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science.evidence_inference | Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:
- Treatment: The specific medical procedure or drug being evaluated.
- Control: The standard or placebo used for comparison. If absent, use `null`.
- Result: The health outcome being assessed.
- Impact: The observed effect of the treatment on the result, categorized as: ("increased", "decreased", "no change").
- Justification: The supporting details for the observed impact.
Format the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].
Abstract:
ABSTRACT.OBJECTIVES:: To compare the use of solifenacin and fesoterodine in treatment of overactive bladder (OAB).
ABSTRACT.METHODS:: This prospective study was conducted on patients diagnosed with OAB who presenting to the Department of Obstetrics and Gynecology and Urology, School of Medicine, Kahramanmaraş Sütçü İmam University, Kahramanmaraş, Turkey between October 2013 and August 2014. Patients were randomized into 2 groups. Group 1 (n=60) received 5 mg solifenacin per day, while Group 2 (n=59) received 4 mg fesoterodine per day. All the patients' OAB symptom scores (OABSS) in weeks 0, 4, and 12 were recorded. In addition, treatment costs and side effects of the drugs were evaluated.
ABSTRACT.RESULTS:: Average OABSS (score 1) was determined as: 9.5 ± 2.8 for Group 1 and 10.7 ± 1.8 for Group 2 at week 0; 2.2 ± 1.2 (Group 1) and 2.4 ± 1.3 (Group 2) at week 4 (score 2); and 1.3 ± 0.5 for Group 1 and 1.3 ± 0.6 for Group 2 at week 12 (score 3). In addition, no statistically significant difference was found between the scores (p=0.062 (score 1), p=0.464 (score 2), and p=0.527 (score 3). The discontinuation rate of medication due to its side effects was 0 (0%) for Group 1, and 6 (10.2%) for Group 2. Intragroup changes in the scores 1-2, 1-3, and 2-3 values was statistically significant in both groups (p<0.001).
ABSTRACT.CONCLUSION:: No significant difference was found between the OABSS of these 2 drugs. However, discontinuation of drugs due to side effects was more frequent in fesoterodine.
Extracted Data: | [["Group 1 received 5 mg solifenacin per day", "Group 2 received 4 mg fesoterodine per day", "overactive bladder (OAB) symptom scores (OABSS)", "no significant difference", "Average OABSS (score 1) was determined as: 9.5 \u00b1 2.8 for Group 1 and 10.7 \u00b1 1.8 for Group 2 at week 0; 2.2 \u00b1 1.2 (Group 1) and 2.4 \u00b1 1.3 (Group 2) at week 4 (score 2); and 1.3 \u00b1 0.5 for Group 1 and 1.3 \u00b1 0.6 for Group 2 at week 12 (score 3). In addition, no statistically significant difference was found between the scores (p=0.062 (score 1), p=0.464 (score 2), and p=0.527 (score 3)."], ["Group 1 received 5 mg solifenacin per day", "Group 2 received 4 mg fesoterodine per day", "discontinuation of drugs due to side effects", "significantly decreased", "The discontinuation rate of medication due to its side effects was 0 (0%) for Group 1, and 6 (10.2%) for Group 2."], ["Group 1 received 5 mg solifenacin per day", "Group 2 received 4 mg fesoterodine per day", "discontinuation of drugs due to side effects", "significantly decreased", "However, discontinuation of drugs due to side effects was more frequent in fesoterodine."]] | science.evidence_inference.121 | [
{
"content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.OBJECTIVES:: To compare the use of solifenacin and fesoterodine in treatment of overactive bladder (OAB).\n\nABSTRACT.METHODS:: This prospective study was conducted on patients diagnosed with OAB who presenting to the Department of Obstetrics and Gynecology and Urology, School of Medicine, Kahramanmaraş Sütçü İmam University, Kahramanmaraş, Turkey between October 2013 and August 2014. Patients were randomized into 2 groups. Group 1 (n=60) received 5 mg solifenacin per day, while Group 2 (n=59) received 4 mg fesoterodine per day. All the patients' OAB symptom scores (OABSS) in weeks 0, 4, and 12 were recorded. In addition, treatment costs and side effects of the drugs were evaluated.\n\nABSTRACT.RESULTS:: Average OABSS (score 1) was determined as: 9.5 ± 2.8 for Group 1 and 10.7 ± 1.8 for Group 2 at week 0; 2.2 ± 1.2 (Group 1) and 2.4 ± 1.3 (Group 2) at week 4 (score 2); and 1.3 ± 0.5 for Group 1 and 1.3 ± 0.6 for Group 2 at week 12 (score 3). In addition, no statistically significant difference was found between the scores (p=0.062 (score 1), p=0.464 (score 2), and p=0.527 (score 3). The discontinuation rate of medication due to its side effects was 0 (0%) for Group 1, and 6 (10.2%) for Group 2. Intragroup changes in the scores 1-2, 1-3, and 2-3 values was statistically significant in both groups (p<0.001).\n\nABSTRACT.CONCLUSION:: No significant difference was found between the OABSS of these 2 drugs. However, discontinuation of drugs due to side effects was more frequent in fesoterodine.\n\nExtracted Data:",
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"content": "[[\"Group 1 received 5 mg solifenacin per day\", \"Group 2 received 4 mg fesoterodine per day\", \"overactive bladder (OAB) symptom scores (OABSS)\", \"no significant difference\", \"Average OABSS (score 1) was determined as: 9.5 \\u00b1 2.8 for Group 1 and 10.7 \\u00b1 1.8 for Group 2 at week 0; 2.2 \\u00b1 1.2 (Group 1) and 2.4 \\u00b1 1.3 (Group 2) at week 4 (score 2); and 1.3 \\u00b1 0.5 for Group 1 and 1.3 \\u00b1 0.6 for Group 2 at week 12 (score 3). In addition, no statistically significant difference was found between the scores (p=0.062 (score 1), p=0.464 (score 2), and p=0.527 (score 3).\"], [\"Group 1 received 5 mg solifenacin per day\", \"Group 2 received 4 mg fesoterodine per day\", \"discontinuation of drugs due to side effects\", \"significantly decreased\", \"The discontinuation rate of medication due to its side effects was 0 (0%) for Group 1, and 6 (10.2%) for Group 2.\"], [\"Group 1 received 5 mg solifenacin per day\", \"Group 2 received 4 mg fesoterodine per day\", \"discontinuation of drugs due to side effects\", \"significantly decreased\", \"However, discontinuation of drugs due to side effects was more frequent in fesoterodine.\"]]",
"role": "assistant"
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|
science.evidence_inference | Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:
- Treatment: The specific medical procedure or drug being evaluated.
- Control: The standard or placebo used for comparison. If absent, use `null`.
- Result: The health outcome being assessed.
- Impact: The observed effect of the treatment on the result, categorized as: ("increased", "decreased", "no change").
- Justification: The supporting details for the observed impact.
Format the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].
Abstract:
ABSTRACT.BACKGROUND: Dietary intervention trials rely on self-reported measures of intake for assessment of energy and macronutrient composition. Dietary fat intake is of particular interest due to strong associations with pathophysiology. In epidemiological trials phospholipid fatty acid composition may reflect composition of habitual diet, although strong correlations have been identified only for essential polyunsaturated fatty acids (PUFAs). Preliminary evidence shows that saturated fatty acids (SFA) C15:0 and C17:0 may be acceptable biomarkers. This study measured changes in erythrocyte membrane fatty acids during a period of strictly controlled fat feeding to investigate their use as a short-term marker of compliance, particularly for intake of SFAs.
ABSTRACT.RESULTS: This was a randomised cross-over trial in which diet was provided and strictly controlled. 20 healthy, male subjects were given a 40 energy % (en%) fat diet, high in saturated (high-SFA, 20 en%) or unsaturated (high-USFA, 24 en%) fatty acids for 2 periods of 3 weeks. Subjects were residential during intervention with all food and beverages provided. Dietary composition was verified by direct chemical analysis. Blood samples were collected on days 1,7,14, 21 and analysed for red blood cell (RBC) membrane fatty acid composition. Pearson correlation showed RBC fatty acid composition to mimic dietary composition by 3 weeks, but the relationships were weak. Of the SFAs only RBC C16:0 decreased in response to decreased dietary content on high-USFA treatment (ANOVA, diet, P < 0.05). Of the USFAs, higher levels of C18:1 MUFA, C20:4 and C22:6 long chain PUFA on high-USFA diet lead to higher C18:1, C20:4 and C22:6 within RBCs (ANOVA, time*diet, P < 0.05). Pearson's correlation was significant between dietary and RBC fatty acids during the 21d dietary manipulation for C18:1, and C20:5, C22:6 only (P < 0.05).
ABSTRACT.CONCLUSION: RBC membrane fatty acids cannot reliably be used as an independent measure of compliance for dietary SFA intake in short-term studies. The MUFA oleic acid and PUFAs EPA and DHA may be more useful as markers of compliance during short term intervention trials.
Extracted Data: | [["saturated fatty acids (SFA)", "baseline", "RBC C16:0", "significantly decreased", "Of the SFAs only RBC C16:0 decreased in response to decreased dietary content on high-USFA treatment (ANOVA, diet, P \u0026lt; 0.05)."]] | science.evidence_inference.188 | [
{
"content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND: Dietary intervention trials rely on self-reported measures of intake for assessment of energy and macronutrient composition. Dietary fat intake is of particular interest due to strong associations with pathophysiology. In epidemiological trials phospholipid fatty acid composition may reflect composition of habitual diet, although strong correlations have been identified only for essential polyunsaturated fatty acids (PUFAs). Preliminary evidence shows that saturated fatty acids (SFA) C15:0 and C17:0 may be acceptable biomarkers. This study measured changes in erythrocyte membrane fatty acids during a period of strictly controlled fat feeding to investigate their use as a short-term marker of compliance, particularly for intake of SFAs.\n\nABSTRACT.RESULTS: This was a randomised cross-over trial in which diet was provided and strictly controlled. 20 healthy, male subjects were given a 40 energy % (en%) fat diet, high in saturated (high-SFA, 20 en%) or unsaturated (high-USFA, 24 en%) fatty acids for 2 periods of 3 weeks. Subjects were residential during intervention with all food and beverages provided. Dietary composition was verified by direct chemical analysis. Blood samples were collected on days 1,7,14, 21 and analysed for red blood cell (RBC) membrane fatty acid composition. Pearson correlation showed RBC fatty acid composition to mimic dietary composition by 3 weeks, but the relationships were weak. Of the SFAs only RBC C16:0 decreased in response to decreased dietary content on high-USFA treatment (ANOVA, diet, P < 0.05). Of the USFAs, higher levels of C18:1 MUFA, C20:4 and C22:6 long chain PUFA on high-USFA diet lead to higher C18:1, C20:4 and C22:6 within RBCs (ANOVA, time*diet, P < 0.05). Pearson's correlation was significant between dietary and RBC fatty acids during the 21d dietary manipulation for C18:1, and C20:5, C22:6 only (P < 0.05).\n\nABSTRACT.CONCLUSION: RBC membrane fatty acids cannot reliably be used as an independent measure of compliance for dietary SFA intake in short-term studies. The MUFA oleic acid and PUFAs EPA and DHA may be more useful as markers of compliance during short term intervention trials.\n\nExtracted Data:",
"role": "user"
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{
"content": "[[\"saturated fatty acids (SFA)\", \"baseline\", \"RBC C16:0\", \"significantly decreased\", \"Of the SFAs only RBC C16:0 decreased in response to decreased dietary content on high-USFA treatment (ANOVA, diet, P \\u0026lt; 0.05).\"]]",
"role": "assistant"
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|
science.discomat_te | | Energy (cm) a | Wavelength (nm) | Absorption cross-section (x10 cm2/OH) a | Bandwidth FWHM (cm-1) | Integrated absorption (normalized) |
| 2800 | 3570 | 15.0 | 920 | 1.000 |
| 3450 | 2900 | 0.70 | 1010 | 0.053 |
| 4200 | 2380 | 0.30 | 645 | 0.011 |
| 4810 | 2080 | 0.30 | 1200 | 0.020 |
Caption: Observed OH infrared band energies and absorption strengths
Examine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:
{"value": "xx", "type": "Other"}
{"value": "xx", "type": "Glass_Compound_Amount", "constituent": "xx", "unit": "xx", "material": "xx"}
Process each cell sequentially, and for any unknown attributes, use "xx" as a placeholder. Each JSON should be on a new line.
Output: | {"value": "2800", "type": "Other"}
{"value": "3570", "type": "Other"}
{"value": "15.0", "type": "Other"}
{"value": "920", "type": "Other"}
{"value": "1.000", "type": "Other"}
{"value": "3450", "type": "Other"}
{"value": "2900", "type": "Other"}
{"value": "0.70", "type": "Other"}
{"value": "1010", "type": "Other"}
{"value": "0.053", "type": "Other"}
{"value": "4200", "type": "Other"}
{"value": "2380", "type": "Other"}
{"value": "0.30", "type": "Other"}
{"value": "645", "type": "Other"}
{"value": "0.011", "type": "Other"}
{"value": "4810", "type": "Other"}
{"value": "2080", "type": "Other"}
{"value": "0.30", "type": "Other"}
{"value": "1200", "type": "Other"}
{"value": "0.020", "type": "Other"} | science.discomat_te.249 | [
{
"content": "| Energy (cm) a | Wavelength (nm) | Absorption cross-section (x10 cm2/OH) a | Bandwidth FWHM (cm-1) | Integrated absorption (normalized) |\n| 2800 | 3570 | 15.0 | 920 | 1.000 |\n| 3450 | 2900 | 0.70 | 1010 | 0.053 |\n| 4200 | 2380 | 0.30 | 645 | 0.011 |\n| 4810 | 2080 | 0.30 | 1200 | 0.020 |\n\nCaption: Observed OH infrared band energies and absorption strengths\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:",
"role": "user"
},
{
"content": "{\"value\": \"2800\", \"type\": \"Other\"}\n{\"value\": \"3570\", \"type\": \"Other\"}\n{\"value\": \"15.0\", \"type\": \"Other\"}\n{\"value\": \"920\", \"type\": \"Other\"}\n{\"value\": \"1.000\", \"type\": \"Other\"}\n{\"value\": \"3450\", \"type\": \"Other\"}\n{\"value\": \"2900\", \"type\": \"Other\"}\n{\"value\": \"0.70\", \"type\": \"Other\"}\n{\"value\": \"1010\", \"type\": \"Other\"}\n{\"value\": \"0.053\", \"type\": \"Other\"}\n{\"value\": \"4200\", \"type\": \"Other\"}\n{\"value\": \"2380\", \"type\": \"Other\"}\n{\"value\": \"0.30\", \"type\": \"Other\"}\n{\"value\": \"645\", \"type\": \"Other\"}\n{\"value\": \"0.011\", \"type\": \"Other\"}\n{\"value\": \"4810\", \"type\": \"Other\"}\n{\"value\": \"2080\", \"type\": \"Other\"}\n{\"value\": \"0.30\", \"type\": \"Other\"}\n{\"value\": \"1200\", \"type\": \"Other\"}\n{\"value\": \"0.020\", \"type\": \"Other\"}",
"role": "assistant"
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|
science.mup_single_document_summarization | Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.
Title of the Paper:
Exploring the Limits of Large Scale Pre-training
Main Content:
1 INTRODUCTION . Recent impressive progress on transfer and few-shot learning ( Brown et al. , 2020 ; Goyal et al. , 2021 ; Kolesnikov et al. , 2019 ; Pham et al. , 2020 ; Dosovitskiy et al. , 2020 ; Dumoulin et al. , 2021 ; Radford et al. , 2021 ) suggests an emerging direction that scaling up models and training them on a huge corpus of data is the main obstacle towards better performance on downstream tasks with less or no data . These developments implicitly encourage two consistent views : 1 ) scaling up the model and data size improves the performance significantly ; 2 ) the performance improvement transfers to downstream tasks in a desirable way . In a more focused empirical study in support of the first view , Kaplan et al . ( 2020 ) show that scaling up the model size , data , and compute appropriately in the language modeling task results in a non-saturating return in performance . Bello et al . ( 2021 ) , Tan & Le ( 2019 ) show that favorable scaling can be achieved in image recognition tasks as well . The second view has also been a subject of recent focused studies . Hernandez et al . ( 2021 ) show that favorable scaling laws similar to that of ( Kaplan et al. , 2020 ; Tay et al. , 2021b ) hold in transfer and few-shot settings in NLP tasks . In perhaps closest prior work to ours , Kornblith et al . ( 2019 ) observe a linear relationship1 between the performances on ImageNet ( Russakovsky et al. , 2015 ) and downstream image recognition tasks . Adopting the above views has major implications moving forward . These views suggest that spending compute and research effort on improving the performance on one massive corpus would pay off because that would enable us to solve many downstream tasks almost for free . It also means while improving our upstream performance , we do not need to be worried about downstream tasks as their improvement is predictable based on a linear trend . While the aforementioned studies provide a compelling story , they suffer from a major shortcoming : due to compute limitations , performance for different choices of hyper-parameter values are not reported . Scaling plots seem more favorable if the hyper-parameter chosen for each scale is fixed or determined by a simple scaling function . 1The linear relationship in ( Kornblith et al. , 2019 ) is achieved after proper logit scaling of accuracy values . We show that with logit or linear scaling , the relationship is not linear . Moreover , often the goal is improving state-of-the-art results , hence naturally most of the efforts in hyper-parameter selection is focused on higher scales , which significantly biases the scaling plots . However , when studying scaling , we are concerned about the best downstream performance of models given all possible values for the hyper-parameters . Additionally , most scaling studies report the behavior within a limited range , and simply extrapolating that scaling without further understanding of the dynamics of scaling can be detrimental as there is no reason , a priori , for the scaling to hold outside of the studied range . In this paper , we systematically investigate the transferability of improvements on a large-scale upstream task to a wide range of downstream tasks in both few-shot and transfer learning scenarios . To address the above shortcomings , part of our work is a meta-study of more than 4800 Vision Transformer ( Dosovitskiy et al. , 2020 ) , MLP-Mixer ( Tolstikhin et al. , 2021 ) and ResNet ( Dosovitskiy et al. , 2020 ) models . The models are pre-trained on either JFT ( Sun et al. , 2017 ) with 303M images and 18K classes or ImageNet21K ( Deng et al. , 2009 ) with 14M images and 21K classes and evaluated on a variety of downstream datasets for few-shot and transfer learning settings . Our 25 downstream tasks cover a wide range of standard datasets that are included in benchmarks like VTAB ( Zhai et al. , 2019 ) , MetaDataset ( Triantafillou et al. , 2019 ) , Wilds ( Koh et al. , 2020 ) and medical imaging . We provide strong empirical evidence that scaling ( and hyper-parameter tuning ) does not lead to a one-model-fits-all solution . There are still many unresolved challenges remaining and at the center is the problem of data diversity for downstream tasks . We provide the first large scale and systematic investigation of this phenomena and discuss the reasons behind it . In Figure 1 , we present downstream ( DS ) vs upstream ( US ) performance plot on variety of models and downstream tasks . We observe that , as we increase US accuracy , for most cases DS accuracy saturates to a value considerably below 100 % . Also , saturating behavior is not an exception but rather the common trend and it is robust to the choice of number of shots and US tasks ( see Figure F.1 ) . We establish that this gap is not due to noise or any other factor that solely depends on DS task ; rather , it depends on the relationship between US , DS tasks . Moreover , given a set of models with similar US accuracy , the best model for different DS tasks varies . Contributions Our main contributions in this paper are as follows : • We establish through extensive study that as we improve the performance of the upstream ( US ) task either by scaling up or hyper-parameter and architectural choices , the performance of downstream ( DS ) tasks shows a saturating behaviour . In our experiments , several DS tasks reach full saturation within the studied range ( Section 2 ) . • We demonstrate that given a set of models with similar US accuracy , the best model for a DS task TDS1 might have much worse performance on another DS task TDS2 compared to the best model for TDS2 ( Figure 5 ) . • Given the scale of experiments , it is crucial for the proposed model to not be impacted by the density of the points in the DS-vs-US plot . We argue and demonstrate that fitting the power law to the convex hull of experiments would circumvent the effect of sampling biases on prediction of downstream accuracy and show the robustness of our model to sample size variations ( Section 2.2 ) . • Having observed the nonlinear relationship between upstream and downstream accuracy , in order to predict downstream performance for a given upstream accuracy , we model their relationship with a power law curve and establish that it captures the behavior well even with small number of samples ( Section 2.2 ) . • We study how scaling up model size , data size , and compute affects DS performance and show that these parameters impact DS performance mainly through the US performance ( Section 3 ) . • We investigate reasons behind the DS performance saturation and show that this behavior can be captured by the usefulness of feature representation in higher layers of the pre-trained model ( Section 4 ) . • We further explore the discrepancy between US and DS performances and show that for some choices of hyper-parameters , they might be at odds with each other . In particular , we showcase how the optimal hyper-parameters for the head used in pre-training ( upstream task ) are different for US and DS . We then uncover the reason behind this discrepancy ( Appendix C , D ) . • Finally , we show our observations are robust to several choices such as size of US data , common scalings of accuracy , number of shots , transfer vs few-shot setting and architecture ( Appendix E ) . Related Work . The closest work to ours is that of Kornblith et al . ( 2019 ) . They investigate the effect of ImageNet ( Russakovsky et al. , 2015 ) pre-training on image classification performance across 12 datasets for few-shot , transfer and random initialization scenarios . They show that performance on ImageNet translates linearly ( in logit scaling ) to performance on DS tasks . However , they do not consider extrapolation of the values . While both works investigate the effect of pre-training via various experiments , there are two main differences in our responses to the question of “ better upstream performance transfer to better downstream performance ? ” . First , we establish that clear “ saturation ” phenomena exists when looking into DS-vs-US performance . In Figure 1 , we see there are various cases when comparing two models , A , B . Where model A has a much higher US accuracy but lower DS accuracy and these are not exceptions to a rule , rather the majority of cases . Essentially , for each DS-vs-US plot two points where one is on the right but lower than the other are instances of such a case . Second , we also establish that , for each DS task you can see best performing models scale with power law as in Equation 1 but for each architecture best performing models are different across DS tasks and this depends on training hyper-parameters , See Figure 5 . In other words , when considering two DS tasks , TDS1 , TDS2 , we have numerous cases where model A has better performance on US and TDS1 but one can not conclude better performance on DS2 . We suspect the difference in conclusion is due to the earlier work being limited in the range of accuracy values they consider . In addition to this difference in conclusions , we investigate the reasons behind this saturation behavior . Moreover , ( in Appendix C ) we consider cases where US and DS performance are at odds with each other , specifically , the scenarios where worse performance on US , leads to performance improvement on DS . Inspired by ( Zhai et al. , 2021 ) who noted that increasing head weight decay during pre-training leads to worse performance on US while improving DS performance ; we investigate head hyper-parameters ( both weight decay and learning rate ) further and show that it can be explained by noting that these manipulations push the information stored in the head down to lower layers . Additional related work are covered in Appendix A . 1.1 EXPERIMENTAL SETUP . The analysis in this paper is based on a study on an exhaustive number of large-scale experiments on image recognition tasks , as well as a set of controlled experiments we conducted to ablate our setup and deepen our understanding of the studied phenomena . We investigate more than 4800 experiments with Vision Transformers , MLP-Mixers and ResNets with different configurations when pre-trained on a large amount of data in a supervised fashion and evaluated on several downstream image recognition tasks through few-shot learning and fine-tuning . For more details , see Appendix G. We emphasize that the large set of experiments we investigate are not trained for the purpose of this paper , rather , we have aggregated models trained by different researchers for different purposes to perform a meta-study on them . This , in fact , positions this meta-study in a unique spot . First , it may not be feasible to run such a number of large-scale trials for the purpose of studying particular phenomena.Second , no implicit or explicit assumption was made in these experiments with respect to the type of analysis we conducted on them afterwards , hence minimizing the systematic biases of the analysis process in the findings . We note that , there might potentially be other biases . For example , researchers usually focus on hyper-parameter tuning to improve SOTA on a specific downstream task ( usually ImageNet ) and this may lead to not do a grid search on high dimensional space of all possible hyper-parameters and possibly affecting the plots . In Section 3 , we investigate this and discuss that in this case the observed trend is similar to performing a grid search . In the main body of the paper , we report the results over eight downstream tasks and provide results for more than 20 downstream tasks in Appendix F. Moreover , the plots corresponding to pre-training on JFT , ImageNet21K are in the main part and Appendix F , respectively . | The paper presents an empirical study (respectively meta-study) of large-scale supervised pre-training for image recognition tasks. By analysing lots of experiments with varying model sizes, dataset sizes and training durations, the paper reaches the conclusion that simply scaling them up for a generic pre-training task will not lead to proportional gains for downstream tasks that build on the pre-trained model. On the contrary, the paper suggests that with increasing pre-training effort the downstream performance will reach a saturation level below its Bayes error. Moreover, that saturation level appears to vary across different downstream tasks (i.e., image characteristics and class definitions), which is seen as a sign that a generic one-fits-all feature extractor cannot be found by just scaling up. | science.mup_single_document_summarization.3800 | [
{
"content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nExploring the Limits of Large Scale Pre-training\n\nMain Content:\n1 INTRODUCTION . Recent impressive progress on transfer and few-shot learning ( Brown et al. , 2020 ; Goyal et al. , 2021 ; Kolesnikov et al. , 2019 ; Pham et al. , 2020 ; Dosovitskiy et al. , 2020 ; Dumoulin et al. , 2021 ; Radford et al. , 2021 ) suggests an emerging direction that scaling up models and training them on a huge corpus of data is the main obstacle towards better performance on downstream tasks with less or no data . These developments implicitly encourage two consistent views : 1 ) scaling up the model and data size improves the performance significantly ; 2 ) the performance improvement transfers to downstream tasks in a desirable way . In a more focused empirical study in support of the first view , Kaplan et al . ( 2020 ) show that scaling up the model size , data , and compute appropriately in the language modeling task results in a non-saturating return in performance . Bello et al . ( 2021 ) , Tan & Le ( 2019 ) show that favorable scaling can be achieved in image recognition tasks as well . The second view has also been a subject of recent focused studies . Hernandez et al . ( 2021 ) show that favorable scaling laws similar to that of ( Kaplan et al. , 2020 ; Tay et al. , 2021b ) hold in transfer and few-shot settings in NLP tasks . In perhaps closest prior work to ours , Kornblith et al . ( 2019 ) observe a linear relationship1 between the performances on ImageNet ( Russakovsky et al. , 2015 ) and downstream image recognition tasks . Adopting the above views has major implications moving forward . These views suggest that spending compute and research effort on improving the performance on one massive corpus would pay off because that would enable us to solve many downstream tasks almost for free . It also means while improving our upstream performance , we do not need to be worried about downstream tasks as their improvement is predictable based on a linear trend . While the aforementioned studies provide a compelling story , they suffer from a major shortcoming : due to compute limitations , performance for different choices of hyper-parameter values are not reported . Scaling plots seem more favorable if the hyper-parameter chosen for each scale is fixed or determined by a simple scaling function . 1The linear relationship in ( Kornblith et al. , 2019 ) is achieved after proper logit scaling of accuracy values . We show that with logit or linear scaling , the relationship is not linear . Moreover , often the goal is improving state-of-the-art results , hence naturally most of the efforts in hyper-parameter selection is focused on higher scales , which significantly biases the scaling plots . However , when studying scaling , we are concerned about the best downstream performance of models given all possible values for the hyper-parameters . Additionally , most scaling studies report the behavior within a limited range , and simply extrapolating that scaling without further understanding of the dynamics of scaling can be detrimental as there is no reason , a priori , for the scaling to hold outside of the studied range . In this paper , we systematically investigate the transferability of improvements on a large-scale upstream task to a wide range of downstream tasks in both few-shot and transfer learning scenarios . To address the above shortcomings , part of our work is a meta-study of more than 4800 Vision Transformer ( Dosovitskiy et al. , 2020 ) , MLP-Mixer ( Tolstikhin et al. , 2021 ) and ResNet ( Dosovitskiy et al. , 2020 ) models . The models are pre-trained on either JFT ( Sun et al. , 2017 ) with 303M images and 18K classes or ImageNet21K ( Deng et al. , 2009 ) with 14M images and 21K classes and evaluated on a variety of downstream datasets for few-shot and transfer learning settings . Our 25 downstream tasks cover a wide range of standard datasets that are included in benchmarks like VTAB ( Zhai et al. , 2019 ) , MetaDataset ( Triantafillou et al. , 2019 ) , Wilds ( Koh et al. , 2020 ) and medical imaging . We provide strong empirical evidence that scaling ( and hyper-parameter tuning ) does not lead to a one-model-fits-all solution . There are still many unresolved challenges remaining and at the center is the problem of data diversity for downstream tasks . We provide the first large scale and systematic investigation of this phenomena and discuss the reasons behind it . In Figure 1 , we present downstream ( DS ) vs upstream ( US ) performance plot on variety of models and downstream tasks . We observe that , as we increase US accuracy , for most cases DS accuracy saturates to a value considerably below 100 % . Also , saturating behavior is not an exception but rather the common trend and it is robust to the choice of number of shots and US tasks ( see Figure F.1 ) . We establish that this gap is not due to noise or any other factor that solely depends on DS task ; rather , it depends on the relationship between US , DS tasks . Moreover , given a set of models with similar US accuracy , the best model for different DS tasks varies . Contributions Our main contributions in this paper are as follows : • We establish through extensive study that as we improve the performance of the upstream ( US ) task either by scaling up or hyper-parameter and architectural choices , the performance of downstream ( DS ) tasks shows a saturating behaviour . In our experiments , several DS tasks reach full saturation within the studied range ( Section 2 ) . • We demonstrate that given a set of models with similar US accuracy , the best model for a DS task TDS1 might have much worse performance on another DS task TDS2 compared to the best model for TDS2 ( Figure 5 ) . • Given the scale of experiments , it is crucial for the proposed model to not be impacted by the density of the points in the DS-vs-US plot . We argue and demonstrate that fitting the power law to the convex hull of experiments would circumvent the effect of sampling biases on prediction of downstream accuracy and show the robustness of our model to sample size variations ( Section 2.2 ) . • Having observed the nonlinear relationship between upstream and downstream accuracy , in order to predict downstream performance for a given upstream accuracy , we model their relationship with a power law curve and establish that it captures the behavior well even with small number of samples ( Section 2.2 ) . • We study how scaling up model size , data size , and compute affects DS performance and show that these parameters impact DS performance mainly through the US performance ( Section 3 ) . • We investigate reasons behind the DS performance saturation and show that this behavior can be captured by the usefulness of feature representation in higher layers of the pre-trained model ( Section 4 ) . • We further explore the discrepancy between US and DS performances and show that for some choices of hyper-parameters , they might be at odds with each other . In particular , we showcase how the optimal hyper-parameters for the head used in pre-training ( upstream task ) are different for US and DS . We then uncover the reason behind this discrepancy ( Appendix C , D ) . • Finally , we show our observations are robust to several choices such as size of US data , common scalings of accuracy , number of shots , transfer vs few-shot setting and architecture ( Appendix E ) . Related Work . The closest work to ours is that of Kornblith et al . ( 2019 ) . They investigate the effect of ImageNet ( Russakovsky et al. , 2015 ) pre-training on image classification performance across 12 datasets for few-shot , transfer and random initialization scenarios . They show that performance on ImageNet translates linearly ( in logit scaling ) to performance on DS tasks . However , they do not consider extrapolation of the values . While both works investigate the effect of pre-training via various experiments , there are two main differences in our responses to the question of “ better upstream performance transfer to better downstream performance ? ” . First , we establish that clear “ saturation ” phenomena exists when looking into DS-vs-US performance . In Figure 1 , we see there are various cases when comparing two models , A , B . Where model A has a much higher US accuracy but lower DS accuracy and these are not exceptions to a rule , rather the majority of cases . Essentially , for each DS-vs-US plot two points where one is on the right but lower than the other are instances of such a case . Second , we also establish that , for each DS task you can see best performing models scale with power law as in Equation 1 but for each architecture best performing models are different across DS tasks and this depends on training hyper-parameters , See Figure 5 . In other words , when considering two DS tasks , TDS1 , TDS2 , we have numerous cases where model A has better performance on US and TDS1 but one can not conclude better performance on DS2 . We suspect the difference in conclusion is due to the earlier work being limited in the range of accuracy values they consider . In addition to this difference in conclusions , we investigate the reasons behind this saturation behavior . Moreover , ( in Appendix C ) we consider cases where US and DS performance are at odds with each other , specifically , the scenarios where worse performance on US , leads to performance improvement on DS . Inspired by ( Zhai et al. , 2021 ) who noted that increasing head weight decay during pre-training leads to worse performance on US while improving DS performance ; we investigate head hyper-parameters ( both weight decay and learning rate ) further and show that it can be explained by noting that these manipulations push the information stored in the head down to lower layers . Additional related work are covered in Appendix A . 1.1 EXPERIMENTAL SETUP . The analysis in this paper is based on a study on an exhaustive number of large-scale experiments on image recognition tasks , as well as a set of controlled experiments we conducted to ablate our setup and deepen our understanding of the studied phenomena . We investigate more than 4800 experiments with Vision Transformers , MLP-Mixers and ResNets with different configurations when pre-trained on a large amount of data in a supervised fashion and evaluated on several downstream image recognition tasks through few-shot learning and fine-tuning . For more details , see Appendix G. We emphasize that the large set of experiments we investigate are not trained for the purpose of this paper , rather , we have aggregated models trained by different researchers for different purposes to perform a meta-study on them . This , in fact , positions this meta-study in a unique spot . First , it may not be feasible to run such a number of large-scale trials for the purpose of studying particular phenomena.Second , no implicit or explicit assumption was made in these experiments with respect to the type of analysis we conducted on them afterwards , hence minimizing the systematic biases of the analysis process in the findings . We note that , there might potentially be other biases . For example , researchers usually focus on hyper-parameter tuning to improve SOTA on a specific downstream task ( usually ImageNet ) and this may lead to not do a grid search on high dimensional space of all possible hyper-parameters and possibly affecting the plots . In Section 3 , we investigate this and discuss that in this case the observed trend is similar to performing a grid search . In the main body of the paper , we report the results over eight downstream tasks and provide results for more than 20 downstream tasks in Appendix F. Moreover , the plots corresponding to pre-training on JFT , ImageNet21K are in the main part and Appendix F , respectively .",
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"content": "The paper presents an empirical study (respectively meta-study) of large-scale supervised pre-training for image recognition tasks. By analysing lots of experiments with varying model sizes, dataset sizes and training durations, the paper reaches the conclusion that simply scaling them up for a generic pre-training task will not lead to proportional gains for downstream tasks that build on the pre-trained model. On the contrary, the paper suggests that with increasing pre-training effort the downstream performance will reach a saturation level below its Bayes error. Moreover, that saturation level appears to vary across different downstream tasks (i.e., image characteristics and class definitions), which is seen as a sign that a generic one-fits-all feature extractor cannot be found by just scaling up.",
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science.mup_single_document_summarization | Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.
Title of the Paper:
Subgroup Generalization and Fairness of Graph Neural Networks
Main Content:
1 Introduction . Graph Neural Networks ( GNNs ) [ 13 , 35 , 20 ] are a family of machine learning models that can be used to model non-Euclidean data as well as inter-related samples in a flexible way . In recent years , there have been enormous successful applications of GNNs in various areas , such as drug discovery [ 18 ] , computer vision [ 29 ] , transportation forecasting [ 49 ] , recommender systems [ 48 ] , etc . Depending on the type of prediction target , the application tasks can be roughly categorized into node-level , edge-level , subgraph-level , and graph-level tasks [ 46 ] . In contrast to the marked empirical success , theoretical understanding of the generalization ability of GNNs has been rather limited . Among the existing literature , some studies [ 9 , 11 , 25 ] focus on the analysis of graph-level tasks where each sample is an entire graph and the samples of graphs are IID . A very limited number of studies [ 36 , 42 ] explore GNN generalization for node-level tasks but they assume the nodes ( and their associated neighborhoods ) are IID samples , which does not align with the commonly seen graph-based semi-supervised learning setups . Baranwal et al . [ 3 ] investigate GNN generalization without IID assumptions but under a specific data generating mechanism . In this work , our first contribution is to provide a novel PAC-Bayesian analysis for the generalization ability of GNNs on node-level tasks with non-IID assumptions . In particular , we assume the node features are fixed and the node labels are independently sampled from distributions conditioned on the node features . We also assume the training set and the test set can be chosen as arbitrary subsets of nodes on the graph . We first prove two general PAC-Bayesian generalization bounds ( Theorem 1 ∗School of Information , University of Michigan , Ann Arbor , Michigan , USA †Equal contribution . ‡Department of EECS , University of Michigan , Ann Arbor , Michigan , USA 4Code available at https : //github.com/TheaperDeng/GNN-Generalization-Fairness . 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . and Theorem 2 ) under this non-IID setup . Subsequently , we derive a generalization bound for GNN ( Theorem 3 ) in terms of characteristics of the GNN models and the node features . Notably , the generalization bound for GNN is influenced by the distance between the test nodes and the training nodes in terms of their aggregated node features . This suggests that , given a fixed training set , test nodes that are “ far away ” from all the training nodes may suffer from larger generalization errors . Based on this analysis , our second contribution is the discovering of a type of unfairness that arises from theoretically predictable accuracy disparity across some subgroups of test nodes . We further conduct a empirical study that investigates the prediction accuracy of four popular GNN models on different subgroups of test nodes . The results on multiple benchmark datasets indicate that there is indeed a significant disparity in test accuracy among these subgroups . We summarize the contributions of this work as follows : ( 1 ) We establish a novel PAC-Bayesian analysis for graph-based semi-supervised learning with non-IID assumptions . ( 2 ) Under this setup , we derive a generalization bound for GNNs that can be applied to an arbitrary subgroup of test nodes . ( 3 ) As an implication of the generalization bound , we predict that there would be an unfairness of GNN predictions that arises from accuracy disparity across subgroups of test nodes . ( 4 ) We empirically verify the existence of accuracy disparity of popular GNN models on multiple benchmark datasets , as predicted by our theoretical analysis . 2 Related Work . 2.1 Generalization of Graph Neural Networks . The majority of existing literature that aims to develop theoretical understandings of GNNs have focused on the expressive power of GNNs ( see Sato [ 34 ] for a survey along this line ) , while the number of studies trying to understand the generalizability of GNNs is rather limited . Among them , some [ 9 , 11 , 25 ] focus on graph-level tasks , the analyses of which can not be easily applied to node-level tasks . As far as we know , Scarselli et al . [ 36 ] , Verma and Zhang [ 42 ] , and Baranwal et al . [ 3 ] are the only existing studies investigating the generalization of GNNs on node-level tasks , even though node-level tasks are more common in reality . Scarselli et al . [ 36 ] present an upper bound of the VC-dimension of GNNs ; Verma and Zhang [ 42 ] derive a stability-based generalization bound for a single-layer GCN [ 20 ] model . Yet , both Scarselli et al . [ 36 ] and Verma and Zhang [ 42 ] ( implicitly ) assume that the training nodes are IID samples from a certain distribution , which does not align with the common practice of node-level semi-supervised learning . Baranwal et al . [ 3 ] investigate the generalization of graph convolution under a specific data generating mechanism ( i.e. , the contextual stochastic block model [ 8 ] ) . Our work presents the first generalization analysis of GNNs for non-IID node-level tasks without strong assumptions on the data generating mechanism . 2.2 Fairness of Machine Learning on Graphs . The fairness issues of machine learning on graphs start to receive research attention recently . Following conventional machine learning fairness literature , the majority of previous work along this line [ 1 , 5–7 , 22 , 32 , 39 , 50 ] concerns about fairness with respect to a given sensitive attribute , such as gender or race , which defines protected groups . In practice , the fairness issues of learning on graphs are much more complicated due to the asymmetric nature of the graph-structured data . However , only a few studies [ 19 ] investigate the unfairness caused by the graph structure without knowing a sensitive feature . Moreover , in a node-level semi-supervised learning task , the non-IID sampling of training nodes brings additional uncertainty to the fairness of the learned models . This work is the first to present a learning theoretic analysis under this setup , which in turn suggests how the graph structure and the selection of training nodes may influence the fairness of machine learning on graphs . 2.3 PAC-Bayesian Analysis . PAC-Bayesian analysis [ 27 ] has become one of the most powerful theoretical framework to analyze the generalization ability of machine learning models . We will briefly introduce the background in Section 3.2 , and refer the readers to a recent tutorial [ 14 ] for a systematic overview of PAC-Bayesian analysis . We note that Liao et al . [ 25 ] recently present a PAC-Bayesian generalization bound for GNNs on IID graph-level tasks . Both Liao et al . [ 25 ] and this work utilize results from Neyshabur et al . [ 30 ] , a PAC-Bayesian analysis for ReLU-activated neural networks , in part of our proofs . Compared to Neyshabur et al . [ 30 ] , the key contribution of Liao et al . [ 25 ] is the derivation of perturbation bounds of two types of GNN architectures ; while the key contribution of this work is the novel analysis under the setup of non-IID node-level tasks . There is also an existing work of PAC-Bayesian analysis for transductive semi-supervised learning [ 4 ] . But it is different from our problem setup and , in particular , it can not be used to analyze the generalization on subgroups . 3 Preliminaries . In this section , we first formulate the problem of node-level semi-supervised learning . We also provide a brief introduction of the PAC-Bayesian framework . 3.1 The Problem Formulation and Notations . Semi-supervised node classification . Let G = ( V , E ) ∈ GN be an undirected graph , with V = { 1 , 2 , . . . , N } being the set of N nodes and E ⊆ V × V being the set of edges . And GN is the space of all undirected graphs with N nodes . The nodes are associated with node features X ∈ RN×D and node labels y ∈ { 1 , 2 , . . . , K } N . In this work , we focus on the transductive node classification setting [ 47 ] , where the node features X and the graph G are observed prior to learning , and every quantity of interest in the analysis will be conditioned on X and G. Without loss of generality , we treat X and G as fixed throughout our analysis , and the randomness comes from the labels y . In particular , we assume that for each node i ∈ V , its label yi is generated from an unknown conditional distribution Pr ( yi | Zi ) , where Z = g ( X , G ) and g : RN×D × GN → RN×D ′ is an aggregation function that aggregates the features over ( multi-hop ) local neighborhoods5 . We also assume that the node labels are generated independently conditional on their respective aggregated features Zi ’ s . Given a small set of the labeled nodes , V0 ⊆ V , the task of node-level semi-supervised learning is to learn a classifier h : RN×D × GN → RN×K from a function family H and perform it on the remaining unlabeled nodes . Given a classifier h , the classification for a node i is obtained by ŷi = argmax k∈ { 1 , ... , K } hi ( X , G ) [ k ] , where hi ( X , G ) is the i-th row of h ( X , G ) and hi ( X , G ) [ k ] refers to the k-th element of hi ( X , G ) . Subgroups . In Section 4 , we will present an analysis of the GNN generalization performance on any subgroup of the set of unlabeled nodes , V \ V0 . Note that the analysis on any subgroup is a stronger result than that on the entire unlabeled set , as any set is a subset of itself . Later we will show that the analysis on subgroups ( rather than on the entire set ) further allows us to investigate the accuracy disparity across subgroups . We denote a collection of subgroups of interest as V1 , V2 , . . . , VM ⊆ V \ V0 . In practice , a subgroup can be defined based on an attribute of the nodes ( e.g. , a gender group ) , certain graph-based properties , or an arbitrary partition of the nodes . We also define the size of each subgroup as Nm : = |Vm| , m = 0 , . . . , M . Margin loss on each subgroup . Now we can define the empirical and expected margin loss of any classifier h ∈ H on each subgroup Vm , m = 0 , 1 , . . . , M . Given a sample of observed node labels yi ’ s , the empirical margin loss of h on Vm for a margin γ ≥ 0 is defined as L̂γm ( h ) : = 1 Nm ∑ i∈Vm 1 [ hi ( X , G ) [ yi ] ≤ γ + max k 6=yi hi ( X , G ) [ k ] ] , ( 1 ) where 1 [ · ] is the indicator function . The expected margin loss is the expectation of Eq . ( 1 ) , i.e. , Lγm ( h ) : = Eyi∼Pr ( y|Zi ) , i∈VmL̂ γ m ( h ) . ( 2 ) 5A simple example is gi ( X , G ) = 1|N ( i ) |+1 ( Xi + ∑ j∈N ( i ) Xj ) , where gi ( X , G ) is the i-th row of g ( X , G ) and N ( i ) : = { j | ( i , j ) ∈ E } is the set of 1-hop neighbors of node i . To simplify the notation , we define ym : = { yi } i∈Vm , ∀m = 0 , . . . , M , so that Eq . ( 2 ) can be written as Lγm ( h ) = EymL̂γm ( h ) . We note that the classification risk and empirical risk of h on Vm are respectively equal to L0m ( h ) and L̂0m ( h ) . | This work presents PAC-Bayesian analysis for GNNs under a non-IID semi-supervised learning setup. It first analyzes the generalization performance for general settings, and then applies the results to GNNs. The theoretical results have been backed up with empirical evaluations on several graph benchmarks. | science.mup_single_document_summarization.13664 | [
{
"content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nSubgroup Generalization and Fairness of Graph Neural Networks\n\nMain Content:\n1 Introduction . Graph Neural Networks ( GNNs ) [ 13 , 35 , 20 ] are a family of machine learning models that can be used to model non-Euclidean data as well as inter-related samples in a flexible way . In recent years , there have been enormous successful applications of GNNs in various areas , such as drug discovery [ 18 ] , computer vision [ 29 ] , transportation forecasting [ 49 ] , recommender systems [ 48 ] , etc . Depending on the type of prediction target , the application tasks can be roughly categorized into node-level , edge-level , subgraph-level , and graph-level tasks [ 46 ] . In contrast to the marked empirical success , theoretical understanding of the generalization ability of GNNs has been rather limited . Among the existing literature , some studies [ 9 , 11 , 25 ] focus on the analysis of graph-level tasks where each sample is an entire graph and the samples of graphs are IID . A very limited number of studies [ 36 , 42 ] explore GNN generalization for node-level tasks but they assume the nodes ( and their associated neighborhoods ) are IID samples , which does not align with the commonly seen graph-based semi-supervised learning setups . Baranwal et al . [ 3 ] investigate GNN generalization without IID assumptions but under a specific data generating mechanism . In this work , our first contribution is to provide a novel PAC-Bayesian analysis for the generalization ability of GNNs on node-level tasks with non-IID assumptions . In particular , we assume the node features are fixed and the node labels are independently sampled from distributions conditioned on the node features . We also assume the training set and the test set can be chosen as arbitrary subsets of nodes on the graph . We first prove two general PAC-Bayesian generalization bounds ( Theorem 1 ∗School of Information , University of Michigan , Ann Arbor , Michigan , USA †Equal contribution . ‡Department of EECS , University of Michigan , Ann Arbor , Michigan , USA 4Code available at https : //github.com/TheaperDeng/GNN-Generalization-Fairness . 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . and Theorem 2 ) under this non-IID setup . Subsequently , we derive a generalization bound for GNN ( Theorem 3 ) in terms of characteristics of the GNN models and the node features . Notably , the generalization bound for GNN is influenced by the distance between the test nodes and the training nodes in terms of their aggregated node features . This suggests that , given a fixed training set , test nodes that are “ far away ” from all the training nodes may suffer from larger generalization errors . Based on this analysis , our second contribution is the discovering of a type of unfairness that arises from theoretically predictable accuracy disparity across some subgroups of test nodes . We further conduct a empirical study that investigates the prediction accuracy of four popular GNN models on different subgroups of test nodes . The results on multiple benchmark datasets indicate that there is indeed a significant disparity in test accuracy among these subgroups . We summarize the contributions of this work as follows : ( 1 ) We establish a novel PAC-Bayesian analysis for graph-based semi-supervised learning with non-IID assumptions . ( 2 ) Under this setup , we derive a generalization bound for GNNs that can be applied to an arbitrary subgroup of test nodes . ( 3 ) As an implication of the generalization bound , we predict that there would be an unfairness of GNN predictions that arises from accuracy disparity across subgroups of test nodes . ( 4 ) We empirically verify the existence of accuracy disparity of popular GNN models on multiple benchmark datasets , as predicted by our theoretical analysis . 2 Related Work . 2.1 Generalization of Graph Neural Networks . The majority of existing literature that aims to develop theoretical understandings of GNNs have focused on the expressive power of GNNs ( see Sato [ 34 ] for a survey along this line ) , while the number of studies trying to understand the generalizability of GNNs is rather limited . Among them , some [ 9 , 11 , 25 ] focus on graph-level tasks , the analyses of which can not be easily applied to node-level tasks . As far as we know , Scarselli et al . [ 36 ] , Verma and Zhang [ 42 ] , and Baranwal et al . [ 3 ] are the only existing studies investigating the generalization of GNNs on node-level tasks , even though node-level tasks are more common in reality . Scarselli et al . [ 36 ] present an upper bound of the VC-dimension of GNNs ; Verma and Zhang [ 42 ] derive a stability-based generalization bound for a single-layer GCN [ 20 ] model . Yet , both Scarselli et al . [ 36 ] and Verma and Zhang [ 42 ] ( implicitly ) assume that the training nodes are IID samples from a certain distribution , which does not align with the common practice of node-level semi-supervised learning . Baranwal et al . [ 3 ] investigate the generalization of graph convolution under a specific data generating mechanism ( i.e. , the contextual stochastic block model [ 8 ] ) . Our work presents the first generalization analysis of GNNs for non-IID node-level tasks without strong assumptions on the data generating mechanism . 2.2 Fairness of Machine Learning on Graphs . The fairness issues of machine learning on graphs start to receive research attention recently . Following conventional machine learning fairness literature , the majority of previous work along this line [ 1 , 5–7 , 22 , 32 , 39 , 50 ] concerns about fairness with respect to a given sensitive attribute , such as gender or race , which defines protected groups . In practice , the fairness issues of learning on graphs are much more complicated due to the asymmetric nature of the graph-structured data . However , only a few studies [ 19 ] investigate the unfairness caused by the graph structure without knowing a sensitive feature . Moreover , in a node-level semi-supervised learning task , the non-IID sampling of training nodes brings additional uncertainty to the fairness of the learned models . This work is the first to present a learning theoretic analysis under this setup , which in turn suggests how the graph structure and the selection of training nodes may influence the fairness of machine learning on graphs . 2.3 PAC-Bayesian Analysis . PAC-Bayesian analysis [ 27 ] has become one of the most powerful theoretical framework to analyze the generalization ability of machine learning models . We will briefly introduce the background in Section 3.2 , and refer the readers to a recent tutorial [ 14 ] for a systematic overview of PAC-Bayesian analysis . We note that Liao et al . [ 25 ] recently present a PAC-Bayesian generalization bound for GNNs on IID graph-level tasks . Both Liao et al . [ 25 ] and this work utilize results from Neyshabur et al . [ 30 ] , a PAC-Bayesian analysis for ReLU-activated neural networks , in part of our proofs . Compared to Neyshabur et al . [ 30 ] , the key contribution of Liao et al . [ 25 ] is the derivation of perturbation bounds of two types of GNN architectures ; while the key contribution of this work is the novel analysis under the setup of non-IID node-level tasks . There is also an existing work of PAC-Bayesian analysis for transductive semi-supervised learning [ 4 ] . But it is different from our problem setup and , in particular , it can not be used to analyze the generalization on subgroups . 3 Preliminaries . In this section , we first formulate the problem of node-level semi-supervised learning . We also provide a brief introduction of the PAC-Bayesian framework . 3.1 The Problem Formulation and Notations . Semi-supervised node classification . Let G = ( V , E ) ∈ GN be an undirected graph , with V = { 1 , 2 , . . . , N } being the set of N nodes and E ⊆ V × V being the set of edges . And GN is the space of all undirected graphs with N nodes . The nodes are associated with node features X ∈ RN×D and node labels y ∈ { 1 , 2 , . . . , K } N . In this work , we focus on the transductive node classification setting [ 47 ] , where the node features X and the graph G are observed prior to learning , and every quantity of interest in the analysis will be conditioned on X and G. Without loss of generality , we treat X and G as fixed throughout our analysis , and the randomness comes from the labels y . In particular , we assume that for each node i ∈ V , its label yi is generated from an unknown conditional distribution Pr ( yi | Zi ) , where Z = g ( X , G ) and g : RN×D × GN → RN×D ′ is an aggregation function that aggregates the features over ( multi-hop ) local neighborhoods5 . We also assume that the node labels are generated independently conditional on their respective aggregated features Zi ’ s . Given a small set of the labeled nodes , V0 ⊆ V , the task of node-level semi-supervised learning is to learn a classifier h : RN×D × GN → RN×K from a function family H and perform it on the remaining unlabeled nodes . Given a classifier h , the classification for a node i is obtained by ŷi = argmax k∈ { 1 , ... , K } hi ( X , G ) [ k ] , where hi ( X , G ) is the i-th row of h ( X , G ) and hi ( X , G ) [ k ] refers to the k-th element of hi ( X , G ) . Subgroups . In Section 4 , we will present an analysis of the GNN generalization performance on any subgroup of the set of unlabeled nodes , V \\ V0 . Note that the analysis on any subgroup is a stronger result than that on the entire unlabeled set , as any set is a subset of itself . Later we will show that the analysis on subgroups ( rather than on the entire set ) further allows us to investigate the accuracy disparity across subgroups . We denote a collection of subgroups of interest as V1 , V2 , . . . , VM ⊆ V \\ V0 . In practice , a subgroup can be defined based on an attribute of the nodes ( e.g. , a gender group ) , certain graph-based properties , or an arbitrary partition of the nodes . We also define the size of each subgroup as Nm : = |Vm| , m = 0 , . . . , M . Margin loss on each subgroup . Now we can define the empirical and expected margin loss of any classifier h ∈ H on each subgroup Vm , m = 0 , 1 , . . . , M . Given a sample of observed node labels yi ’ s , the empirical margin loss of h on Vm for a margin γ ≥ 0 is defined as L̂γm ( h ) : = 1 Nm ∑ i∈Vm 1 [ hi ( X , G ) [ yi ] ≤ γ + max k 6=yi hi ( X , G ) [ k ] ] , ( 1 ) where 1 [ · ] is the indicator function . The expected margin loss is the expectation of Eq . ( 1 ) , i.e. , Lγm ( h ) : = Eyi∼Pr ( y|Zi ) , i∈VmL̂ γ m ( h ) . ( 2 ) 5A simple example is gi ( X , G ) = 1|N ( i ) |+1 ( Xi + ∑ j∈N ( i ) Xj ) , where gi ( X , G ) is the i-th row of g ( X , G ) and N ( i ) : = { j | ( i , j ) ∈ E } is the set of 1-hop neighbors of node i . To simplify the notation , we define ym : = { yi } i∈Vm , ∀m = 0 , . . . , M , so that Eq . ( 2 ) can be written as Lγm ( h ) = EymL̂γm ( h ) . We note that the classification risk and empirical risk of h on Vm are respectively equal to L0m ( h ) and L̂0m ( h ) .",
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"content": "This work presents PAC-Bayesian analysis for GNNs under a non-IID semi-supervised learning setup. It first analyzes the generalization performance for general settings, and then applies the results to GNNs. The theoretical results have been backed up with empirical evaluations on several graph benchmarks.",
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science.mup_single_document_summarization | Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.
Title of the Paper:
Recurrent Neural Networks are Universal Filters
Main Content:
∀ > 0 , ∃ RNN-based filter , s.t . lim sup k→∞ ∥∥x̂k|k − E [ xk|Yk ] ∥∥ < , where x̂k|k is RNN-based filter ’ s estimate of state xk at step k conditioned on the observation history and E [ xk|Yk ] is the conditional mean of xk , known as the optimal estimate of the state in minimum mean square error sense . As an interesting special case , the widely used Kalman filter ( KF ) can be synthesized by RNN . 1 INTRODUCTION . Recurrent neural network ( RNN ) is a certain type of neural networks characterized by hidden variables that memorize the history of input sequences , and it has been successfully applied and brought amazing results in many different disciplines including computer vision , natural language processing and optimal control , etc . ( Mikolov et al . ( 2010 ) , Graves et al . ( 2013 ) , Du et al . ( 2015 ) , Fei & Lu ( 2017 ) , Ma et al . ( 2015 ) ) . Its huge empirical success in different engineering disciplines is grounded on the expressive power of RNN . However , how to understand the expressive power of RNN in a quantitative way is not fully understood . Even what RNN expresses is not totally clear . Another type of neural network , deep feed forward neural network has been well characterized as a universal function approximator ( Hornik et al . ( 1989 ) , Park & Sandberg ( 1991 ) ) . However , a similar way to characterize the expressive power of RNN is not obvious . DNN is a mapping from a finite dimensional Euclidean space to another finite dimensional Euclidean space , that is to say it can be regarded as a vector-valued multivariate function . However , RNN is a mapping from a sequence space to another sequence space and the current output depends on both current input and the whole observation history . The input sequence , in principle , can be infinite . The function of RNN is capturing the relationship between input process and output process . For example , in machine translation , the input process ( or the observation process ) is sentence in one language and the output process ( or the state process ) is sentence in another language . And in many other RNN ’ s application scenarios such as traffic forecast and optimal control , the input is a noisy observation or measurement sequence and the output is an estimate sequence of a certain quantity , e.g. , the traffic speed .. We observe that the function of RNN is quite similar to a filter . In our paper , we propose to characterize the expressive power of RNN in a quantitative way from the perspective of filtering . We consider a discrete filtering system as 1 . { xk = f ( xk−1 ) + g ( xk−1 ) wk−1 , yk = h ( xk ) + vk , ( 1 ) where the state xk at time instant k is an n-dimensional vector , f is an n-dimensional vectorvalued function , g is an n × r matrix-valued function , { wk , k = 0 , 1 , · · · } is an r-dimensional white Gaussian process and wk ∼ N ( 0 , Q ) , where Q is the covariance matrix of wk . yk is the m-dimensional observation ( measurement ) process , h is an m-dimensional vector-valued function , { vk , k = 1 , · · · } is an m-dimensional white Gaussian process and vk ∼ N ( 0 , R ) , where R is the covariance matrix of vk . And we assume that { wk , k = 0 , 1 , · · · } , { vk , k = 1 , · · · } and the initial state x0 are jointly independent . We use Yk to denote the sequence of observations up to time instant k , i.e. , Yk : = { y1 , · · · , yk } . ( 2 ) Given the realization of the sequence of observations Yk , the aim of filtering problem is to compute the optimal estimate of xk conditioned on Yk . Not surprisingly , recurrent neural network has been proposed to do filtering . James Ting Ho Lo showed that Recurrent Multi-layer Perceptron can be used to synthesize optimal filter ( Lo ( 1994 ) ) . However , Lo ’ s approach is based on simply copying and storing the whole observation history in the hidden variables and thus require the time horizon to be finite , which is fundamentally limited . Besides Lo ’ s work , many efforts have been made to connect RNN and dynamical system . Wilson & Finkel ( 2009 ) implemented a neural network based Kalman Filter ( KF ) but did not provide theoretical analysis on the approximation error . Parlos et al . ( 2001 ) proposed an algorithmic approach to do nonlinear filtering using recurrent neural network architecture but did not provide theoretical gurantee . Schäfer & Zimmermann ( 2006 ) shows that recurrent neural network is a universal approximator of dynamical system . However , they only consider the deterministic system dynamics and do not analyze the filtering relationship between two stochastic processes . Around the same time our work was under review , a similar paper was also uploaded in arXiv by Lim et al . ( 2019 ) , The results in the arXiv paper are similar to the June , 2018 graduation thesis of the first author of the current paper in the use of sufficient statistics , and emphasize on the design and implementation of the algorithm . Our current paper has been dramatically improved by introducing the error analysis and asymptotic convergence of the algorithm . And our work is inspired by the finite dimensional filter such as Beneš filter Beneš ( 1981 ) . Compared to the existing work , we make the following specific contributions : • Motivated by the similarity between RNN and filter , we propose to use the ability to approximate optimal filter to characterize the expressive power of RNN . Unlike existing work on expressive power of RNN ( Schäfer & Zimmermann ( 2006 ) ) where only deterministic system is considered , we consider a stochastic dynamic system with noisy observations and analyze the capability of RNN to estimate unknown state . • We unify RNN-based filter into Bayesian filtering framework . In this framework , the hidden variables of RNN are interpreted as statistics of the observation history . And the evolution of hidden variables are interpreted as the evolution of statistics . • Based on the Bayesian filtering framework , we derive our main result : Recurrent Neural Networks ( RNN ) are universal approximators of a large class of optimal finite dimensional filters . That is to say , RNN estimator ’ s asymptotic estimation error can be as close to minimum mean square error as desired . As an interesting special case , the widely used Kalman Filter can be synthesized by RNN . The consideration of asymptotic error differentiates us from existing work on expressive power of RNN ( Schäfer & Zimmermann ( 2006 ) ) . 2 PRELIMINARY : THE BAYESIAN FRAMEWORK OF FILTERING . We first introduce the definition of minimum mean square error estimate . Definition 1 ( Minimum Mean Square Error ( MMSE ) Estimate ( Jazwinski ( 1970 ) ) ) . Let x̂ be an estimate of random variable x and LMSE : = ( x− x̂ ) T ( x− x̂ ) . The estimate that minimizes E [ LMSE ] is called the minimum mean square error estimate . Theorem 1 ( Theorem 5.3 in Jazwinski ( 1970 ) ) . Conditioned on the observation history Yk , the minimum mean square error estimate of state xk is the conditional mean E [ xk|Yk ] . Proof . Proof can be found in our appendix A.1 . The Bayesian filtering consists of recursive prediction and update procedures ( Jazwinski ( 1970 ) ) : Prediction Step p ( xk−1|Yk−1 ) → p ( xk|Yk−1 ) : Given the posterior distribution p ( xk−1|Yk−1 ) at instant k − 1 , the prior distribution p ( xk|Yk−1 ) of xk satisfies the Chapman-Kolmogorov equation : p ( xk|Yk−1 ) = ∫ p ( xk|xk−1 ) p ( xk−1|Yk−1 ) dxk−1 ; ( 3 ) Updating Step p ( xk|Yk−1 ) → p ( xk|Yk ) : Given prior distribution p ( xk|Yk−1 ) , when the observation yk at instant k arrives , the posterior distribution p ( xk|Yk ) at instant k is given by equation 4 , p ( xk|Yk ) = p ( yk|xk ) p ( xk|Yk−1 ) ∫ p ( yk|xk ) p ( xk|Yk−1 ) dxk . ( 4 ) Then we can get the MMSE estimate by simply doing integration : E [ xk|Yk ] = ∫ p ( xk|Yk ) xk dxk . ( 5 ) To facilitate subsequent discussion , we make the following definition . Definition 2 ( Sufficient Statistic ( Beneš ( 1981 ) ) ) . If the conditional distribution p ( xk|Yk ) ( or p ( xk|Yk−1 ) ) can be fully determined by a vector-valued function sk|k ( or sk|k−1 ) of the observation sequence Yk ( or Yk−1 ) , then we say sk|k ( or sk|k−1 ) is a sufficient statistic for p ( xk|Yk ) ( or p ( xk|Yk−1 ) ) .1 Because sufficient statistic sk|k fully determines the posterior distribution p ( xk|Yk ) and MMSE estimate E ( xk|Yk ) is a functional of p ( xk|Yk ) , there exists a function γ that maps sk|k to E [ xk|Yk ] ,2 i.e. , E [ xk|Yk ] = γ ( sk|k ) . ( 6 ) 3 RNN BASED FILTER ’ S ARCHITECTURE . Motivated by the Bayesian framework of filtering , we propose the RNN based filter ’ s architecture as shown in Fig . 1 . Our RNN based filter ’ s architecture , Bayesian Filter Net ( BFN ) , consists of three parts : prediction network , update network and estimation network . To mimic the prediction step in Bayesian filtering , we use the prediction network to map the posterior distribution representation vector to a prior distribution representation vector . To mimic the update step in Bayesian filtering , we then use an update network to update the prior state distribution representation vector and the observation to get the posterior state distribution representation vector . Finally , we use an estimation network to map the current posterior state distribution representation vector to current estimation x̂k . We will see in the subsequent discussions , the so-called representation vector or hidden variables indeed can be interpreted as statistics of the underlying conditional distribution . 1 The sufficient statistic sk|k ( or sk|k−1 ) can be any vector-valued function of Yk ( or Yk−1 ) as long as it can fully determine p ( xk|Yk ) ( or p ( xk|Yk−1 ) ) . Taking linear system as an example , sk|k can be the vector composed of the conditional mean and covariance because these two quantities sufficiently determine the conditional density function of the state in linear filtering system . ( See more details in our Appendix A.3 . ) 2For instance , as for the Kalman Filter in linear system , the sufficient statistic sk|k can be chosen to be the conditional mean and conditional covariance , thus γ function can be the function that copy the conditional mean part of sk|k . | This paper shows that RNN (of infinite horizon) can be universal approximators for any stochastic dynamics system. It does that by using the Bayesian framework of filtering, which shows that if sufficient statistics given the observations so far can be computed at each stage of filtering, then the expected hidden states at each stage can be estimated as well. The paper then follows this framework by constructing deep neural network mapping from “observations so-far” to “sufficient statistics” and by universal approximation theorem this is possible. Then as long as after each stage, the image of such a mapping is bounded into a certain region, and if the mapping is contractive (|C_\psi C_\phi| < 1), this can be applied to arbitrarily long sequence (and thus infinite horizon). | science.mup_single_document_summarization.17714 | [
{
"content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nRecurrent Neural Networks are Universal Filters\n\nMain Content:\n∀ > 0 , ∃ RNN-based filter , s.t . lim sup k→∞ ∥∥x̂k|k − E [ xk|Yk ] ∥∥ < , where x̂k|k is RNN-based filter ’ s estimate of state xk at step k conditioned on the observation history and E [ xk|Yk ] is the conditional mean of xk , known as the optimal estimate of the state in minimum mean square error sense . As an interesting special case , the widely used Kalman filter ( KF ) can be synthesized by RNN . 1 INTRODUCTION . Recurrent neural network ( RNN ) is a certain type of neural networks characterized by hidden variables that memorize the history of input sequences , and it has been successfully applied and brought amazing results in many different disciplines including computer vision , natural language processing and optimal control , etc . ( Mikolov et al . ( 2010 ) , Graves et al . ( 2013 ) , Du et al . ( 2015 ) , Fei & Lu ( 2017 ) , Ma et al . ( 2015 ) ) . Its huge empirical success in different engineering disciplines is grounded on the expressive power of RNN . However , how to understand the expressive power of RNN in a quantitative way is not fully understood . Even what RNN expresses is not totally clear . Another type of neural network , deep feed forward neural network has been well characterized as a universal function approximator ( Hornik et al . ( 1989 ) , Park & Sandberg ( 1991 ) ) . However , a similar way to characterize the expressive power of RNN is not obvious . DNN is a mapping from a finite dimensional Euclidean space to another finite dimensional Euclidean space , that is to say it can be regarded as a vector-valued multivariate function . However , RNN is a mapping from a sequence space to another sequence space and the current output depends on both current input and the whole observation history . The input sequence , in principle , can be infinite . The function of RNN is capturing the relationship between input process and output process . For example , in machine translation , the input process ( or the observation process ) is sentence in one language and the output process ( or the state process ) is sentence in another language . And in many other RNN ’ s application scenarios such as traffic forecast and optimal control , the input is a noisy observation or measurement sequence and the output is an estimate sequence of a certain quantity , e.g. , the traffic speed .. We observe that the function of RNN is quite similar to a filter . In our paper , we propose to characterize the expressive power of RNN in a quantitative way from the perspective of filtering . We consider a discrete filtering system as 1 . { xk = f ( xk−1 ) + g ( xk−1 ) wk−1 , yk = h ( xk ) + vk , ( 1 ) where the state xk at time instant k is an n-dimensional vector , f is an n-dimensional vectorvalued function , g is an n × r matrix-valued function , { wk , k = 0 , 1 , · · · } is an r-dimensional white Gaussian process and wk ∼ N ( 0 , Q ) , where Q is the covariance matrix of wk . yk is the m-dimensional observation ( measurement ) process , h is an m-dimensional vector-valued function , { vk , k = 1 , · · · } is an m-dimensional white Gaussian process and vk ∼ N ( 0 , R ) , where R is the covariance matrix of vk . And we assume that { wk , k = 0 , 1 , · · · } , { vk , k = 1 , · · · } and the initial state x0 are jointly independent . We use Yk to denote the sequence of observations up to time instant k , i.e. , Yk : = { y1 , · · · , yk } . ( 2 ) Given the realization of the sequence of observations Yk , the aim of filtering problem is to compute the optimal estimate of xk conditioned on Yk . Not surprisingly , recurrent neural network has been proposed to do filtering . James Ting Ho Lo showed that Recurrent Multi-layer Perceptron can be used to synthesize optimal filter ( Lo ( 1994 ) ) . However , Lo ’ s approach is based on simply copying and storing the whole observation history in the hidden variables and thus require the time horizon to be finite , which is fundamentally limited . Besides Lo ’ s work , many efforts have been made to connect RNN and dynamical system . Wilson & Finkel ( 2009 ) implemented a neural network based Kalman Filter ( KF ) but did not provide theoretical analysis on the approximation error . Parlos et al . ( 2001 ) proposed an algorithmic approach to do nonlinear filtering using recurrent neural network architecture but did not provide theoretical gurantee . Schäfer & Zimmermann ( 2006 ) shows that recurrent neural network is a universal approximator of dynamical system . However , they only consider the deterministic system dynamics and do not analyze the filtering relationship between two stochastic processes . Around the same time our work was under review , a similar paper was also uploaded in arXiv by Lim et al . ( 2019 ) , The results in the arXiv paper are similar to the June , 2018 graduation thesis of the first author of the current paper in the use of sufficient statistics , and emphasize on the design and implementation of the algorithm . Our current paper has been dramatically improved by introducing the error analysis and asymptotic convergence of the algorithm . And our work is inspired by the finite dimensional filter such as Beneš filter Beneš ( 1981 ) . Compared to the existing work , we make the following specific contributions : • Motivated by the similarity between RNN and filter , we propose to use the ability to approximate optimal filter to characterize the expressive power of RNN . Unlike existing work on expressive power of RNN ( Schäfer & Zimmermann ( 2006 ) ) where only deterministic system is considered , we consider a stochastic dynamic system with noisy observations and analyze the capability of RNN to estimate unknown state . • We unify RNN-based filter into Bayesian filtering framework . In this framework , the hidden variables of RNN are interpreted as statistics of the observation history . And the evolution of hidden variables are interpreted as the evolution of statistics . • Based on the Bayesian filtering framework , we derive our main result : Recurrent Neural Networks ( RNN ) are universal approximators of a large class of optimal finite dimensional filters . That is to say , RNN estimator ’ s asymptotic estimation error can be as close to minimum mean square error as desired . As an interesting special case , the widely used Kalman Filter can be synthesized by RNN . The consideration of asymptotic error differentiates us from existing work on expressive power of RNN ( Schäfer & Zimmermann ( 2006 ) ) . 2 PRELIMINARY : THE BAYESIAN FRAMEWORK OF FILTERING . We first introduce the definition of minimum mean square error estimate . Definition 1 ( Minimum Mean Square Error ( MMSE ) Estimate ( Jazwinski ( 1970 ) ) ) . Let x̂ be an estimate of random variable x and LMSE : = ( x− x̂ ) T ( x− x̂ ) . The estimate that minimizes E [ LMSE ] is called the minimum mean square error estimate . Theorem 1 ( Theorem 5.3 in Jazwinski ( 1970 ) ) . Conditioned on the observation history Yk , the minimum mean square error estimate of state xk is the conditional mean E [ xk|Yk ] . Proof . Proof can be found in our appendix A.1 . The Bayesian filtering consists of recursive prediction and update procedures ( Jazwinski ( 1970 ) ) : Prediction Step p ( xk−1|Yk−1 ) → p ( xk|Yk−1 ) : Given the posterior distribution p ( xk−1|Yk−1 ) at instant k − 1 , the prior distribution p ( xk|Yk−1 ) of xk satisfies the Chapman-Kolmogorov equation : p ( xk|Yk−1 ) = ∫ p ( xk|xk−1 ) p ( xk−1|Yk−1 ) dxk−1 ; ( 3 ) Updating Step p ( xk|Yk−1 ) → p ( xk|Yk ) : Given prior distribution p ( xk|Yk−1 ) , when the observation yk at instant k arrives , the posterior distribution p ( xk|Yk ) at instant k is given by equation 4 , p ( xk|Yk ) = p ( yk|xk ) p ( xk|Yk−1 ) ∫ p ( yk|xk ) p ( xk|Yk−1 ) dxk . ( 4 ) Then we can get the MMSE estimate by simply doing integration : E [ xk|Yk ] = ∫ p ( xk|Yk ) xk dxk . ( 5 ) To facilitate subsequent discussion , we make the following definition . Definition 2 ( Sufficient Statistic ( Beneš ( 1981 ) ) ) . If the conditional distribution p ( xk|Yk ) ( or p ( xk|Yk−1 ) ) can be fully determined by a vector-valued function sk|k ( or sk|k−1 ) of the observation sequence Yk ( or Yk−1 ) , then we say sk|k ( or sk|k−1 ) is a sufficient statistic for p ( xk|Yk ) ( or p ( xk|Yk−1 ) ) .1 Because sufficient statistic sk|k fully determines the posterior distribution p ( xk|Yk ) and MMSE estimate E ( xk|Yk ) is a functional of p ( xk|Yk ) , there exists a function γ that maps sk|k to E [ xk|Yk ] ,2 i.e. , E [ xk|Yk ] = γ ( sk|k ) . ( 6 ) 3 RNN BASED FILTER ’ S ARCHITECTURE . Motivated by the Bayesian framework of filtering , we propose the RNN based filter ’ s architecture as shown in Fig . 1 . Our RNN based filter ’ s architecture , Bayesian Filter Net ( BFN ) , consists of three parts : prediction network , update network and estimation network . To mimic the prediction step in Bayesian filtering , we use the prediction network to map the posterior distribution representation vector to a prior distribution representation vector . To mimic the update step in Bayesian filtering , we then use an update network to update the prior state distribution representation vector and the observation to get the posterior state distribution representation vector . Finally , we use an estimation network to map the current posterior state distribution representation vector to current estimation x̂k . We will see in the subsequent discussions , the so-called representation vector or hidden variables indeed can be interpreted as statistics of the underlying conditional distribution . 1 The sufficient statistic sk|k ( or sk|k−1 ) can be any vector-valued function of Yk ( or Yk−1 ) as long as it can fully determine p ( xk|Yk ) ( or p ( xk|Yk−1 ) ) . Taking linear system as an example , sk|k can be the vector composed of the conditional mean and covariance because these two quantities sufficiently determine the conditional density function of the state in linear filtering system . ( See more details in our Appendix A.3 . ) 2For instance , as for the Kalman Filter in linear system , the sufficient statistic sk|k can be chosen to be the conditional mean and conditional covariance , thus γ function can be the function that copy the conditional mean part of sk|k .",
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"content": "This paper shows that RNN (of infinite horizon) can be universal approximators for any stochastic dynamics system. It does that by using the Bayesian framework of filtering, which shows that if sufficient statistics given the observations so far can be computed at each stage of filtering, then the expected hidden states at each stage can be estimated as well. The paper then follows this framework by constructing deep neural network mapping from “observations so-far” to “sufficient statistics” and by universal approximation theorem this is possible. Then as long as after each stage, the image of such a mapping is bounded into a certain region, and if the mapping is contractive (|C_\\psi C_\\phi| < 1), this can be applied to arbitrarily long sequence (and thus infinite horizon).",
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science.bioasq_list_qa | Examine the following excerpts from biomedical literature. Your objective is to extract relevant information to answer the posed question. Format your answer as a JSON array.
Context: MECOM-associated syndrome: a heterogeneous inherited bone marrow failure syndrome with amegakaryocytic thrombocytopenia.
Heterozygous mutations in MECOM (MDS1 and EVI1 complex locus) have been reported to be causative of a rare association of congenital amegakaryocytic thrombocytopenia and radioulnar synostosis. Here we report on 12 patients with congenital hypomegakaryocytic thrombocytopenia caused by MECOM mutations (including 10 novel mutations). The mutations affected different functional domains of the EVI1 protein. The spectrum of phenotypes was much broader than initially reported for the first 3 patients; we found familial as well as sporadic cases, and the clinical spectrum ranged from isolated radioulnar synostosis with no or mild hematological involvement to severe bone marrow failure without obvious skeletal abnormality. The clinical picture included radioulnar synostosis, bone marrow failure, clinodactyly, cardiac and renal malformations, B-cell deficiency, and presenile hearing loss. No single clinical manifestation was detected in all patients affected by MECOM mutations. Radioulnar synostosis and B-cell deficiency were observed only in patients with mutations affecting a short region in the C-terminal zinc finger domain of EVI1. We propose the term MECOM-associated syndrome for this heterogeneous hereditary disease and inclusion of MECOM sequencing in the diagnostic workup of congenital bone marrow failure.
Heterozygous mutations in MECOM (MDS1 and EVI1 complex locus) have been reported to be causative of a rare association of congenital amegakaryocytic thrombocytopenia and radioulnar synostosis.
Radioulnar synostosis and B-cell deficiency were observed only in patients with mutations affecting a short region in the C-terminal zinc finger domain of EVI1.
The spectrum of phenotypes was much broader than initially reported for the first 3 patients; we found familial as well as sporadic cases, and the clinical spectrum ranged from isolated radioulnar synostosis with no or mild hematological involvement to severe bone marrow failure without obvious skeletal abnormality.
The clinical picture included radioulnar synostosis, bone marrow failure, clinodactyly, cardiac and renal malformations, B-cell deficiency, and presenile hearing loss.
Question: List clinical symptoms of the MECOM-associated syndrome | ["radioulnar synostosis", "bone marrow failure", "clinodactyly", "cardiac malformations", "renal malformations", "B-cell deficiency", "presenile hearing loss"] | science.bioasq_list_qa.611 | [
{
"content": "Examine the following excerpts from biomedical literature. Your objective is to extract relevant information to answer the posed question. Format your answer as a JSON array.\n\nContext: MECOM-associated syndrome: a heterogeneous inherited bone marrow failure syndrome with amegakaryocytic thrombocytopenia.\nHeterozygous mutations in MECOM (MDS1 and EVI1 complex locus) have been reported to be causative of a rare association of congenital amegakaryocytic thrombocytopenia and radioulnar synostosis. Here we report on 12 patients with congenital hypomegakaryocytic thrombocytopenia caused by MECOM mutations (including 10 novel mutations). The mutations affected different functional domains of the EVI1 protein. The spectrum of phenotypes was much broader than initially reported for the first 3 patients; we found familial as well as sporadic cases, and the clinical spectrum ranged from isolated radioulnar synostosis with no or mild hematological involvement to severe bone marrow failure without obvious skeletal abnormality. The clinical picture included radioulnar synostosis, bone marrow failure, clinodactyly, cardiac and renal malformations, B-cell deficiency, and presenile hearing loss. No single clinical manifestation was detected in all patients affected by MECOM mutations. Radioulnar synostosis and B-cell deficiency were observed only in patients with mutations affecting a short region in the C-terminal zinc finger domain of EVI1. We propose the term MECOM-associated syndrome for this heterogeneous hereditary disease and inclusion of MECOM sequencing in the diagnostic workup of congenital bone marrow failure.\nHeterozygous mutations in MECOM (MDS1 and EVI1 complex locus) have been reported to be causative of a rare association of congenital amegakaryocytic thrombocytopenia and radioulnar synostosis.\nRadioulnar synostosis and B-cell deficiency were observed only in patients with mutations affecting a short region in the C-terminal zinc finger domain of EVI1.\nThe spectrum of phenotypes was much broader than initially reported for the first 3 patients; we found familial as well as sporadic cases, and the clinical spectrum ranged from isolated radioulnar synostosis with no or mild hematological involvement to severe bone marrow failure without obvious skeletal abnormality.\nThe clinical picture included radioulnar synostosis, bone marrow failure, clinodactyly, cardiac and renal malformations, B-cell deficiency, and presenile hearing loss.\n\nQuestion: List clinical symptoms of the MECOM-associated syndrome",
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"content": "[\"radioulnar synostosis\", \"bone marrow failure\", \"clinodactyly\", \"cardiac malformations\", \"renal malformations\", \"B-cell deficiency\", \"presenile hearing loss\"]",
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science.scifact_entailment | Determine the relationship between them and generate a JSON response. Scientific Claim:
Guanine nucleotide dissociation inhibitor (Rho-GDI) interacts with the p75 NTR death domain
Research Abstract:
The multifunctional signaling protein p75 neurotrophin receptor (p75(NTR)) is a central regulator and major contributor to the highly invasive nature of malignant gliomas.
Here, we show that neurotrophin-dependent regulated intramembrane proteolysis (RIP) of p75(NTR) is required for p75(NTR)-mediated glioma invasion, and identify a previously unnamed process for targeted glioma therapy.
Expression of cleavage-resistant chimeras of p75(NTR) or treatment of animals bearing p75(NTR)-positive intracranial tumors with clinically applicable gamma-secretase inhibitors resulted in dramatically decreased glioma invasion and prolonged survival.
Importantly, proteolytic processing of p75(NTR) was observed in p75(NTR)-positive patient tumor specimens and brain tumor initiating cells.
This work highlights the importance of p75(NTR) as a therapeutic target, suggesting that gamma-secretase inhibitors may have direct clinical application for the treatment of malignant glioma. | {"verdict": "NEI", "evidence": []} | science.scifact_entailment.313 | [
{
"content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nGuanine nucleotide dissociation inhibitor (Rho-GDI) interacts with the p75 NTR death domain\n\nResearch Abstract:\nThe multifunctional signaling protein p75 neurotrophin receptor (p75(NTR)) is a central regulator and major contributor to the highly invasive nature of malignant gliomas.\nHere, we show that neurotrophin-dependent regulated intramembrane proteolysis (RIP) of p75(NTR) is required for p75(NTR)-mediated glioma invasion, and identify a previously unnamed process for targeted glioma therapy.\nExpression of cleavage-resistant chimeras of p75(NTR) or treatment of animals bearing p75(NTR)-positive intracranial tumors with clinically applicable gamma-secretase inhibitors resulted in dramatically decreased glioma invasion and prolonged survival.\nImportantly, proteolytic processing of p75(NTR) was observed in p75(NTR)-positive patient tumor specimens and brain tumor initiating cells.\nThis work highlights the importance of p75(NTR) as a therapeutic target, suggesting that gamma-secretase inhibitors may have direct clinical application for the treatment of malignant glioma.",
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"content": "{\"verdict\": \"NEI\", \"evidence\": []}",
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science.mup_single_document_summarization | Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.
Title of the Paper:
Stochastic Solutions for Linear Inverse Problems using the Prior Implicit in a Denoiser
Main Content:
1 Introduction . Many problems in image processing and computer vision rely , explicitly or implicitly , on prior probability models . Describing the full density of natural images is a daunting problem , given the high dimensionality of the signal space . Traditionally , models have been developed by combining assumed symmetry properties ( e.g. , translation-invariance , dilation-invariance ) , with simple parametric forms ( e.g. , Gaussian , exponential , Gaussian mixtures ) , often within pre-specified transformed coordinate systems ( e.g. , Fourier transform , multi-scale wavelets ) . While these models have led to steady advances in problems such as denoising ( e.g. , [ 1–7 ] ) , they are too simplistic to generate complex features that occur in our visual world , or to solve more demanding statistical inference problems . In recent years , nearly all problems in image processing and computer vision have been revolutionalized by the use of deep Convolutional Neural Networks ( CNNs ) . These networks are generally optimized in supervised fashion to obtain a direct input-output mapping for a specific task . This approach does not explicitly rely on a known prior , and offers performance far superior to prior-based methods . The downside , however , is that the learned mappings are intertwined with the task for which they are optimized , and in most cases require training a separate network for each new application . In contrast , a prior probability model can provide a universal substrate for solving inference 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . problems . The superior performance of CNNs suggests that they embed , implicitly , sophisticated prior knowledge of images . These implicit priors arise from a combination of the distribution of the training data , the architecture of the network [ 8 ] , regularization terms included in the optimization objective , and the optimization algorithm . Here , our goal is to extract the implicit prior from a network trained for denoising and use it to solve other inverse problems without further training . We choose denoising not because it is of particular importance or interest , but because we can make the relationship between mapping of a denoiser and a prior explicit . We combine the advantages of prior-based and mapping-based approaches , deriving a general algorithm for solving linear inverse problems using the prior implicit in a trained denoiser . We start with a result from classical statistics [ 9 ] that states that a denoiser that aims to minimize squared error of images corrupted by additive Gaussian noise may be interpreted as computing the gradient of the log of the density of noisy images . This result is related to Score Matching [ 10 ] , but provides a more direct relationship between least-squares optimal denoising and the embedded prior [ 11 , 12 ] . We develop a stochastic ascent algorithm that uses this denoiser-estimated gradient to draw high-probability samples from the embedded prior . Importantly , we use a blind denoiser that can handle noise contamination of unknown amplitude , which provides a means of adaptively controlling the gradient step sizes and the amplitude of injected noise , enabling robust and efficient convergence . We then modify the algorithm to incorporate constraints arising from any deterministic linear measurement of an image . The resulting procedure generates high-probability samples from the prior conditioned on the measurements , thus providing a general stochastic solution for any deterministic linear inverse problem . We demonstrate that our method produces visually high-quality results in recovering missing pixels , and state-of-the-art levels of unsupervised performance on superresolution , deblurring and compressive sensing.1 Earlier versions of this work were presented in [ 13 ] . This work is closely related to two lines of research . Nearly a decade ago , a strategy known as Plug-and-Play ( P & P ) was proposed for using a denoiser as a regularizer in solving other inverse problems [ 14 ] , and a number of recent extensions have used this concept to develop MAP solutions for linear inverse problems [ 15–22 , 12 ] . Generally , the objective is decoupled into data fidelity and regularization terms , introducing a slack variable for use in a proximal optimization algorithm ( e.g. , ADMM ) . The proximal operator of the regularization term is interpreted as the MAP solution of a denoising problem , and is replaced by a denoiser . Of particular relevance to our work , recent publications have proven convergence of such algorithms when used in conjunction with MMSE denoisers [ 23 , 24 ] . A parallel line of research has focused on the use of generative models based on Score Matching [ 25–30 ] . The connection between Score Matching [ 10 ] and denoising autoencoders [ 31 ] was first shown in [ 32 ] , by proving that the training criterion of a denoising autoencoder is equivalent to matching the score of the model and a Parzan density estimate of the data . Most recently , this idea has been used as the basis for an MCMC algorithm for sampling from the prior implicit in a CNN denoiser [ 29 ] . In Section 4 we elaborate on how these methods are related to our results . 1.1 Image priors , manifolds , and noisy observations . Digital photographic images lie in a high-dimensional space ( RN , where N is the number of pixels ) , and simple thought experiments suggest that they are concentrated on or near low-dimensional manifolds whose local coordinates represent continuous deformations and intensity variations . In contrast , images generated with random pixels are almost always feature and content free , and thus not considered to be part of this manifold . We can associate with this manifold a prior probability model , p ( x ) , by assuming that images within the manifold have constant or slowly-varying probability , while unnatural or distorted images ( which lie off the manifold ) have low or zero probability . Suppose we make a noisy observation of an image , y = x + z , where x 2 RN is the original image drawn from p ( x ) , and z ⇠ N ( 0 , 2IN ) is a sample of Gaussian white noise . The observation density p ( y ) is related to the prior p ( x ) via marginalization : p ( y ) = Z p ( y|x ) p ( x ) dx = Z g ( y x ) p ( x ) dx , ( 1 ) where g ( z ) is the Gaussian noise distribution . Equation ( 1 ) is in the form of a convolution , and thus p ( y ) is a Gaussian-blurred version of the signal prior , p ( x ) . Moreover , the family of observation 1A software implementation of the sampling and linear inverse algorithms is available at https : //github . com/LabForComputationalVision/universal_inverse_problem densities over different noise variances , p ( y ) , forms a Gaussian scale-space representation of the prior [ 33 , 34 ] , analogous to the temporal evolution of a diffusion process . 1.2 Least squares denoising and CNNs . Given a noisy observation , y , the minimum mean squared error ( MMSE ) estimate of the true signal is well known to be the conditional mean of the posterior density : x̂ ( y ) = Z xp ( x|y ) dx = Z x p ( y|x ) p ( x ) p ( y ) dx ( 2 ) The structure of the equation mirrors the traditional approach to the problem : one chooses a prior probability model , p ( x ) , combines it with a likelihood function describing the noisy measurement process , p ( y|x ) , and solves . Modern denoising solutions , on the other hand , are often based on supervised learning of a direct mapping from noisy to denoised images . One expresses the estimation function ( as opposed to the prior ) in parametric form , and sets the parameters by minimizing the denoising MSE over a large training set of example signals and their noise-corrupted counterparts [ 35– 38 ] . Current state-of-the-art denoising results using CNNs obtained with this supervised approach are far superior to results of previous methods [ 39–41 ] . Recent analysis of these networks demonstrates that when they are trained to handle a broad range of noise levels , they perform an approximate projection onto a low-dimensional subspace [ 42 ] . In our context , we interpret this subspace as a tangent hyperplane of the image manifold . 1.3 Exposing the implicit prior through Empirical Bayes estimation . Trained CNN denoisers contain detailed prior knowledge of image structure , but Eq . ( 2 ) suggests that it is embedded within a high-dimensional integral . How can we make use of this implicit prior ? Recent results have derived relationships between Score Matching density estimates and denoising , and have used these relationships to make use of implicit prior information [ 43 , 44 , 29 , 45 ] . Here , we exploit a more direct but less-known result from the literature on Empirical Bayesian estimation . The idea was introduced in [ 46 ] , extended to the case of Gaussian additive noise in [ 9 ] ( see also [ 12 ] ) , and generalized to many other measurement models [ 11 ] . In the case of additive Gaussian noise , one can rewrite the estimator of Eq . ( 2 ) as : x̂ ( y ) = y + 2ry log p ( y ) . ( 3 ) The proof is relatively straightforward . The gradient of the observation density of Eq . ( 1 ) is : ry p ( y ) = 1 2 Z ( x y ) g ( y x ) p ( x ) dx = 1 2 Z ( x y ) p ( y , x ) dx . Multiplying both sides by 2/p ( y ) and separating the right side into two terms gives : 2 ry p ( y ) p ( y ) = Z xp ( x|y ) dx Z yp ( x|y ) dx = x̂ ( y ) y . Rearranging terms and using the chain rule to compute the gradient of the log gives Eq . ( 3 ) . This remarkable result re-expresses the integral over the prior and likelihood of Eq . ( 2 ) in terms of a gradient . Note that 1 ) the relevant density is not the prior , p ( x ) , but the noisy observation density , p ( y ) ; 2 ) the gradient is computed on the log density ( the associated “ energy function ” ) ; and 3 ) the gradient adjustment is not iterative - the estimate is achieved in a single step , and holds for any noise level , . 2 Drawing high-probability samples from the implicit prior . Suppose we wish to draw a sample from the prior implicit in a denoiser . Equation ( 3 ) allows us to generate an image proportional to the gradient of log p ( y ) by computing the denoiser residual , f ( y ) = x̂ ( y ) y . Song and Ermon [ 29 ] developed a Markov chain Monte Carlo ( MCMC ) scheme , combining gradient steps derived from Score Matching and injected noise in a Langevin sampling algorithm to draw samples from a sequence of densities p ( y ) , while reducing in a sequence of discrete steps , each associated with an appropriately trained denoiser . In contrast , starting from a random initialization , y0 , we aim to find a high-probability image ( i.e. , an image from the manifold ) using a more direct and efficient stochastic gradient ascent procedure . | The paper derives an algorithm for solving linear inverse problems using the prior that is implicit in an end-to-end denoiser in the form of a CNN. Consider a denoising algorithm in form of a CNN that takes a noisy observation $y = x+z$ as input and yields an estimate of the signal $x$. CNNs trained end-to-end yield state-of-the-art denoising performance. After training, the image/signal prior is implicit in the CNN. The paper notes that the least-squares-estimate of the signal given a prior $p(x)$ is $$ \hat x(y) = y + \sigma^2 \nabla_y \log p(y), $$ where $p(y)$ is the noisy observation density. Based on this relation, the paper proposes a method to obtain a high-probability image (sample) from the prior implicit in a denoiser. The idea is as follows: We can denoise a given image by evaluating $\hat x(y)$, and by computing the residual $f(y) = \hat x(y) - y$ we obtain an image that is proportional to $\nabla_y \log p(y)$. Based on this insight, the paper proposes to sample from the prior implicit in a denoiser by running stochastic gradient ascent through iterating $y_{t+1} = y_t + h_t f(y_t) + \gamma_t z_t$, where $z_t$ is Gaussian noise, and $h_t$ and $\gamma_t$ are tuning parameters. The paper demonstrates (Fig. 2) that this method can yield realistic samples of low-dimensional images. Next, the paper then modifies this approach to solve linear inverse problems by obtaining a high-probability sample based on partial linear measurements of a signal/image. The paper evaluates the method on standard inverse problems, including super-resolution, deblurring, and compressive sensing. The paper shows that the method works well, both in numbers and by providing example images. | science.mup_single_document_summarization.11891 | [
{
"content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nStochastic Solutions for Linear Inverse Problems using the Prior Implicit in a Denoiser\n\nMain Content:\n1 Introduction . Many problems in image processing and computer vision rely , explicitly or implicitly , on prior probability models . Describing the full density of natural images is a daunting problem , given the high dimensionality of the signal space . Traditionally , models have been developed by combining assumed symmetry properties ( e.g. , translation-invariance , dilation-invariance ) , with simple parametric forms ( e.g. , Gaussian , exponential , Gaussian mixtures ) , often within pre-specified transformed coordinate systems ( e.g. , Fourier transform , multi-scale wavelets ) . While these models have led to steady advances in problems such as denoising ( e.g. , [ 1–7 ] ) , they are too simplistic to generate complex features that occur in our visual world , or to solve more demanding statistical inference problems . In recent years , nearly all problems in image processing and computer vision have been revolutionalized by the use of deep Convolutional Neural Networks ( CNNs ) . These networks are generally optimized in supervised fashion to obtain a direct input-output mapping for a specific task . This approach does not explicitly rely on a known prior , and offers performance far superior to prior-based methods . The downside , however , is that the learned mappings are intertwined with the task for which they are optimized , and in most cases require training a separate network for each new application . In contrast , a prior probability model can provide a universal substrate for solving inference 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . problems . The superior performance of CNNs suggests that they embed , implicitly , sophisticated prior knowledge of images . These implicit priors arise from a combination of the distribution of the training data , the architecture of the network [ 8 ] , regularization terms included in the optimization objective , and the optimization algorithm . Here , our goal is to extract the implicit prior from a network trained for denoising and use it to solve other inverse problems without further training . We choose denoising not because it is of particular importance or interest , but because we can make the relationship between mapping of a denoiser and a prior explicit . We combine the advantages of prior-based and mapping-based approaches , deriving a general algorithm for solving linear inverse problems using the prior implicit in a trained denoiser . We start with a result from classical statistics [ 9 ] that states that a denoiser that aims to minimize squared error of images corrupted by additive Gaussian noise may be interpreted as computing the gradient of the log of the density of noisy images . This result is related to Score Matching [ 10 ] , but provides a more direct relationship between least-squares optimal denoising and the embedded prior [ 11 , 12 ] . We develop a stochastic ascent algorithm that uses this denoiser-estimated gradient to draw high-probability samples from the embedded prior . Importantly , we use a blind denoiser that can handle noise contamination of unknown amplitude , which provides a means of adaptively controlling the gradient step sizes and the amplitude of injected noise , enabling robust and efficient convergence . We then modify the algorithm to incorporate constraints arising from any deterministic linear measurement of an image . The resulting procedure generates high-probability samples from the prior conditioned on the measurements , thus providing a general stochastic solution for any deterministic linear inverse problem . We demonstrate that our method produces visually high-quality results in recovering missing pixels , and state-of-the-art levels of unsupervised performance on superresolution , deblurring and compressive sensing.1 Earlier versions of this work were presented in [ 13 ] . This work is closely related to two lines of research . Nearly a decade ago , a strategy known as Plug-and-Play ( P & P ) was proposed for using a denoiser as a regularizer in solving other inverse problems [ 14 ] , and a number of recent extensions have used this concept to develop MAP solutions for linear inverse problems [ 15–22 , 12 ] . Generally , the objective is decoupled into data fidelity and regularization terms , introducing a slack variable for use in a proximal optimization algorithm ( e.g. , ADMM ) . The proximal operator of the regularization term is interpreted as the MAP solution of a denoising problem , and is replaced by a denoiser . Of particular relevance to our work , recent publications have proven convergence of such algorithms when used in conjunction with MMSE denoisers [ 23 , 24 ] . A parallel line of research has focused on the use of generative models based on Score Matching [ 25–30 ] . The connection between Score Matching [ 10 ] and denoising autoencoders [ 31 ] was first shown in [ 32 ] , by proving that the training criterion of a denoising autoencoder is equivalent to matching the score of the model and a Parzan density estimate of the data . Most recently , this idea has been used as the basis for an MCMC algorithm for sampling from the prior implicit in a CNN denoiser [ 29 ] . In Section 4 we elaborate on how these methods are related to our results . 1.1 Image priors , manifolds , and noisy observations . Digital photographic images lie in a high-dimensional space ( RN , where N is the number of pixels ) , and simple thought experiments suggest that they are concentrated on or near low-dimensional manifolds whose local coordinates represent continuous deformations and intensity variations . In contrast , images generated with random pixels are almost always feature and content free , and thus not considered to be part of this manifold . We can associate with this manifold a prior probability model , p ( x ) , by assuming that images within the manifold have constant or slowly-varying probability , while unnatural or distorted images ( which lie off the manifold ) have low or zero probability . Suppose we make a noisy observation of an image , y = x + z , where x 2 RN is the original image drawn from p ( x ) , and z ⇠ N ( 0 , 2IN ) is a sample of Gaussian white noise . The observation density p ( y ) is related to the prior p ( x ) via marginalization : p ( y ) = Z p ( y|x ) p ( x ) dx = Z g ( y x ) p ( x ) dx , ( 1 ) where g ( z ) is the Gaussian noise distribution . Equation ( 1 ) is in the form of a convolution , and thus p ( y ) is a Gaussian-blurred version of the signal prior , p ( x ) . Moreover , the family of observation 1A software implementation of the sampling and linear inverse algorithms is available at https : //github . com/LabForComputationalVision/universal_inverse_problem densities over different noise variances , p ( y ) , forms a Gaussian scale-space representation of the prior [ 33 , 34 ] , analogous to the temporal evolution of a diffusion process . 1.2 Least squares denoising and CNNs . Given a noisy observation , y , the minimum mean squared error ( MMSE ) estimate of the true signal is well known to be the conditional mean of the posterior density : x̂ ( y ) = Z xp ( x|y ) dx = Z x p ( y|x ) p ( x ) p ( y ) dx ( 2 ) The structure of the equation mirrors the traditional approach to the problem : one chooses a prior probability model , p ( x ) , combines it with a likelihood function describing the noisy measurement process , p ( y|x ) , and solves . Modern denoising solutions , on the other hand , are often based on supervised learning of a direct mapping from noisy to denoised images . One expresses the estimation function ( as opposed to the prior ) in parametric form , and sets the parameters by minimizing the denoising MSE over a large training set of example signals and their noise-corrupted counterparts [ 35– 38 ] . Current state-of-the-art denoising results using CNNs obtained with this supervised approach are far superior to results of previous methods [ 39–41 ] . Recent analysis of these networks demonstrates that when they are trained to handle a broad range of noise levels , they perform an approximate projection onto a low-dimensional subspace [ 42 ] . In our context , we interpret this subspace as a tangent hyperplane of the image manifold . 1.3 Exposing the implicit prior through Empirical Bayes estimation . Trained CNN denoisers contain detailed prior knowledge of image structure , but Eq . ( 2 ) suggests that it is embedded within a high-dimensional integral . How can we make use of this implicit prior ? Recent results have derived relationships between Score Matching density estimates and denoising , and have used these relationships to make use of implicit prior information [ 43 , 44 , 29 , 45 ] . Here , we exploit a more direct but less-known result from the literature on Empirical Bayesian estimation . The idea was introduced in [ 46 ] , extended to the case of Gaussian additive noise in [ 9 ] ( see also [ 12 ] ) , and generalized to many other measurement models [ 11 ] . In the case of additive Gaussian noise , one can rewrite the estimator of Eq . ( 2 ) as : x̂ ( y ) = y + 2ry log p ( y ) . ( 3 ) The proof is relatively straightforward . The gradient of the observation density of Eq . ( 1 ) is : ry p ( y ) = 1 2 Z ( x y ) g ( y x ) p ( x ) dx = 1 2 Z ( x y ) p ( y , x ) dx . Multiplying both sides by 2/p ( y ) and separating the right side into two terms gives : 2 ry p ( y ) p ( y ) = Z xp ( x|y ) dx Z yp ( x|y ) dx = x̂ ( y ) y . Rearranging terms and using the chain rule to compute the gradient of the log gives Eq . ( 3 ) . This remarkable result re-expresses the integral over the prior and likelihood of Eq . ( 2 ) in terms of a gradient . Note that 1 ) the relevant density is not the prior , p ( x ) , but the noisy observation density , p ( y ) ; 2 ) the gradient is computed on the log density ( the associated “ energy function ” ) ; and 3 ) the gradient adjustment is not iterative - the estimate is achieved in a single step , and holds for any noise level , . 2 Drawing high-probability samples from the implicit prior . Suppose we wish to draw a sample from the prior implicit in a denoiser . Equation ( 3 ) allows us to generate an image proportional to the gradient of log p ( y ) by computing the denoiser residual , f ( y ) = x̂ ( y ) y . Song and Ermon [ 29 ] developed a Markov chain Monte Carlo ( MCMC ) scheme , combining gradient steps derived from Score Matching and injected noise in a Langevin sampling algorithm to draw samples from a sequence of densities p ( y ) , while reducing in a sequence of discrete steps , each associated with an appropriately trained denoiser . In contrast , starting from a random initialization , y0 , we aim to find a high-probability image ( i.e. , an image from the manifold ) using a more direct and efficient stochastic gradient ascent procedure .",
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"content": "The paper derives an algorithm for solving linear inverse problems using the prior that is implicit in an end-to-end denoiser in the form of a CNN. Consider a denoising algorithm in form of a CNN that takes a noisy observation $y = x+z$ as input and yields an estimate of the signal $x$. CNNs trained end-to-end yield state-of-the-art denoising performance. After training, the image/signal prior is implicit in the CNN. The paper notes that the least-squares-estimate of the signal given a prior $p(x)$ is $$ \\hat x(y) = y + \\sigma^2 \\nabla_y \\log p(y), $$ where $p(y)$ is the noisy observation density. Based on this relation, the paper proposes a method to obtain a high-probability image (sample) from the prior implicit in a denoiser. The idea is as follows: We can denoise a given image by evaluating $\\hat x(y)$, and by computing the residual $f(y) = \\hat x(y) - y$ we obtain an image that is proportional to $\\nabla_y \\log p(y)$. Based on this insight, the paper proposes to sample from the prior implicit in a denoiser by running stochastic gradient ascent through iterating $y_{t+1} = y_t + h_t f(y_t) + \\gamma_t z_t$, where $z_t$ is Gaussian noise, and $h_t$ and $\\gamma_t$ are tuning parameters. The paper demonstrates (Fig. 2) that this method can yield realistic samples of low-dimensional images. Next, the paper then modifies this approach to solve linear inverse problems by obtaining a high-probability sample based on partial linear measurements of a signal/image. The paper evaluates the method on standard inverse problems, including super-resolution, deblurring, and compressive sensing. The paper shows that the method works well, both in numbers and by providing example images.",
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science.mup_single_document_summarization | Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.
Title of the Paper:
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness
Main Content:
1 INTRODUCTION . Recent studies on mode connectivity show that two independently trained deep neural network ( DNN ) models with the same architecture and loss function can be connected on their loss landscape using a high-accuracy/low-loss path characterized by a simple curve ( Garipov et al. , 2018 ; Gotmare et al. , 2018 ; Draxler et al. , 2018 ) . This insight on the loss landscape geometry provides us with easy access to a large number of similar-performing models on the low-loss path between two given models , and Garipov et al . ( 2018 ) use this to devise a new model ensembling method . Another line of recent research reveals interesting geometric properties relating to adversarial robustness of DNNs ( Fawzi et al. , 2017 ; 2018 ; Wang et al. , 2018b ; Yu et al. , 2018 ) . An adversarial data or model is defined to be one that is close to a bonafide data or model in some space , but exhibits unwanted or malicious behavior . Motivated by these geometric perspectives , in this study , we propose to employ mode connectivity to study and improve adversarial robustness of DNNs against different types of threats . A DNN can be possibly tampered by an adversary during different phases in its life cycle . For example , during the training phase , the training data can be corrupted with a designated trigger pattern associated with a target label to implant a backdoor for trojan attack on DNNs ( Gu et al. , 2019 ; Liu et al. , 2018 ) . During the inference phase when a trained model is deployed for task-solving , prediction-evasive attacks are plausible ( Biggio & Roli , 2018 ; Goodfellow et al. , 2015 ; Zhao et al. , 2018 ) , even when the model internal details are unknown to an attacker ( Chen et al. , 2017 ; Ilyas et al. , 2018 ; Zhao et al. , 2019a ) . In this research , we will demonstrate that by using mode connectivity in loss landscapes , we can repair backdoored or error-injected DNNs . We also show that mode 1The code is available at https : //github.com/IBM/model-sanitization connectivity analysis reveals the existence of a robustness loss barrier on the path connecting regular and adversarially-trained models . We motivate the novelty and benefit of using mode connectivity for mitigating training-phase adversarial threats through the following practical scenario : as training DNNs is both time- and resource-consuming , it has become a common trend for users to leverage pre-trained models released in the public domain2 . Users may then perform model fine-tuning or transfer learning with a small set of bonafide data that they have . However , publicly available pre-trained models may carry an unknown but significant risk of tampering by an adversary . It can also be challenging to detect this tampering , as in the case of a backdoor attack3 , since a backdoored model will behave like a regular model in the absence of the embedded trigger . Therefore , it is practically helpful to provide tools to users who wish to utilize pre-trained models while mitigating such adversarial threats . We show that our proposed method using mode connectivity with limited amount of bonafide data can repair backdoored or error-injected DNNs , while greatly countering their adversarial effects . Our main contributions are summarized as follows : • For backdoor and error-injection attacks , we show that the path trained using limited bonafide data connecting two tampered models can be used to repair and redeem the attacked models , thereby resulting in high-accuracy and low-risk models . The performance of mode connectivity is significantly better than several baselines including fine-tuning , training from scratch , pruning , and random weight perturbations . We also provide technical explanations for the effectiveness of our path connection method based on model weight space exploration and similarity analysis of input gradients for clean and tampered data . • For evasion attacks , we use mode connectivity to study standard and adversarial-robustness loss landscapes . We find that between a regular and an adversarially-trained model , training a path with standard loss reveals no barrier , whereas the robustness loss on the same path reveals a barrier . This insight provides a geometric interpretation of the “ no free lunch ” hypothesis in adversarial robustness ( Tsipras et al. , 2019 ; Dohmatob , 2018 ; Bubeck et al. , 2019 ) . We also provide technical explanations for the high correlation observed between the robustness loss and the largest eigenvalue of the input Hessian matrix on the path . • Our experimental results on different DNN architectures ( ResNet and VGG ) and datasets ( CIFAR10 and SVHN ) corroborate the effectiveness of using mode connectivity in loss landscapes to understand and improve adversarial robustness . We also show that our path connection is resilient to the considered adaptive attacks that are aware of our defense . To the best of our knowledge , this is the first work that proposes using mode connectivity approaches for adversarial robustness . 2 BACKGROUND AND RELATED WORK . 2.1 MODE CONNECTIVITY IN LOSS LANDSCAPES . Let w1 and w2 be two sets of model weights corresponding to two neural networks independently trained by minimizing any user-specified loss l ( w ) , such as the cross-entropy loss . Moreover , let φθ ( t ) with t ∈ [ 0 , 1 ] be a continuous piece-wise smooth parametric curve , with parameters θ , such that its two ends are φθ ( 0 ) = w1 and φθ ( 1 ) = w2 . To find a high-accuracy path between w1 and w2 , it is proposed to find the parameters θ that minimize the expectation over a uniform distribution on the curve ( Garipov et al. , 2018 ) , L ( θ ) = Et∼qθ ( t ) [ l ( φθ ( t ) ) ] ( 1 ) where qθ ( t ) is the distribution for sampling the models on the path indexed by t. Since qθ ( t ) depends on θ , in order to render the training of high-accuracy path connection more computationally tractable , ( Garipov et al. , 2018 ; Gotmare et al. , 2018 ) proposed to instead use the following loss term , L ( θ ) = Et∼U ( 0,1 ) [ l ( φθ ( t ) ) ] ( 2 ) 2For example , the Model Zoo project : https : //modelzoo.co 3See the recent call for proposals on Trojans in AI announced by IARPA : https : //www.iarpa.gov/ index.php/research-programs/trojai/trojai-baa where U ( 0 , 1 ) is the uniform distribution on [ 0 , 1 ] . The following functions are commonly used for characterizing the parametric curve function φθ ( t ) . Polygonal chain ( Gomes et al. , 2012 ) . The two trained networks w1 and w2 serve as the endpoints of the chain and the bends of the chain are parameterized by θ . For instance , the case of a chain with one bend is φθ ( t ) = { 2 ( tθ + ( 0.5− t ) ω1 ) , 0 ≤ t ≤ 0.5 2 ( ( t− 0.5 ) ω2 + ( 1− t ) θ ) , 0.5 ≤ t ≤ 1 . ( 3 ) Bezier curve ( Farouki , 2012 ) . A Bezier curve provides a convenient parametrization of smoothness on the paths connecting endpoints . For instance , a quadratic Bezier curve with endpoints w1 and w2 is given by φθ ( t ) = ( 1− t ) 2ω1 + 2t ( 1− t ) θ + t2ω2 , 0 ≤ t ≤ 1 . ( 4 ) It is worth noting that , while current research on mode connectivity mainly focuses on generalization analysis ( Garipov et al. , 2018 ; Gotmare et al. , 2018 ; Draxler et al. , 2018 ; Wang et al. , 2018a ) and has found remarkable applications such as fast model ensembling ( Garipov et al. , 2018 ) , our results show that its implication on adversarial robustness through the lens of loss landscape analysis is a promising , yet largely unexplored , research direction . Yu et al . ( 2018 ) scratched the surface but focused on interpreting decision surface of input space and only considered evasion attacks . 2.2 BACKDOOR , EVASION , AND ERROR-INJECTION ADVERSARIAL ATTACKS . Backdoor attack . Backdoor attack on DNNs is often accomplished by designing a designated trigger pattern with a target label implanted to a subset of training data , which is a specific form of data poisoning ( Biggio et al. , 2012 ; Shafahi et al. , 2018 ; Jagielski et al. , 2018 ) . A backdoored model trained on the corrupted data will output the target label for any data input with the trigger ; and it will behave as a normal model when the trigger is absent . For mitigating backdoor attacks , majority of research focuses on backdoor detection or filtering anomalous data samples from training data for re-training ( Chen et al. , 2018 ; Wang et al. , 2019 ; Tran et al. , 2018 ) , while our aim is to repair backdoored models for models using mode connectivity and limited amount of bonafide data . Evasion attack . Evasion attack is a type of inference-phase adversarial threat that generates adversarial examples by mounting slight modification on a benign data sample to manipulate model prediction ( Biggio & Roli , 2018 ) . For image classification models , evasion attack can be accomplished by adding imperceptible noises to natural images and resulting in misclassification ( Goodfellow et al. , 2015 ; Carlini & Wagner , 2017 ; Xu et al. , 2018 ) . Different from training-phase attacks , evasion attack does not assume access to training data . Moreover , it can be executed even when the model details are unknown to an adversary , via black-box or transfer attacks ( Papernot et al. , 2017 ; Chen et al. , 2017 ; Zhao et al. , 2020 ) . Error-injection attack . Different from attacks modifying data inputs , error-injection attack injects errors to model weights at the inference phase and aims to cause misclassification of certain input samples ( Liu et al. , 2017 ; Zhao et al. , 2019b ) . At the hardware level of a deployed machine learning system , it can be made plausible via laser beam ( Barenghi et al. , 2012 ) and row hammer ( Van Der Veen et al. , 2016 ) to change or flip the logic values of the corresponding bits and thus modifying the model parameters saved in memory . 3 MAIN RESULTS . Here we report the experimental results , provide technical explanations , and elucidate the effectiveness of using mode connectivity for studying and enhancing adversarial robustness in three representative themes : ( i ) backdoor attack ; ( ii ) error-injection attack ; and ( iii ) evasion attack . Our experiments were conducted on different network architectures ( VGG and ResNet ) and datasets ( CIFAR-10 and SVHN ) . The details on experiment setups are given in Appendix A . When connecting models , we use the cross entropy loss and the quadratic Bezier curve as described in ( 4 ) . In what follows , we begin by illustrating the problem setups bridging mode connectivity and adversarial robustness , summarizing the results of high-accuracy ( low-loss ) pathways between untampered models for reference , and then delving into detailed discussions . Depending on the context , we will use the terms error rate and accuracy on clean/adversarial samples interchangeably . The error rate of adversarial samples is equivalent to their attack failure rate as well as 100 % - attack accuracy . | This paper studies leveraging mode connectivity to defend against different types of attacks, including backdoor attacks, adversarial examples, and error-injection attacks. They perform a comprehensive evaluation to show the benign test accuracy and attack success rate over the models in the connected path between pairs of models with the same or different properties, e.g., both are attacked, both are benign, one attacked and one benign, etc., where the connected path is learned using existing algorithms to find the high-accuracy path over two different models. Their evaluation suggests that in certain attack scenarios, exploring the mode connectivity could help find a model that has a high benign accuracy, while with a significantly lower attack success rate than the models at the end points. | science.mup_single_document_summarization.18686 | [
{
"content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nBridging Mode Connectivity in Loss Landscapes and Adversarial Robustness\n\nMain Content:\n1 INTRODUCTION . Recent studies on mode connectivity show that two independently trained deep neural network ( DNN ) models with the same architecture and loss function can be connected on their loss landscape using a high-accuracy/low-loss path characterized by a simple curve ( Garipov et al. , 2018 ; Gotmare et al. , 2018 ; Draxler et al. , 2018 ) . This insight on the loss landscape geometry provides us with easy access to a large number of similar-performing models on the low-loss path between two given models , and Garipov et al . ( 2018 ) use this to devise a new model ensembling method . Another line of recent research reveals interesting geometric properties relating to adversarial robustness of DNNs ( Fawzi et al. , 2017 ; 2018 ; Wang et al. , 2018b ; Yu et al. , 2018 ) . An adversarial data or model is defined to be one that is close to a bonafide data or model in some space , but exhibits unwanted or malicious behavior . Motivated by these geometric perspectives , in this study , we propose to employ mode connectivity to study and improve adversarial robustness of DNNs against different types of threats . A DNN can be possibly tampered by an adversary during different phases in its life cycle . For example , during the training phase , the training data can be corrupted with a designated trigger pattern associated with a target label to implant a backdoor for trojan attack on DNNs ( Gu et al. , 2019 ; Liu et al. , 2018 ) . During the inference phase when a trained model is deployed for task-solving , prediction-evasive attacks are plausible ( Biggio & Roli , 2018 ; Goodfellow et al. , 2015 ; Zhao et al. , 2018 ) , even when the model internal details are unknown to an attacker ( Chen et al. , 2017 ; Ilyas et al. , 2018 ; Zhao et al. , 2019a ) . In this research , we will demonstrate that by using mode connectivity in loss landscapes , we can repair backdoored or error-injected DNNs . We also show that mode 1The code is available at https : //github.com/IBM/model-sanitization connectivity analysis reveals the existence of a robustness loss barrier on the path connecting regular and adversarially-trained models . We motivate the novelty and benefit of using mode connectivity for mitigating training-phase adversarial threats through the following practical scenario : as training DNNs is both time- and resource-consuming , it has become a common trend for users to leverage pre-trained models released in the public domain2 . Users may then perform model fine-tuning or transfer learning with a small set of bonafide data that they have . However , publicly available pre-trained models may carry an unknown but significant risk of tampering by an adversary . It can also be challenging to detect this tampering , as in the case of a backdoor attack3 , since a backdoored model will behave like a regular model in the absence of the embedded trigger . Therefore , it is practically helpful to provide tools to users who wish to utilize pre-trained models while mitigating such adversarial threats . We show that our proposed method using mode connectivity with limited amount of bonafide data can repair backdoored or error-injected DNNs , while greatly countering their adversarial effects . Our main contributions are summarized as follows : • For backdoor and error-injection attacks , we show that the path trained using limited bonafide data connecting two tampered models can be used to repair and redeem the attacked models , thereby resulting in high-accuracy and low-risk models . The performance of mode connectivity is significantly better than several baselines including fine-tuning , training from scratch , pruning , and random weight perturbations . We also provide technical explanations for the effectiveness of our path connection method based on model weight space exploration and similarity analysis of input gradients for clean and tampered data . • For evasion attacks , we use mode connectivity to study standard and adversarial-robustness loss landscapes . We find that between a regular and an adversarially-trained model , training a path with standard loss reveals no barrier , whereas the robustness loss on the same path reveals a barrier . This insight provides a geometric interpretation of the “ no free lunch ” hypothesis in adversarial robustness ( Tsipras et al. , 2019 ; Dohmatob , 2018 ; Bubeck et al. , 2019 ) . We also provide technical explanations for the high correlation observed between the robustness loss and the largest eigenvalue of the input Hessian matrix on the path . • Our experimental results on different DNN architectures ( ResNet and VGG ) and datasets ( CIFAR10 and SVHN ) corroborate the effectiveness of using mode connectivity in loss landscapes to understand and improve adversarial robustness . We also show that our path connection is resilient to the considered adaptive attacks that are aware of our defense . To the best of our knowledge , this is the first work that proposes using mode connectivity approaches for adversarial robustness . 2 BACKGROUND AND RELATED WORK . 2.1 MODE CONNECTIVITY IN LOSS LANDSCAPES . Let w1 and w2 be two sets of model weights corresponding to two neural networks independently trained by minimizing any user-specified loss l ( w ) , such as the cross-entropy loss . Moreover , let φθ ( t ) with t ∈ [ 0 , 1 ] be a continuous piece-wise smooth parametric curve , with parameters θ , such that its two ends are φθ ( 0 ) = w1 and φθ ( 1 ) = w2 . To find a high-accuracy path between w1 and w2 , it is proposed to find the parameters θ that minimize the expectation over a uniform distribution on the curve ( Garipov et al. , 2018 ) , L ( θ ) = Et∼qθ ( t ) [ l ( φθ ( t ) ) ] ( 1 ) where qθ ( t ) is the distribution for sampling the models on the path indexed by t. Since qθ ( t ) depends on θ , in order to render the training of high-accuracy path connection more computationally tractable , ( Garipov et al. , 2018 ; Gotmare et al. , 2018 ) proposed to instead use the following loss term , L ( θ ) = Et∼U ( 0,1 ) [ l ( φθ ( t ) ) ] ( 2 ) 2For example , the Model Zoo project : https : //modelzoo.co 3See the recent call for proposals on Trojans in AI announced by IARPA : https : //www.iarpa.gov/ index.php/research-programs/trojai/trojai-baa where U ( 0 , 1 ) is the uniform distribution on [ 0 , 1 ] . The following functions are commonly used for characterizing the parametric curve function φθ ( t ) . Polygonal chain ( Gomes et al. , 2012 ) . The two trained networks w1 and w2 serve as the endpoints of the chain and the bends of the chain are parameterized by θ . For instance , the case of a chain with one bend is φθ ( t ) = { 2 ( tθ + ( 0.5− t ) ω1 ) , 0 ≤ t ≤ 0.5 2 ( ( t− 0.5 ) ω2 + ( 1− t ) θ ) , 0.5 ≤ t ≤ 1 . ( 3 ) Bezier curve ( Farouki , 2012 ) . A Bezier curve provides a convenient parametrization of smoothness on the paths connecting endpoints . For instance , a quadratic Bezier curve with endpoints w1 and w2 is given by φθ ( t ) = ( 1− t ) 2ω1 + 2t ( 1− t ) θ + t2ω2 , 0 ≤ t ≤ 1 . ( 4 ) It is worth noting that , while current research on mode connectivity mainly focuses on generalization analysis ( Garipov et al. , 2018 ; Gotmare et al. , 2018 ; Draxler et al. , 2018 ; Wang et al. , 2018a ) and has found remarkable applications such as fast model ensembling ( Garipov et al. , 2018 ) , our results show that its implication on adversarial robustness through the lens of loss landscape analysis is a promising , yet largely unexplored , research direction . Yu et al . ( 2018 ) scratched the surface but focused on interpreting decision surface of input space and only considered evasion attacks . 2.2 BACKDOOR , EVASION , AND ERROR-INJECTION ADVERSARIAL ATTACKS . Backdoor attack . Backdoor attack on DNNs is often accomplished by designing a designated trigger pattern with a target label implanted to a subset of training data , which is a specific form of data poisoning ( Biggio et al. , 2012 ; Shafahi et al. , 2018 ; Jagielski et al. , 2018 ) . A backdoored model trained on the corrupted data will output the target label for any data input with the trigger ; and it will behave as a normal model when the trigger is absent . For mitigating backdoor attacks , majority of research focuses on backdoor detection or filtering anomalous data samples from training data for re-training ( Chen et al. , 2018 ; Wang et al. , 2019 ; Tran et al. , 2018 ) , while our aim is to repair backdoored models for models using mode connectivity and limited amount of bonafide data . Evasion attack . Evasion attack is a type of inference-phase adversarial threat that generates adversarial examples by mounting slight modification on a benign data sample to manipulate model prediction ( Biggio & Roli , 2018 ) . For image classification models , evasion attack can be accomplished by adding imperceptible noises to natural images and resulting in misclassification ( Goodfellow et al. , 2015 ; Carlini & Wagner , 2017 ; Xu et al. , 2018 ) . Different from training-phase attacks , evasion attack does not assume access to training data . Moreover , it can be executed even when the model details are unknown to an adversary , via black-box or transfer attacks ( Papernot et al. , 2017 ; Chen et al. , 2017 ; Zhao et al. , 2020 ) . Error-injection attack . Different from attacks modifying data inputs , error-injection attack injects errors to model weights at the inference phase and aims to cause misclassification of certain input samples ( Liu et al. , 2017 ; Zhao et al. , 2019b ) . At the hardware level of a deployed machine learning system , it can be made plausible via laser beam ( Barenghi et al. , 2012 ) and row hammer ( Van Der Veen et al. , 2016 ) to change or flip the logic values of the corresponding bits and thus modifying the model parameters saved in memory . 3 MAIN RESULTS . Here we report the experimental results , provide technical explanations , and elucidate the effectiveness of using mode connectivity for studying and enhancing adversarial robustness in three representative themes : ( i ) backdoor attack ; ( ii ) error-injection attack ; and ( iii ) evasion attack . Our experiments were conducted on different network architectures ( VGG and ResNet ) and datasets ( CIFAR-10 and SVHN ) . The details on experiment setups are given in Appendix A . When connecting models , we use the cross entropy loss and the quadratic Bezier curve as described in ( 4 ) . In what follows , we begin by illustrating the problem setups bridging mode connectivity and adversarial robustness , summarizing the results of high-accuracy ( low-loss ) pathways between untampered models for reference , and then delving into detailed discussions . Depending on the context , we will use the terms error rate and accuracy on clean/adversarial samples interchangeably . The error rate of adversarial samples is equivalent to their attack failure rate as well as 100 % - attack accuracy .",
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"content": "This paper studies leveraging mode connectivity to defend against different types of attacks, including backdoor attacks, adversarial examples, and error-injection attacks. They perform a comprehensive evaluation to show the benign test accuracy and attack success rate over the models in the connected path between pairs of models with the same or different properties, e.g., both are attacked, both are benign, one attacked and one benign, etc., where the connected path is learned using existing algorithms to find the high-accuracy path over two different models. Their evaluation suggests that in certain attack scenarios, exploring the mode connectivity could help find a model that has a high benign accuracy, while with a significantly lower attack success rate than the models at the end points.",
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science.scifact_entailment | Determine the relationship between them and generate a JSON response. Scientific Claim:
ART has no effect on the infectiveness of HIV-positive people.
Research Abstract:
BACKGROUND Expanded access to antiretroviral therapy (ART) using universal test and treat (UTT) has been suggested as a strategy to eliminate HIV in South Africa within 7 y based on an influential mathematical modeling study.
However, the underlying deterministic model was criticized widely, and other modeling studies did not always confirm the study's finding.
The objective of our study is to better understand the implications of different model structures and assumptions, so as to arrive at the best possible predictions of the long-term impact of UTT and the possibility of elimination of HIV.
METHODS AND FINDINGS We developed nine structurally different mathematical models of the South African HIV epidemic in a stepwise approach of increasing complexity and realism.
The simplest model resembles the initial deterministic model, while the most comprehensive model is the stochastic microsimulation model STDSIM, which includes sexual networks and HIV stages with different degrees of infectiousness.
We defined UTT as annual screening and immediate ART for all HIV-infected adults, starting at 13% in January 2012 and scaled up to 90% coverage by January 2019.
All models predict elimination, yet those that capture more processes underlying the HIV transmission dynamics predict elimination at a later point in time, after 20 to 25 y. Importantly, the most comprehensive model predicts that the current strategy of ART at CD4 count ≤350 cells/µl will also lead to elimination, albeit 10 y later compared to UTT.
Still, UTT remains cost-effective, as many additional life-years would be saved.
The study's major limitations are that elimination was defined as incidence below 1/1,000 person-years rather than 0% prevalence, and drug resistance was not modeled.
CONCLUSIONS Our results confirm previous predictions that the HIV epidemic in South Africa can be eliminated through universal testing and immediate treatment at 90% coverage.
However, more realistic models show that elimination is likely to occur at a much later point in time than the initial model suggested.
Also, UTT is a cost-effective intervention, but less cost-effective than previously predicted because the current South African ART treatment policy alone could already drive HIV into elimination.
Please see later in the article for the Editors' Summary. | {"verdict": "NEI", "evidence": []} | science.scifact_entailment.41 | [
{
"content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nART has no effect on the infectiveness of HIV-positive people.\n\nResearch Abstract:\nBACKGROUND Expanded access to antiretroviral therapy (ART) using universal test and treat (UTT) has been suggested as a strategy to eliminate HIV in South Africa within 7 y based on an influential mathematical modeling study.\nHowever, the underlying deterministic model was criticized widely, and other modeling studies did not always confirm the study's finding.\nThe objective of our study is to better understand the implications of different model structures and assumptions, so as to arrive at the best possible predictions of the long-term impact of UTT and the possibility of elimination of HIV.\nMETHODS AND FINDINGS We developed nine structurally different mathematical models of the South African HIV epidemic in a stepwise approach of increasing complexity and realism.\nThe simplest model resembles the initial deterministic model, while the most comprehensive model is the stochastic microsimulation model STDSIM, which includes sexual networks and HIV stages with different degrees of infectiousness.\nWe defined UTT as annual screening and immediate ART for all HIV-infected adults, starting at 13% in January 2012 and scaled up to 90% coverage by January 2019.\nAll models predict elimination, yet those that capture more processes underlying the HIV transmission dynamics predict elimination at a later point in time, after 20 to 25 y. Importantly, the most comprehensive model predicts that the current strategy of ART at CD4 count ≤350 cells/µl will also lead to elimination, albeit 10 y later compared to UTT.\nStill, UTT remains cost-effective, as many additional life-years would be saved.\nThe study's major limitations are that elimination was defined as incidence below 1/1,000 person-years rather than 0% prevalence, and drug resistance was not modeled.\nCONCLUSIONS Our results confirm previous predictions that the HIV epidemic in South Africa can be eliminated through universal testing and immediate treatment at 90% coverage.\nHowever, more realistic models show that elimination is likely to occur at a much later point in time than the initial model suggested.\nAlso, UTT is a cost-effective intervention, but less cost-effective than previously predicted because the current South African ART treatment policy alone could already drive HIV into elimination.\nPlease see later in the article for the Editors' Summary.",
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"content": "{\"verdict\": \"NEI\", \"evidence\": []}",
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science.multicite_intent_classification | Examine the text below, which includes a citation marked by "<cite>" and "</cite>". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.
Provide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.
Text with Citation:
However, the feature value pruning method of <cite>(Ng and Lee, 1996)</cite> only selects surrounding words and local collocations as feature values if they are indicative of some sense class as measured by conditional probability (See <cite>(Ng and Lee, 1996)</cite> for details). | ["Background"] | science.multicite_intent_classification.1969 | [
{
"content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nHowever, the feature value pruning method of <cite>(Ng and Lee, 1996)</cite> only selects surrounding words and local collocations as feature values if they are indicative of some sense class as measured by conditional probability (See <cite>(Ng and Lee, 1996)</cite> for details).",
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"content": "[\"Background\"]",
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science.biored_ner | Analyze the provided biomedical abstract and identify distinct entities. Focus on the specified categories: ["CellLine", "Variant", "Species", "Disease", "Chemical", "Gene"].
Ensure the output is strictly the JSON object.
Source Text:
Identification of PVT1 as a candidate gene for end-stage renal disease in type 2 diabetes using a pooling-based genome-wide single nucleotide polymorphism association study. To identify genetic variants contributing to end-stage renal disease (ESRD) in type 2 diabetes, we performed a genome-wide analysis of 115,352 single nucleotide polymorphisms (SNPs) in pools of 105 unrelated case subjects with ESRD and 102 unrelated control subjects who have had type 2 diabetes for > or =10 years without macroalbuminuria. Using a sliding window statistic of ranked SNPs, we identified a 200-kb region on 8q24 harboring three SNPs showing substantial differences in allelic frequency between case and control pools. These SNPs were genotyped in individuals comprising each pool, and strong evidence for association was found with rs2720709 (P = 0.000021; odds ratio 2.57 [95% CI 1.66-3.96]), which is located in the plasmacytoma variant translocation gene PVT1. We sequenced all exons, exon-intron boundaries, and the promoter of PVT1 and identified 47 variants, 11 of which represented nonredundant markers with minor allele frequency > or =0.05. We subsequently genotyped these 11 variants and an additional 87 SNPs identified through public databases in 319-kb flanking rs2720709 ( approximately 1 SNP/3.5 kb); 23 markers were associated with ESRD at P < 0.01. The strongest evidence for association was found for rs2648875 (P = 0.0000018; 2.97 [1.90-4.65]), which maps to intron 8 of PVT1. Together, these results suggest that PVT1 may contribute to ESRD susceptibility in diabetes. | {"CellLine": [], "Chemical": [], "Disease": ["type 2 diabetes", "end-stage renal disease", "diabetes", "ESRD"], "Gene": ["PVT1"], "Species": [], "Variant": ["rs2648875", "rs2720709"]} | science.biored_ner.67 | [
{
"content": "Analyze the provided biomedical abstract and identify distinct entities. Focus on the specified categories: [\"CellLine\", \"Variant\", \"Species\", \"Disease\", \"Chemical\", \"Gene\"].\n\nEnsure the output is strictly the JSON object.\n\nSource Text:\n\nIdentification of PVT1 as a candidate gene for end-stage renal disease in type 2 diabetes using a pooling-based genome-wide single nucleotide polymorphism association study. To identify genetic variants contributing to end-stage renal disease (ESRD) in type 2 diabetes, we performed a genome-wide analysis of 115,352 single nucleotide polymorphisms (SNPs) in pools of 105 unrelated case subjects with ESRD and 102 unrelated control subjects who have had type 2 diabetes for > or =10 years without macroalbuminuria. Using a sliding window statistic of ranked SNPs, we identified a 200-kb region on 8q24 harboring three SNPs showing substantial differences in allelic frequency between case and control pools. These SNPs were genotyped in individuals comprising each pool, and strong evidence for association was found with rs2720709 (P = 0.000021; odds ratio 2.57 [95% CI 1.66-3.96]), which is located in the plasmacytoma variant translocation gene PVT1. We sequenced all exons, exon-intron boundaries, and the promoter of PVT1 and identified 47 variants, 11 of which represented nonredundant markers with minor allele frequency > or =0.05. We subsequently genotyped these 11 variants and an additional 87 SNPs identified through public databases in 319-kb flanking rs2720709 ( approximately 1 SNP/3.5 kb); 23 markers were associated with ESRD at P < 0.01. The strongest evidence for association was found for rs2648875 (P = 0.0000018; 2.97 [1.90-4.65]), which maps to intron 8 of PVT1. Together, these results suggest that PVT1 may contribute to ESRD susceptibility in diabetes.",
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"content": "{\"CellLine\": [], \"Chemical\": [], \"Disease\": [\"type 2 diabetes\", \"end-stage renal disease\", \"diabetes\", \"ESRD\"], \"Gene\": [\"PVT1\"], \"Species\": [], \"Variant\": [\"rs2648875\", \"rs2720709\"]}",
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science.discomat_te | | Cluster type | Probability | Nominal molar fraction of Fe2O3 | Nominal molar fraction of Fe2O3 | Nominal molar fraction of Fe2O3 | Nominal molar fraction of Fe2O3 | Nominal molar fraction of Fe2O3 | Nominal molar fraction of Fe2O3 |
| | | x=0.0 | 0.10 | 0.20 | 0.25 | [A] 0.30 | [B] 0.30 |
| No magnetic ions | (1-q)4 | 0.545 | 0.171 | 0.160 | 0.134 | 0.130 | 0.109 |
| Single | 4q(1-q)3 | 0.357 | 0.380 | 0.372 | 0.350 | 0.346 | 0.322 |
| Dimer | 6q 2(1-q)2 | 0.088 | 0.316 | 0.324 | 0.343 | 0.345 | 0.359 |
| Closed-trimer | 4q 3(1-q) | 0.010 | 0.117 | 0.126 | 0.149 | 0.153 | 0.178 |
| `Quad' | q 4 | 0.000 | 0.016 | 0.018 | 0.024 | 0.025 | 0.033 |
Caption: Probability distribution of a given magnetic cluster type in the P4O10 structure
Examine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:
{"value": "xx", "type": "Other"}
{"value": "xx", "type": "Glass_Compound_Amount", "constituent": "xx", "unit": "xx", "material": "xx"}
Process each cell sequentially, and for any unknown attributes, use "xx" as a placeholder. Each JSON should be on a new line.
Output: | {"value": "0.10", "type": "Other"}
{"value": "0.20", "type": "Other"}
{"value": "0.25", "type": "Other"}
{"value": "0.545", "type": "Other"}
{"value": "0.171", "type": "Other"}
{"value": "0.160", "type": "Other"}
{"value": "0.134", "type": "Other"}
{"value": "0.130", "type": "Other"}
{"value": "0.109", "type": "Other"}
{"value": "0.357", "type": "Other"}
{"value": "0.380", "type": "Other"}
{"value": "0.372", "type": "Other"}
{"value": "0.350", "type": "Other"}
{"value": "0.346", "type": "Other"}
{"value": "0.322", "type": "Other"}
{"value": "0.088", "type": "Other"}
{"value": "0.316", "type": "Other"}
{"value": "0.324", "type": "Other"}
{"value": "0.343", "type": "Other"}
{"value": "0.345", "type": "Other"}
{"value": "0.359", "type": "Other"}
{"value": "0.010", "type": "Other"}
{"value": "0.117", "type": "Other"}
{"value": "0.126", "type": "Other"}
{"value": "0.149", "type": "Other"}
{"value": "0.153", "type": "Other"}
{"value": "0.178", "type": "Other"}
{"value": "0.000", "type": "Other"}
{"value": "0.016", "type": "Other"}
{"value": "0.018", "type": "Other"}
{"value": "0.024", "type": "Other"}
{"value": "0.025", "type": "Other"}
{"value": "0.033", "type": "Other"} | science.discomat_te.171 | [
{
"content": "| Cluster type | Probability | Nominal molar fraction of Fe2O3 | Nominal molar fraction of Fe2O3 | Nominal molar fraction of Fe2O3 | Nominal molar fraction of Fe2O3 | Nominal molar fraction of Fe2O3 | Nominal molar fraction of Fe2O3 |\n| | | x=0.0 | 0.10 | 0.20 | 0.25 | [A] 0.30 | [B] 0.30 |\n| No magnetic ions | (1-q)4 | 0.545 | 0.171 | 0.160 | 0.134 | 0.130 | 0.109 |\n| Single | 4q(1-q)3 | 0.357 | 0.380 | 0.372 | 0.350 | 0.346 | 0.322 |\n| Dimer | 6q 2(1-q)2 | 0.088 | 0.316 | 0.324 | 0.343 | 0.345 | 0.359 |\n| Closed-trimer | 4q 3(1-q) | 0.010 | 0.117 | 0.126 | 0.149 | 0.153 | 0.178 |\n| `Quad' | q 4 | 0.000 | 0.016 | 0.018 | 0.024 | 0.025 | 0.033 |\n\nCaption: Probability distribution of a given magnetic cluster type in the P4O10 structure\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:",
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"content": "{\"value\": \"0.10\", \"type\": \"Other\"}\n{\"value\": \"0.20\", \"type\": \"Other\"}\n{\"value\": \"0.25\", \"type\": \"Other\"}\n{\"value\": \"0.545\", \"type\": \"Other\"}\n{\"value\": \"0.171\", \"type\": \"Other\"}\n{\"value\": \"0.160\", \"type\": \"Other\"}\n{\"value\": \"0.134\", \"type\": \"Other\"}\n{\"value\": \"0.130\", \"type\": \"Other\"}\n{\"value\": \"0.109\", \"type\": \"Other\"}\n{\"value\": \"0.357\", \"type\": \"Other\"}\n{\"value\": \"0.380\", \"type\": \"Other\"}\n{\"value\": \"0.372\", \"type\": \"Other\"}\n{\"value\": \"0.350\", \"type\": \"Other\"}\n{\"value\": \"0.346\", \"type\": \"Other\"}\n{\"value\": \"0.322\", \"type\": \"Other\"}\n{\"value\": \"0.088\", \"type\": \"Other\"}\n{\"value\": \"0.316\", \"type\": \"Other\"}\n{\"value\": \"0.324\", \"type\": \"Other\"}\n{\"value\": \"0.343\", \"type\": \"Other\"}\n{\"value\": \"0.345\", \"type\": \"Other\"}\n{\"value\": \"0.359\", \"type\": \"Other\"}\n{\"value\": \"0.010\", \"type\": \"Other\"}\n{\"value\": \"0.117\", \"type\": \"Other\"}\n{\"value\": \"0.126\", \"type\": \"Other\"}\n{\"value\": \"0.149\", \"type\": \"Other\"}\n{\"value\": \"0.153\", \"type\": \"Other\"}\n{\"value\": \"0.178\", \"type\": \"Other\"}\n{\"value\": \"0.000\", \"type\": \"Other\"}\n{\"value\": \"0.016\", \"type\": \"Other\"}\n{\"value\": \"0.018\", \"type\": \"Other\"}\n{\"value\": \"0.024\", \"type\": \"Other\"}\n{\"value\": \"0.025\", \"type\": \"Other\"}\n{\"value\": \"0.033\", \"type\": \"Other\"}",
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science.mup_single_document_summarization | Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.
Title of the Paper:
Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage
Main Content:
1 Introduction . Covariate shift is a core issue in Imitation Learning ( IL ) . Traditional IL methods like behavior cloning ( BC ) [ 49 ] , while simple , suffer from covariate shift , learning a policy that can make arbitrary mistakes in parts of the state space not covered by the expert dataset . This leads to compounding errors in the agent ’ s performance [ 57 ] , hurting the generalization capabilities in practice . Prior works have presented several means to combat this phenomenon in IL . One line of thought utilizes an interactive expert , i.e . an expert that can be queried at an arbitrary state encountered during the training procedure . Interactive IL algorithms such as DAgger [ 59 ] , LOLS [ 15 ] , DART [ 40 ] , and AggreVaTe ( D ) [ 58 ; 66 ] utilize a reduction to no-regret online learning and demonstrate that under certain conditions , they can successfully learn a policy that imitates the expert . These interactive IL algorithms , however , can not provably avoid covariate shift if the expert is not recoverable . That is , ∗Equal contribution †Work done outside Amazon 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) , virtual . Aπe ( s , a ) = Θ ( H ) where πe is the expert , Aπ is the usual ( dis ) advantage function,3 and H is the planning horizon [ 52 ; 4 , Chapter 15 ] . A second line of work that avoids covariate shift utilizes either a known transition dynamics model [ 2 ; 86 ] or uses real world interactions [ 27 ; 10 ; 65 ; 38 ; 56 ; 33 ] . Prior works have shown that with known transition dynamics or real world interactions , agents can provably avoid covariate shift in both tabular and general MDPs [ 4 ; 52 ] even without a recoverable expert . While these results offer strong theoretical guarantees and empirical performance , online interactions are often costly and prohibitive for real world applications where active trial-and-error exploration in the environment could be unsafe or impossible . A third perspective towards addressing this issue is to assume that the expert visits the entire state space [ 62 ] , where the expert effectively informs the learner what actions to take in every state . Unfortunately , such a full coverage expert distribution might be rare and holds only for special MDPs and expert policies ( for e.g . an expert that induces ergodicity in the MDP ) . In this work , we consider a new perspective towards handling the covariate shift issue in IL . In particular , we investigate a pure offline learning setting where the learner has access to neither the expert nor the environment for additional interactions . The learner , instead , has access to a small pre-collected dataset of state-action pairs sampled from the expert and a large batch offline dataset of state-action-next state transition triples sampled from a behavior policy that could be highly sub-optimal ( see Figure 1 where BC on the offline data results in a low-quality policy ) . Unlike prior works that require online interactions , our proposed method , MILO performs high fidelity imitation in an offline , data-driven manner . Moreover , different from interactive IL , we do not require the expert to be present during learning , significantly relieving the expert ’ s burden . Finally , in contrast to the prior work [ 62 ] that assumes the expert distribution covers the entire state-action space ( i.e. , maxπ maxs , a d π ( s , a ) /dπ e ( s , a ) < ∞ where dπ denotes the state-action distribution of policy π ) , we require the offline dataset to provide partial coverage , i.e. , it only needs to cover the expert ’ s state-actions ( i.e. , maxs , a dπ e ( s , a ) /ρ ( s , a ) < ∞ where ρ is the offline distribution of some behavior policy ) .4 In summary , we list our main contributions below : 1 . We propose Model based Imitation Learning from Offline data , MILO : a model-based framework that leverages offline batch data with only partial coverage ( see Section 4.1 for definition ) to overcome covariate shift in IL . 3in this work , we use cost instead of reward , thus we call Aπ the disadvantage function . 4In our analysis , we refine the density ratio dπ e ( s , a ) /ρ ( s , a ) via the concept of relative conditional number which allows us to extend it to large MDPs where the ratio is infinite but the relative condition number is finite . 2 . Our analysis is modular and covers common models such as discrete MDPs and linear models . Notably , our new result on non-parametric models ( e.g . Gaussian Processes ) with relative condition number is new even considering all existing results in offline RL ( see Remark 4,7,11 ) . 3 . The practical instantiation of our general framework leverages neural network model ensembles , and demonstrates its efficacy on benchmark MuJoCo continuous control problems . Specifically , even under low-quality behavior policies , our approach can successfully imitate using an extremely small number of expert samples while algorithms like BC completely fail ( Figure 1 ) . 1.1 Related work . Imitation Learning As summarized above , avoiding covariate shift in IL is an important topic . Another relevant line of research is IL algorithms that use offline or off-policy learning . ValueDICE [ 38 ] presents a principled way to leverage off-policy data for IL . In theory , the techniques from ValueDICE ( and more broadly , DICE [ 46 ; 84 ] ) require the data provided to the agent to have global coverage . Moreover in practice , ValueDICE uses online interaction and maintains an increasing replay buffer which may eventually provide global coverage . Instead , we aim to study offline IL without any online interactions and are interested in the setting where offline data does not have global coverage . Another line of work [ 29 ; 14 ] studies IL in an offline setting by only using the expert dataset . In contrast to these works , our goal is to study the use of an additional offline dataset collected from a behavior policy to mitigate covariate shift , as information theoretically any algorithm that relies solely on expert data will still suffer from covariate shift in the worst case [ 52 ] . Similar in setting , Cascaded Supervised IRL ( CSI ) [ 35 ] performs imitation offline with both an expert dataset and a static offline dataset by first fitting a reward function that is then used in Least Squares Policy Iteration . CSI , however , requires the expert data to have global coverage and does not mitigate covariate shift with partial coverage like MILO does . Finally , Variational Model-Based Adversarial IL ( V-MAIL ) [ 50 ] learns a dynamics model from a static offline dataset and performs offline imitation within the model . V-MAIL , however , studies zero-shot IL where the static offline data is samples from a variety of source tasks , and the expert dataset is samples from the transfer target task . In contrast , MILO investigates avoiding covariate shift in IL with an offline dataset collected by a potentially suboptimal policy from the same task . Offline RL In offline RL , algorithms such as FQI [ 22 ] have finite-sample error guarantees under the global coverage [ 45 ; 5 ] . Recently , many algorithms to tackle this problem have been proposed from both model-free [ 76 ; 68 ; 43 ; 26 ; 23 ; 39 ] and model-based perspectives [ 82 ; 34 ; 44 ] with some pessimism ideas . The idea of pessimism features in offline RL with an eye to penalize the learner from visiting unknown regions of the state-action space [ 54 ; 31 ; 79 ; 12 ] . We utilize pessimism within the IL context where , unlike RL , the learner does not have access to an underlying reward signal . Our work expands prior theoretical results by ( a ) formalizing the partial coverage condition using a notion of a relative condition number , and ( b ) offering distribution-dependent results when working with non-parametric models including gaussian processes . See Appendix C for a detailed literature review . 2 Setting . We consider an episodic finite-horizon Markov Decision Process ( MDP ) , M = { S , A , P , H , c , d0 } , where S is the state space , A is the action space , P : S × A → ∆ ( S ) is the MDP ’ s transition , H is the horizon , d0 is an initial distribution , and c : S × A → [ 0 , 1 ] is the cost function . A policy π : S → ∆ ( A ) maps from state to distribution over actions . We denote dπP ∈ ∆ ( S × A ) as the average state-action distribution of π under transition kernel P , that is , dπP = 1/H ∑H t=1 d π P , t , where dπP , t ∈ ∆ ( S × A ) is the distribution of ( s ( t ) , a ( t ) ) under π at t. Given a cost function f : S ×A 7→ [ 0 , 1 ] , V πP , f denotes the expected cumulative cost of π under the transition kernel P and cost function f . Following a standard IL setting , the ground truth cost function c is unknown . Instead , we have the demonstrations by the expert specified by πe : S → ∆ ( A ) ( potentially stochastic and not necessarily optimal ) . Concretely , we have an expert dataset in the form of i.i.d tuples De = { si , ai } nei=1 sampled from distribution d πe P . In our setting , we also have an offline static dataset consisting of i.i.d tuples Do = { si , ai , s′i } no i=1 s.t . ( s , a ) ∼ ρ ( s , a ) , s′ ∼ P ( s , a ) , where ρ ∈ ∆ ( S × A ) is an offline distribution resulting from some behavior policies . Note behavior policy could be a much worse policy than the expert πe . Our goal is Algorithm 1 Framework for model-based Imitation Learning with offline data ( MILO ) 1 : Require : IPM class F , model class P , policy class Π , datasets De = { s , a } , Do : = { s , a , s′ } 2 : Train Dynamics Model and Bonus : P̂ : S ×A → S and b : S ×A → R+ on offline data Do 3 : Pessimistic model-based min-max IL : with P̂ , b , De , obtain π̂IL by solving the following : π̂IL = argminπ∈Π maxf∈F [ E ( s , a ) ∼dπ P̂ [ f ( s , a ) + b ( s , a ) ] − E ( s , a ) ∼De [ f ( s , a ) ] ] ( 1 ) to only leverage ( De +Do ) to learn a policy π that performs as well as πe with regard to optimizing the ground truth cost c. More specifically , our goal is to utilize the offline static data Do to combat covariate shift and learn a policy that can significantly outperform traditional offline IL methods such as Behavior cloning ( BC ) , without any interaction with the real world or expert . Function classes We introduce function approximation . Since we do not know the true cost function c and transition kernel P , we introduce a cost function class F ⊂ S × A → [ 0 , 1 ] and a transition model class P : S × A → ∆ ( S ) . We also need a policy class Π . For the analysis , we assume realizability : Assumption 1. c ∈ F , P ∈ P , πe ∈ Π . We use Integral Probability Metric ( IPM ) as a distribution distance measure , i.e. , given two distributions ρ1 and ρ2 , IPM with F is defined as maxf∈F [ E ( s , a ) ∼ρ1 [ f ( s , a ) ] − E ( s , a ) ∼ρ2 [ f ( s , a ) ] ] . | This work considers an offline imitation learning setting where both expert and sub-optimal demonstrations are assumed to be known. While the covariate shift issue (the gap between training and test distributions) is a critical problem in offline imitation learning, e.g., behavioral cloning, authors address this issue by training a dynamics model through the sub-optimal demonstration (having state-action-successor state triples) and applying pessimistic penalty (incentivizing visiting the state-action pairs in the expert demonstration). With the theoretical derivations for various settings for dynamics modeling and the assumption that the demonstration does not have to cover the entire state-action space, authors show the theoretical guarantee of their proposed method. Also, the empirical results support their claim by showing that MILO (proposed algorithm) outperforms ValueDICE and BC. | science.mup_single_document_summarization.14661 | [
{
"content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nMitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage\n\nMain Content:\n1 Introduction . Covariate shift is a core issue in Imitation Learning ( IL ) . Traditional IL methods like behavior cloning ( BC ) [ 49 ] , while simple , suffer from covariate shift , learning a policy that can make arbitrary mistakes in parts of the state space not covered by the expert dataset . This leads to compounding errors in the agent ’ s performance [ 57 ] , hurting the generalization capabilities in practice . Prior works have presented several means to combat this phenomenon in IL . One line of thought utilizes an interactive expert , i.e . an expert that can be queried at an arbitrary state encountered during the training procedure . Interactive IL algorithms such as DAgger [ 59 ] , LOLS [ 15 ] , DART [ 40 ] , and AggreVaTe ( D ) [ 58 ; 66 ] utilize a reduction to no-regret online learning and demonstrate that under certain conditions , they can successfully learn a policy that imitates the expert . These interactive IL algorithms , however , can not provably avoid covariate shift if the expert is not recoverable . That is , ∗Equal contribution †Work done outside Amazon 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) , virtual . Aπe ( s , a ) = Θ ( H ) where πe is the expert , Aπ is the usual ( dis ) advantage function,3 and H is the planning horizon [ 52 ; 4 , Chapter 15 ] . A second line of work that avoids covariate shift utilizes either a known transition dynamics model [ 2 ; 86 ] or uses real world interactions [ 27 ; 10 ; 65 ; 38 ; 56 ; 33 ] . Prior works have shown that with known transition dynamics or real world interactions , agents can provably avoid covariate shift in both tabular and general MDPs [ 4 ; 52 ] even without a recoverable expert . While these results offer strong theoretical guarantees and empirical performance , online interactions are often costly and prohibitive for real world applications where active trial-and-error exploration in the environment could be unsafe or impossible . A third perspective towards addressing this issue is to assume that the expert visits the entire state space [ 62 ] , where the expert effectively informs the learner what actions to take in every state . Unfortunately , such a full coverage expert distribution might be rare and holds only for special MDPs and expert policies ( for e.g . an expert that induces ergodicity in the MDP ) . In this work , we consider a new perspective towards handling the covariate shift issue in IL . In particular , we investigate a pure offline learning setting where the learner has access to neither the expert nor the environment for additional interactions . The learner , instead , has access to a small pre-collected dataset of state-action pairs sampled from the expert and a large batch offline dataset of state-action-next state transition triples sampled from a behavior policy that could be highly sub-optimal ( see Figure 1 where BC on the offline data results in a low-quality policy ) . Unlike prior works that require online interactions , our proposed method , MILO performs high fidelity imitation in an offline , data-driven manner . Moreover , different from interactive IL , we do not require the expert to be present during learning , significantly relieving the expert ’ s burden . Finally , in contrast to the prior work [ 62 ] that assumes the expert distribution covers the entire state-action space ( i.e. , maxπ maxs , a d π ( s , a ) /dπ e ( s , a ) < ∞ where dπ denotes the state-action distribution of policy π ) , we require the offline dataset to provide partial coverage , i.e. , it only needs to cover the expert ’ s state-actions ( i.e. , maxs , a dπ e ( s , a ) /ρ ( s , a ) < ∞ where ρ is the offline distribution of some behavior policy ) .4 In summary , we list our main contributions below : 1 . We propose Model based Imitation Learning from Offline data , MILO : a model-based framework that leverages offline batch data with only partial coverage ( see Section 4.1 for definition ) to overcome covariate shift in IL . 3in this work , we use cost instead of reward , thus we call Aπ the disadvantage function . 4In our analysis , we refine the density ratio dπ e ( s , a ) /ρ ( s , a ) via the concept of relative conditional number which allows us to extend it to large MDPs where the ratio is infinite but the relative condition number is finite . 2 . Our analysis is modular and covers common models such as discrete MDPs and linear models . Notably , our new result on non-parametric models ( e.g . Gaussian Processes ) with relative condition number is new even considering all existing results in offline RL ( see Remark 4,7,11 ) . 3 . The practical instantiation of our general framework leverages neural network model ensembles , and demonstrates its efficacy on benchmark MuJoCo continuous control problems . Specifically , even under low-quality behavior policies , our approach can successfully imitate using an extremely small number of expert samples while algorithms like BC completely fail ( Figure 1 ) . 1.1 Related work . Imitation Learning As summarized above , avoiding covariate shift in IL is an important topic . Another relevant line of research is IL algorithms that use offline or off-policy learning . ValueDICE [ 38 ] presents a principled way to leverage off-policy data for IL . In theory , the techniques from ValueDICE ( and more broadly , DICE [ 46 ; 84 ] ) require the data provided to the agent to have global coverage . Moreover in practice , ValueDICE uses online interaction and maintains an increasing replay buffer which may eventually provide global coverage . Instead , we aim to study offline IL without any online interactions and are interested in the setting where offline data does not have global coverage . Another line of work [ 29 ; 14 ] studies IL in an offline setting by only using the expert dataset . In contrast to these works , our goal is to study the use of an additional offline dataset collected from a behavior policy to mitigate covariate shift , as information theoretically any algorithm that relies solely on expert data will still suffer from covariate shift in the worst case [ 52 ] . Similar in setting , Cascaded Supervised IRL ( CSI ) [ 35 ] performs imitation offline with both an expert dataset and a static offline dataset by first fitting a reward function that is then used in Least Squares Policy Iteration . CSI , however , requires the expert data to have global coverage and does not mitigate covariate shift with partial coverage like MILO does . Finally , Variational Model-Based Adversarial IL ( V-MAIL ) [ 50 ] learns a dynamics model from a static offline dataset and performs offline imitation within the model . V-MAIL , however , studies zero-shot IL where the static offline data is samples from a variety of source tasks , and the expert dataset is samples from the transfer target task . In contrast , MILO investigates avoiding covariate shift in IL with an offline dataset collected by a potentially suboptimal policy from the same task . Offline RL In offline RL , algorithms such as FQI [ 22 ] have finite-sample error guarantees under the global coverage [ 45 ; 5 ] . Recently , many algorithms to tackle this problem have been proposed from both model-free [ 76 ; 68 ; 43 ; 26 ; 23 ; 39 ] and model-based perspectives [ 82 ; 34 ; 44 ] with some pessimism ideas . The idea of pessimism features in offline RL with an eye to penalize the learner from visiting unknown regions of the state-action space [ 54 ; 31 ; 79 ; 12 ] . We utilize pessimism within the IL context where , unlike RL , the learner does not have access to an underlying reward signal . Our work expands prior theoretical results by ( a ) formalizing the partial coverage condition using a notion of a relative condition number , and ( b ) offering distribution-dependent results when working with non-parametric models including gaussian processes . See Appendix C for a detailed literature review . 2 Setting . We consider an episodic finite-horizon Markov Decision Process ( MDP ) , M = { S , A , P , H , c , d0 } , where S is the state space , A is the action space , P : S × A → ∆ ( S ) is the MDP ’ s transition , H is the horizon , d0 is an initial distribution , and c : S × A → [ 0 , 1 ] is the cost function . A policy π : S → ∆ ( A ) maps from state to distribution over actions . We denote dπP ∈ ∆ ( S × A ) as the average state-action distribution of π under transition kernel P , that is , dπP = 1/H ∑H t=1 d π P , t , where dπP , t ∈ ∆ ( S × A ) is the distribution of ( s ( t ) , a ( t ) ) under π at t. Given a cost function f : S ×A 7→ [ 0 , 1 ] , V πP , f denotes the expected cumulative cost of π under the transition kernel P and cost function f . Following a standard IL setting , the ground truth cost function c is unknown . Instead , we have the demonstrations by the expert specified by πe : S → ∆ ( A ) ( potentially stochastic and not necessarily optimal ) . Concretely , we have an expert dataset in the form of i.i.d tuples De = { si , ai } nei=1 sampled from distribution d πe P . In our setting , we also have an offline static dataset consisting of i.i.d tuples Do = { si , ai , s′i } no i=1 s.t . ( s , a ) ∼ ρ ( s , a ) , s′ ∼ P ( s , a ) , where ρ ∈ ∆ ( S × A ) is an offline distribution resulting from some behavior policies . Note behavior policy could be a much worse policy than the expert πe . Our goal is Algorithm 1 Framework for model-based Imitation Learning with offline data ( MILO ) 1 : Require : IPM class F , model class P , policy class Π , datasets De = { s , a } , Do : = { s , a , s′ } 2 : Train Dynamics Model and Bonus : P̂ : S ×A → S and b : S ×A → R+ on offline data Do 3 : Pessimistic model-based min-max IL : with P̂ , b , De , obtain π̂IL by solving the following : π̂IL = argminπ∈Π maxf∈F [ E ( s , a ) ∼dπ P̂ [ f ( s , a ) + b ( s , a ) ] − E ( s , a ) ∼De [ f ( s , a ) ] ] ( 1 ) to only leverage ( De +Do ) to learn a policy π that performs as well as πe with regard to optimizing the ground truth cost c. More specifically , our goal is to utilize the offline static data Do to combat covariate shift and learn a policy that can significantly outperform traditional offline IL methods such as Behavior cloning ( BC ) , without any interaction with the real world or expert . Function classes We introduce function approximation . Since we do not know the true cost function c and transition kernel P , we introduce a cost function class F ⊂ S × A → [ 0 , 1 ] and a transition model class P : S × A → ∆ ( S ) . We also need a policy class Π . For the analysis , we assume realizability : Assumption 1. c ∈ F , P ∈ P , πe ∈ Π . We use Integral Probability Metric ( IPM ) as a distribution distance measure , i.e. , given two distributions ρ1 and ρ2 , IPM with F is defined as maxf∈F [ E ( s , a ) ∼ρ1 [ f ( s , a ) ] − E ( s , a ) ∼ρ2 [ f ( s , a ) ] ] .",
"role": "user"
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"content": "This work considers an offline imitation learning setting where both expert and sub-optimal demonstrations are assumed to be known. While the covariate shift issue (the gap between training and test distributions) is a critical problem in offline imitation learning, e.g., behavioral cloning, authors address this issue by training a dynamics model through the sub-optimal demonstration (having state-action-successor state triples) and applying pessimistic penalty (incentivizing visiting the state-action pairs in the expert demonstration). With the theoretical derivations for various settings for dynamics modeling and the assumption that the demonstration does not have to cover the entire state-action space, authors show the theoretical guarantee of their proposed method. Also, the empirical results support their claim by showing that MILO (proposed algorithm) outperforms ValueDICE and BC.",
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science.evidence_inference | Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:
- Treatment: The specific medical procedure or drug being evaluated.
- Control: The standard or placebo used for comparison. If absent, use `null`.
- Result: The health outcome being assessed.
- Impact: The observed effect of the treatment on the result, categorized as: ("increased", "decreased", "no change").
- Justification: The supporting details for the observed impact.
Format the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].
Abstract:
ABSTRACT.BACKGROUND: The present studies evaluated the effects of cryoprotectants, the vitrification procedure and time in the warming solution containing sucrose on cleavage and embryo development of immature (GV stage) bovine cumulus-oocyte complexes (COCs).
ABSTRACT.METHODS: Two experiments were conducted. In Experiment 1, COCs (n = 420) were randomly assigned to four groups: 1) Control group: no treatment; 2) VS1 group: COCs were exposed to vitrification solution 1 (VS1) containing 7.5% ethylene glycol [EG] + 7.5% dimethyl sulfoxide [DMSO] + 20% calf serum [CS] in TCM-199 at 37 C for 5 min; 3) VS1 + VS2 group: COCs were exposed to VS1 for 5 min followed by VS2 (15% EG + 15% DMSO + 17.1% sucrose + 20% CS) at 37 C for 45–60 sec; and 4) Vitrified group: COCs were exposed to VS1 and VS2, loaded on cryotops, vitrified in liquid nitrogen and then warmed in TCM-199 + 17.1% sucrose + 20% CS at 37 C for 1 min. In Experiment 2, COCs (n = 581) were assigned to the same groups, but those in VS1, VS1 + VS2 and Vitrified groups were sub-divided and exposed to the warming solution for either 1 or 5 min. After treatment and/or warming, all COCs in both experiments underwent in vitro maturation, in vitro fertilization and in vitro culture.
ABSTRACT.RESULTS: Cleavage and blastocyst rates did not differ among Control, VS1 and VS1 + VS2 groups in either experiment. In Experiment 2, there was no effect of time in the warming solution. <p> However, both cleavage and blastocyst rates were lower (P < 0.001) in the Vitrified group than in the Control, VS1 and VS1 + VS2 groups (40.9 and 1.6% vs 92.2 and 34.4%, 79.4 and 25.2%, and 80.2 and 20.8%, respectively in Experiment 1, and 25.0 and 1.7% vs 75.3 and 27.2%, 67.9 and 19.5%, and 62.7 and 22.5%, respectively in Experiment 2).
ABSTRACT.CONCLUSIONS: The permeating cryoprotectants (EG and DMSO) present in VS1 and VS2 solutions and the time in the warming solution containing sucrose had no adverse effects on cleavage and blastocyst rates of immature bovine COCs. However, cleavage rate and early embryo development were reduced following the vitrification and warming.
Extracted Data: | [["Vitrified group: COCs were exposed to VS1 and VS2, loaded on cryotops, vitrified in liquid nitrogen and then warmed in TCM-199 + 17.1% sucrose + 20% CS at 37 C for 1 min.", "Control group: no treatment; VS1 group: COCs were exposed to vitrification solution 1 (VS1) containing 7.5% ethylene glycol [EG] + 7.5% dimethyl sulfoxide [DMSO] + 20% calf serum [CS] in TCM-199 at 37 C for 5 min; VS1 + VS2 group: COCs were exposed to VS1 for 5 min followed by VS2 (15% EG + 15% DMSO + 17.1% sucrose + 20% CS) at 37 C for 45\u201360 sec;", "cleavage and blastocyst rates", "significantly decreased", "However, both cleavage and blastocyst rates were lower (P \u003c 0.001) in the Vitrified group than in the Control, VS1 and VS1 + VS2 groups (40.9 and 1.6% vs 92.2 and 34.4%, 79.4 and 25.2%, and 80.2 and 20.8%, respectively in Experiment 1, and 25.0 and 1.7% vs 75.3 and 27.2%, 67.9 and 19.5%, and 62.7 and 22.5%, respectively in Experiment 2)."], ["VS1 group: COCs were exposed to vitrification solution 1 (VS1) containing 7.5% ethylene glycol [EG] + 7.5% dimethyl sulfoxide [DMSO] + 20% calf serum [CS] in TCM-199 at 37 C for 5 min; VS1 + VS2 group: COCs were exposed to VS1 for 5 min followed by VS2 (15% EG + 15% DMSO + 17.1% sucrose + 20% CS) at 37 C for 45\u201360 sec; ", "Control group: no treatment", "Cleavage and blastocyst rates", "significantly decreased", "both cleavage and blastocyst rates were lower (P \u003c 0.001) in the Vitrified group than in the Control,"]] | science.evidence_inference.128 | [
{
"content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND: The present studies evaluated the effects of cryoprotectants, the vitrification procedure and time in the warming solution containing sucrose on cleavage and embryo development of immature (GV stage) bovine cumulus-oocyte complexes (COCs).\n\nABSTRACT.METHODS: Two experiments were conducted. In Experiment 1, COCs (n = 420) were randomly assigned to four groups: 1) Control group: no treatment; 2) VS1 group: COCs were exposed to vitrification solution 1 (VS1) containing 7.5% ethylene glycol [EG] + 7.5% dimethyl sulfoxide [DMSO] + 20% calf serum [CS] in TCM-199 at 37 C for 5 min; 3) VS1 + VS2 group: COCs were exposed to VS1 for 5 min followed by VS2 (15% EG + 15% DMSO + 17.1% sucrose + 20% CS) at 37 C for 45–60 sec; and 4) Vitrified group: COCs were exposed to VS1 and VS2, loaded on cryotops, vitrified in liquid nitrogen and then warmed in TCM-199 + 17.1% sucrose + 20% CS at 37 C for 1 min. In Experiment 2, COCs (n = 581) were assigned to the same groups, but those in VS1, VS1 + VS2 and Vitrified groups were sub-divided and exposed to the warming solution for either 1 or 5 min. After treatment and/or warming, all COCs in both experiments underwent in vitro maturation, in vitro fertilization and in vitro culture.\n\nABSTRACT.RESULTS: Cleavage and blastocyst rates did not differ among Control, VS1 and VS1 + VS2 groups in either experiment. In Experiment 2, there was no effect of time in the warming solution. <p> However, both cleavage and blastocyst rates were lower (P < 0.001) in the Vitrified group than in the Control, VS1 and VS1 + VS2 groups (40.9 and 1.6% vs 92.2 and 34.4%, 79.4 and 25.2%, and 80.2 and 20.8%, respectively in Experiment 1, and 25.0 and 1.7% vs 75.3 and 27.2%, 67.9 and 19.5%, and 62.7 and 22.5%, respectively in Experiment 2).\n\nABSTRACT.CONCLUSIONS: The permeating cryoprotectants (EG and DMSO) present in VS1 and VS2 solutions and the time in the warming solution containing sucrose had no adverse effects on cleavage and blastocyst rates of immature bovine COCs. However, cleavage rate and early embryo development were reduced following the vitrification and warming.\n\nExtracted Data:",
"role": "user"
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"content": "[[\"Vitrified group: COCs were exposed to VS1 and VS2, loaded on cryotops, vitrified in liquid nitrogen and then warmed in TCM-199 + 17.1% sucrose + 20% CS at 37 C for 1 min.\", \"Control group: no treatment; VS1 group: COCs were exposed to vitrification solution 1 (VS1) containing 7.5% ethylene glycol [EG] + 7.5% dimethyl sulfoxide [DMSO] + 20% calf serum [CS] in TCM-199 at 37 C for 5 min; VS1 + VS2 group: COCs were exposed to VS1 for 5 min followed by VS2 (15% EG + 15% DMSO + 17.1% sucrose + 20% CS) at 37 C for 45\\u201360 sec;\", \"cleavage and blastocyst rates\", \"significantly decreased\", \"However, both cleavage and blastocyst rates were lower (P \\u003c 0.001) in the Vitrified group than in the Control, VS1 and VS1 + VS2 groups (40.9 and 1.6% vs 92.2 and 34.4%, 79.4 and 25.2%, and 80.2 and 20.8%, respectively in Experiment 1, and 25.0 and 1.7% vs 75.3 and 27.2%, 67.9 and 19.5%, and 62.7 and 22.5%, respectively in Experiment 2).\"], [\"VS1 group: COCs were exposed to vitrification solution 1 (VS1) containing 7.5% ethylene glycol [EG] + 7.5% dimethyl sulfoxide [DMSO] + 20% calf serum [CS] in TCM-199 at 37 C for 5 min; VS1 + VS2 group: COCs were exposed to VS1 for 5 min followed by VS2 (15% EG + 15% DMSO + 17.1% sucrose + 20% CS) at 37 C for 45\\u201360 sec; \", \"Control group: no treatment\", \"Cleavage and blastocyst rates\", \"significantly decreased\", \"both cleavage and blastocyst rates were lower (P \\u003c 0.001) in the Vitrified group than in the Control,\"]]",
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science.scifact_entailment | Determine the relationship between them and generate a JSON response. Scientific Claim:
Side effects associated with antidepressants lower risk of myocardial infarction.
Research Abstract:
OBJECTIVES To investigate the association between antidepressant treatment and risk of several potential adverse outcomes in older people with depression and to examine risks by class of antidepressant, duration of use, and dose.
DESIGN Cohort study of people aged 65 and over diagnosed as having depression.
SETTING 570 general practices in the United Kingdom supplying data to the QResearch primary care database.
PARTICIPANTS 60,746 patients diagnosed as having a new episode of depression between the ages of 65 and 100 years from 1 January 1996 to 31 December 2007 and followed up until 31 December 2008.
MAIN OUTCOME MEASURES Hazard ratios associated with antidepressant use for all cause mortality, attempted suicide/self harm, myocardial infarction, stroke/transient ischaemic attack, falls, fractures, upper gastrointestinal bleeding, epilepsy/seizures, road traffic accidents, adverse drug reactions, and hyponatraemia, adjusted for a range of potential confounding variables.
Hazard ratios were calculated for antidepressant class (tricyclic and related antidepressants, selective serotonin reuptake inhibitors, other antidepressants), dose, and duration of use and for commonly prescribed individual drugs.
RESULTS 54,038 (89.0%) patients received at least one prescription for an antidepressant during follow-up.
A total of 1,398,359 antidepressant prescriptions were issued: 764,659 (54.7%) for selective serotonin reuptake inhibitors, 442,192 (31.6%) for tricyclic antidepressants, 2203 (0.2%) for monoamine oxidase inhibitors, and 189,305 (13.5%) for the group of other antidepressants.
The associations with the adverse outcomes differed significantly between the antidepressant classes for seven outcomes.
Selective serotonin reuptake inhibitors were associated with the highest adjusted hazard ratios for falls (1.66, 95% confidence interval 1.58 to 1.73) and hyponatraemia (1.52, 1.33 to 1.75) compared with when antidepressants were not being used.
The group of other antidepressants was associated with the highest adjusted hazard ratios for all cause mortality (1.66, 1.56 to 1.77), attempted suicide/self harm (5.16, 3.90 to 6.83), stroke/transient ischaemic attack (1.37, 1.22 to 1.55), fracture (1.64, 1.46 to 1.84), and epilepsy/seizures (2.24, 1.60 to 3.15), compared with when antidepressants were not being used.
Tricyclic antidepressants did not have the highest hazard ratio for any of the outcomes.
Significantly different associations also existed between the individual drugs for the same seven outcomes; trazodone (tricyclic antidepressant), mirtazapine, and venlafaxine (both in the group of other antidepressants) were associated with the highest rates for some of these outcomes.
Absolute risks over 1 year for all cause mortality were 7.04% for patients while not taking antidepressants, 8.12% for those taking tricyclic antidepressants, 10.61% for selective serotonin reuptake inhibitors, and 11.43% for other antidepressants.
CONCLUSIONS Selective serotonin reuptake inhibitors and drugs in the group of other antidepressants were associated with an increased risk of several adverse outcomes compared with tricyclic antidepressants.
Among individual drugs, trazodone, mirtazapine, and venlafaxine were associated with the highest risks for some outcomes.
As this is an observational study, it is susceptible to confounding by indication, channelling bias, and residual confounding, so differences in characteristics between patients prescribed different antidepressant drugs that could account for some of the associations between the drugs and the adverse outcomes may remain.
Further research is needed to confirm these findings, but the risks and benefits of different antidepressants should be carefully evaluated when these drugs are prescribed to older people. | {"verdict": "NEI", "evidence": []} | science.scifact_entailment.710 | [
{
"content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nSide effects associated with antidepressants lower risk of myocardial infarction.\n\nResearch Abstract:\nOBJECTIVES To investigate the association between antidepressant treatment and risk of several potential adverse outcomes in older people with depression and to examine risks by class of antidepressant, duration of use, and dose.\nDESIGN Cohort study of people aged 65 and over diagnosed as having depression.\nSETTING 570 general practices in the United Kingdom supplying data to the QResearch primary care database.\nPARTICIPANTS 60,746 patients diagnosed as having a new episode of depression between the ages of 65 and 100 years from 1 January 1996 to 31 December 2007 and followed up until 31 December 2008.\nMAIN OUTCOME MEASURES Hazard ratios associated with antidepressant use for all cause mortality, attempted suicide/self harm, myocardial infarction, stroke/transient ischaemic attack, falls, fractures, upper gastrointestinal bleeding, epilepsy/seizures, road traffic accidents, adverse drug reactions, and hyponatraemia, adjusted for a range of potential confounding variables.\nHazard ratios were calculated for antidepressant class (tricyclic and related antidepressants, selective serotonin reuptake inhibitors, other antidepressants), dose, and duration of use and for commonly prescribed individual drugs.\nRESULTS 54,038 (89.0%) patients received at least one prescription for an antidepressant during follow-up.\nA total of 1,398,359 antidepressant prescriptions were issued: 764,659 (54.7%) for selective serotonin reuptake inhibitors, 442,192 (31.6%) for tricyclic antidepressants, 2203 (0.2%) for monoamine oxidase inhibitors, and 189,305 (13.5%) for the group of other antidepressants.\nThe associations with the adverse outcomes differed significantly between the antidepressant classes for seven outcomes.\nSelective serotonin reuptake inhibitors were associated with the highest adjusted hazard ratios for falls (1.66, 95% confidence interval 1.58 to 1.73) and hyponatraemia (1.52, 1.33 to 1.75) compared with when antidepressants were not being used.\nThe group of other antidepressants was associated with the highest adjusted hazard ratios for all cause mortality (1.66, 1.56 to 1.77), attempted suicide/self harm (5.16, 3.90 to 6.83), stroke/transient ischaemic attack (1.37, 1.22 to 1.55), fracture (1.64, 1.46 to 1.84), and epilepsy/seizures (2.24, 1.60 to 3.15), compared with when antidepressants were not being used.\nTricyclic antidepressants did not have the highest hazard ratio for any of the outcomes.\nSignificantly different associations also existed between the individual drugs for the same seven outcomes; trazodone (tricyclic antidepressant), mirtazapine, and venlafaxine (both in the group of other antidepressants) were associated with the highest rates for some of these outcomes.\nAbsolute risks over 1 year for all cause mortality were 7.04% for patients while not taking antidepressants, 8.12% for those taking tricyclic antidepressants, 10.61% for selective serotonin reuptake inhibitors, and 11.43% for other antidepressants.\nCONCLUSIONS Selective serotonin reuptake inhibitors and drugs in the group of other antidepressants were associated with an increased risk of several adverse outcomes compared with tricyclic antidepressants.\nAmong individual drugs, trazodone, mirtazapine, and venlafaxine were associated with the highest risks for some outcomes.\nAs this is an observational study, it is susceptible to confounding by indication, channelling bias, and residual confounding, so differences in characteristics between patients prescribed different antidepressant drugs that could account for some of the associations between the drugs and the adverse outcomes may remain.\nFurther research is needed to confirm these findings, but the risks and benefits of different antidepressants should be carefully evaluated when these drugs are prescribed to older people.",
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"content": "{\"verdict\": \"NEI\", \"evidence\": []}",
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science.evidence_inference | Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:
- Treatment: The specific medical procedure or drug being evaluated.
- Control: The standard or placebo used for comparison. If absent, use `null`.
- Result: The health outcome being assessed.
- Impact: The observed effect of the treatment on the result, categorized as: ("increased", "decreased", "no change").
- Justification: The supporting details for the observed impact.
Format the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].
Abstract:
ABSTRACT.BACKGROUND:: Some evidence has shown benefits of probiotics in the management of minimal hepatic encephalopathy (MHE). We evaluated the efficacy of a multistrain probiotic compound, alone and in combination with lactulose, in the treatment of MHE.
ABSTRACT.MATERIALS AND METHODS:: This study has two parts. First, consecutive adult patients with MHE were randomized to receive lactulose (30–60 mL/day) + probiotic (200 million colony forming units of seven bacteria species/day) (Gp-LPr) or lactulose + placebo (Gp-L). In second part, a non-randomized group of patients received probiotic alone (Gp-Pr). Medication duration was for 2 weeks and patients were followed-up for another 8 weeks. Improvement in MHE status was assessed by psychometric hepatic encephalopathy score (PHES). Development of overt encephalopathy, hospitalization, and death were considered as secondary outcomes.
ABSTRACT.RESULTS:: Sixty patients (80% male, mean age 38.4 ± 9.6 years) completed the intervention. PHES significantly improved after medication in all the three groups (Gp-LPr: −3.8 ± 3.9 to −1.6 ± 3.0; Gp-L: −4.8 ± 4.1 to −1.6 ± 2.9; and Gp-Pr: −4.9 ± 3.7 to −2.1 ± 2.5, P < 0.001). After 8 weeks follow-up, improvement was maintained in Gp-LPr and Gp-Pr, but there was deterioration in those who did not receive probiotics (Gp-L: PHES score reversed to −4.8 ± 4.2). Two patients (one each in Gp-L and Gp-Pr) experienced overt encephalopathy. One patient was hospitalized due to worsening of ascites (Gp-LPr) and one due to spontaneous bacterial peritonitis (Gp-L). Side effects were mild and not significantly different among the groups.
ABSTRACT.CONCLUSION:: Lactulose and probiotics are effective for the treatment of MHE; however, probiotics, but not lactulose, have long-term effects. More studies are required before suggesting probiotics for the standard treatment of MHE.
Extracted Data: | [["receive lactulose (30\u201360 mL/day) + probiotic (200 million colony forming units of seven bacteria species/day) (Gp-LPr) or probiotic alone (Gp-Pr) or lactulose + placebo (Gp-L)", "baseline", "psychometric hepatic encephalopathy score (PHES)", "significantly increased", "PHES significantly improved after medication in all the three groups (Gp-LPr: \u22123.8 \u00b1 3.9 to \u22121.6 \u00b1 3.0; Gp-L: \u22124.8 \u00b1 4.1 to \u22121.6 \u00b1 2.9; and Gp-Pr: \u22124.9 \u00b1 3.7 to \u22122.1 \u00b1 2.5, P \u003c 0.001)."]] | science.evidence_inference.28 | [
{
"content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND:: Some evidence has shown benefits of probiotics in the management of minimal hepatic encephalopathy (MHE). We evaluated the efficacy of a multistrain probiotic compound, alone and in combination with lactulose, in the treatment of MHE.\n\nABSTRACT.MATERIALS AND METHODS:: This study has two parts. First, consecutive adult patients with MHE were randomized to receive lactulose (30–60 mL/day) + probiotic (200 million colony forming units of seven bacteria species/day) (Gp-LPr) or lactulose + placebo (Gp-L). In second part, a non-randomized group of patients received probiotic alone (Gp-Pr). Medication duration was for 2 weeks and patients were followed-up for another 8 weeks. Improvement in MHE status was assessed by psychometric hepatic encephalopathy score (PHES). Development of overt encephalopathy, hospitalization, and death were considered as secondary outcomes.\n\nABSTRACT.RESULTS:: Sixty patients (80% male, mean age 38.4 ± 9.6 years) completed the intervention. PHES significantly improved after medication in all the three groups (Gp-LPr: −3.8 ± 3.9 to −1.6 ± 3.0; Gp-L: −4.8 ± 4.1 to −1.6 ± 2.9; and Gp-Pr: −4.9 ± 3.7 to −2.1 ± 2.5, P < 0.001). After 8 weeks follow-up, improvement was maintained in Gp-LPr and Gp-Pr, but there was deterioration in those who did not receive probiotics (Gp-L: PHES score reversed to −4.8 ± 4.2). Two patients (one each in Gp-L and Gp-Pr) experienced overt encephalopathy. One patient was hospitalized due to worsening of ascites (Gp-LPr) and one due to spontaneous bacterial peritonitis (Gp-L). Side effects were mild and not significantly different among the groups.\n\nABSTRACT.CONCLUSION:: Lactulose and probiotics are effective for the treatment of MHE; however, probiotics, but not lactulose, have long-term effects. More studies are required before suggesting probiotics for the standard treatment of MHE.\n\nExtracted Data:",
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"content": "[[\"receive lactulose (30\\u201360 mL/day) + probiotic (200 million colony forming units of seven bacteria species/day) (Gp-LPr) or probiotic alone (Gp-Pr) or lactulose + placebo (Gp-L)\", \"baseline\", \"psychometric hepatic encephalopathy score (PHES)\", \"significantly increased\", \"PHES significantly improved after medication in all the three groups (Gp-LPr: \\u22123.8 \\u00b1 3.9 to \\u22121.6 \\u00b1 3.0; Gp-L: \\u22124.8 \\u00b1 4.1 to \\u22121.6 \\u00b1 2.9; and Gp-Pr: \\u22124.9 \\u00b1 3.7 to \\u22122.1 \\u00b1 2.5, P \\u003c 0.001).\"]]",
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science.evidence_inference | Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:
- Treatment: The specific medical procedure or drug being evaluated.
- Control: The standard or placebo used for comparison. If absent, use `null`.
- Result: The health outcome being assessed.
- Impact: The observed effect of the treatment on the result, categorized as: ("increased", "decreased", "no change").
- Justification: The supporting details for the observed impact.
Format the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].
Abstract:
ABSTRACT.OBJECTIVES:: This trial investigated whether probiotics improved mood, stress and anxiety in a sample selected for low mood. We also tested whether the presence or severity of irritable bowel syndrome symptoms, and levels of proinflammatory cytokines, brain-derived neurotrophic factor and other blood markers, would predict or impact treatment response.
ABSTRACT.METHOD:: Seventy-nine participants (10 dropouts) not currently taking psychotropic medications with at least moderate scores on self-report mood measures were randomly allocated to receive either a probiotic preparation (containing Lactobacillus helveticus and Bifidobacterium longum) or a matched placebo, in a double-blind trial for 8 weeks. Data were analysed as intent-to-treat.
ABSTRACT.RESULTS:: No significant difference was found between the probiotic and placebo groups on any psychological outcome measure (Cohen's d range = 0.07–0.16) or any blood-based biomarker. At end-point, 9 (23%) of those in the probiotic group showed a ⩾60% change on the Montgomery–Åsberg Depression Rating Scale (responders), compared to 10 (26%) of those in the placebo group (χ12=0.107, p = ns). Baseline vitamin D level was found to moderate treatment effect on several outcome measures. Dry mouth and sleep disruption were reported more frequently in the placebo group.
ABSTRACT.CONCLUSIONS:: This study found no evidence that the probiotic formulation is effective in treating low mood, or in moderating the levels of inflammatory and other biomarkers. The lack of observed effect on mood symptoms may be due to the severity, chronicity or treatment resistance of the sample; recruiting an antidepressant-naive sample experiencing mild, acute symptoms of low mood, may well yield a different result. Future studies taking a preventative approach or using probiotics as an adjuvant treatment may also be more effective. Vitamin D levels should be monitored in future studies in the area. The results of this trial are preliminary; future studies in the area should not be discouraged.
Extracted Data: | [["Probiotics ", "Placebo", "Montgomery\u2013A\u030asberg Depression Rating Scale ", "no significant difference", "At end-point, 9 (23%) of those in the probiotic group showed a \u2a7e60% change on the Montgomery\u2013A\u030asberg Depression Rating Scale (responders), compared to 10 (26%) of those in the placebo group (\u03c712=0.107, p = ns)."], ["Probiotics ", "Placebo", "Depression", "no significant difference", "No significant difference was found between the probiotic and placebo groups on any psychological outcome measure (Cohen\u0027s d range = 0.07\u20130.16)"], ["Probiotics ", "Placebo", "Depression", "no significant difference", "At end-point, 9 (23%) of those in the probiotic group showed a \u2a7e60% change on the Montgomery\u2013A\u030asberg Depression Rating Scale (responders), compared to 10 (26%) of those in the placebo group (\u03c712=0.107, p = ns)."], ["Probiotics ", "Placebo", "Anxiety", "no significant difference", "No significant difference was found between the probiotic and placebo groups on any psychological outcome measure (Cohen\u0027s d range = 0.07\u20130.16)"]] | science.evidence_inference.142 | [
{
"content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.OBJECTIVES:: This trial investigated whether probiotics improved mood, stress and anxiety in a sample selected for low mood. We also tested whether the presence or severity of irritable bowel syndrome symptoms, and levels of proinflammatory cytokines, brain-derived neurotrophic factor and other blood markers, would predict or impact treatment response.\n\nABSTRACT.METHOD:: Seventy-nine participants (10 dropouts) not currently taking psychotropic medications with at least moderate scores on self-report mood measures were randomly allocated to receive either a probiotic preparation (containing Lactobacillus helveticus and Bifidobacterium longum) or a matched placebo, in a double-blind trial for 8 weeks. Data were analysed as intent-to-treat.\n\nABSTRACT.RESULTS:: No significant difference was found between the probiotic and placebo groups on any psychological outcome measure (Cohen's d range = 0.07–0.16) or any blood-based biomarker. At end-point, 9 (23%) of those in the probiotic group showed a ⩾60% change on the Montgomery–Åsberg Depression Rating Scale (responders), compared to 10 (26%) of those in the placebo group (χ12=0.107, p = ns). Baseline vitamin D level was found to moderate treatment effect on several outcome measures. Dry mouth and sleep disruption were reported more frequently in the placebo group.\n\nABSTRACT.CONCLUSIONS:: This study found no evidence that the probiotic formulation is effective in treating low mood, or in moderating the levels of inflammatory and other biomarkers. The lack of observed effect on mood symptoms may be due to the severity, chronicity or treatment resistance of the sample; recruiting an antidepressant-naive sample experiencing mild, acute symptoms of low mood, may well yield a different result. Future studies taking a preventative approach or using probiotics as an adjuvant treatment may also be more effective. Vitamin D levels should be monitored in future studies in the area. The results of this trial are preliminary; future studies in the area should not be discouraged.\n\nExtracted Data:",
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"content": "[[\"Probiotics \", \"Placebo\", \"Montgomery\\u2013A\\u030asberg Depression Rating Scale \", \"no significant difference\", \"At end-point, 9 (23%) of those in the probiotic group showed a \\u2a7e60% change on the Montgomery\\u2013A\\u030asberg Depression Rating Scale (responders), compared to 10 (26%) of those in the placebo group (\\u03c712=0.107, p = ns).\"], [\"Probiotics \", \"Placebo\", \"Depression\", \"no significant difference\", \"No significant difference was found between the probiotic and placebo groups on any psychological outcome measure (Cohen\\u0027s d range = 0.07\\u20130.16)\"], [\"Probiotics \", \"Placebo\", \"Depression\", \"no significant difference\", \"At end-point, 9 (23%) of those in the probiotic group showed a \\u2a7e60% change on the Montgomery\\u2013A\\u030asberg Depression Rating Scale (responders), compared to 10 (26%) of those in the placebo group (\\u03c712=0.107, p = ns).\"], [\"Probiotics \", \"Placebo\", \"Anxiety\", \"no significant difference\", \"No significant difference was found between the probiotic and placebo groups on any psychological outcome measure (Cohen\\u0027s d range = 0.07\\u20130.16)\"]]",
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science.multicite_intent_classification | Examine the text below, which includes a citation marked by "<cite>" and "</cite>". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.
Provide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.
Text with Citation:
<cite>Zhang et al. (2017)</cite> utilize adversarial training to obtain cross-lingual word embeddings without any parallel data. However, their performance is still significantly worse than supervised methods. <cite>Zhang et al. (2017)</cite> apply adversarial training to align monolingual word vector spaces with no supervision. | ["Background"] | science.multicite_intent_classification.579 | [
{
"content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\n<cite>Zhang et al. (2017)</cite> utilize adversarial training to obtain cross-lingual word embeddings without any parallel data. However, their performance is still significantly worse than supervised methods. <cite>Zhang et al. (2017)</cite> apply adversarial training to align monolingual word vector spaces with no supervision.",
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"content": "[\"Background\"]",
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science.bioasq_list_qa | Examine the following excerpts from biomedical literature. Your objective is to extract relevant information to answer the posed question. Format your answer as a JSON array.
Context: : Previous controversy was generated over the hypothesis that a paramyxovirus such as measles
Measles are a systemic infectious disease caused by a single stranded ribonucleic acid virus (measles virus) from the paramyxovirus family.
Mumps is a Paramyxoviridae virus.
Since chronic paramyxovirus infection with measles is known to be accompanied by increased production of antiviral antibodies
Nipah virus (NiV) is a highly pathogenic paramyxovirus which causes fatal encephalitis in up to 75% of infected humans.
Measles is caused by the paramyxovirus which spreads through airborne droplets
Newcastle disease virus (NDV) is classified as a member of the superfamily Mononegavirales in the family Paramyxoviridae.
Many of these identified genes were also required for infection by two other NS RNA viruses, the lymphocytic choriomeningitis virus of the Arenaviridae family and human parainfluenza virus type 3 of the Paramyxoviridae family.
Measles virus ( MeV) , a highly contagious member of the Paramyxoviridae family , causes measles in humans .
The human metapneumovirus ( hMPV ) is a newly reported respiratory virus belonging to the Paramyxoviridae family that has been associated with bronchiolitis and pneumonia in young children .
Two members of the morbillivirus genus of the family Paramyxoviridae , canine distemper virus ( CDV ) and measles virus ( MV) , are well-known for their ability to cause a chronic demyelinating disease of the CNS in their natural hosts , dogs and humans , respectively .
Respiratory syncytial virus ( RSV ) belongs to the family Paramyxoviridae and is the single most important cause of serious lower respiratory tract infections in young children , yet no highly effective treatment or vaccine is available.
Human respiratory syncytial virus ( RSV) , a member of the Paramyxoviridae family , is the most important viral agent of pediatric respiratory tract disease worldwide.
Respiratory syncytial virus ( RSV) , an RNA virus in the family Paramyxoviridae , causes respiratory disease in humans .
Measles virus ( MV ) is a member of the Paramyxoviridae family and an important human pathogen causing strong immunosuppression in affected individuals and a considerable number of deaths worldwide.
Hendra ( HeV ) and Nipah ( NiV ) viruses ( genus Henipavirus ( HNV; family Paramyxoviridae ) are emerging zoonotic agents that can cause severe respiratory distress and acute encephalitis in humans .
Many of the common respiratory illnesses of infancy and childhood are caused by viruses of the Paramyxoviridae family, in particular measles virus, respiratory syncytial (RS) virus and parainfluenzavirus type 3 (PI3).
Here we report simultaneous outbreaks of two distinct human respiratory viruses, human metapneumovirus (MPV; Pneumoviridae: Metapneumovirus) and human respirovirus 3 (HRV3; Paramyxoviridae; Respirovirus, formerly known as parainfluenza virus 3), in two chimpanzee (Pan troglodytes schweinfurthii) communities in the same forest in Uganda in December 2016 and January 2017.
The large virus family Paramyxoviridae includes some of the most significant human and livestock viruses, such as measles-, distemper-, mumps-, parainfluenza-, Newcastle disease-, respiratory syncytial virus and metapneumoviruses.
Question: List 3 human diseases caused by viruses in the family Paramyxoviridae. | ["measles", "mumps", "encephalitis", "respiratory disease", "newcastle disease"] | science.bioasq_list_qa.718 | [
{
"content": "Examine the following excerpts from biomedical literature. Your objective is to extract relevant information to answer the posed question. Format your answer as a JSON array.\n\nContext: : Previous controversy was generated over the hypothesis that a paramyxovirus such as measles\nMeasles are a systemic infectious disease caused by a single stranded ribonucleic acid virus (measles virus) from the paramyxovirus family.\nMumps is a Paramyxoviridae virus.\nSince chronic paramyxovirus infection with measles is known to be accompanied by increased production of antiviral antibodies\nNipah virus (NiV) is a highly pathogenic paramyxovirus which causes fatal encephalitis in up to 75% of infected humans.\nMeasles is caused by the paramyxovirus which spreads through airborne droplets\nNewcastle disease virus (NDV) is classified as a member of the superfamily Mononegavirales in the family Paramyxoviridae.\nMany of these identified genes were also required for infection by two other NS RNA viruses, the lymphocytic choriomeningitis virus of the Arenaviridae family and human parainfluenza virus type 3 of the Paramyxoviridae family.\nMeasles virus ( MeV) , a highly contagious member of the Paramyxoviridae family , causes measles in humans .\nThe human metapneumovirus ( hMPV ) is a newly reported respiratory virus belonging to the Paramyxoviridae family that has been associated with bronchiolitis and pneumonia in young children .\nTwo members of the morbillivirus genus of the family Paramyxoviridae , canine distemper virus ( CDV ) and measles virus ( MV) , are well-known for their ability to cause a chronic demyelinating disease of the CNS in their natural hosts , dogs and humans , respectively .\nRespiratory syncytial virus ( RSV ) belongs to the family Paramyxoviridae and is the single most important cause of serious lower respiratory tract infections in young children , yet no highly effective treatment or vaccine is available.\nHuman respiratory syncytial virus ( RSV) , a member of the Paramyxoviridae family , is the most important viral agent of pediatric respiratory tract disease worldwide.\nRespiratory syncytial virus ( RSV) , an RNA virus in the family Paramyxoviridae , causes respiratory disease in humans .\nMeasles virus ( MV ) is a member of the Paramyxoviridae family and an important human pathogen causing strong immunosuppression in affected individuals and a considerable number of deaths worldwide.\nHendra ( HeV ) and Nipah ( NiV ) viruses ( genus Henipavirus ( HNV; family Paramyxoviridae ) are emerging zoonotic agents that can cause severe respiratory distress and acute encephalitis in humans .\nMany of the common respiratory illnesses of infancy and childhood are caused by viruses of the Paramyxoviridae family, in particular measles virus, respiratory syncytial (RS) virus and parainfluenzavirus type 3 (PI3).\nHere we report simultaneous outbreaks of two distinct human respiratory viruses, human metapneumovirus (MPV; Pneumoviridae: Metapneumovirus) and human respirovirus 3 (HRV3; Paramyxoviridae; Respirovirus, formerly known as parainfluenza virus 3), in two chimpanzee (Pan troglodytes schweinfurthii) communities in the same forest in Uganda in December 2016 and January 2017.\nThe large virus family Paramyxoviridae includes some of the most significant human and livestock viruses, such as measles-, distemper-, mumps-, parainfluenza-, Newcastle disease-, respiratory syncytial virus and metapneumoviruses.\n\nQuestion: List 3 human diseases caused by viruses in the family Paramyxoviridae.",
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"content": "[\"measles\", \"mumps\", \"encephalitis\", \"respiratory disease\", \"newcastle disease\"]",
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science.scifact_entailment | Determine the relationship between them and generate a JSON response. Scientific Claim:
Monoclonal antibody targeting of N-cadherin encourages castration resistance.
Research Abstract:
The transition from androgen-dependent to castration-resistant prostate cancer (CRPC) is a lethal event of uncertain molecular etiology.
Comparing gene expression in isogenic androgen-dependent and CRPC xenografts, we found a reproducible increase in N-cadherin expression, which was also elevated in primary and metastatic tumors of individuals with CRPC.
Ectopic expression of N-cadherin in nonmetastatic, androgen-dependent prostate cancer models caused castration resistance, invasion and metastasis.
Monoclonal antibodies against the ectodomain of N-cadherin reduced proliferation, adhesion and invasion of prostate cancer cells in vitro.
In vivo, these antibodies slowed the growth of multiple established CRPC xenografts, blocked local invasion and metastasis and, at higher doses, led to complete regression.
N-cadherin–specific antibodies markedly delayed the time to emergence of castration resistance, markedly affected tumor histology and angiogenesis, and reduced both AKT serine-threonine kinase activity and serum interleukin-8 (IL-8) secretion.
These data indicate that N-cadherin is a major cause of both prostate cancer metastasis and castration resistance.
Therapeutic targeting of this factor with monoclonal antibodies may have considerable clinical benefit. | {"verdict": "CONTRADICT", "evidence": ["N-cadherin\u2013specific antibodies markedly delayed the time to emergence of castration resistance, markedly affected tumor histology and angiogenesis, and reduced both AKT serine-threonine kinase activity and serum interleukin-8 (IL-8) secretion."]} | science.scifact_entailment.515 | [
{
"content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nMonoclonal antibody targeting of N-cadherin encourages castration resistance.\n\nResearch Abstract:\nThe transition from androgen-dependent to castration-resistant prostate cancer (CRPC) is a lethal event of uncertain molecular etiology.\nComparing gene expression in isogenic androgen-dependent and CRPC xenografts, we found a reproducible increase in N-cadherin expression, which was also elevated in primary and metastatic tumors of individuals with CRPC.\nEctopic expression of N-cadherin in nonmetastatic, androgen-dependent prostate cancer models caused castration resistance, invasion and metastasis.\nMonoclonal antibodies against the ectodomain of N-cadherin reduced proliferation, adhesion and invasion of prostate cancer cells in vitro.\nIn vivo, these antibodies slowed the growth of multiple established CRPC xenografts, blocked local invasion and metastasis and, at higher doses, led to complete regression.\nN-cadherin–specific antibodies markedly delayed the time to emergence of castration resistance, markedly affected tumor histology and angiogenesis, and reduced both AKT serine-threonine kinase activity and serum interleukin-8 (IL-8) secretion.\nThese data indicate that N-cadherin is a major cause of both prostate cancer metastasis and castration resistance.\nTherapeutic targeting of this factor with monoclonal antibodies may have considerable clinical benefit.",
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"content": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"N-cadherin\\u2013specific antibodies markedly delayed the time to emergence of castration resistance, markedly affected tumor histology and angiogenesis, and reduced both AKT serine-threonine kinase activity and serum interleukin-8 (IL-8) secretion.\"]}",
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science.bioasq_list_qa | Examine the following excerpts from biomedical literature. Your objective is to extract relevant information to answer the posed question. Format your answer as a JSON array.
Context: Expression of TBC1D16 Is Associated with Favorable Prognosis of Epithelial Ovarian Cancer.
Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy with high recurrence and poor prognosis duo to the lack of effective biomarkers. TBC1 domain family member 16 (TBC1D16), a GTPase-activating protein, is involved in regulating intracellular trafficking in tumorigenesis and metastasis. However, the clinical significance of TBC1D16 in EOC remains unknown. In the present study, we investigated the expression and prognostic significance of TBC1D16 in EOC and its relationship with the expression of vascular endothelial growth factor (VEGF). The tissue specimens included 156 histologically confirmed EOC and 30 normal ovarian tissues. The expression of TBC1D16 and VEGF was detected by immunohistochemistry (IHC), and the immunoreactive score was calculated with signal intensity and percentage of positive cells. IHC results showed that TBC1D16 and VEGF were both mainly localized in cytoplasm of epithelial cells in normal ovarian tissues and were expressed in cancer cells. Based on the immunoreactive score, TBC1D16 expression in EOC was categorized as "high expression," compared with normal ovarian tissues (P < 0.05). The Chi-square test showed that high TBC1D16 expression was related to advanced pT stages (P = 0.029), but not correlated with other clinical features. Moreover, the TBC1D16 expression was significantly higher in EOC specimens with low VEGF expression (P < 0.001). Importantly, in both univariate and multivariate survival analyses, high expression of TBC1D16 was significantly correlated with good overall survival (OS). In conclusion, TBC1D16 is a predictive marker for favorable prognosis of EOC.
Epigenetic activation of a cryptic TBC1D16 transcript enhances melanoma progression by targeting EGFR.
Metastasis is responsible for most cancer-related deaths, and, among common tumor types, melanoma is one with great potential to metastasize. Here we study the contribution of epigenetic changes to the dissemination process by analyzing the changes that occur at the DNA methylation level between primary cancer cells and metastases. We found a hypomethylation event that reactivates a cryptic transcript of the Rab GTPase activating protein TBC1D16 (TBC1D16-47 kDa; referred to hereafter as TBC1D16-47KD) to be a characteristic feature of the metastatic cascade. This short isoform of TBC1D16 exacerbates melanoma growth and metastasis both in vitro and in vivo. By combining immunoprecipitation and mass spectrometry, we identified RAB5C as a new TBC1D16 target and showed that it regulates EGFR in melanoma cells. We also found that epigenetic reactivation of TBC1D16-47KD is associated with poor clinical outcome in melanoma, while conferring greater sensitivity to BRAF and MEK inhibitors.
IHC results showed that TBC1D16 and VEGF were both mainly localized in cytoplasm of epithelial cells in normal ovarian tissues and were expressed in cancer cells.
Based on the immunoreactive score, TBC1D16 expression in EOC was categorized as "high expression," compared with normal ovarian tissues (P < 0.05).
Expression of TBC1D16 Is Associated with Favorable Prognosis of Epithelial Ovarian Cancer .
Based on the immunoreactive score , TBC1D16 expression in EOC was categorized as " high expression , " compared with normal ovarian tissues ( P < 0.05) .
In addition, hypomethylation of TBC1D16 was observed in multiple tumours, including a breast cancer primary/metastasis pair, and to a lesser degree in melanoma, although again not all tumours or cancer primary/metastasis pairs exhibited altered patterns of methylation.
BACKGROUND Characteristic DNA methylation differences have been identified between primary and metastatic melanomas at EBF3 and/or TBC1D16 gene loci.
We also found that epigenetic reactivation of TBC1D16-47KD is associated with poor clinical outcome in melanoma, while conferring greater sensitivity to BRAF and MEK inhibitors.
In addition, hypomethylation of TBC1D16 was observed in multiple tumours, including a breast cancer primary/metastasis pair, and to a lesser degree in melanoma, although again not all tumours or cancer primary/metastasis pairs exhibited altered patterns of methylation.
In addition, hypomethylation of TBC1D16 was observed in multiple tumours, including a breast cancer primary/metastasis pair, and to a lesser degree in melanoma, although again not all tumours or cancer primary/metastasis pairs exhibited altered patterns of methylation.
Question: List types of cancer where TBC1 domain family member 16 (TBC1D16) is involved | ["Epithelial ovarian cancer (EOC)", "Melanoma"] | science.bioasq_list_qa.708 | [
{
"content": "Examine the following excerpts from biomedical literature. Your objective is to extract relevant information to answer the posed question. Format your answer as a JSON array.\n\nContext: Expression of TBC1D16 Is Associated with Favorable Prognosis of Epithelial Ovarian Cancer.\nEpithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy with high recurrence and poor prognosis duo to the lack of effective biomarkers. TBC1 domain family member 16 (TBC1D16), a GTPase-activating protein, is involved in regulating intracellular trafficking in tumorigenesis and metastasis. However, the clinical significance of TBC1D16 in EOC remains unknown. In the present study, we investigated the expression and prognostic significance of TBC1D16 in EOC and its relationship with the expression of vascular endothelial growth factor (VEGF). The tissue specimens included 156 histologically confirmed EOC and 30 normal ovarian tissues. The expression of TBC1D16 and VEGF was detected by immunohistochemistry (IHC), and the immunoreactive score was calculated with signal intensity and percentage of positive cells. IHC results showed that TBC1D16 and VEGF were both mainly localized in cytoplasm of epithelial cells in normal ovarian tissues and were expressed in cancer cells. Based on the immunoreactive score, TBC1D16 expression in EOC was categorized as \"high expression,\" compared with normal ovarian tissues (P < 0.05). The Chi-square test showed that high TBC1D16 expression was related to advanced pT stages (P = 0.029), but not correlated with other clinical features. Moreover, the TBC1D16 expression was significantly higher in EOC specimens with low VEGF expression (P < 0.001). Importantly, in both univariate and multivariate survival analyses, high expression of TBC1D16 was significantly correlated with good overall survival (OS). In conclusion, TBC1D16 is a predictive marker for favorable prognosis of EOC.\nEpigenetic activation of a cryptic TBC1D16 transcript enhances melanoma progression by targeting EGFR.\nMetastasis is responsible for most cancer-related deaths, and, among common tumor types, melanoma is one with great potential to metastasize. Here we study the contribution of epigenetic changes to the dissemination process by analyzing the changes that occur at the DNA methylation level between primary cancer cells and metastases. We found a hypomethylation event that reactivates a cryptic transcript of the Rab GTPase activating protein TBC1D16 (TBC1D16-47 kDa; referred to hereafter as TBC1D16-47KD) to be a characteristic feature of the metastatic cascade. This short isoform of TBC1D16 exacerbates melanoma growth and metastasis both in vitro and in vivo. By combining immunoprecipitation and mass spectrometry, we identified RAB5C as a new TBC1D16 target and showed that it regulates EGFR in melanoma cells. We also found that epigenetic reactivation of TBC1D16-47KD is associated with poor clinical outcome in melanoma, while conferring greater sensitivity to BRAF and MEK inhibitors.\nIHC results showed that TBC1D16 and VEGF were both mainly localized in cytoplasm of epithelial cells in normal ovarian tissues and were expressed in cancer cells.\nBased on the immunoreactive score, TBC1D16 expression in EOC was categorized as \"high expression,\" compared with normal ovarian tissues (P < 0.05).\nExpression of TBC1D16 Is Associated with Favorable Prognosis of Epithelial Ovarian Cancer .\nBased on the immunoreactive score , TBC1D16 expression in EOC was categorized as \" high expression , \" compared with normal ovarian tissues ( P < 0.05) .\nIn addition, hypomethylation of TBC1D16 was observed in multiple tumours, including a breast cancer primary/metastasis pair, and to a lesser degree in melanoma, although again not all tumours or cancer primary/metastasis pairs exhibited altered patterns of methylation.\nBACKGROUND Characteristic DNA methylation differences have been identified between primary and metastatic melanomas at EBF3 and/or TBC1D16 gene loci.\nWe also found that epigenetic reactivation of TBC1D16-47KD is associated with poor clinical outcome in melanoma, while conferring greater sensitivity to BRAF and MEK inhibitors.\nIn addition, hypomethylation of TBC1D16 was observed in multiple tumours, including a breast cancer primary/metastasis pair, and to a lesser degree in melanoma, although again not all tumours or cancer primary/metastasis pairs exhibited altered patterns of methylation.\nIn addition, hypomethylation of TBC1D16 was observed in multiple tumours, including a breast cancer primary/metastasis pair, and to a lesser degree in melanoma, although again not all tumours or cancer primary/metastasis pairs exhibited altered patterns of methylation.\n\nQuestion: List types of cancer where TBC1 domain family member 16 (TBC1D16) is involved",
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"content": "[\"Epithelial ovarian cancer (EOC)\", \"Melanoma\"]",
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science.scifact_entailment | Determine the relationship between them and generate a JSON response. Scientific Claim:
Oxidative phosphorylation is one of the primary glycometabolic pathways in cells.
Research Abstract:
Fast axonal transport (FAT) requires consistent energy over long distances to fuel the molecular motors that transport vesicles.
We demonstrate that glycolysis provides ATP for the FAT of vesicles.
Although inhibiting ATP production from mitochondria did not affect vesicles motility, pharmacological or genetic inhibition of the glycolytic enzyme GAPDH reduced transport in cultured neurons and in Drosophila larvae.
GAPDH localizes on vesicles via a huntingtin-dependent mechanism and is transported on fast-moving vesicles within axons.
Purified motile vesicles showed GAPDH enzymatic activity and produced ATP.
Finally, we show that vesicular GAPDH is necessary and sufficient to provide on-board energy for fast vesicular transport.
Although detaching GAPDH from vesicles reduced transport, targeting GAPDH to vesicles was sufficient to promote FAT in GAPDH deficient neurons.
This specifically localized glycolytic machinery may supply constant energy, independent of mitochondria, for the processive movement of vesicles over long distances in axons. | {"verdict": "NEI", "evidence": []} | science.scifact_entailment.569 | [
{
"content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nOxidative phosphorylation is one of the primary glycometabolic pathways in cells.\n\nResearch Abstract:\nFast axonal transport (FAT) requires consistent energy over long distances to fuel the molecular motors that transport vesicles.\nWe demonstrate that glycolysis provides ATP for the FAT of vesicles.\nAlthough inhibiting ATP production from mitochondria did not affect vesicles motility, pharmacological or genetic inhibition of the glycolytic enzyme GAPDH reduced transport in cultured neurons and in Drosophila larvae.\nGAPDH localizes on vesicles via a huntingtin-dependent mechanism and is transported on fast-moving vesicles within axons.\nPurified motile vesicles showed GAPDH enzymatic activity and produced ATP.\nFinally, we show that vesicular GAPDH is necessary and sufficient to provide on-board energy for fast vesicular transport.\nAlthough detaching GAPDH from vesicles reduced transport, targeting GAPDH to vesicles was sufficient to promote FAT in GAPDH deficient neurons.\nThis specifically localized glycolytic machinery may supply constant energy, independent of mitochondria, for the processive movement of vesicles over long distances in axons.",
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"content": "{\"verdict\": \"NEI\", \"evidence\": []}",
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science.scierc_ner | Analyze the given abstract from a computer science paper. Identify and list entities in a structured JSON format. Focus on capturing distinct elements relevant to the research context.
Input Abstract:
This paper describes three relatively domain-independent capabilities recently added to the Paramax spoken language understanding system: non-monotonic reasoning, implicit reference resolution, and database query paraphrase. In addition, we discuss the results of the February 1992 ATIS benchmark tests. We describe a variation on the standard evaluation metric which provides a more tightly controlled measure of progress. Finally, we briefly describe an experiment which we have done in extending the n-best speech/language integration architecture to improving OCR accuracy. | Output Entities:
{"Generic": ["domain-independent capabilities"], "Material": ["February 1992 ATIS benchmark tests"], "Method": ["Paramax spoken language understanding system", "n-best speech/language integration architecture"], "Metric": ["OCR accuracy"], "OtherScientificTerm": [], "Task": ["implicit reference resolution", "database query paraphrase", "non-monotonic reasoning"]} | science.scierc_ner.42 | [
{
"content": "Analyze the given abstract from a computer science paper. Identify and list entities in a structured JSON format. Focus on capturing distinct elements relevant to the research context.\n\nInput Abstract:\n\nThis paper describes three relatively domain-independent capabilities recently added to the Paramax spoken language understanding system: non-monotonic reasoning, implicit reference resolution, and database query paraphrase. In addition, we discuss the results of the February 1992 ATIS benchmark tests. We describe a variation on the standard evaluation metric which provides a more tightly controlled measure of progress. Finally, we briefly describe an experiment which we have done in extending the n-best speech/language integration architecture to improving OCR accuracy.",
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"content": "Output Entities:\n\n{\"Generic\": [\"domain-independent capabilities\"], \"Material\": [\"February 1992 ATIS benchmark tests\"], \"Method\": [\"Paramax spoken language understanding system\", \"n-best speech/language integration architecture\"], \"Metric\": [\"OCR accuracy\"], \"OtherScientificTerm\": [], \"Task\": [\"implicit reference resolution\", \"database query paraphrase\", \"non-monotonic reasoning\"]}",
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science.multicite_intent_classification | Examine the text below, which includes a citation marked by "<cite>" and "</cite>". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.
Provide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.
Text with Citation:
Working with German-to-English translation, <cite>Collins et al. (2005)</cite> parse input sentences with a constituent-structure parser and apply six hand-crafted rules to reorder the German text toward English word order. | ["Background"] | science.multicite_intent_classification.275 | [
{
"content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nWorking with German-to-English translation, <cite>Collins et al. (2005)</cite> parse input sentences with a constituent-structure parser and apply six hand-crafted rules to reorder the German text toward English word order.",
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{
"content": "[\"Background\"]",
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|
science.discomat_te | | Indenter | Material | Type | Load | Hv (kg/mm2) | Hv (kg/mm2) | Hv (GPa) | Hv (GPa) |
| Micro- | Clear CVD ZnS (Vitron) | Ceramic | 200g | 148.3 | +-3.6 | 1.45 | +-0.04 |
| Micro- | FLIR CVD ZnS (Vitron) | Ceramic | 200g | 178.3 | +-4.4 | 1.75 | +-0.04 |
| Micro- | Crystran GLS | Glass | 200g | 378.1 | +-11.2 | 3.71 | +-0.11 |
| Micro- | GGLS-15CdS-e-w | Glass | 200g | 248.7 | +-8.5 | 2.44 | +-0.08 |
| Micro- | GaLS65-20Ca-w | Glass | 200g | 342.3 | +-16.7 | 3.36 | +-0.16 |
| Nano- | GaLS72-a | Phase-separated glass | 200mN | 432.0 | +-38.7 | 4.24 | +-0.38 |
| Nano- | GaLS72-10Zn-a -- phase 1 | Glass ceramic | 200mN | 261.8 | +-40.2 | 2.78 | +-0.43 |
| Nano- | GaLS72-10Zn-a -- phase 2 | Glass ceramic | 200mN | 527.6 | +-95.1 | 5.58 | +-1.01 |
| Nano- | GeLS-900-a -- phase 1 | Glass ceramic | 50mN | 373.7 | +-63.1 | 3.95 | +-0.67 |
| Nano- | GeLS-900-a -- phase 2 | Glass ceramic | 10mN | 1259.9 | +-13.3 | 13.3 | +-4.36 |
Caption: Hardness testing results.
Examine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:
{"value": "xx", "type": "Other"}
{"value": "xx", "type": "Glass_Compound_Amount", "constituent": "xx", "unit": "xx", "material": "xx"}
Process each cell sequentially, and for any unknown attributes, use "xx" as a placeholder. Each JSON should be on a new line.
Output: | {"value": "148.3", "type": "Other"}
{"value": "1.45", "type": "Other"}
{"value": "178.3", "type": "Other"}
{"value": "1.75", "type": "Other"}
{"value": "378.1", "type": "Other"}
{"value": "3.71", "type": "Other"}
{"value": "248.7", "type": "Other"}
{"value": "2.44", "type": "Other"}
{"value": "342.3", "type": "Other"}
{"value": "3.36", "type": "Other"}
{"value": "432.0", "type": "Other"}
{"value": "4.24", "type": "Other"}
{"value": "261.8", "type": "Other"}
{"value": "2.78", "type": "Other"}
{"value": "527.6", "type": "Other"}
{"value": "5.58", "type": "Other"}
{"value": "373.7", "type": "Other"}
{"value": "3.95", "type": "Other"}
{"value": "1259.9", "type": "Other"}
{"value": "13.3", "type": "Other"} | science.discomat_te.366 | [
{
"content": "| Indenter | Material | Type | Load | Hv (kg/mm2) | Hv (kg/mm2) | Hv (GPa) | Hv (GPa) |\n| Micro- | Clear CVD ZnS (Vitron) | Ceramic | 200g | 148.3 | +-3.6 | 1.45 | +-0.04 |\n| Micro- | FLIR CVD ZnS (Vitron) | Ceramic | 200g | 178.3 | +-4.4 | 1.75 | +-0.04 |\n| Micro- | Crystran GLS | Glass | 200g | 378.1 | +-11.2 | 3.71 | +-0.11 |\n| Micro- | GGLS-15CdS-e-w | Glass | 200g | 248.7 | +-8.5 | 2.44 | +-0.08 |\n| Micro- | GaLS65-20Ca-w | Glass | 200g | 342.3 | +-16.7 | 3.36 | +-0.16 |\n| Nano- | GaLS72-a | Phase-separated glass | 200mN | 432.0 | +-38.7 | 4.24 | +-0.38 |\n| Nano- | GaLS72-10Zn-a -- phase 1 | Glass ceramic | 200mN | 261.8 | +-40.2 | 2.78 | +-0.43 |\n| Nano- | GaLS72-10Zn-a -- phase 2 | Glass ceramic | 200mN | 527.6 | +-95.1 | 5.58 | +-1.01 |\n| Nano- | GeLS-900-a -- phase 1 | Glass ceramic | 50mN | 373.7 | +-63.1 | 3.95 | +-0.67 |\n| Nano- | GeLS-900-a -- phase 2 | Glass ceramic | 10mN | 1259.9 | +-13.3 | 13.3 | +-4.36 |\n\nCaption: Hardness testing results.\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:",
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"content": "{\"value\": \"148.3\", \"type\": \"Other\"}\n{\"value\": \"1.45\", \"type\": \"Other\"}\n{\"value\": \"178.3\", \"type\": \"Other\"}\n{\"value\": \"1.75\", \"type\": \"Other\"}\n{\"value\": \"378.1\", \"type\": \"Other\"}\n{\"value\": \"3.71\", \"type\": \"Other\"}\n{\"value\": \"248.7\", \"type\": \"Other\"}\n{\"value\": \"2.44\", \"type\": \"Other\"}\n{\"value\": \"342.3\", \"type\": \"Other\"}\n{\"value\": \"3.36\", \"type\": \"Other\"}\n{\"value\": \"432.0\", \"type\": \"Other\"}\n{\"value\": \"4.24\", \"type\": \"Other\"}\n{\"value\": \"261.8\", \"type\": \"Other\"}\n{\"value\": \"2.78\", \"type\": \"Other\"}\n{\"value\": \"527.6\", \"type\": \"Other\"}\n{\"value\": \"5.58\", \"type\": \"Other\"}\n{\"value\": \"373.7\", \"type\": \"Other\"}\n{\"value\": \"3.95\", \"type\": \"Other\"}\n{\"value\": \"1259.9\", \"type\": \"Other\"}\n{\"value\": \"13.3\", \"type\": \"Other\"}",
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science.mup_single_document_summarization | Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.
Title of the Paper:
How Data Augmentation affects Optimization for Linear Regression
Main Content:
1 Introduction . Data augmentation , a popular set of techniques in which data is augmented ( i.e . modified ) at every optimization step , has become increasingly crucial to training models using gradient-based optimization . However , in modern overparametrized settings where there are many different minimizers of the training loss , the specific minimizer selected by training and the quality of the resulting model can be highly sensitive to choices of augmentation hyperparameters . As a result , practitioners use methods ranging from simple grid search to Bayesian optimization and reinforcement learning [ 8 , 9 , 17 ] to select and schedule augmentations by changing hyperparameters over the course of optimization . Such approaches , while effective , often require extensive compute and lack theoretical grounding . These empirical practices stand in contrast to theoretical results from the implicit bias and stochastic optimization literature . The extensive recent literature on implicit bias [ 15 , 29 , 32 ] gives provable guarantees on which minimizer of the training loss is selected by GD and SGD in simple settings , but considers cases without complex scheduling . On the other hand , classical theorems in stochastic optimization , building on the Monro-Robbins theorem in [ 25 ] , give provably optimal learning rate schedules for strongly convex objectives . However , neither line of work addresses the myriad augmentation and hyperparameter choices crucial to gradient-based training effective in practice . The present work takes a step towards bridging this gap . We consider two main questions for a learning rate schedule and data augmentation policy : ∗Equal contribution 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . 1 . When and at what rate will optimization converge ? 2 . Assuming optimization converges , what point does it converge to ? To isolate the effect on optimization of jointly scheduling learning rate and data augmentation schemes , we consider these questions in the simple convex model of linear regression with MSE loss : L ( W ; D ) = 1 N ‖Y −WX‖2F . ( 1.1 ) In this setting , we analyze the effect of the data augmentation policy on the optimization trajectory Wt of augmented ( stochastic ) gradient descent2 , which follows the update equation Wt+1 = Wt − ηt∇WL ( W ; Dt ) ∣∣ W=Wt . ( 1.2 ) Here , the dataset D = ( X , Y ) contains N datapoints arranged into data matrices X ∈ Rn×N and Y ∈ Rp×N whose columns consist of inputs xi ∈ Rn and outputs yi ∈ Rp . In this context , we take a flexible definition data augmentation scheme as any procedure that consists , at every step of optimization , of replacing the dataset D by a randomly augmented variant which we denote by Dt = ( Xt , Yt ) . This framework is flexible enough to handle SGD and commonly used augmentations such as additive noise [ 14 ] , CutOut [ 12 ] , SpecAugment [ 23 ] , Mixup [ 35 ] , and label-preserving transformations ( e.g . color jitter , geometric transformations [ 26 ] ) ) . We give a general answer to Questions 1 and 2 for arbitrary data augmentation schemes . Our main result ( Theorem 3.1 ) gives sufficient conditions for optimization to converge in terms of the learning rate schedule and simple 2nd and 4th order moment statistics of augmented data matrices . When convergence occurs , we explicitly characterize the resulting optimum in terms of these statistics . We then specialize our results to ( S ) GD with modern augmentations such as additive noise [ 14 ] and random projections ( e.g . CutOut [ 12 ] and SpecAugment [ 23 ] ) . In these cases , we find learning rate and augmentation parameters which ensure convergence with rates to the minimum norm optimum for overparametrized linear regression . To sum up , our main contributions are : 1 . We analyze arbitrary data augmentation schemes for linear regression with MSE loss , obtaining explicit sufficient conditions on the joint schedule of the data augmentation policy and the learning rate for ( stochastic ) gradient descent that guarantee convergence with rates in Theorems 3.1 and 3.2 . The resulting augmentation-dependent optimum encodes the ultimate effect of augmentation on optimization , and we characterize it in Theorem 3.1 . Our results generalize Monro-Robbins theorems [ 25 ] to situations where the stochastic optimization objective may change at each step . 2 . We specialize our results to ( stochastic ) gradient descent with additive input noise ( §4 ) or random projections of the input ( §5 ) , a proxy for the popular CutOut and SpecAugment augmentations [ 12 , 23 ] . In each case , we find that jointly scheduling learning rate and augmentation strength is critical for allowing convergence with rates to the minimum norm optimizer . We find specific schedule choices which guarantee this convergence with rates ( Theorems 4.1 , 4.2 , and 5.1 ) and validate our results empirically ( Figure 4.1 ) . This suggests explicitly adding learning rate schedules to the search space for learned augmentations as in [ 8 , 9 ] , which we leave to future work . 2 Related Work . In addition to the extensive empirical work on data augmentation cited elsewhere in this article , we briefly catalog other theoretical work on data augmentation and learning rate schedules . The latter were first considered in the seminal work [ 25 ] . This spawned a vast literature on rates of convergence for GD , SGD , and their variants . We mention only the relatively recent articles [ 1 , 11 , 4 , 27 , 22 ] and the references therein . The last of these , namely [ 22 ] , finds optimal choices of learning rate and batch size for SGD in the overparametrized linear setting . A number of articles have also pointed out in various regimes that data augmentation and more general transformations such as feature dropout correspond in part to ` 2-type regularization on model parameters , features , gradients , and Hessians . The first article of this kind of which we are aware is [ 3 ] , 2Both GD and SGD fall into our framework . To implement SGD , we take Dt to be a subset of D. which treats the case of additive Gaussian noise ( see §4 ) . More recent work in this direction includes [ 5 , 30 , 19 , 21 ] . There are also several articles investigating optimal choices of ` 2-regularization for linear models ( cf e.g . [ 33 , 31 , 2 ] ) . These articles focus directly on the generalization effects of ridge-regularized minima but not on the dynamics of optimization . We also point the reader to [ 20 ] , which considers optimal choices for the weight decay coefficient empirically in neural networks and analytically in simple models . We also refer the reader to a number of recent attempts to characterize the benefits of data augmentation . In [ 24 ] , for example , the authors quantify how much augmented data , produced via additive noise , is needed to learn positive margin classifiers . [ 6 ] , in contrast , focuses on the case of data invariant under the action of a group . Using the group action to generate label-preserving augmentations , the authors prove that the variance of any function depending only on the trained model will decrease . This applies in particular to estimators for the trainable parameters themselves . [ 10 ] shows augmented k-NN classification reduces to a kernel method for augmentations transforming each datapoint to a finite orbit of possibilities . It also gives a second order expansion for the proxy loss of a kernel method under such augmentations and interprets how each term affects generalization . Finally , the article [ 34 ] considers both label preserving and noising augmentations , pointing out the conceptually distinct roles such augmentations play . 3 Time-varying Monro-Robbins for linear models under augmentation . We seek to isolate the impact of data augmentation on optimization in the simple setting of augmented ( stochastic ) gradient descent for linear regression with the MSE loss ( 1.1 ) . Since the augmented dataset Dt at time t is a stochastic function of D , we may view the update rule ( 1.2 ) as a form of stochastic optimization for the proxy loss at time t Lt ( W ) : = EDt [ L ( W ; Dt ) ] ( 3.1 ) which uses an unbiased estimate of the gradient of L ( W ; Dt ) from a single draw of Dt . The connection between data augmentation and this proxy loss was introduced in [ 3 , 5 ] , but we now consider it in the context of stochastic optimization . In particular , we consider scheduling the augmentation , which allows the distribution of Dt to change with t and thus enables optimization to converge to points which are not minimizers of the proxy loss Lt ( W ) at any fixed time . Our main results , Theorems 3.1 and 3.2 , provide sufficient conditions for jointly scheduling learning rates and general augmentation schemes to guarantee convergence of augmented gradient descent in the linear regression model ( 1.1 ) . Before stating them , we first give examples of augmentations falling into our framework , which we analyze using our general results in §4 and §5 . • Additive Gaussian noise : For SGD with batch size Bt and noise level σt > 0 , this corresponds to Xt = ct ( XAt + σt · Gt ) and Yt = ctY At , where Gt is a matrix of i.i.d . standard Gaussians , At ∈ RN×Bt has i.i.d . columns with a single non-zero entry equal to 1 chosen uniformly at random and ct = √ N/Bt is a normalizing factor . The proxy loss is Lt ( W ) = L ( W ; D ) + σ2t ‖W‖2F , ( 3.2 ) which adds an ` 2 penalty . We analyze this case in §4 . • Random projection : This corresponds to Xt = ΠtX and Yt = Y , where Πt is an orthogonal projection onto a random subspace . For γt = tr ( Πt ) /n , the proxy loss is Lt ( W ) = 1 N ‖Y − γtWX‖2F + 1 N γt ( 1− γt ) 1 n ‖X‖2F · ‖W‖2F +O ( n−1 ) , adding a data-dependent ` 2 penalty and applying Stein-type shrinkage on input data . We analyze this in §5 . In addition to these augmentations , the augmentations below also fit into our framework , and Theorems 3.1 and 3.2 apply . However , in these cases , explicitly characterizing the learned minimum beyond the general description given in Theorems 3.1 and 3.2 is more difficult , and we thus leave interpretion of these specializations to future work . ( a ) The time t proxy loss Lt is non-degenerate on V‖ with minimal norm minimizer W ∗t . The increment Wt+1 −Wt is in V‖ , so only the projection WtQ‖ of Wt onto V‖ changes . ( b ) The proxy losses Lt and Lt+1 at consecutive times will generally have different minimal norm minimizers W ∗t , W ∗t+1 . The increment Ξ∗t = W ∗t+1 − W ∗t measures this change . 3.1 A general time-varying Monro-Robbins theorem . Given an augmentation scheme for the overparameterized linear model ( 1.1 ) , the time t gradient update at learning rate ηt is Wt+1 : = Wt + 2ηt N · ( Yt −WtXt ) XTt , ( 3.3 ) where Dt = ( Xt , Yt ) is the augmented dataset at time t. The minimum norm minimizer of the corresponding proxy loss Lt ( see ( 3.1 ) ) is W ∗t : = E [ YtXTt ] E [ XtXTt ] + , ( 3.4 ) where E [ XtXTt ] + denotes the Moore-Penrose pseudo-inverse ( see Figure 3.1a ) . In this section we state a rigorous result , Theorem 3.1 , giving sufficient conditions on the learning rate ηt and distributions of the augmented matrices Xt , Yt under which augmented gradient descent converges . To state it , note that ( 3.3 ) implies that each row of Wt+1 −Wt is contained in the column span of the Hessian XtXTt of the augmented loss and therefore almost surely belongs to the subspace V‖ : = column span of E [ XtXTt ] ⊆ Rn , ( 3.5 ) as illustrated in Figure 3.1a . The reason is that , in the orthogonal complement to V‖ , the augmented loss L ( W ; Dt ) has zero gradient with probability 1 . To ease notation , we assume that V‖ is independent of t. This assumption holds for additive Gaussian noise , random projection , MixUp , SGD , and their combinations . It is not necessary in general , however , and we refer the interested reader to Remark B.2 in the Appendix for how to treat the general case . Let us denote by Q‖ : Rn → Rn the orthogonal projection onto V‖ ( see Figure 3.1a ) . As we already pointed out , at time t , gradient descent leaves the matrix of projections Wt ( Id−Q‖ ) of each row of Wt onto the orthogonal complement of V‖ unchanged . In contrast , ‖WtQ‖ −W ∗t ‖F decreases at a rate governed by the smallest positive eigenvalue λmin , V‖ ( E [ XtX T t ] ) of the Hessian for the proxy loss Lt , which is obtained by restricting its full Hessian E [ XtX T t ] to V‖ . Moreover , whether and at what rate WtQ‖ −W ∗t converges to 0 depends on how quickly the distance Ξ∗t : = W ∗ t+1 −W ∗t ( 3.6 ) between proxy loss optima at successive steps tends to zero ( see Figure 3.1b ) . Theorem 3.1 ( Special case of Theorem B.1 ) . Suppose that V‖ is independent of t , that the learning rate satisfies ηt → 0 , that the proxy optima satisfy ∞∑ t=0 ‖Ξ∗t ‖F < ∞ , ( 3.7 ) ensuring the existence of a limit W ∗∞ : = limt→∞W ∗ t , that ∞∑ t=0 ηtλmin , V‖ ( E [ XtX T t ] ) =∞ ( 3.8 ) and finally that ∞∑ t=0 η2tE [ ‖XtXTt − E [ XtXTt ] ‖2F + ‖YtXTt − E [ YtXTt ] ‖2F ] < ∞ . ( 3.9 ) Then , for any initialization W0 , we have that WtQ‖ converges in probability to W ∗∞ . If the same augmentation is applied with different strength parameters at each step t ( e.g . the noise level σ2t for additive Gaussian noise ) , we may specialize Theorem 3.1 to this augmentation scheme . More precisely , translating conditions ( 3.7 ) , ( 3.8 ) , ( 3.9 ) into conditions on the learning rate and augmentation strength gives conditions on the schedule for ηt and these strength parameters to ensure convergence . In §4 and §5 , we do this for additive Gaussian noise and random projections . When the augmentation scheme is static in t , Theorem 3.1 reduces to a standard Monro-Robbins theorem [ 25 ] for the ( static ) proxy loss Lt ( W ) . As in that setting , condition ( 3.8 ) enforces that the learning trajectory travels far enough to reach an optimum , and the summand in Condition ( 3.9 ) bounds the variance of the gradient of the augmented loss L ( W ; Dt ) to ensure the total variance of the stochastic gradients is summable . Condition ( 3.7 ) is new and enforces that the minimizers W ∗t of the proxy losses Lt ( W ) change slowly enough for augmented optimization procedure to keep pace . | This paper rigorously analyzed the convergence of data augmentation in linear regression under the convex setting. Its main result gives sufficient condition on the joint schedule of the learning rate and the augmentation strength under which asymptotic convergence rates are provided. Specializations The above result is then instantiated for additive Gaussian noise, random projections. | science.mup_single_document_summarization.13132 | [
{
"content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nHow Data Augmentation affects Optimization for Linear Regression\n\nMain Content:\n1 Introduction . Data augmentation , a popular set of techniques in which data is augmented ( i.e . modified ) at every optimization step , has become increasingly crucial to training models using gradient-based optimization . However , in modern overparametrized settings where there are many different minimizers of the training loss , the specific minimizer selected by training and the quality of the resulting model can be highly sensitive to choices of augmentation hyperparameters . As a result , practitioners use methods ranging from simple grid search to Bayesian optimization and reinforcement learning [ 8 , 9 , 17 ] to select and schedule augmentations by changing hyperparameters over the course of optimization . Such approaches , while effective , often require extensive compute and lack theoretical grounding . These empirical practices stand in contrast to theoretical results from the implicit bias and stochastic optimization literature . The extensive recent literature on implicit bias [ 15 , 29 , 32 ] gives provable guarantees on which minimizer of the training loss is selected by GD and SGD in simple settings , but considers cases without complex scheduling . On the other hand , classical theorems in stochastic optimization , building on the Monro-Robbins theorem in [ 25 ] , give provably optimal learning rate schedules for strongly convex objectives . However , neither line of work addresses the myriad augmentation and hyperparameter choices crucial to gradient-based training effective in practice . The present work takes a step towards bridging this gap . We consider two main questions for a learning rate schedule and data augmentation policy : ∗Equal contribution 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . 1 . When and at what rate will optimization converge ? 2 . Assuming optimization converges , what point does it converge to ? To isolate the effect on optimization of jointly scheduling learning rate and data augmentation schemes , we consider these questions in the simple convex model of linear regression with MSE loss : L ( W ; D ) = 1 N ‖Y −WX‖2F . ( 1.1 ) In this setting , we analyze the effect of the data augmentation policy on the optimization trajectory Wt of augmented ( stochastic ) gradient descent2 , which follows the update equation Wt+1 = Wt − ηt∇WL ( W ; Dt ) ∣∣ W=Wt . ( 1.2 ) Here , the dataset D = ( X , Y ) contains N datapoints arranged into data matrices X ∈ Rn×N and Y ∈ Rp×N whose columns consist of inputs xi ∈ Rn and outputs yi ∈ Rp . In this context , we take a flexible definition data augmentation scheme as any procedure that consists , at every step of optimization , of replacing the dataset D by a randomly augmented variant which we denote by Dt = ( Xt , Yt ) . This framework is flexible enough to handle SGD and commonly used augmentations such as additive noise [ 14 ] , CutOut [ 12 ] , SpecAugment [ 23 ] , Mixup [ 35 ] , and label-preserving transformations ( e.g . color jitter , geometric transformations [ 26 ] ) ) . We give a general answer to Questions 1 and 2 for arbitrary data augmentation schemes . Our main result ( Theorem 3.1 ) gives sufficient conditions for optimization to converge in terms of the learning rate schedule and simple 2nd and 4th order moment statistics of augmented data matrices . When convergence occurs , we explicitly characterize the resulting optimum in terms of these statistics . We then specialize our results to ( S ) GD with modern augmentations such as additive noise [ 14 ] and random projections ( e.g . CutOut [ 12 ] and SpecAugment [ 23 ] ) . In these cases , we find learning rate and augmentation parameters which ensure convergence with rates to the minimum norm optimum for overparametrized linear regression . To sum up , our main contributions are : 1 . We analyze arbitrary data augmentation schemes for linear regression with MSE loss , obtaining explicit sufficient conditions on the joint schedule of the data augmentation policy and the learning rate for ( stochastic ) gradient descent that guarantee convergence with rates in Theorems 3.1 and 3.2 . The resulting augmentation-dependent optimum encodes the ultimate effect of augmentation on optimization , and we characterize it in Theorem 3.1 . Our results generalize Monro-Robbins theorems [ 25 ] to situations where the stochastic optimization objective may change at each step . 2 . We specialize our results to ( stochastic ) gradient descent with additive input noise ( §4 ) or random projections of the input ( §5 ) , a proxy for the popular CutOut and SpecAugment augmentations [ 12 , 23 ] . In each case , we find that jointly scheduling learning rate and augmentation strength is critical for allowing convergence with rates to the minimum norm optimizer . We find specific schedule choices which guarantee this convergence with rates ( Theorems 4.1 , 4.2 , and 5.1 ) and validate our results empirically ( Figure 4.1 ) . This suggests explicitly adding learning rate schedules to the search space for learned augmentations as in [ 8 , 9 ] , which we leave to future work . 2 Related Work . In addition to the extensive empirical work on data augmentation cited elsewhere in this article , we briefly catalog other theoretical work on data augmentation and learning rate schedules . The latter were first considered in the seminal work [ 25 ] . This spawned a vast literature on rates of convergence for GD , SGD , and their variants . We mention only the relatively recent articles [ 1 , 11 , 4 , 27 , 22 ] and the references therein . The last of these , namely [ 22 ] , finds optimal choices of learning rate and batch size for SGD in the overparametrized linear setting . A number of articles have also pointed out in various regimes that data augmentation and more general transformations such as feature dropout correspond in part to ` 2-type regularization on model parameters , features , gradients , and Hessians . The first article of this kind of which we are aware is [ 3 ] , 2Both GD and SGD fall into our framework . To implement SGD , we take Dt to be a subset of D. which treats the case of additive Gaussian noise ( see §4 ) . More recent work in this direction includes [ 5 , 30 , 19 , 21 ] . There are also several articles investigating optimal choices of ` 2-regularization for linear models ( cf e.g . [ 33 , 31 , 2 ] ) . These articles focus directly on the generalization effects of ridge-regularized minima but not on the dynamics of optimization . We also point the reader to [ 20 ] , which considers optimal choices for the weight decay coefficient empirically in neural networks and analytically in simple models . We also refer the reader to a number of recent attempts to characterize the benefits of data augmentation . In [ 24 ] , for example , the authors quantify how much augmented data , produced via additive noise , is needed to learn positive margin classifiers . [ 6 ] , in contrast , focuses on the case of data invariant under the action of a group . Using the group action to generate label-preserving augmentations , the authors prove that the variance of any function depending only on the trained model will decrease . This applies in particular to estimators for the trainable parameters themselves . [ 10 ] shows augmented k-NN classification reduces to a kernel method for augmentations transforming each datapoint to a finite orbit of possibilities . It also gives a second order expansion for the proxy loss of a kernel method under such augmentations and interprets how each term affects generalization . Finally , the article [ 34 ] considers both label preserving and noising augmentations , pointing out the conceptually distinct roles such augmentations play . 3 Time-varying Monro-Robbins for linear models under augmentation . We seek to isolate the impact of data augmentation on optimization in the simple setting of augmented ( stochastic ) gradient descent for linear regression with the MSE loss ( 1.1 ) . Since the augmented dataset Dt at time t is a stochastic function of D , we may view the update rule ( 1.2 ) as a form of stochastic optimization for the proxy loss at time t Lt ( W ) : = EDt [ L ( W ; Dt ) ] ( 3.1 ) which uses an unbiased estimate of the gradient of L ( W ; Dt ) from a single draw of Dt . The connection between data augmentation and this proxy loss was introduced in [ 3 , 5 ] , but we now consider it in the context of stochastic optimization . In particular , we consider scheduling the augmentation , which allows the distribution of Dt to change with t and thus enables optimization to converge to points which are not minimizers of the proxy loss Lt ( W ) at any fixed time . Our main results , Theorems 3.1 and 3.2 , provide sufficient conditions for jointly scheduling learning rates and general augmentation schemes to guarantee convergence of augmented gradient descent in the linear regression model ( 1.1 ) . Before stating them , we first give examples of augmentations falling into our framework , which we analyze using our general results in §4 and §5 . • Additive Gaussian noise : For SGD with batch size Bt and noise level σt > 0 , this corresponds to Xt = ct ( XAt + σt · Gt ) and Yt = ctY At , where Gt is a matrix of i.i.d . standard Gaussians , At ∈ RN×Bt has i.i.d . columns with a single non-zero entry equal to 1 chosen uniformly at random and ct = √ N/Bt is a normalizing factor . The proxy loss is Lt ( W ) = L ( W ; D ) + σ2t ‖W‖2F , ( 3.2 ) which adds an ` 2 penalty . We analyze this case in §4 . • Random projection : This corresponds to Xt = ΠtX and Yt = Y , where Πt is an orthogonal projection onto a random subspace . For γt = tr ( Πt ) /n , the proxy loss is Lt ( W ) = 1 N ‖Y − γtWX‖2F + 1 N γt ( 1− γt ) 1 n ‖X‖2F · ‖W‖2F +O ( n−1 ) , adding a data-dependent ` 2 penalty and applying Stein-type shrinkage on input data . We analyze this in §5 . In addition to these augmentations , the augmentations below also fit into our framework , and Theorems 3.1 and 3.2 apply . However , in these cases , explicitly characterizing the learned minimum beyond the general description given in Theorems 3.1 and 3.2 is more difficult , and we thus leave interpretion of these specializations to future work . ( a ) The time t proxy loss Lt is non-degenerate on V‖ with minimal norm minimizer W ∗t . The increment Wt+1 −Wt is in V‖ , so only the projection WtQ‖ of Wt onto V‖ changes . ( b ) The proxy losses Lt and Lt+1 at consecutive times will generally have different minimal norm minimizers W ∗t , W ∗t+1 . The increment Ξ∗t = W ∗t+1 − W ∗t measures this change . 3.1 A general time-varying Monro-Robbins theorem . Given an augmentation scheme for the overparameterized linear model ( 1.1 ) , the time t gradient update at learning rate ηt is Wt+1 : = Wt + 2ηt N · ( Yt −WtXt ) XTt , ( 3.3 ) where Dt = ( Xt , Yt ) is the augmented dataset at time t. The minimum norm minimizer of the corresponding proxy loss Lt ( see ( 3.1 ) ) is W ∗t : = E [ YtXTt ] E [ XtXTt ] + , ( 3.4 ) where E [ XtXTt ] + denotes the Moore-Penrose pseudo-inverse ( see Figure 3.1a ) . In this section we state a rigorous result , Theorem 3.1 , giving sufficient conditions on the learning rate ηt and distributions of the augmented matrices Xt , Yt under which augmented gradient descent converges . To state it , note that ( 3.3 ) implies that each row of Wt+1 −Wt is contained in the column span of the Hessian XtXTt of the augmented loss and therefore almost surely belongs to the subspace V‖ : = column span of E [ XtXTt ] ⊆ Rn , ( 3.5 ) as illustrated in Figure 3.1a . The reason is that , in the orthogonal complement to V‖ , the augmented loss L ( W ; Dt ) has zero gradient with probability 1 . To ease notation , we assume that V‖ is independent of t. This assumption holds for additive Gaussian noise , random projection , MixUp , SGD , and their combinations . It is not necessary in general , however , and we refer the interested reader to Remark B.2 in the Appendix for how to treat the general case . Let us denote by Q‖ : Rn → Rn the orthogonal projection onto V‖ ( see Figure 3.1a ) . As we already pointed out , at time t , gradient descent leaves the matrix of projections Wt ( Id−Q‖ ) of each row of Wt onto the orthogonal complement of V‖ unchanged . In contrast , ‖WtQ‖ −W ∗t ‖F decreases at a rate governed by the smallest positive eigenvalue λmin , V‖ ( E [ XtX T t ] ) of the Hessian for the proxy loss Lt , which is obtained by restricting its full Hessian E [ XtX T t ] to V‖ . Moreover , whether and at what rate WtQ‖ −W ∗t converges to 0 depends on how quickly the distance Ξ∗t : = W ∗ t+1 −W ∗t ( 3.6 ) between proxy loss optima at successive steps tends to zero ( see Figure 3.1b ) . Theorem 3.1 ( Special case of Theorem B.1 ) . Suppose that V‖ is independent of t , that the learning rate satisfies ηt → 0 , that the proxy optima satisfy ∞∑ t=0 ‖Ξ∗t ‖F < ∞ , ( 3.7 ) ensuring the existence of a limit W ∗∞ : = limt→∞W ∗ t , that ∞∑ t=0 ηtλmin , V‖ ( E [ XtX T t ] ) =∞ ( 3.8 ) and finally that ∞∑ t=0 η2tE [ ‖XtXTt − E [ XtXTt ] ‖2F + ‖YtXTt − E [ YtXTt ] ‖2F ] < ∞ . ( 3.9 ) Then , for any initialization W0 , we have that WtQ‖ converges in probability to W ∗∞ . If the same augmentation is applied with different strength parameters at each step t ( e.g . the noise level σ2t for additive Gaussian noise ) , we may specialize Theorem 3.1 to this augmentation scheme . More precisely , translating conditions ( 3.7 ) , ( 3.8 ) , ( 3.9 ) into conditions on the learning rate and augmentation strength gives conditions on the schedule for ηt and these strength parameters to ensure convergence . In §4 and §5 , we do this for additive Gaussian noise and random projections . When the augmentation scheme is static in t , Theorem 3.1 reduces to a standard Monro-Robbins theorem [ 25 ] for the ( static ) proxy loss Lt ( W ) . As in that setting , condition ( 3.8 ) enforces that the learning trajectory travels far enough to reach an optimum , and the summand in Condition ( 3.9 ) bounds the variance of the gradient of the augmented loss L ( W ; Dt ) to ensure the total variance of the stochastic gradients is summable . Condition ( 3.7 ) is new and enforces that the minimizers W ∗t of the proxy losses Lt ( W ) change slowly enough for augmented optimization procedure to keep pace .",
"role": "user"
},
{
"content": "This paper rigorously analyzed the convergence of data augmentation in linear regression under the convex setting. Its main result gives sufficient condition on the joint schedule of the learning rate and the augmentation strength under which asymptotic convergence rates are provided. Specializations The above result is then instantiated for additive Gaussian noise, random projections.",
"role": "assistant"
}
]
|
science.evidence_inference | Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:
- Treatment: The specific medical procedure or drug being evaluated.
- Control: The standard or placebo used for comparison. If absent, use `null`.
- Result: The health outcome being assessed.
- Impact: The observed effect of the treatment on the result, categorized as: ("increased", "decreased", "no change").
- Justification: The supporting details for the observed impact.
Format the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].
Abstract:
ABSTRACT.OBJECTIVE: The aim of this study was to demonstrate the efficacy of an ultra-low-dose vaginal estriol 0.03 mg in combination with viable Lactobacillus acidophilus KS400 (Gynoflor® vaginal tablets) in the short-term therapy and to investigate the long-term maintenance dose in the treatment of vaginal atrophy.
ABSTRACT.METHODS: This was a double-blind, randomized, placebo-controlled study (Controlled phase – initial therapy) followed by an open-label follow-up (Open phase – test medication initial and maintenance therapy). Included were postmenopausal women with vaginal atrophy symptoms and Vaginal Maturation Index (VMI) of ≤ 40%. The method of treatment was initial therapy with test medication (or placebo in first phase), one vaginal tablet daily for 12 days, followed by maintenance therapy, one tablet on two consecutive days weekly for 12 weeks.
ABSTRACT.RESULTS: A total of 87 women completed the study. The Controlled phase results for a change in VMI demonstrated superiority of the 0.03 mg estriol–lactobacilli combination to placebo (p < 0.001). In the test group, the positive change in VMI was 35.2%, compared to 9.9% in the placebo group. In the Open phase after the initial therapy, the VMI was increased to 55.4% and, during maintenance therapy, it stayed at a comparable level (52.8–49.4%). The maturation of epithelium was followed by improvement of clinical symptoms and normalization of the vaginal ecosystem.
ABSTRACT.CONCLUSIONS: The ultra-low-dose, vaginal 0.03 mg estriol–lactobacilli combination (Gynoflor®) was superior to placebo with respect to changes in VMI after the 12-day initial therapy, and the maintenance therapy of two tablets weekly was sufficient to prevent the relapse of vaginal atrophy.
Extracted Data: | [["Vaginal estriol plus viable Lactobacillus acidophilus KS400 ", "Placebo", "Improvement in Vaginal Maturation Index", "significantly increased", "The Controlled phase results for a change in VMI demonstrated superiority of the 0.03 mg estriol\u2013lactobacilli combination to placebo (p \u003c 0.001). In the test group, the positive change in VMI was 35.2%, compared to 9.9% in the placebo group. In the Open phase after the initial therapy, the VMI was increased to 55.4% and, during maintenance therapy, it stayed at a comparable level (52.8\u201349.4%)."]] | science.evidence_inference.103 | [
{
"content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.OBJECTIVE: The aim of this study was to demonstrate the efficacy of an ultra-low-dose vaginal estriol 0.03 mg in combination with viable Lactobacillus acidophilus KS400 (Gynoflor® vaginal tablets) in the short-term therapy and to investigate the long-term maintenance dose in the treatment of vaginal atrophy.\n\nABSTRACT.METHODS: This was a double-blind, randomized, placebo-controlled study (Controlled phase – initial therapy) followed by an open-label follow-up (Open phase – test medication initial and maintenance therapy). Included were postmenopausal women with vaginal atrophy symptoms and Vaginal Maturation Index (VMI) of ≤ 40%. The method of treatment was initial therapy with test medication (or placebo in first phase), one vaginal tablet daily for 12 days, followed by maintenance therapy, one tablet on two consecutive days weekly for 12 weeks.\n\nABSTRACT.RESULTS: A total of 87 women completed the study. The Controlled phase results for a change in VMI demonstrated superiority of the 0.03 mg estriol–lactobacilli combination to placebo (p < 0.001). In the test group, the positive change in VMI was 35.2%, compared to 9.9% in the placebo group. In the Open phase after the initial therapy, the VMI was increased to 55.4% and, during maintenance therapy, it stayed at a comparable level (52.8–49.4%). The maturation of epithelium was followed by improvement of clinical symptoms and normalization of the vaginal ecosystem.\n\nABSTRACT.CONCLUSIONS: The ultra-low-dose, vaginal 0.03 mg estriol–lactobacilli combination (Gynoflor®) was superior to placebo with respect to changes in VMI after the 12-day initial therapy, and the maintenance therapy of two tablets weekly was sufficient to prevent the relapse of vaginal atrophy.\n\nExtracted Data:",
"role": "user"
},
{
"content": "[[\"Vaginal estriol plus viable Lactobacillus acidophilus KS400 \", \"Placebo\", \"Improvement in Vaginal Maturation Index\", \"significantly increased\", \"The Controlled phase results for a change in VMI demonstrated superiority of the 0.03 mg estriol\\u2013lactobacilli combination to placebo (p \\u003c 0.001). In the test group, the positive change in VMI was 35.2%, compared to 9.9% in the placebo group. In the Open phase after the initial therapy, the VMI was increased to 55.4% and, during maintenance therapy, it stayed at a comparable level (52.8\\u201349.4%).\"]]",
"role": "assistant"
}
]
|
science.discomat_te | | | Molar fraction (nominal) | Molar fraction (nominal) | Molar fraction (nominal) | C expt | th expt | | | |
| | Fe2O3 | V2O5 | P2O5 | (emu K/g Oe) | (K) | | | |
| | 0.00 | 0.60 | 0.40 | 7.35x10-4 | -0.97 | | | |
| | 0.10 | 0.50 | 0.40 | 6.09x10-3 | -21.4 | | | |
| | 0.20 | 0.40 | 0.40 | 1.03x10-2 | -40.4 | | | |
| | 0.25 | 0.35 | 0.40 | 1.35x10-2 | -57.9 | | | |
| [A] | 0.30 | 0.30 | 0.40 | 1.53x10-2 | -63.7 | | | |
| [B] | 0.30 | 0.30 | 0.40 | 1.61x10-2 | -69.5 | | | |
| | | | | | | | | |
| | Molar fraction (from ICP) | Molar fraction (from ICP) | Molar fraction (from ICP) | Ion fraction (from XPS) | Ion fraction (from XPS) | MW | C calc | C expt |
| | Fe2O3 | V2O5 | P2O5 | Fe3+/Fetotal | V5+/Vtotal | (g/mol) | (emu K/g Oe) | (emu K/g Oe) |
| | | | | | | | | |
| | 0.000 | 0.602 | 0.398 | 0.000 | 0.766 | 163.591 | 6.97x10-4 | 7.35x10-4 |
| | 0.097 | 0.502 | 0.401 | 0.366 | 0.482 | 158.570 | 6.10x10-3 | 6.09x10-3 |
| | 0.191 | 0.402 | 0.407 | 0.459 | 0.561 | 156.912 | 1.06x10-2 | 1.03x10-2 |
| | 0.250 | 0.349 | 0.401 | 0.436 | 0.585 | 155.746 | 1.34x10-2 | 1.35x10-2 |
| | 0.279 | 0.302 | 0.421 | 0.476 | 0.602 | 154.979 | 1.50x10-2 | 1.53x10-2 |
| | 0.309 | 0.294 | 0.398 | 0.476 | 0.602 | 154.849 | 1.65x10-2 | 1.61x10-2 |
Caption: Magnetic susceptibility results for the vanadium phosphate glass samples and comparison to calculated paramagnetic Curie constants based on ICP and XPS results to experimental results
Examine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:
{"value": "xx", "type": "Other"}
{"value": "xx", "type": "Glass_Compound_Amount", "constituent": "xx", "unit": "xx", "material": "xx"}
Process each cell sequentially, and for any unknown attributes, use "xx" as a placeholder. Each JSON should be on a new line.
Output: | {"value": "0.00", "type": "Other"}
{"value": "0.60", "type": "Glass_Compound_Amount", "constituent": "V2O5", "unit": "mol", "material": "xx"}
{"value": "0.40", "type": "Glass_Compound_Amount", "constituent": "P2O5", "unit": "mol", "material": "xx"}
{"value": "0.10", "type": "Glass_Compound_Amount", "constituent": "Fe2O3", "unit": "mol", "material": "xx"}
{"value": "0.50", "type": "Glass_Compound_Amount", "constituent": "V2O5", "unit": "mol", "material": "xx"}
{"value": "0.40", "type": "Glass_Compound_Amount", "constituent": "P2O5", "unit": "mol", "material": "xx"}
{"value": "0.20", "type": "Glass_Compound_Amount", "constituent": "Fe2O3", "unit": "mol", "material": "xx"}
{"value": "0.40", "type": "Glass_Compound_Amount", "constituent": "V2O5", "unit": "mol", "material": "xx"}
{"value": "0.40", "type": "Glass_Compound_Amount", "constituent": "P2O5", "unit": "mol", "material": "xx"}
{"value": "0.25", "type": "Glass_Compound_Amount", "constituent": "Fe2O3", "unit": "mol", "material": "xx"}
{"value": "0.35", "type": "Glass_Compound_Amount", "constituent": "V2O5", "unit": "mol", "material": "xx"}
{"value": "0.40", "type": "Glass_Compound_Amount", "constituent": "P2O5", "unit": "mol", "material": "xx"}
{"value": "0.30", "type": "Glass_Compound_Amount", "constituent": "Fe2O3", "unit": "mol", "material": "xx"}
{"value": "0.30", "type": "Glass_Compound_Amount", "constituent": "V2O5", "unit": "mol", "material": "xx"}
{"value": "0.40", "type": "Glass_Compound_Amount", "constituent": "P2O5", "unit": "mol", "material": "xx"}
{"value": "0.30", "type": "Glass_Compound_Amount", "constituent": "Fe2O3", "unit": "mol", "material": "xx"}
{"value": "0.30", "type": "Glass_Compound_Amount", "constituent": "V2O5", "unit": "mol", "material": "xx"}
{"value": "0.40", "type": "Glass_Compound_Amount", "constituent": "P2O5", "unit": "mol", "material": "xx"}
{"value": "0.000", "type": "Other"}
{"value": "0.602", "type": "Glass_Compound_Amount", "constituent": "V2O5", "unit": "mol", "material": "xx"}
{"value": "0.398", "type": "Glass_Compound_Amount", "constituent": "P2O5", "unit": "mol", "material": "xx"}
{"value": "0.000", "type": "Other"}
{"value": "0.766", "type": "Other"}
{"value": "163.591", "type": "Other"}
{"value": "0.097", "type": "Glass_Compound_Amount", "constituent": "Fe2O3", "unit": "mol", "material": "xx"}
{"value": "0.502", "type": "Glass_Compound_Amount", "constituent": "V2O5", "unit": "mol", "material": "xx"}
{"value": "0.401", "type": "Glass_Compound_Amount", "constituent": "P2O5", "unit": "mol", "material": "xx"}
{"value": "0.366", "type": "Other"}
{"value": "0.482", "type": "Other"}
{"value": "158.570", "type": "Other"}
{"value": "0.191", "type": "Glass_Compound_Amount", "constituent": "Fe2O3", "unit": "mol", "material": "xx"}
{"value": "0.402", "type": "Glass_Compound_Amount", "constituent": "V2O5", "unit": "mol", "material": "xx"}
{"value": "0.407", "type": "Glass_Compound_Amount", "constituent": "P2O5", "unit": "mol", "material": "xx"}
{"value": "0.459", "type": "Other"}
{"value": "0.561", "type": "Other"}
{"value": "156.912", "type": "Other"}
{"value": "0.250", "type": "Glass_Compound_Amount", "constituent": "Fe2O3", "unit": "mol", "material": "xx"}
{"value": "0.349", "type": "Glass_Compound_Amount", "constituent": "V2O5", "unit": "mol", "material": "xx"}
{"value": "0.401", "type": "Glass_Compound_Amount", "constituent": "P2O5", "unit": "mol", "material": "xx"}
{"value": "0.436", "type": "Other"}
{"value": "0.585", "type": "Other"}
{"value": "155.746", "type": "Other"}
{"value": "0.279", "type": "Glass_Compound_Amount", "constituent": "Fe2O3", "unit": "mol", "material": "xx"}
{"value": "0.302", "type": "Glass_Compound_Amount", "constituent": "V2O5", "unit": "mol", "material": "xx"}
{"value": "0.421", "type": "Glass_Compound_Amount", "constituent": "P2O5", "unit": "mol", "material": "xx"}
{"value": "0.476", "type": "Other"}
{"value": "0.602", "type": "Other"}
{"value": "154.979", "type": "Other"}
{"value": "0.309", "type": "Glass_Compound_Amount", "constituent": "Fe2O3", "unit": "mol", "material": "xx"}
{"value": "0.294", "type": "Glass_Compound_Amount", "constituent": "V2O5", "unit": "mol", "material": "xx"}
{"value": "0.398", "type": "Glass_Compound_Amount", "constituent": "P2O5", "unit": "mol", "material": "xx"}
{"value": "0.476", "type": "Other"}
{"value": "0.602", "type": "Other"}
{"value": "154.849", "type": "Other"} | science.discomat_te.170 | [
{
"content": "| | Molar fraction (nominal) | Molar fraction (nominal) | Molar fraction (nominal) | C expt | th expt | | | |\n| | Fe2O3 | V2O5 | P2O5 | (emu K/g Oe) | (K) | | | |\n| | 0.00 | 0.60 | 0.40 | 7.35x10-4 | -0.97 | | | |\n| | 0.10 | 0.50 | 0.40 | 6.09x10-3 | -21.4 | | | |\n| | 0.20 | 0.40 | 0.40 | 1.03x10-2 | -40.4 | | | |\n| | 0.25 | 0.35 | 0.40 | 1.35x10-2 | -57.9 | | | |\n| [A] | 0.30 | 0.30 | 0.40 | 1.53x10-2 | -63.7 | | | |\n| [B] | 0.30 | 0.30 | 0.40 | 1.61x10-2 | -69.5 | | | |\n| | | | | | | | | |\n| | Molar fraction (from ICP) | Molar fraction (from ICP) | Molar fraction (from ICP) | Ion fraction (from XPS) | Ion fraction (from XPS) | MW | C calc | C expt |\n| | Fe2O3 | V2O5 | P2O5 | Fe3+/Fetotal | V5+/Vtotal | (g/mol) | (emu K/g Oe) | (emu K/g Oe) |\n| | | | | | | | | |\n| | 0.000 | 0.602 | 0.398 | 0.000 | 0.766 | 163.591 | 6.97x10-4 | 7.35x10-4 |\n| | 0.097 | 0.502 | 0.401 | 0.366 | 0.482 | 158.570 | 6.10x10-3 | 6.09x10-3 |\n| | 0.191 | 0.402 | 0.407 | 0.459 | 0.561 | 156.912 | 1.06x10-2 | 1.03x10-2 |\n| | 0.250 | 0.349 | 0.401 | 0.436 | 0.585 | 155.746 | 1.34x10-2 | 1.35x10-2 |\n| | 0.279 | 0.302 | 0.421 | 0.476 | 0.602 | 154.979 | 1.50x10-2 | 1.53x10-2 |\n| | 0.309 | 0.294 | 0.398 | 0.476 | 0.602 | 154.849 | 1.65x10-2 | 1.61x10-2 |\n\nCaption: Magnetic susceptibility results for the vanadium phosphate glass samples and comparison to calculated paramagnetic Curie constants based on ICP and XPS results to experimental results\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:",
"role": "user"
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{
"content": "{\"value\": \"0.00\", \"type\": \"Other\"}\n{\"value\": \"0.60\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"V2O5\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.40\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"P2O5\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.10\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Fe2O3\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.50\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"V2O5\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.40\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"P2O5\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.20\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Fe2O3\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.40\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"V2O5\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.40\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"P2O5\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.25\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Fe2O3\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.35\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"V2O5\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.40\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"P2O5\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.30\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Fe2O3\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.30\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"V2O5\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.40\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"P2O5\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.30\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Fe2O3\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.30\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"V2O5\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.40\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"P2O5\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.000\", \"type\": \"Other\"}\n{\"value\": \"0.602\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"V2O5\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.398\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"P2O5\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.000\", \"type\": \"Other\"}\n{\"value\": \"0.766\", \"type\": \"Other\"}\n{\"value\": \"163.591\", \"type\": \"Other\"}\n{\"value\": \"0.097\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Fe2O3\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.502\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"V2O5\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.401\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"P2O5\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.366\", \"type\": \"Other\"}\n{\"value\": \"0.482\", \"type\": \"Other\"}\n{\"value\": \"158.570\", \"type\": \"Other\"}\n{\"value\": \"0.191\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Fe2O3\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.402\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"V2O5\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.407\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"P2O5\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.459\", \"type\": \"Other\"}\n{\"value\": \"0.561\", \"type\": \"Other\"}\n{\"value\": \"156.912\", \"type\": \"Other\"}\n{\"value\": \"0.250\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Fe2O3\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.349\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"V2O5\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.401\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"P2O5\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.436\", \"type\": \"Other\"}\n{\"value\": \"0.585\", \"type\": \"Other\"}\n{\"value\": \"155.746\", \"type\": \"Other\"}\n{\"value\": \"0.279\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Fe2O3\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.302\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"V2O5\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.421\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"P2O5\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.476\", \"type\": \"Other\"}\n{\"value\": \"0.602\", \"type\": \"Other\"}\n{\"value\": \"154.979\", \"type\": \"Other\"}\n{\"value\": \"0.309\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"Fe2O3\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.294\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"V2O5\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.398\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"P2O5\", \"unit\": \"mol\", \"material\": \"xx\"}\n{\"value\": \"0.476\", \"type\": \"Other\"}\n{\"value\": \"0.602\", \"type\": \"Other\"}\n{\"value\": \"154.849\", \"type\": \"Other\"}",
"role": "assistant"
}
]
|
science.scifact_entailment | Determine the relationship between them and generate a JSON response. Scientific Claim:
PrimPol generates short DNA replication intermediates on the leading strand during DNA replication.
Research Abstract:
We describe a second primase in human cells, PrimPol, which has the ability to start DNA chains with deoxynucleotides unlike regular primases, which use exclusively ribonucleotides.
Moreover, PrimPol is also a DNA polymerase tailored to bypass the most common oxidative lesions in DNA, such as abasic sites and 8-oxoguanine.
Subcellular fractionation and immunodetection studies indicated that PrimPol is present in both nuclear and mitochondrial DNA compartments.
PrimPol activity is detectable in mitochondrial lysates from human and mouse cells but is absent from mitochondria derived from PRIMPOL knockout mice.
PRIMPOL gene silencing or ablation in human and mouse cells impaired mitochondrial DNA replication.
On the basis of the synergy observed with replicative DNA polymerases Polγ and Polε, PrimPol is proposed to facilitate replication fork progression by acting as a translesion DNA polymerase or as a specific DNA primase reinitiating downstream of lesions that block synthesis during both mitochondrial and nuclear DNA replication. | {"verdict": "NEI", "evidence": []} | science.scifact_entailment.622 | [
{
"content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nPrimPol generates short DNA replication intermediates on the leading strand during DNA replication.\n\nResearch Abstract:\nWe describe a second primase in human cells, PrimPol, which has the ability to start DNA chains with deoxynucleotides unlike regular primases, which use exclusively ribonucleotides.\nMoreover, PrimPol is also a DNA polymerase tailored to bypass the most common oxidative lesions in DNA, such as abasic sites and 8-oxoguanine.\nSubcellular fractionation and immunodetection studies indicated that PrimPol is present in both nuclear and mitochondrial DNA compartments.\nPrimPol activity is detectable in mitochondrial lysates from human and mouse cells but is absent from mitochondria derived from PRIMPOL knockout mice.\nPRIMPOL gene silencing or ablation in human and mouse cells impaired mitochondrial DNA replication.\nOn the basis of the synergy observed with replicative DNA polymerases Polγ and Polε, PrimPol is proposed to facilitate replication fork progression by acting as a translesion DNA polymerase or as a specific DNA primase reinitiating downstream of lesions that block synthesis during both mitochondrial and nuclear DNA replication.",
"role": "user"
},
{
"content": "{\"verdict\": \"NEI\", \"evidence\": []}",
"role": "assistant"
}
]
|
science.qasper_abstractive_qa | Examine the provided sections from a research document and address the accompanying query. The text is divided by newlines.
Document Content:
Dynamic Compositionality in Recursive Neural Networks with Structure-aware Tag Representations
Most existing recursive neural network (RvNN) architectures utilize only the structure of parse trees, ignoring syntactic tags which are provided as by-products of parsing. We present a novel RvNN architecture that can provide dynamic compositionality by considering comprehensive syntactic information derived from both the structure and linguistic tags. Specifically, we introduce a structure-aware tag representation constructed by a separate tag-level tree-LSTM. With this, we can control the composition function of the existing word-level tree-LSTM by augmenting the representation as a supplementary input to the gate functions of the tree-LSTM. In extensive experiments, we show that models built upon the proposed architecture obtain superior or competitive performance on several sentence-level tasks such as sentiment analysis and natural language inference when compared against previous tree-structured models and other sophisticated neural models.
Related Work
Recursive neural networks (RvNN) are a kind of neural architecture which model sentences by exploiting syntactic structure. While earlier RvNN models proposed utilizing diverse composition functions, including feed-forward neural networks BIBREF12 , matrix-vector multiplication BIBREF5 , and tensor computation BIBREF6 , tree-LSTMs BIBREF13 remain the standard for several sentence-level tasks.
Even though classic RvNNs have demonstrated superior performance on a variety of tasks, their inflexibility, i.e. their inability to handle dynamic compositionality for different syntactic configurations, is a considerable weakness. For instance, it would be desirable if our model could distinguish e.g. adjective-noun composition from that of verb-noun or preposition-noun composition, as models failing to make such a distinction ignore real-world syntactic considerations such as `-arity' of function words (i.e. types), and the adjunct/argument distinction.
To enable dynamic compositionality in recursive neural networks, many previous works BIBREF14 , BIBREF15 , BIBREF16 , BIBREF9 , BIBREF17 , BIBREF18 , BIBREF19 have proposed various methods.
One main direction of research leverages tag information, which is produced as a by-product of parsing. In detail, BIBREF16 ( BIBREF16 ) suggested TG-RNN, a model employing different composition functions according to POS tags, and TE-RNN/TE-RNTN, models which leverage tag embeddings as additional inputs for the existing tree-structured models. Despite the novelty of utilizing tag information, the explosion of the number of parameters (in case of the TG-RNN) and the limited performance of the original models (in case of the TE-RNN/TE-RNTN) have prevented these models from being widely adopted. Meanwhile, BIBREF9 ( BIBREF9 ) and BIBREF18 ( BIBREF18 ) proposed models based on a tree-LSTM which also uses the tag vectors to control the gate functions of the tree-LSTM. In spite of their impressive results, there is a limitation that the trained tag embeddings are too simple to reflect the rich information which tags provide in different syntactic structures. To alleviate this problem, we introduce structure-aware tag representations in the next section.
Another way of building dynamic compositionality into RvNNs is to take advantage of a meta-network (or hyper-network). Inspired by recent works on dynamic parameter prediction, DC-TreeLSTMs BIBREF17 dynamically create the parameters for compositional functions in a tree-LSTM. Specifically, the model has two separate tree-LSTM networks whose architectures are similar, but the smaller of the two is utilized to calculate the weights of the bigger one. A possible problem for this model is that it may be easy to be trained such that the role of each tree-LSTM is ambiguous, as they share the same input, i.e. word information. Therefore, we design two disentangled tree-LSTMs in our model so that one focuses on extracting useful features from only syntactic information while the other composes semantic units with the aid of the features. Furthermore, our model reduces the complexity of computation by utilizing typical tree-LSTM frameworks instead of computing the weights for each example.
Finally, some recent works BIBREF10 , BIBREF11 have proposed latent tree-structured models that learn how to formulate tree structures from only sequences of tokens, without the aid of syntactic trees or linguistic information. The latent tree models have the advantage of being able to find the optimized task-specific order of composition rather than a sequential or syntactic one. In experiments, we compare our model with not only syntactic tree-based models but also latent tree models, demonstrating that modeling with explicit linguistic knowledge can be an attractive option.
Quantitative Analysis
One of the most basic approaches to evaluate a sentence encoder is to measure the classification performance with the sentence representations made by the encoder. Thus, we conduct experiments on the following five datasets. (Summary statistics for the datasets are reported in the supplemental materials.)
MR: A group of movie reviews with binary (positive / negative) classes. BIBREF22
SST-2: Stanford Sentiment Treebank BIBREF6 . Similar to MR, but each review is provided in the form of a binary parse tree whose nodes are annotated with numeric sentiment values. For SST-2, we only consider binary (positive / negative) classes.
SST-5: Identical to SST-2, but the reviews are grouped into fine-grained (very negative, negative, neutral, positive, very positive) classes.
SUBJ: Sentences grouped as being either subjective or objective (binary classes). BIBREF23
TREC: A dataset which groups questions into six different question types (classes). BIBREF24
As a preprocessing step, we construct parse trees for the sentences in the datasets using the Stanford PCFG parser BIBREF25 . Because syntactic tags are by-products of constituency parsing, we do not need further preprocessing.
To classify the sentence given our sentence representation ( $\check{\mathbf {h}}_\text{root}$ ), we use one fully-connected layer with a ReLU activation, followed by a softmax classifier. The final predicted probability distribution of the class $y$ given the sentence $w_{1:n}$ is defined as follows,
$$\mathbf {s} = \text{ReLU}(\mathbf {W}_\text{s} \check{\mathbf {h}}_\text{root}+ \mathbf {b}_\text{s})$$ (Eq. 37)
$$p(y|w_{1:n}) = \text{softmax}(\mathbf {W}_\text{c}\mathbf {s} + \mathbf {b}_\text{c})$$ (Eq. 38)
where $\textbf {s} \in \mathbb {R}^{d_\text{s}}$ is the computed task-specific sentence representation for the classifier, and $\textbf {W}_\text{s} \in \mathbb {R}^{d_\text{s} \times d_h}$ , $\textbf {W}_\text{c} \in \mathbb {R}^{d_\text{c} \times d_s}$ , $\textbf {b}_\text{s} \in \mathbb {R}^{d_s}$ , $\textbf {b}_\text{c} \in \mathbb {R}^{d_c}$ are trainable parameters. As an objective function, we use the cross entropy of the predicted and true class distributions.
The results of the experiments on the five datasets are shown in table 1 . In this table, we report the test accuracy of our model and various other models on each dataset in terms of percentage. To consider the effects of random initialization, we report the best numbers obtained from each several runs with hyper-parameters fixed.
Compared with the previous syntactic tree-based models as well as other neural models, our SATA Tree-LSTM shows superior or competitive performance on all tasks. Specifically, our model achieves new state-of-the-art results within the tree-structured model class on 4 out of 5 sentence classification tasks—SST-2, SST-5, MR, and TREC. The model shows its strength, in particular, when the datasets provide phrase-level supervision to facilitate tree structure learning (i.e. SST-2, SST-5). Moreover, the numbers we report for SST-5 and TREC are competitive to the existing state-of-the-art results including ones from structurally pre-trained models such as ELMo BIBREF26 , proving our model's superiority. Note that the SATA Tree-LSTM also outperforms the recent latent tree-based model, indicating that modeling a neural model with explicit linguistic knowledge can be an attractive option.
On the other hand, a remaining concern is that our SATA Tree-LSTM is not robust to random seeds when the size of a dataset is relatively small, as tag embeddings are randomly initialized rather than relying on pre-trained ones in contrast with the case of words. From this observation, we could find out there needs a direction of research towards pre-trained tag embeddings.
To estimate the performance of our model beyond the tasks requiring only one sentence at a time, we conduct an experiment on the Stanford Natural Language Inference BIBREF34 dataset, each example of which consists of two sentences, the premise and the hypothesis. Our objective given the data is to predict the correct relationship between the two sentences among three options— contradiction, neutral, or entailment.
We use the siamese architecture to encode both the premise ( $p_{1:m}$ ) and hypothesis ( $h_{1:n}$ ) following the standard of sentence-encoding models in the literature. (Specifically, $p_{1:m}$ is encoded as $\check{\mathbf {h}}_\text{root}^p \in \mathbb {R}^{d_h}$ and $h_{1:n}$ is encoded as $\check{\mathbf {h}}_\text{root}^h \in \mathbb {R}^{d_h}$ with the same encoder.) Then, we leverage some heuristics BIBREF35 , followed by one fully-connected layer with a ReLU activation and a softmax classifier. Specifically,
$$\mathbf {z} = \left[ \check{\mathbf {h}}_\text{root}^p; \check{\mathbf {h}}_\text{root}^h; | \check{\mathbf {h}}_\text{root}^p - \check{\mathbf {h}}_\text{root}^h |; \check{\mathbf {h}}_\text{root}^p \odot \check{\mathbf {h}}_\text{root}^h \right]$$ (Eq. 41)
$$\mathbf {s} = \text{ReLU}(\mathbf {W}_\text{s} \mathbf {z} + \mathbf {b}_\text{s})$$ (Eq. 42)
where $\textbf {z} \in \mathbb {R}^{4d_h}$ , $\textbf {s} \in \mathbb {R}^{d_s}$ are intermediate features for the classifier and $\textbf {W}_\text{s} \in \mathbb {R}^{d_\text{s} \times 4d_h}$ , $\textbf {W}_\text{c} \in \mathbb {R}^{d_\text{c} \times d_s}$ , $\textbf {b}_\text{s} \in \mathbb {R}^{d_s}$ , $\textbf {b}_\text{c} \in \mathbb {R}^{d_c}$ are again trainable parameters.
Our experimental results on the SNLI dataset are shown in table 2 . In this table, we report the test accuracy and number of trainable parameters for each model. Our SATA-LSTM again demonstrates its decent performance compared against the neural models built on both syntactic trees and latent trees, as well as the non-tree models. (Latent Syntax Tree-LSTM: BIBREF10 ( BIBREF10 ), Tree-based CNN: BIBREF35 ( BIBREF35 ), Gumbel Tree-LSTM: BIBREF11 ( BIBREF11 ), NSE: BIBREF36 ( BIBREF36 ), Reinforced Self-Attention Network: BIBREF4 ( BIBREF4 ), Residual stacked encoders: BIBREF37 ( BIBREF37 ), BiLSTM with generalized pooling: BIBREF38 ( BIBREF38 ).) Note that the number of learned parameters in our model is also comparable to other sophisticated models, showing the efficiency of our model.
Even though our model has proven its mettle, the effect of tag information seems relatively weak in the case of SNLI, which contains a large amount of data compared to the others. One possible explanation is that neural models may learn some syntactic rules from large amounts of text when the text size is large enough, reducing the necessity of external linguistic knowledge. We leave the exploration of the effectiveness of tags relative to data size for future work.
Here we go over the settings common across our models during experimentation. For more task-specific details, refer to the supplemental materials.
For our input embeddings, we used 300 dimensional 840B GloVe BIBREF39 as pre-trained word embeddings, and tag representations were randomly sampled from the uniform distribution [-0.005, 0.005]. Tag vectors are revised during training while the fine-tuning of the word embedding depends on the task. Our models were trained using the Adam BIBREF40 or Adadelta BIBREF41 optimizer, depending on task. For regularization, weight decay is added to the loss function except for SNLI following BIBREF42 ( BIBREF42 ) and Dropout BIBREF43 is also applied for the word embeddings and task-specific classifiers. Moreover, batch normalization BIBREF44 is adopted for the classifiers. As a default, all the weights in the model are initialized following BIBREF45 ( BIBREF45 ) and the biases are set to 0. The total norm of the gradients of the parameters is clipped not to be over 5 during training.
Our best models for each dataset were chosen by validation accuracy in cases where a validation set was provided as a part of the dataset. Otherwise, we perform a grid search on probable hyper-parameter settings, or run 10-fold cross-validation in cases where even a test set does not exist.
Figure 1: A constituency tree example from Stanford Sentiment Treebank.
Figure 2: A diagram of SATA Tree-LSTM. The model has two separate tree-LSTM modules, the right of which (tag tree-LSTM) extracts a structure-aware tag representation to control the composition function of the remaining tree-LSTM (word tree-LSTM). Fully-connected: onelayered non-linear transformation.
Figure 3: An ablation study on the core modules of our model. The test accuracy of each model on SST-2 is reported. The results demonstrate that the modules play an important role for achieving the superior performance of our model. FC: A fully connected-layer with a tanh function. w/o tags: Tag embeddings are not used. w/ tags: The naive tag embeddings are directly inserted into each node of a tree.
Query: Which baselines did they compare against?
JSON Response: | {"answer": "Various tree structured neural networks including variants of Tree-LSTM, Tree-based CNN, RNTN, and non-tree models including variants of LSTMs, CNNs, residual, and self-attention based networks", "evidence": ["Our experimental results on the SNLI dataset are shown in table 2 . In this table, we report the test accuracy and number of trainable parameters for each model. Our SATA-LSTM again demonstrates its decent performance compared against the neural models built on both syntactic trees and latent trees, as well as the non-tree models. (Latent Syntax Tree-LSTM: BIBREF10 ( BIBREF10 ), Tree-based CNN: BIBREF35 ( BIBREF35 ), Gumbel Tree-LSTM: BIBREF11 ( BIBREF11 ), NSE: BIBREF36 ( BIBREF36 ), Reinforced Self-Attention Network: BIBREF4 ( BIBREF4 ), Residual stacked encoders: BIBREF37 ( BIBREF37 ), BiLSTM with generalized pooling: BIBREF38 ( BIBREF38 ).) Note that the number of learned parameters in our model is also comparable to other sophisticated models, showing the efficiency of our model."]} | science.qasper_abstractive_qa.44.0 | [
{
"content": "Examine the provided sections from a research document and address the accompanying query. The text is divided by newlines.\n\nDocument Content:\n\nDynamic Compositionality in Recursive Neural Networks with Structure-aware Tag Representations\n\nMost existing recursive neural network (RvNN) architectures utilize only the structure of parse trees, ignoring syntactic tags which are provided as by-products of parsing. We present a novel RvNN architecture that can provide dynamic compositionality by considering comprehensive syntactic information derived from both the structure and linguistic tags. Specifically, we introduce a structure-aware tag representation constructed by a separate tag-level tree-LSTM. With this, we can control the composition function of the existing word-level tree-LSTM by augmenting the representation as a supplementary input to the gate functions of the tree-LSTM. In extensive experiments, we show that models built upon the proposed architecture obtain superior or competitive performance on several sentence-level tasks such as sentiment analysis and natural language inference when compared against previous tree-structured models and other sophisticated neural models.\n\nRelated Work\nRecursive neural networks (RvNN) are a kind of neural architecture which model sentences by exploiting syntactic structure. While earlier RvNN models proposed utilizing diverse composition functions, including feed-forward neural networks BIBREF12 , matrix-vector multiplication BIBREF5 , and tensor computation BIBREF6 , tree-LSTMs BIBREF13 remain the standard for several sentence-level tasks.\nEven though classic RvNNs have demonstrated superior performance on a variety of tasks, their inflexibility, i.e. their inability to handle dynamic compositionality for different syntactic configurations, is a considerable weakness. For instance, it would be desirable if our model could distinguish e.g. adjective-noun composition from that of verb-noun or preposition-noun composition, as models failing to make such a distinction ignore real-world syntactic considerations such as `-arity' of function words (i.e. types), and the adjunct/argument distinction.\nTo enable dynamic compositionality in recursive neural networks, many previous works BIBREF14 , BIBREF15 , BIBREF16 , BIBREF9 , BIBREF17 , BIBREF18 , BIBREF19 have proposed various methods.\nOne main direction of research leverages tag information, which is produced as a by-product of parsing. In detail, BIBREF16 ( BIBREF16 ) suggested TG-RNN, a model employing different composition functions according to POS tags, and TE-RNN/TE-RNTN, models which leverage tag embeddings as additional inputs for the existing tree-structured models. Despite the novelty of utilizing tag information, the explosion of the number of parameters (in case of the TG-RNN) and the limited performance of the original models (in case of the TE-RNN/TE-RNTN) have prevented these models from being widely adopted. Meanwhile, BIBREF9 ( BIBREF9 ) and BIBREF18 ( BIBREF18 ) proposed models based on a tree-LSTM which also uses the tag vectors to control the gate functions of the tree-LSTM. In spite of their impressive results, there is a limitation that the trained tag embeddings are too simple to reflect the rich information which tags provide in different syntactic structures. To alleviate this problem, we introduce structure-aware tag representations in the next section.\nAnother way of building dynamic compositionality into RvNNs is to take advantage of a meta-network (or hyper-network). Inspired by recent works on dynamic parameter prediction, DC-TreeLSTMs BIBREF17 dynamically create the parameters for compositional functions in a tree-LSTM. Specifically, the model has two separate tree-LSTM networks whose architectures are similar, but the smaller of the two is utilized to calculate the weights of the bigger one. A possible problem for this model is that it may be easy to be trained such that the role of each tree-LSTM is ambiguous, as they share the same input, i.e. word information. Therefore, we design two disentangled tree-LSTMs in our model so that one focuses on extracting useful features from only syntactic information while the other composes semantic units with the aid of the features. Furthermore, our model reduces the complexity of computation by utilizing typical tree-LSTM frameworks instead of computing the weights for each example.\nFinally, some recent works BIBREF10 , BIBREF11 have proposed latent tree-structured models that learn how to formulate tree structures from only sequences of tokens, without the aid of syntactic trees or linguistic information. The latent tree models have the advantage of being able to find the optimized task-specific order of composition rather than a sequential or syntactic one. In experiments, we compare our model with not only syntactic tree-based models but also latent tree models, demonstrating that modeling with explicit linguistic knowledge can be an attractive option.\n\nQuantitative Analysis\nOne of the most basic approaches to evaluate a sentence encoder is to measure the classification performance with the sentence representations made by the encoder. Thus, we conduct experiments on the following five datasets. (Summary statistics for the datasets are reported in the supplemental materials.)\nMR: A group of movie reviews with binary (positive / negative) classes. BIBREF22 \nSST-2: Stanford Sentiment Treebank BIBREF6 . Similar to MR, but each review is provided in the form of a binary parse tree whose nodes are annotated with numeric sentiment values. For SST-2, we only consider binary (positive / negative) classes.\nSST-5: Identical to SST-2, but the reviews are grouped into fine-grained (very negative, negative, neutral, positive, very positive) classes.\nSUBJ: Sentences grouped as being either subjective or objective (binary classes). BIBREF23 \nTREC: A dataset which groups questions into six different question types (classes). BIBREF24 \nAs a preprocessing step, we construct parse trees for the sentences in the datasets using the Stanford PCFG parser BIBREF25 . Because syntactic tags are by-products of constituency parsing, we do not need further preprocessing.\nTo classify the sentence given our sentence representation ( $\\check{\\mathbf {h}}_\\text{root}$ ), we use one fully-connected layer with a ReLU activation, followed by a softmax classifier. The final predicted probability distribution of the class $y$ given the sentence $w_{1:n}$ is defined as follows, \n$$\\mathbf {s} = \\text{ReLU}(\\mathbf {W}_\\text{s} \\check{\\mathbf {h}}_\\text{root}+ \\mathbf {b}_\\text{s})$$ (Eq. 37) \n$$p(y|w_{1:n}) = \\text{softmax}(\\mathbf {W}_\\text{c}\\mathbf {s} + \\mathbf {b}_\\text{c})$$ (Eq. 38) \nwhere $\\textbf {s} \\in \\mathbb {R}^{d_\\text{s}}$ is the computed task-specific sentence representation for the classifier, and $\\textbf {W}_\\text{s} \\in \\mathbb {R}^{d_\\text{s} \\times d_h}$ , $\\textbf {W}_\\text{c} \\in \\mathbb {R}^{d_\\text{c} \\times d_s}$ , $\\textbf {b}_\\text{s} \\in \\mathbb {R}^{d_s}$ , $\\textbf {b}_\\text{c} \\in \\mathbb {R}^{d_c}$ are trainable parameters. As an objective function, we use the cross entropy of the predicted and true class distributions.\nThe results of the experiments on the five datasets are shown in table 1 . In this table, we report the test accuracy of our model and various other models on each dataset in terms of percentage. To consider the effects of random initialization, we report the best numbers obtained from each several runs with hyper-parameters fixed.\nCompared with the previous syntactic tree-based models as well as other neural models, our SATA Tree-LSTM shows superior or competitive performance on all tasks. Specifically, our model achieves new state-of-the-art results within the tree-structured model class on 4 out of 5 sentence classification tasks—SST-2, SST-5, MR, and TREC. The model shows its strength, in particular, when the datasets provide phrase-level supervision to facilitate tree structure learning (i.e. SST-2, SST-5). Moreover, the numbers we report for SST-5 and TREC are competitive to the existing state-of-the-art results including ones from structurally pre-trained models such as ELMo BIBREF26 , proving our model's superiority. Note that the SATA Tree-LSTM also outperforms the recent latent tree-based model, indicating that modeling a neural model with explicit linguistic knowledge can be an attractive option.\nOn the other hand, a remaining concern is that our SATA Tree-LSTM is not robust to random seeds when the size of a dataset is relatively small, as tag embeddings are randomly initialized rather than relying on pre-trained ones in contrast with the case of words. From this observation, we could find out there needs a direction of research towards pre-trained tag embeddings.\nTo estimate the performance of our model beyond the tasks requiring only one sentence at a time, we conduct an experiment on the Stanford Natural Language Inference BIBREF34 dataset, each example of which consists of two sentences, the premise and the hypothesis. Our objective given the data is to predict the correct relationship between the two sentences among three options— contradiction, neutral, or entailment.\nWe use the siamese architecture to encode both the premise ( $p_{1:m}$ ) and hypothesis ( $h_{1:n}$ ) following the standard of sentence-encoding models in the literature. (Specifically, $p_{1:m}$ is encoded as $\\check{\\mathbf {h}}_\\text{root}^p \\in \\mathbb {R}^{d_h}$ and $h_{1:n}$ is encoded as $\\check{\\mathbf {h}}_\\text{root}^h \\in \\mathbb {R}^{d_h}$ with the same encoder.) Then, we leverage some heuristics BIBREF35 , followed by one fully-connected layer with a ReLU activation and a softmax classifier. Specifically, \n$$\\mathbf {z} = \\left[ \\check{\\mathbf {h}}_\\text{root}^p; \\check{\\mathbf {h}}_\\text{root}^h; | \\check{\\mathbf {h}}_\\text{root}^p - \\check{\\mathbf {h}}_\\text{root}^h |; \\check{\\mathbf {h}}_\\text{root}^p \\odot \\check{\\mathbf {h}}_\\text{root}^h \\right]$$ (Eq. 41) \n$$\\mathbf {s} = \\text{ReLU}(\\mathbf {W}_\\text{s} \\mathbf {z} + \\mathbf {b}_\\text{s})$$ (Eq. 42) \nwhere $\\textbf {z} \\in \\mathbb {R}^{4d_h}$ , $\\textbf {s} \\in \\mathbb {R}^{d_s}$ are intermediate features for the classifier and $\\textbf {W}_\\text{s} \\in \\mathbb {R}^{d_\\text{s} \\times 4d_h}$ , $\\textbf {W}_\\text{c} \\in \\mathbb {R}^{d_\\text{c} \\times d_s}$ , $\\textbf {b}_\\text{s} \\in \\mathbb {R}^{d_s}$ , $\\textbf {b}_\\text{c} \\in \\mathbb {R}^{d_c}$ are again trainable parameters.\nOur experimental results on the SNLI dataset are shown in table 2 . In this table, we report the test accuracy and number of trainable parameters for each model. Our SATA-LSTM again demonstrates its decent performance compared against the neural models built on both syntactic trees and latent trees, as well as the non-tree models. (Latent Syntax Tree-LSTM: BIBREF10 ( BIBREF10 ), Tree-based CNN: BIBREF35 ( BIBREF35 ), Gumbel Tree-LSTM: BIBREF11 ( BIBREF11 ), NSE: BIBREF36 ( BIBREF36 ), Reinforced Self-Attention Network: BIBREF4 ( BIBREF4 ), Residual stacked encoders: BIBREF37 ( BIBREF37 ), BiLSTM with generalized pooling: BIBREF38 ( BIBREF38 ).) Note that the number of learned parameters in our model is also comparable to other sophisticated models, showing the efficiency of our model.\nEven though our model has proven its mettle, the effect of tag information seems relatively weak in the case of SNLI, which contains a large amount of data compared to the others. One possible explanation is that neural models may learn some syntactic rules from large amounts of text when the text size is large enough, reducing the necessity of external linguistic knowledge. We leave the exploration of the effectiveness of tags relative to data size for future work.\nHere we go over the settings common across our models during experimentation. For more task-specific details, refer to the supplemental materials.\nFor our input embeddings, we used 300 dimensional 840B GloVe BIBREF39 as pre-trained word embeddings, and tag representations were randomly sampled from the uniform distribution [-0.005, 0.005]. Tag vectors are revised during training while the fine-tuning of the word embedding depends on the task. Our models were trained using the Adam BIBREF40 or Adadelta BIBREF41 optimizer, depending on task. For regularization, weight decay is added to the loss function except for SNLI following BIBREF42 ( BIBREF42 ) and Dropout BIBREF43 is also applied for the word embeddings and task-specific classifiers. Moreover, batch normalization BIBREF44 is adopted for the classifiers. As a default, all the weights in the model are initialized following BIBREF45 ( BIBREF45 ) and the biases are set to 0. The total norm of the gradients of the parameters is clipped not to be over 5 during training.\nOur best models for each dataset were chosen by validation accuracy in cases where a validation set was provided as a part of the dataset. Otherwise, we perform a grid search on probable hyper-parameter settings, or run 10-fold cross-validation in cases where even a test set does not exist.\n\nFigure 1: A constituency tree example from Stanford Sentiment Treebank.\n\nFigure 2: A diagram of SATA Tree-LSTM. The model has two separate tree-LSTM modules, the right of which (tag tree-LSTM) extracts a structure-aware tag representation to control the composition function of the remaining tree-LSTM (word tree-LSTM). Fully-connected: onelayered non-linear transformation.\n\nFigure 3: An ablation study on the core modules of our model. The test accuracy of each model on SST-2 is reported. The results demonstrate that the modules play an important role for achieving the superior performance of our model. FC: A fully connected-layer with a tanh function. w/o tags: Tag embeddings are not used. w/ tags: The naive tag embeddings are directly inserted into each node of a tree.\n\nQuery: Which baselines did they compare against?\n\nJSON Response:",
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"content": "{\"answer\": \"Various tree structured neural networks including variants of Tree-LSTM, Tree-based CNN, RNTN, and non-tree models including variants of LSTMs, CNNs, residual, and self-attention based networks\", \"evidence\": [\"Our experimental results on the SNLI dataset are shown in table 2 . In this table, we report the test accuracy and number of trainable parameters for each model. Our SATA-LSTM again demonstrates its decent performance compared against the neural models built on both syntactic trees and latent trees, as well as the non-tree models. (Latent Syntax Tree-LSTM: BIBREF10 ( BIBREF10 ), Tree-based CNN: BIBREF35 ( BIBREF35 ), Gumbel Tree-LSTM: BIBREF11 ( BIBREF11 ), NSE: BIBREF36 ( BIBREF36 ), Reinforced Self-Attention Network: BIBREF4 ( BIBREF4 ), Residual stacked encoders: BIBREF37 ( BIBREF37 ), BiLSTM with generalized pooling: BIBREF38 ( BIBREF38 ).) Note that the number of learned parameters in our model is also comparable to other sophisticated models, showing the efficiency of our model.\"]}",
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science.multicite_intent_classification | Examine the text below, which includes a citation marked by "<cite>" and "</cite>". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.
Provide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.
Text with Citation:
The recent approaches used pair-wise alignment algorithms based on symmetric alignments from a HMM alignment model (Matusov et al., 2006) or edit distance alignments allowing shifts <cite>(Rosti et al., 2007)</cite> . The alignment method described in this paper extends the latter by incrementally aligning the hypotheses as in MSA but also allowing shifts as in the TER alignment. | ["Extends"] | science.multicite_intent_classification.1530 | [
{
"content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nThe recent approaches used pair-wise alignment algorithms based on symmetric alignments from a HMM alignment model (Matusov et al., 2006) or edit distance alignments allowing shifts <cite>(Rosti et al., 2007)</cite> . The alignment method described in this paper extends the latter by incrementally aligning the hypotheses as in MSA but also allowing shifts as in the TER alignment.",
"role": "user"
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{
"content": "[\"Extends\"]",
"role": "assistant"
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science.multicite_intent_classification | Examine the text below, which includes a citation marked by "<cite>" and "</cite>". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.
Provide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.
Text with Citation:
We also tested the state of the art model of implicit discourse relation classification proposed by<cite> Bai and Zhao (2018)</cite> on BioDRB. | ["Uses"] | science.multicite_intent_classification.21 | [
{
"content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nWe also tested the state of the art model of implicit discourse relation classification proposed by<cite> Bai and Zhao (2018)</cite> on BioDRB.",
"role": "user"
},
{
"content": "[\"Uses\"]",
"role": "assistant"
}
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science.multicite_intent_classification | Examine the text below, which includes a citation marked by "<cite>" and "</cite>". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.
Provide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.
Text with Citation:
The reordering operations (gaps and jumps) are generated by looking at the position of the translator, the last foreign word generated etc. (Refer to Algorithm 1 in <cite>Durrani et al. (2011)</cite> ). | ["Uses"] | science.multicite_intent_classification.1599 | [
{
"content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nThe reordering operations (gaps and jumps) are generated by looking at the position of the translator, the last foreign word generated etc. (Refer to Algorithm 1 in <cite>Durrani et al. (2011)</cite> ).",
"role": "user"
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{
"content": "[\"Uses\"]",
"role": "assistant"
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]
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science.scifact_entailment | Determine the relationship between them and generate a JSON response. Scientific Claim:
NAC inhibits the generation of angiotensin-converting enzyme.
Research Abstract:
CONTEXT The antioxidant acetylcysteine prevents acute contrast nephrotoxicity in patients with impaired renal function who undergo computed tomography scanning.
However, its role in coronary angiography is unclear.
OBJECTIVE To determine whether oral acetylcysteine prevents acute deterioration in renal function in patients with moderate renal insufficiency who undergo elective coronary angiography.
DESIGN AND SETTING Prospective, randomized, double-blind, placebo-controlled trial conducted from May 2000 to December 2001 at the Grantham Hospital at the University of Hong Kong.
PARTICIPANTS Two hundred Chinese patients aged mean (SD) 68 (6.5) years with stable moderate renal insufficiency (creatinine clearance <60 mL/min [1.00 mL/s]) who were undergoing elective coronary angiography with or without intervention.
INTERVENTION Participants were randomly assigned to receive oral acetylcysteine(600 mg twice per day; n = 102) or matching placebo tablets (n = 98) on the day before and the day of angiography.
All patients received low-osmolality contrast agent.
MAIN OUTCOME MEASURES Occurrence of more than a 25% increase in serum creatinine level within 48 hours after contrast administration; change in creatinine clearance and serum creatinine level.
RESULTS Twelve control patients (12%) and 4 acetylcysteine patients (4%) developed a more than 25% increase in serum creatinine level within 48 hours after contrast administration (relative risk, 0.32; 95% confidence interval [CI], 0.10-0.96; P =.03).
Serum creatinine was lower in the acetylcysteine group (1.22 mg/dL [107.8 micromol/L]; 95% CI, 1.11-1.33 mg/dL vs 1.38 mg/dL [122.9 micromol/L]; 95% CI, 1.27-1.49 mg/dL; P =.006) during the first 48 hours after angiography.
Acetylcysteine treatment significantly increased creatinine clearance from 44.8 mL/min (0.75 mL/s) (95% CI, 42.7-47.6 mL/min) to 58.9 mL/min (0.98 mL/s) (95% CI, 55.6-62.3 mL/min) 2 days after the contrast administration (P<.001).
The increase was not significant in the control group (from 42.1 to 44.1 mL/min [0.70 to 0.74 mL/s]; P =.15).
The benefit of acetylcysteine was consistent among various patient subgroups and persistent for at least 7 days.
There were no major treatment-related adverse events.
CONCLUSION Acetylcysteine protects patients with moderate chronic renal insufficiency from contrast-induced deterioration in renal function after coronary angiographic procedures, with minimal adverse effects and at a low cost. | {"verdict": "NEI", "evidence": []} | science.scifact_entailment.530 | [
{
"content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nNAC inhibits the generation of angiotensin-converting enzyme.\n\nResearch Abstract:\nCONTEXT The antioxidant acetylcysteine prevents acute contrast nephrotoxicity in patients with impaired renal function who undergo computed tomography scanning.\nHowever, its role in coronary angiography is unclear.\nOBJECTIVE To determine whether oral acetylcysteine prevents acute deterioration in renal function in patients with moderate renal insufficiency who undergo elective coronary angiography.\nDESIGN AND SETTING Prospective, randomized, double-blind, placebo-controlled trial conducted from May 2000 to December 2001 at the Grantham Hospital at the University of Hong Kong.\nPARTICIPANTS Two hundred Chinese patients aged mean (SD) 68 (6.5) years with stable moderate renal insufficiency (creatinine clearance <60 mL/min [1.00 mL/s]) who were undergoing elective coronary angiography with or without intervention.\nINTERVENTION Participants were randomly assigned to receive oral acetylcysteine(600 mg twice per day; n = 102) or matching placebo tablets (n = 98) on the day before and the day of angiography.\nAll patients received low-osmolality contrast agent.\nMAIN OUTCOME MEASURES Occurrence of more than a 25% increase in serum creatinine level within 48 hours after contrast administration; change in creatinine clearance and serum creatinine level.\nRESULTS Twelve control patients (12%) and 4 acetylcysteine patients (4%) developed a more than 25% increase in serum creatinine level within 48 hours after contrast administration (relative risk, 0.32; 95% confidence interval [CI], 0.10-0.96; P =.03).\nSerum creatinine was lower in the acetylcysteine group (1.22 mg/dL [107.8 micromol/L]; 95% CI, 1.11-1.33 mg/dL vs 1.38 mg/dL [122.9 micromol/L]; 95% CI, 1.27-1.49 mg/dL; P =.006) during the first 48 hours after angiography.\nAcetylcysteine treatment significantly increased creatinine clearance from 44.8 mL/min (0.75 mL/s) (95% CI, 42.7-47.6 mL/min) to 58.9 mL/min (0.98 mL/s) (95% CI, 55.6-62.3 mL/min) 2 days after the contrast administration (P<.001).\nThe increase was not significant in the control group (from 42.1 to 44.1 mL/min [0.70 to 0.74 mL/s]; P =.15).\nThe benefit of acetylcysteine was consistent among various patient subgroups and persistent for at least 7 days.\nThere were no major treatment-related adverse events.\nCONCLUSION Acetylcysteine protects patients with moderate chronic renal insufficiency from contrast-induced deterioration in renal function after coronary angiographic procedures, with minimal adverse effects and at a low cost.",
"role": "user"
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"content": "{\"verdict\": \"NEI\", \"evidence\": []}",
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science.evidence_inference | Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:
- Treatment: The specific medical procedure or drug being evaluated.
- Control: The standard or placebo used for comparison. If absent, use `null`.
- Result: The health outcome being assessed.
- Impact: The observed effect of the treatment on the result, categorized as: ("increased", "decreased", "no change").
- Justification: The supporting details for the observed impact.
Format the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].
Abstract:
ABSTRACT: [Purpose] This study analyzed the cognitive functions according to risk level for the
Driver 65 Plus measure, and examined the cognitive basis of self-assessment for screening
the driving risk of elderly drivers. [Subjects and Methods] A total of 46 older drivers
with a driver's license participated in this study. All participants were evaluated with
Driver 65 Plus. They were classified into three groups of "safe," "caution" and "stop,"
and examined for cognitive functions with Trail Making Test and Montreal Cognitive
Assessment-K. The cognitive test results of the three groups were compared. [Results]
Trail Making Test-A, Trail Making Test-B, and Montreal Cognitive Assessment-K showed a
significant difference between the three groups. The safe group showed significantly
higher ability than the caution and stop groups in the three cognitive tests. In addition,
cognitive functions of naming, attention, language, and delayed recall were significantly
different between the three groups. [Conclusion] Self-assessment of older drivers is a
useful tool for screening the cognitive aspects of driving risk. The cognitive functions,
such as attention and recall, are the critical factors for screening the driving risk of
elderly drivers.
Extracted Data: | [["Being in the Safe gruop", "Being in the Caution gruop", "Good TMT-A results", "significantly increased", "The cognitive test results of the three groups were compared. [Results]\r\r\r\r\nTrail Making Test-A, Trail Making Test-B, and Montreal Cognitive Assessment-K showed a\r\r\r\r\nsignificant difference between the three groups. The safe group showed significantly\r\r\r\r\nhigher ability than the caution and stop groups in the three cognitive tests. "]] | science.evidence_inference.74 | [
{
"content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT: [Purpose] This study analyzed the cognitive functions according to risk level for the\nDriver 65 Plus measure, and examined the cognitive basis of self-assessment for screening\nthe driving risk of elderly drivers. [Subjects and Methods] A total of 46 older drivers\nwith a driver's license participated in this study. All participants were evaluated with\nDriver 65 Plus. They were classified into three groups of \"safe,\" \"caution\" and \"stop,\"\nand examined for cognitive functions with Trail Making Test and Montreal Cognitive\nAssessment-K. The cognitive test results of the three groups were compared. [Results]\nTrail Making Test-A, Trail Making Test-B, and Montreal Cognitive Assessment-K showed a\nsignificant difference between the three groups. The safe group showed significantly\nhigher ability than the caution and stop groups in the three cognitive tests. In addition,\ncognitive functions of naming, attention, language, and delayed recall were significantly\ndifferent between the three groups. [Conclusion] Self-assessment of older drivers is a\nuseful tool for screening the cognitive aspects of driving risk. The cognitive functions,\nsuch as attention and recall, are the critical factors for screening the driving risk of\nelderly drivers.\n\nExtracted Data:",
"role": "user"
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{
"content": "[[\"Being in the Safe gruop\", \"Being in the Caution gruop\", \"Good TMT-A results\", \"significantly increased\", \"The cognitive test results of the three groups were compared. [Results]\\r\\r\\r\\r\\nTrail Making Test-A, Trail Making Test-B, and Montreal Cognitive Assessment-K showed a\\r\\r\\r\\r\\nsignificant difference between the three groups. The safe group showed significantly\\r\\r\\r\\r\\nhigher ability than the caution and stop groups in the three cognitive tests. \"]]",
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science.discomat_te | | b (K/min) | T g (K) | F | T c1 (K) | T p1 (K) | S (K) | T p2 (K) |
| 5 | 1059 | 21.39 | 1138 | 1152 | 1.98 | 1325 |
| 10 | 1068 | | 1150 | 1168 | | 1348 |
| 20 | 1079 | | 1171 | 1197 | | 1369 |
| 25 | 1082 | | 1168 | 1201 | | 1372 |
| 30 | 1086 | | 1174 | 1207 | | 1377 |
Caption: Thermal parameters of the glass obtained from DTA
Examine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:
{"value": "xx", "type": "Other"}
{"value": "xx", "type": "Glass_Compound_Amount", "constituent": "xx", "unit": "xx", "material": "xx"}
Process each cell sequentially, and for any unknown attributes, use "xx" as a placeholder. Each JSON should be on a new line.
Output: | {"value": "5", "type": "Other"}
{"value": "1059", "type": "Other"}
{"value": "21.39", "type": "Other"}
{"value": "1138", "type": "Other"}
{"value": "1152", "type": "Other"}
{"value": "1.98", "type": "Other"}
{"value": "1325", "type": "Other"}
{"value": "10", "type": "Other"}
{"value": "1068", "type": "Other"}
{"value": "1150", "type": "Other"}
{"value": "1168", "type": "Other"}
{"value": "1348", "type": "Other"}
{"value": "20", "type": "Other"}
{"value": "1079", "type": "Other"}
{"value": "1171", "type": "Other"}
{"value": "1197", "type": "Other"}
{"value": "1369", "type": "Other"}
{"value": "25", "type": "Other"}
{"value": "1082", "type": "Other"}
{"value": "1168", "type": "Other"}
{"value": "1201", "type": "Other"}
{"value": "1372", "type": "Other"}
{"value": "30", "type": "Other"}
{"value": "1086", "type": "Other"}
{"value": "1174", "type": "Other"}
{"value": "1207", "type": "Other"}
{"value": "1377", "type": "Other"} | science.discomat_te.356 | [
{
"content": "| b (K/min) | T g (K) | F | T c1 (K) | T p1 (K) | S (K) | T p2 (K) |\n| 5 | 1059 | 21.39 | 1138 | 1152 | 1.98 | 1325 |\n| 10 | 1068 | | 1150 | 1168 | | 1348 |\n| 20 | 1079 | | 1171 | 1197 | | 1369 |\n| 25 | 1082 | | 1168 | 1201 | | 1372 |\n| 30 | 1086 | | 1174 | 1207 | | 1377 |\n\nCaption: Thermal parameters of the glass obtained from DTA\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:",
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{
"content": "{\"value\": \"5\", \"type\": \"Other\"}\n{\"value\": \"1059\", \"type\": \"Other\"}\n{\"value\": \"21.39\", \"type\": \"Other\"}\n{\"value\": \"1138\", \"type\": \"Other\"}\n{\"value\": \"1152\", \"type\": \"Other\"}\n{\"value\": \"1.98\", \"type\": \"Other\"}\n{\"value\": \"1325\", \"type\": \"Other\"}\n{\"value\": \"10\", \"type\": \"Other\"}\n{\"value\": \"1068\", \"type\": \"Other\"}\n{\"value\": \"1150\", \"type\": \"Other\"}\n{\"value\": \"1168\", \"type\": \"Other\"}\n{\"value\": \"1348\", \"type\": \"Other\"}\n{\"value\": \"20\", \"type\": \"Other\"}\n{\"value\": \"1079\", \"type\": \"Other\"}\n{\"value\": \"1171\", \"type\": \"Other\"}\n{\"value\": \"1197\", \"type\": \"Other\"}\n{\"value\": \"1369\", \"type\": \"Other\"}\n{\"value\": \"25\", \"type\": \"Other\"}\n{\"value\": \"1082\", \"type\": \"Other\"}\n{\"value\": \"1168\", \"type\": \"Other\"}\n{\"value\": \"1201\", \"type\": \"Other\"}\n{\"value\": \"1372\", \"type\": \"Other\"}\n{\"value\": \"30\", \"type\": \"Other\"}\n{\"value\": \"1086\", \"type\": \"Other\"}\n{\"value\": \"1174\", \"type\": \"Other\"}\n{\"value\": \"1207\", \"type\": \"Other\"}\n{\"value\": \"1377\", \"type\": \"Other\"}",
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science.discomat_te | | Sample code | R (O) at 303K (+-0.5%) | R (O) at 423K (+-0.5%) | A at 303K (+-0.5%) | A at 423K (+-0.5%) | a at 303K (+-0.5%) | a at 423K (+-0.5%) |
| F40 | 1.00x1010 | 1.95x107 | 7.62x10-12 | 2.34x10-11 | 0.84 | 0.83 |
| B3 | 5.19x108 | 2.08x106 | 1.09x10-11 | 3.09x10-11 | 0.85 | 0.83 |
| B10 | 4.83x108 | 2.00x106 | 1.53x10-11 | 3.73x10-11 | 0.85 | 0.85 |
| B20 | 2.65x109 | 9.45x106 | 1.53x10-11 | 3.22x10-11 | 0.85 | 0.86 |
Caption: Fitting parameters of impedance spectra at 303 and 423K for bismuth iron phosphate glasses
Examine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:
{"value": "xx", "type": "Other"}
{"value": "xx", "type": "Glass_Compound_Amount", "constituent": "xx", "unit": "xx", "material": "xx"}
Process each cell sequentially, and for any unknown attributes, use "xx" as a placeholder. Each JSON should be on a new line.
Output: | {"value": "0.84", "type": "Other"}
{"value": "0.83", "type": "Other"}
{"value": "0.85", "type": "Other"}
{"value": "0.83", "type": "Other"}
{"value": "0.85", "type": "Other"}
{"value": "0.85", "type": "Other"}
{"value": "0.85", "type": "Other"}
{"value": "0.86", "type": "Other"} | science.discomat_te.275 | [
{
"content": "| Sample code | R (O) at 303K (+-0.5%) | R (O) at 423K (+-0.5%) | A at 303K (+-0.5%) | A at 423K (+-0.5%) | a at 303K (+-0.5%) | a at 423K (+-0.5%) |\n| F40 | 1.00x1010 | 1.95x107 | 7.62x10-12 | 2.34x10-11 | 0.84 | 0.83 |\n| B3 | 5.19x108 | 2.08x106 | 1.09x10-11 | 3.09x10-11 | 0.85 | 0.83 |\n| B10 | 4.83x108 | 2.00x106 | 1.53x10-11 | 3.73x10-11 | 0.85 | 0.85 |\n| B20 | 2.65x109 | 9.45x106 | 1.53x10-11 | 3.22x10-11 | 0.85 | 0.86 |\n\nCaption: Fitting parameters of impedance spectra at 303 and 423K for bismuth iron phosphate glasses\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:",
"role": "user"
},
{
"content": "{\"value\": \"0.84\", \"type\": \"Other\"}\n{\"value\": \"0.83\", \"type\": \"Other\"}\n{\"value\": \"0.85\", \"type\": \"Other\"}\n{\"value\": \"0.83\", \"type\": \"Other\"}\n{\"value\": \"0.85\", \"type\": \"Other\"}\n{\"value\": \"0.85\", \"type\": \"Other\"}\n{\"value\": \"0.85\", \"type\": \"Other\"}\n{\"value\": \"0.86\", \"type\": \"Other\"}",
"role": "assistant"
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science.bioasq_list_qa | Examine the following excerpts from biomedical literature. Your objective is to extract relevant information to answer the posed question. Format your answer as a JSON array.
Context: The RING domain of RING1 was required for its E3 Ub ligase activity.
The TRIM proteins are classically characterized by the presence of an amino-terminal RING domain and a B-box domain followed by a coiled coil domain
the detailed mechanism by which the TRAF6 RING dimer promotes ubiquitin transfer was unknown.
RING ubiquitin E3 ligases enclose a RING domain for ubiquitin ligase activity
UHRF1 RING domain
MARCH7, a RING domain-containing ubiquitin E3 ligase,
Seven in absentia (SINA) protein is one subgroup of ubiquitin ligases possessing an N-terminal cysteine-rich really interesting new gene (RING) domain,
The BRCA1 RING domain is a ubiquitin ligase
Question: List proteins with RING domain. | ["RING1", "TRIM proteins", "TRAF6", "UHRF1", "MARCH7", "SINA", "BRCA1"] | science.bioasq_list_qa.642 | [
{
"content": "Examine the following excerpts from biomedical literature. Your objective is to extract relevant information to answer the posed question. Format your answer as a JSON array.\n\nContext: The RING domain of RING1 was required for its E3 Ub ligase activity.\nThe TRIM proteins are classically characterized by the presence of an amino-terminal RING domain and a B-box domain followed by a coiled coil domain\nthe detailed mechanism by which the TRAF6 RING dimer promotes ubiquitin transfer was unknown.\nRING ubiquitin E3 ligases enclose a RING domain for ubiquitin ligase activity\nUHRF1 RING domain\nMARCH7, a RING domain-containing ubiquitin E3 ligase,\nSeven in absentia (SINA) protein is one subgroup of ubiquitin ligases possessing an N-terminal cysteine-rich really interesting new gene (RING) domain,\nThe BRCA1 RING domain is a ubiquitin ligase\n\nQuestion: List proteins with RING domain.",
"role": "user"
},
{
"content": "[\"RING1\", \"TRIM proteins\", \"TRAF6\", \"UHRF1\", \"MARCH7\", \"SINA\", \"BRCA1\"]",
"role": "assistant"
}
]
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science.scifact_entailment | Determine the relationship between them and generate a JSON response. Scientific Claim:
Moderate consumption of candy and chocolate reduces the risk of cardiovascular disease (CVD).
Research Abstract:
Our attitude towards candy—“if it tastes that good, it can't be healthy”—betrays society's puritanical stance towards pleasure.
Candy has been blamed for various ills, including hyperactivity in children; however, clinical trials have not supported this.1 Candy—sugar confectionery and chocolate—is not a recent invention: the ancient Arabs, Chinese, and Egyptians candied fruits and nuts in honey, and the Aztecs made a chocolate drink from the bean of the cacao tree.
Today, Americans gratify themselves with, on average, 5.4 kg of sugar candy and 6.5 kg of chocolate per person annually.2 Since candy has existed for centuries, we surmised that it cannot be totally unhealthy.
We decided to investigate whether candy consumption was associated with longevity.
Subjects were from the Harvard alumni health study, an ongoing study of men entering Harvard University as undergraduates between 1916 and 1950.
We included 7841 men, free of cardiovascular disease and cancer, who responded to a health survey … | {"verdict": "NEI", "evidence": []} | science.scifact_entailment.514 | [
{
"content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nModerate consumption of candy and chocolate reduces the risk of cardiovascular disease (CVD).\n\nResearch Abstract:\nOur attitude towards candy—“if it tastes that good, it can't be healthy”—betrays society's puritanical stance towards pleasure.\nCandy has been blamed for various ills, including hyperactivity in children; however, clinical trials have not supported this.1 Candy—sugar confectionery and chocolate—is not a recent invention: the ancient Arabs, Chinese, and Egyptians candied fruits and nuts in honey, and the Aztecs made a chocolate drink from the bean of the cacao tree.\nToday, Americans gratify themselves with, on average, 5.4 kg of sugar candy and 6.5 kg of chocolate per person annually.2 Since candy has existed for centuries, we surmised that it cannot be totally unhealthy.\nWe decided to investigate whether candy consumption was associated with longevity.\nSubjects were from the Harvard alumni health study, an ongoing study of men entering Harvard University as undergraduates between 1916 and 1950.\nWe included 7841 men, free of cardiovascular disease and cancer, who responded to a health survey …",
"role": "user"
},
{
"content": "{\"verdict\": \"NEI\", \"evidence\": []}",
"role": "assistant"
}
]
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science.evidence_inference | Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:
- Treatment: The specific medical procedure or drug being evaluated.
- Control: The standard or placebo used for comparison. If absent, use `null`.
- Result: The health outcome being assessed.
- Impact: The observed effect of the treatment on the result, categorized as: ("increased", "decreased", "no change").
- Justification: The supporting details for the observed impact.
Format the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].
Abstract:
ABSTRACT.BACKGROUND:: Adjuvants have been used to prolong analgesic effects of epidural local anaesthetics. We studied two different doses of neostigmine.
ABSTRACT.PATIENTS & METHODS:: A randomized double blind study was conducted on ninety adult females scheduled for lower intra abdominal surgeries. The study was designed to compare two doses of epidural neostigmine co administered with lignocaine, with regard to its analgesic efficacy and its effect on sedation in postoperative period. Patients were divided into three groups of 30 each. Group I received lignocaine 1% (9ml) with normal saline (1ml), group II lignocaine1% (9ml) with neostigmine 100μg in saline (1ml) and group III received lignocaine 1% (9ml) with neostigmine 200μg in NS (1ml). Group I served as a control. In operating room, after putting epidural catheter, general anesthesia was administered with propofol (2mg kg-1), succinylcholine (2mg kg-1) and maintained with O2, N2O, relaxant technique. At the end of surgery, patients were reversed. Epidural analgesic medication was administered to after proper recovery from anesthesia. Intensity of pain relief on VAS, duration of analgesia, level of sensory block, motor blockade, sedation by sedation score and complications were assessed.
ABSTRACT.RESULTS:: The addition of neostigmine resulted in significant longer duration of analgesia (dose independent) and sedation (dose dependent). Sensory and motor blockade were identical in all three groups. There was no incidence of respiratory depression, pruritus, bradycardia or hypotension in any group. Two patients in control group and one, receiving neostigmine (200μg), developed nausea/vomiting.
ABSTRACT.CONCLUSION:: Co administration of epidural neostigmine and lignocaine appears to be a useful technique for postoperative analgesia as it increases the duration of analgesia and provides desirable sedation at the same time.
Extracted Data: | [["Group I received lignocaine 1% (9ml) with normal saline (1ml)", "group II lignocaine1% (9ml) with neostigmine 100\u03bcg in saline (1ml) and group III received lignocaine 1% (9ml) with neostigmine 200\u03bcg in NS (1ml)", "sensory blockade", "no significant difference", "Sensory and motor blockade were identical in all three groups."], ["Group I received lignocaine 1% (9ml) with normal saline (1ml)", "group II lignocaine1% (9ml) with neostigmine 100\u03bcg in saline (1ml) and group III received lignocaine 1% (9ml) with neostigmine 200\u03bcg in NS (1ml)", "duration of analgesia", "significantly decreased", "The addition of neostigmine resulted in significant longer duration of analgesia (dose independent) and sedation (dose dependent)."]] | science.evidence_inference.120 | [
{
"content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND:: Adjuvants have been used to prolong analgesic effects of epidural local anaesthetics. We studied two different doses of neostigmine.\n\nABSTRACT.PATIENTS & METHODS:: A randomized double blind study was conducted on ninety adult females scheduled for lower intra abdominal surgeries. The study was designed to compare two doses of epidural neostigmine co administered with lignocaine, with regard to its analgesic efficacy and its effect on sedation in postoperative period. Patients were divided into three groups of 30 each. Group I received lignocaine 1% (9ml) with normal saline (1ml), group II lignocaine1% (9ml) with neostigmine 100μg in saline (1ml) and group III received lignocaine 1% (9ml) with neostigmine 200μg in NS (1ml). Group I served as a control. In operating room, after putting epidural catheter, general anesthesia was administered with propofol (2mg kg-1), succinylcholine (2mg kg-1) and maintained with O2, N2O, relaxant technique. At the end of surgery, patients were reversed. Epidural analgesic medication was administered to after proper recovery from anesthesia. Intensity of pain relief on VAS, duration of analgesia, level of sensory block, motor blockade, sedation by sedation score and complications were assessed.\n\nABSTRACT.RESULTS:: The addition of neostigmine resulted in significant longer duration of analgesia (dose independent) and sedation (dose dependent). Sensory and motor blockade were identical in all three groups. There was no incidence of respiratory depression, pruritus, bradycardia or hypotension in any group. Two patients in control group and one, receiving neostigmine (200μg), developed nausea/vomiting.\n\nABSTRACT.CONCLUSION:: Co administration of epidural neostigmine and lignocaine appears to be a useful technique for postoperative analgesia as it increases the duration of analgesia and provides desirable sedation at the same time.\n\nExtracted Data:",
"role": "user"
},
{
"content": "[[\"Group I received lignocaine 1% (9ml) with normal saline (1ml)\", \"group II lignocaine1% (9ml) with neostigmine 100\\u03bcg in saline (1ml) and group III received lignocaine 1% (9ml) with neostigmine 200\\u03bcg in NS (1ml)\", \"sensory blockade\", \"no significant difference\", \"Sensory and motor blockade were identical in all three groups.\"], [\"Group I received lignocaine 1% (9ml) with normal saline (1ml)\", \"group II lignocaine1% (9ml) with neostigmine 100\\u03bcg in saline (1ml) and group III received lignocaine 1% (9ml) with neostigmine 200\\u03bcg in NS (1ml)\", \"duration of analgesia\", \"significantly decreased\", \"The addition of neostigmine resulted in significant longer duration of analgesia (dose independent) and sedation (dose dependent).\"]]",
"role": "assistant"
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science.evidence_inference | Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:
- Treatment: The specific medical procedure or drug being evaluated.
- Control: The standard or placebo used for comparison. If absent, use `null`.
- Result: The health outcome being assessed.
- Impact: The observed effect of the treatment on the result, categorized as: ("increased", "decreased", "no change").
- Justification: The supporting details for the observed impact.
Format the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].
Abstract:
ABSTRACT.BACKGROUND:: Infertility means failure to achieve pregnancy after one year of regular unprotected sexual intercourse. Infertile women may experience severe stress and depression. Numerous studies have indicated that auriculotherapy could reduce stress. Thus, the aim of the present study was to determine the effect of auriculotherapy on the stress and the outcome assisted reproductive technology in infertile women.
ABSTRACT.MATERIALS AND METHODS:: The present study was a clinical trial that was conducted on 56 infertile women aged 20–45, who were assigned into two groups of intervention and control, from November 2014 to November 2015. The control group only received the routine treatments, while the intervention group, in addition to their routine treatment, received auriculotherapy for 8–10 sessions during menstrual cycle. Both groups completed Newton's Fertility Problem Inventory in three stages. The datasets collected for the study were analyzed using independent t-test, repeated-measures analysis of variance, and Chi-square test.
ABSTRACT.RESULTS:: The mean score of stress in the intervention group decreased significantly, compared to the control group prior to the embryo transfer and pregnancy test stages. Although insignificant, the rate of pregnancy in the intervention group was higher than the control group. There was a significant increase in the rate of clinical pregnancy in the intervention group, compared to the control.
ABSTRACT.CONCLUSIONS:: The results indicated that auriculotherapy might be effective in reducing stress and improving the outcome of assisted reproductive treatment.
Extracted Data: | [["in addition to their routine treatment, received auriculotherapy for 8\u201310 sessions during menstrual cycle.", "routine treatments", "rate of pregnancy", "no significant difference", "Although insignificant, the rate of pregnancy in the intervention group was higher than the control group."], ["in addition to their routine treatment, received auriculotherapy for 8\u201310 sessions during menstrual cycle.", "routine treatments", "the frequency of clinical pregnancy", "significantly increased", "There was a significant increase in the rate of clinical pregnancy in the intervention group, compared to the control."]] | science.evidence_inference.244 | [
{
"content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND:: Infertility means failure to achieve pregnancy after one year of regular unprotected sexual intercourse. Infertile women may experience severe stress and depression. Numerous studies have indicated that auriculotherapy could reduce stress. Thus, the aim of the present study was to determine the effect of auriculotherapy on the stress and the outcome assisted reproductive technology in infertile women.\n\nABSTRACT.MATERIALS AND METHODS:: The present study was a clinical trial that was conducted on 56 infertile women aged 20–45, who were assigned into two groups of intervention and control, from November 2014 to November 2015. The control group only received the routine treatments, while the intervention group, in addition to their routine treatment, received auriculotherapy for 8–10 sessions during menstrual cycle. Both groups completed Newton's Fertility Problem Inventory in three stages. The datasets collected for the study were analyzed using independent t-test, repeated-measures analysis of variance, and Chi-square test.\n\nABSTRACT.RESULTS:: The mean score of stress in the intervention group decreased significantly, compared to the control group prior to the embryo transfer and pregnancy test stages. Although insignificant, the rate of pregnancy in the intervention group was higher than the control group. There was a significant increase in the rate of clinical pregnancy in the intervention group, compared to the control.\n\nABSTRACT.CONCLUSIONS:: The results indicated that auriculotherapy might be effective in reducing stress and improving the outcome of assisted reproductive treatment.\n\nExtracted Data:",
"role": "user"
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{
"content": "[[\"in addition to their routine treatment, received auriculotherapy for 8\\u201310 sessions during menstrual cycle.\", \"routine treatments\", \"rate of pregnancy\", \"no significant difference\", \"Although insignificant, the rate of pregnancy in the intervention group was higher than the control group.\"], [\"in addition to their routine treatment, received auriculotherapy for 8\\u201310 sessions during menstrual cycle.\", \"routine treatments\", \"the frequency of clinical pregnancy\", \"significantly increased\", \"There was a significant increase in the rate of clinical pregnancy in the intervention group, compared to the control.\"]]",
"role": "assistant"
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science.mup_single_document_summarization | Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.
Title of the Paper:
Shape-Tailored Deep Neural Networks Using PDEs for Segmentation
Main Content:
1 INTRODUCTION . Convolutional neural networks ( CNNs ) have been used extensively for segmentation problems in computer vision He et al . ( 2017 ) ; He et al . ( 2016 ) ; Chen et al . ( 2017 ) ; Xie & Tu ( 2015 ) . CNNs provide a framework for learning descriptors that are able to discriminate different textured or semantic regions within images . Much progress has been made in segmentation with CNNs but results are still far from human performance . Also , significant engineering must be performed to adapt CNNs to segmentation problems . A basic component in the architecture for segmentation problems involves labeling or grouping dense descriptors returned by a backbone CNN . A difficulty in grouping these descriptors arises , especially near the boundaries of segmentation regions , as CNN descriptors aggregate data from fixed shape ( square neighborhoods ) at each pixel and may thus aggregate data from different regions . This makes grouping these descriptors into a unique region difficult , which often results in errors in the grouping . In segmentation problems ( e.g. , semantic segmentation ) , current methods attempt to mitigate these errors by adding post-processing layers that aim to group simultaneously the ( coarse-scale ) descriptors from the CNN backbone and the fine-level pixel data . However , the errors introduced might not always be fixed . A more natural approach to avoid this problem is to consider the coarse and fine structure together , avoiding aggregation across boundaries , to prevent errors at the outset . To avoid such errors , one could design descriptors that aggregate data only within boundaries . To this end , Khan et al . ( 2015 ) introduced “ shape-tailored ” descriptors that aggregate data within a region of interest , and used these descriptors for segmentation . However , these descriptors are hand-crafted and do not perform on-par with learned approaches . Khan & Sundaramoorthi ( 2018 ) introduced learned shape-tailored descriptors by learning a neural network operating on the input channel dimension of input hand-crafted shape-tailored descriptors for segmentation . However , these networks , though deep in the channel dimension , did not filter data spatially within layers . Since an advantage of CNNs comes from exploiting spatial filtering at each depth of the network , in this work , we design shape-tailored networks that are deep and perform shape-tailored filtering in space at each layer using solutions of the Poisson PDE . This results in shape-tailored networks that provide more discriminative descriptors than a single shape-tailored kernel . This extension requires development of techniques to back-propagate through PDEs , which we derive in this work . Our contributions are specifically : 1 . We construct and show how to train ST-DNN , deep networks that perform shape-tailored spatial filtering via the Poisson PDE at each depth so as to generalize a CNN to arbitrarily shaped regions . 2 . We show analytically and empirically that ST-DNNs are covariant to translations and rotations as they inherit this property from the Poisson PDE . In segmentation , covariance ( a.k.a. , equivariance ) to translation and rotation is a desired property : if a segment in an image is found , then the corresponding segment should be found in the translated / rotated image ( or object ) . This property is not generally present with existing CNN-based segmentation methods even when trained with augmented translated and rotated images Azulay & Weiss ( 2019 ) , and requires special consideration . 3 . We show analytically and empirically that ST-DNNs are robust to domain deformations . These result from viewpoint change or object articulation , and so they should not affect the descriptor . 4 . To demonstrate ST-DNN and the properties above , we validate them on the task of segmentation , an important problem in low-level vision Malik & Perona ( 1990 ) ; Arbelaez et al . ( 2011b ) . Because of properties of the PDE , ST-DNN also have desirable generalization properties . This is because : a ) The robustness and covariance properties are built into our descriptors and do not need to be learned from data , b ) The PDE solutions , generalizations of Gabor-like filters Olshausen & Field ( 1996 ) ; Zador ( 2019 ) , have natural image structure inherent in their solutions and so this does not need to be learned from data , and c ) Our networks have fewer parameters compared to existing networks in segmentation . This is because the PDE solutions form a basis and only linear combinations of a few basis elements are needed to learn discriminative descriptors for segmentation . In contrast , CNNs spend a lot of parameters to learn this structure . 1.1 RELATED WORK . Traditional approaches to segmentation rely on hand-crafted features , e.g. , through a filter bank Haralick & Shapiro ( 1985 ) . These features are ambiguous near the boundaries of objects . In Khan et al . ( 2015 ) hand-crafted descriptors that aggregate data within object boundaries are constructed to avoid this , but lack sufficient capacity to capture the diversity of textures or be invariant to nuisances . Deep-learning based approaches have showed state-of-the-art results in edge-based methods Xie & Tu ( 2017 ) ; He et al . ( 2019 ) ; Deng et al . ( 2018 ) . Watershed is applied on edge-maps to obtain the segmentation . The main drawback of these methods is it is often difficult to form segmentations due to extraneous or faint edges , particularly when `` textons '' in textures are large . CNNs have been applied to compute descriptors for semantic segmentation , where pixels in an image are classified into certain semantic object classes Li et al . ( 2019 ) ; Huang et al . ( 2019 ) ; Du et al . ( 2019 ) ; Pang et al . ( 2019 ) ; Zhu et al . ( 2019 ) ; Liu et al . ( 2019 ) . Usually these classes are limited to a few object classes and do not tackle general textures , where the number of classes may be far greater , and thus such approaches are not directly applicable to texture segmentation . But semantic segmentation approaches may eventually benefit from our methodology as descriptors aggregating data only within objects or regions are also relevant to these problems . A learned shape-tailored descriptor Khan & Sundaramoorthi ( 2018 ) is constructed with a Siamese network on hand-crafted shape-tailored descriptors . However , Khan & Sundaramoorthi ( 2018 ) only does shape-tailored filtering in pre-processing as layering these requires new methods to train . We further examine covariance and robustness , not examined in Khan & Sundaramoorthi ( 2018 ) . Covariance to rotation in CNNs has been examined in recent works , e.g. , Weiler et al . ( 2018 ) ; Yin et al . ( 2019 ) ; Anderson et al . ( 2019 ) . They , however , are not shape-tailored so do not aggregate data only within shaped regions . Lack of robustness to deformation ( and translation ) in CNNs is examined in Azulay & Weiss ( 2019 ) and theoretically in Bietti & Mairal ( 2017 ) . Sifre & Mallat ( 2013 ) constructs deformation robust descriptors inspired by CNNs , but are hand-crafted . 2 CONSTRUCTION OF SHAPE-TAILORED DNN AND PROPERTIES . In this section , we design a deep neural network that outputs descriptors at each pixel within an arbitrary shaped region of interest and aggregates data only from within the region . We want the descriptors to be discriminative of different texture , yet robust to nuisances within the region ( e.g. , local photometric and geometric variability ) to be useful for segmentation . Our construction uses a Poisson PDE , which naturally smooths data only within a region of interest . Smoothing naturally yields robustness to geometric nuisances ( domain deformations ) . By taking linear combinations of derivatives of the output of the PDE , we can approximate the effect of general convolutional kernels but avoid mixing data across the boundary of region of interest . ST-DNN is also covariant to translations and rotations , inheriting it from the Poisson equation , which leads to the segmentation algorithm being covariant to such transformations . 2.1 SHAPE-TAILORED DNN DESCRIPTORS THROUGH POISSON PDE . Shape-tailored Smoothing via Poisson PDE : To construct a shape-tailored deep network , we first smooth the input to a layer using the Poisson PDE so as to aggregate data only within the region of interest , similar to what is done in Khan et al . ( 2015 ) for just the first layer . Let R ⊂ Ω ⊂ R2 be the region of interest , where Ω is the domain of the input image I : Ω→ Rk and k is the number of input channels to the layer . Let u : R → RM ( M is the number of output channels ) be the result of the smoothing ; the components u of u solve the PDE within R : { u ( x ) − α∆u ( x ) = I ( x ) x ∈ R ∇u ( x ) ·N = 0 x ∈ ∂R , ( 1 ) where I is a channel of I , ∂R is the boundary of R , N is normal to ∂R , α is the scale of smoothing and ∆/∇ are the Laplacian and the gradient respectively . It can be shown that the smoothing can be written in the form u ( x ) = ∫ R K ( x , y ) I ( y ) dy where K ( . , . ) is the Green ’ s function of the PDE , a smoothing kernel , which further shows that the PDE aggregates data only within R. Shape-tailored Deep Network : We can now generalize the operation of convolution tailored to the region of interest by taking linear combinations of partial derivatives of the output of the PDE equation 1 . This is motivated by the fact that in R = R2 , linear combinations of derivatives of Gaussians can approximate any kernel arbitrarily well . Gaussian filters are the solution of the heat equation , and the PDE equation 1 relates to the heat equation , i.e. , equation 1 is the steady state solution of a heat equation . Thus , linear combinations of derivatives of equation 1 generalize convolution to an arbitrary shape R ; in experiments , a few first order directional derivatives are sufficient for our segmentation tasks ( see Section 5 for details ) . A layer of the ST-DNN takes such linear combinations and rectifies it as follows : fi ( x ) = r ◦ Li ◦ T [ I ] ( x ) , ( 2 ) where I : R→ Rk is the input to the layer , T is an operator that outputs derivatives of the solution of the Poisson PDE equation 1 , Li ( y ) = wiy+bi is a point-wise linear function ( i.e. , a 1×1 convolution applied to combine different channels ) , r is the rectified linear function , and i indexes the layer of the network . Notice that since r and Li are pointwise operations , they preserve the property of T that it aggregates data only within the region R. We now compose layers to construct a ST-DNN as follows : F [ I ] ( x ) = s ◦ fm ◦ fm−1 ◦ fm−2 ◦ .... f0 ◦ I ( x ) , ( 3 ) where F [ I ] ( x ) is the output of the ST-DNN , f0 , ... , fm are the m+ 1 layers of the network , I is the input image , and s represents the soft-max operation ( to bound the output values ) . ST-DNN does not have a pooling layer because the PDE already aggregates data from a neighborhood by smoothing ; further , the lack of reduction in spatial dimension allows for more accurate shape estimation in our subsequent segmentation , and avoids the need for up-sampling layers . We will show that we can retain efficiency in training and inference . | The paper proposed a new "shape-tailored" convolutional layer for improving the accuracy of semantic segmentation. The shape-tailored layer is inspired by the Poisson partial differential equation which aggregate features from neighboring pixels through the linear combinations of partial derivatives of the output of PDEs. The paper proves several properties of the proposed ST-DNN and demonstrated good results in comparisons with previous semantic segmentation algorithms. | science.mup_single_document_summarization.3051 | [
{
"content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nShape-Tailored Deep Neural Networks Using PDEs for Segmentation\n\nMain Content:\n1 INTRODUCTION . Convolutional neural networks ( CNNs ) have been used extensively for segmentation problems in computer vision He et al . ( 2017 ) ; He et al . ( 2016 ) ; Chen et al . ( 2017 ) ; Xie & Tu ( 2015 ) . CNNs provide a framework for learning descriptors that are able to discriminate different textured or semantic regions within images . Much progress has been made in segmentation with CNNs but results are still far from human performance . Also , significant engineering must be performed to adapt CNNs to segmentation problems . A basic component in the architecture for segmentation problems involves labeling or grouping dense descriptors returned by a backbone CNN . A difficulty in grouping these descriptors arises , especially near the boundaries of segmentation regions , as CNN descriptors aggregate data from fixed shape ( square neighborhoods ) at each pixel and may thus aggregate data from different regions . This makes grouping these descriptors into a unique region difficult , which often results in errors in the grouping . In segmentation problems ( e.g. , semantic segmentation ) , current methods attempt to mitigate these errors by adding post-processing layers that aim to group simultaneously the ( coarse-scale ) descriptors from the CNN backbone and the fine-level pixel data . However , the errors introduced might not always be fixed . A more natural approach to avoid this problem is to consider the coarse and fine structure together , avoiding aggregation across boundaries , to prevent errors at the outset . To avoid such errors , one could design descriptors that aggregate data only within boundaries . To this end , Khan et al . ( 2015 ) introduced “ shape-tailored ” descriptors that aggregate data within a region of interest , and used these descriptors for segmentation . However , these descriptors are hand-crafted and do not perform on-par with learned approaches . Khan & Sundaramoorthi ( 2018 ) introduced learned shape-tailored descriptors by learning a neural network operating on the input channel dimension of input hand-crafted shape-tailored descriptors for segmentation . However , these networks , though deep in the channel dimension , did not filter data spatially within layers . Since an advantage of CNNs comes from exploiting spatial filtering at each depth of the network , in this work , we design shape-tailored networks that are deep and perform shape-tailored filtering in space at each layer using solutions of the Poisson PDE . This results in shape-tailored networks that provide more discriminative descriptors than a single shape-tailored kernel . This extension requires development of techniques to back-propagate through PDEs , which we derive in this work . Our contributions are specifically : 1 . We construct and show how to train ST-DNN , deep networks that perform shape-tailored spatial filtering via the Poisson PDE at each depth so as to generalize a CNN to arbitrarily shaped regions . 2 . We show analytically and empirically that ST-DNNs are covariant to translations and rotations as they inherit this property from the Poisson PDE . In segmentation , covariance ( a.k.a. , equivariance ) to translation and rotation is a desired property : if a segment in an image is found , then the corresponding segment should be found in the translated / rotated image ( or object ) . This property is not generally present with existing CNN-based segmentation methods even when trained with augmented translated and rotated images Azulay & Weiss ( 2019 ) , and requires special consideration . 3 . We show analytically and empirically that ST-DNNs are robust to domain deformations . These result from viewpoint change or object articulation , and so they should not affect the descriptor . 4 . To demonstrate ST-DNN and the properties above , we validate them on the task of segmentation , an important problem in low-level vision Malik & Perona ( 1990 ) ; Arbelaez et al . ( 2011b ) . Because of properties of the PDE , ST-DNN also have desirable generalization properties . This is because : a ) The robustness and covariance properties are built into our descriptors and do not need to be learned from data , b ) The PDE solutions , generalizations of Gabor-like filters Olshausen & Field ( 1996 ) ; Zador ( 2019 ) , have natural image structure inherent in their solutions and so this does not need to be learned from data , and c ) Our networks have fewer parameters compared to existing networks in segmentation . This is because the PDE solutions form a basis and only linear combinations of a few basis elements are needed to learn discriminative descriptors for segmentation . In contrast , CNNs spend a lot of parameters to learn this structure . 1.1 RELATED WORK . Traditional approaches to segmentation rely on hand-crafted features , e.g. , through a filter bank Haralick & Shapiro ( 1985 ) . These features are ambiguous near the boundaries of objects . In Khan et al . ( 2015 ) hand-crafted descriptors that aggregate data within object boundaries are constructed to avoid this , but lack sufficient capacity to capture the diversity of textures or be invariant to nuisances . Deep-learning based approaches have showed state-of-the-art results in edge-based methods Xie & Tu ( 2017 ) ; He et al . ( 2019 ) ; Deng et al . ( 2018 ) . Watershed is applied on edge-maps to obtain the segmentation . The main drawback of these methods is it is often difficult to form segmentations due to extraneous or faint edges , particularly when `` textons '' in textures are large . CNNs have been applied to compute descriptors for semantic segmentation , where pixels in an image are classified into certain semantic object classes Li et al . ( 2019 ) ; Huang et al . ( 2019 ) ; Du et al . ( 2019 ) ; Pang et al . ( 2019 ) ; Zhu et al . ( 2019 ) ; Liu et al . ( 2019 ) . Usually these classes are limited to a few object classes and do not tackle general textures , where the number of classes may be far greater , and thus such approaches are not directly applicable to texture segmentation . But semantic segmentation approaches may eventually benefit from our methodology as descriptors aggregating data only within objects or regions are also relevant to these problems . A learned shape-tailored descriptor Khan & Sundaramoorthi ( 2018 ) is constructed with a Siamese network on hand-crafted shape-tailored descriptors . However , Khan & Sundaramoorthi ( 2018 ) only does shape-tailored filtering in pre-processing as layering these requires new methods to train . We further examine covariance and robustness , not examined in Khan & Sundaramoorthi ( 2018 ) . Covariance to rotation in CNNs has been examined in recent works , e.g. , Weiler et al . ( 2018 ) ; Yin et al . ( 2019 ) ; Anderson et al . ( 2019 ) . They , however , are not shape-tailored so do not aggregate data only within shaped regions . Lack of robustness to deformation ( and translation ) in CNNs is examined in Azulay & Weiss ( 2019 ) and theoretically in Bietti & Mairal ( 2017 ) . Sifre & Mallat ( 2013 ) constructs deformation robust descriptors inspired by CNNs , but are hand-crafted . 2 CONSTRUCTION OF SHAPE-TAILORED DNN AND PROPERTIES . In this section , we design a deep neural network that outputs descriptors at each pixel within an arbitrary shaped region of interest and aggregates data only from within the region . We want the descriptors to be discriminative of different texture , yet robust to nuisances within the region ( e.g. , local photometric and geometric variability ) to be useful for segmentation . Our construction uses a Poisson PDE , which naturally smooths data only within a region of interest . Smoothing naturally yields robustness to geometric nuisances ( domain deformations ) . By taking linear combinations of derivatives of the output of the PDE , we can approximate the effect of general convolutional kernels but avoid mixing data across the boundary of region of interest . ST-DNN is also covariant to translations and rotations , inheriting it from the Poisson equation , which leads to the segmentation algorithm being covariant to such transformations . 2.1 SHAPE-TAILORED DNN DESCRIPTORS THROUGH POISSON PDE . Shape-tailored Smoothing via Poisson PDE : To construct a shape-tailored deep network , we first smooth the input to a layer using the Poisson PDE so as to aggregate data only within the region of interest , similar to what is done in Khan et al . ( 2015 ) for just the first layer . Let R ⊂ Ω ⊂ R2 be the region of interest , where Ω is the domain of the input image I : Ω→ Rk and k is the number of input channels to the layer . Let u : R → RM ( M is the number of output channels ) be the result of the smoothing ; the components u of u solve the PDE within R : { u ( x ) − α∆u ( x ) = I ( x ) x ∈ R ∇u ( x ) ·N = 0 x ∈ ∂R , ( 1 ) where I is a channel of I , ∂R is the boundary of R , N is normal to ∂R , α is the scale of smoothing and ∆/∇ are the Laplacian and the gradient respectively . It can be shown that the smoothing can be written in the form u ( x ) = ∫ R K ( x , y ) I ( y ) dy where K ( . , . ) is the Green ’ s function of the PDE , a smoothing kernel , which further shows that the PDE aggregates data only within R. Shape-tailored Deep Network : We can now generalize the operation of convolution tailored to the region of interest by taking linear combinations of partial derivatives of the output of the PDE equation 1 . This is motivated by the fact that in R = R2 , linear combinations of derivatives of Gaussians can approximate any kernel arbitrarily well . Gaussian filters are the solution of the heat equation , and the PDE equation 1 relates to the heat equation , i.e. , equation 1 is the steady state solution of a heat equation . Thus , linear combinations of derivatives of equation 1 generalize convolution to an arbitrary shape R ; in experiments , a few first order directional derivatives are sufficient for our segmentation tasks ( see Section 5 for details ) . A layer of the ST-DNN takes such linear combinations and rectifies it as follows : fi ( x ) = r ◦ Li ◦ T [ I ] ( x ) , ( 2 ) where I : R→ Rk is the input to the layer , T is an operator that outputs derivatives of the solution of the Poisson PDE equation 1 , Li ( y ) = wiy+bi is a point-wise linear function ( i.e. , a 1×1 convolution applied to combine different channels ) , r is the rectified linear function , and i indexes the layer of the network . Notice that since r and Li are pointwise operations , they preserve the property of T that it aggregates data only within the region R. We now compose layers to construct a ST-DNN as follows : F [ I ] ( x ) = s ◦ fm ◦ fm−1 ◦ fm−2 ◦ .... f0 ◦ I ( x ) , ( 3 ) where F [ I ] ( x ) is the output of the ST-DNN , f0 , ... , fm are the m+ 1 layers of the network , I is the input image , and s represents the soft-max operation ( to bound the output values ) . ST-DNN does not have a pooling layer because the PDE already aggregates data from a neighborhood by smoothing ; further , the lack of reduction in spatial dimension allows for more accurate shape estimation in our subsequent segmentation , and avoids the need for up-sampling layers . We will show that we can retain efficiency in training and inference .",
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"content": "The paper proposed a new \"shape-tailored\" convolutional layer for improving the accuracy of semantic segmentation. The shape-tailored layer is inspired by the Poisson partial differential equation which aggregate features from neighboring pixels through the linear combinations of partial derivatives of the output of PDEs. The paper proves several properties of the proposed ST-DNN and demonstrated good results in comparisons with previous semantic segmentation algorithms.",
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science.evidence_inference | Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:
- Treatment: The specific medical procedure or drug being evaluated.
- Control: The standard or placebo used for comparison. If absent, use `null`.
- Result: The health outcome being assessed.
- Impact: The observed effect of the treatment on the result, categorized as: ("increased", "decreased", "no change").
- Justification: The supporting details for the observed impact.
Format the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].
Abstract:
ABSTRACT.BACKGROUND:: Ketamine, a non-competitive NMDA (N-Methyl-D-Aspartate) receptor antagonist, is recognized as an intraoperative anesthetic agent. Increasing interest in the use of low-dose ketamine for postoperative analgesia has developed in part because of its NMDA-antagonistic properties, which may be important in attenuating central sensitization and opioid tolerance. Despite of many trial evaluations which have been done on the effect of low-dose ketamine in postoperative pain, the role of ketamine, as a component of perioperative analgesia, remains unclear. We evaluated the analgesic effect of low-dose ketamine during anesthesia induction in painful ophthalmic surgery.
ABSTRACT.MATERIALS AND METHODS:: After institutional approval and written informed consent, 88 patients undergoing retinal detachment, strabismus, and keratoplasty surgery aged 18-80 years old were randomly divided intoequal case and control groups. Anesthesia was induced with sodium thiopental, fentanyl, atracurium, and liducaine, and maintained with N2O, O2, and propofol. Ketamine 0.5 mg/kg was administered intravenously to patients in the case group during anesthetic induction. Mean blood pressure and pulse rate were listed in questionnaire every 5 minutes. The consumption of anesthetic, perioperative additional analgesic, extubation time, postoperative pain and nausea scores (based on Visual Analog Scale), vomiting frequency, and the recovery time were recorded.
ABSTRACT.RESULTS:: There were no differences in the recovery time (17.3 ± 3.4 in the case group vs. 16.3 ± 3 in the control group, P < 0.05), postoperative pain scores (5 ± 1 in the case group vs. 5.6 ± 2 in the control group, P < 0.05), the consumption of anesthetic (9376.9 ± 1245.8 in the case group vs. 9012.9 ± 1620 in the control group, P < 0.05), the analgesic requirements (1000 in the case group vs. 940.9 ± 135.6 in the control group, P < 0.05), and perioperative additional analgesic (63.4 ± 26.5 in the case group vs. 69.4 ± 25.6 in the control group, P < 0.05) between two groups. The extubation time in the case group (13.59 ± 4.83) was significantly shorter than in the control group (15.9 ± 3.6) (P = 0.01).
ABSTRACT.CONCLUSION:: This study demonstrates that a low dose administration of ketamine during anesthesia induction in retinal detachment, strabismus, and keratoplasty surgery improves the extubation time but have no effect on postoperative pain, nausea and vomiting, and perioperative additional analgesic requirements.
Extracted Data: | [["Ketamine", "Control", "Recovery time", "no significant difference", "There were no differences in the recovery time (17.3 \u00b1 3.4 in the case group vs. 16.3 \u00b1 3 in the control group, P \u003c 0.05),"], ["Ketamine", "Control", "Post-Surgical Pain", "no significant difference", "There were no differences in the recovery time (17.3 \u00b1 3.4 in the case group vs. 16.3 \u00b1 3 in the control group, P \u003c 0.05), postoperative pain scores (5 \u00b1 1 in the case group vs. 5.6 \u00b1 2 in the control group, P \u003c 0.05),"], ["Ketamine", "Control", "Anesthetic usage", "no significant difference", "There were no differences in the recovery time (17.3 \u00b1 3.4 in the case group vs. 16.3 \u00b1 3 in the control group, P \u003c 0.05), postoperative pain scores (5 \u00b1 1 in the case group vs. 5.6 \u00b1 2 in the control group, P \u003c 0.05), the consumption of anesthetic (9376.9 \u00b1 1245.8 in the case group vs. 9012.9 \u00b1 1620 in the control group, P \u003c 0.05),"], ["Ketamine", "Control", "Usage of analgesics", "no significant difference", "the analgesic requirements (1000 in the case group vs. 940.9 \u00b1 135.6 in the control group"], ["Ketamine", "Control", "Time for extubation ", "significantly decreased", "The extubation time in the case group (13.59 \u00b1 4.83) was significantly shorter than in the control group (15.9 \u00b1 3.6) (P = 0.01)."]] | science.evidence_inference.63 | [
{
"content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND:: Ketamine, a non-competitive NMDA (N-Methyl-D-Aspartate) receptor antagonist, is recognized as an intraoperative anesthetic agent. Increasing interest in the use of low-dose ketamine for postoperative analgesia has developed in part because of its NMDA-antagonistic properties, which may be important in attenuating central sensitization and opioid tolerance. Despite of many trial evaluations which have been done on the effect of low-dose ketamine in postoperative pain, the role of ketamine, as a component of perioperative analgesia, remains unclear. We evaluated the analgesic effect of low-dose ketamine during anesthesia induction in painful ophthalmic surgery.\n\nABSTRACT.MATERIALS AND METHODS:: After institutional approval and written informed consent, 88 patients undergoing retinal detachment, strabismus, and keratoplasty surgery aged 18-80 years old were randomly divided intoequal case and control groups. Anesthesia was induced with sodium thiopental, fentanyl, atracurium, and liducaine, and maintained with N2O, O2, and propofol. Ketamine 0.5 mg/kg was administered intravenously to patients in the case group during anesthetic induction. Mean blood pressure and pulse rate were listed in questionnaire every 5 minutes. The consumption of anesthetic, perioperative additional analgesic, extubation time, postoperative pain and nausea scores (based on Visual Analog Scale), vomiting frequency, and the recovery time were recorded.\n\nABSTRACT.RESULTS:: There were no differences in the recovery time (17.3 ± 3.4 in the case group vs. 16.3 ± 3 in the control group, P < 0.05), postoperative pain scores (5 ± 1 in the case group vs. 5.6 ± 2 in the control group, P < 0.05), the consumption of anesthetic (9376.9 ± 1245.8 in the case group vs. 9012.9 ± 1620 in the control group, P < 0.05), the analgesic requirements (1000 in the case group vs. 940.9 ± 135.6 in the control group, P < 0.05), and perioperative additional analgesic (63.4 ± 26.5 in the case group vs. 69.4 ± 25.6 in the control group, P < 0.05) between two groups. The extubation time in the case group (13.59 ± 4.83) was significantly shorter than in the control group (15.9 ± 3.6) (P = 0.01).\n\nABSTRACT.CONCLUSION:: This study demonstrates that a low dose administration of ketamine during anesthesia induction in retinal detachment, strabismus, and keratoplasty surgery improves the extubation time but have no effect on postoperative pain, nausea and vomiting, and perioperative additional analgesic requirements.\n\nExtracted Data:",
"role": "user"
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"content": "[[\"Ketamine\", \"Control\", \"Recovery time\", \"no significant difference\", \"There were no differences in the recovery time (17.3 \\u00b1 3.4 in the case group vs. 16.3 \\u00b1 3 in the control group, P \\u003c 0.05),\"], [\"Ketamine\", \"Control\", \"Post-Surgical Pain\", \"no significant difference\", \"There were no differences in the recovery time (17.3 \\u00b1 3.4 in the case group vs. 16.3 \\u00b1 3 in the control group, P \\u003c 0.05), postoperative pain scores (5 \\u00b1 1 in the case group vs. 5.6 \\u00b1 2 in the control group, P \\u003c 0.05),\"], [\"Ketamine\", \"Control\", \"Anesthetic usage\", \"no significant difference\", \"There were no differences in the recovery time (17.3 \\u00b1 3.4 in the case group vs. 16.3 \\u00b1 3 in the control group, P \\u003c 0.05), postoperative pain scores (5 \\u00b1 1 in the case group vs. 5.6 \\u00b1 2 in the control group, P \\u003c 0.05), the consumption of anesthetic (9376.9 \\u00b1 1245.8 in the case group vs. 9012.9 \\u00b1 1620 in the control group, P \\u003c 0.05),\"], [\"Ketamine\", \"Control\", \"Usage of analgesics\", \"no significant difference\", \"the analgesic requirements (1000 in the case group vs. 940.9 \\u00b1 135.6 in the control group\"], [\"Ketamine\", \"Control\", \"Time for extubation \", \"significantly decreased\", \"The extubation time in the case group (13.59 \\u00b1 4.83) was significantly shorter than in the control group (15.9 \\u00b1 3.6) (P = 0.01).\"]]",
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science.biored_ner | Analyze the provided biomedical abstract and identify distinct entities. Focus on the specified categories: ["CellLine", "Variant", "Species", "Disease", "Chemical", "Gene"].
Ensure the output is strictly the JSON object.
Source Text:
Sex-differential genetic effect of phosphodiesterase 4D (PDE4D) on carotid atherosclerosis. BACKGROUND: The phosphodiesterase 4D (PDE4D) gene was reported as a susceptibility gene to stroke. The genetic effect might be attributed to its role in modulating the atherogenic process in the carotid arteries. Using carotid intima-media thickness (IMT) and plaque index as phenotypes, the present study sought to determine the influence of this gene on subclinical atherosclerosis. METHODS: Carotid ultrasonography was performed on 1013 stroke-free subjects who participated in the health screening programs (age 52.6 +/- 12.2; 47.6% men). Genotype distribution was compared among the high-risk (plaque index > or = 4), low-risk (index = 1-3), and reference (index = 0) groups. We analyzed continuous IMT data and further dichotomized IMT data using mean plus one standard deviation as the cutoff level. Because the plaque prevalence and IMT values displayed a notable difference between men and women, we carried out sex-specific analyses in addition to analyzing the overall data. Rs702553 at the PDE4D gene was selected because it conferred a risk for young stroke in our previous report. Previous young stroke data (190 cases and 211 controls) with an additional 532 control subjects without ultrasonic data were shown as a cross-validation for the genetic effect. RESULTS: In the overall analyses, the rare homozygote of rs702553 led to an OR of 3.1 (p = 0.034) for a plaque index > or = 4. When subjects were stratified by sex, the genetic effect was only evident in men but not in women. Comparing male subjects with plaque index > or = 4 and those with plaque index = 0, the TT genotype was over-represented (27.6% vs. 13.4%, p = 0.008). For dichotomized IMT data in men, the TT genotype had an OR of 2.1 (p = 0.032) for a thicker IMT at the common carotid artery compared with the (AA + AT) genotypes. In women, neither IMT nor plaque index was associated with rs702553. Similarly, SNP rs702553 was only significant in young stroke men (OR = 1.8, p = 0.025) but not in women (p = 0.27). CONCLUSIONS: The present study demonstrates a sex-differential effect of PDE4D on IMT, plaque index and stroke, which highlights its influence on various aspects of atherogenesis. | {"CellLine": [], "Chemical": [], "Disease": ["atherosclerosis", "atherogenesis", "stroke", "carotid atherosclerosis", "atherogenic"], "Gene": ["PDE4D", "phosphodiesterase 4D"], "Species": ["men", "women"], "Variant": ["rs702553", "Rs702553"]} | science.biored_ner.34 | [
{
"content": "Analyze the provided biomedical abstract and identify distinct entities. Focus on the specified categories: [\"CellLine\", \"Variant\", \"Species\", \"Disease\", \"Chemical\", \"Gene\"].\n\nEnsure the output is strictly the JSON object.\n\nSource Text:\n\nSex-differential genetic effect of phosphodiesterase 4D (PDE4D) on carotid atherosclerosis. BACKGROUND: The phosphodiesterase 4D (PDE4D) gene was reported as a susceptibility gene to stroke. The genetic effect might be attributed to its role in modulating the atherogenic process in the carotid arteries. Using carotid intima-media thickness (IMT) and plaque index as phenotypes, the present study sought to determine the influence of this gene on subclinical atherosclerosis. METHODS: Carotid ultrasonography was performed on 1013 stroke-free subjects who participated in the health screening programs (age 52.6 +/- 12.2; 47.6% men). Genotype distribution was compared among the high-risk (plaque index > or = 4), low-risk (index = 1-3), and reference (index = 0) groups. We analyzed continuous IMT data and further dichotomized IMT data using mean plus one standard deviation as the cutoff level. Because the plaque prevalence and IMT values displayed a notable difference between men and women, we carried out sex-specific analyses in addition to analyzing the overall data. Rs702553 at the PDE4D gene was selected because it conferred a risk for young stroke in our previous report. Previous young stroke data (190 cases and 211 controls) with an additional 532 control subjects without ultrasonic data were shown as a cross-validation for the genetic effect. RESULTS: In the overall analyses, the rare homozygote of rs702553 led to an OR of 3.1 (p = 0.034) for a plaque index > or = 4. When subjects were stratified by sex, the genetic effect was only evident in men but not in women. Comparing male subjects with plaque index > or = 4 and those with plaque index = 0, the TT genotype was over-represented (27.6% vs. 13.4%, p = 0.008). For dichotomized IMT data in men, the TT genotype had an OR of 2.1 (p = 0.032) for a thicker IMT at the common carotid artery compared with the (AA + AT) genotypes. In women, neither IMT nor plaque index was associated with rs702553. Similarly, SNP rs702553 was only significant in young stroke men (OR = 1.8, p = 0.025) but not in women (p = 0.27). CONCLUSIONS: The present study demonstrates a sex-differential effect of PDE4D on IMT, plaque index and stroke, which highlights its influence on various aspects of atherogenesis.",
"role": "user"
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{
"content": "{\"CellLine\": [], \"Chemical\": [], \"Disease\": [\"atherosclerosis\", \"atherogenesis\", \"stroke\", \"carotid atherosclerosis\", \"atherogenic\"], \"Gene\": [\"PDE4D\", \"phosphodiesterase 4D\"], \"Species\": [\"men\", \"women\"], \"Variant\": [\"rs702553\", \"Rs702553\"]}",
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science.evidence_inference | Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:
- Treatment: The specific medical procedure or drug being evaluated.
- Control: The standard or placebo used for comparison. If absent, use `null`.
- Result: The health outcome being assessed.
- Impact: The observed effect of the treatment on the result, categorized as: ("increased", "decreased", "no change").
- Justification: The supporting details for the observed impact.
Format the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].
Abstract:
ABSTRACT.BACKGROUND: Dietary trans-rich and interesterified fats were compared to an unmodified saturated fat for their relative impact on blood lipids and plasma glucose. Each fat had melting characteristics, plasticity and solids fat content suitable for use as hardstock in margarine and other solid fat formulations.
ABSTRACT.METHODS: Thirty human volunteers were fed complete, whole food diets during 4 wk periods, where total fat (~31% daily energy, >70% from the test fats) and fatty acid composition were tightly controlled. A crossover design was used with 3 randomly-assigned diet rotations and repeated-measures analysis. One test fat rotation was based on palm olein (POL) and provided 12.0 percent of energy (%en) as palmitic acid (16:0); a second contained trans-rich partially hydrogenated soybean oil (PHSO) and provided 3.2 %en as trans fatty acids plus 6.5 %en as 16:0, while the third used an interesterified fat (IE) and provided 12.5 %en as stearic acid (18:0). After 4 wk the plasma lipoproteins, fatty acid profile, as well as fasting glucose and insulin were assessed. In addition, after 2 wk into each period an 8 h postprandial challenge was initiated in a subset of 19 subjects who consumed a meal containing 53 g of test fat.
ABSTRACT.RESULTS: After 4 wk, both PHSO and IE fats significantly elevated both the LDL/HDL ratio and fasting blood glucose, the latter almost 20% in the IE group relative to POL. Fasting 4 wk insulin was 10% lower after PHSO (p > 0.05) and 22% lower after IE (p < 0.001) compared to POL. For the postprandial study the glucose incremental area under the curve (IAUC) following the IE meal was 40% greater than after either other meal (p < 0.001), and was linked to relatively depressed insulin and C-peptide (p < 0.05).
ABSTRACT.CONCLUSION: Both PHSO and IE fats altered the metabolism of lipoproteins and glucose relative to an unmodified saturated fat when fed to humans under identical circumstances.
Extracted Data: | [["partially hydrogenated soybean oil (PHSO) , interesterified fat (IE)", "palm olein (POL)", "LDL/HDL ratio and fasting blood glucose", "significantly increased", "After 4 wk, both PHSO and IE fats significantly elevated both the LDL/HDL ratio and fasting blood glucose, the latter almost 20% in the IE group relative to POL."], ["partially hydrogenated soybean oil (PHSO) , interesterified fat (IE)", "palm olein (POL)", "insulin", "significantly decreased", "Fasting 4 wk insulin was 10% lower after PHSO (p \u0026gt; 0.05) and 22% lower after IE (p \u0026lt; 0.001) compared to POL."], ["partially hydrogenated soybean oil (PHSO) , interesterified fat (IE)", "palm olein (POL)", "insulin", "significantly decreased", "Fasting 4 wk insulin was 10% lower after PHSO (p \u003e 0.05) and 22% lower after IE (p \u003c 0.001) compared to POL"], [" interesterified fat (IE)", "palm olein (POL), partially hydrogenated soybean oil (PHSO) ,", "postprandial study the glucose incremental area", "significantly increased", "For the postprandial study the glucose incremental area under the curve (IAUC) following the IE meal was 40% greater than after either other meal (p \u0026lt; 0.001),"], [" interesterified fat (IE)", "palm olein (POL), partially hydrogenated soybean oil (PHSO) ,", "postprandial study the glucose incremental area", "significantly increased", "For the postprandial study the glucose incremental area under the curve (IAUC) following the IE meal was 40% greater than after either other meal (p \u003c 0.001),"], [" interesterified fat (IE)", "palm olein (POL), partially hydrogenated soybean oil (PHSO) ,", "insulin and C-peptide", "significantly decreased", "For the postprandial study the glucose incremental area under the curve (IAUC) following the IE meal was 40% greater than after either other meal (p \u0026lt; 0.001), and was linked to relatively depressed insulin and C-peptide (p \u0026lt; 0.05)."], [" interesterified fat (IE)", "palm olein (POL), partially hydrogenated soybean oil (PHSO) ,", "insulin and C-peptide", "significantly decreased", "For the postprandial study the glucose incremental area under the curve (IAUC) following the IE meal was 40% greater than after either other meal (p \u003c 0.001), and was linked to relatively depressed insulin and C-peptide (p \u003c 0.05)."]] | science.evidence_inference.194 | [
{
"content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND: Dietary trans-rich and interesterified fats were compared to an unmodified saturated fat for their relative impact on blood lipids and plasma glucose. Each fat had melting characteristics, plasticity and solids fat content suitable for use as hardstock in margarine and other solid fat formulations.\n\nABSTRACT.METHODS: Thirty human volunteers were fed complete, whole food diets during 4 wk periods, where total fat (~31% daily energy, >70% from the test fats) and fatty acid composition were tightly controlled. A crossover design was used with 3 randomly-assigned diet rotations and repeated-measures analysis. One test fat rotation was based on palm olein (POL) and provided 12.0 percent of energy (%en) as palmitic acid (16:0); a second contained trans-rich partially hydrogenated soybean oil (PHSO) and provided 3.2 %en as trans fatty acids plus 6.5 %en as 16:0, while the third used an interesterified fat (IE) and provided 12.5 %en as stearic acid (18:0). After 4 wk the plasma lipoproteins, fatty acid profile, as well as fasting glucose and insulin were assessed. In addition, after 2 wk into each period an 8 h postprandial challenge was initiated in a subset of 19 subjects who consumed a meal containing 53 g of test fat.\n\nABSTRACT.RESULTS: After 4 wk, both PHSO and IE fats significantly elevated both the LDL/HDL ratio and fasting blood glucose, the latter almost 20% in the IE group relative to POL. Fasting 4 wk insulin was 10% lower after PHSO (p > 0.05) and 22% lower after IE (p < 0.001) compared to POL. For the postprandial study the glucose incremental area under the curve (IAUC) following the IE meal was 40% greater than after either other meal (p < 0.001), and was linked to relatively depressed insulin and C-peptide (p < 0.05).\n\nABSTRACT.CONCLUSION: Both PHSO and IE fats altered the metabolism of lipoproteins and glucose relative to an unmodified saturated fat when fed to humans under identical circumstances.\n\nExtracted Data:",
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"content": "[[\"partially hydrogenated soybean oil (PHSO) , interesterified fat (IE)\", \"palm olein (POL)\", \"LDL/HDL ratio and fasting blood glucose\", \"significantly increased\", \"After 4 wk, both PHSO and IE fats significantly elevated both the LDL/HDL ratio and fasting blood glucose, the latter almost 20% in the IE group relative to POL.\"], [\"partially hydrogenated soybean oil (PHSO) , interesterified fat (IE)\", \"palm olein (POL)\", \"insulin\", \"significantly decreased\", \"Fasting 4 wk insulin was 10% lower after PHSO (p \\u0026gt; 0.05) and 22% lower after IE (p \\u0026lt; 0.001) compared to POL.\"], [\"partially hydrogenated soybean oil (PHSO) , interesterified fat (IE)\", \"palm olein (POL)\", \"insulin\", \"significantly decreased\", \"Fasting 4 wk insulin was 10% lower after PHSO (p \\u003e 0.05) and 22% lower after IE (p \\u003c 0.001) compared to POL\"], [\" interesterified fat (IE)\", \"palm olein (POL), partially hydrogenated soybean oil (PHSO) ,\", \"postprandial study the glucose incremental area\", \"significantly increased\", \"For the postprandial study the glucose incremental area under the curve (IAUC) following the IE meal was 40% greater than after either other meal (p \\u0026lt; 0.001),\"], [\" interesterified fat (IE)\", \"palm olein (POL), partially hydrogenated soybean oil (PHSO) ,\", \"postprandial study the glucose incremental area\", \"significantly increased\", \"For the postprandial study the glucose incremental area under the curve (IAUC) following the IE meal was 40% greater than after either other meal (p \\u003c 0.001),\"], [\" interesterified fat (IE)\", \"palm olein (POL), partially hydrogenated soybean oil (PHSO) ,\", \"insulin and C-peptide\", \"significantly decreased\", \"For the postprandial study the glucose incremental area under the curve (IAUC) following the IE meal was 40% greater than after either other meal (p \\u0026lt; 0.001), and was linked to relatively depressed insulin and C-peptide (p \\u0026lt; 0.05).\"], [\" interesterified fat (IE)\", \"palm olein (POL), partially hydrogenated soybean oil (PHSO) ,\", \"insulin and C-peptide\", \"significantly decreased\", \"For the postprandial study the glucose incremental area under the curve (IAUC) following the IE meal was 40% greater than after either other meal (p \\u003c 0.001), and was linked to relatively depressed insulin and C-peptide (p \\u003c 0.05).\"]]",
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science.qasper_abstractive_qa | Examine the provided sections from a research document and address the accompanying query. The text is divided by newlines.
Document Content:
Attention-based method for categorizing different types of online harassment language
In the era of social media and networking platforms, Twitter has been doomed for abuse and harassment toward users specifically women. Monitoring the contents including sexism and sexual harassment in traditional media is easier than monitoring on the online social media platforms like Twitter, because of the large amount of user generated content in these media. So, the research about the automated detection of content containing sexual or racist harassment is an important issue and could be the basis for removing that content or flagging it for human evaluation. Previous studies have been focused on collecting data about sexism and racism in very broad terms. However, there is not much study focusing on different types of online harassment alone attracting natural language processing techniques. In this work, we present an attention-based approach for the detection of harassment in tweets and the detection of different types of harassment as well. Our approach is based on the Recurrent Neural Networks and particularly we are using a deep, classification specific attention mechanism. Moreover, we present a comparison between different variations of this attention-based approach.
Introduction
In the era of social media and networking platforms, Twitter has been doomed for abuse and harassment toward users specifically women. In fact, online harassment becomes very common in Twitter and there have been a lot of critics that Twitter has become the platform for many racists, misogynists and hate groups which can express themselves openly. Online harassment is usually in the form of verbal or graphical formats and is considered harassment, because it is neither invited nor has the consent of the receipt. Monitoring the contents including sexism and sexual harassment in traditional media is easier than monitoring on the online social media platforms like Twitter. The main reason is because of the large amount of user generated content in these media. So, the research about the automated detection of content containing sexual harassment is an important issue and could be the basis for removing that content or flagging it for human evaluation. The basic goal of this automatic classification is that it will significantly improve the process of detecting these types of hate speech on social media by reducing the time and effort required by human beings.
Previous studies have been focused on collecting data about sexism and racism in very broad terms or have proposed two categories of sexism as benevolent or hostile sexism BIBREF0, which undermines other types of online harassment. However, there is no much study focusing on different types online harassment alone attracting natural language processing techniques.
In this paper we present our work, which is a part of the SociaL Media And Harassment Competition of the ECML PKDD 2019 Conference. The topic of the competition is the classification of different types of harassment and it is divided in two tasks. The first one is the classification of the tweets in harassment and non-harassment categories, while the second one is the classification in specific harassment categories like indirect harassment, physical and sexual harassment as well. We are using the dataset of the competition, which includes text from tweets having the aforementioned categories. Our approach is based on the Recurrent Neural Networks and particularly we are using a deep, classification specific attention mechanism. Moreover, we present a comparison between different variations of this attention-based approach like multi-attention and single attention models. The next Section includes a short description of the related work, while the third Section includes a description of the dataset. After that, we describe our methodology. Finally, we describe the experiments and we present the results and our conclusion.
Related Work
Waseem et al. BIBREF1 were the first who collected hateful tweets and categorized them into being sexist, racist or neither. However, they did not provide specific definitions for each category. Jha and Mamidi BIBREF0 focused on just sexist tweets and proposed two categories of hostile and benevolent sexism. However, these categories were general as they ignored other types of sexism happening in social media. Sharifirad S. and Matwin S. BIBREF2 proposed complimentary categories of sexist language inspired from social science work. They categorized the sexist tweets into the categories of indirect harassment, information threat, sexual harassment and physical harassment. In the next year the same authors proposed BIBREF3 a more comprehensive categorization of online harassment in social media e.g. twitter into the following categories, indirect harassment, information threat, sexual harassment, physical harassment and not sexist.
For the detection of hate speech in social media like twitter, many approaches have been proposed. Jha and Mamidi BIBREF0 tested support vector machine, bi-directional RNN encoder-decoder and FastText on hostile and benevolent sexist tweets. They also used SentiWordNet and subjectivity lexicon on the extracted phrases to show the polarity of the tweets. Sharifirad et al. BIBREF4 trained, tested and evaluated different classification methods on the SemEval2018 dataset and chose the classifier with the highest accuracy for testing on each category of sexist tweets to know the mental state and the affectual state of the user who tweets in each category. To overcome the limitations of small data sets on sexist speech detection, Sharifirad S. et al. BIBREF5 have applied text augmentation and text generation with certain success. They have generated new tweets by replacing words in order to increase the size of our training set. Moreover, in the presented text augmentation approach, the number of tweets in each class remains the same, but their words are augmented with words extracted from their ConceptNet relations and their description extracted from Wikidata. Zhang et al. BIBREF6 combined convolutional and gated recurrent networks to detect hate speech in tweets. Others have proposed different methods, which are not based on deep learning. Burnap and Williams BIBREF7 used Support Vector Machines, Random Forests and a meta-classifier to distinguish between hateful and non-hateful messages. A survey of recent research in the field is presented in BIBREF8. For the problem of the hate speech detection a few approaches have been proposed that are based on the Attention mechanism. Pavlopoulos et al. BIBREF9 have proposed a novel, classification-specific attention mechanism that improves the performance of the RNN further for the detection of abusive content in the web. Xie et al. BIBREF10 for emotion intensity prediction, which is a similar problem to ours, have proposed a novel attention mechanism for CNN model that associates attention-based weights for every convolution window. Park and Fung BIBREF11 transformed the classification into a 2-step problem, where abusive text first is distinguished from the non-abusive, and then the class of abuse (Sexism or Racism) is determined. However, while the first part of the two step classification performs quite well, it falls short in detecting the particular class the abusive text belongs to. Pitsilis et al. BIBREF12 have proposed a detection scheme that is an ensemble of RNN classifiers, which incorporates various features associated with user related information, such as the users’ tendency towards racism or sexism
Dataset description
The dataset from Twitter that we are using in our work, consists of a train set, a validation set and a test set. It was published for the "First workshop on categorizing different types of online harassment languages in social media". The whole dataset is divided into two categories, which are harassment and non-harassment tweets. Moreover, considering the type of the harassment, the tweets are divided into three sub-categories which are indirect harassment, sexual and physical harassment. We can see in Table TABREF1 the class distribution of our dataset. One important issue here is that the categories of indirect and physical harassment seem to be more rare in the train set than in the validation and test sets. To tackle this issue, as we describe in the next section, we are performing data augmentation techniques. However, the dataset is imbalanced and this has a significant impact in our results.
Proposed methodology ::: Text processing
Before training our models we are processing the given tweets using a tweet pre-processor. The scope here is the cleaning and tokenization of the dataset.
Proposed methodology ::: RNN Model and Attention Mechanism
We are presenting an attention-based approach for the problem of the harassment detection in tweets. In this section, we describe the basic approach of our work. We are using RNN models because of their ability to deal with sequence information. The RNN model is a chain of GRU cells BIBREF15 that transforms the tokens $w_{1}, w_{2},..., w_{k}$ of each tweet to the hidden states $h_{1}, h_{2},..., h_{k}$, followed by an LR Layer that uses $h_{k}$ to classify the tweet as harassment or non-harassment (similarly for the other categories). Given the vocabulary V and a matrix E $\in $ $R^{d \times \vert V \vert }$ containing d-dimensional word embeddings, an initial $h_{0}$ and a tweet $w = <w_{1},.., w_{k}>$, the RNN computes $h_{1}, h_{2},..., h_{k}$, with $h_{t} \in R^{m}$, as follows:
where $h^{^{\prime }}_{t} \in R^{m}$ is the proposed hidden state at position t, obtained using the word embedding $x_{t}$ of token $w_{t}$ and the previous hidden state $h_{t-1}$, $\odot $ represents the element-wise multiplication, $r_{t} \in R^{m}$ is the reset gate, $z_{t} \in R^{m}$ is the update gate, $\sigma $ is the sigmoid function. Also $W_{h}, W_{z}, W_{r} \in R^{m \times d}$ and $U_{h}, U_{z}, U_{r} \in R^{m \times m}$, $b_{h}, b_{z}, b_{r} \in R^{m}$. After the computation of state $h_{k}$ the LR Layer estimates the probability that tweet w should be considered as harassment, with $W_{p} \in R^{1 \times m}, b_{p} \in R$:
We would like to add an attention mechanism similar to the one presented in BIBREF9, so that the LR Layer will consider the weighted sum $h_{sum}$ of all the hidden states instead of $h_{k}$:
$h_{sum} = \sum _{t=1}^{k} \alpha _{t}h_{t}$
$P_{attentionRNN} = \sigma (W_{p}h_{sum} + b_{p})$
Alternatively, we could pass $h_{sum}$ through an MLP with k layers and then the LR layer will estimate the corresponding probability. More formally,
$P_{attentionRNN} = \sigma (W_{p}h_{*} + b_{p})$
where $h_{*}$ is the state that comes out from the MLP. The weights $\alpha _{t}$ are produced by an attention mechanism presented in BIBREF9 (see Fig. FIGREF7), which is an MLP with l layers. This attention mechanism differs from most previous ones BIBREF16, BIBREF17, because it is used in a classification setting, where there is no previously generated output sub-sequence to drive the attention. It assigns larger weights $\alpha _{t}$ to hidden states $h_{t}$ corresponding to positions, where there is more evidence that the tweet should be harassment (or any other specific type of harassment) or not. In our work we are using four attention mechanisms instead of one that is presented in BIBREF9. Particularly, we are using one attention mechanism per category. Another element that differentiates our approach from Pavlopoulos et al. BIBREF9 is that we are using a projection layer for the word embeddings (see Fig. FIGREF2). In the next subsection we describe the Model Architecture of our approach.
Experiments ::: Evaluation and Results
Each model produces four scores and each score is the probability that a tweet includes harassment language, indirect, physical and sexual harassment language respectively. For any tweet, we first check the score of the harassment language and if it is less than a specified threshold, then the harassment label is zero, so the other three labels are zero as well. If it is greater than or equal to that threshold, then the harassment label is one and the type of harassment is the one among these three having that has the greatest score (highest probability). We set this threshold equal to 0.33.
We compare eight different models in our experiments. Four of them have a Projected Layer (see Fig. FIGREF2), while the others do not have, and this is the only difference between these two groups of our models. So, we actually include four models in our experiments (having a projected layer or not). Firstly, LastStateRNN is the classic RNN model, where the last state passes through an MLP and then the LR Layer estimates the corresponding probability. In contrast, in the AvgRNN model we consider the average vector of all states that come out of the cells. The AttentionRNN model is the one that it has been presented in BIBREF9. Moreover, we introduce the MultiAttentionRNN model for the harassment language detection, which instead of one attention, it includes four attentions, one for each category.
We have evaluated our models considering the F1 Score, which is the harmonic mean of precision and recall. We have run ten times the experiment for each model and considered the average F1 Score. The results are mentioned in Table TABREF11. Considering F1 Macro the models that include the multi-attention mechanism outperform the others and particularly the one with the Projected Layer has the highest performance. In three out of four pairs of models, the ones with the Projected Layer achieved better performance, so in most cases the addition of the Projected Layer had a significant enhancement.
Conclusion - Future work
We present an attention-based approach for the detection of harassment language in tweets and the detection of different types of harassment as well. Our approach is based on the Recurrent Neural Networks and particularly we are using a deep, classification specific attention mechanism. Moreover, we present a comparison between different variations of this attention-based approach and a few baseline methods. According to the results of our experiments and considering the F1 Score, the multi-attention method having a projected layer, achieved the highest performance. Also, we tackled the problem of the imbalance between the training, validation and test sets performing the technique of back-translation.
In the future, we would like to perform more experiments with this dataset applying different models using BERT BIBREF21. Also, we would like to apply the models presented in this work, in other datasets about hate speech in social media.
Table 1. Class distribution of the dataset.
Fig. 1. Projection Layer
Query: What were the datasets used in this paper?
JSON Response: | {"answer": "The dataset from the SociaL Media And Harassment Competition of the ECML PKDD 2019 Conference. ", "evidence": ["In this paper we present our work, which is a part of the SociaL Media And Harassment Competition of the ECML PKDD 2019 Conference. The topic of the competition is the classification of different types of harassment and it is divided in two tasks. The first one is the classification of the tweets in harassment and non-harassment categories, while the second one is the classification in specific harassment categories like indirect harassment, physical and sexual harassment as well. We are using the dataset of the competition, which includes text from tweets having the aforementioned categories. Our approach is based on the Recurrent Neural Networks and particularly we are using a deep, classi\ufb01cation specific attention mechanism. Moreover, we present a comparison between different variations of this attention-based approach like multi-attention and single attention models. The next Section includes a short description of the related work, while the third Section includes a description of the dataset. After that, we describe our methodology. Finally, we describe the experiments and we present the results and our conclusion."]} | science.qasper_abstractive_qa.113.3 | [
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"content": "Examine the provided sections from a research document and address the accompanying query. The text is divided by newlines.\n\nDocument Content:\n\nAttention-based method for categorizing different types of online harassment language\n\nIn the era of social media and networking platforms, Twitter has been doomed for abuse and harassment toward users specifically women. Monitoring the contents including sexism and sexual harassment in traditional media is easier than monitoring on the online social media platforms like Twitter, because of the large amount of user generated content in these media. So, the research about the automated detection of content containing sexual or racist harassment is an important issue and could be the basis for removing that content or flagging it for human evaluation. Previous studies have been focused on collecting data about sexism and racism in very broad terms. However, there is not much study focusing on different types of online harassment alone attracting natural language processing techniques. In this work, we present an attention-based approach for the detection of harassment in tweets and the detection of different types of harassment as well. Our approach is based on the Recurrent Neural Networks and particularly we are using a deep, classification specific attention mechanism. Moreover, we present a comparison between different variations of this attention-based approach.\n\nIntroduction\nIn the era of social media and networking platforms, Twitter has been doomed for abuse and harassment toward users specifically women. In fact, online harassment becomes very common in Twitter and there have been a lot of critics that Twitter has become the platform for many racists, misogynists and hate groups which can express themselves openly. Online harassment is usually in the form of verbal or graphical formats and is considered harassment, because it is neither invited nor has the consent of the receipt. Monitoring the contents including sexism and sexual harassment in traditional media is easier than monitoring on the online social media platforms like Twitter. The main reason is because of the large amount of user generated content in these media. So, the research about the automated detection of content containing sexual harassment is an important issue and could be the basis for removing that content or flagging it for human evaluation. The basic goal of this automatic classification is that it will significantly improve the process of detecting these types of hate speech on social media by reducing the time and effort required by human beings.\nPrevious studies have been focused on collecting data about sexism and racism in very broad terms or have proposed two categories of sexism as benevolent or hostile sexism BIBREF0, which undermines other types of online harassment. However, there is no much study focusing on different types online harassment alone attracting natural language processing techniques.\nIn this paper we present our work, which is a part of the SociaL Media And Harassment Competition of the ECML PKDD 2019 Conference. The topic of the competition is the classification of different types of harassment and it is divided in two tasks. The first one is the classification of the tweets in harassment and non-harassment categories, while the second one is the classification in specific harassment categories like indirect harassment, physical and sexual harassment as well. We are using the dataset of the competition, which includes text from tweets having the aforementioned categories. Our approach is based on the Recurrent Neural Networks and particularly we are using a deep, classification specific attention mechanism. Moreover, we present a comparison between different variations of this attention-based approach like multi-attention and single attention models. The next Section includes a short description of the related work, while the third Section includes a description of the dataset. After that, we describe our methodology. Finally, we describe the experiments and we present the results and our conclusion.\n\nRelated Work\nWaseem et al. BIBREF1 were the first who collected hateful tweets and categorized them into being sexist, racist or neither. However, they did not provide specific definitions for each category. Jha and Mamidi BIBREF0 focused on just sexist tweets and proposed two categories of hostile and benevolent sexism. However, these categories were general as they ignored other types of sexism happening in social media. Sharifirad S. and Matwin S. BIBREF2 proposed complimentary categories of sexist language inspired from social science work. They categorized the sexist tweets into the categories of indirect harassment, information threat, sexual harassment and physical harassment. In the next year the same authors proposed BIBREF3 a more comprehensive categorization of online harassment in social media e.g. twitter into the following categories, indirect harassment, information threat, sexual harassment, physical harassment and not sexist.\nFor the detection of hate speech in social media like twitter, many approaches have been proposed. Jha and Mamidi BIBREF0 tested support vector machine, bi-directional RNN encoder-decoder and FastText on hostile and benevolent sexist tweets. They also used SentiWordNet and subjectivity lexicon on the extracted phrases to show the polarity of the tweets. Sharifirad et al. BIBREF4 trained, tested and evaluated different classification methods on the SemEval2018 dataset and chose the classifier with the highest accuracy for testing on each category of sexist tweets to know the mental state and the affectual state of the user who tweets in each category. To overcome the limitations of small data sets on sexist speech detection, Sharifirad S. et al. BIBREF5 have applied text augmentation and text generation with certain success. They have generated new tweets by replacing words in order to increase the size of our training set. Moreover, in the presented text augmentation approach, the number of tweets in each class remains the same, but their words are augmented with words extracted from their ConceptNet relations and their description extracted from Wikidata. Zhang et al. BIBREF6 combined convolutional and gated recurrent networks to detect hate speech in tweets. Others have proposed different methods, which are not based on deep learning. Burnap and Williams BIBREF7 used Support Vector Machines, Random Forests and a meta-classifier to distinguish between hateful and non-hateful messages. A survey of recent research in the field is presented in BIBREF8. For the problem of the hate speech detection a few approaches have been proposed that are based on the Attention mechanism. Pavlopoulos et al. BIBREF9 have proposed a novel, classification-specific attention mechanism that improves the performance of the RNN further for the detection of abusive content in the web. Xie et al. BIBREF10 for emotion intensity prediction, which is a similar problem to ours, have proposed a novel attention mechanism for CNN model that associates attention-based weights for every convolution window. Park and Fung BIBREF11 transformed the classification into a 2-step problem, where abusive text first is distinguished from the non-abusive, and then the class of abuse (Sexism or Racism) is determined. However, while the first part of the two step classification performs quite well, it falls short in detecting the particular class the abusive text belongs to. Pitsilis et al. BIBREF12 have proposed a detection scheme that is an ensemble of RNN classifiers, which incorporates various features associated with user related information, such as the users’ tendency towards racism or sexism\n\nDataset description\nThe dataset from Twitter that we are using in our work, consists of a train set, a validation set and a test set. It was published for the \"First workshop on categorizing different types of online harassment languages in social media\". The whole dataset is divided into two categories, which are harassment and non-harassment tweets. Moreover, considering the type of the harassment, the tweets are divided into three sub-categories which are indirect harassment, sexual and physical harassment. We can see in Table TABREF1 the class distribution of our dataset. One important issue here is that the categories of indirect and physical harassment seem to be more rare in the train set than in the validation and test sets. To tackle this issue, as we describe in the next section, we are performing data augmentation techniques. However, the dataset is imbalanced and this has a significant impact in our results.\n\nProposed methodology ::: Text processing\nBefore training our models we are processing the given tweets using a tweet pre-processor. The scope here is the cleaning and tokenization of the dataset.\n\nProposed methodology ::: RNN Model and Attention Mechanism\nWe are presenting an attention-based approach for the problem of the harassment detection in tweets. In this section, we describe the basic approach of our work. We are using RNN models because of their ability to deal with sequence information. The RNN model is a chain of GRU cells BIBREF15 that transforms the tokens $w_{1}, w_{2},..., w_{k}$ of each tweet to the hidden states $h_{1}, h_{2},..., h_{k}$, followed by an LR Layer that uses $h_{k}$ to classify the tweet as harassment or non-harassment (similarly for the other categories). Given the vocabulary V and a matrix E $\\in $ $R^{d \\times \\vert V \\vert }$ containing d-dimensional word embeddings, an initial $h_{0}$ and a tweet $w = <w_{1},.., w_{k}>$, the RNN computes $h_{1}, h_{2},..., h_{k}$, with $h_{t} \\in R^{m}$, as follows:\nwhere $h^{^{\\prime }}_{t} \\in R^{m}$ is the proposed hidden state at position t, obtained using the word embedding $x_{t}$ of token $w_{t}$ and the previous hidden state $h_{t-1}$, $\\odot $ represents the element-wise multiplication, $r_{t} \\in R^{m}$ is the reset gate, $z_{t} \\in R^{m}$ is the update gate, $\\sigma $ is the sigmoid function. Also $W_{h}, W_{z}, W_{r} \\in R^{m \\times d}$ and $U_{h}, U_{z}, U_{r} \\in R^{m \\times m}$, $b_{h}, b_{z}, b_{r} \\in R^{m}$. After the computation of state $h_{k}$ the LR Layer estimates the probability that tweet w should be considered as harassment, with $W_{p} \\in R^{1 \\times m}, b_{p} \\in R$:\nWe would like to add an attention mechanism similar to the one presented in BIBREF9, so that the LR Layer will consider the weighted sum $h_{sum}$ of all the hidden states instead of $h_{k}$:\n$h_{sum} = \\sum _{t=1}^{k} \\alpha _{t}h_{t}$\n$P_{attentionRNN} = \\sigma (W_{p}h_{sum} + b_{p})$\nAlternatively, we could pass $h_{sum}$ through an MLP with k layers and then the LR layer will estimate the corresponding probability. More formally,\n$P_{attentionRNN} = \\sigma (W_{p}h_{*} + b_{p})$\nwhere $h_{*}$ is the state that comes out from the MLP. The weights $\\alpha _{t}$ are produced by an attention mechanism presented in BIBREF9 (see Fig. FIGREF7), which is an MLP with l layers. This attention mechanism differs from most previous ones BIBREF16, BIBREF17, because it is used in a classification setting, where there is no previously generated output sub-sequence to drive the attention. It assigns larger weights $\\alpha _{t}$ to hidden states $h_{t}$ corresponding to positions, where there is more evidence that the tweet should be harassment (or any other specific type of harassment) or not. In our work we are using four attention mechanisms instead of one that is presented in BIBREF9. Particularly, we are using one attention mechanism per category. Another element that differentiates our approach from Pavlopoulos et al. BIBREF9 is that we are using a projection layer for the word embeddings (see Fig. FIGREF2). In the next subsection we describe the Model Architecture of our approach.\n\nExperiments ::: Evaluation and Results\nEach model produces four scores and each score is the probability that a tweet includes harassment language, indirect, physical and sexual harassment language respectively. For any tweet, we first check the score of the harassment language and if it is less than a specified threshold, then the harassment label is zero, so the other three labels are zero as well. If it is greater than or equal to that threshold, then the harassment label is one and the type of harassment is the one among these three having that has the greatest score (highest probability). We set this threshold equal to 0.33.\nWe compare eight different models in our experiments. Four of them have a Projected Layer (see Fig. FIGREF2), while the others do not have, and this is the only difference between these two groups of our models. So, we actually include four models in our experiments (having a projected layer or not). Firstly, LastStateRNN is the classic RNN model, where the last state passes through an MLP and then the LR Layer estimates the corresponding probability. In contrast, in the AvgRNN model we consider the average vector of all states that come out of the cells. The AttentionRNN model is the one that it has been presented in BIBREF9. Moreover, we introduce the MultiAttentionRNN model for the harassment language detection, which instead of one attention, it includes four attentions, one for each category.\nWe have evaluated our models considering the F1 Score, which is the harmonic mean of precision and recall. We have run ten times the experiment for each model and considered the average F1 Score. The results are mentioned in Table TABREF11. Considering F1 Macro the models that include the multi-attention mechanism outperform the others and particularly the one with the Projected Layer has the highest performance. In three out of four pairs of models, the ones with the Projected Layer achieved better performance, so in most cases the addition of the Projected Layer had a significant enhancement.\n\nConclusion - Future work\nWe present an attention-based approach for the detection of harassment language in tweets and the detection of different types of harassment as well. Our approach is based on the Recurrent Neural Networks and particularly we are using a deep, classification specific attention mechanism. Moreover, we present a comparison between different variations of this attention-based approach and a few baseline methods. According to the results of our experiments and considering the F1 Score, the multi-attention method having a projected layer, achieved the highest performance. Also, we tackled the problem of the imbalance between the training, validation and test sets performing the technique of back-translation.\nIn the future, we would like to perform more experiments with this dataset applying different models using BERT BIBREF21. Also, we would like to apply the models presented in this work, in other datasets about hate speech in social media.\n\nTable 1. Class distribution of the dataset.\n\nFig. 1. Projection Layer\n\nQuery: What were the datasets used in this paper?\n\nJSON Response:",
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"content": "{\"answer\": \"The dataset from the SociaL Media And Harassment Competition of the ECML PKDD 2019 Conference. \", \"evidence\": [\"In this paper we present our work, which is a part of the SociaL Media And Harassment Competition of the ECML PKDD 2019 Conference. The topic of the competition is the classification of different types of harassment and it is divided in two tasks. The first one is the classification of the tweets in harassment and non-harassment categories, while the second one is the classification in specific harassment categories like indirect harassment, physical and sexual harassment as well. We are using the dataset of the competition, which includes text from tweets having the aforementioned categories. Our approach is based on the Recurrent Neural Networks and particularly we are using a deep, classi\\ufb01cation specific attention mechanism. Moreover, we present a comparison between different variations of this attention-based approach like multi-attention and single attention models. The next Section includes a short description of the related work, while the third Section includes a description of the dataset. After that, we describe our methodology. Finally, we describe the experiments and we present the results and our conclusion.\"]}",
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science.multicite_intent_classification | Examine the text below, which includes a citation marked by "<cite>" and "</cite>". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.
Provide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.
Text with Citation:
In related work, <cite>Bansal et al. (2014)</cite> also use dependency context to tailor word embeddings to dependency parsing. However, <cite>their</cite> embedding features are still based on the sparse set of n-ary, word-based templates from previous work (McDonald et al., 2005a; Koo et al., 2008) . Our structured link embeddings achieve similar improvements <cite>as theirs</cite> (and better in the case of direct, per-dimension bucket features) with a substantially smaller and simpler (unary) set of features that are aimed to directly capture hidden relationships between the substructures that dependency parsing factors on. | ["Differences", "Motivation"] | science.multicite_intent_classification.1956 | [
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"content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nIn related work, <cite>Bansal et al. (2014)</cite> also use dependency context to tailor word embeddings to dependency parsing. However, <cite>their</cite> embedding features are still based on the sparse set of n-ary, word-based templates from previous work (McDonald et al., 2005a; Koo et al., 2008) . Our structured link embeddings achieve similar improvements <cite>as theirs</cite> (and better in the case of direct, per-dimension bucket features) with a substantially smaller and simpler (unary) set of features that are aimed to directly capture hidden relationships between the substructures that dependency parsing factors on.",
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"content": "[\"Differences\", \"Motivation\"]",
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science.mup_single_document_summarization | Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.
Title of the Paper:
Measuring Calibration in Deep Learning
Main Content:
1 INTRODUCTION . The reliability of a machine learning model ’ s confidence in its predictions is critical for high risk applications , such as deciding whether to trust a medical diagnosis prediction ( Crowson et al. , 2016 ; Jiang et al. , 2011 ; Raghu et al. , 2018 ) . One mathematical formulation of the reliability of confidence is calibration ( Murphy & Epstein , 1967 ; Dawid , 1982 ) . Intuitively , for class predictions , calibration means that if a model assigns a class with 90 % probability , that class should appear 90 % of the time . Calibration is not directly measured by proper scoring rules like negative log likelihood or the quadratic loss . The reason that we need to assess the quality of calibrated probabilities is that presently , we optimize against a proper scoring rule and our models often yield uncalibrated probabilities . Recent work proposed Expected Calibration Error ( ECE ; Naeini et al. , 2015 ) , a measure of calibration error which has lead to a surge of works developing methods for calibrated deep neural networks ( e.g. , Guo et al. , 2017 ; Kuleshov et al. , 2018 ) . In this paper , we show that ECE has numerous pathologies , and that recent calibration methods , which have been shown to successfully recalibrate models according to ECE , can not be properly evaluated via ECE . Calibration ( and uncertainty quantification generally ) is critical in autonomous vehicles , the exploration phase of may algorithms , medical applications , and many more safety-critical applications of machine learning . A suite of recent papers ( e.g. , Lee et al. , 2017 ; Vaicenavicius et al. , 2019 ; Thulasidasan et al. , 2019 ; Kumar et al. , 2018 ; Guo et al. , 2017 ; Seo et al. , 2019 ) use ECE to validate their models ’ calibration . We identify concerns with that methodology . As this metric has become the default choice for measuring calibration in industry and for researchers , we expect our criticism to change the decisions of anyone attempting to train well-calibrated classification models . We recommend that rather than using Expected Calibration Error , practitioners use Static Calibratinon Error or Adaptive Calibration . 2 BACKGROUND & RELATED WORK . 2.1 MEASUREMENT OF CALIBRATION . Assume the dataset of features and outcomes { ( x , y ) } are i.i.d . realizations of the random variables X , Y ∼ P. We focus on class predictions . Suppose a model predicts a class y with probability p̂ . The model is calibrated if p̂ is always the true probability . Formally , P ( Y = y | p̂ = p ) = p for all probability values p ∈ [ 0 , 1 ] and class labels y ∈ { 0 , . . . , K − 1 } . The left-hand-side denotes the true data distribution ’ s probability of a label given that the model predicts p̂ = p ; the right-handside denotes that value . Any difference between the left and right sides for a given p is known as calibration error . Expected Calibration Error ( ECE ) . To approximately measure the calibration error in expectation , ECE discretizes the probability interval into a fixed number of bins , and assigns each predicted probability to the bin that encompasses it . The calibration error is the difference between the fraction of predictions in the bin that are correct ( accuracy ) and the mean of the probabilities in the bin ( confidence ) . Intuitively , the accuracy estimates P ( Y = y | p̂ = p ) , and the average confidence is a setting of p. ECE computes a weighted average of this error across bins : ECE = B∑ b=1 nb N |acc ( b ) − conf ( b ) | , where nb is the number of predictions in bin b , N is the total number of data points , and acc ( b ) and conf ( b ) are the accuracy and confidence of bin b , respectively . ECE as framed in Naeini et al . ( 2015 ) leaves ambiguity in both its binning implementation and how to compute calibration for multiple classes . In Guo et al . ( 2017 ) , they bin the probability interval [ 0 , 1 ] into equally spaced subintervals , and they take the maximum probability output for each datapoint ( i.e. , the predicted class ’ s probability ) . We use this for our ECE implementation . 2.2 PROPER SCORING RULES & OTHER MEASURES OF CALIBRATION .. Many classic methods exist to measure the accuracy of predicted probabilities . For example , the Brier score ( quadratic loss ) measures the mean squared difference between the predicted probability and the actual outcome ( Gneiting & Raftery , 2007 ) . This score can be shown to decompose into a sum of metrics , including calibration error . In practice , training against this proper scoring rule , like training against log likelihood , does not guarantee a well calibrated model . The Hosmer-Lemeshow test is a popular hypothesis test for assessing whether a model ’ s predictions significantly deviate from perfect calibration ( Hosmer & Lemesbow , 1980 ) . The reliability diagram provides a visualization of how well-calibrated a model is ( DeGroot & Fienberg , 1983 ) . Adaptivity was explored by ( Nguyen & O ’ Connor , 2015 ) and applied in ( Hendrycks et al. , 2018 ) . Kuleshov et al . ( 2018 ) extends ECE to the regression setting . Unlike the Brier score and Hosmer-Lemeshow , we ’ d like the metric to be scalar-valued in order to easily benchmark methods , and to only measure calibration . 2.3 POST-PROCESSING METHODS . 2.3.1 STANDARD APPROACHES FOR MULTICLASS CLASSIFICATION . One common approach to calibration is to apply post-processing methods to the output of any classifier without retraining . The two most popular post-processing methods are the parametric approach of Platt scaling ( Platt et al. , 1999 ) and the non-parametric approach of isotonic regression ( Zadrozny & Elkan , 2002 ) . Platt scaling fits a logistic regression model to the logits of a classifier , on the validation set , which can be used to compute calibrated predictions at test time . The original formulation of Platt scaling for neural networks ( Niculescu-Mizil & Caruana , 2005 ) involves learning scalar parameters a , b ∈ R on a held-out validation set and then computing calibrated probabilities p̂i given the uncalibrated logits vector zi on the test set as p̂i = σ ( azi + b ) . These parameters are typically estimated by minimizing the negative log likelihood . Platt scaling can be extended to the multiclass setting by considering higher-dimensional parameters . In matrix scaling a is replaced with W ∈ RK∗K while we consider b ∈ RK . As for vector scaling ; W ∈ RK . Calibrated probabilities for either of these extensions can then be computed as p̂i = max k σ ( Wzi + b ) . An even simpler extension is temperature scaling ( Guo et al. , 2017 ) which reduces the set of regression parameters to the inverse of a single scalar T > 0 such that p̂i = max k σ ( zi/T ) . On the other hand , isotonic regression is a common non-parametric processing method that finds the stepwise-constant non-decreasing ( isotonic ) function f that best fits the data according to a mean-squared loss function ∑ i ( f ( pi ) − yi ) 2 where pi are the uncalibrated probabilities and yi the labels . The standard approach for extending isotonic regression to the multiclass setting is to break the problem into many binary classification problems ( e.g . one-versus-all problems ) , to calibrate each problem separately , and then to combine the calibrated probabilities ( Zadrozny & Elkan , 2002 ) . 3 ISSUES WITH CALIBRATION METRICS . 3.1 NOT COMPUTING CALIBRATION ACROSS ALL PREDICTIONS . Expected Calibration Error was crafted to mirror reliability diagrams , which are structured around binary classification such as rain vs not rain ( DeGroot & Fienberg , 1983 ) ( Figure 1 ) . A consequence is that the error metric is reductive in a multi-class setting . In particular , ECE is computed using only the predicted class ’ s probability , which implies the metric does not assess how accurate a model is with respect to the K − 1 other probabilities . We examine increasing the importance of omitted predictions via label noise . As label noise increases , trained models become less confident in their predictions . Secondary and tertiary predictions are correct with higher frequency , and so for many samples the prediction with the correct label isn ’ t included in ECE ’ s measure of calibration error . We find that ECE becomes a worse approximation of the calibration error as relevant predictions not included in the ECE metric become common ( Figure 1 ) . In Figure 2 , we observe that calibration error , when measured in a per-class manner , is non-uniform across classes . Guaranteeing calibration for all class probabilities is an important property for many applications . For example , a physician may care deeply about a secondary or tertiary prediction of a disease . Decision costs along both ethical and monetary axes are non-uniform , and thus clinical decision policies necessitate a comprehensive view of the likelihoods of potential outcomes ( e.g. , Tsoukalas et al. , 2015 ) . Alternatively , if a true data distribution exhibits high data noise ( also known as high aleatoric uncertainty , or weak labels ) , then we ’ d like models to be calibrated across all predictions as no true class label is likely . 3.2 FIXED CALIBRATION RANGES . One major weakness of evenly spaced binning metrics is caused by the dispersion of data across ranges . In computing ECE , there is often a large leftward skew in the output probabilities , with the left end of the region being sparsely populated and the rightward end being densely populated . Sharpness , which is the desire for models to always predict with high confidence , i.e. , predicted probabilities concentrate to 0 or 1 , is a fundamental property ( Gneiting et al. , 2007 ) . ( That is , network predictions are typically very confident . ) This causes only a few bins to contribute the most to ECE—typically one or two as bin sizes are 10-20 in practice ( Guo et al. , 2017 ) . 3.3 BIAS-VARIANCE TRADEOFF . ‘ Ground truth ’ is the calibration error we would measure if we could see an infinite number of predictions at a particular confidence level . That would give us a long-run error for every prediction , which we could aggregate into an overall calibration error . Because we don ’ t have that infinite set of reference predictions for every real-valued prediction , we try to approximate that set by using the values that are close together as reference predictions . As we increase the number of datapoints in a bin , we get a more accurate estimate of the calibration in a given range through the added ( seen as a reduction in variance ) . But as the datapoints in the bin start to differ from one another due to the extension of the range , they bias the estimate of the calibration within that bin ( we refer to this as bias ) . Selecting the number of bins has a bias-variance tradeoff as it determines how many data points fall into each bin and therefore the quality of the estimate of calibration from that bin ’ s range . In particular , a larger number of bins causes more granular measures of calibration error ( low bias ) but also a high variance of each bin ’ s measurement as bins become sparsely populated . This tradeoff compounds particularly with the problem of fixed calibration ranges , as due to sharpness certain bins have many more data points than others . | This paper proposes two novel scalar-valued evaluation measures, namely Static Calibration Error (SCE) and Adaptive Calibration Error (ACE) for evaluating the quality of calibration (the accuracy of estimating p(y|x) in classification) in deep learning. The authors suggested that the existing Expected Calibration Error (ECE), which is the current most popular scalar-valued metric for calibration, has several drawbacks, especially in the multi-class scenario. Intuitively, ECE only focuses on the predicted class probability (argmax) and ignores class probabilities of other K-1 classes, which implies that ECE may fail to capture the class probability estimates for all classes. They also illustrated the drawback of ECE under label noise. | science.mup_single_document_summarization.17235 | [
{
"content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nMeasuring Calibration in Deep Learning\n\nMain Content:\n1 INTRODUCTION . The reliability of a machine learning model ’ s confidence in its predictions is critical for high risk applications , such as deciding whether to trust a medical diagnosis prediction ( Crowson et al. , 2016 ; Jiang et al. , 2011 ; Raghu et al. , 2018 ) . One mathematical formulation of the reliability of confidence is calibration ( Murphy & Epstein , 1967 ; Dawid , 1982 ) . Intuitively , for class predictions , calibration means that if a model assigns a class with 90 % probability , that class should appear 90 % of the time . Calibration is not directly measured by proper scoring rules like negative log likelihood or the quadratic loss . The reason that we need to assess the quality of calibrated probabilities is that presently , we optimize against a proper scoring rule and our models often yield uncalibrated probabilities . Recent work proposed Expected Calibration Error ( ECE ; Naeini et al. , 2015 ) , a measure of calibration error which has lead to a surge of works developing methods for calibrated deep neural networks ( e.g. , Guo et al. , 2017 ; Kuleshov et al. , 2018 ) . In this paper , we show that ECE has numerous pathologies , and that recent calibration methods , which have been shown to successfully recalibrate models according to ECE , can not be properly evaluated via ECE . Calibration ( and uncertainty quantification generally ) is critical in autonomous vehicles , the exploration phase of may algorithms , medical applications , and many more safety-critical applications of machine learning . A suite of recent papers ( e.g. , Lee et al. , 2017 ; Vaicenavicius et al. , 2019 ; Thulasidasan et al. , 2019 ; Kumar et al. , 2018 ; Guo et al. , 2017 ; Seo et al. , 2019 ) use ECE to validate their models ’ calibration . We identify concerns with that methodology . As this metric has become the default choice for measuring calibration in industry and for researchers , we expect our criticism to change the decisions of anyone attempting to train well-calibrated classification models . We recommend that rather than using Expected Calibration Error , practitioners use Static Calibratinon Error or Adaptive Calibration . 2 BACKGROUND & RELATED WORK . 2.1 MEASUREMENT OF CALIBRATION . Assume the dataset of features and outcomes { ( x , y ) } are i.i.d . realizations of the random variables X , Y ∼ P. We focus on class predictions . Suppose a model predicts a class y with probability p̂ . The model is calibrated if p̂ is always the true probability . Formally , P ( Y = y | p̂ = p ) = p for all probability values p ∈ [ 0 , 1 ] and class labels y ∈ { 0 , . . . , K − 1 } . The left-hand-side denotes the true data distribution ’ s probability of a label given that the model predicts p̂ = p ; the right-handside denotes that value . Any difference between the left and right sides for a given p is known as calibration error . Expected Calibration Error ( ECE ) . To approximately measure the calibration error in expectation , ECE discretizes the probability interval into a fixed number of bins , and assigns each predicted probability to the bin that encompasses it . The calibration error is the difference between the fraction of predictions in the bin that are correct ( accuracy ) and the mean of the probabilities in the bin ( confidence ) . Intuitively , the accuracy estimates P ( Y = y | p̂ = p ) , and the average confidence is a setting of p. ECE computes a weighted average of this error across bins : ECE = B∑ b=1 nb N |acc ( b ) − conf ( b ) | , where nb is the number of predictions in bin b , N is the total number of data points , and acc ( b ) and conf ( b ) are the accuracy and confidence of bin b , respectively . ECE as framed in Naeini et al . ( 2015 ) leaves ambiguity in both its binning implementation and how to compute calibration for multiple classes . In Guo et al . ( 2017 ) , they bin the probability interval [ 0 , 1 ] into equally spaced subintervals , and they take the maximum probability output for each datapoint ( i.e. , the predicted class ’ s probability ) . We use this for our ECE implementation . 2.2 PROPER SCORING RULES & OTHER MEASURES OF CALIBRATION .. Many classic methods exist to measure the accuracy of predicted probabilities . For example , the Brier score ( quadratic loss ) measures the mean squared difference between the predicted probability and the actual outcome ( Gneiting & Raftery , 2007 ) . This score can be shown to decompose into a sum of metrics , including calibration error . In practice , training against this proper scoring rule , like training against log likelihood , does not guarantee a well calibrated model . The Hosmer-Lemeshow test is a popular hypothesis test for assessing whether a model ’ s predictions significantly deviate from perfect calibration ( Hosmer & Lemesbow , 1980 ) . The reliability diagram provides a visualization of how well-calibrated a model is ( DeGroot & Fienberg , 1983 ) . Adaptivity was explored by ( Nguyen & O ’ Connor , 2015 ) and applied in ( Hendrycks et al. , 2018 ) . Kuleshov et al . ( 2018 ) extends ECE to the regression setting . Unlike the Brier score and Hosmer-Lemeshow , we ’ d like the metric to be scalar-valued in order to easily benchmark methods , and to only measure calibration . 2.3 POST-PROCESSING METHODS . 2.3.1 STANDARD APPROACHES FOR MULTICLASS CLASSIFICATION . One common approach to calibration is to apply post-processing methods to the output of any classifier without retraining . The two most popular post-processing methods are the parametric approach of Platt scaling ( Platt et al. , 1999 ) and the non-parametric approach of isotonic regression ( Zadrozny & Elkan , 2002 ) . Platt scaling fits a logistic regression model to the logits of a classifier , on the validation set , which can be used to compute calibrated predictions at test time . The original formulation of Platt scaling for neural networks ( Niculescu-Mizil & Caruana , 2005 ) involves learning scalar parameters a , b ∈ R on a held-out validation set and then computing calibrated probabilities p̂i given the uncalibrated logits vector zi on the test set as p̂i = σ ( azi + b ) . These parameters are typically estimated by minimizing the negative log likelihood . Platt scaling can be extended to the multiclass setting by considering higher-dimensional parameters . In matrix scaling a is replaced with W ∈ RK∗K while we consider b ∈ RK . As for vector scaling ; W ∈ RK . Calibrated probabilities for either of these extensions can then be computed as p̂i = max k σ ( Wzi + b ) . An even simpler extension is temperature scaling ( Guo et al. , 2017 ) which reduces the set of regression parameters to the inverse of a single scalar T > 0 such that p̂i = max k σ ( zi/T ) . On the other hand , isotonic regression is a common non-parametric processing method that finds the stepwise-constant non-decreasing ( isotonic ) function f that best fits the data according to a mean-squared loss function ∑ i ( f ( pi ) − yi ) 2 where pi are the uncalibrated probabilities and yi the labels . The standard approach for extending isotonic regression to the multiclass setting is to break the problem into many binary classification problems ( e.g . one-versus-all problems ) , to calibrate each problem separately , and then to combine the calibrated probabilities ( Zadrozny & Elkan , 2002 ) . 3 ISSUES WITH CALIBRATION METRICS . 3.1 NOT COMPUTING CALIBRATION ACROSS ALL PREDICTIONS . Expected Calibration Error was crafted to mirror reliability diagrams , which are structured around binary classification such as rain vs not rain ( DeGroot & Fienberg , 1983 ) ( Figure 1 ) . A consequence is that the error metric is reductive in a multi-class setting . In particular , ECE is computed using only the predicted class ’ s probability , which implies the metric does not assess how accurate a model is with respect to the K − 1 other probabilities . We examine increasing the importance of omitted predictions via label noise . As label noise increases , trained models become less confident in their predictions . Secondary and tertiary predictions are correct with higher frequency , and so for many samples the prediction with the correct label isn ’ t included in ECE ’ s measure of calibration error . We find that ECE becomes a worse approximation of the calibration error as relevant predictions not included in the ECE metric become common ( Figure 1 ) . In Figure 2 , we observe that calibration error , when measured in a per-class manner , is non-uniform across classes . Guaranteeing calibration for all class probabilities is an important property for many applications . For example , a physician may care deeply about a secondary or tertiary prediction of a disease . Decision costs along both ethical and monetary axes are non-uniform , and thus clinical decision policies necessitate a comprehensive view of the likelihoods of potential outcomes ( e.g. , Tsoukalas et al. , 2015 ) . Alternatively , if a true data distribution exhibits high data noise ( also known as high aleatoric uncertainty , or weak labels ) , then we ’ d like models to be calibrated across all predictions as no true class label is likely . 3.2 FIXED CALIBRATION RANGES . One major weakness of evenly spaced binning metrics is caused by the dispersion of data across ranges . In computing ECE , there is often a large leftward skew in the output probabilities , with the left end of the region being sparsely populated and the rightward end being densely populated . Sharpness , which is the desire for models to always predict with high confidence , i.e. , predicted probabilities concentrate to 0 or 1 , is a fundamental property ( Gneiting et al. , 2007 ) . ( That is , network predictions are typically very confident . ) This causes only a few bins to contribute the most to ECE—typically one or two as bin sizes are 10-20 in practice ( Guo et al. , 2017 ) . 3.3 BIAS-VARIANCE TRADEOFF . ‘ Ground truth ’ is the calibration error we would measure if we could see an infinite number of predictions at a particular confidence level . That would give us a long-run error for every prediction , which we could aggregate into an overall calibration error . Because we don ’ t have that infinite set of reference predictions for every real-valued prediction , we try to approximate that set by using the values that are close together as reference predictions . As we increase the number of datapoints in a bin , we get a more accurate estimate of the calibration in a given range through the added ( seen as a reduction in variance ) . But as the datapoints in the bin start to differ from one another due to the extension of the range , they bias the estimate of the calibration within that bin ( we refer to this as bias ) . Selecting the number of bins has a bias-variance tradeoff as it determines how many data points fall into each bin and therefore the quality of the estimate of calibration from that bin ’ s range . In particular , a larger number of bins causes more granular measures of calibration error ( low bias ) but also a high variance of each bin ’ s measurement as bins become sparsely populated . This tradeoff compounds particularly with the problem of fixed calibration ranges , as due to sharpness certain bins have many more data points than others .",
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},
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"content": "This paper proposes two novel scalar-valued evaluation measures, namely Static Calibration Error (SCE) and Adaptive Calibration Error (ACE) for evaluating the quality of calibration (the accuracy of estimating p(y|x) in classification) in deep learning. The authors suggested that the existing Expected Calibration Error (ECE), which is the current most popular scalar-valued metric for calibration, has several drawbacks, especially in the multi-class scenario. Intuitively, ECE only focuses on the predicted class probability (argmax) and ignores class probabilities of other K-1 classes, which implies that ECE may fail to capture the class probability estimates for all classes. They also illustrated the drawback of ECE under label noise.",
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science.mup_single_document_summarization | Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.
Title of the Paper:
Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation
Main Content:
1 Introduction . Blind source separation , a long-standing problem in signal processing , aims to recover multiple intrinsic components from their mixture , the difficulty of which comes from its ill-posedness , i.e. , without extra information , there is an infinite number of feasible decompositions . Particularly in computer vision , image reflection separation ( IRS ) is a representative scenario that often occurs when taking pictures through a transparent medium such as glass . In such cases , the captured images will contain both the scene transmitted through the medium ( transmission ) and reflection . On the one hand , reflections are annoying for high-quality imaging , and may interfere with the performance of most , if not all , of classic and contemporary vision oriented algorithms such as object detection and segmentation . On the other hand , one may also want to see what happens in the reflection . Hence , developing effective transmission-reflection decomposition techniques is desired . Formally , the captured superimposed image I can be typically modeled as a linear combination of a transmission layer T and a reflection R , i.e . I = T + R.1 Over last decades , a large number of schemes have been devised to solve the decomposition problem . Various statistical priors and regularizers have been proposed to mitigate the ill-posed dilemma , while diverse deep networks have been recently built for the sake of performance improvement , please see Sec . 2 for details . However , ∗Corresponding Author 1Many problems follow the same additive model , such as denoising ( I = B +N , where B and N denote clean image and noise , respectively ) , and intrinsic image decomposition ( log I = logA+ logS , where A and S stand for albedo and shading , respectively . ) The proposed strategy can be potentially applied to all these tasks , but due to page limit , we concentrate on the task of SIRS to verify primary claims in this paper . 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . in the literature , the additive property , say I = T +R , has been hardly investigated , except for acting as a reconstruction constraint . Let us consider that , for any estimation pair T̂ and R̂ satisfying the additive property , there always exists an error/residual Q subject to I = T̂ + R̂ = ( T +Q ) + ( R−Q ) . Once Q is somehow obtained , the only thing needed to do is subtracting it from T̂ , and adding it into R̂ instead of simply discarding . Under the circumstances , no information is trash , only misplaced . To be symmetric , we rewrite Q as Q : = QT − QR , yielding T = T̂ − QT + QR and R = R̂ − QR + QT . In other words , the two targets including T̂ and R̂ can gain from each other by exchanging , rather than discarding , their respective “ trash ” factors QT and QR . Driven by the above fact , a question naturally arises : Can such an interaction/exchange be applied to intermediate deep features of dual-stream networks ? Contributions . This paper answers the above question by designing a general interactive dualstream/branch strategy , namely your trash is my treasure ( YTMT ) . An obstacle to realizing YTMT was how to determine exchanging information . Intuitively , activation functions are competent for the job , which are developed to select ( activate ) a part of features from inputs . In this work , we adopt the ReLU that is arguably the most representative and widely-used activation manner , while others could be also qualified like [ 4 , 24 , 12 , 2 ] . Please notice that , instead of simply discarding the deactivated features ( trash ) of the one stream , we alternatively deliver them to the other stream as compensation ( treasure ) . By doing so , there are two main merits : 1 ) the information losing and dead ReLU problems can be consequently mitigated , and 2 ) the decreasing speed in training error can be significantly accelerated . The implementation is quite simple and flexible . We provide two optional YTMT blocks as examples and apply them on both plain and UNet architectures to verify the primary claims . Both ablation studies and experimental results on widely-used SIRS datasets are conducted to demonstrate the efficacy of YTMT , and reveal its superiority over other state-of-the-art alternatives . 2 Related Work . Over the past decades , much attention to resolving the image reflection separation problem has been drawn from the community . From the perspective of the required input amount , existing methods can be divided into two classes , i.e. , multi-image based and single image based methods . Multiple Image Reflection Separation ( MIRS ) . Utilizing multiple images [ 7 , 28 , 33 , 27 , 1 , 8 , 31 , 22 , 9 , 29 , 40 , 32 , 41 , 10 ] used to be a prevalent way to cope with the task , as more information complementary to single superimposed images , like varied conditions and relative motions , can be explored from a sequence of images to accomplish the task . For instance , Agrawal et al . [ 1 ] use flash and no-flash image pairs to remove reflections and highlights , while Szeliski et al . [ 33 ] employ focused and defocused pairs . A variety of approaches [ 7 , 27 , 8 , 22 , 33 , 40 , 41 ] have been proposed to seek relationships between different images , among which Farid and Adelson [ 7 ] employ independent component analysis to reduce reflections and lighting . Gai et al . [ 8 ] alternatively develop an algorithm to estimate layer motions and linear mixing coefficients for the recovery . Li and Brown [ 22 ] adopt the SIFT-flow to align the images , while Xue et al . [ 40 ] enforce a heavy tailed distribution on the obstruction and background components to penalize the overlapped gradients , making the two layers independent . Moreover , Yang et al . [ 41 ] introduce a generalized double-layer brightness consistency constraint to connect optical flow estimation and layer separation . Despite the satisfactory performance of MIRS methods , the need for specified shooting conditions and/or professional tools heavily limits their applicability . Single Image Reflection Separation ( SIRS ) . In practice , single image based schemes are more attractive . No doubt that , compared with those MIRS techniques , single image based ones are also more challenging due to less information available , demanding extra priors to regularize the solution . To mitigate the ill-posedness , Levin et al . [ 19 , 18 ] favor decompositions that have fewer edges and corners by imposing sparse gradients on the layers , in the same spirit as [ 40 ] . Levin and Weiss [ 17 ] allow users to manually annotate some dominant edges for the layers as explicit constraints for the problem , which requires careful human efforts to obtain favorable results . Li and Brown [ 23 ] assume one layer is smoother than the other , and penalize differently on the two layers in the gradient domain to split the reflection and transmission . Despite a progress made toward addressing the problem , the assumption and requirement could be frequently violated in real situations . Besides , these methods all rely on handcrafted features that may considerably restrict their performance . With the emergence of deep neural networks , learning based methods [ 6 , 44 , 42 , 38 , 20 , 39 ] have shown their power and become dominant for the task . Concretely , CEILNet [ 6 ] comprises of two stages , similarly to [ 17 , 37 ] . An edge map is estimated in the first stage , which performs as the guidance for the second stage to produce the final transmission layer . However , the network is hard to capture high-level information , thus having trouble guaranteeing the perceptual quality . Zhang et al . [ 44 ] further take the perception cue into consideration by coordinating hyper-column features [ 11 ] , the perceptual and adversarial losses . The exclusivity loss ( see also the gradient production penalization in [ 40 ] ) is designed for assuring gradients to be exclusive between the two components . This manner is also adopted by [ 38 ] . The main drawback is the insufficient capacity in processing cases with large transmission-reflection overlapped regions . Recently , ERRNet [ 38 ] was proposed , which enlarges the receptive field by introducing channel attention and pyramid pooling modules . It does not explicitly predict the reflection layer , which directly discards possible benefit from the supervision of reflection . IBCLN [ 20 ] proposes an iterative boost convolutional LSTM network to progressively split the two layers . However , such an iterative scheme slows down the training and predicting procedure . The errors will also be accumulated due to the high dependency on the outputs of previous stages . Though the learning based strategies have further stepped forward in single image reflection separation compared with the traditional methods , they either restore the target layers individually , or together with some concerns dealing with outputs ( e.g. , enforcing the linear combination constraint and/or concatenating them together as input ) . In other words , they barely take into account the interaction across the two streams/branches , which is key to the target task and also other dual-stream decomposition tasks . This study is mainly to demonstrate the effectiveness of such an interaction consideration . 3 Deep Interactive Dual-Stream Learning for SIRS . 3.1 YTMT Strategy . In this part , the proposed deep interactive dual-stream strategy will be detailed by centering around the concept that your trash is my treasure , which would be beneficial to a variety of two-component decomposition tasks using dual-branch networks . Prior works [ 21 , 35 ] have shown evidence on the effectiveness of passing information between two branches though , our YTMT strategy performs in a novel and principled way . We first give the definition of the negative ReLU function as follows : ReLU− ( x ) : = x− ReLU ( x ) = min ( x , 0 ) , ( 1 ) where ReLU ( x ) : = max ( x , 0 ) . By the negative ReLU , the deactivated features can be easily retained . Figure 1 ( a ) exhibits the behaviors of the ReLU and negative ReLU . Here , let x0 be the input to the first layer of the network , and x̃li ( i ∈ { 1 , 2 } for two branches ) denotes the feature obtained by the i-th branch after l stacked layers , i.e . x̃li : = Hli ( x0 ) . The inputs to the ( l + 1 ) -th layer are as follows : xl1 : = ReLU ( x̃ l 1 ) ⊕ ReLU − ( x̃l2 ) ; x l 2 : = ReLU ( x̃ l 2 ) ⊕ ReLU − ( x̃l1 ) , ( 2 ) where ⊕ can be either the concatenation operation or the addition between features activated by the ReLU function ( called normal connection ) and those by the ReLU− ( YTMT connection ) , as shown in Fig . 1 ( b ) and ( c ) . As can be obtained from Eq . ( 2 ) , the amount of information in xl1 and x l 2 is equivalent to that in x̃l1 and x̃ l 2 . This property guarantees no information flowing away from the interaction , which substantially avoids the problems of vanishing/exploding gradients and dead ReLU . Figure 2 ( c ) depicts a visual example of producing xL1 and x L 2 from x̃ L 1 and x̃ L 2 ( in this case , L = 6 ) . It shows that ReLU− ( x̃L1 ) is complementary to the ReLU ( x̃ L 2 ) and ReLU − ( x̃L2 ) is complementary to the ReLU ( x̃L1 ) . By merging the complementary counterparts , there is no information wasted by the rectifiers . In addition , this strategy can significantly speed up the decreasing of training error similarly to the ResNet design [ 13 ] , which will be empirically validated in Sec . 4 . Moreover , generally speaking , our strategy can be compatible with most , if not all , of the activation pairs ( e.g . Softmax and Softmin ) . But for the additive problems , the pair of ReLU and negative ReLU is more suitable due to its “ either A or B ” nature that satisfies the task of SIRS . We again emphasize that the proposed YTMT strategy is general and flexible , which can be implemented in various forms according to different demands . Figure 1 provides two YTMT block options . The one in ( b ) fuses features from the normal and YTMT connections by channel concatenation and 1× 1 convolutions , while the second in ( c ) simply employs feature addition . In addition , pixel and channel attention mechanism is introduced in both ( b ) and ( c ) to select and re-weight the merged features ( see [ 25 ] for details ) . Moreover , the YTMT blocks can be applied to most , if not all , of dualstream backbones by simple modification . In Fig . 2 ( a ) and ( b ) , two commonly-used architectures , i.e . U-shaped [ 26 ] and plain [ 43 ] networks , are present . We will shortly show how to construct these YTMT based networks , and demonstrate their improvement over the backbones and the superior performance on specific applications over other competitors . | This paper presents a nonlinear module that leverages both positive and negative ReLUs for feature separation. This enables applications such as reflection removal, blind denoising and other layer separation tasks. The paper shows several variants of adding the proposed module into existing CNN frameworks. Results show superior performance on several benchmark datasets. | science.mup_single_document_summarization.14670 | [
{
"content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nTrash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation\n\nMain Content:\n1 Introduction . Blind source separation , a long-standing problem in signal processing , aims to recover multiple intrinsic components from their mixture , the difficulty of which comes from its ill-posedness , i.e. , without extra information , there is an infinite number of feasible decompositions . Particularly in computer vision , image reflection separation ( IRS ) is a representative scenario that often occurs when taking pictures through a transparent medium such as glass . In such cases , the captured images will contain both the scene transmitted through the medium ( transmission ) and reflection . On the one hand , reflections are annoying for high-quality imaging , and may interfere with the performance of most , if not all , of classic and contemporary vision oriented algorithms such as object detection and segmentation . On the other hand , one may also want to see what happens in the reflection . Hence , developing effective transmission-reflection decomposition techniques is desired . Formally , the captured superimposed image I can be typically modeled as a linear combination of a transmission layer T and a reflection R , i.e . I = T + R.1 Over last decades , a large number of schemes have been devised to solve the decomposition problem . Various statistical priors and regularizers have been proposed to mitigate the ill-posed dilemma , while diverse deep networks have been recently built for the sake of performance improvement , please see Sec . 2 for details . However , ∗Corresponding Author 1Many problems follow the same additive model , such as denoising ( I = B +N , where B and N denote clean image and noise , respectively ) , and intrinsic image decomposition ( log I = logA+ logS , where A and S stand for albedo and shading , respectively . ) The proposed strategy can be potentially applied to all these tasks , but due to page limit , we concentrate on the task of SIRS to verify primary claims in this paper . 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . in the literature , the additive property , say I = T +R , has been hardly investigated , except for acting as a reconstruction constraint . Let us consider that , for any estimation pair T̂ and R̂ satisfying the additive property , there always exists an error/residual Q subject to I = T̂ + R̂ = ( T +Q ) + ( R−Q ) . Once Q is somehow obtained , the only thing needed to do is subtracting it from T̂ , and adding it into R̂ instead of simply discarding . Under the circumstances , no information is trash , only misplaced . To be symmetric , we rewrite Q as Q : = QT − QR , yielding T = T̂ − QT + QR and R = R̂ − QR + QT . In other words , the two targets including T̂ and R̂ can gain from each other by exchanging , rather than discarding , their respective “ trash ” factors QT and QR . Driven by the above fact , a question naturally arises : Can such an interaction/exchange be applied to intermediate deep features of dual-stream networks ? Contributions . This paper answers the above question by designing a general interactive dualstream/branch strategy , namely your trash is my treasure ( YTMT ) . An obstacle to realizing YTMT was how to determine exchanging information . Intuitively , activation functions are competent for the job , which are developed to select ( activate ) a part of features from inputs . In this work , we adopt the ReLU that is arguably the most representative and widely-used activation manner , while others could be also qualified like [ 4 , 24 , 12 , 2 ] . Please notice that , instead of simply discarding the deactivated features ( trash ) of the one stream , we alternatively deliver them to the other stream as compensation ( treasure ) . By doing so , there are two main merits : 1 ) the information losing and dead ReLU problems can be consequently mitigated , and 2 ) the decreasing speed in training error can be significantly accelerated . The implementation is quite simple and flexible . We provide two optional YTMT blocks as examples and apply them on both plain and UNet architectures to verify the primary claims . Both ablation studies and experimental results on widely-used SIRS datasets are conducted to demonstrate the efficacy of YTMT , and reveal its superiority over other state-of-the-art alternatives . 2 Related Work . Over the past decades , much attention to resolving the image reflection separation problem has been drawn from the community . From the perspective of the required input amount , existing methods can be divided into two classes , i.e. , multi-image based and single image based methods . Multiple Image Reflection Separation ( MIRS ) . Utilizing multiple images [ 7 , 28 , 33 , 27 , 1 , 8 , 31 , 22 , 9 , 29 , 40 , 32 , 41 , 10 ] used to be a prevalent way to cope with the task , as more information complementary to single superimposed images , like varied conditions and relative motions , can be explored from a sequence of images to accomplish the task . For instance , Agrawal et al . [ 1 ] use flash and no-flash image pairs to remove reflections and highlights , while Szeliski et al . [ 33 ] employ focused and defocused pairs . A variety of approaches [ 7 , 27 , 8 , 22 , 33 , 40 , 41 ] have been proposed to seek relationships between different images , among which Farid and Adelson [ 7 ] employ independent component analysis to reduce reflections and lighting . Gai et al . [ 8 ] alternatively develop an algorithm to estimate layer motions and linear mixing coefficients for the recovery . Li and Brown [ 22 ] adopt the SIFT-flow to align the images , while Xue et al . [ 40 ] enforce a heavy tailed distribution on the obstruction and background components to penalize the overlapped gradients , making the two layers independent . Moreover , Yang et al . [ 41 ] introduce a generalized double-layer brightness consistency constraint to connect optical flow estimation and layer separation . Despite the satisfactory performance of MIRS methods , the need for specified shooting conditions and/or professional tools heavily limits their applicability . Single Image Reflection Separation ( SIRS ) . In practice , single image based schemes are more attractive . No doubt that , compared with those MIRS techniques , single image based ones are also more challenging due to less information available , demanding extra priors to regularize the solution . To mitigate the ill-posedness , Levin et al . [ 19 , 18 ] favor decompositions that have fewer edges and corners by imposing sparse gradients on the layers , in the same spirit as [ 40 ] . Levin and Weiss [ 17 ] allow users to manually annotate some dominant edges for the layers as explicit constraints for the problem , which requires careful human efforts to obtain favorable results . Li and Brown [ 23 ] assume one layer is smoother than the other , and penalize differently on the two layers in the gradient domain to split the reflection and transmission . Despite a progress made toward addressing the problem , the assumption and requirement could be frequently violated in real situations . Besides , these methods all rely on handcrafted features that may considerably restrict their performance . With the emergence of deep neural networks , learning based methods [ 6 , 44 , 42 , 38 , 20 , 39 ] have shown their power and become dominant for the task . Concretely , CEILNet [ 6 ] comprises of two stages , similarly to [ 17 , 37 ] . An edge map is estimated in the first stage , which performs as the guidance for the second stage to produce the final transmission layer . However , the network is hard to capture high-level information , thus having trouble guaranteeing the perceptual quality . Zhang et al . [ 44 ] further take the perception cue into consideration by coordinating hyper-column features [ 11 ] , the perceptual and adversarial losses . The exclusivity loss ( see also the gradient production penalization in [ 40 ] ) is designed for assuring gradients to be exclusive between the two components . This manner is also adopted by [ 38 ] . The main drawback is the insufficient capacity in processing cases with large transmission-reflection overlapped regions . Recently , ERRNet [ 38 ] was proposed , which enlarges the receptive field by introducing channel attention and pyramid pooling modules . It does not explicitly predict the reflection layer , which directly discards possible benefit from the supervision of reflection . IBCLN [ 20 ] proposes an iterative boost convolutional LSTM network to progressively split the two layers . However , such an iterative scheme slows down the training and predicting procedure . The errors will also be accumulated due to the high dependency on the outputs of previous stages . Though the learning based strategies have further stepped forward in single image reflection separation compared with the traditional methods , they either restore the target layers individually , or together with some concerns dealing with outputs ( e.g. , enforcing the linear combination constraint and/or concatenating them together as input ) . In other words , they barely take into account the interaction across the two streams/branches , which is key to the target task and also other dual-stream decomposition tasks . This study is mainly to demonstrate the effectiveness of such an interaction consideration . 3 Deep Interactive Dual-Stream Learning for SIRS . 3.1 YTMT Strategy . In this part , the proposed deep interactive dual-stream strategy will be detailed by centering around the concept that your trash is my treasure , which would be beneficial to a variety of two-component decomposition tasks using dual-branch networks . Prior works [ 21 , 35 ] have shown evidence on the effectiveness of passing information between two branches though , our YTMT strategy performs in a novel and principled way . We first give the definition of the negative ReLU function as follows : ReLU− ( x ) : = x− ReLU ( x ) = min ( x , 0 ) , ( 1 ) where ReLU ( x ) : = max ( x , 0 ) . By the negative ReLU , the deactivated features can be easily retained . Figure 1 ( a ) exhibits the behaviors of the ReLU and negative ReLU . Here , let x0 be the input to the first layer of the network , and x̃li ( i ∈ { 1 , 2 } for two branches ) denotes the feature obtained by the i-th branch after l stacked layers , i.e . x̃li : = Hli ( x0 ) . The inputs to the ( l + 1 ) -th layer are as follows : xl1 : = ReLU ( x̃ l 1 ) ⊕ ReLU − ( x̃l2 ) ; x l 2 : = ReLU ( x̃ l 2 ) ⊕ ReLU − ( x̃l1 ) , ( 2 ) where ⊕ can be either the concatenation operation or the addition between features activated by the ReLU function ( called normal connection ) and those by the ReLU− ( YTMT connection ) , as shown in Fig . 1 ( b ) and ( c ) . As can be obtained from Eq . ( 2 ) , the amount of information in xl1 and x l 2 is equivalent to that in x̃l1 and x̃ l 2 . This property guarantees no information flowing away from the interaction , which substantially avoids the problems of vanishing/exploding gradients and dead ReLU . Figure 2 ( c ) depicts a visual example of producing xL1 and x L 2 from x̃ L 1 and x̃ L 2 ( in this case , L = 6 ) . It shows that ReLU− ( x̃L1 ) is complementary to the ReLU ( x̃ L 2 ) and ReLU − ( x̃L2 ) is complementary to the ReLU ( x̃L1 ) . By merging the complementary counterparts , there is no information wasted by the rectifiers . In addition , this strategy can significantly speed up the decreasing of training error similarly to the ResNet design [ 13 ] , which will be empirically validated in Sec . 4 . Moreover , generally speaking , our strategy can be compatible with most , if not all , of the activation pairs ( e.g . Softmax and Softmin ) . But for the additive problems , the pair of ReLU and negative ReLU is more suitable due to its “ either A or B ” nature that satisfies the task of SIRS . We again emphasize that the proposed YTMT strategy is general and flexible , which can be implemented in various forms according to different demands . Figure 1 provides two YTMT block options . The one in ( b ) fuses features from the normal and YTMT connections by channel concatenation and 1× 1 convolutions , while the second in ( c ) simply employs feature addition . In addition , pixel and channel attention mechanism is introduced in both ( b ) and ( c ) to select and re-weight the merged features ( see [ 25 ] for details ) . Moreover , the YTMT blocks can be applied to most , if not all , of dualstream backbones by simple modification . In Fig . 2 ( a ) and ( b ) , two commonly-used architectures , i.e . U-shaped [ 26 ] and plain [ 43 ] networks , are present . We will shortly show how to construct these YTMT based networks , and demonstrate their improvement over the backbones and the superior performance on specific applications over other competitors .",
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"content": "This paper presents a nonlinear module that leverages both positive and negative ReLUs for feature separation. This enables applications such as reflection removal, blind denoising and other layer separation tasks. The paper shows several variants of adding the proposed module into existing CNN frameworks. Results show superior performance on several benchmark datasets.",
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science.evidence_inference | Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:
- Treatment: The specific medical procedure or drug being evaluated.
- Control: The standard or placebo used for comparison. If absent, use `null`.
- Result: The health outcome being assessed.
- Impact: The observed effect of the treatment on the result, categorized as: ("increased", "decreased", "no change").
- Justification: The supporting details for the observed impact.
Format the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].
Abstract:
ABSTRACT.BACKGROUND: Weight loss is common among patients with squamous cell carcinoma of the head and neck (SCCHN) and is mainly due to tumor and treatment related factors. The aim of the present study was to evaluate weight loss in patients with SCCHN undergoing two different radiotherapy (RT) schedules.
ABSTRACT.MATERIAL AND METHODS: Nutritional data were analyzed from the ARTSCAN study, a controlled randomized prospective Swedish multicenter study conducted with the aim of comparing conventional fractionation (2.0 Gy per day, total 68 Gy during 7 weeks) and accelerated fractionation (1.1 + 2.0 Gy per day, total 68 Gy during 4.5 weeks). Seven hundred and fifty patients were randomized and 712 patients were followed from the start of RT in the present nutritional study.
ABSTRACT.RESULTS: The patients had a weight loss of 11.3% (± 8.6%) during the acute phase (start of RT up to five months after the termination of RT). No difference in weight loss was seen between the two RT fractionation schedules (p = 0.839). Three factors were significantly predictive for weight loss during the acute phase, i.e. tumor site, overweight/obesity or lack of tube feeding at the start of RT. Moreover, the nadir point of weight loss occurred at five months after the termination of RT.
ABSTRACT.CONCLUSION: The results of the present study showed no difference in weight loss between the two RT fractionation schedules and also highlight that weight loss in SCCHN is a multifactorial problem. Moreover, the nadir of weight loss occurred at five months after the termination of treatment which calls for more intense nutritional interventions during the period after treatment.
Extracted Data: | [["conventional fractionation (2.0 Gy per day, total 68 Gy during 7 weeks)", "accelerated fractionation (1.1 + 2.0 Gy per day, total 68 Gy during 4.5 weeks)", "weight loss", "no significant difference", "No difference in weight loss was seen between the two RT fractionation schedules (p = 0.839)."]] | science.evidence_inference.215 | [
{
"content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND: Weight loss is common among patients with squamous cell carcinoma of the head and neck (SCCHN) and is mainly due to tumor and treatment related factors. The aim of the present study was to evaluate weight loss in patients with SCCHN undergoing two different radiotherapy (RT) schedules.\n\nABSTRACT.MATERIAL AND METHODS: Nutritional data were analyzed from the ARTSCAN study, a controlled randomized prospective Swedish multicenter study conducted with the aim of comparing conventional fractionation (2.0 Gy per day, total 68 Gy during 7 weeks) and accelerated fractionation (1.1 + 2.0 Gy per day, total 68 Gy during 4.5 weeks). Seven hundred and fifty patients were randomized and 712 patients were followed from the start of RT in the present nutritional study.\n\nABSTRACT.RESULTS: The patients had a weight loss of 11.3% (± 8.6%) during the acute phase (start of RT up to five months after the termination of RT). No difference in weight loss was seen between the two RT fractionation schedules (p = 0.839). Three factors were significantly predictive for weight loss during the acute phase, i.e. tumor site, overweight/obesity or lack of tube feeding at the start of RT. Moreover, the nadir point of weight loss occurred at five months after the termination of RT.\n\nABSTRACT.CONCLUSION: The results of the present study showed no difference in weight loss between the two RT fractionation schedules and also highlight that weight loss in SCCHN is a multifactorial problem. Moreover, the nadir of weight loss occurred at five months after the termination of treatment which calls for more intense nutritional interventions during the period after treatment.\n\nExtracted Data:",
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"content": "[[\"conventional fractionation (2.0 Gy per day, total 68 Gy during 7 weeks)\", \"accelerated fractionation (1.1 + 2.0 Gy per day, total 68 Gy during 4.5 weeks)\", \"weight loss\", \"no significant difference\", \"No difference in weight loss was seen between the two RT fractionation schedules (p = 0.839).\"]]",
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science.evidence_inference | Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:
- Treatment: The specific medical procedure or drug being evaluated.
- Control: The standard or placebo used for comparison. If absent, use `null`.
- Result: The health outcome being assessed.
- Impact: The observed effect of the treatment on the result, categorized as: ("increased", "decreased", "no change").
- Justification: The supporting details for the observed impact.
Format the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].
Abstract:
ABSTRACT.BACKGROUND: Direct-to-consumer (DTC) promotion of prescription drugs can affect consumer behaviors and health outcomes, and Internet drug promotion is growing rapidly. Branded drug websites often capitalize on the multimedia capabilities of the Internet by using videos to emphasize drug benefits and characteristics. However, it is unknown how such videos affect consumer processing of drug information.
ABSTRACT.OBJECTIVE: This study aimed to examine how videos on prescription drug websites, and the inclusion of risk information in those videos, influence consumer knowledge and perceptions.
ABSTRACT.METHODS: We conducted an experimental study in which online panel participants with acid reflux (n=1070) or high blood pressure (n=1055) were randomly assigned to view 1 of the 10 fictitious prescription drug websites and complete a short questionnaire. On each website, we manipulated the type of video (patient testimonial, mechanism of action animation, or none) and whether the video mentioned drug risks.
ABSTRACT.RESULTS: Participants who viewed any video were less likely to recognize drug risks presented only in the website text (P≤.01). Including risk information in videos increased participants' recognition of the risks presented in the videos (P≤.01). However, in some cases, including risk information in videos decreased participants' recognition of the risks not presented in the videos (ie, risks presented in text only; P≤.04). Participants who viewed a video without drug risk information thought that the website placed more emphasis on benefits, compared with participants who viewed the video with drug risk information (P≤.01). Compared with participants who viewed a video without drug risk information, participants who viewed a video with drug risk information thought that the drug was less effective in the high blood pressure sample (P=.03) and thought that risks were more serious in the acid reflux sample (P=.01). There were no significant differences between risk and nonrisk video conditions on other perception measures (P>.05). In addition, we noted a few differences among the types of videos.
ABSTRACT.CONCLUSIONS: Including risks in branded drug website videos may increase in-video risk retention at the expense of text-only risk retention.
Extracted Data: | [["websites presented a video of a personal testimonial, an informational mechanism of action- video viewed a video without drug risk information", " viewed the video with drug risk information ", "emphasis on benefits", "significantly increased", "Participants who viewed a video without drug risk information thought that the website placed more emphasis on benefits, compared with participants who viewed the video with drug risk information (P\u2264.01)."], ["websites presented a video of a personal testimonial, an informational mechanism of action- video viewed a video without drug risk information", " viewed the video with drug risk information ", "patients thought that the drug was less effective in the high blood pressure sample", "significantly decreased", "Compared with participants who viewed a video without drug risk information, participants who viewed a video with drug risk information thought that the drug was less effective in the high blood pressure sample (P=.03)"]] | science.evidence_inference.197 | [
{
"content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND: Direct-to-consumer (DTC) promotion of prescription drugs can affect consumer behaviors and health outcomes, and Internet drug promotion is growing rapidly. Branded drug websites often capitalize on the multimedia capabilities of the Internet by using videos to emphasize drug benefits and characteristics. However, it is unknown how such videos affect consumer processing of drug information.\n\nABSTRACT.OBJECTIVE: This study aimed to examine how videos on prescription drug websites, and the inclusion of risk information in those videos, influence consumer knowledge and perceptions.\n\nABSTRACT.METHODS: We conducted an experimental study in which online panel participants with acid reflux (n=1070) or high blood pressure (n=1055) were randomly assigned to view 1 of the 10 fictitious prescription drug websites and complete a short questionnaire. On each website, we manipulated the type of video (patient testimonial, mechanism of action animation, or none) and whether the video mentioned drug risks.\n\nABSTRACT.RESULTS: Participants who viewed any video were less likely to recognize drug risks presented only in the website text (P≤.01). Including risk information in videos increased participants' recognition of the risks presented in the videos (P≤.01). However, in some cases, including risk information in videos decreased participants' recognition of the risks not presented in the videos (ie, risks presented in text only; P≤.04). Participants who viewed a video without drug risk information thought that the website placed more emphasis on benefits, compared with participants who viewed the video with drug risk information (P≤.01). Compared with participants who viewed a video without drug risk information, participants who viewed a video with drug risk information thought that the drug was less effective in the high blood pressure sample (P=.03) and thought that risks were more serious in the acid reflux sample (P=.01). There were no significant differences between risk and nonrisk video conditions on other perception measures (P>.05). In addition, we noted a few differences among the types of videos.\n\nABSTRACT.CONCLUSIONS: Including risks in branded drug website videos may increase in-video risk retention at the expense of text-only risk retention.\n\nExtracted Data:",
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"content": "[[\"websites presented a video of a personal testimonial, an informational mechanism of action- video viewed a video without drug risk information\", \" viewed the video with drug risk information \", \"emphasis on benefits\", \"significantly increased\", \"Participants who viewed a video without drug risk information thought that the website placed more emphasis on benefits, compared with participants who viewed the video with drug risk information (P\\u2264.01).\"], [\"websites presented a video of a personal testimonial, an informational mechanism of action- video viewed a video without drug risk information\", \" viewed the video with drug risk information \", \"patients thought that the drug was less effective in the high blood pressure sample\", \"significantly decreased\", \"Compared with participants who viewed a video without drug risk information, participants who viewed a video with drug risk information thought that the drug was less effective in the high blood pressure sample (P=.03)\"]]",
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science.multicite_intent_classification | Examine the text below, which includes a citation marked by "<cite>" and "</cite>". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.
Provide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.
Text with Citation:
(Structural Correspondence Learning)<cite> (Blitzer et al., 2006</cite>; Blitzer, 2008 ) is a recently proposed domain adaptation technique which uses unlabeled data from both source and target domain to learn correspondences between features from different domains. | ["Background"] | science.multicite_intent_classification.1439 | [
{
"content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\n(Structural Correspondence Learning)<cite> (Blitzer et al., 2006</cite>; Blitzer, 2008 ) is a recently proposed domain adaptation technique which uses unlabeled data from both source and target domain to learn correspondences between features from different domains.",
"role": "user"
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"content": "[\"Background\"]",
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science.mup_single_document_summarization | Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.
Title of the Paper:
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
Main Content:
1 INTRODUCTION . Current machine learning models depend on the ability of training data to faithfully represent the data encountered during deployment . In practice , data distributions evolve ( Lipton et al. , 2018 ) , models encounter new scenarios ( Hendrycks & Gimpel , 2017 ) , and data curation procedures may capture only a narrow slice of the underlying data distribution ( Torralba & Efros , 2011 ) . Mismatches between the train and test data are commonplace , yet the study of this problem is not . As it stands , models do not robustly generalize across shifts in the data distribution . If models could identify when they are likely to be mistaken , or estimate uncertainty accurately , then the impact of such fragility might be ameliorated . Unfortunately , modern models already produce overconfident predictions when the training examples are independent and identically distributed to the test distribution . This overconfidence and miscalibration is greatly exacerbated by mismatched training and testing distributions . Small corruptions to the data distribution are enough to subvert existing classifiers , and techniques to improve corruption robustness remain few in number . Hendrycks & Dietterich ( 2019 ) show that classification error of modern models rises from 22 % on the usual ImageNet test set to 64 % on ImageNet-C , a test set consisting of various corruptions applied to ImageNet test images . Even methods which aim to explicitly quantify uncertainty , such as probabilistic and Bayesian neural networks , struggle under data shift , as recently demonstrated by Ovadia et al . ( 2019 ) . Improving performance in this setting has been difficult . One reason is that training against corruptions only encourages networks to memorize the specific corruptions seen during training and leaves models unable to generalize to new corruptions ( Vasiljevic et al. , 2016 ; Geirhos et al. , 2018 ) . Further , networks trained on translation augmentations remain highly sensitive to images shifted by a single pixel ( Gu et al. , 2019 ; Hendrycks & Dietterich , 2019 ) . Others have proposed aggressive data augmentation schemes ( Cubuk et al. , 2018 ) , though at the cost of a computational increase . Chun et al . ( 2019 ) demonstrates that many techniques may improve clean accuracy at the cost of robustness while many techniques which improve robustness harm uncertainty , and contrariwise . In all , existing techniques have considerable trade-offs . ∗Equal Contribution . †Corresponding author . CutOut MixUp CutMix AugMix In this work , we propose a technique to improve both the robustness and uncertainty estimates of classifiers under data shift . We propose AUGMIX , a method which simultaneously achieves new state-of-the-art results for robustness and uncertainty estimation while maintaining or improving accuracy on standard benchmark datasets . AUGMIX utilizes stochasticity and diverse augmentations , a Jensen-Shannon Divergence consistency loss , and a formulation to mix multiple augmented images to achieve state-of-the-art performance . On CIFAR-10 and CIFAR-100 , our method roughly halves the corruption robustness error of standard training procedures from 28.4 % to 12.4 % and 54.3 % to 37.8 % error , respectively . On ImageNet , AUGMIX also achieves state-of-the-art corruption robustness and decreases perturbation instability from 57.2 % to 37.4 % . Code is available at https : //github.com/google-research/augmix . 2 RELATED WORK Robustness under Data Shift . Geirhos et al . ( 2018 ) show that training against distortions can often fail to generalize to unseen distortions , as networks have a tendency to memorize properties of the specific training distortion . Vasiljevic et al . ( 2016 ) show training with various blur augmentations can fail to generalize to unseen blurs or blurs with different parameter settings . Hendrycks & Dietterich ( 2019 ) propose measuring generalization to unseen corruptions and provide benchmarks for doing so . Kang et al . ( 2019 ) construct an adversarial version of the aforementioned benchmark . Gilmer et al . ( 2018 ) ; Gilmer & Hendrycks ( 2019 ) argue that robustness to data shift is a pressing problem which greatly affects the reliability of real-world machine learning systems . Calibration under Data Shift . Guo et al . ( 2017 ) ; Nguyen & O ’ Connor ( 2015 ) propose metrics for determining the calibration of machine learning models . Lakshminarayanan et al . ( 2017 ) find that simply ensembling classifier predictions improves prediction calibration . Hendrycks et al . ( 2019a ) show that pre-training can also improve calibration . Ovadia et al . ( 2019 ) demonstrate that model calibration substantially deteriorates under data shift . Data Augmentation . Data augmentation can greatly improve generalization performance . For image data , random left-right flipping and cropping are commonly used He et al . ( 2015 ) . Random occlusion techniques such as Cutout can also improve accuracy on clean data ( Devries & Taylor , 2017 ; Zhong et al. , 2017 ) . Rather than occluding a portion of an image , CutMix replaces a portion of an image with a portion of a different image ( Yun et al. , 2019 ) . Mixup also uses information from two images . Rather than implanting one portion of an image inside another , Mixup produces an elementwise convex combination of two images ( Zhang et al. , 2017 ; Tokozume et al. , 2018 ) . Guo et al . ( 2019 ) show that Mixup can be improved with an adaptive mixing policy , so as to prevent manifold intrusion . Separate from these approaches are learned augmentation methods such as AutoAugment ( Cubuk et al. , 2018 ) , where a group of augmentations is tuned to optimize performance on a downstream task . Patch Gaussian augments data with Gaussian noise applied to a randomly chosen portion of an image ( Lopes et al. , 2019 ) . A popular way to make networks robust to ` p adversarial examples is with adversarial training ( Madry et al. , 2018 ) , which we use in this paper . However , this tends to increase training time by an order of magnitude and substantially degrades accuracy on non-adversarial images ( Raghunathan et al. , 2019 ) . 3 AUGMIX . AUGMIX is a data augmentation technique which improves model robustness and uncertainty estimates , and slots in easily to existing training pipelines . At a high level , AugMix is characterized by its utilization of simple augmentation operations in concert with a consistency loss . These augmentation operations are sampled stochastically and layered to produce a high diversity of augmented images . We then enforce a consistent embedding by the classifier across diverse augmentations of the same input image through the use of Jensen-Shannon divergence as a consistency loss . Mixing augmentations allows us to generate diverse transformations , which are important for inducing robustness , as a common failure mode of deep models in the arena of corruption robustness is the memorization of fixed augmentations ( Vasiljevic et al. , 2016 ; Geirhos et al. , 2018 ) . Previous methods have attempted to increase diversity by directly composing augmentation primitives in a chain , but this can cause the image to quickly degrade and drift off the data manifold , as depicted in Figure 3 . Such image degradation can be mitigated and the augmentation diversity can be maintained by mixing together the results of several augmentation chains in convex combinations . A concrete account of the algorithm is given in the pseudocode below . Algorithm AUGMIX Pseudocode 1 : Input : Model p̂ , Classification Loss L , Image xorig , Operations O = { rotate , . . . , posterize } 2 : function AugmentAndMix ( xorig , k = 3 , α = 1 ) 3 : Fill xaug with zeros 4 : Sample mixing weights ( w1 , w2 , . . . , wk ) ∼ Dirichlet ( α , α , . . . , α ) 5 : for i = 1 , . . . , k do 6 : Sample operations op1 , op2 , op3 ∼ O 7 : Compose operations with varying depth op12 = op2 ◦ op1 and op123 = op3 ◦ op2 ◦ op1 8 : Sample uniformly from one of these operations chain ∼ { op1 , op12 , op123 } 9 : xaug += wi · chain ( xorig ) . Addition is elementwise 10 : end for 11 : Sample weight m ∼ Beta ( α , α ) 12 : Interpolate with rule xaugmix = mxorig + ( 1−m ) xaug 13 : returnxaugmix 14 : end function 15 : xaugmix1 = AugmentAndMix ( xorig ) . xaugmix1 is stochastically generated 16 : xaugmix2 = AugmentAndMix ( xorig ) . xaugmix1 6= xaugmix2 17 : Loss Output : L ( p̂ ( y | xorig ) , y ) + λ Jensen-Shannon ( p̂ ( y | xorig ) ; p̂ ( y|xaugmix1 ) ; p̂ ( y|xaugmix2 ) ) Augmentations . Our method consists of mixing the results from augmentation chains or compositions of augmentation operations . We use operations from AutoAugment . Each operation is visualized in Appendix C. Crucially , we exclude operations which overlap with ImageNet-C corruptions . In particular , we remove the contrast , color , brightness , sharpness , and Cutout operations so that our set of operations and the ImageNet-C corruptions are disjoint . In turn , we do not use any image noising nor image blurring operations so that ImageNet-C corruptions are encountered only at test time . Operations such as rotate can be realized with varying severities , like 2◦ or −15◦ . For operations with varying severities , we uniformly sample the severity upon each application . Next , we randomly sample k augmentation chains , where k = 3 by default . Each augmentation chain is constructed by composing from one to three randomly selected augmentation operations . Mixing . The resulting images from these augmentation chains are combined by mixing . While we considered mixing by alpha compositing , we chose to use elementwise convex combinations for simplicity . The k-dimensional vector of convex coefficients is randomly sampled from a Dirichlet ( α , . . . , α ) distribution . Once these images are mixed , we use a “ skip connection ” to combine the result of the augmentation chain and the original image through a second random convex combination sampled from a Beta ( α , α ) distribution . The final image incorporates several sources of randomness from the choice of operations , the severity of these operations , the lengths of the augmentation chains , and the mixing weights . Jensen-Shannon Divergence Consistency Loss . We couple with this augmentation scheme a loss that enforces smoother neural network responses . Since the semantic content of an image is approximately preserved with AUGMIX , we should like the model to embed xorig , xaugmix1 , xaugmix2 similarly . Toward this end , we minimize the Jensen-Shannon divergence among the posterior distributions of the original sample xorig and its augmented variants . That is , for porig = p̂ ( y | xorig ) , paugmix1 = p̂ ( y | xaugmix1 ) , paugmix2 = p̂ ( y|xaugmix2 ) , we replace the original loss L with the loss L ( porig , y ) + λ JS ( porig ; paugmix1 ; paugmix2 ) . ( 1 ) To interpret this loss , imagine a sample from one of the three distributions porig , paugmix1 , paugmix2 . The Jensen-Shannon divergence can be understood to measure the average information that the sample reveals about the identity of the distribution from which it was sampled . This loss can be computed by first obtaining M = ( porig + paugmix1 + paugmix2 ) /3 and then computing JS ( porig ; paugmix1 ; paugmix2 ) = 1 3 ( KL [ porig‖M ] + KL [ paugmix1‖M ] + KL [ paugmix2‖M ] ) . ( 2 ) Unlike an arbitrary KL Divergence between porig and paugmix , the Jensen-Shannon divergence is upper bounded , in this case by the logarithm of the number of classes . Note that we could instead compute JS ( porig ; paugmix1 ) , though this does not perform as well . The gain of training with JS ( porig ; paugmix1 ; paugmix2 ; paugmix3 ) is marginal . The Jensen-Shannon Consistency Loss impels to model to be stable , consistent , and insensitive across to a diverse range of inputs ( Zheng et al. , 2016 ; Kannan et al. , 2018 ; Xie et al. , 2019 ) . Ablations are in Section 4.3 and Appendix A . | The paper discusses a new data augmentation method which improves the accuracy of the network for several specific shifted domain scenarios. The main goal of the paper is to increase the robustness of the deep model trained on the augmented data to generalize well beyond the data corruption like the rotation, translation, noise,.... For each input, they apply $k$ different operation of image shift and make the weighted combination of them. The weight vector is generated randomly from Dirichlet distribution with the parameter $\alpha$. The weighted combined images would be added to the original image in convex combination. The convex weights are generated from distribution Beta with parameter $\beta$. Later they train the network with adding the Jensen-Shannon divergence for the posterior distributions of augmented images as the consistency regularizer. They show this data augmentation will increase the accuracy of the model for shifted and non-shifted domains and also it leads to more calibrated model for domain shift problem. | science.mup_single_document_summarization.18093 | [
{
"content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nAugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty\n\nMain Content:\n1 INTRODUCTION . Current machine learning models depend on the ability of training data to faithfully represent the data encountered during deployment . In practice , data distributions evolve ( Lipton et al. , 2018 ) , models encounter new scenarios ( Hendrycks & Gimpel , 2017 ) , and data curation procedures may capture only a narrow slice of the underlying data distribution ( Torralba & Efros , 2011 ) . Mismatches between the train and test data are commonplace , yet the study of this problem is not . As it stands , models do not robustly generalize across shifts in the data distribution . If models could identify when they are likely to be mistaken , or estimate uncertainty accurately , then the impact of such fragility might be ameliorated . Unfortunately , modern models already produce overconfident predictions when the training examples are independent and identically distributed to the test distribution . This overconfidence and miscalibration is greatly exacerbated by mismatched training and testing distributions . Small corruptions to the data distribution are enough to subvert existing classifiers , and techniques to improve corruption robustness remain few in number . Hendrycks & Dietterich ( 2019 ) show that classification error of modern models rises from 22 % on the usual ImageNet test set to 64 % on ImageNet-C , a test set consisting of various corruptions applied to ImageNet test images . Even methods which aim to explicitly quantify uncertainty , such as probabilistic and Bayesian neural networks , struggle under data shift , as recently demonstrated by Ovadia et al . ( 2019 ) . Improving performance in this setting has been difficult . One reason is that training against corruptions only encourages networks to memorize the specific corruptions seen during training and leaves models unable to generalize to new corruptions ( Vasiljevic et al. , 2016 ; Geirhos et al. , 2018 ) . Further , networks trained on translation augmentations remain highly sensitive to images shifted by a single pixel ( Gu et al. , 2019 ; Hendrycks & Dietterich , 2019 ) . Others have proposed aggressive data augmentation schemes ( Cubuk et al. , 2018 ) , though at the cost of a computational increase . Chun et al . ( 2019 ) demonstrates that many techniques may improve clean accuracy at the cost of robustness while many techniques which improve robustness harm uncertainty , and contrariwise . In all , existing techniques have considerable trade-offs . ∗Equal Contribution . †Corresponding author . CutOut MixUp CutMix AugMix In this work , we propose a technique to improve both the robustness and uncertainty estimates of classifiers under data shift . We propose AUGMIX , a method which simultaneously achieves new state-of-the-art results for robustness and uncertainty estimation while maintaining or improving accuracy on standard benchmark datasets . AUGMIX utilizes stochasticity and diverse augmentations , a Jensen-Shannon Divergence consistency loss , and a formulation to mix multiple augmented images to achieve state-of-the-art performance . On CIFAR-10 and CIFAR-100 , our method roughly halves the corruption robustness error of standard training procedures from 28.4 % to 12.4 % and 54.3 % to 37.8 % error , respectively . On ImageNet , AUGMIX also achieves state-of-the-art corruption robustness and decreases perturbation instability from 57.2 % to 37.4 % . Code is available at https : //github.com/google-research/augmix . 2 RELATED WORK Robustness under Data Shift . Geirhos et al . ( 2018 ) show that training against distortions can often fail to generalize to unseen distortions , as networks have a tendency to memorize properties of the specific training distortion . Vasiljevic et al . ( 2016 ) show training with various blur augmentations can fail to generalize to unseen blurs or blurs with different parameter settings . Hendrycks & Dietterich ( 2019 ) propose measuring generalization to unseen corruptions and provide benchmarks for doing so . Kang et al . ( 2019 ) construct an adversarial version of the aforementioned benchmark . Gilmer et al . ( 2018 ) ; Gilmer & Hendrycks ( 2019 ) argue that robustness to data shift is a pressing problem which greatly affects the reliability of real-world machine learning systems . Calibration under Data Shift . Guo et al . ( 2017 ) ; Nguyen & O ’ Connor ( 2015 ) propose metrics for determining the calibration of machine learning models . Lakshminarayanan et al . ( 2017 ) find that simply ensembling classifier predictions improves prediction calibration . Hendrycks et al . ( 2019a ) show that pre-training can also improve calibration . Ovadia et al . ( 2019 ) demonstrate that model calibration substantially deteriorates under data shift . Data Augmentation . Data augmentation can greatly improve generalization performance . For image data , random left-right flipping and cropping are commonly used He et al . ( 2015 ) . Random occlusion techniques such as Cutout can also improve accuracy on clean data ( Devries & Taylor , 2017 ; Zhong et al. , 2017 ) . Rather than occluding a portion of an image , CutMix replaces a portion of an image with a portion of a different image ( Yun et al. , 2019 ) . Mixup also uses information from two images . Rather than implanting one portion of an image inside another , Mixup produces an elementwise convex combination of two images ( Zhang et al. , 2017 ; Tokozume et al. , 2018 ) . Guo et al . ( 2019 ) show that Mixup can be improved with an adaptive mixing policy , so as to prevent manifold intrusion . Separate from these approaches are learned augmentation methods such as AutoAugment ( Cubuk et al. , 2018 ) , where a group of augmentations is tuned to optimize performance on a downstream task . Patch Gaussian augments data with Gaussian noise applied to a randomly chosen portion of an image ( Lopes et al. , 2019 ) . A popular way to make networks robust to ` p adversarial examples is with adversarial training ( Madry et al. , 2018 ) , which we use in this paper . However , this tends to increase training time by an order of magnitude and substantially degrades accuracy on non-adversarial images ( Raghunathan et al. , 2019 ) . 3 AUGMIX . AUGMIX is a data augmentation technique which improves model robustness and uncertainty estimates , and slots in easily to existing training pipelines . At a high level , AugMix is characterized by its utilization of simple augmentation operations in concert with a consistency loss . These augmentation operations are sampled stochastically and layered to produce a high diversity of augmented images . We then enforce a consistent embedding by the classifier across diverse augmentations of the same input image through the use of Jensen-Shannon divergence as a consistency loss . Mixing augmentations allows us to generate diverse transformations , which are important for inducing robustness , as a common failure mode of deep models in the arena of corruption robustness is the memorization of fixed augmentations ( Vasiljevic et al. , 2016 ; Geirhos et al. , 2018 ) . Previous methods have attempted to increase diversity by directly composing augmentation primitives in a chain , but this can cause the image to quickly degrade and drift off the data manifold , as depicted in Figure 3 . Such image degradation can be mitigated and the augmentation diversity can be maintained by mixing together the results of several augmentation chains in convex combinations . A concrete account of the algorithm is given in the pseudocode below . Algorithm AUGMIX Pseudocode 1 : Input : Model p̂ , Classification Loss L , Image xorig , Operations O = { rotate , . . . , posterize } 2 : function AugmentAndMix ( xorig , k = 3 , α = 1 ) 3 : Fill xaug with zeros 4 : Sample mixing weights ( w1 , w2 , . . . , wk ) ∼ Dirichlet ( α , α , . . . , α ) 5 : for i = 1 , . . . , k do 6 : Sample operations op1 , op2 , op3 ∼ O 7 : Compose operations with varying depth op12 = op2 ◦ op1 and op123 = op3 ◦ op2 ◦ op1 8 : Sample uniformly from one of these operations chain ∼ { op1 , op12 , op123 } 9 : xaug += wi · chain ( xorig ) . Addition is elementwise 10 : end for 11 : Sample weight m ∼ Beta ( α , α ) 12 : Interpolate with rule xaugmix = mxorig + ( 1−m ) xaug 13 : returnxaugmix 14 : end function 15 : xaugmix1 = AugmentAndMix ( xorig ) . xaugmix1 is stochastically generated 16 : xaugmix2 = AugmentAndMix ( xorig ) . xaugmix1 6= xaugmix2 17 : Loss Output : L ( p̂ ( y | xorig ) , y ) + λ Jensen-Shannon ( p̂ ( y | xorig ) ; p̂ ( y|xaugmix1 ) ; p̂ ( y|xaugmix2 ) ) Augmentations . Our method consists of mixing the results from augmentation chains or compositions of augmentation operations . We use operations from AutoAugment . Each operation is visualized in Appendix C. Crucially , we exclude operations which overlap with ImageNet-C corruptions . In particular , we remove the contrast , color , brightness , sharpness , and Cutout operations so that our set of operations and the ImageNet-C corruptions are disjoint . In turn , we do not use any image noising nor image blurring operations so that ImageNet-C corruptions are encountered only at test time . Operations such as rotate can be realized with varying severities , like 2◦ or −15◦ . For operations with varying severities , we uniformly sample the severity upon each application . Next , we randomly sample k augmentation chains , where k = 3 by default . Each augmentation chain is constructed by composing from one to three randomly selected augmentation operations . Mixing . The resulting images from these augmentation chains are combined by mixing . While we considered mixing by alpha compositing , we chose to use elementwise convex combinations for simplicity . The k-dimensional vector of convex coefficients is randomly sampled from a Dirichlet ( α , . . . , α ) distribution . Once these images are mixed , we use a “ skip connection ” to combine the result of the augmentation chain and the original image through a second random convex combination sampled from a Beta ( α , α ) distribution . The final image incorporates several sources of randomness from the choice of operations , the severity of these operations , the lengths of the augmentation chains , and the mixing weights . Jensen-Shannon Divergence Consistency Loss . We couple with this augmentation scheme a loss that enforces smoother neural network responses . Since the semantic content of an image is approximately preserved with AUGMIX , we should like the model to embed xorig , xaugmix1 , xaugmix2 similarly . Toward this end , we minimize the Jensen-Shannon divergence among the posterior distributions of the original sample xorig and its augmented variants . That is , for porig = p̂ ( y | xorig ) , paugmix1 = p̂ ( y | xaugmix1 ) , paugmix2 = p̂ ( y|xaugmix2 ) , we replace the original loss L with the loss L ( porig , y ) + λ JS ( porig ; paugmix1 ; paugmix2 ) . ( 1 ) To interpret this loss , imagine a sample from one of the three distributions porig , paugmix1 , paugmix2 . The Jensen-Shannon divergence can be understood to measure the average information that the sample reveals about the identity of the distribution from which it was sampled . This loss can be computed by first obtaining M = ( porig + paugmix1 + paugmix2 ) /3 and then computing JS ( porig ; paugmix1 ; paugmix2 ) = 1 3 ( KL [ porig‖M ] + KL [ paugmix1‖M ] + KL [ paugmix2‖M ] ) . ( 2 ) Unlike an arbitrary KL Divergence between porig and paugmix , the Jensen-Shannon divergence is upper bounded , in this case by the logarithm of the number of classes . Note that we could instead compute JS ( porig ; paugmix1 ) , though this does not perform as well . The gain of training with JS ( porig ; paugmix1 ; paugmix2 ; paugmix3 ) is marginal . The Jensen-Shannon Consistency Loss impels to model to be stable , consistent , and insensitive across to a diverse range of inputs ( Zheng et al. , 2016 ; Kannan et al. , 2018 ; Xie et al. , 2019 ) . Ablations are in Section 4.3 and Appendix A .",
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"content": "The paper discusses a new data augmentation method which improves the accuracy of the network for several specific shifted domain scenarios. The main goal of the paper is to increase the robustness of the deep model trained on the augmented data to generalize well beyond the data corruption like the rotation, translation, noise,.... For each input, they apply $k$ different operation of image shift and make the weighted combination of them. The weight vector is generated randomly from Dirichlet distribution with the parameter $\\alpha$. The weighted combined images would be added to the original image in convex combination. The convex weights are generated from distribution Beta with parameter $\\beta$. Later they train the network with adding the Jensen-Shannon divergence for the posterior distributions of augmented images as the consistency regularizer. They show this data augmentation will increase the accuracy of the model for shifted and non-shifted domains and also it leads to more calibrated model for domain shift problem.",
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science.multicite_intent_classification | Examine the text below, which includes a citation marked by "<cite>" and "</cite>". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.
Provide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.
Text with Citation:
Yet, in contrast to <cite>Barzilay and Lapata (2008)</cite> , <cite>who</cite> employ the syntactic properties of the respective occurrences, we reduce the accounts to whether or not the entities occur in subsequent sentences (similar to Karamanis (2004) 's NOCB metric). | ["Differences"] | science.multicite_intent_classification.972 | [
{
"content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nYet, in contrast to <cite>Barzilay and Lapata (2008)</cite> , <cite>who</cite> employ the syntactic properties of the respective occurrences, we reduce the accounts to whether or not the entities occur in subsequent sentences (similar to Karamanis (2004) 's NOCB metric).",
"role": "user"
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"content": "[\"Differences\"]",
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science.evidence_inference | Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:
- Treatment: The specific medical procedure or drug being evaluated.
- Control: The standard or placebo used for comparison. If absent, use `null`.
- Result: The health outcome being assessed.
- Impact: The observed effect of the treatment on the result, categorized as: ("increased", "decreased", "no change").
- Justification: The supporting details for the observed impact.
Format the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].
Abstract:
ABSTRACT: We examined the effects of a 10–14-weeks inpatient lifestyle modification program, including minimum 90 min of physical activity (PA) five days/week, on body composition, CVD risk factors, and eating behavior in 139 obese subjects (BMI 42.6 ± 5.2 kg/m2). Completion rate was 71% (n = 71) in the intensive lifestyle intervention (ILI) group and 85% (n = 33) among waiting list controls. Compared to controls body weight (−17.0 (95% CI: −18.7, −15.3) kg, P < 0.0001), fat mass (−15.2 (95% CI: −17.4, −13.1) kg, P < 0.0001), fat free mass (−1.2 (95% CI: −2.2, −0.2) kg, P = 0.016) and visceral fat (−86.6(95% CI: −97.4, −75.7) cm2, P < 0.0001) were reduced in the ILI-group after 10–14 weeks. Within the ILI-group weight loss was −23.8 (95% CI: −25.9, −21.7) kg, P < 0.0001 and -20.3 (95% CI: −23.3, −17.3) kg, P < 0.0001, after six and 12 months, respectively. Systolic BP, glucose, triglycerides, and LDL-C were reduced, and HDL-C was increased (all P ≤ 0.006) after 10–14 weeks within the ILI group. The reduction in glucose and increase in HDL-C were sustained after 12 months (all P < 0.0001). After one year, weight loss was related to increased cognitive restraint and decreased uncontrolled eating (all P < 0.05). Thus, ILI including high volume of PA resulted in weight loss with almost maintenance of fat-free mass, favorable changes in CVD risk factors, and eating behavior in subjects with severe obesity.
Extracted Data: | [["Intensive lifestyle intervention (ILI) group", "Waiting list control", "Body weight", "significantly decreased", "Compared to controls body weight (\u221217.0 (95% CI: \u221218.7, \u221215.3) kg, P \u003c 0.0001), fat mass (\u221215.2 (95% CI: \u221217.4, \u221213.1) kg, P \u003c 0.0001), fat free mass (\u22121.2 (95% CI: \u22122.2, \u22120.2) kg, P = 0.016) and visceral fat (\u221286.6(95% CI: \u221297.4, \u221275.7) cm2, P \u003c 0.0001) were reduced in the ILI-group after 10\u201314 weeks."], ["Intensive lifestyle intervention (ILI) group", "Waiting list control", "Fat mass", "significantly decreased", "Compared to controls body weight (\u221217.0 (95% CI: \u221218.7, \u221215.3) kg, P \u003c 0.0001), fat mass (\u221215.2 (95% CI: \u221217.4, \u221213.1) kg, P \u003c 0.0001), fat free mass (\u22121.2 (95% CI: \u22122.2, \u22120.2) kg, P = 0.016) and visceral fat (\u221286.6(95% CI: \u221297.4, \u221275.7) cm2, P \u003c 0.0001) were reduced in the ILI-group after 10\u201314 weeks."], ["Intensive lifestyle intervention (ILI) group", "Waiting list control", "Fat mass", "significantly decreased", ". Compared to controls body weight (\u221217.0 (95% CI: \u221218.7, \u221215.3) kg, P \u003c 0.0001), fat mass (\u221215.2 (95% CI: \u221217.4, \u221213.1) kg, P \u003c 0.0001), fat free mass (\u22121.2 (95% CI: \u22122.2, \u22120.2) kg, P = 0.016) and visceral fat (\u221286.6(95% CI: \u221297.4, \u221275.7) cm2, P \u003c 0.0001) were reduced in the ILI-group after 10\u201314 weeks"], ["Intensive lifestyle intervention (ILI) group", "Waiting list control", "Visceral fat", "significantly decreased", "Compared to controls body weight (\u221217.0 (95% CI: \u221218.7, \u221215.3) kg, P \u003c 0.0001), fat mass (\u221215.2 (95% CI: \u221217.4, \u221213.1) kg, P \u003c 0.0001), fat free mass (\u22121.2 (95% CI: \u22122.2, \u22120.2) kg, P = 0.016) and visceral fat (\u221286.6(95% CI: \u221297.4, \u221275.7) cm2, P \u003c 0.0001) were reduced in the ILI-group after 10\u201314 weeks."]] | science.evidence_inference.104 | [
{
"content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT: We examined the effects of a 10–14-weeks inpatient lifestyle modification program, including minimum 90 min of physical activity (PA) five days/week, on body composition, CVD risk factors, and eating behavior in 139 obese subjects (BMI 42.6 ± 5.2 kg/m2). Completion rate was 71% (n = 71) in the intensive lifestyle intervention (ILI) group and 85% (n = 33) among waiting list controls. Compared to controls body weight (−17.0 (95% CI: −18.7, −15.3) kg, P < 0.0001), fat mass (−15.2 (95% CI: −17.4, −13.1) kg, P < 0.0001), fat free mass (−1.2 (95% CI: −2.2, −0.2) kg, P = 0.016) and visceral fat (−86.6(95% CI: −97.4, −75.7) cm2, P < 0.0001) were reduced in the ILI-group after 10–14 weeks. Within the ILI-group weight loss was −23.8 (95% CI: −25.9, −21.7) kg, P < 0.0001 and -20.3 (95% CI: −23.3, −17.3) kg, P < 0.0001, after six and 12 months, respectively. Systolic BP, glucose, triglycerides, and LDL-C were reduced, and HDL-C was increased (all P ≤ 0.006) after 10–14 weeks within the ILI group. The reduction in glucose and increase in HDL-C were sustained after 12 months (all P < 0.0001). After one year, weight loss was related to increased cognitive restraint and decreased uncontrolled eating (all P < 0.05). Thus, ILI including high volume of PA resulted in weight loss with almost maintenance of fat-free mass, favorable changes in CVD risk factors, and eating behavior in subjects with severe obesity.\n\nExtracted Data:",
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"content": "[[\"Intensive lifestyle intervention (ILI) group\", \"Waiting list control\", \"Body weight\", \"significantly decreased\", \"Compared to controls body weight (\\u221217.0 (95% CI: \\u221218.7, \\u221215.3) kg, P \\u003c 0.0001), fat mass (\\u221215.2 (95% CI: \\u221217.4, \\u221213.1) kg, P \\u003c 0.0001), fat free mass (\\u22121.2 (95% CI: \\u22122.2, \\u22120.2) kg, P = 0.016) and visceral fat (\\u221286.6(95% CI: \\u221297.4, \\u221275.7) cm2, P \\u003c 0.0001) were reduced in the ILI-group after 10\\u201314 weeks.\"], [\"Intensive lifestyle intervention (ILI) group\", \"Waiting list control\", \"Fat mass\", \"significantly decreased\", \"Compared to controls body weight (\\u221217.0 (95% CI: \\u221218.7, \\u221215.3) kg, P \\u003c 0.0001), fat mass (\\u221215.2 (95% CI: \\u221217.4, \\u221213.1) kg, P \\u003c 0.0001), fat free mass (\\u22121.2 (95% CI: \\u22122.2, \\u22120.2) kg, P = 0.016) and visceral fat (\\u221286.6(95% CI: \\u221297.4, \\u221275.7) cm2, P \\u003c 0.0001) were reduced in the ILI-group after 10\\u201314 weeks.\"], [\"Intensive lifestyle intervention (ILI) group\", \"Waiting list control\", \"Fat mass\", \"significantly decreased\", \". Compared to controls body weight (\\u221217.0 (95% CI: \\u221218.7, \\u221215.3) kg, P \\u003c 0.0001), fat mass (\\u221215.2 (95% CI: \\u221217.4, \\u221213.1) kg, P \\u003c 0.0001), fat free mass (\\u22121.2 (95% CI: \\u22122.2, \\u22120.2) kg, P = 0.016) and visceral fat (\\u221286.6(95% CI: \\u221297.4, \\u221275.7) cm2, P \\u003c 0.0001) were reduced in the ILI-group after 10\\u201314 weeks\"], [\"Intensive lifestyle intervention (ILI) group\", \"Waiting list control\", \"Visceral fat\", \"significantly decreased\", \"Compared to controls body weight (\\u221217.0 (95% CI: \\u221218.7, \\u221215.3) kg, P \\u003c 0.0001), fat mass (\\u221215.2 (95% CI: \\u221217.4, \\u221213.1) kg, P \\u003c 0.0001), fat free mass (\\u22121.2 (95% CI: \\u22122.2, \\u22120.2) kg, P = 0.016) and visceral fat (\\u221286.6(95% CI: \\u221297.4, \\u221275.7) cm2, P \\u003c 0.0001) were reduced in the ILI-group after 10\\u201314 weeks.\"]]",
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science.evidence_inference | Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:
- Treatment: The specific medical procedure or drug being evaluated.
- Control: The standard or placebo used for comparison. If absent, use `null`.
- Result: The health outcome being assessed.
- Impact: The observed effect of the treatment on the result, categorized as: ("increased", "decreased", "no change").
- Justification: The supporting details for the observed impact.
Format the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].
Abstract:
ABSTRACT: This study was undertaken to evaluate the influence of treatment with rifaximin followed by the probiotic VSL#3 versus no treatment on the progression of chronic prostatitis toward chronic microbial prostate-vesiculitis (PV) or prostate-vesiculo-epididymitis (PVE). A total of 106 selected infertile male patients with bacteriologically cured chronic bacterial prostatitis (CBP) and irritable bowel syndrome (IBS) were randomly prescribed rifaximin (200 mg, 2 tablets bid, for 7 days monthly for 12 months) and probiotic containing multiple strains VSL#3 (450 × 109 CFU per day) or no treatment. Ninety-five of them (89.6%) complied with the therapeutic plan and were included in this study. Group A = "6Tx/6-": treatment for the initial 6 and no treatment for the following 6 months (n = 26); Group B = "12Tx": 12 months of treatment (n = 22); Group C = "6-/6Tx": no treatment for the initial 6 months and treatment in the last 6 months (n = 23); Group D = "12-": no treatment (n = 24). The patients of Groups A = "6Tx/6-" and B = "12Tx" had the highest frequency of chronic prostatitis (88.5% and 86.4%, respectively). In contrast, group "12-": patients had the lowest frequency of prostatitis (33.4%). The progression of prostatitis into PV in groups "6Tx/6-" (15.5%) and "6-/6Tx" (13.6%) was lower than that found in the patients of group "12-" (45.8%). Finally, no patient of groups "6Tx/6-" and "6-/6Tx" had PVE, whereas it was diagnosed in 20.8% of group "12-" patients. Long-term treatment with rifaximin and the probiotic VSL#3 is effective in lowering the progression of prostatitis into more complicated forms of male accessory gland infections in infertile patients with bacteriologically cured CBP plus IBS.
Extracted Data: | [["rifaximin followed by the probiotic VSL#3", "no treatment", "progression of chronic prostatitis plus IBS toward chronic microbial prostate-vesiculitis (PV) or prostate-vesiculo-epididymitis (PVE) in infertile males", "significantly decreased", "Long-term treatment with rifaximin and the probiotic VSL#3 is effective in lowering the progression of prostatitis into more complicated forms of male accessory gland infections"]] | science.evidence_inference.82 | [
{
"content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT: This study was undertaken to evaluate the influence of treatment with rifaximin followed by the probiotic VSL#3 versus no treatment on the progression of chronic prostatitis toward chronic microbial prostate-vesiculitis (PV) or prostate-vesiculo-epididymitis (PVE). A total of 106 selected infertile male patients with bacteriologically cured chronic bacterial prostatitis (CBP) and irritable bowel syndrome (IBS) were randomly prescribed rifaximin (200 mg, 2 tablets bid, for 7 days monthly for 12 months) and probiotic containing multiple strains VSL#3 (450 × 109 CFU per day) or no treatment. Ninety-five of them (89.6%) complied with the therapeutic plan and were included in this study. Group A = \"6Tx/6-\": treatment for the initial 6 and no treatment for the following 6 months (n = 26); Group B = \"12Tx\": 12 months of treatment (n = 22); Group C = \"6-/6Tx\": no treatment for the initial 6 months and treatment in the last 6 months (n = 23); Group D = \"12-\": no treatment (n = 24). The patients of Groups A = \"6Tx/6-\" and B = \"12Tx\" had the highest frequency of chronic prostatitis (88.5% and 86.4%, respectively). In contrast, group \"12-\": patients had the lowest frequency of prostatitis (33.4%). The progression of prostatitis into PV in groups \"6Tx/6-\" (15.5%) and \"6-/6Tx\" (13.6%) was lower than that found in the patients of group \"12-\" (45.8%). Finally, no patient of groups \"6Tx/6-\" and \"6-/6Tx\" had PVE, whereas it was diagnosed in 20.8% of group \"12-\" patients. Long-term treatment with rifaximin and the probiotic VSL#3 is effective in lowering the progression of prostatitis into more complicated forms of male accessory gland infections in infertile patients with bacteriologically cured CBP plus IBS.\n\nExtracted Data:",
"role": "user"
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"content": "[[\"rifaximin followed by the probiotic VSL#3\", \"no treatment\", \"progression of chronic prostatitis plus IBS toward chronic microbial prostate-vesiculitis (PV) or prostate-vesiculo-epididymitis (PVE) in infertile males\", \"significantly decreased\", \"Long-term treatment with rifaximin and the probiotic VSL#3 is effective in lowering the progression of prostatitis into more complicated forms of male accessory gland infections\"]]",
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science.biored_ner | Analyze the provided biomedical abstract and identify distinct entities. Focus on the specified categories: ["CellLine", "Variant", "Species", "Disease", "Chemical", "Gene"].
Ensure the output is strictly the JSON object.
Source Text:
Risks and benefits of COX-2 inhibitors vs non-selective NSAIDs: does their cardiovascular risk exceed their gastrointestinal benefit? A retrospective cohort study. OBJECTIVES: The risk of acute myocardial infarction (AMI) with COX-2 inhibitors may offset their gastrointestinal (GI) benefit compared with non-selective (NS) non-steroidal anti-inflammatory drugs (NSAIDs). We aimed to compare the risks of hospitalization for AMI and GI bleeding among elderly patients using COX-2 inhibitors, NS-NSAIDs and acetaminophen. METHODS: We conducted a retrospective cohort study using administrative data of patients > or =65 years of age who filled a prescription for NSAID or acetaminophen during 1999-2002. Outcomes were compared using Cox regression models with time-dependent exposures. RESULTS: Person-years of exposure among non-users of aspirin were: 75,761 to acetaminophen, 42,671 to rofecoxib 65,860 to celecoxib, and 37,495 to NS-NSAIDs. Among users of aspirin, they were: 14,671 to rofecoxib, 22,875 to celecoxib, 9,832 to NS-NSAIDs and 38,048 to acetaminophen. Among non-users of aspirin, the adjusted hazard ratios (95% confidence interval) of hospitalization for AMI/GI vs the acetaminophen (with no aspirin) group were: rofecoxib 1.27 (1.13, 1.42), celecoxib 0.93 (0.83, 1.03), naproxen 1.59 (1.31, 1.93), diclofenac 1.17 (0.99, 1.38) and ibuprofen 1.05 (0.74, 1.51). Among users of aspirin, they were: rofecoxib 1.73 (1.52, 1.98), celecoxib 1.34 (1.19, 1.52), ibuprofen 1.51 (0.95, 2.41), diclofenac 1.69 (1.35, 2.10), naproxen 1.35 (0.97, 1.88) and acetaminophen 1.29 (1.17, 1.42). CONCLUSION: Among non-users of aspirin, naproxen seemed to carry the highest risk for AMI/GI bleeding. The AMI/GI toxicity of celecoxib was similar to that of acetaminophen and seemed to be better than those of rofecoxib and NS-NSAIDs. Among users of aspirin, both celecoxib and naproxen seemed to be the least toxic. | {"CellLine": [], "Chemical": ["non-steroidal anti-inflammatory drugs", "celecoxib", "ibuprofen", "rofecoxib", "aspirin", "naproxen", "COX-2 inhibitors", "NSAID", "acetaminophen", "NSAIDs", "diclofenac"], "Disease": ["GI toxicity", "GI bleeding", "acute myocardial infarction", "AMI"], "Gene": [], "Species": ["patients"], "Variant": []} | science.biored_ner.85 | [
{
"content": "Analyze the provided biomedical abstract and identify distinct entities. Focus on the specified categories: [\"CellLine\", \"Variant\", \"Species\", \"Disease\", \"Chemical\", \"Gene\"].\n\nEnsure the output is strictly the JSON object.\n\nSource Text:\n\nRisks and benefits of COX-2 inhibitors vs non-selective NSAIDs: does their cardiovascular risk exceed their gastrointestinal benefit? A retrospective cohort study. OBJECTIVES: The risk of acute myocardial infarction (AMI) with COX-2 inhibitors may offset their gastrointestinal (GI) benefit compared with non-selective (NS) non-steroidal anti-inflammatory drugs (NSAIDs). We aimed to compare the risks of hospitalization for AMI and GI bleeding among elderly patients using COX-2 inhibitors, NS-NSAIDs and acetaminophen. METHODS: We conducted a retrospective cohort study using administrative data of patients > or =65 years of age who filled a prescription for NSAID or acetaminophen during 1999-2002. Outcomes were compared using Cox regression models with time-dependent exposures. RESULTS: Person-years of exposure among non-users of aspirin were: 75,761 to acetaminophen, 42,671 to rofecoxib 65,860 to celecoxib, and 37,495 to NS-NSAIDs. Among users of aspirin, they were: 14,671 to rofecoxib, 22,875 to celecoxib, 9,832 to NS-NSAIDs and 38,048 to acetaminophen. Among non-users of aspirin, the adjusted hazard ratios (95% confidence interval) of hospitalization for AMI/GI vs the acetaminophen (with no aspirin) group were: rofecoxib 1.27 (1.13, 1.42), celecoxib 0.93 (0.83, 1.03), naproxen 1.59 (1.31, 1.93), diclofenac 1.17 (0.99, 1.38) and ibuprofen 1.05 (0.74, 1.51). Among users of aspirin, they were: rofecoxib 1.73 (1.52, 1.98), celecoxib 1.34 (1.19, 1.52), ibuprofen 1.51 (0.95, 2.41), diclofenac 1.69 (1.35, 2.10), naproxen 1.35 (0.97, 1.88) and acetaminophen 1.29 (1.17, 1.42). CONCLUSION: Among non-users of aspirin, naproxen seemed to carry the highest risk for AMI/GI bleeding. The AMI/GI toxicity of celecoxib was similar to that of acetaminophen and seemed to be better than those of rofecoxib and NS-NSAIDs. Among users of aspirin, both celecoxib and naproxen seemed to be the least toxic.",
"role": "user"
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{
"content": "{\"CellLine\": [], \"Chemical\": [\"non-steroidal anti-inflammatory drugs\", \"celecoxib\", \"ibuprofen\", \"rofecoxib\", \"aspirin\", \"naproxen\", \"COX-2 inhibitors\", \"NSAID\", \"acetaminophen\", \"NSAIDs\", \"diclofenac\"], \"Disease\": [\"GI toxicity\", \"GI bleeding\", \"acute myocardial infarction\", \"AMI\"], \"Gene\": [], \"Species\": [\"patients\"], \"Variant\": []}",
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science.mup_single_document_summarization | Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.
Title of the Paper:
Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge Distillation
Main Content:
1 Introduction . Measuring the similarity across graphs , i.e. , Graph Similarity Computation ( GSC ) , is one of the core problems of graph data mining , centered around by multiple downstream tasks such as graph retrieval [ 1 , 2 ] , plagiarism/anomaly detection [ 22 , 41 ] , graph clustering [ 39 ] , etc . As shown in Fig . 1 , the graph similarity can be defined as distances between graphs , such as Graph Edit Distance ( GED ) . The conventional solutions towards GSC are the exact computation of these graph distances , which , however , is an NP-hard problem . Therefore , such exact solutions are less favorable when handling large-scale graphs due to the expensive computation cost . Computational time , especially run time in inference stage , is particularly important in industrial scenarios . As a motivating example , in graph-structured molecules or chemical compounds query for in-silico drug screening , fast identifying similar compounds in a large database is a key process [ 25 ] . Leveraging the strong representational power of graph neural network ( GNN ) [ 21 , 13 , 43 , 42 ] , the GNN-based approximate GSC solutions have gained increasing popularity . To adapt GNNs to the GSC task , the target similarity score ( e.g. , GED ) is normalized into the range of ( 0 , 1 ] . In this way , the ∗Corresponding Author : qin.ca @ northeastern.edu 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . GSC can be regarded as a single-value regression problem that outputs a similarity score given two graphs as inputs . A standard design can be summarized as a twin of GNNs bridged by a co-attention with a Multi-layer Perceptron ( MLP ) stacked as the regression head . Such approaches can be trained in a fully supervised way using the Mean Square Error ( MSE ) loss computed over the ground truth similarity score . Many GNN-based GSC methods [ 1 , 2 , 22 ] followed such strategy , which , however , suffers from the fusion issue . The paper presents a novel solution to both effectively and efficiently address the task of approximate GSC . Compared to the commonly used graph convolutional network as the backbone [ 1 , 2 ] , this paper adopts a more robust network , i.e. , Graph Isomorphism Network ( GIN ) [ 43 ] . Cross-graph fusion is essential to the model . The multi-scale features within different GIN layers are fused with a new design . We have adopted an attention layer stacked over the concatenated cross-graph features for smooth feature fusion . To this end , similar features will be assigned with more weights to contribute to the desired task . Moreover , to make the model easier to deploy , we take an MLP for feature learning which is simple but effective to achieve cutting-edge performance . Intuitively , speed and accuracy can be considered as a trade-off . GSC naturally requires dense connections/interactions between the two input graphs , which will consequently cause increasing computations as the cost . This paper focuses on the efficiency of inference speed which can be addressed by either model compression or a faster data loading pipeline . Especially in industrial scenarios , the raw graph data are usually pre-processed as the embeddings off-line that can be easily applied to the real-time downstream tasks , e.g. , molecular graph retrieval . However , as shown in Fig . 2 , most of the co-attention-based GSC solutions employ feature fusion in the early stage , which only outputs the joint embedding of pairing graphs . Inspired by [ 26 ] , we propose a lightweight model that removes all the early feature fusion modules in the encoder for efficient GSC . In this way , as shown in Fig . 2 , the individual embedding of each graph can be collected by a Siamese GNN . Such pairing graph embeddings will be fused with an attention layer to predict the final similarity score . To overcome the accuracy drop of such a small network , we take a novel paradigm of Knowledge Distillation ( KD ) specifically designed for our task . As shown in Fig . 3 , we propose an early-feature fusion network regarded as the teacher model , and the student model is a siamese network without co-attention . It is found that the direct distillation of joint embeddings fails to work where the KD loss disturbs largely during training . To solve this , we generate the pseudo individual embeddings of the teacher model and use them for KD by minimizing their relational distances [ 29 ] . To ensure pseudo individual embeddings fully cover the information of raw graphs , we further apply an MSE loss on the reconstructed joint embeddings concatenated from pseudo individual ones . We have verified that there is only a marginal accuracy drop compared with the original joint embeddings , which justifies the claim above . To sum up , our contributions can be summarized in three folds : • We introduce a new early-feature fusion model to achieve the competitive accuracy by designing a strong co-attention network and taking the GIN as the backbone . • For efficient inference and off-line embedding collection , we propose a novel Knowledge Distillation method for GSC where the joint embeddings are decomposed to distill . • Extensive experiments on the popular GED benchmarks demonstrate the superiority of our model over the state-of-the-art GSC methods on both accuracy and efficiency . Compared with the co-attention models , there is a more than 10 times faster in inference speed compared with the best competitor on AIDS dataset . † 2 Related Works . 2.1 Graph Similarity Computation ( GSC ) . Graph similarity computation measures the similarity of two given graphs , where similarity metrics can be defined as Graph Edit-Distance [ 6 ] , Graph Isomorphism [ 8 ] , and Maximum Common Subgraph [ 7 ] . Exact computation of these metrics is generally an NP-complete problem [ 48 ] . To speed up the computation , kernel-based methods have been extensively proposed to approximate the exact solvers [ 44 , 3 , 28 , 45 ] . Recently , inspired by the strong representation power of deep neural network , a number of neural network based methods have been proposed and demonstrated a huge success [ 47 , 1 , 2 , 24 , 22 , 41 , 40 ] . Among them , regression-based similarity learning has a great promise due to the competitive performance in both efficiency and efficacy [ 1 , 22 , 2 ] . The intuition here is to learn an embedding vector using a graph neural network ( GNN ) , and then measure the similarity of graph embeddings . While such a graph-level embedding encoded by GNN alone is not sufficient to well distinguish the nuances of subgraph level structures . To integrate subgraph information for final similarity computation , several methods are proposed recently , such as node-level pairwise comparison [ 1 ] , cross-graph attention-based matching [ 22 ] , multi-scale neighbor aggregation [ 2 ] , etc . Despite the superior efficacy reported under various metrics ( such as Accuracy , Mean Squared Error ( mse ) , Spearman ’ s Rank Correlation Coefficient ) , the complex subgraph matching/fusion components ( termed ‘ early-fusion ’ in Fig . 2 ) in different layers dramatically slow down the similarity measure . Moreover , early-fusion prevents pre-computing the embeddings for all candidate graphs for further reducing inference time in the graph retrieval scenario . Motivated by this , we propose a slow learning and fast inference method by leveraging the knowledge distillation idea to transfer the fine-grained but slowly learned early-fusion teach model to the fast-inference student model . 2.2 Knowledge Distillation ( KD ) . Knowledge distillation is a general neural network training method , where a ( typically pretrained ) teacher network is introduced to guide the learning of a student network . Its idea was first pioneered by Bucilua et al . [ 5 ] to compress large machine learning models , where they proposed to transfer the knowledge of a model ensemble into a neural network by labeling unlabeled data as transfer set . This idea was later refined by Hinton et al . [ 16 ] , where they adopted softened probabilities of the teacher as a target for the student to learn and coined the term “ knowledge distillation ” . Ever since , many methods have been proposed revolving around the central question in KD : “ what is the definition of knowledge to be distilled ” . Popular definitions include feature distance [ 34 ] , feature map attention [ 46 ] , feature distribution [ 30 ] , activation boundary [ 15 ] , inter-sample distance structure [ 29 , 32 , 23 , 36 ] , and mutual information [ 35 ] . See [ 38 , 11 ] for a more comprehensive survey . [ 26 ] is proposed to distill separate models from a co-attention one . Despite the progress , they mainly focus on convolutional neural networks for vision tasks ( mainly image recognition ) or recurrent neural networks for sequential data tasks ( e.g. , for natural language understanding [ 19 ] ) . 3 Approach . This section will introduce 1 ) the architecture of the early-fusion network ( i.e. , teacher model ) ; 2 ) the KD process and its interpretation . Before that , we start from the formalized problem definition . 3.1 Problem Formulation . Formally , a graph G is defined upon the node set V and edge set E as G = ( V , E ) . In specific , the edge linking a pair of nodes including u ∈ V and v ∈ V can be denoted as the ( u , v ) ∈ E . In our setting , all the accessible graphs are undirected , i.e. , ( u , v ) ∈ E ↔ ( v , u ) ∈ E . The quantity of nodes is represented as N = |V| . A convenient way to represent the graphs is the adjacency matrix †The code is uploaded on https : //github.com/canqin001/Efficient_Graph_Similarity_ Computation A ∈ R|V|×|V| . We denote the presence of edges as A [ u , v ] = 1 if ( u , v ) ∈ E and A [ u , v ] = 0 otherwise . Mostly , graph attributes ( e.g. , node labels ) are available . Such node-level features can be denoted as a real-value matrix X ∈ R|V|×m with the m dimension and the order of feature matrix X is consistent with the adjacency matrix [ 12 ] . In GSC task , we have the access to pairing graphs Gi and Gj ∈ D , whereD = { G0 , G1 , ... } is the graph set . The similarity of such two graphs can be represented as Graph Edit Distance ( GED ) or Maximum Common Subgraph ( MCS ) . As shown in Fig . 1 , the GED is defined as the number of edit operations in the optimal trajectory to transform the source graph to the target . The MCS is the maximum subgraph common to both two graphs . To well fit GNN , the standard GED value is normalized as the nGED , i.e. , nGED ( Gi , Gj ) = GED ( Gi , Gj ) ( |G1|+|G2| ) /2 . In the following , nGED should be transformed to the value ranging ( 0 , 1 ] as the ground truth similarity score sij , i.e. , sij = exp ( −nGED ( Gi , Gj ) ) ∈ S , where S ∈ R|D|×|D| indicates the similarity matrix among all the graphs [ 2 ] . | This paper presents a novel approach to address Graph Similarity Computation (GSC) both accurately and efficiently. From my point of view, the technique contributions of this paper can be summarized in two perspectives, i.e., feature fusion and knowledge distillation. The proposed slow learning employs multi-scale integration and attention-based feature fusion for cross-graph interaction, making it achieve cutting-edge accuracy on most datasets within a comparable computation cost. The fast inference module is the major contribution and the most attractive point of this paper. The authors apply the embedding decomposition to separate the individual features for knowledge distillation, which successfully improves efficiency by vast scale without much accuracy drop. Overall, this paper has made a solid contribution to this field with comprehensive experimental results to support it. | science.mup_single_document_summarization.14589 | [
{
"content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nSlow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge Distillation\n\nMain Content:\n1 Introduction . Measuring the similarity across graphs , i.e. , Graph Similarity Computation ( GSC ) , is one of the core problems of graph data mining , centered around by multiple downstream tasks such as graph retrieval [ 1 , 2 ] , plagiarism/anomaly detection [ 22 , 41 ] , graph clustering [ 39 ] , etc . As shown in Fig . 1 , the graph similarity can be defined as distances between graphs , such as Graph Edit Distance ( GED ) . The conventional solutions towards GSC are the exact computation of these graph distances , which , however , is an NP-hard problem . Therefore , such exact solutions are less favorable when handling large-scale graphs due to the expensive computation cost . Computational time , especially run time in inference stage , is particularly important in industrial scenarios . As a motivating example , in graph-structured molecules or chemical compounds query for in-silico drug screening , fast identifying similar compounds in a large database is a key process [ 25 ] . Leveraging the strong representational power of graph neural network ( GNN ) [ 21 , 13 , 43 , 42 ] , the GNN-based approximate GSC solutions have gained increasing popularity . To adapt GNNs to the GSC task , the target similarity score ( e.g. , GED ) is normalized into the range of ( 0 , 1 ] . In this way , the ∗Corresponding Author : qin.ca @ northeastern.edu 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . GSC can be regarded as a single-value regression problem that outputs a similarity score given two graphs as inputs . A standard design can be summarized as a twin of GNNs bridged by a co-attention with a Multi-layer Perceptron ( MLP ) stacked as the regression head . Such approaches can be trained in a fully supervised way using the Mean Square Error ( MSE ) loss computed over the ground truth similarity score . Many GNN-based GSC methods [ 1 , 2 , 22 ] followed such strategy , which , however , suffers from the fusion issue . The paper presents a novel solution to both effectively and efficiently address the task of approximate GSC . Compared to the commonly used graph convolutional network as the backbone [ 1 , 2 ] , this paper adopts a more robust network , i.e. , Graph Isomorphism Network ( GIN ) [ 43 ] . Cross-graph fusion is essential to the model . The multi-scale features within different GIN layers are fused with a new design . We have adopted an attention layer stacked over the concatenated cross-graph features for smooth feature fusion . To this end , similar features will be assigned with more weights to contribute to the desired task . Moreover , to make the model easier to deploy , we take an MLP for feature learning which is simple but effective to achieve cutting-edge performance . Intuitively , speed and accuracy can be considered as a trade-off . GSC naturally requires dense connections/interactions between the two input graphs , which will consequently cause increasing computations as the cost . This paper focuses on the efficiency of inference speed which can be addressed by either model compression or a faster data loading pipeline . Especially in industrial scenarios , the raw graph data are usually pre-processed as the embeddings off-line that can be easily applied to the real-time downstream tasks , e.g. , molecular graph retrieval . However , as shown in Fig . 2 , most of the co-attention-based GSC solutions employ feature fusion in the early stage , which only outputs the joint embedding of pairing graphs . Inspired by [ 26 ] , we propose a lightweight model that removes all the early feature fusion modules in the encoder for efficient GSC . In this way , as shown in Fig . 2 , the individual embedding of each graph can be collected by a Siamese GNN . Such pairing graph embeddings will be fused with an attention layer to predict the final similarity score . To overcome the accuracy drop of such a small network , we take a novel paradigm of Knowledge Distillation ( KD ) specifically designed for our task . As shown in Fig . 3 , we propose an early-feature fusion network regarded as the teacher model , and the student model is a siamese network without co-attention . It is found that the direct distillation of joint embeddings fails to work where the KD loss disturbs largely during training . To solve this , we generate the pseudo individual embeddings of the teacher model and use them for KD by minimizing their relational distances [ 29 ] . To ensure pseudo individual embeddings fully cover the information of raw graphs , we further apply an MSE loss on the reconstructed joint embeddings concatenated from pseudo individual ones . We have verified that there is only a marginal accuracy drop compared with the original joint embeddings , which justifies the claim above . To sum up , our contributions can be summarized in three folds : • We introduce a new early-feature fusion model to achieve the competitive accuracy by designing a strong co-attention network and taking the GIN as the backbone . • For efficient inference and off-line embedding collection , we propose a novel Knowledge Distillation method for GSC where the joint embeddings are decomposed to distill . • Extensive experiments on the popular GED benchmarks demonstrate the superiority of our model over the state-of-the-art GSC methods on both accuracy and efficiency . Compared with the co-attention models , there is a more than 10 times faster in inference speed compared with the best competitor on AIDS dataset . † 2 Related Works . 2.1 Graph Similarity Computation ( GSC ) . Graph similarity computation measures the similarity of two given graphs , where similarity metrics can be defined as Graph Edit-Distance [ 6 ] , Graph Isomorphism [ 8 ] , and Maximum Common Subgraph [ 7 ] . Exact computation of these metrics is generally an NP-complete problem [ 48 ] . To speed up the computation , kernel-based methods have been extensively proposed to approximate the exact solvers [ 44 , 3 , 28 , 45 ] . Recently , inspired by the strong representation power of deep neural network , a number of neural network based methods have been proposed and demonstrated a huge success [ 47 , 1 , 2 , 24 , 22 , 41 , 40 ] . Among them , regression-based similarity learning has a great promise due to the competitive performance in both efficiency and efficacy [ 1 , 22 , 2 ] . The intuition here is to learn an embedding vector using a graph neural network ( GNN ) , and then measure the similarity of graph embeddings . While such a graph-level embedding encoded by GNN alone is not sufficient to well distinguish the nuances of subgraph level structures . To integrate subgraph information for final similarity computation , several methods are proposed recently , such as node-level pairwise comparison [ 1 ] , cross-graph attention-based matching [ 22 ] , multi-scale neighbor aggregation [ 2 ] , etc . Despite the superior efficacy reported under various metrics ( such as Accuracy , Mean Squared Error ( mse ) , Spearman ’ s Rank Correlation Coefficient ) , the complex subgraph matching/fusion components ( termed ‘ early-fusion ’ in Fig . 2 ) in different layers dramatically slow down the similarity measure . Moreover , early-fusion prevents pre-computing the embeddings for all candidate graphs for further reducing inference time in the graph retrieval scenario . Motivated by this , we propose a slow learning and fast inference method by leveraging the knowledge distillation idea to transfer the fine-grained but slowly learned early-fusion teach model to the fast-inference student model . 2.2 Knowledge Distillation ( KD ) . Knowledge distillation is a general neural network training method , where a ( typically pretrained ) teacher network is introduced to guide the learning of a student network . Its idea was first pioneered by Bucilua et al . [ 5 ] to compress large machine learning models , where they proposed to transfer the knowledge of a model ensemble into a neural network by labeling unlabeled data as transfer set . This idea was later refined by Hinton et al . [ 16 ] , where they adopted softened probabilities of the teacher as a target for the student to learn and coined the term “ knowledge distillation ” . Ever since , many methods have been proposed revolving around the central question in KD : “ what is the definition of knowledge to be distilled ” . Popular definitions include feature distance [ 34 ] , feature map attention [ 46 ] , feature distribution [ 30 ] , activation boundary [ 15 ] , inter-sample distance structure [ 29 , 32 , 23 , 36 ] , and mutual information [ 35 ] . See [ 38 , 11 ] for a more comprehensive survey . [ 26 ] is proposed to distill separate models from a co-attention one . Despite the progress , they mainly focus on convolutional neural networks for vision tasks ( mainly image recognition ) or recurrent neural networks for sequential data tasks ( e.g. , for natural language understanding [ 19 ] ) . 3 Approach . This section will introduce 1 ) the architecture of the early-fusion network ( i.e. , teacher model ) ; 2 ) the KD process and its interpretation . Before that , we start from the formalized problem definition . 3.1 Problem Formulation . Formally , a graph G is defined upon the node set V and edge set E as G = ( V , E ) . In specific , the edge linking a pair of nodes including u ∈ V and v ∈ V can be denoted as the ( u , v ) ∈ E . In our setting , all the accessible graphs are undirected , i.e. , ( u , v ) ∈ E ↔ ( v , u ) ∈ E . The quantity of nodes is represented as N = |V| . A convenient way to represent the graphs is the adjacency matrix †The code is uploaded on https : //github.com/canqin001/Efficient_Graph_Similarity_ Computation A ∈ R|V|×|V| . We denote the presence of edges as A [ u , v ] = 1 if ( u , v ) ∈ E and A [ u , v ] = 0 otherwise . Mostly , graph attributes ( e.g. , node labels ) are available . Such node-level features can be denoted as a real-value matrix X ∈ R|V|×m with the m dimension and the order of feature matrix X is consistent with the adjacency matrix [ 12 ] . In GSC task , we have the access to pairing graphs Gi and Gj ∈ D , whereD = { G0 , G1 , ... } is the graph set . The similarity of such two graphs can be represented as Graph Edit Distance ( GED ) or Maximum Common Subgraph ( MCS ) . As shown in Fig . 1 , the GED is defined as the number of edit operations in the optimal trajectory to transform the source graph to the target . The MCS is the maximum subgraph common to both two graphs . To well fit GNN , the standard GED value is normalized as the nGED , i.e. , nGED ( Gi , Gj ) = GED ( Gi , Gj ) ( |G1|+|G2| ) /2 . In the following , nGED should be transformed to the value ranging ( 0 , 1 ] as the ground truth similarity score sij , i.e. , sij = exp ( −nGED ( Gi , Gj ) ) ∈ S , where S ∈ R|D|×|D| indicates the similarity matrix among all the graphs [ 2 ] .",
"role": "user"
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"content": "This paper presents a novel approach to address Graph Similarity Computation (GSC) both accurately and efficiently. From my point of view, the technique contributions of this paper can be summarized in two perspectives, i.e., feature fusion and knowledge distillation. The proposed slow learning employs multi-scale integration and attention-based feature fusion for cross-graph interaction, making it achieve cutting-edge accuracy on most datasets within a comparable computation cost. The fast inference module is the major contribution and the most attractive point of this paper. The authors apply the embedding decomposition to separate the individual features for knowledge distillation, which successfully improves efficiency by vast scale without much accuracy drop. Overall, this paper has made a solid contribution to this field with comprehensive experimental results to support it.",
"role": "assistant"
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science.scifact_entailment | Determine the relationship between them and generate a JSON response. Scientific Claim:
Sn is present on mature DCs during inflammation in vivo.
Research Abstract:
Dendritic cells (DCs) are essential antigen-presenting cells for the induction of immunity against pathogens.
However, HIV-1 spread is strongly enhanced in clusters of DCs and CD4(+) T cells.
Uninfected DCs capture HIV-1 and mediate viral transfer to bystander CD4(+) T cells through a process termed trans-infection.
Initial studies identified the C-type lectin DC-SIGN as the HIV-1 binding factor on DCs, which interacts with the viral envelope glycoproteins.
Upon DC maturation, however, DC-SIGN is down-regulated, while HIV-1 capture and trans-infection is strongly enhanced via a glycoprotein-independent capture pathway that recognizes sialyllactose-containing membrane gangliosides.
Here we show that the sialic acid-binding Ig-like lectin 1 (Siglec-1, CD169), which is highly expressed on mature DCs, specifically binds HIV-1 and vesicles carrying sialyllactose.
Furthermore, Siglec-1 is essential for trans-infection by mature DCs.
These findings identify Siglec-1 as a key factor for HIV-1 spread via infectious DC/T-cell synapses, highlighting a novel mechanism that mediates HIV-1 dissemination in activated tissues. | {"verdict": "NEI", "evidence": []} | science.scifact_entailment.713 | [
{
"content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nSn is present on mature DCs during inflammation in vivo.\n\nResearch Abstract:\nDendritic cells (DCs) are essential antigen-presenting cells for the induction of immunity against pathogens.\nHowever, HIV-1 spread is strongly enhanced in clusters of DCs and CD4(+) T cells.\nUninfected DCs capture HIV-1 and mediate viral transfer to bystander CD4(+) T cells through a process termed trans-infection.\nInitial studies identified the C-type lectin DC-SIGN as the HIV-1 binding factor on DCs, which interacts with the viral envelope glycoproteins.\nUpon DC maturation, however, DC-SIGN is down-regulated, while HIV-1 capture and trans-infection is strongly enhanced via a glycoprotein-independent capture pathway that recognizes sialyllactose-containing membrane gangliosides.\nHere we show that the sialic acid-binding Ig-like lectin 1 (Siglec-1, CD169), which is highly expressed on mature DCs, specifically binds HIV-1 and vesicles carrying sialyllactose.\nFurthermore, Siglec-1 is essential for trans-infection by mature DCs.\nThese findings identify Siglec-1 as a key factor for HIV-1 spread via infectious DC/T-cell synapses, highlighting a novel mechanism that mediates HIV-1 dissemination in activated tissues.",
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"content": "{\"verdict\": \"NEI\", \"evidence\": []}",
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science.evidence_inference | Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:
- Treatment: The specific medical procedure or drug being evaluated.
- Control: The standard or placebo used for comparison. If absent, use `null`.
- Result: The health outcome being assessed.
- Impact: The observed effect of the treatment on the result, categorized as: ("increased", "decreased", "no change").
- Justification: The supporting details for the observed impact.
Format the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].
Abstract:
ABSTRACT.BACKGROUND:: Caudal analgesia is the most commonly used technique providing intra- and postoperative analgesia for various pediatric infraumbilical surgical procedures but with the disadvantage of short duration of action after single injection. Caudal dexamethasone and magnesium could offer significant analgesic benefits. We compared the analgesic effects and side-effects of dexamethasone or magnesium added to caudal ropivacaine in pediatric patients undergoing inguinal hernia repair.
ABSTRACT.MATERIALS AND METHODS:: A total of 105 (1-6 years) were randomly assigned into three groups in a double-blinded manner. After a standardized sevoflurane in oxygen anesthesia, each patient received a single caudal dose of ropivacaine 0.15% 1.5 mL/kg combined with either magnesium 50 mg in normal saline 1 mL (group RM), dexamethasone 0.1 mg/kg in normal saline 1 mL (group RD), or corresponding volume of normal saline (group R) according to group assignment. Postoperative analgesia, use of analgesics, and side-effects were assessed during the first 24 h.
ABSTRACT.RESULTS:: Addition of magnesium or dexamethasone to caudal ropivacaine significantly prolonged analgesia duration 8 (5-11) h and 12 (8-16) h, respectively compared with 4 (3-5) h with the use of ropivacaine alone. The incidence of postoperative rescue analgesia was significantly higher in group R compared with groups RM and RD. The time to 1st analgesic dose was significantly longer in groups RM and RD (500 ± 190 and 730 ± 260 min) respectively compared with group R (260 ± 65 min). Group R patients achieved significantly higher Children's Hospital of Eastern Ontario Pain Scale and Faces Legs Activity Cry Consolability scores (4th hourly) compared with groups RM and RD patients (8th and 12th hourly, respectively).
ABSTRACT.CONCLUSION:: The addition of dexamethasone or magnesium to caudal ropivacaine significantly prolonged the duration of postoperative analgesia in children undergoing inguinal hernia repair. Also the time to 1st analgesic dose was longer and the need for rescue postoperative analgesic was reduced and without increase in incidence of side effects.
Extracted Data: | [["magnesium 50 mg in normal saline 1 mL or dexamethasone 0.1 mg/kg in normal saline 1 mL", "corresponding volume of normal saline ", "duration of postoperative analgesia ", "significantly increased", "Addition of magnesium or dexamethasone to caudal ropivacaine significantly prolonged analgesia duration 8 (5-11) h and 12 (8-16) h, respectively compared with 4 (3-5) h with the use of ropivacaine alone. The incidence of postoperative rescue analgesia was significantly higher in group R compared with groups RM and RD."], ["magnesium 50 mg in normal saline 1 mL or dexamethasone 0.1 mg/kg in normal saline 1 mL", "corresponding volume of normal saline ", "The incidence of rescue pethidine in the PACU and rescue oral paracetamol in ward", "significantly decreased", "The incidence of postoperative rescue analgesia was significantly higher in group R compared with groups RM and RD."]] | science.evidence_inference.212 | [
{
"content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND:: Caudal analgesia is the most commonly used technique providing intra- and postoperative analgesia for various pediatric infraumbilical surgical procedures but with the disadvantage of short duration of action after single injection. Caudal dexamethasone and magnesium could offer significant analgesic benefits. We compared the analgesic effects and side-effects of dexamethasone or magnesium added to caudal ropivacaine in pediatric patients undergoing inguinal hernia repair.\n\nABSTRACT.MATERIALS AND METHODS:: A total of 105 (1-6 years) were randomly assigned into three groups in a double-blinded manner. After a standardized sevoflurane in oxygen anesthesia, each patient received a single caudal dose of ropivacaine 0.15% 1.5 mL/kg combined with either magnesium 50 mg in normal saline 1 mL (group RM), dexamethasone 0.1 mg/kg in normal saline 1 mL (group RD), or corresponding volume of normal saline (group R) according to group assignment. Postoperative analgesia, use of analgesics, and side-effects were assessed during the first 24 h.\n\nABSTRACT.RESULTS:: Addition of magnesium or dexamethasone to caudal ropivacaine significantly prolonged analgesia duration 8 (5-11) h and 12 (8-16) h, respectively compared with 4 (3-5) h with the use of ropivacaine alone. The incidence of postoperative rescue analgesia was significantly higher in group R compared with groups RM and RD. The time to 1st analgesic dose was significantly longer in groups RM and RD (500 ± 190 and 730 ± 260 min) respectively compared with group R (260 ± 65 min). Group R patients achieved significantly higher Children's Hospital of Eastern Ontario Pain Scale and Faces Legs Activity Cry Consolability scores (4th hourly) compared with groups RM and RD patients (8th and 12th hourly, respectively).\n\nABSTRACT.CONCLUSION:: The addition of dexamethasone or magnesium to caudal ropivacaine significantly prolonged the duration of postoperative analgesia in children undergoing inguinal hernia repair. Also the time to 1st analgesic dose was longer and the need for rescue postoperative analgesic was reduced and without increase in incidence of side effects.\n\nExtracted Data:",
"role": "user"
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"content": "[[\"magnesium 50 mg in normal saline 1 mL or dexamethasone 0.1 mg/kg in normal saline 1 mL\", \"corresponding volume of normal saline \", \"duration of postoperative analgesia \", \"significantly increased\", \"Addition of magnesium or dexamethasone to caudal ropivacaine significantly prolonged analgesia duration 8 (5-11) h and 12 (8-16) h, respectively compared with 4 (3-5) h with the use of ropivacaine alone. The incidence of postoperative rescue analgesia was significantly higher in group R compared with groups RM and RD.\"], [\"magnesium 50 mg in normal saline 1 mL or dexamethasone 0.1 mg/kg in normal saline 1 mL\", \"corresponding volume of normal saline \", \"The incidence of rescue pethidine in the PACU and rescue oral paracetamol in ward\", \"significantly decreased\", \"The incidence of postoperative rescue analgesia was significantly higher in group R compared with groups RM and RD.\"]]",
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science.evidence_inference | Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:
- Treatment: The specific medical procedure or drug being evaluated.
- Control: The standard or placebo used for comparison. If absent, use `null`.
- Result: The health outcome being assessed.
- Impact: The observed effect of the treatment on the result, categorized as: ("increased", "decreased", "no change").
- Justification: The supporting details for the observed impact.
Format the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].
Abstract:
ABSTRACT.OBJECTIVE: To determine if a programme of progressive resistance exercise, mobilisation and orientation, in addition to usual care, was superior to usual care alone in the prevention of incident delirium in older hospitalised patients.
ABSTRACT.DESIGN: A randomised controlled trial.
ABSTRACT.SETTING: The study was performed at a secondary referral hospital in Melbourne, Australia between May 2005 and December 2007.
ABSTRACT.PARTICIPANTS: 648 consecutive medical inpatients aged 65 years or older who had been in hospital for less than 48 h and who did not have delirium.
ABSTRACT.INTERVENTION: Participants were randomly allocated to a twice-daily programme of progressive resistance exercise tailored to individual ability, mobilisation and orientation in addition to usual care or to usual care alone.
ABSTRACT.MEASUREMENTS: Delirium was measured using the Confusion Assessment Method at baseline and every 48 h until discharge. Secondary outcome measures were severity and duration of delirium, discharge destination and length of stay.
ABSTRACT.RESULTS: Delirium occurred in 4.9% (95% CI 2.3% to 7.3%) of the intervention group (15/305) and in 5.9% (20/339; 95% CI 3.8% to 9.2%) of the group receiving usual care. No difference was observed between groups (χ2; p=0.5). The intervention had no effect on delirium duration, severity, discharge destination or length of stay.
ABSTRACT.CONCLUSION: A programme of progressive resistance exercise and orientation was not effective in reducing incident delirium in hospitalised elderly patients.
Extracted Data: | [["mobilisation and orientation in addition to usual care", "usual care alone", "Delirium", "no significant difference", "Delirium occurred in 4.9% (95% CI 2.3% to 7.3%) of the intervention group (15/305) and in 5.9% (20/339; 95% CI 3.8% to 9.2%) of the group receiving usual care. No difference was observed between groups (\u03c72; p=0.5). The intervention had no effect on delirium duration, severity, discharge destination or length of stay."], ["mobilisation and orientation in addition to usual care", "usual care alone", "duration of delirium, length of stay", "no significant difference", "The intervention had no effect on delirium duration, severity, discharge destination or length of stay."], ["mobilisation and orientation in addition to usual care", "usual care alone", "Rates of delirium", "no significant difference", "Delirium occurred in 4.9% (95% CI 2.3% to 7.3%) of the intervention group (15/305) and in 5.9% (20/339; 95% CI 3.8% to 9.2%) of the group receiving usual care. No difference was observed between groups (\u03c72; p=0.5)."]] | science.evidence_inference.230 | [
{
"content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.OBJECTIVE: To determine if a programme of progressive resistance exercise, mobilisation and orientation, in addition to usual care, was superior to usual care alone in the prevention of incident delirium in older hospitalised patients.\n\nABSTRACT.DESIGN: A randomised controlled trial.\n\nABSTRACT.SETTING: The study was performed at a secondary referral hospital in Melbourne, Australia between May 2005 and December 2007.\n\nABSTRACT.PARTICIPANTS: 648 consecutive medical inpatients aged 65 years or older who had been in hospital for less than 48 h and who did not have delirium.\n\nABSTRACT.INTERVENTION: Participants were randomly allocated to a twice-daily programme of progressive resistance exercise tailored to individual ability, mobilisation and orientation in addition to usual care or to usual care alone.\n\nABSTRACT.MEASUREMENTS: Delirium was measured using the Confusion Assessment Method at baseline and every 48 h until discharge. Secondary outcome measures were severity and duration of delirium, discharge destination and length of stay.\n\nABSTRACT.RESULTS: Delirium occurred in 4.9% (95% CI 2.3% to 7.3%) of the intervention group (15/305) and in 5.9% (20/339; 95% CI 3.8% to 9.2%) of the group receiving usual care. No difference was observed between groups (χ2; p=0.5). The intervention had no effect on delirium duration, severity, discharge destination or length of stay.\n\nABSTRACT.CONCLUSION: A programme of progressive resistance exercise and orientation was not effective in reducing incident delirium in hospitalised elderly patients.\n\nExtracted Data:",
"role": "user"
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{
"content": "[[\"mobilisation and orientation in addition to usual care\", \"usual care alone\", \"Delirium\", \"no significant difference\", \"Delirium occurred in 4.9% (95% CI 2.3% to 7.3%) of the intervention group (15/305) and in 5.9% (20/339; 95% CI 3.8% to 9.2%) of the group receiving usual care. No difference was observed between groups (\\u03c72; p=0.5). The intervention had no effect on delirium duration, severity, discharge destination or length of stay.\"], [\"mobilisation and orientation in addition to usual care\", \"usual care alone\", \"duration of delirium, length of stay\", \"no significant difference\", \"The intervention had no effect on delirium duration, severity, discharge destination or length of stay.\"], [\"mobilisation and orientation in addition to usual care\", \"usual care alone\", \"Rates of delirium\", \"no significant difference\", \"Delirium occurred in 4.9% (95% CI 2.3% to 7.3%) of the intervention group (15/305) and in 5.9% (20/339; 95% CI 3.8% to 9.2%) of the group receiving usual care. No difference was observed between groups (\\u03c72; p=0.5).\"]]",
"role": "assistant"
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science.qasper_abstractive_qa | Examine the provided sections from a research document and address the accompanying query. The text is divided by newlines.
Document Content:
Embracing data abundance: BookTest Dataset for Reading Comprehension
There is a practically unlimited amount of natural language data available. Still, recent work in text comprehension has focused on datasets which are small relative to current computing possibilities. This article is making a case for the community to move to larger data and as a step in that direction it is proposing the BookTest, a new dataset similar to the popular Children's Book Test (CBT), however more than 60 times larger. We show that training on the new data improves the accuracy of our Attention-Sum Reader model on the original CBT test data by a much larger margin than many recent attempts to improve the model architecture. On one version of the dataset our ensemble even exceeds the human baseline provided by Facebook. We then show in our own human study that there is still space for further improvement.
Introduction
Since humans amass more and more generally available data in the form of unstructured text it would be very useful to teach machines to read and comprehend such data and then use this understanding to answer our questions. A significant amount of research has recently focused on answering one particular kind of questions the answer to which depends on understanding a context document. These are cloze-style questions BIBREF0 which require the reader to fill in a missing word in a sentence. An important advantage of such questions is that they can be generated automatically from a suitable text corpus which allows us to produce a practically unlimited amount of them. That opens the task to notoriously data-hungry deep-learning techniques which now seem to outperform all alternative approaches.
Two such large-scale datasets have recently been proposed by researchers from Google DeepMind and Facebook AI: the CNN/Daily Mail dataset BIBREF1 and the Children's Book Test (CBT) BIBREF2 respectively. These have attracted a lot of attention from the research community BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 with a new state-of-the-art model coming out every few weeks.
However if our goal is a production-level system actually capable of helping humans, we want the model to use all available resources as efficiently as possible. Given that
we believe that if the community is striving to bring the performance as far as possible, it should move its work to larger data.
This thinking goes in line with recent developments in the area of language modelling. For a long time models were being compared on several "standard" datasets with publications often presenting minuscule improvements in performance. Then the large-scale One Billion Word corpus dataset appeared BIBREF15 and it allowed Jozefowicz et al. to train much larger LSTM models BIBREF16 that almost halved the state-of-the-art perplexity on this dataset.
We think it is time to make a similar step in the area of text comprehension. Hence we are introducing the BookTest, a new dataset very similar to the Children's Book test but more than 60 times larger to enable training larger models even in the domain of text comprehension. Furthermore the methodology used to create our data can later be used to create even larger datasets when the need arises thanks to further technological progress.
We show that if we evaluate a model trained on the new dataset on the now standard Children's Book Test dataset, we see an improvement in accuracy much larger than other research groups achieved by enhancing the model architecture itself (while still using the original CBT training data). By training on the new dataset, we reduce the prediction error by almost one third. On the named-entity version of CBT this brings the ensemble of our models to the level of human baseline as reported by Facebook BIBREF2 . However in the final section we show in our own human study that there is still room for improvement on the CBT beyond the performance of our model.
Datasets
A crucial condition for applying deep-learning techniques is to have a huge amount of data available for training. For question answering this specifically means having a large number of document-question-answer triples available. While there is an unlimited amount of text available, coming up with relevant questions and the corresponding answers can be extremely labour-intensive if done by human annotators. There were efforts to provide such human-generated datasets, e.g. Microsoft's MCTest BIBREF17 , however their scale is not suitable for deep learning without pre-training on other data BIBREF18 (such as using pre-trained word embedding vectors).
Google DeepMind managed to avoid this scale issue with their way of generating document-question-answer triples automatically, closely followed by Facebook with a similar method. Let us now briefly introduce the two resulting datasets whose properties are summarized in Table TABREF8 .
These two datasets BIBREF1 exploit a useful feature of online news articles – many articles include a short summarizing sentence near the top of the page. Since all information in the summary sentence is also presented in the article body, we get a nice cloze-style question about the article contents by removing a word from the short summary.
The dataset's authors also replaced all named entities in the dataset by anonymous tokens which are further shuffled for each new batch. This forces the model to rely solely on information from the context document, not being able to transfer any meaning of the named entities between documents.
This restricts the task to one specific aspect of context-dependent question answering which may be useful however it moves the task further from the real application scenario, where we would like the model to use all information available to answer questions. Furthermore Chen et al. BIBREF5 have suggested that this can make about 17% of the questions unanswerable even by humans. They also claim that more than a half of the question sentences are mere paraphrases or exact matches of a single sentence from the context document. This raises a question to what extent the dataset can test deeper understanding of the articles.
The Children's Book Test BIBREF2 uses a different source - books freely available thanks to Project Gutenberg. Since no summary is available, each example consists of a context document formed from 20 consecutive sentences from the story together with a question formed from the subsequent sentence.
The dataset comes in four flavours depending on what type of word is omitted from the question sentence. Based on human evaluation done in BIBREF2 it seems that NE and CN are more context dependent than the other two types – prepositions and verbs. Therefore we (and all of the recent publications) focus only on these two word types.
Several new datasets related to the (now almost standard) ones above emerged recently. We will now briefly present them and explain how the dataset we are introducing in this article differs from them.
The LAMBADA dataset BIBREF19 is designed to measure progress in understanding common-sense questions about short stories that can be easily answered by humans but cannot be answered by current standard machine-learning models (e.g. plain LSTM language models). This dataset is useful for measuring the gap between humans and machine learning algorithms. However, by contrast to our BookTest dataset, it will not allow us to track progress towards the performance of the baseline systems or on examples where machine learning may show super-human performance. Also LAMBADA is just a diagnostic dataset and does not provide ready-to-use question-answering training data, just a plain-text corpus which may moreover include copyrighted books making its use potentially problematic for some purposes. We are providing ready training data consisting of copyright-free books only.
The SQuAD dataset BIBREF20 based on Wikipedia and the Who-did-What dataset BIBREF21 based on Gigaword news articles are factoid question-answering datasets where a multi-word answer should be extracted from a context document. This is in contrast to the previous datasets, including CNN/DM, CBT, LAMBADA and our new dataset, which require only single-word answers. Both these datasets however provide less than 130,000 training questions, two orders of magnitude less than our dataset does.
The Story Cloze Test BIBREF22 provides a crowd-sourced corpus of 49,255 commonsense stories for training and 3,744 testing stories with right and wrong endings. Hence the dataset is again rather small. Similarly to LAMBADA, the Story Cloze Test was designed to be easily answerable by humans.
In the WikiReading BIBREF23 dataset the context document is formed from a Wikipedia article and the question-answer pair is taken from the corresponding WikiData page. For each entity (e.g. Hillary Clinton), WikiData contain a number of property-value pairs (e.g. place of birth: Chicago) which form the datasets's question-answer pairs. The dataset is certainly relevant to the community, however the questions are of very limited variety with only 20 properties (and hence unique questions) covering INLINEFORM0 of the dataset. Furthermore many of the frequent properties are mentioned at a set spot within the article (e.g. the date of birth is almost always in brackets behind the name of a person) which may make the task easier for machines. We are trying to provide a more varied dataset.
Although there are several datasets related to task we are aiming to solve, they differ sufficiently for our dataset to bring new value to the community. Its biggest advantage is its size which can furthermore be easily upscaled without expensive human annotation. Finally while we are emphasizing the differences, models could certainly benefit from as diverse a collection of datasets as possible.
Possible Directions for Improvements
Accuracy in any machine learning tasks can be enhanced either by improving a machine learning model or by using more in-domain training data. Current state of the art models BIBREF6 , BIBREF7 , BIBREF8 , BIBREF11 improve over AS Reader's accuracy on CBT NE and CN datasets by 1-2 percent absolute. This suggests that with current techniques there is only limited room for improvement on the algorithmic side.
The other possibility to improve performance is simply to use more training data. The importance of training data was highlighted by the frequently quoted Mercer's statement that “There is no data like more data.” The observation that having more data is often more important than having better algorithms has been frequently stressed since then BIBREF13 , BIBREF14 .
As a step in the direction of exploiting the potential of more data in the domain of text comprehension, we created a new dataset called BookTest similar to, but much larger than the widely used CBT and CNN/DM datasets.
BookTest
Similarly to the CBT, our BookTest dataset is derived from books available through project Gutenberg. We used 3555 copyright-free books to extract CN examples and 10507 books for NE examples, for comparison the CBT dataset was extracted from just 108 books.
When creating our dataset we follow the same procedure as was used to create the CBT dataset BIBREF2 . That is, we detect whether each sentence contains either a named entity or a common noun that already appeared in one of the preceding twenty sentences. This word is then replaced by a gap tag (XXXXX) in this sentence which is hence turned into a cloze-style question. The preceding 20 sentences are used as the context document. For common noun and named entity detection we use the Stanford POS tagger BIBREF27 and Stanford NER BIBREF28 .
The training dataset consists of the original CBT NE and CN data extended with new NE and CN examples. The new BookTest dataset hence contains INLINEFORM0 training examples and INLINEFORM1 tokens.
The validation dataset consists of INLINEFORM0 NE and INLINEFORM1 CN questions. We have one test set for NE and one for CN, each containing INLINEFORM2 examples. The training, validation and test sets were generated from non-overlapping sets of books.
When generating the dataset we removed all editions of books used to create CBT validation and test sets from our training dataset. Therefore the models trained on the BookTest corpus can be evaluated on the original CBT data and they can be compared with recent text-comprehension models utilizing this dataset BIBREF2 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 .
Results
Table TABREF25 shows the accuracy of the psr and other architectures on the CBT validation and test data. The last two rows show the performance of the psr trained on the BookTest dataset; all the other models have been trained on the original CBT training data.
If we take the best psr ensemble trained on CBT as a baseline, improving the model architecture as in BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , continuing to use the original CBT training data, lead to improvements of INLINEFORM0 and INLINEFORM1 absolute on named entities and common nouns respectively. By contrast, inflating the training dataset provided a boost of INLINEFORM2 while using the same model. The ensemble of our models even exceeded the human baseline provided by Facebook BIBREF2 on the Common Noun dataset.
Our model takes approximately two weeks to converge when trained on the BookTest dataset on a single Nvidia Tesla K40 GPU.
Human Study
After adding more data we have the performance on the CBT validation and test datasets soaring. However is there still potential for much further growth beyond the results we have observed?
We decided to explore the remaining space for improvement on the CBT by testing humans on a random subset of 50 named entity and 50 common noun validation questions that the psr ensemble could not answer correctly. These questions were answered by 10 non-native English speakers from our research laboratory, each on a disjoint subset of questions.. Participants had unlimited time to answer the questions and were told that these questions were not correctly answered by a machine, providing additional motivation to prove they are better than computers. The results of the human study are summarized in Table TABREF28 . They show that a majority of questions that our system could not answer so far are in fact answerable. This suggests that 1) the original human baselines might have been underestimated, however, it might also be the case that there are some examples that can be answered by machines and not by humans; 2) there is still space for improvement.
A system that would answer correctly every time when either our ensemble or human answered correctly would achieve accuracy over 92% percent on both validation and test NE datasets and over 96% on both CN datasets. Hence it still makes sense to use CBT dataset to study further improvements of text-comprehension systems.
Conclusion
Few ways of improving model performance are as solidly established as using more training data. Yet we believe this principle has been somewhat neglected by recent research in text comprehension. While there is a practically unlimited amount of data available in this field, most research was performed on unnecessarily small datasets.
As a gentle reminder to the community we have shown that simply infusing a model with more data can yield performance improvements of up to INLINEFORM0 where several attempts to improve the model architecture on the same training data have given gains of at most INLINEFORM1 compared to our best ensemble result. Yes, experiments on small datasets certainly can bring useful insights. However we believe that the community should also embrace the real-world scenario of data abundance.
The BookTest dataset we are proposing gives the reading-comprehension community an opportunity to make a step in that direction.
Table 4: The best hyperparameters and range of values that we tested on the BookTest train dataset. We report number of hidden units of the unidirectional GRU; the bidirectional GRU has twice as many hidden units.
Query: How large are the improvements of the Attention-Sum Reader model when using the BookTest dataset?
JSON Response: | {"answer": "Answer with content missing: (Table 2) Accuracy of best AS reader results including ensembles are 78.4 and 83.7 when trained on BookTest compared to 71.0 and 68.9 when trained on CBT for Named endity and Common noun respectively.", "evidence": ["Table TABREF25 shows the accuracy of the psr and other architectures on the CBT validation and test data. The last two rows show the performance of the psr trained on the BookTest dataset; all the other models have been trained on the original CBT training data."]} | science.qasper_abstractive_qa.172.0 | [
{
"content": "Examine the provided sections from a research document and address the accompanying query. The text is divided by newlines.\n\nDocument Content:\n\nEmbracing data abundance: BookTest Dataset for Reading Comprehension\n\nThere is a practically unlimited amount of natural language data available. Still, recent work in text comprehension has focused on datasets which are small relative to current computing possibilities. This article is making a case for the community to move to larger data and as a step in that direction it is proposing the BookTest, a new dataset similar to the popular Children's Book Test (CBT), however more than 60 times larger. We show that training on the new data improves the accuracy of our Attention-Sum Reader model on the original CBT test data by a much larger margin than many recent attempts to improve the model architecture. On one version of the dataset our ensemble even exceeds the human baseline provided by Facebook. We then show in our own human study that there is still space for further improvement.\n\nIntroduction\nSince humans amass more and more generally available data in the form of unstructured text it would be very useful to teach machines to read and comprehend such data and then use this understanding to answer our questions. A significant amount of research has recently focused on answering one particular kind of questions the answer to which depends on understanding a context document. These are cloze-style questions BIBREF0 which require the reader to fill in a missing word in a sentence. An important advantage of such questions is that they can be generated automatically from a suitable text corpus which allows us to produce a practically unlimited amount of them. That opens the task to notoriously data-hungry deep-learning techniques which now seem to outperform all alternative approaches.\nTwo such large-scale datasets have recently been proposed by researchers from Google DeepMind and Facebook AI: the CNN/Daily Mail dataset BIBREF1 and the Children's Book Test (CBT) BIBREF2 respectively. These have attracted a lot of attention from the research community BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 with a new state-of-the-art model coming out every few weeks.\nHowever if our goal is a production-level system actually capable of helping humans, we want the model to use all available resources as efficiently as possible. Given that\nwe believe that if the community is striving to bring the performance as far as possible, it should move its work to larger data.\nThis thinking goes in line with recent developments in the area of language modelling. For a long time models were being compared on several \"standard\" datasets with publications often presenting minuscule improvements in performance. Then the large-scale One Billion Word corpus dataset appeared BIBREF15 and it allowed Jozefowicz et al. to train much larger LSTM models BIBREF16 that almost halved the state-of-the-art perplexity on this dataset.\nWe think it is time to make a similar step in the area of text comprehension. Hence we are introducing the BookTest, a new dataset very similar to the Children's Book test but more than 60 times larger to enable training larger models even in the domain of text comprehension. Furthermore the methodology used to create our data can later be used to create even larger datasets when the need arises thanks to further technological progress.\nWe show that if we evaluate a model trained on the new dataset on the now standard Children's Book Test dataset, we see an improvement in accuracy much larger than other research groups achieved by enhancing the model architecture itself (while still using the original CBT training data). By training on the new dataset, we reduce the prediction error by almost one third. On the named-entity version of CBT this brings the ensemble of our models to the level of human baseline as reported by Facebook BIBREF2 . However in the final section we show in our own human study that there is still room for improvement on the CBT beyond the performance of our model.\n\nDatasets\nA crucial condition for applying deep-learning techniques is to have a huge amount of data available for training. For question answering this specifically means having a large number of document-question-answer triples available. While there is an unlimited amount of text available, coming up with relevant questions and the corresponding answers can be extremely labour-intensive if done by human annotators. There were efforts to provide such human-generated datasets, e.g. Microsoft's MCTest BIBREF17 , however their scale is not suitable for deep learning without pre-training on other data BIBREF18 (such as using pre-trained word embedding vectors).\nGoogle DeepMind managed to avoid this scale issue with their way of generating document-question-answer triples automatically, closely followed by Facebook with a similar method. Let us now briefly introduce the two resulting datasets whose properties are summarized in Table TABREF8 .\nThese two datasets BIBREF1 exploit a useful feature of online news articles – many articles include a short summarizing sentence near the top of the page. Since all information in the summary sentence is also presented in the article body, we get a nice cloze-style question about the article contents by removing a word from the short summary.\nThe dataset's authors also replaced all named entities in the dataset by anonymous tokens which are further shuffled for each new batch. This forces the model to rely solely on information from the context document, not being able to transfer any meaning of the named entities between documents.\nThis restricts the task to one specific aspect of context-dependent question answering which may be useful however it moves the task further from the real application scenario, where we would like the model to use all information available to answer questions. Furthermore Chen et al. BIBREF5 have suggested that this can make about 17% of the questions unanswerable even by humans. They also claim that more than a half of the question sentences are mere paraphrases or exact matches of a single sentence from the context document. This raises a question to what extent the dataset can test deeper understanding of the articles.\nThe Children's Book Test BIBREF2 uses a different source - books freely available thanks to Project Gutenberg. Since no summary is available, each example consists of a context document formed from 20 consecutive sentences from the story together with a question formed from the subsequent sentence.\nThe dataset comes in four flavours depending on what type of word is omitted from the question sentence. Based on human evaluation done in BIBREF2 it seems that NE and CN are more context dependent than the other two types – prepositions and verbs. Therefore we (and all of the recent publications) focus only on these two word types.\nSeveral new datasets related to the (now almost standard) ones above emerged recently. We will now briefly present them and explain how the dataset we are introducing in this article differs from them.\nThe LAMBADA dataset BIBREF19 is designed to measure progress in understanding common-sense questions about short stories that can be easily answered by humans but cannot be answered by current standard machine-learning models (e.g. plain LSTM language models). This dataset is useful for measuring the gap between humans and machine learning algorithms. However, by contrast to our BookTest dataset, it will not allow us to track progress towards the performance of the baseline systems or on examples where machine learning may show super-human performance. Also LAMBADA is just a diagnostic dataset and does not provide ready-to-use question-answering training data, just a plain-text corpus which may moreover include copyrighted books making its use potentially problematic for some purposes. We are providing ready training data consisting of copyright-free books only.\nThe SQuAD dataset BIBREF20 based on Wikipedia and the Who-did-What dataset BIBREF21 based on Gigaword news articles are factoid question-answering datasets where a multi-word answer should be extracted from a context document. This is in contrast to the previous datasets, including CNN/DM, CBT, LAMBADA and our new dataset, which require only single-word answers. Both these datasets however provide less than 130,000 training questions, two orders of magnitude less than our dataset does.\nThe Story Cloze Test BIBREF22 provides a crowd-sourced corpus of 49,255 commonsense stories for training and 3,744 testing stories with right and wrong endings. Hence the dataset is again rather small. Similarly to LAMBADA, the Story Cloze Test was designed to be easily answerable by humans.\nIn the WikiReading BIBREF23 dataset the context document is formed from a Wikipedia article and the question-answer pair is taken from the corresponding WikiData page. For each entity (e.g. Hillary Clinton), WikiData contain a number of property-value pairs (e.g. place of birth: Chicago) which form the datasets's question-answer pairs. The dataset is certainly relevant to the community, however the questions are of very limited variety with only 20 properties (and hence unique questions) covering INLINEFORM0 of the dataset. Furthermore many of the frequent properties are mentioned at a set spot within the article (e.g. the date of birth is almost always in brackets behind the name of a person) which may make the task easier for machines. We are trying to provide a more varied dataset.\nAlthough there are several datasets related to task we are aiming to solve, they differ sufficiently for our dataset to bring new value to the community. Its biggest advantage is its size which can furthermore be easily upscaled without expensive human annotation. Finally while we are emphasizing the differences, models could certainly benefit from as diverse a collection of datasets as possible.\n\nPossible Directions for Improvements\nAccuracy in any machine learning tasks can be enhanced either by improving a machine learning model or by using more in-domain training data. Current state of the art models BIBREF6 , BIBREF7 , BIBREF8 , BIBREF11 improve over AS Reader's accuracy on CBT NE and CN datasets by 1-2 percent absolute. This suggests that with current techniques there is only limited room for improvement on the algorithmic side.\nThe other possibility to improve performance is simply to use more training data. The importance of training data was highlighted by the frequently quoted Mercer's statement that “There is no data like more data.” The observation that having more data is often more important than having better algorithms has been frequently stressed since then BIBREF13 , BIBREF14 .\nAs a step in the direction of exploiting the potential of more data in the domain of text comprehension, we created a new dataset called BookTest similar to, but much larger than the widely used CBT and CNN/DM datasets.\n\nBookTest\nSimilarly to the CBT, our BookTest dataset is derived from books available through project Gutenberg. We used 3555 copyright-free books to extract CN examples and 10507 books for NE examples, for comparison the CBT dataset was extracted from just 108 books.\nWhen creating our dataset we follow the same procedure as was used to create the CBT dataset BIBREF2 . That is, we detect whether each sentence contains either a named entity or a common noun that already appeared in one of the preceding twenty sentences. This word is then replaced by a gap tag (XXXXX) in this sentence which is hence turned into a cloze-style question. The preceding 20 sentences are used as the context document. For common noun and named entity detection we use the Stanford POS tagger BIBREF27 and Stanford NER BIBREF28 .\nThe training dataset consists of the original CBT NE and CN data extended with new NE and CN examples. The new BookTest dataset hence contains INLINEFORM0 training examples and INLINEFORM1 tokens.\nThe validation dataset consists of INLINEFORM0 NE and INLINEFORM1 CN questions. We have one test set for NE and one for CN, each containing INLINEFORM2 examples. The training, validation and test sets were generated from non-overlapping sets of books.\nWhen generating the dataset we removed all editions of books used to create CBT validation and test sets from our training dataset. Therefore the models trained on the BookTest corpus can be evaluated on the original CBT data and they can be compared with recent text-comprehension models utilizing this dataset BIBREF2 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 .\n\nResults\nTable TABREF25 shows the accuracy of the psr and other architectures on the CBT validation and test data. The last two rows show the performance of the psr trained on the BookTest dataset; all the other models have been trained on the original CBT training data.\nIf we take the best psr ensemble trained on CBT as a baseline, improving the model architecture as in BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , continuing to use the original CBT training data, lead to improvements of INLINEFORM0 and INLINEFORM1 absolute on named entities and common nouns respectively. By contrast, inflating the training dataset provided a boost of INLINEFORM2 while using the same model. The ensemble of our models even exceeded the human baseline provided by Facebook BIBREF2 on the Common Noun dataset.\nOur model takes approximately two weeks to converge when trained on the BookTest dataset on a single Nvidia Tesla K40 GPU.\n\nHuman Study\nAfter adding more data we have the performance on the CBT validation and test datasets soaring. However is there still potential for much further growth beyond the results we have observed?\nWe decided to explore the remaining space for improvement on the CBT by testing humans on a random subset of 50 named entity and 50 common noun validation questions that the psr ensemble could not answer correctly. These questions were answered by 10 non-native English speakers from our research laboratory, each on a disjoint subset of questions.. Participants had unlimited time to answer the questions and were told that these questions were not correctly answered by a machine, providing additional motivation to prove they are better than computers. The results of the human study are summarized in Table TABREF28 . They show that a majority of questions that our system could not answer so far are in fact answerable. This suggests that 1) the original human baselines might have been underestimated, however, it might also be the case that there are some examples that can be answered by machines and not by humans; 2) there is still space for improvement.\nA system that would answer correctly every time when either our ensemble or human answered correctly would achieve accuracy over 92% percent on both validation and test NE datasets and over 96% on both CN datasets. Hence it still makes sense to use CBT dataset to study further improvements of text-comprehension systems.\n\nConclusion\nFew ways of improving model performance are as solidly established as using more training data. Yet we believe this principle has been somewhat neglected by recent research in text comprehension. While there is a practically unlimited amount of data available in this field, most research was performed on unnecessarily small datasets.\nAs a gentle reminder to the community we have shown that simply infusing a model with more data can yield performance improvements of up to INLINEFORM0 where several attempts to improve the model architecture on the same training data have given gains of at most INLINEFORM1 compared to our best ensemble result. Yes, experiments on small datasets certainly can bring useful insights. However we believe that the community should also embrace the real-world scenario of data abundance.\nThe BookTest dataset we are proposing gives the reading-comprehension community an opportunity to make a step in that direction.\n\nTable 4: The best hyperparameters and range of values that we tested on the BookTest train dataset. We report number of hidden units of the unidirectional GRU; the bidirectional GRU has twice as many hidden units.\n\nQuery: How large are the improvements of the Attention-Sum Reader model when using the BookTest dataset?\n\nJSON Response:",
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"content": "{\"answer\": \"Answer with content missing: (Table 2) Accuracy of best AS reader results including ensembles are 78.4 and 83.7 when trained on BookTest compared to 71.0 and 68.9 when trained on CBT for Named endity and Common noun respectively.\", \"evidence\": [\"Table TABREF25 shows the accuracy of the psr and other architectures on the CBT validation and test data. The last two rows show the performance of the psr trained on the BookTest dataset; all the other models have been trained on the original CBT training data.\"]}",
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science.multicite_intent_classification | Examine the text below, which includes a citation marked by "<cite>" and "</cite>". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.
Provide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.
Text with Citation:
In addition, <cite>Sudo et al. (2003)</cite> only generated subtrees which appeared in at least three documents. | ["Background"] | science.multicite_intent_classification.1675 | [
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"content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nIn addition, <cite>Sudo et al. (2003)</cite> only generated subtrees which appeared in at least three documents.",
"role": "user"
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"content": "[\"Background\"]",
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science.discomat_te | | Energy level 4I15/2- | TBZNbEr10 * | TBZNbEr10 * | TBZNbEr10 * | fexp (reported) | fexp (reported) | fexp (reported) | fexp (reported) |
| Energy level 4I15/2- | l (nm) | fexp | fcal | BLNEr [28] | P1 [29] | SLBiBEr10 [30] | LBTAFEr10 [31] |
| 4I13/2 | 1531+-1 | 2.90+-0.03 | 2.49+-0.13 | - | 0.86 | 2.47 | 1.59 |
| 4I11/2 | 977+-1 | 1.00+-0.02 | 0.98+-0.07 | 0.43 | 0.54 | 0.80 | 0.54 |
| 4I9/2 | - | - | - | 0.23 | 0.23 | 0.30 | 0.28 |
| 4F9/2 | 653+-1 | 3.75+-0.07 | 3.13+-0.31 | 1.54 | 1.44 | 2.69 | 1.88 |
| 4S3/2 | 545+-1 | 0.78+-0.02 | 1.01+-0.05 | 0.35 | 0.39 | 0.20 | 0.30 |
| 2H11/2 | 522+-1 | 11.19+-0.07 | 10.81+-0.29 | 4.97 | 8.41 | 9.21 | 8.79 |
| 4F7/2 | 489+-1 | 2.76+-0.08 | 3.59+-0.45 | 1.35 | 1.47 | 1.75 | 1.74 |
| 4F5/2 | 452+-1 | 0.65+-0.02 | 1.19+-0.06 | 0.36 | - | - | 0.32 |
| 4F3/2 | 443+-1 | 0.16+-0.02 | 0.65+-0.07 | 0.19 | - | - | - |
| 2G9/2 | 407+-1 | 1.20+-0.17 | 0.71+-0.05 | 0.91 | 0.54 | - | - |
Caption: Experimental and calculated oscillator stengths (f) (x10-6) of certain excited states of Er3+ ions in TBZNbEr10 glass and experimental oscillator strengths of some reported glasses.
Examine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:
{"value": "xx", "type": "Other"}
{"value": "xx", "type": "Glass_Compound_Amount", "constituent": "xx", "unit": "xx", "material": "xx"}
Process each cell sequentially, and for any unknown attributes, use "xx" as a placeholder. Each JSON should be on a new line.
Output: | {"value": "1531", "type": "Other"}
{"value": "2.90", "type": "Other"}
{"value": "2.49", "type": "Other"}
{"value": "0.86", "type": "Other"}
{"value": "2.47", "type": "Other"}
{"value": "1.59", "type": "Other"}
{"value": "977", "type": "Other"}
{"value": "1.00", "type": "Other"}
{"value": "0.98", "type": "Other"}
{"value": "0.43", "type": "Other"}
{"value": "0.54", "type": "Other"}
{"value": "0.80", "type": "Other"}
{"value": "0.54", "type": "Other"}
{"value": "0.23", "type": "Other"}
{"value": "0.23", "type": "Other"}
{"value": "0.30", "type": "Other"}
{"value": "0.28", "type": "Other"}
{"value": "653", "type": "Other"}
{"value": "3.75", "type": "Other"}
{"value": "3.13", "type": "Other"}
{"value": "1.54", "type": "Other"}
{"value": "1.44", "type": "Other"}
{"value": "2.69", "type": "Other"}
{"value": "1.88", "type": "Other"}
{"value": "545", "type": "Other"}
{"value": "0.78", "type": "Other"}
{"value": "1.01", "type": "Other"}
{"value": "0.35", "type": "Other"}
{"value": "0.39", "type": "Other"}
{"value": "0.20", "type": "Other"}
{"value": "0.30", "type": "Other"}
{"value": "522", "type": "Other"}
{"value": "11.19", "type": "Other"}
{"value": "10.81", "type": "Other"}
{"value": "4.97", "type": "Other"}
{"value": "8.41", "type": "Other"}
{"value": "9.21", "type": "Other"}
{"value": "8.79", "type": "Other"}
{"value": "489", "type": "Other"}
{"value": "2.76", "type": "Other"}
{"value": "3.59", "type": "Other"}
{"value": "1.35", "type": "Other"}
{"value": "1.47", "type": "Other"}
{"value": "1.75", "type": "Other"}
{"value": "1.74", "type": "Other"}
{"value": "452", "type": "Other"}
{"value": "0.65", "type": "Other"}
{"value": "1.19", "type": "Other"}
{"value": "0.36", "type": "Other"}
{"value": "0.32", "type": "Other"}
{"value": "443", "type": "Other"}
{"value": "0.16", "type": "Other"}
{"value": "0.65", "type": "Other"}
{"value": "0.19", "type": "Other"}
{"value": "407", "type": "Other"}
{"value": "1.20", "type": "Other"}
{"value": "0.71", "type": "Other"}
{"value": "0.91", "type": "Other"}
{"value": "0.54", "type": "Other"} | science.discomat_te.73 | [
{
"content": "| Energy level 4I15/2- | TBZNbEr10 * | TBZNbEr10 * | TBZNbEr10 * | fexp (reported) | fexp (reported) | fexp (reported) | fexp (reported) |\n| Energy level 4I15/2- | l (nm) | fexp | fcal | BLNEr [28] | P1 [29] | SLBiBEr10 [30] | LBTAFEr10 [31] |\n| 4I13/2 | 1531+-1 | 2.90+-0.03 | 2.49+-0.13 | - | 0.86 | 2.47 | 1.59 |\n| 4I11/2 | 977+-1 | 1.00+-0.02 | 0.98+-0.07 | 0.43 | 0.54 | 0.80 | 0.54 |\n| 4I9/2 | - | - | - | 0.23 | 0.23 | 0.30 | 0.28 |\n| 4F9/2 | 653+-1 | 3.75+-0.07 | 3.13+-0.31 | 1.54 | 1.44 | 2.69 | 1.88 |\n| 4S3/2 | 545+-1 | 0.78+-0.02 | 1.01+-0.05 | 0.35 | 0.39 | 0.20 | 0.30 |\n| 2H11/2 | 522+-1 | 11.19+-0.07 | 10.81+-0.29 | 4.97 | 8.41 | 9.21 | 8.79 |\n| 4F7/2 | 489+-1 | 2.76+-0.08 | 3.59+-0.45 | 1.35 | 1.47 | 1.75 | 1.74 |\n| 4F5/2 | 452+-1 | 0.65+-0.02 | 1.19+-0.06 | 0.36 | - | - | 0.32 |\n| 4F3/2 | 443+-1 | 0.16+-0.02 | 0.65+-0.07 | 0.19 | - | - | - |\n| 2G9/2 | 407+-1 | 1.20+-0.17 | 0.71+-0.05 | 0.91 | 0.54 | - | - |\n\nCaption: Experimental and calculated oscillator stengths (f) (x10-6) of certain excited states of Er3+ ions in TBZNbEr10 glass and experimental oscillator strengths of some reported glasses.\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:",
"role": "user"
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{
"content": "{\"value\": \"1531\", \"type\": \"Other\"}\n{\"value\": \"2.90\", \"type\": \"Other\"}\n{\"value\": \"2.49\", \"type\": \"Other\"}\n{\"value\": \"0.86\", \"type\": \"Other\"}\n{\"value\": \"2.47\", \"type\": \"Other\"}\n{\"value\": \"1.59\", \"type\": \"Other\"}\n{\"value\": \"977\", \"type\": \"Other\"}\n{\"value\": \"1.00\", \"type\": \"Other\"}\n{\"value\": \"0.98\", \"type\": \"Other\"}\n{\"value\": \"0.43\", \"type\": \"Other\"}\n{\"value\": \"0.54\", \"type\": \"Other\"}\n{\"value\": \"0.80\", \"type\": \"Other\"}\n{\"value\": \"0.54\", \"type\": \"Other\"}\n{\"value\": \"0.23\", \"type\": \"Other\"}\n{\"value\": \"0.23\", \"type\": \"Other\"}\n{\"value\": \"0.30\", \"type\": \"Other\"}\n{\"value\": \"0.28\", \"type\": \"Other\"}\n{\"value\": \"653\", \"type\": \"Other\"}\n{\"value\": \"3.75\", \"type\": \"Other\"}\n{\"value\": \"3.13\", \"type\": \"Other\"}\n{\"value\": \"1.54\", \"type\": \"Other\"}\n{\"value\": \"1.44\", \"type\": \"Other\"}\n{\"value\": \"2.69\", \"type\": \"Other\"}\n{\"value\": \"1.88\", \"type\": \"Other\"}\n{\"value\": \"545\", \"type\": \"Other\"}\n{\"value\": \"0.78\", \"type\": \"Other\"}\n{\"value\": \"1.01\", \"type\": \"Other\"}\n{\"value\": \"0.35\", \"type\": \"Other\"}\n{\"value\": \"0.39\", \"type\": \"Other\"}\n{\"value\": \"0.20\", \"type\": \"Other\"}\n{\"value\": \"0.30\", \"type\": \"Other\"}\n{\"value\": \"522\", \"type\": \"Other\"}\n{\"value\": \"11.19\", \"type\": \"Other\"}\n{\"value\": \"10.81\", \"type\": \"Other\"}\n{\"value\": \"4.97\", \"type\": \"Other\"}\n{\"value\": \"8.41\", \"type\": \"Other\"}\n{\"value\": \"9.21\", \"type\": \"Other\"}\n{\"value\": \"8.79\", \"type\": \"Other\"}\n{\"value\": \"489\", \"type\": \"Other\"}\n{\"value\": \"2.76\", \"type\": \"Other\"}\n{\"value\": \"3.59\", \"type\": \"Other\"}\n{\"value\": \"1.35\", \"type\": \"Other\"}\n{\"value\": \"1.47\", \"type\": \"Other\"}\n{\"value\": \"1.75\", \"type\": \"Other\"}\n{\"value\": \"1.74\", \"type\": \"Other\"}\n{\"value\": \"452\", \"type\": \"Other\"}\n{\"value\": \"0.65\", \"type\": \"Other\"}\n{\"value\": \"1.19\", \"type\": \"Other\"}\n{\"value\": \"0.36\", \"type\": \"Other\"}\n{\"value\": \"0.32\", \"type\": \"Other\"}\n{\"value\": \"443\", \"type\": \"Other\"}\n{\"value\": \"0.16\", \"type\": \"Other\"}\n{\"value\": \"0.65\", \"type\": \"Other\"}\n{\"value\": \"0.19\", \"type\": \"Other\"}\n{\"value\": \"407\", \"type\": \"Other\"}\n{\"value\": \"1.20\", \"type\": \"Other\"}\n{\"value\": \"0.71\", \"type\": \"Other\"}\n{\"value\": \"0.91\", \"type\": \"Other\"}\n{\"value\": \"0.54\", \"type\": \"Other\"}",
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science.evidence_inference | Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:
- Treatment: The specific medical procedure or drug being evaluated.
- Control: The standard or placebo used for comparison. If absent, use `null`.
- Result: The health outcome being assessed.
- Impact: The observed effect of the treatment on the result, categorized as: ("increased", "decreased", "no change").
- Justification: The supporting details for the observed impact.
Format the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].
Abstract:
ABSTRACT.OBJECTIVE: Axitinib is a potent and selective second-generation inhibitor of vascular endothelial growth factor receptors 1, 2 and 3. The efficacy and safety of axitinib in Japanese patients with metastatic renal cell carcinoma were evaluated.
ABSTRACT.METHODS: A subgroup analysis was conducted in Japanese patients enrolled in the randomized Phase III trial of axitinib versus sorafenib after failure of one prior systemic therapy for metastatic renal cell carcinoma.
ABSTRACT.RESULTS: Twenty-five (of 361) and 29 (of 362) patients randomized to the axitinib and sorafenib arms, respectively, were Japanese and included in this analysis. Median progression-free survival in Japanese patients was 12.1 months (95% confidence interval 8.6 to not estimable) for axitinib and 4.9 months (95% confidence interval 2.8–6.6) for sorafenib (hazard ratio 0.390; 95% confidence interval 0.130–1.173; stratified one-sided P = 0.0401). The objective response rate was 52.0% for axitinib and 3.4% for sorafenib (P = 0.0001). The common all-causality adverse events (all grades) in Japanese patients were dysphonia (68%), hypertension (64%), hand–foot syndrome (64%) and diarrhea (56%) for axitinib, and hand–foot syndrome (86%), hypertension (62%) and diarrhea (52%) for sorafenib. The safety profiles of axitinib and sorafenib in Japanese patients were generally similar to those observed in the overall population, with the exceptions of higher incidences of hypertension, dysphonia, hand–foot syndrome, hypothyroidism and stomatitis.
ABSTRACT.CONCLUSIONS: Axitinib is efficacious and well tolerated in Japanese patients with previously treated metastatic renal cell carcinoma, consistent with the results in the overall population, providing a new targeted therapy for these Japanese patients.
Extracted Data: | [["axitinib", "sorafenib", "Median progression-free survival", "significantly increased", "Median progression-free survival in Japanese patients was 12.1 months (95% confidence interval 8.6 to not estimable) for axitinib and 4.9 months (95% confidence interval 2.8\u20136.6) for sorafenib (hazard ratio 0.390; 95% confidence interval 0.130\u20131.173; stratified one-sided P = 0.0401)."]] | science.evidence_inference.247 | [
{
"content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.OBJECTIVE: Axitinib is a potent and selective second-generation inhibitor of vascular endothelial growth factor receptors 1, 2 and 3. The efficacy and safety of axitinib in Japanese patients with metastatic renal cell carcinoma were evaluated.\n\nABSTRACT.METHODS: A subgroup analysis was conducted in Japanese patients enrolled in the randomized Phase III trial of axitinib versus sorafenib after failure of one prior systemic therapy for metastatic renal cell carcinoma.\n\nABSTRACT.RESULTS: Twenty-five (of 361) and 29 (of 362) patients randomized to the axitinib and sorafenib arms, respectively, were Japanese and included in this analysis. Median progression-free survival in Japanese patients was 12.1 months (95% confidence interval 8.6 to not estimable) for axitinib and 4.9 months (95% confidence interval 2.8–6.6) for sorafenib (hazard ratio 0.390; 95% confidence interval 0.130–1.173; stratified one-sided P = 0.0401). The objective response rate was 52.0% for axitinib and 3.4% for sorafenib (P = 0.0001). The common all-causality adverse events (all grades) in Japanese patients were dysphonia (68%), hypertension (64%), hand–foot syndrome (64%) and diarrhea (56%) for axitinib, and hand–foot syndrome (86%), hypertension (62%) and diarrhea (52%) for sorafenib. The safety profiles of axitinib and sorafenib in Japanese patients were generally similar to those observed in the overall population, with the exceptions of higher incidences of hypertension, dysphonia, hand–foot syndrome, hypothyroidism and stomatitis.\n\nABSTRACT.CONCLUSIONS: Axitinib is efficacious and well tolerated in Japanese patients with previously treated metastatic renal cell carcinoma, consistent with the results in the overall population, providing a new targeted therapy for these Japanese patients.\n\nExtracted Data:",
"role": "user"
},
{
"content": "[[\"axitinib\", \"sorafenib\", \"Median progression-free survival\", \"significantly increased\", \"Median progression-free survival in Japanese patients was 12.1 months (95% confidence interval 8.6 to not estimable) for axitinib and 4.9 months (95% confidence interval 2.8\\u20136.6) for sorafenib (hazard ratio 0.390; 95% confidence interval 0.130\\u20131.173; stratified one-sided P = 0.0401).\"]]",
"role": "assistant"
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science.biored_ner | Analyze the provided biomedical abstract and identify distinct entities. Focus on the specified categories: ["CellLine", "Variant", "Species", "Disease", "Chemical", "Gene"].
Ensure the output is strictly the JSON object.
Source Text:
Psoriasin promotes invasion, aggregation and survival of pancreatic cancer cells; association with disease progression. Psoriasin (S100A7) is an 11-kDa small calcium binding protein initially isolated from psoriatic skin lesions. It belongs to the S100 family of proteins which play an important role in a range of cell functions including proliferation, differentiation, migration and apoptosis. Aberrant Psoriasin expression has been implicated in a range of cancers and is often associated with poor prognosis. This study examined the role of Psoriasin on pancreatic cancer cell functions and the implication in progression of the disease. Expression of Psoriasin was determined in a cohort of pancreatic tissues comprised of 126 pancreatic tumours and 114 adjacent non-tumour pancreatic tissues. Knockdown and overexpression of Psoriasin in pancreatic cancer cells was performed using specifically constructed plasmids, which either had anti-Psoriasin ribozyme transgene or the full length human Psoriasin coding sequence. Psoriasin knockdown and overexpression was verified using conventional RT-PCR and qPCR. The effect of manipulating Psoriasin expression on pancreatic cancer cell functions was assessed using several in vitro cell function assays. Local invasive pancreatic cancers extended beyond the pancreas expressed higher levels of Psoriasin transcripts compared with the cancers confined to the pancreas. Primary tumours with distant metastases exhibited a reduced expression of Psoriasin. Psoriasin overexpression cell lines exhibited significantly increased growth and migration compared to control cells. In addition, Psoriasin overexpression resulted in increased pancreatic cancer cell invasion which was associated with upregulation of matrix metalloproteinase-2 (MMP-2) and MMP-9. Overexpression of Psoriasin also promoted aggregation and survival of pancreatic cancer cells when they lost anchorage. Taken together, higher expression of Psoriasin was associated with local invasion in pancreatic cancers. Psoriasin expression is associated with pancreatic cancer cell growth, migration, cell-matrix adhesion, and invasion via regulation of MMPs. As such, the proposed implications of Psoriasin in invasion, disease progression and as a potential therapeutic target warrant further investigation. | {"CellLine": [], "Chemical": [], "Disease": ["psoriatic skin lesions", "pancreatic cancer", "pancreatic tumours", "cancers", "pancreatic cancers", "metastases", "Primary tumours"], "Gene": ["MMPs", "matrix metalloproteinase-2", "S100A7", "MMP-9", "S100", "Psoriasin", "MMP-2", "calcium binding protein"], "Species": ["human"], "Variant": []} | science.biored_ner.36 | [
{
"content": "Analyze the provided biomedical abstract and identify distinct entities. Focus on the specified categories: [\"CellLine\", \"Variant\", \"Species\", \"Disease\", \"Chemical\", \"Gene\"].\n\nEnsure the output is strictly the JSON object.\n\nSource Text:\n\nPsoriasin promotes invasion, aggregation and survival of pancreatic cancer cells; association with disease progression. Psoriasin (S100A7) is an 11-kDa small calcium binding protein initially isolated from psoriatic skin lesions. It belongs to the S100 family of proteins which play an important role in a range of cell functions including proliferation, differentiation, migration and apoptosis. Aberrant Psoriasin expression has been implicated in a range of cancers and is often associated with poor prognosis. This study examined the role of Psoriasin on pancreatic cancer cell functions and the implication in progression of the disease. Expression of Psoriasin was determined in a cohort of pancreatic tissues comprised of 126 pancreatic tumours and 114 adjacent non-tumour pancreatic tissues. Knockdown and overexpression of Psoriasin in pancreatic cancer cells was performed using specifically constructed plasmids, which either had anti-Psoriasin ribozyme transgene or the full length human Psoriasin coding sequence. Psoriasin knockdown and overexpression was verified using conventional RT-PCR and qPCR. The effect of manipulating Psoriasin expression on pancreatic cancer cell functions was assessed using several in vitro cell function assays. Local invasive pancreatic cancers extended beyond the pancreas expressed higher levels of Psoriasin transcripts compared with the cancers confined to the pancreas. Primary tumours with distant metastases exhibited a reduced expression of Psoriasin. Psoriasin overexpression cell lines exhibited significantly increased growth and migration compared to control cells. In addition, Psoriasin overexpression resulted in increased pancreatic cancer cell invasion which was associated with upregulation of matrix metalloproteinase-2 (MMP-2) and MMP-9. Overexpression of Psoriasin also promoted aggregation and survival of pancreatic cancer cells when they lost anchorage. Taken together, higher expression of Psoriasin was associated with local invasion in pancreatic cancers. Psoriasin expression is associated with pancreatic cancer cell growth, migration, cell-matrix adhesion, and invasion via regulation of MMPs. As such, the proposed implications of Psoriasin in invasion, disease progression and as a potential therapeutic target warrant further investigation.",
"role": "user"
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{
"content": "{\"CellLine\": [], \"Chemical\": [], \"Disease\": [\"psoriatic skin lesions\", \"pancreatic cancer\", \"pancreatic tumours\", \"cancers\", \"pancreatic cancers\", \"metastases\", \"Primary tumours\"], \"Gene\": [\"MMPs\", \"matrix metalloproteinase-2\", \"S100A7\", \"MMP-9\", \"S100\", \"Psoriasin\", \"MMP-2\", \"calcium binding protein\"], \"Species\": [\"human\"], \"Variant\": []}",
"role": "assistant"
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science.biored_ner | Analyze the provided biomedical abstract and identify distinct entities. Focus on the specified categories: ["CellLine", "Variant", "Species", "Disease", "Chemical", "Gene"].
Ensure the output is strictly the JSON object.
Source Text:
MicroRNA deep sequencing in two adult stem cell populations identifies miR-501 as a novel regulator of myosin heavy chain during muscle regeneration. MicroRNAs (miRNAs) are important regulators of skeletal muscle regeneration, but the underlying mechanisms are still incompletely understood. Here, comparative miRNA sequencing analysis of myogenic progenitor cells (MPs) and non-myogenic fibroblast-adipocyte progenitors (FAPs) during cardiotoxin (CTX)-induced muscle injury uncovered miR-501 as a novel muscle-specific miRNA. miR-501 is an intronic miRNA and its expression levels in MPs correlated with its host gene, chloride channel, voltage-sensitive 5 (Clcn5). Pharmacological inhibition of miR-501 dramatically blunted the induction of embryonic myosin heavy chain (MYH3) and, to a lesser extent, adult myosin isoforms during muscle regeneration, and promoted small-diameter neofibers. An unbiased target identification approach in primary myoblasts validated gigaxonin as a target of miR-501 that mimicked the effect of miR-501 inhibition on MYH3 expression. In the mdx mouse model, which models a pathological disease state, not only was miR-501 induced in regenerating skeletal muscle, but also its serum levels were increased, which correlated with the disease state of the animals. Our results suggest that miR-501 plays a key role in adult muscle regeneration and might serve as a novel serum biomarker for the activation of adult muscle stem cells. | {"CellLine": [], "Chemical": ["CTX", "cardiotoxin"], "Disease": ["muscle injury"], "Gene": ["myosin heavy chain", "adult myosin isoforms", "miR-501", "Clcn5", "chloride channel, voltage-sensitive 5", "MYH3", "gigaxonin"], "Species": ["mouse"], "Variant": []} | science.biored_ner.25 | [
{
"content": "Analyze the provided biomedical abstract and identify distinct entities. Focus on the specified categories: [\"CellLine\", \"Variant\", \"Species\", \"Disease\", \"Chemical\", \"Gene\"].\n\nEnsure the output is strictly the JSON object.\n\nSource Text:\n\nMicroRNA deep sequencing in two adult stem cell populations identifies miR-501 as a novel regulator of myosin heavy chain during muscle regeneration. MicroRNAs (miRNAs) are important regulators of skeletal muscle regeneration, but the underlying mechanisms are still incompletely understood. Here, comparative miRNA sequencing analysis of myogenic progenitor cells (MPs) and non-myogenic fibroblast-adipocyte progenitors (FAPs) during cardiotoxin (CTX)-induced muscle injury uncovered miR-501 as a novel muscle-specific miRNA. miR-501 is an intronic miRNA and its expression levels in MPs correlated with its host gene, chloride channel, voltage-sensitive 5 (Clcn5). Pharmacological inhibition of miR-501 dramatically blunted the induction of embryonic myosin heavy chain (MYH3) and, to a lesser extent, adult myosin isoforms during muscle regeneration, and promoted small-diameter neofibers. An unbiased target identification approach in primary myoblasts validated gigaxonin as a target of miR-501 that mimicked the effect of miR-501 inhibition on MYH3 expression. In the mdx mouse model, which models a pathological disease state, not only was miR-501 induced in regenerating skeletal muscle, but also its serum levels were increased, which correlated with the disease state of the animals. Our results suggest that miR-501 plays a key role in adult muscle regeneration and might serve as a novel serum biomarker for the activation of adult muscle stem cells.",
"role": "user"
},
{
"content": "{\"CellLine\": [], \"Chemical\": [\"CTX\", \"cardiotoxin\"], \"Disease\": [\"muscle injury\"], \"Gene\": [\"myosin heavy chain\", \"adult myosin isoforms\", \"miR-501\", \"Clcn5\", \"chloride channel, voltage-sensitive 5\", \"MYH3\", \"gigaxonin\"], \"Species\": [\"mouse\"], \"Variant\": []}",
"role": "assistant"
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science.multicite_intent_classification | Examine the text below, which includes a citation marked by "<cite>" and "</cite>". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.
Provide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.
Text with Citation:
Experiments on multiple dialog act datasets showed that our model outperforms state-of-the-art deep leaning methods (Lee and Dernoncourt, 2016; Khanpour et al., 2016;<cite> Ortega and Vu, 2017)</cite> . | ["Differences"] | science.multicite_intent_classification.542 | [
{
"content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nExperiments on multiple dialog act datasets showed that our model outperforms state-of-the-art deep leaning methods (Lee and Dernoncourt, 2016; Khanpour et al., 2016;<cite> Ortega and Vu, 2017)</cite> .",
"role": "user"
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{
"content": "[\"Differences\"]",
"role": "assistant"
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science.evidence_inference | Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:
- Treatment: The specific medical procedure or drug being evaluated.
- Control: The standard or placebo used for comparison. If absent, use `null`.
- Result: The health outcome being assessed.
- Impact: The observed effect of the treatment on the result, categorized as: ("increased", "decreased", "no change").
- Justification: The supporting details for the observed impact.
Format the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].
Abstract:
ABSTRACT.PURPOSE:: To compare two phakic intraocular lenses, Artiflex and STAAR® implantable contact lens (ICL), in high myopia.
ABSTRACT.SETTING:: Isfahan Ophthalmology Clinic, Iran.
ABSTRACT.MATERIALS AND METHODS:: In a randomized, prospective clinical trial study, 40 eyes of 20 patients who came to Isfahan ophthalmology clinic to correct their refractive error were examined. Artiflex was inserted in 20 eyes and ICL in the other 20 eyes. After 1 year, uncorrected visual acuity (UCVA), best corrected visual acuity (BCVA), contrast sensitivity, intraocular pressure (IOP), and specular microscopy of corneal endothelium were assessed in both the groups.
ABSTRACT.RESULTS:: In this study, there was no statistical difference between UCVA, BCVA, IOP, cataract incidence, contrast sensitivity, and specular microscopy, 1 year after surgery. 40% of eyes in the Artiflex group developed anterior chamber cell and flare reaction 1 year postoperatively, but no patient in the ICL group developed the same. So, there is obvious difference in the rate of anterior chamber cell and flare reaction between these two groups.
ABSTRACT.CONCLUSION:: These two lenses have similar outcomes except in the incidence rate of anterior chamber cell and flare reaction that is greater in the Artiflex group. So, these two lenses are safe with predictable outcome in treating high myopia.
Extracted Data: | [["STAAR\u00ae implantable contact lens (ICL)", "Artiflex", "UCVA or BCVA", "no significant difference", "there was no statistical difference between UCVA, BCVA, IOP, cataract incidence, contrast sensitivity, and specular microscopy, 1 year after surgery."], ["STAAR\u00ae implantable contact lens (ICL)", "Artiflex", "intraocular pressure (IOP)", "no significant difference", ", there was no statistical difference between UCVA, BCVA, IOP, cataract incidence, contrast sensitivity, and specular microscopy, 1 year after surgery"]] | science.evidence_inference.165 | [
{
"content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.PURPOSE:: To compare two phakic intraocular lenses, Artiflex and STAAR® implantable contact lens (ICL), in high myopia.\n\nABSTRACT.SETTING:: Isfahan Ophthalmology Clinic, Iran.\n\nABSTRACT.MATERIALS AND METHODS:: In a randomized, prospective clinical trial study, 40 eyes of 20 patients who came to Isfahan ophthalmology clinic to correct their refractive error were examined. Artiflex was inserted in 20 eyes and ICL in the other 20 eyes. After 1 year, uncorrected visual acuity (UCVA), best corrected visual acuity (BCVA), contrast sensitivity, intraocular pressure (IOP), and specular microscopy of corneal endothelium were assessed in both the groups.\n\nABSTRACT.RESULTS:: In this study, there was no statistical difference between UCVA, BCVA, IOP, cataract incidence, contrast sensitivity, and specular microscopy, 1 year after surgery. 40% of eyes in the Artiflex group developed anterior chamber cell and flare reaction 1 year postoperatively, but no patient in the ICL group developed the same. So, there is obvious difference in the rate of anterior chamber cell and flare reaction between these two groups.\n\nABSTRACT.CONCLUSION:: These two lenses have similar outcomes except in the incidence rate of anterior chamber cell and flare reaction that is greater in the Artiflex group. So, these two lenses are safe with predictable outcome in treating high myopia.\n\nExtracted Data:",
"role": "user"
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{
"content": "[[\"STAAR\\u00ae implantable contact lens (ICL)\", \"Artiflex\", \"UCVA or BCVA\", \"no significant difference\", \"there was no statistical difference between UCVA, BCVA, IOP, cataract incidence, contrast sensitivity, and specular microscopy, 1 year after surgery.\"], [\"STAAR\\u00ae implantable contact lens (ICL)\", \"Artiflex\", \"intraocular pressure (IOP)\", \"no significant difference\", \", there was no statistical difference between UCVA, BCVA, IOP, cataract incidence, contrast sensitivity, and specular microscopy, 1 year after surgery\"]]",
"role": "assistant"
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science.mup_single_document_summarization | Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.
Title of the Paper:
Collaborate to Defend Against Adversarial Attacks
Main Content:
Adversarially robust learning methods require invariant predictions to a small neighborhood of its natural inputs , thus often encountering insufficient model capacity . Learning multiple sub-models in an ensemble can mitigate this insufficiency , further improving both generalization and robustness . However , an ensemble still wastes the limited capacity of multiple models . To optimally utilize the limited capacity , this paper proposes to learn a collaboration among multiple sub-models . Compared with the ensemble , the collaboration enables the possibility of correct predictions even if there exists a single correct sub-model . Besides , learning a collaboration could enable every sub-model to fit its vulnerability area and reserve the rest of the sub-models to fit other vulnerability areas . To implement the idea , we propose a collaboration framework—CDA2 the abbreviation for Collaborate to Defend against Adversarial Attacks . CDA2 could effectively minimize the vulnerability overlap of all sub-models and then choose a representative sub-model to make correct predictions . Empirical experiments verify that CDA2 outperforms various ensemble methods against black-box and white-box adversarial attacks . 1 INTRODUCTION . Safety-critical applications ( such as in medicine and finance ) require the adversarial robustness of deep models ( Goodfellow et al. , 2015 ; Szegedy et al. , 2014 ) . An adversarially robust learning method requires invariant predictions to a small neighborhood of its natural inputs , thus often encountering insufficient model capacity ( Zhang et al. , 2021 ; Yu et al. , 2021a ) . This limits the further improvement of robustness and has the undesirable degradation of generalization ( Madry et al. , 2018 ) . Learning multiple sub-models in an ensemble ( Breiman , 1996 ; Freund et al. , 1996 ) can mitigate this insufficiency ( Pang et al. , 2019 ; Kariyappa & Qureshi , 2019 ; Yang et al. , 2020a ) . Remarkably , Pang et al . ( 2019 ) , Kariyappa & Qureshi ( 2019 ) and Yang et al . ( 2020a ) minimized the vulnerability overlaps between each pair of sub-models and improved both robustness and generalization over a single model . However , an ensemble wastes the limited capacity of multiple models . In the example of three sub-models ( see Figure 1 ( b ) ) , the adversarial input that lies in the black areas can fool the ensemble successfully , i.e. , more than half of sub-models must correctly classify the adversarial input . Therefore , the ensemble ’ s voting-based strategy excludes the possibility that true predictions remain with the minority . Besides , learning an ensemble requires more than half of the sub-models to fit the same vulnerability areas , which leaves the following question unanswered whether we could only leverage a single sub-model to fit a vulnerability area and reserve the rest of the sub-models to fit other vulnerability areas . To optimally utilize the limited capacity , this paper proposes to learn a collaboration among multiple sub-models . As shown in Figure 1 ( c ) , the adversarial input that lies in the vulnerability overlaps of all sub-models can undoubtedly fool the collaboration . Compared with the ensemble in Figure 1 ( b ) ) , collaboration enables the possibility of correct predictions even if there exists a single correct submodel merely . Besides , learning a collaboration could enable every sub-model to fit its vulnerability area , which could collectively fix broader vulnerability areas than the ensemble does . Then , submodels could collaboratively choose trustworthy ones to make the final predictions . To realize the idea , we propose a collaboration framework—Collaborate to Defend against Adversarial Attacks ( CDA2 ) ( Algorithms 1 and 2 ) . In CDA2 , each sub-model has dual heads : one outputs a vector of predicted probability fθ ( · ) ; another outputs a scalar that measures posterior probability density ( PPD ) of the prediction . In the training phase , given a natural or adversarial input x , each sub-model chooses an easy one ( s ) to feed itself . The PPD head is meanwhile updated by comparing the predicted probability on the true label—fyθ ( · ) ( a scalar ) . In the inference phase , given an input , CDA2 chooses a sub-model with the largest PPD value as the representative to output the prediction . We highlight our key contributions as follows . • We provide a new perspective on learning multiple sub-models for defending against adversarial attacks . We theoretically show the collaboration makes better decisions than the ensemble , which implies collaboration may fix broader vulnerability areas . • We propose a novel collaboration framework—CDA2 ( see Section 3.2 ) . In the training phase , CDA2could effectively minimize the vulnerability overlap of all sub-models ; In the inference phase , CDA2 could effectively choose a representative sub-model to make correct predictions . We also provide a comprehensive analysis illustrating the rationale of CDA2 . • Empirical experiments verify that CDA2 outperforms various ensemble methods against black-box and white-box adversarial attacks . 2 RELATED WORKS . Adversarial attack Adversarial attacks aim to craft the human-imperceptible adversarial input to fool the deep models . Adversarial attacks could be roughly divided into white-box attacks in which the adversary is fully aware of the model ’ s structures ( Goodfellow et al. , 2015 ; Moosavi-Dezfooli et al. , 2016 ; Carlini & Wagner , 2017b ; Chen et al. , 2018 ; Athalye et al. , 2018 ; Xiao et al. , 2018 ; Zheng et al. , 2019 ; Wong et al. , 2019 ; Mopuri et al. , 2019 ; Alaifari et al. , 2019 ; Sriramanan et al. , 2020 ; Wu et al. , 2020b ; Croce & Hein , 2020 ; Yu et al. , 2021b ) and black-box attacks in which the deep models are treated as black boxes to the adversary ( Cheng et al. , 2019 ; 2020 ; Wu et al. , 2020a ; Chen et al. , 2020a ; Li et al. , 2020a ; Rahmati et al. , 2020 ; Yan et al. , 2021b ; Hendrycks et al. , 2021 ; Dong et al. , 2018 ; Xie et al. , 2019 ) . This paper focuses on building effective defense and select both white-box and black-box attack methods as our robustness evaluation metrics . Adversarial defense Defending adversarial attacks is a challenging task and researchers have proposed various solutions . Certified defense tries to learn provably robust deep models against norm-bounded ( e.g. , ` 2 and ` ∞ ) perturbations ( Wong & Kolter , 2018 ; Tsuzuku et al. , 2018 ; Weng et al. , 2018 ; Mirman et al. , 2018 ; Hein & Andriushchenko , 2017 ; Lécuyer et al. , 2019 ; Xiao et al. , 2019 ; Cohen et al. , 2019 ; Balunovic & Vechev , 2020a ; Zhang et al. , 2020a ; Singla & Feizi , 2020 ; Balunovic & Vechev , 2020b ; Zou et al. , 2021 ) . Empirical defense leverages adversarial data to build effective defense such as adversary detection ( Metzen et al. , 2017 ; Li & Li , 2017 ; Carlini & Wagner , 2017a ; Tian et al. , 2018 ; Ma et al. , 2018b ; Lee et al. , 2018 ; Pang et al. , 2018 ; Smith & Gal , 2018 ; Roth et al. , 2019 ; Liu et al. , 2019 ; Yin & Rohde , 2020 ; Sperl et al. , 2020 ; Cohen et al. , 2020 ; Sheikholeslami et al. , 2021 ; Chen et al. , 2021a ; Yang et al. , 2020b ; Qin et al. , 2020 ; Tian et al. , 2021 ; Wu et al. , 2021 ) and adversarial training ( AT ) , in which AT stands out as the most effective defense . Researchers have investigated various aspects of AT , such as improving AT ’ s robustness or generalization ( Madry et al. , 2018 ; Yan et al. , 2018 ; Wu et al. , 2018 ; Cai et al. , 2018 ; Najafi et al. , 2019 ; Alayrac et al. , 2019 ; Carmon et al. , 2019 ; Farnia et al. , 2019 ; Song et al. , 2019 ; Zhang et al. , 2019b ; Wang et al. , 2019 ; Tramèr & Boneh , 2019 ; Zhang & Wang , 2019 ; Stutz et al. , 2020 ; Pang et al. , 2020 ; Gan et al. , 2020 ; Dong et al. , 2020 ; Zhang et al. , 2020b ; Chen et al. , 2020b ; Song et al. , 2020 ; Ding et al. , 2020 ; Wang et al. , 2020b ; Zhang et al. , 2021 ) , fixing AT ’ s undesirable robust overfitting ( Rice et al. , 2020 ; Chen et al. , 2021b ) , improving AT ’ s training efficiency ( Zhang et al. , 2019a ; Shafahi et al. , 2019 ; Zheng et al. , 2020 ; B.S . & Babu , 2020 ; Andriushchenko & Flammarion , 2020 ; Wong et al. , 2020 ) , understanding/interpreting AT ’ s unique traits ( Nakkiran , 2019 ; Yin et al. , 2019 ; Gao et al. , 2019 ; Cranko et al. , 2019 ; Zhang et al. , 2019c ; Liu et al. , 2020 ; Roth et al. , 2020 ; Wang et al. , 2020a ; Zhang et al. , 2020c ; Li et al. , 2020b ; Zou et al. , 2021 ; Mehrabi et al. , 2021 ; Xu et al. , 2021 ) , etc . Besides , researchers have alao actively investigated robust-structured models ( Cisse et al. , 2017 ; Xie et al. , 2020 ; Moosavi-Dezfooli et al. , 2019 ; Xie & Yuille , 2020 ; Yan et al. , 2021a ; Du et al. , 2021 ; Pang et al. , 2021 ) . Nevertheless , the above research thoroughly investigated a single model ; this paper focuses on the collaboration among multiple models for adversarial defense . Ensemble methods for adversarial robustness The most relevant studies are the ensemble methods . Ensemble methods such as bagging ( Breiman , 1996 ) and boosting ( Freund et al. , 1996 ) have been investigated for significantly improving the model ’ s generalization . Motivated by the benefits of ensemble methods in improving generalization , researchers introduced an ensemble to improve the model robustness ( Yang et al. , 2020a ; Kariyappa & Qureshi , 2019 ; Pang et al. , 2019 ; Tramèr et al. , 2018 ) . Tramèr et al . ( 2018 ) proposed to reduce the adversarial transferability by training a single model with adversarial examples from multiple pretrained sub-models . Pang et al . ( 2019 ) introduce a regularization method—ADP—to encourage high diversity in the non-maximal predictions of sub-models . Kariyappa & Qureshi ( 2019 ) improved the ensemble diversity by maximizing the introduced cosine distance between the gradients of sub-models with respect to the input . Yang et al . ( 2020a ) proposed to distill non-robust features in the input and diversify the adversarial vulnerability . These methods reduced overlaps of vulnerability areas between sub-models ( Yang et al. , 2020a ) . To further improve the ensembles , mixture-of-experts ( MOE ) assume that the problem space can be divided into multiple sub-problems through a gate module ; the gate module specifies each sub-model on a specific sub-problem ( Jacobs et al. , 1991 ; Ma et al. , 2018a ) . Nevertheless , to the best of our knowledge , MOE-based methods have been not applied to help adversarial robustness . Inspired by MOE , we propose the collaboration framework to defend against adversary attacks . | This paper presents a new paradigm for defending against adversarial attacks with multiple sub-models. Different from ensemble, the proposed collaboration paradigm, a representative sub-model is chosen to make the decision, instead of letting all sub-models vote. The proposed method has been validated on CIFAR-10 dataset, against both white-box and transferrability-based black-box attacks. | science.mup_single_document_summarization.5399 | [
{
"content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nCollaborate to Defend Against Adversarial Attacks\n\nMain Content:\nAdversarially robust learning methods require invariant predictions to a small neighborhood of its natural inputs , thus often encountering insufficient model capacity . Learning multiple sub-models in an ensemble can mitigate this insufficiency , further improving both generalization and robustness . However , an ensemble still wastes the limited capacity of multiple models . To optimally utilize the limited capacity , this paper proposes to learn a collaboration among multiple sub-models . Compared with the ensemble , the collaboration enables the possibility of correct predictions even if there exists a single correct sub-model . Besides , learning a collaboration could enable every sub-model to fit its vulnerability area and reserve the rest of the sub-models to fit other vulnerability areas . To implement the idea , we propose a collaboration framework—CDA2 the abbreviation for Collaborate to Defend against Adversarial Attacks . CDA2 could effectively minimize the vulnerability overlap of all sub-models and then choose a representative sub-model to make correct predictions . Empirical experiments verify that CDA2 outperforms various ensemble methods against black-box and white-box adversarial attacks . 1 INTRODUCTION . Safety-critical applications ( such as in medicine and finance ) require the adversarial robustness of deep models ( Goodfellow et al. , 2015 ; Szegedy et al. , 2014 ) . An adversarially robust learning method requires invariant predictions to a small neighborhood of its natural inputs , thus often encountering insufficient model capacity ( Zhang et al. , 2021 ; Yu et al. , 2021a ) . This limits the further improvement of robustness and has the undesirable degradation of generalization ( Madry et al. , 2018 ) . Learning multiple sub-models in an ensemble ( Breiman , 1996 ; Freund et al. , 1996 ) can mitigate this insufficiency ( Pang et al. , 2019 ; Kariyappa & Qureshi , 2019 ; Yang et al. , 2020a ) . Remarkably , Pang et al . ( 2019 ) , Kariyappa & Qureshi ( 2019 ) and Yang et al . ( 2020a ) minimized the vulnerability overlaps between each pair of sub-models and improved both robustness and generalization over a single model . However , an ensemble wastes the limited capacity of multiple models . In the example of three sub-models ( see Figure 1 ( b ) ) , the adversarial input that lies in the black areas can fool the ensemble successfully , i.e. , more than half of sub-models must correctly classify the adversarial input . Therefore , the ensemble ’ s voting-based strategy excludes the possibility that true predictions remain with the minority . Besides , learning an ensemble requires more than half of the sub-models to fit the same vulnerability areas , which leaves the following question unanswered whether we could only leverage a single sub-model to fit a vulnerability area and reserve the rest of the sub-models to fit other vulnerability areas . To optimally utilize the limited capacity , this paper proposes to learn a collaboration among multiple sub-models . As shown in Figure 1 ( c ) , the adversarial input that lies in the vulnerability overlaps of all sub-models can undoubtedly fool the collaboration . Compared with the ensemble in Figure 1 ( b ) ) , collaboration enables the possibility of correct predictions even if there exists a single correct submodel merely . Besides , learning a collaboration could enable every sub-model to fit its vulnerability area , which could collectively fix broader vulnerability areas than the ensemble does . Then , submodels could collaboratively choose trustworthy ones to make the final predictions . To realize the idea , we propose a collaboration framework—Collaborate to Defend against Adversarial Attacks ( CDA2 ) ( Algorithms 1 and 2 ) . In CDA2 , each sub-model has dual heads : one outputs a vector of predicted probability fθ ( · ) ; another outputs a scalar that measures posterior probability density ( PPD ) of the prediction . In the training phase , given a natural or adversarial input x , each sub-model chooses an easy one ( s ) to feed itself . The PPD head is meanwhile updated by comparing the predicted probability on the true label—fyθ ( · ) ( a scalar ) . In the inference phase , given an input , CDA2 chooses a sub-model with the largest PPD value as the representative to output the prediction . We highlight our key contributions as follows . • We provide a new perspective on learning multiple sub-models for defending against adversarial attacks . We theoretically show the collaboration makes better decisions than the ensemble , which implies collaboration may fix broader vulnerability areas . • We propose a novel collaboration framework—CDA2 ( see Section 3.2 ) . In the training phase , CDA2could effectively minimize the vulnerability overlap of all sub-models ; In the inference phase , CDA2 could effectively choose a representative sub-model to make correct predictions . We also provide a comprehensive analysis illustrating the rationale of CDA2 . • Empirical experiments verify that CDA2 outperforms various ensemble methods against black-box and white-box adversarial attacks . 2 RELATED WORKS . Adversarial attack Adversarial attacks aim to craft the human-imperceptible adversarial input to fool the deep models . Adversarial attacks could be roughly divided into white-box attacks in which the adversary is fully aware of the model ’ s structures ( Goodfellow et al. , 2015 ; Moosavi-Dezfooli et al. , 2016 ; Carlini & Wagner , 2017b ; Chen et al. , 2018 ; Athalye et al. , 2018 ; Xiao et al. , 2018 ; Zheng et al. , 2019 ; Wong et al. , 2019 ; Mopuri et al. , 2019 ; Alaifari et al. , 2019 ; Sriramanan et al. , 2020 ; Wu et al. , 2020b ; Croce & Hein , 2020 ; Yu et al. , 2021b ) and black-box attacks in which the deep models are treated as black boxes to the adversary ( Cheng et al. , 2019 ; 2020 ; Wu et al. , 2020a ; Chen et al. , 2020a ; Li et al. , 2020a ; Rahmati et al. , 2020 ; Yan et al. , 2021b ; Hendrycks et al. , 2021 ; Dong et al. , 2018 ; Xie et al. , 2019 ) . This paper focuses on building effective defense and select both white-box and black-box attack methods as our robustness evaluation metrics . Adversarial defense Defending adversarial attacks is a challenging task and researchers have proposed various solutions . Certified defense tries to learn provably robust deep models against norm-bounded ( e.g. , ` 2 and ` ∞ ) perturbations ( Wong & Kolter , 2018 ; Tsuzuku et al. , 2018 ; Weng et al. , 2018 ; Mirman et al. , 2018 ; Hein & Andriushchenko , 2017 ; Lécuyer et al. , 2019 ; Xiao et al. , 2019 ; Cohen et al. , 2019 ; Balunovic & Vechev , 2020a ; Zhang et al. , 2020a ; Singla & Feizi , 2020 ; Balunovic & Vechev , 2020b ; Zou et al. , 2021 ) . Empirical defense leverages adversarial data to build effective defense such as adversary detection ( Metzen et al. , 2017 ; Li & Li , 2017 ; Carlini & Wagner , 2017a ; Tian et al. , 2018 ; Ma et al. , 2018b ; Lee et al. , 2018 ; Pang et al. , 2018 ; Smith & Gal , 2018 ; Roth et al. , 2019 ; Liu et al. , 2019 ; Yin & Rohde , 2020 ; Sperl et al. , 2020 ; Cohen et al. , 2020 ; Sheikholeslami et al. , 2021 ; Chen et al. , 2021a ; Yang et al. , 2020b ; Qin et al. , 2020 ; Tian et al. , 2021 ; Wu et al. , 2021 ) and adversarial training ( AT ) , in which AT stands out as the most effective defense . Researchers have investigated various aspects of AT , such as improving AT ’ s robustness or generalization ( Madry et al. , 2018 ; Yan et al. , 2018 ; Wu et al. , 2018 ; Cai et al. , 2018 ; Najafi et al. , 2019 ; Alayrac et al. , 2019 ; Carmon et al. , 2019 ; Farnia et al. , 2019 ; Song et al. , 2019 ; Zhang et al. , 2019b ; Wang et al. , 2019 ; Tramèr & Boneh , 2019 ; Zhang & Wang , 2019 ; Stutz et al. , 2020 ; Pang et al. , 2020 ; Gan et al. , 2020 ; Dong et al. , 2020 ; Zhang et al. , 2020b ; Chen et al. , 2020b ; Song et al. , 2020 ; Ding et al. , 2020 ; Wang et al. , 2020b ; Zhang et al. , 2021 ) , fixing AT ’ s undesirable robust overfitting ( Rice et al. , 2020 ; Chen et al. , 2021b ) , improving AT ’ s training efficiency ( Zhang et al. , 2019a ; Shafahi et al. , 2019 ; Zheng et al. , 2020 ; B.S . & Babu , 2020 ; Andriushchenko & Flammarion , 2020 ; Wong et al. , 2020 ) , understanding/interpreting AT ’ s unique traits ( Nakkiran , 2019 ; Yin et al. , 2019 ; Gao et al. , 2019 ; Cranko et al. , 2019 ; Zhang et al. , 2019c ; Liu et al. , 2020 ; Roth et al. , 2020 ; Wang et al. , 2020a ; Zhang et al. , 2020c ; Li et al. , 2020b ; Zou et al. , 2021 ; Mehrabi et al. , 2021 ; Xu et al. , 2021 ) , etc . Besides , researchers have alao actively investigated robust-structured models ( Cisse et al. , 2017 ; Xie et al. , 2020 ; Moosavi-Dezfooli et al. , 2019 ; Xie & Yuille , 2020 ; Yan et al. , 2021a ; Du et al. , 2021 ; Pang et al. , 2021 ) . Nevertheless , the above research thoroughly investigated a single model ; this paper focuses on the collaboration among multiple models for adversarial defense . Ensemble methods for adversarial robustness The most relevant studies are the ensemble methods . Ensemble methods such as bagging ( Breiman , 1996 ) and boosting ( Freund et al. , 1996 ) have been investigated for significantly improving the model ’ s generalization . Motivated by the benefits of ensemble methods in improving generalization , researchers introduced an ensemble to improve the model robustness ( Yang et al. , 2020a ; Kariyappa & Qureshi , 2019 ; Pang et al. , 2019 ; Tramèr et al. , 2018 ) . Tramèr et al . ( 2018 ) proposed to reduce the adversarial transferability by training a single model with adversarial examples from multiple pretrained sub-models . Pang et al . ( 2019 ) introduce a regularization method—ADP—to encourage high diversity in the non-maximal predictions of sub-models . Kariyappa & Qureshi ( 2019 ) improved the ensemble diversity by maximizing the introduced cosine distance between the gradients of sub-models with respect to the input . Yang et al . ( 2020a ) proposed to distill non-robust features in the input and diversify the adversarial vulnerability . These methods reduced overlaps of vulnerability areas between sub-models ( Yang et al. , 2020a ) . To further improve the ensembles , mixture-of-experts ( MOE ) assume that the problem space can be divided into multiple sub-problems through a gate module ; the gate module specifies each sub-model on a specific sub-problem ( Jacobs et al. , 1991 ; Ma et al. , 2018a ) . Nevertheless , to the best of our knowledge , MOE-based methods have been not applied to help adversarial robustness . Inspired by MOE , we propose the collaboration framework to defend against adversary attacks .",
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"content": "This paper presents a new paradigm for defending against adversarial attacks with multiple sub-models. Different from ensemble, the proposed collaboration paradigm, a representative sub-model is chosen to make the decision, instead of letting all sub-models vote. The proposed method has been validated on CIFAR-10 dataset, against both white-box and transferrability-based black-box attacks.",
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science.evidence_inference | Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:
- Treatment: The specific medical procedure or drug being evaluated.
- Control: The standard or placebo used for comparison. If absent, use `null`.
- Result: The health outcome being assessed.
- Impact: The observed effect of the treatment on the result, categorized as: ("increased", "decreased", "no change").
- Justification: The supporting details for the observed impact.
Format the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].
Abstract:
ABSTRACT.BACKGROUND: Administration of normal saline might increase circulating levels of pro-inflammatory cytokines and may cause variation of plasmatic electrolytic and hyperchloremic acidosis, which in turn can impair renal function. Hence the use of balanced solutions could influence the inflammatory cascade triggered by the surgical procedures, the plasmatic electrolyte concentration, the acid–base equilibrium, and the renal function.
ABSTRACT.METHODS: This is a double blind randomized trial. Forty patients undergoing major abdominal surgery (bowel cancer) were allocated in two groups, the balanced solution (BS) group in which the fluids administered were balanced solutions (colloids and crystalloids); and the unbalanced solution (UBS) group in which the fluids administered were unbalanced solutions (colloids and crystalloids). Measurements were performed after anaesthesia induction (T0), at the end of surgery (T1), within 2 h after surgery (T2) and 24 h after the beginning of surgery (T3). The following data were collected: 1) active matrix metalloproteinase 9 (MMP-9) and its tissue inhibitor (TIMP-1), IL-6, IL-8, IL-10; 2) blood gases variables; 3) electrolytes, albumin, total serum protein and the strong ion difference; 4) neutrophil gelatinase-associated lipocalin (NGAL) from urinary sample.
ABSTRACT.RESULTS: The BS group exhibited higher circulating level of IL-10 and TIMP-1 and lower level of active MMP-9. The UBS group experienced hypercloremia, hypocalcemia, hypomagnesemia, worse acid–base equilibrium and higher level of NGAL.
ABSTRACT.CONCLUSIONS: The use of balanced solutions was responsible of less alteration of plasmatic electrolytes, acid–base equilibrium, kidney function and it might be associated with an early anti-inflammatory mechanisms triggering.
ABSTRACT.TRIAL REGISTRATION: ClinicalTrials.gov (Ref: NCT01320891).
Extracted Data: | [["Balanced solutions", "Unbalanced solutions", "IL-10", "significantly increased", "The BS group exhibited higher circulating level of IL-10 and TIMP-1 and lower level of active MMP-9"]] | science.evidence_inference.157 | [
{
"content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND: Administration of normal saline might increase circulating levels of pro-inflammatory cytokines and may cause variation of plasmatic electrolytic and hyperchloremic acidosis, which in turn can impair renal function. Hence the use of balanced solutions could influence the inflammatory cascade triggered by the surgical procedures, the plasmatic electrolyte concentration, the acid–base equilibrium, and the renal function.\n\nABSTRACT.METHODS: This is a double blind randomized trial. Forty patients undergoing major abdominal surgery (bowel cancer) were allocated in two groups, the balanced solution (BS) group in which the fluids administered were balanced solutions (colloids and crystalloids); and the unbalanced solution (UBS) group in which the fluids administered were unbalanced solutions (colloids and crystalloids). Measurements were performed after anaesthesia induction (T0), at the end of surgery (T1), within 2 h after surgery (T2) and 24 h after the beginning of surgery (T3). The following data were collected: 1) active matrix metalloproteinase 9 (MMP-9) and its tissue inhibitor (TIMP-1), IL-6, IL-8, IL-10; 2) blood gases variables; 3) electrolytes, albumin, total serum protein and the strong ion difference; 4) neutrophil gelatinase-associated lipocalin (NGAL) from urinary sample.\n\nABSTRACT.RESULTS: The BS group exhibited higher circulating level of IL-10 and TIMP-1 and lower level of active MMP-9. The UBS group experienced hypercloremia, hypocalcemia, hypomagnesemia, worse acid–base equilibrium and higher level of NGAL.\n\nABSTRACT.CONCLUSIONS: The use of balanced solutions was responsible of less alteration of plasmatic electrolytes, acid–base equilibrium, kidney function and it might be associated with an early anti-inflammatory mechanisms triggering.\n\nABSTRACT.TRIAL REGISTRATION: ClinicalTrials.gov (Ref: NCT01320891).\n\nExtracted Data:",
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"content": "[[\"Balanced solutions\", \"Unbalanced solutions\", \"IL-10\", \"significantly increased\", \"The BS group exhibited higher circulating level of IL-10 and TIMP-1 and lower level of active MMP-9\"]]",
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science.qasper_abstractive_qa | Examine the provided sections from a research document and address the accompanying query. The text is divided by newlines.
Document Content:
FacTweet: Profiling Fake News Twitter Accounts
We present an approach to detect fake news in Twitter at the account level using a neural recurrent model and a variety of different semantic and stylistic features. Our method extracts a set of features from the timelines of news Twitter accounts by reading their posts as chunks, rather than dealing with each tweet independently. We show the experimental benefits of modeling latent stylistic signatures of mixed fake and real news with a sequential model over a wide range of strong baselines.
Introduction
Social media platforms have made the spreading of fake news easier, faster as well as able to reach a wider audience. Social media offer another feature which is the anonymity for the authors, and this opens the door to many suspicious individuals or organizations to utilize these platforms. Recently, there has been an increased number of spreading fake news and rumors over the web and social media BIBREF0. Fake news in social media vary considering the intention to mislead. Some of these news are spread with the intention to be ironic or to deliver the news in an ironic way (satirical news). Others, such as propaganda, hoaxes, and clickbaits, are spread to mislead the audience or to manipulate their opinions. In the case of Twitter, suspicious news annotations should be done on a tweet rather than an account level, since some accounts mix fake with real news. However, these annotations are extremely costly and time consuming – i.e., due to high volume of available tweets Consequently, a first step in this direction, e.g., as a pre-filtering step, can be viewed as the task of detecting fake news at the account level.
The main obstacle for detecting suspicious Twitter accounts is due to the behavior of mixing some real news with the misleading ones. Consequently, we investigate ways to detect suspicious accounts by considering their tweets in groups (chunks). Our hypothesis is that suspicious accounts have a unique pattern in posting tweet sequences. Since their intention is to mislead, the way they transition from one set of tweets to the next has a hidden signature, biased by their intentions. Therefore, reading these tweets in chunks has the potential to improve the detection of the fake news accounts.
In this work, we investigate the problem of discriminating between factual and non-factual accounts in Twitter. To this end, we collect a large dataset of tweets using a list of propaganda, hoax and clickbait accounts and compare different versions of sequential chunk-based approaches using a variety of feature sets against several baselines. Several approaches have been proposed for news verification, whether in social media (rumors detection) BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, or in news claims BIBREF5, BIBREF6, BIBREF7, BIBREF8. The main orientation in the previous works is to verify the textual claims/tweets but not their sources. To the best of our knowledge, this is the first work aiming to detect factuality at the account level, and especially from a textual perspective. Our contributions are:
[leftmargin=4mm]
We propose an approach to detect non-factual Twitter accounts by treating post streams as a sequence of tweets' chunks. We test several semantic and dictionary-based features together with a neural sequential approach, and apply an ablation test to investigate their contribution.
We benchmark our approach against other approaches that discard the chronological order of the tweets or read the tweets individually. The results show that our approach produces superior results at detecting non-factual accounts.
Methodology
Given a news Twitter account, we read its tweets from the account's timeline. Then we sort the tweets by the posting date in ascending way and we split them into $N$ chunks. Each chunk consists of a sorted sequence of tweets labeled by the label of its corresponding account. We extract a set of features from each chunk and we feed them into a recurrent neural network to model the sequential flow of the chunks' tweets. We use an attention layer with dropout to attend over the most important tweets in each chunk. Finally, the representation is fed into a softmax layer to produce a probability distribution over the account types and thus predict the factuality of the accounts. Since we have many chunks for each account, the label for an account is obtained by taking the majority class of the account's chunks.
Input Representation. Let $t$ be a Twitter account that contains $m$ tweets. These tweets are sorted by date and split into a sequence of chunks $ck = \langle ck_1, \ldots , ck_n \rangle $, where each $ck_i$ contains $s$ tweets. Each tweet in $ck_i$ is represented by a vector $v \in {\rm I\!R}^d$ , where $v$ is the concatenation of a set of features' vectors, that is $v = \langle f_1, \ldots , f_n \rangle $. Each feature vector $f_i$ is built by counting the presence of tweet's words in a set of lexical lists. The final representation of the tweet is built by averaging the single word vectors.
Features. We argue that different kinds of features like the sentiment of the text, morality, and other text-based features are critical to detect the nonfactual Twitter accounts by utilizing their occurrence during reporting the news in an account's timeline. We employ a rich set of features borrowed from previous works in fake news, bias, and rumors detection BIBREF0, BIBREF1, BIBREF8, BIBREF9.
[leftmargin=4mm]
Emotion: We build an emotions vector using word occurrences of 15 emotion types from two available emotional lexicons. We use the NRC lexicon BIBREF10, which contains $\sim $14K words labeled using the eight Plutchik's emotions BIBREF11. The other lexicon is SentiSense BIBREF12 which is a concept-based affective lexicon that attaches emotional meanings to concepts from the WordNet lexical database. It has $\sim $5.5 words labeled with emotions from a set of 14 emotional categories We use the categories that do not exist in the NRC lexicon.
Sentiment: We extract the sentiment of the tweets by employing EffectWordNet BIBREF13, SenticNet BIBREF14, NRC BIBREF10, and subj_lexicon BIBREF15, where each has the two sentiment classes, positive and negative.
Morality: Features based on morality foundation theory BIBREF16 where words are labeled in one of the following 10 categories (care, harm, fairness, cheating, loyalty, betrayal, authority, subversion, sanctity, and degradation).
Style: We use canonical stylistic features, such as the count of question marks, exclamation marks, consecutive characters and letters, links, hashtags, users' mentions. In addition, we extract the uppercase ratio and the tweet length.
Words embeddings: We extract words embeddings of the words of the tweet using $Glove\-840B-300d$ BIBREF17 pretrained model. The tweet final representation is obtained by averaging its words embeddings.
Model. To account for chunk sequences we make use of a de facto standard approach and opt for a recurrent neural model using long short-term memory (LSTM) BIBREF18. In our model, the sequence consists of a sequence of tweets belonging to one chunk (Figure FIGREF11). The LSTM learns the hidden state $h\textsubscript {t}$ by capturing the previous timesteps (past tweets). The produced hidden state $h\textsubscript {t}$ at each time step is passed to the attention layer which computes a `context' vector $c\textsubscript {t}$ as the weighted mean of the state sequence $h$ by:
Where $T$ is the total number of timesteps in the input sequence and $\alpha \textsubscript {tj}$ is a weight computed at each time step $j$ for each state hj.
Experiments and Results
Data. We build a dataset of Twitter accounts based on two lists annotated in previous works. For the non-factual accounts, we rely on a list of 180 Twitter accounts from BIBREF1. This list was created based on public resources where suspicious Twitter accounts were annotated with the main fake news types (clickbait, propaganda, satire, and hoax). We discard the satire labeled accounts since their intention is not to mislead or deceive. On the other hand, for the factual accounts, we use a list with another 32 Twitter accounts from BIBREF19 that are considered trustworthy by independent third parties. We discard some accounts that publish news in languages other than English (e.g., Russian or Arabic). Moreover, to ensure the quality of the data, we remove the duplicate, media-based, and link-only tweets. For each account, we collect the maximum amount of tweets allowed by Twitter API. Table TABREF13 presents statistics on our dataset.
Baselines. We compare our approach (FacTweet) to the following set of baselines:
[leftmargin=4mm]
LR + Bag-of-words: We aggregate the tweets of a feed and we use a bag-of-words representation with a logistic regression (LR) classifier.
Tweet2vec: We use the Bidirectional Gated recurrent neural network model proposed in BIBREF20. We keep the default parameters that were provided with the implementation. To represent the tweets, we use the decoded embedding produced by the model. With this baseline we aim at assessing if the tweets' hashtags may help detecting the non-factual accounts.
LR + All Features (tweet-level): We extract all our features from each tweet and feed them into a LR classifier. Here, we do not aggregate over tweets and thus view each tweet independently.
LR + All Features (chunk-level): We concatenate the features' vectors of the tweets in a chunk and feed them into a LR classifier.
FacTweet (tweet-level): Similar to the FacTweet approach, but at tweet-level; the sequential flow of the tweets is not utilized. We aim at investigating the importance of the sequential flow of tweets.
Top-$k$ replies, likes, or re-tweets: Some approaches in rumors detection use the number of replies, likes, and re-tweets to detect rumors BIBREF21. Thus, we extract top $k$ replied, liked or re-tweeted tweets from each account to assess the accounts factuality. We tested different $k$ values between 10 tweets to the max number of tweets from each account. Figure FIGREF24 shows the macro-F1 values for different $k$ values. It seems that $k=500$ for the top replied tweets achieves the highest result. Therefore, we consider this as a baseline.
Experimental Setup. We apply a 5 cross-validation on the account's level. For the FacTweet model, we experiment with 25% of the accounts for validation and parameters selection. We use hyperopt library to select the hyper-parameters on the following values: LSTM layer size (16, 32, 64), dropout ($0.0-0.9$), activation function ($relu$, $selu$, $tanh$), optimizer ($sgd$, $adam$, $rmsprop$) with varying the value of the learning rate (1e-1,..,-5), and batch size (4, 8, 16). The validation split is extracted on the class level using stratified sampling: we took a random 25% of the accounts from each class since the dataset is unbalanced. Discarding the classes' size in the splitting process may affect the minority classes (e.g. hoax). For the baselines' classifier, we tested many classifiers and the LR showed the best overall performance.
Results. Table TABREF25 presents the results. We present the results using a chunk size of 20, which was found to be the best size on the held-out data. Figure FIGREF24 shows the results of different chunks sizes. FacTweet performs better than the proposed baselines and obtains the highest macro-F1 value of $0.565$. Our results indicate the importance of taking into account the sequence of the tweets in the accounts' timelines. The sequence of these tweets is better captured by our proposed model sequence-agnostic or non-neural classifiers. Moreover, the results demonstrate that the features at tweet-level do not perform well to detect the Twitter accounts factuality, since they obtain a result near to the majority class ($0.18$). Another finding from our experiments shows that the performance of the Tweet2vec is weak. This demonstrates that tweets' hashtags are not informative to detect non-factual accounts. In Table TABREF25, we present ablation tests so as to quantify the contribution of subset of features. The results indicate that most performance gains come from words embeddings, style, and morality features. Other features (emotion and sentiment) show lower importance: nevertheless, they still improve the overall system performance (on average 0.35% Macro-F$_1$ improvement). These performance figures suggest that non-factual accounts use semantic and stylistic hidden signatures mostly while tweeting news, so as to be able to mislead the readers and behave as reputable (i.e., factual) sources. We leave a more fine-grained, diachronic analysis of semantic and stylistic features – how semantic and stylistic signature evolve across time and change across the accounts' timelines – for future work.
Conclusions
In this paper, we proposed a model that utilizes chunked timelines of tweets and a recurrent neural model in order to infer the factuality of a Twitter news account. Our experimental results indicate the importance of analyzing tweet stream into chunks, as well as the benefits of heterogeneous knowledge source (i.e., lexica as well as text) in order to capture factuality. In future work, we would like to extend this line of research with further in-depth analysis to understand the flow change of the used features in the accounts' streams. Moreover, we would like to take our approach one step further incorporating explicit temporal information, e.g., using timestamps. Crucially, we are also interested in developing a multilingual version of our approach, for instance by leveraging the now ubiquitous cross-lingual embeddings BIBREF22, BIBREF23.
Fig. 1: The FacTweet’s architecture.
Table 1: Statistics on the data with respect to each account type: propaganda (P), clickbait (C), hoax (H), and real news (R).
Fig. 3: The FacTweet performance on difference chunk sizes.
Table 2: FacTweet: experimental results.
Query: What activation function do they use in their model?
JSON Response: | {"answer": "Activation function is hyperparameter. Possible values: relu, selu, tanh.", "evidence": ["Experimental Setup. We apply a 5 cross-validation on the account\u0027s level. For the FacTweet model, we experiment with 25% of the accounts for validation and parameters selection. We use hyperopt library to select the hyper-parameters on the following values: LSTM layer size (16, 32, 64), dropout ($0.0-0.9$), activation function ($relu$, $selu$, $tanh$), optimizer ($sgd$, $adam$, $rmsprop$) with varying the value of the learning rate (1e-1,..,-5), and batch size (4, 8, 16). The validation split is extracted on the class level using stratified sampling: we took a random 25% of the accounts from each class since the dataset is unbalanced. Discarding the classes\u0027 size in the splitting process may affect the minority classes (e.g. hoax). For the baselines\u0027 classifier, we tested many classifiers and the LR showed the best overall performance."]} | science.qasper_abstractive_qa.124.0 | [
{
"content": "Examine the provided sections from a research document and address the accompanying query. The text is divided by newlines.\n\nDocument Content:\n\nFacTweet: Profiling Fake News Twitter Accounts\n\nWe present an approach to detect fake news in Twitter at the account level using a neural recurrent model and a variety of different semantic and stylistic features. Our method extracts a set of features from the timelines of news Twitter accounts by reading their posts as chunks, rather than dealing with each tweet independently. We show the experimental benefits of modeling latent stylistic signatures of mixed fake and real news with a sequential model over a wide range of strong baselines.\n\nIntroduction\nSocial media platforms have made the spreading of fake news easier, faster as well as able to reach a wider audience. Social media offer another feature which is the anonymity for the authors, and this opens the door to many suspicious individuals or organizations to utilize these platforms. Recently, there has been an increased number of spreading fake news and rumors over the web and social media BIBREF0. Fake news in social media vary considering the intention to mislead. Some of these news are spread with the intention to be ironic or to deliver the news in an ironic way (satirical news). Others, such as propaganda, hoaxes, and clickbaits, are spread to mislead the audience or to manipulate their opinions. In the case of Twitter, suspicious news annotations should be done on a tweet rather than an account level, since some accounts mix fake with real news. However, these annotations are extremely costly and time consuming – i.e., due to high volume of available tweets Consequently, a first step in this direction, e.g., as a pre-filtering step, can be viewed as the task of detecting fake news at the account level.\nThe main obstacle for detecting suspicious Twitter accounts is due to the behavior of mixing some real news with the misleading ones. Consequently, we investigate ways to detect suspicious accounts by considering their tweets in groups (chunks). Our hypothesis is that suspicious accounts have a unique pattern in posting tweet sequences. Since their intention is to mislead, the way they transition from one set of tweets to the next has a hidden signature, biased by their intentions. Therefore, reading these tweets in chunks has the potential to improve the detection of the fake news accounts.\nIn this work, we investigate the problem of discriminating between factual and non-factual accounts in Twitter. To this end, we collect a large dataset of tweets using a list of propaganda, hoax and clickbait accounts and compare different versions of sequential chunk-based approaches using a variety of feature sets against several baselines. Several approaches have been proposed for news verification, whether in social media (rumors detection) BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, or in news claims BIBREF5, BIBREF6, BIBREF7, BIBREF8. The main orientation in the previous works is to verify the textual claims/tweets but not their sources. To the best of our knowledge, this is the first work aiming to detect factuality at the account level, and especially from a textual perspective. Our contributions are:\n[leftmargin=4mm]\nWe propose an approach to detect non-factual Twitter accounts by treating post streams as a sequence of tweets' chunks. We test several semantic and dictionary-based features together with a neural sequential approach, and apply an ablation test to investigate their contribution.\nWe benchmark our approach against other approaches that discard the chronological order of the tweets or read the tweets individually. The results show that our approach produces superior results at detecting non-factual accounts.\n\nMethodology\nGiven a news Twitter account, we read its tweets from the account's timeline. Then we sort the tweets by the posting date in ascending way and we split them into $N$ chunks. Each chunk consists of a sorted sequence of tweets labeled by the label of its corresponding account. We extract a set of features from each chunk and we feed them into a recurrent neural network to model the sequential flow of the chunks' tweets. We use an attention layer with dropout to attend over the most important tweets in each chunk. Finally, the representation is fed into a softmax layer to produce a probability distribution over the account types and thus predict the factuality of the accounts. Since we have many chunks for each account, the label for an account is obtained by taking the majority class of the account's chunks.\nInput Representation. Let $t$ be a Twitter account that contains $m$ tweets. These tweets are sorted by date and split into a sequence of chunks $ck = \\langle ck_1, \\ldots , ck_n \\rangle $, where each $ck_i$ contains $s$ tweets. Each tweet in $ck_i$ is represented by a vector $v \\in {\\rm I\\!R}^d$ , where $v$ is the concatenation of a set of features' vectors, that is $v = \\langle f_1, \\ldots , f_n \\rangle $. Each feature vector $f_i$ is built by counting the presence of tweet's words in a set of lexical lists. The final representation of the tweet is built by averaging the single word vectors.\nFeatures. We argue that different kinds of features like the sentiment of the text, morality, and other text-based features are critical to detect the nonfactual Twitter accounts by utilizing their occurrence during reporting the news in an account's timeline. We employ a rich set of features borrowed from previous works in fake news, bias, and rumors detection BIBREF0, BIBREF1, BIBREF8, BIBREF9.\n[leftmargin=4mm]\nEmotion: We build an emotions vector using word occurrences of 15 emotion types from two available emotional lexicons. We use the NRC lexicon BIBREF10, which contains $\\sim $14K words labeled using the eight Plutchik's emotions BIBREF11. The other lexicon is SentiSense BIBREF12 which is a concept-based affective lexicon that attaches emotional meanings to concepts from the WordNet lexical database. It has $\\sim $5.5 words labeled with emotions from a set of 14 emotional categories We use the categories that do not exist in the NRC lexicon.\nSentiment: We extract the sentiment of the tweets by employing EffectWordNet BIBREF13, SenticNet BIBREF14, NRC BIBREF10, and subj_lexicon BIBREF15, where each has the two sentiment classes, positive and negative.\nMorality: Features based on morality foundation theory BIBREF16 where words are labeled in one of the following 10 categories (care, harm, fairness, cheating, loyalty, betrayal, authority, subversion, sanctity, and degradation).\nStyle: We use canonical stylistic features, such as the count of question marks, exclamation marks, consecutive characters and letters, links, hashtags, users' mentions. In addition, we extract the uppercase ratio and the tweet length.\nWords embeddings: We extract words embeddings of the words of the tweet using $Glove\\-840B-300d$ BIBREF17 pretrained model. The tweet final representation is obtained by averaging its words embeddings.\nModel. To account for chunk sequences we make use of a de facto standard approach and opt for a recurrent neural model using long short-term memory (LSTM) BIBREF18. In our model, the sequence consists of a sequence of tweets belonging to one chunk (Figure FIGREF11). The LSTM learns the hidden state $h\\textsubscript {t}$ by capturing the previous timesteps (past tweets). The produced hidden state $h\\textsubscript {t}$ at each time step is passed to the attention layer which computes a `context' vector $c\\textsubscript {t}$ as the weighted mean of the state sequence $h$ by:\nWhere $T$ is the total number of timesteps in the input sequence and $\\alpha \\textsubscript {tj}$ is a weight computed at each time step $j$ for each state hj.\n\nExperiments and Results\nData. We build a dataset of Twitter accounts based on two lists annotated in previous works. For the non-factual accounts, we rely on a list of 180 Twitter accounts from BIBREF1. This list was created based on public resources where suspicious Twitter accounts were annotated with the main fake news types (clickbait, propaganda, satire, and hoax). We discard the satire labeled accounts since their intention is not to mislead or deceive. On the other hand, for the factual accounts, we use a list with another 32 Twitter accounts from BIBREF19 that are considered trustworthy by independent third parties. We discard some accounts that publish news in languages other than English (e.g., Russian or Arabic). Moreover, to ensure the quality of the data, we remove the duplicate, media-based, and link-only tweets. For each account, we collect the maximum amount of tweets allowed by Twitter API. Table TABREF13 presents statistics on our dataset.\nBaselines. We compare our approach (FacTweet) to the following set of baselines:\n[leftmargin=4mm]\nLR + Bag-of-words: We aggregate the tweets of a feed and we use a bag-of-words representation with a logistic regression (LR) classifier.\nTweet2vec: We use the Bidirectional Gated recurrent neural network model proposed in BIBREF20. We keep the default parameters that were provided with the implementation. To represent the tweets, we use the decoded embedding produced by the model. With this baseline we aim at assessing if the tweets' hashtags may help detecting the non-factual accounts.\nLR + All Features (tweet-level): We extract all our features from each tweet and feed them into a LR classifier. Here, we do not aggregate over tweets and thus view each tweet independently.\nLR + All Features (chunk-level): We concatenate the features' vectors of the tweets in a chunk and feed them into a LR classifier.\nFacTweet (tweet-level): Similar to the FacTweet approach, but at tweet-level; the sequential flow of the tweets is not utilized. We aim at investigating the importance of the sequential flow of tweets.\nTop-$k$ replies, likes, or re-tweets: Some approaches in rumors detection use the number of replies, likes, and re-tweets to detect rumors BIBREF21. Thus, we extract top $k$ replied, liked or re-tweeted tweets from each account to assess the accounts factuality. We tested different $k$ values between 10 tweets to the max number of tweets from each account. Figure FIGREF24 shows the macro-F1 values for different $k$ values. It seems that $k=500$ for the top replied tweets achieves the highest result. Therefore, we consider this as a baseline.\nExperimental Setup. We apply a 5 cross-validation on the account's level. For the FacTweet model, we experiment with 25% of the accounts for validation and parameters selection. We use hyperopt library to select the hyper-parameters on the following values: LSTM layer size (16, 32, 64), dropout ($0.0-0.9$), activation function ($relu$, $selu$, $tanh$), optimizer ($sgd$, $adam$, $rmsprop$) with varying the value of the learning rate (1e-1,..,-5), and batch size (4, 8, 16). The validation split is extracted on the class level using stratified sampling: we took a random 25% of the accounts from each class since the dataset is unbalanced. Discarding the classes' size in the splitting process may affect the minority classes (e.g. hoax). For the baselines' classifier, we tested many classifiers and the LR showed the best overall performance.\nResults. Table TABREF25 presents the results. We present the results using a chunk size of 20, which was found to be the best size on the held-out data. Figure FIGREF24 shows the results of different chunks sizes. FacTweet performs better than the proposed baselines and obtains the highest macro-F1 value of $0.565$. Our results indicate the importance of taking into account the sequence of the tweets in the accounts' timelines. The sequence of these tweets is better captured by our proposed model sequence-agnostic or non-neural classifiers. Moreover, the results demonstrate that the features at tweet-level do not perform well to detect the Twitter accounts factuality, since they obtain a result near to the majority class ($0.18$). Another finding from our experiments shows that the performance of the Tweet2vec is weak. This demonstrates that tweets' hashtags are not informative to detect non-factual accounts. In Table TABREF25, we present ablation tests so as to quantify the contribution of subset of features. The results indicate that most performance gains come from words embeddings, style, and morality features. Other features (emotion and sentiment) show lower importance: nevertheless, they still improve the overall system performance (on average 0.35% Macro-F$_1$ improvement). These performance figures suggest that non-factual accounts use semantic and stylistic hidden signatures mostly while tweeting news, so as to be able to mislead the readers and behave as reputable (i.e., factual) sources. We leave a more fine-grained, diachronic analysis of semantic and stylistic features – how semantic and stylistic signature evolve across time and change across the accounts' timelines – for future work.\n\nConclusions\nIn this paper, we proposed a model that utilizes chunked timelines of tweets and a recurrent neural model in order to infer the factuality of a Twitter news account. Our experimental results indicate the importance of analyzing tweet stream into chunks, as well as the benefits of heterogeneous knowledge source (i.e., lexica as well as text) in order to capture factuality. In future work, we would like to extend this line of research with further in-depth analysis to understand the flow change of the used features in the accounts' streams. Moreover, we would like to take our approach one step further incorporating explicit temporal information, e.g., using timestamps. Crucially, we are also interested in developing a multilingual version of our approach, for instance by leveraging the now ubiquitous cross-lingual embeddings BIBREF22, BIBREF23.\n\nFig. 1: The FacTweet’s architecture.\n\nTable 1: Statistics on the data with respect to each account type: propaganda (P), clickbait (C), hoax (H), and real news (R).\n\nFig. 3: The FacTweet performance on difference chunk sizes.\n\nTable 2: FacTweet: experimental results.\n\nQuery: What activation function do they use in their model?\n\nJSON Response:",
"role": "user"
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"content": "{\"answer\": \"Activation function is hyperparameter. Possible values: relu, selu, tanh.\", \"evidence\": [\"Experimental Setup. We apply a 5 cross-validation on the account\\u0027s level. For the FacTweet model, we experiment with 25% of the accounts for validation and parameters selection. We use hyperopt library to select the hyper-parameters on the following values: LSTM layer size (16, 32, 64), dropout ($0.0-0.9$), activation function ($relu$, $selu$, $tanh$), optimizer ($sgd$, $adam$, $rmsprop$) with varying the value of the learning rate (1e-1,..,-5), and batch size (4, 8, 16). The validation split is extracted on the class level using stratified sampling: we took a random 25% of the accounts from each class since the dataset is unbalanced. Discarding the classes\\u0027 size in the splitting process may affect the minority classes (e.g. hoax). For the baselines\\u0027 classifier, we tested many classifiers and the LR showed the best overall performance.\"]}",
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science.evidence_inference | Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:
- Treatment: The specific medical procedure or drug being evaluated.
- Control: The standard or placebo used for comparison. If absent, use `null`.
- Result: The health outcome being assessed.
- Impact: The observed effect of the treatment on the result, categorized as: ("increased", "decreased", "no change").
- Justification: The supporting details for the observed impact.
Format the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].
Abstract:
ABSTRACT.INTRODUCTION: Hyperglycaemia occurs frequently in the critically ill, affects outcome adversely, and is exacerbated by enteral feeding. Furthermore, treatment with insulin in this group is frequently complicated by hypoglycaemia. In healthy patients and those with type 2 diabetes, exogenous glucagon-like peptide-1 (GLP-1) decreases blood glucose by suppressing glucagon, stimulating insulin and slowing gastric emptying. Because the former effects are glucose-dependent, the use of GLP-1 is not associated with hypoglycaemia. The objective of this study was to establish if exogenous GLP-1 attenuates the glycaemic response to enteral nutrition in patients with critical illness induced hyperglycaemia.
ABSTRACT.METHODS: Seven mechanically ventilated critically ill patients, not previously known to have diabetes, received two intravenous infusions of GLP-1 (1.2 pmol/kg/min) and placebo (4% albumin) over 270 minutes. Infusions were administered on consecutive days in a randomised, double-blind fashion. On both days a mixed nutrient liquid was infused, via a post-pyloric feeding catheter, at a rate of 1.5 kcal/min between 30 and 270 minutes. Blood glucose and plasma GLP-1, insulin and glucagon concentrations were measured.
ABSTRACT.RESULTS: In all patients, exogenous GLP-1 infusion reduced the overall glycaemic response during enteral nutrient stimulation (AUC30–270 min GLP-1 (2077 ± 144 mmol/l min) vs placebo (2568 ± 208 mmol/l min); P = 0.02) and the peak blood glucose (GLP-1 (10.1 ± 0.7 mmol/l) vs placebo (12.7 ± 1.0 mmol/l); P < 0.01). The insulin/glucose ratio at 270 minutes was increased with GLP-1 infusion (GLP-1 (9.1 ± 2.7) vs. placebo (5.8 ± 1.8); P = 0.02) but there was no difference in absolute insulin concentrations. There was a transient, non-sustained, reduction in plasma glucagon concentrations during GLP-1 infusion (t = 30 minutes GLP-1 (90 ± 12 pmol/ml) vs. placebo (104 ± 10 pmol/ml); P < 0.01).
ABSTRACT.CONCLUSIONS: Acute, exogenous GLP-1 infusion markedly attenuates the glycaemic response to enteral nutrition in the critically ill. These observations suggest that GLP-1 and/or its analogues have the potential to manage hyperglycaemia in the critically ill.
ABSTRACT.TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry number: ACTRN12609000093280.
Extracted Data: | [["Exogenous glucagon-like peptide-1 infusion", "Control", "Peak blood glucose", "significantly decreased", "In all patients, exogenous GLP-1 infusion reduced the overall glycaemic response during enteral nutrient stimulation (AUC30\u2013270 min GLP-1 (2077 \u00b1 144 mmol/l min) vs placebo (2568 \u00b1 208 mmol/l min); P = 0.02) and the peak blood glucose (GLP-1 (10.1 \u00b1 0.7 mmol/l) vs placebo (12.7 \u00b1 1.0 mmol/l); P \u003c 0.01)."], ["Exogenous glucagon-like peptide-1 infusion", "Control", "Absolute insulin concentrations", "no significant difference", "The insulin/glucose ratio at 270 minutes was increased with GLP-1 infusion (GLP-1 (9.1 \u00b1 2.7) vs. placebo (5.8 \u00b1 1.8); P = 0.02) but there was no difference in absolute insulin concentrations."], ["Exogenous glucagon-like peptide-1 infusion", "Control", "Glucagon concentrations during infusion", "significantly decreased", "There was a transient, non-sustained, reduction in plasma glucagon concentrations during GLP-1 infusion (t = 30 minutes GLP-1 (90 \u00b1 12 pmol/ml) vs. placebo (104 \u00b1 10 pmol/ml); P \u003c 0.01)."], ["Exogenous glucagon-like peptide-1 infusion", "Control", "Glycaemic response", "significantly decreased", "In all patients, exogenous GLP-1 infusion reduced the overall glycaemic response during enteral nutrient stimulation (AUC30\u2013270 min GLP-1 (2077 \u00b1 144 mmol/l min) vs placebo (2568 \u00b1 208 mmol/l min); P = 0.02) and the peak blood glucose (GLP-1 (10.1 \u00b1 0.7 mmol/l) vs placebo (12.7 \u00b1 1.0 mmol/l); P \u003c 0.01)."]] | science.evidence_inference.136 | [
{
"content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.INTRODUCTION: Hyperglycaemia occurs frequently in the critically ill, affects outcome adversely, and is exacerbated by enteral feeding. Furthermore, treatment with insulin in this group is frequently complicated by hypoglycaemia. In healthy patients and those with type 2 diabetes, exogenous glucagon-like peptide-1 (GLP-1) decreases blood glucose by suppressing glucagon, stimulating insulin and slowing gastric emptying. Because the former effects are glucose-dependent, the use of GLP-1 is not associated with hypoglycaemia. The objective of this study was to establish if exogenous GLP-1 attenuates the glycaemic response to enteral nutrition in patients with critical illness induced hyperglycaemia.\n\nABSTRACT.METHODS: Seven mechanically ventilated critically ill patients, not previously known to have diabetes, received two intravenous infusions of GLP-1 (1.2 pmol/kg/min) and placebo (4% albumin) over 270 minutes. Infusions were administered on consecutive days in a randomised, double-blind fashion. On both days a mixed nutrient liquid was infused, via a post-pyloric feeding catheter, at a rate of 1.5 kcal/min between 30 and 270 minutes. Blood glucose and plasma GLP-1, insulin and glucagon concentrations were measured.\n\nABSTRACT.RESULTS: In all patients, exogenous GLP-1 infusion reduced the overall glycaemic response during enteral nutrient stimulation (AUC30–270 min GLP-1 (2077 ± 144 mmol/l min) vs placebo (2568 ± 208 mmol/l min); P = 0.02) and the peak blood glucose (GLP-1 (10.1 ± 0.7 mmol/l) vs placebo (12.7 ± 1.0 mmol/l); P < 0.01). The insulin/glucose ratio at 270 minutes was increased with GLP-1 infusion (GLP-1 (9.1 ± 2.7) vs. placebo (5.8 ± 1.8); P = 0.02) but there was no difference in absolute insulin concentrations. There was a transient, non-sustained, reduction in plasma glucagon concentrations during GLP-1 infusion (t = 30 minutes GLP-1 (90 ± 12 pmol/ml) vs. placebo (104 ± 10 pmol/ml); P < 0.01).\n\nABSTRACT.CONCLUSIONS: Acute, exogenous GLP-1 infusion markedly attenuates the glycaemic response to enteral nutrition in the critically ill. These observations suggest that GLP-1 and/or its analogues have the potential to manage hyperglycaemia in the critically ill.\n\nABSTRACT.TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry number: ACTRN12609000093280.\n\nExtracted Data:",
"role": "user"
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{
"content": "[[\"Exogenous glucagon-like peptide-1 infusion\", \"Control\", \"Peak blood glucose\", \"significantly decreased\", \"In all patients, exogenous GLP-1 infusion reduced the overall glycaemic response during enteral nutrient stimulation (AUC30\\u2013270 min GLP-1 (2077 \\u00b1 144 mmol/l min) vs placebo (2568 \\u00b1 208 mmol/l min); P = 0.02) and the peak blood glucose (GLP-1 (10.1 \\u00b1 0.7 mmol/l) vs placebo (12.7 \\u00b1 1.0 mmol/l); P \\u003c 0.01).\"], [\"Exogenous glucagon-like peptide-1 infusion\", \"Control\", \"Absolute insulin concentrations\", \"no significant difference\", \"The insulin/glucose ratio at 270 minutes was increased with GLP-1 infusion (GLP-1 (9.1 \\u00b1 2.7) vs. placebo (5.8 \\u00b1 1.8); P = 0.02) but there was no difference in absolute insulin concentrations.\"], [\"Exogenous glucagon-like peptide-1 infusion\", \"Control\", \"Glucagon concentrations during infusion\", \"significantly decreased\", \"There was a transient, non-sustained, reduction in plasma glucagon concentrations during GLP-1 infusion (t = 30 minutes GLP-1 (90 \\u00b1 12 pmol/ml) vs. placebo (104 \\u00b1 10 pmol/ml); P \\u003c 0.01).\"], [\"Exogenous glucagon-like peptide-1 infusion\", \"Control\", \"Glycaemic response\", \"significantly decreased\", \"In all patients, exogenous GLP-1 infusion reduced the overall glycaemic response during enteral nutrient stimulation (AUC30\\u2013270 min GLP-1 (2077 \\u00b1 144 mmol/l min) vs placebo (2568 \\u00b1 208 mmol/l min); P = 0.02) and the peak blood glucose (GLP-1 (10.1 \\u00b1 0.7 mmol/l) vs placebo (12.7 \\u00b1 1.0 mmol/l); P \\u003c 0.01).\"]]",
"role": "assistant"
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science.evidence_inference | Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:
- Treatment: The specific medical procedure or drug being evaluated.
- Control: The standard or placebo used for comparison. If absent, use `null`.
- Result: The health outcome being assessed.
- Impact: The observed effect of the treatment on the result, categorized as: ("increased", "decreased", "no change").
- Justification: The supporting details for the observed impact.
Format the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].
Abstract:
ABSTRACT.BACKGROUND: Angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers are renoprotective but both may increase serum potassium concentrations in patients with chronic kidney disease (CKD). The proportion of affected patients, the optimum follow-up period and whether there are differences between drugs in the development of this complication remain to be ascertained.
ABSTRACT.METHODS: In a randomized, double-blind, phase IV, controlled, crossover study we recruited 30 patients with stage 3 CKD under restrictive eligibility criteria and strict dietary control. With the exception of withdrawals, each patient was treated with olmesartan and enalapril separately for 3 months each, with a 1-week wash-out period between treatments. Patients were clinically assessed on 10 occasions via measurements of serum and urine samples. We used the Cochran–Mantel–Haenszel statistics for comparison of categorical data between groups. Comparisons were also made using independent two-sample t-tests and Welch's t-test. Analysis of variance (ANOVA) was performed when necessary. We used either a Mann–Whitney or Kruskal-Wallis test if the distribution was not normal or the variance not homogeneous.
ABSTRACT.RESULTS: Enalapril and olmesartan increased serum potassium levels similarly (0.3 mmol/L and 0.24 mmol/L respectively). The percentage of patients presenting hyperkalemia higher than 5 mmol/L did not differ between treatments: 37% for olmesartan and 40% for enalapril. The mean e-GFR ranged 46.3 to 48.59 ml/mint/1.73 m2 in those treated with olmesartan and 46.8 to 48.3 ml/mint/1.73 m2 in those with enalapril and remained unchanged at the end of the study. The decreases in microalbuminuria were also similar (23% in olmesartan and 29% in enalapril patients) in the 4 weeks time point. The percentage of patients presenting hyperkalemia, even after a two month period, did not differ between treatments. There were no appreciable changes in sodium and potassium urinary excretion.
ABSTRACT.CONCLUSIONS: Disturbances in potassium balance upon treatment with either olmesartan or enalapril are frequent and without differences between groups. The follow-up of these patients should include control of potassium levels, at least after the first week and the first and second month after initiating treatment.
ABSTRACT.TRIAL REGISTRATION: The trial EudraCT "2008-002191-98".
Extracted Data: | [["Enalapril", "Olmesartan", "Glomerular filtration rate increase ", "no significant difference", "The mean e-GFR ranged 46.3 to 48.59 ml/mint/1.73 m2 in those treated with olmesartan and 46.8 to 48.3 ml/mint/1.73 m2 in those with enalapril and remained unchanged at the end of the study."], ["Enalapril", "Olmesartan", "Microalbuminuria decrease", "no significant difference", "The decreases in microalbuminuria were also similar (23% in olmesartan and 29% in enalapril patients) in the 4 weeks time point."], ["Enalapril", "Olmesartan", "Hyperkalemia higher than 5 mmol/L", "no significant difference", "The percentage of patients presenting hyperkalemia higher than 5 mmol/L did not differ between treatments: 37% for olmesartan and 40% for enalapril."], ["Enalapril", "Olmesartan", "Urinary potassium", "no significant difference", "There were no appreciable changes in sodium and potassium urinary excretion."]] | science.evidence_inference.184 | [
{
"content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND: Angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers are renoprotective but both may increase serum potassium concentrations in patients with chronic kidney disease (CKD). The proportion of affected patients, the optimum follow-up period and whether there are differences between drugs in the development of this complication remain to be ascertained.\n\nABSTRACT.METHODS: In a randomized, double-blind, phase IV, controlled, crossover study we recruited 30 patients with stage 3 CKD under restrictive eligibility criteria and strict dietary control. With the exception of withdrawals, each patient was treated with olmesartan and enalapril separately for 3 months each, with a 1-week wash-out period between treatments. Patients were clinically assessed on 10 occasions via measurements of serum and urine samples. We used the Cochran–Mantel–Haenszel statistics for comparison of categorical data between groups. Comparisons were also made using independent two-sample t-tests and Welch's t-test. Analysis of variance (ANOVA) was performed when necessary. We used either a Mann–Whitney or Kruskal-Wallis test if the distribution was not normal or the variance not homogeneous.\n\nABSTRACT.RESULTS: Enalapril and olmesartan increased serum potassium levels similarly (0.3 mmol/L and 0.24 mmol/L respectively). The percentage of patients presenting hyperkalemia higher than 5 mmol/L did not differ between treatments: 37% for olmesartan and 40% for enalapril. The mean e-GFR ranged 46.3 to 48.59 ml/mint/1.73 m2 in those treated with olmesartan and 46.8 to 48.3 ml/mint/1.73 m2 in those with enalapril and remained unchanged at the end of the study. The decreases in microalbuminuria were also similar (23% in olmesartan and 29% in enalapril patients) in the 4 weeks time point. The percentage of patients presenting hyperkalemia, even after a two month period, did not differ between treatments. There were no appreciable changes in sodium and potassium urinary excretion.\n\nABSTRACT.CONCLUSIONS: Disturbances in potassium balance upon treatment with either olmesartan or enalapril are frequent and without differences between groups. The follow-up of these patients should include control of potassium levels, at least after the first week and the first and second month after initiating treatment.\n\nABSTRACT.TRIAL REGISTRATION: The trial EudraCT \"2008-002191-98\".\n\nExtracted Data:",
"role": "user"
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{
"content": "[[\"Enalapril\", \"Olmesartan\", \"Glomerular filtration rate increase \", \"no significant difference\", \"The mean e-GFR ranged 46.3 to 48.59 ml/mint/1.73 m2 in those treated with olmesartan and 46.8 to 48.3 ml/mint/1.73 m2 in those with enalapril and remained unchanged at the end of the study.\"], [\"Enalapril\", \"Olmesartan\", \"Microalbuminuria decrease\", \"no significant difference\", \"The decreases in microalbuminuria were also similar (23% in olmesartan and 29% in enalapril patients) in the 4 weeks time point.\"], [\"Enalapril\", \"Olmesartan\", \"Hyperkalemia higher than 5 mmol/L\", \"no significant difference\", \"The percentage of patients presenting hyperkalemia higher than 5 mmol/L did not differ between treatments: 37% for olmesartan and 40% for enalapril.\"], [\"Enalapril\", \"Olmesartan\", \"Urinary potassium\", \"no significant difference\", \"There were no appreciable changes in sodium and potassium urinary excretion.\"]]",
"role": "assistant"
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science.evidence_inference | Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:
- Treatment: The specific medical procedure or drug being evaluated.
- Control: The standard or placebo used for comparison. If absent, use `null`.
- Result: The health outcome being assessed.
- Impact: The observed effect of the treatment on the result, categorized as: ("increased", "decreased", "no change").
- Justification: The supporting details for the observed impact.
Format the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].
Abstract:
ABSTRACT.BACKGROUND AND AIMS:: Transurethral resection of the prostate is a commonly performed urological procedure in elderly men with spinal anaesthesia being the technique of choice. Use of low-dose spinal anesthetic drug with adjuvants is desirable. This study compares the sensorimotor effects of addition of buprenorphine or dexmedetomidine to low-dose bupivacaine.
ABSTRACT.METHODS:: Sixty patients were randomly allocated to three different groups. All received 1.8 mL 0.5% hyperbaric bupivacaine intrathecally. Sterile water (0.2 mL) or buprenorphine (60 μg) or dexmedetomidine (5 μg) was added to control group (Group C), buprenorphine group (Group B), and dexmedetomidine group (Group D), respectively. Time to the first analgesic request was the primary objective, and other objectives included the level of sensory-motor block, time to two-segment regression, time to S1 sensory regression and time to complete motor recovery. ANOVA and post hoc test were used for statistical analysis. The value of P < 0.05 was considered statistically significant.
ABSTRACT.RESULTS:: All sixty patients completed the study. Postoperative analgesia was not required in the first 24 h in a total of 10 (50%), 12 (60%) and 15 (75%) patients in groups C, B, and D, respectively. Time to S1 regression was 130 ± 46 min (Group C), 144 ± 51.3 min (Group B) and 164 ± 55.99 min (Group D), P = 0.117. Time to complete motor recovery was 177 ± 56.9 min (Group C), 236 ± 60 min (Group B) and 234 ± 61.71 min (Group D), P < 0.001.
ABSTRACT.CONCLUSION:: Addition of buprenorphine (60 μg) or dexmedetomidine (5 μg) to intrathecal bupivacaine for transurethral resection prolongs the time to the first analgesic request with comparable recovery profile.
Extracted Data: | [["buprenorphine (60 \u03bcg) or dexmedetomidine (5 \u03bcg)", "Sterile water (0.2 mL)", "time to S1 sensory regression", "significantly increased", "Addition of buprenorphine (60 \u03bcg) or dexmedetomidine (5 \u03bcg) to intrathecal bupivacaine for transurethral resection prolongs the time to the first analgesic request with comparable recovery profile."], ["buprenorphine (60 \u03bcg) or dexmedetomidine (5 \u03bcg)", "Sterile water (0.2 mL)", "Time to complete motor recovery", "significantly increased", "Time to complete motor recovery was 177 \u00b1 56.9 min (Group C), 236 \u00b1 60 min (Group B) and 234 \u00b1 61.71 min (Group D), P \u0026lt; 0.001."]] | science.evidence_inference.213 | [
{
"content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND AND AIMS:: Transurethral resection of the prostate is a commonly performed urological procedure in elderly men with spinal anaesthesia being the technique of choice. Use of low-dose spinal anesthetic drug with adjuvants is desirable. This study compares the sensorimotor effects of addition of buprenorphine or dexmedetomidine to low-dose bupivacaine.\n\nABSTRACT.METHODS:: Sixty patients were randomly allocated to three different groups. All received 1.8 mL 0.5% hyperbaric bupivacaine intrathecally. Sterile water (0.2 mL) or buprenorphine (60 μg) or dexmedetomidine (5 μg) was added to control group (Group C), buprenorphine group (Group B), and dexmedetomidine group (Group D), respectively. Time to the first analgesic request was the primary objective, and other objectives included the level of sensory-motor block, time to two-segment regression, time to S1 sensory regression and time to complete motor recovery. ANOVA and post hoc test were used for statistical analysis. The value of P < 0.05 was considered statistically significant.\n\nABSTRACT.RESULTS:: All sixty patients completed the study. Postoperative analgesia was not required in the first 24 h in a total of 10 (50%), 12 (60%) and 15 (75%) patients in groups C, B, and D, respectively. Time to S1 regression was 130 ± 46 min (Group C), 144 ± 51.3 min (Group B) and 164 ± 55.99 min (Group D), P = 0.117. Time to complete motor recovery was 177 ± 56.9 min (Group C), 236 ± 60 min (Group B) and 234 ± 61.71 min (Group D), P < 0.001.\n\nABSTRACT.CONCLUSION:: Addition of buprenorphine (60 μg) or dexmedetomidine (5 μg) to intrathecal bupivacaine for transurethral resection prolongs the time to the first analgesic request with comparable recovery profile.\n\nExtracted Data:",
"role": "user"
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{
"content": "[[\"buprenorphine (60 \\u03bcg) or dexmedetomidine (5 \\u03bcg)\", \"Sterile water (0.2 mL)\", \"time to S1 sensory regression\", \"significantly increased\", \"Addition of buprenorphine (60 \\u03bcg) or dexmedetomidine (5 \\u03bcg) to intrathecal bupivacaine for transurethral resection prolongs the time to the first analgesic request with comparable recovery profile.\"], [\"buprenorphine (60 \\u03bcg) or dexmedetomidine (5 \\u03bcg)\", \"Sterile water (0.2 mL)\", \"Time to complete motor recovery\", \"significantly increased\", \"Time to complete motor recovery was 177 \\u00b1 56.9 min (Group C), 236 \\u00b1 60 min (Group B) and 234 \\u00b1 61.71 min (Group D), P \\u0026lt; 0.001.\"]]",
"role": "assistant"
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science.biored_ner | Analyze the provided biomedical abstract and identify distinct entities. Focus on the specified categories: ["CellLine", "Variant", "Species", "Disease", "Chemical", "Gene"].
Ensure the output is strictly the JSON object.
Source Text:
Pleomorphic adenoma gene like-2 induces epithelial-mesenchymal transition via Wnt/b-catenin signaling pathway in human colorectal adenocarcinoma. Epithelial-mesenchymal transition (EMT) is a critical step in the acquisition of metastatic and invasive power for tumor cells. Colorectal adenocarcinoma (CRC) is a common cancer where metastasis is directly linked to patient survival. Recent studies show that pleomorphic adenoma gene like-2 (PLAGL2) could induce tumor EMT and is an independent predictive factor associated with poor prognosis in cancer. In the present study, we confirmed the role of PLAGL2 in the prognosis of CRC patients and provide molecular evidence of PLAGL2 promoted EMT in CRC cell line SW480. We found that PLAGL2 expression was upregulated in the paraffin-embedded CRC tissues compared to borderline or benign tissues. Experimental EMT induced by PLAGL2 plasmid transfection proved PLAGL2 protein overexpression could enhance the cell scratch wound-healing and transwell ability and significantly upregulated mesenchymal marker proteins, N-cadherin and vimentin and concurrently downregulated epithelial marker of E-cadherin. Subsequently, through western blot assay, we found that PLAGL2 could activate the wnt-signaling component b-catenin in the nuclei. More CRC cell metastasis to the lungs was observed when the PLAGL2 overexpressing SW480 cells were injected into the tail vein of rats, compared with the cell control and PLAGL2 silence group. Our findings indicated that PLAGL2 might be a very upstream key molecule regulating EMT involved in Wnt/b-catenin signaling pathway. | {"CellLine": ["SW480"], "Chemical": ["paraffin"], "Disease": ["metastasis", "colorectal adenocarcinoma", "CRC", "cancer", "Colorectal adenocarcinoma", "tumor"], "Gene": ["Pleomorphic adenoma gene like-2", "E-cadherin", "Wnt", "wnt", "vimentin", "N-cadherin", "PLAGL2", "pleomorphic adenoma gene like-2", "b-catenin"], "Species": ["rats", "patient", "human", "patients"], "Variant": []} | science.biored_ner.62 | [
{
"content": "Analyze the provided biomedical abstract and identify distinct entities. Focus on the specified categories: [\"CellLine\", \"Variant\", \"Species\", \"Disease\", \"Chemical\", \"Gene\"].\n\nEnsure the output is strictly the JSON object.\n\nSource Text:\n\nPleomorphic adenoma gene like-2 induces epithelial-mesenchymal transition via Wnt/b-catenin signaling pathway in human colorectal adenocarcinoma. Epithelial-mesenchymal transition (EMT) is a critical step in the acquisition of metastatic and invasive power for tumor cells. Colorectal adenocarcinoma (CRC) is a common cancer where metastasis is directly linked to patient survival. Recent studies show that pleomorphic adenoma gene like-2 (PLAGL2) could induce tumor EMT and is an independent predictive factor associated with poor prognosis in cancer. In the present study, we confirmed the role of PLAGL2 in the prognosis of CRC patients and provide molecular evidence of PLAGL2 promoted EMT in CRC cell line SW480. We found that PLAGL2 expression was upregulated in the paraffin-embedded CRC tissues compared to borderline or benign tissues. Experimental EMT induced by PLAGL2 plasmid transfection proved PLAGL2 protein overexpression could enhance the cell scratch wound-healing and transwell ability and significantly upregulated mesenchymal marker proteins, N-cadherin and vimentin and concurrently downregulated epithelial marker of E-cadherin. Subsequently, through western blot assay, we found that PLAGL2 could activate the wnt-signaling component b-catenin in the nuclei. More CRC cell metastasis to the lungs was observed when the PLAGL2 overexpressing SW480 cells were injected into the tail vein of rats, compared with the cell control and PLAGL2 silence group. Our findings indicated that PLAGL2 might be a very upstream key molecule regulating EMT involved in Wnt/b-catenin signaling pathway.",
"role": "user"
},
{
"content": "{\"CellLine\": [\"SW480\"], \"Chemical\": [\"paraffin\"], \"Disease\": [\"metastasis\", \"colorectal adenocarcinoma\", \"CRC\", \"cancer\", \"Colorectal adenocarcinoma\", \"tumor\"], \"Gene\": [\"Pleomorphic adenoma gene like-2\", \"E-cadherin\", \"Wnt\", \"wnt\", \"vimentin\", \"N-cadherin\", \"PLAGL2\", \"pleomorphic adenoma gene like-2\", \"b-catenin\"], \"Species\": [\"rats\", \"patient\", \"human\", \"patients\"], \"Variant\": []}",
"role": "assistant"
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science.scifact_entailment | Determine the relationship between them and generate a JSON response. Scientific Claim:
Noninvasive positive pressure ventilation is not predictive of acute respiratory failure after solid organ transplantation.
Research Abstract:
CONTEXT Noninvasive ventilation (NIV) has been associated with lower rates of endotracheal intubation in populations of patients with acute respiratory failure.
OBJECTIVE To compare NIV with standard treatment using supplemental oxygen administration to avoid endotracheal intubation in recipients of solid organ transplantation with acute hypoxemic respiratory failure.
DESIGN AND SETTING Prospective randomized study conducted at a 14-bed, general intensive care unit of a university hospital.
PATIENTS Of 238 patients who underwent solid organ transplantation from December 1995 to October 1997, 51 were treated for acute respiratory failure.
Of these, 40 were eligible and 20 were randomized to each group.
INTERVENTION Noninvasive ventilation vs standard treatment with supplemental oxygen administration.
MAIN OUTCOME MEASURES The need for endotracheal intubation and mechanical ventilation at any time during the study, complications not present on admission, duration of ventilatory assistance, length of hospital stay, and intensive care unit mortality.
RESULTS The 2 groups were similar at study entry.
Within the first hour of treatment, 14 patients (70%) in the NIV group, and 5 patients (25%) in the standard treatment group improved their ratio of the PaO2 to the fraction of inspired oxygen (FIO2).
Over time, a sustained improvement in PaO2 to FIO2 was noted in 12 patients (60%) in the NIV group, and in 5 patients (25%) randomized to standard treatment (P = .03).
The use of NIV was associated with a significant reduction in the rate of endotracheal intubation (20% vs 70%; P = .002), rate of fatal complications (20% vs 50%; P = .05), length of stay in the intensive care unit by survivors (mean [SD] days, 5.5 [3] vs 9 [4]; P = .03), and intensive care unit mortality (20% vs 50%; P = .05).
Hospital mortality did not differ.
CONCLUSIONS These results indicate that transplantation programs should consider NIV in the treatment of selected recipients of transplantation with acute respiratory failure. | {"verdict": "CONTRADICT", "evidence": ["Within the first hour of treatment, 14 patients (70%) in the NIV group, and 5 patients (25%) in the standard treatment group improved their ratio of the PaO2 to the fraction of inspired oxygen (FIO2).", "Over time, a sustained improvement in PaO2 to FIO2 was noted in 12 patients (60%) in the NIV group, and in 5 patients (25%) randomized to standard treatment (P = .03).", "The use of NIV was associated with a significant reduction in the rate of endotracheal intubation (20% vs 70%; P = .002), rate of fatal complications (20% vs 50%; P = .05), length of stay in the intensive care unit by survivors (mean [SD] days, 5.5 [3] vs 9 [4]; P = .03), and intensive care unit mortality (20% vs 50%; P = .05).", "CONCLUSIONS These results indicate that transplantation programs should consider NIV in the treatment of selected recipients of transplantation with acute respiratory failure."]} | science.scifact_entailment.540 | [
{
"content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nNoninvasive positive pressure ventilation is not predictive of acute respiratory failure after solid organ transplantation.\n\nResearch Abstract:\nCONTEXT Noninvasive ventilation (NIV) has been associated with lower rates of endotracheal intubation in populations of patients with acute respiratory failure.\nOBJECTIVE To compare NIV with standard treatment using supplemental oxygen administration to avoid endotracheal intubation in recipients of solid organ transplantation with acute hypoxemic respiratory failure.\nDESIGN AND SETTING Prospective randomized study conducted at a 14-bed, general intensive care unit of a university hospital.\nPATIENTS Of 238 patients who underwent solid organ transplantation from December 1995 to October 1997, 51 were treated for acute respiratory failure.\nOf these, 40 were eligible and 20 were randomized to each group.\nINTERVENTION Noninvasive ventilation vs standard treatment with supplemental oxygen administration.\nMAIN OUTCOME MEASURES The need for endotracheal intubation and mechanical ventilation at any time during the study, complications not present on admission, duration of ventilatory assistance, length of hospital stay, and intensive care unit mortality.\nRESULTS The 2 groups were similar at study entry.\nWithin the first hour of treatment, 14 patients (70%) in the NIV group, and 5 patients (25%) in the standard treatment group improved their ratio of the PaO2 to the fraction of inspired oxygen (FIO2).\nOver time, a sustained improvement in PaO2 to FIO2 was noted in 12 patients (60%) in the NIV group, and in 5 patients (25%) randomized to standard treatment (P = .03).\nThe use of NIV was associated with a significant reduction in the rate of endotracheal intubation (20% vs 70%; P = .002), rate of fatal complications (20% vs 50%; P = .05), length of stay in the intensive care unit by survivors (mean [SD] days, 5.5 [3] vs 9 [4]; P = .03), and intensive care unit mortality (20% vs 50%; P = .05).\nHospital mortality did not differ.\nCONCLUSIONS These results indicate that transplantation programs should consider NIV in the treatment of selected recipients of transplantation with acute respiratory failure.",
"role": "user"
},
{
"content": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Within the first hour of treatment, 14 patients (70%) in the NIV group, and 5 patients (25%) in the standard treatment group improved their ratio of the PaO2 to the fraction of inspired oxygen (FIO2).\", \"Over time, a sustained improvement in PaO2 to FIO2 was noted in 12 patients (60%) in the NIV group, and in 5 patients (25%) randomized to standard treatment (P = .03).\", \"The use of NIV was associated with a significant reduction in the rate of endotracheal intubation (20% vs 70%; P = .002), rate of fatal complications (20% vs 50%; P = .05), length of stay in the intensive care unit by survivors (mean [SD] days, 5.5 [3] vs 9 [4]; P = .03), and intensive care unit mortality (20% vs 50%; P = .05).\", \"CONCLUSIONS These results indicate that transplantation programs should consider NIV in the treatment of selected recipients of transplantation with acute respiratory failure.\"]}",
"role": "assistant"
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science.evidence_inference | Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:
- Treatment: The specific medical procedure or drug being evaluated.
- Control: The standard or placebo used for comparison. If absent, use `null`.
- Result: The health outcome being assessed.
- Impact: The observed effect of the treatment on the result, categorized as: ("increased", "decreased", "no change").
- Justification: The supporting details for the observed impact.
Format the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].
Abstract:
ABSTRACT: Little is known about the humoral immune response against DNA prime-recombinant adenovirus 5 (rAd5) boost HIV vaccine among HIV-infected patients on long-term suppressive antiretroviral therapy (ART). Previous studies emphasized cellular immune responses; however, current research suggests both cellular and humoral responses are likely required for a successful therapeutic vaccine. Thus, we aimed to understand antibody response and function induced by vaccination of ART-treated HIV-1-infected patients with immune recovery. All subjects participated in EraMune 02, an open-label randomized clinical trial of ART intensification followed by a six plasmid DNA prime (envA, envB, envC, gagB, polB, nefB) and rAd5 boost HIV vaccine with matching inserts. Antibody binding levels were determined with a recently developed microarray approach. We also analyzed neutralization efficiency and antibody-dependent cellular cytotoxicity (ADCC). We found that the DNA prime-rAd5 boost vaccine induced a significant cross-clade HIV-specific antibody response, which correlated with antibody neutralization efficiency. However, despite the increase in antibody binding levels, the vaccine did not significantly stimulate neutralization or ADCC responses. This finding was also reflected by a lack of change in total CD4+ cell associated HIV DNA in those who received the vaccine. Our results have important implications for further therapeutic vaccine design and administration, especially in HIV-1 infected patients, as boosting of preexisting antibody responses are unlikely to lead to clearance of latent proviruses in the HIV reservoir.
Extracted Data: | [["DNA prime-rAd5 boost vaccine", "Control", "HIV-specific antibody response", "significantly increased", "We found that the DNA prime-rAd5 boost vaccine induced a significant cross-clade HIV-specific antibody response, which correlated with antibody neutralization efficiency."]] | science.evidence_inference.133 | [
{
"content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT: Little is known about the humoral immune response against DNA prime-recombinant adenovirus 5 (rAd5) boost HIV vaccine among HIV-infected patients on long-term suppressive antiretroviral therapy (ART). Previous studies emphasized cellular immune responses; however, current research suggests both cellular and humoral responses are likely required for a successful therapeutic vaccine. Thus, we aimed to understand antibody response and function induced by vaccination of ART-treated HIV-1-infected patients with immune recovery. All subjects participated in EraMune 02, an open-label randomized clinical trial of ART intensification followed by a six plasmid DNA prime (envA, envB, envC, gagB, polB, nefB) and rAd5 boost HIV vaccine with matching inserts. Antibody binding levels were determined with a recently developed microarray approach. We also analyzed neutralization efficiency and antibody-dependent cellular cytotoxicity (ADCC). We found that the DNA prime-rAd5 boost vaccine induced a significant cross-clade HIV-specific antibody response, which correlated with antibody neutralization efficiency. However, despite the increase in antibody binding levels, the vaccine did not significantly stimulate neutralization or ADCC responses. This finding was also reflected by a lack of change in total CD4+ cell associated HIV DNA in those who received the vaccine. Our results have important implications for further therapeutic vaccine design and administration, especially in HIV-1 infected patients, as boosting of preexisting antibody responses are unlikely to lead to clearance of latent proviruses in the HIV reservoir.\n\nExtracted Data:",
"role": "user"
},
{
"content": "[[\"DNA prime-rAd5 boost vaccine\", \"Control\", \"HIV-specific antibody response\", \"significantly increased\", \"We found that the DNA prime-rAd5 boost vaccine induced a significant cross-clade HIV-specific antibody response, which correlated with antibody neutralization efficiency.\"]]",
"role": "assistant"
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science.scifact_entailment | Determine the relationship between them and generate a JSON response. Scientific Claim:
Primary cervical cancer screening with HPV detection has lower longitudinal sensitivity than conventional cytology to detect cervical intraepithelial neoplasia grade 2.
Research Abstract:
More than ever, clinicians need regularly updated reviews given the continuously increasing amount of new information regarding innovative cervical cancer prevention methods.
A summary is given from recent meta-analyses and systematic reviews on 3 possible clinical applications of human papillomavirus (HPV) testing: triage of women with equivocal or low-grade cytologic abnormalities; prediction of the therapeutic outcome after treatment of cervical intraepithelial neoplasia (CIN) lesions, and last not but not least, primary screening for cervical cancer and pre-cancer.
Consistent evidence is available indicating that HPV-triage with the Hybrid Capture(®) 2 assay (Qiagen Gaithersburg, Inc., MD, USA [previously Digene Corp.] (HC2) is more accurate (higher sensitivity, similar specificity) than repeat cytology to triage women with equivocal Pap smear results.
Several other tests show at least similar accuracy but mRNA testing with the APTIMA(®) (Gen-Probe Inc., San Diego, CA, USA) test is similarly sensitive but more specific compared to HC2.
In triage of low-grade squamous intraepithelial lesions (LSIL), HC2 is more sensitive but its specificity is substantially lower compared to repeat cytology.
The APTIMA(®) test is more specific than HC2 without showing a loss in sensitivity.
Identification of DNA of HPV types 16 and/or 18, or RNA from the five most carcinogenic HPV types allow selecting women at highest risk for CIN3+ but the sensitivity and negative predictive value of these markers are lower than full-range high-risk HPV (hrHPV) testing.
After conservative treatment of cervical pre-cancer, HPV testing picks up more quickly, with higher sensitivity and not lower specificity, residual or recurrent high-grade CIN than follow-up cytology.
Primary screening for hrHPV generally detects more CIN2, CIN3 or cancer compared to cytology at cut-off atypical squamous cells of undetermined significance (ASC-US) or LSIL, but is less specific.
Combined HPV and cytology screening provides a further small gain in sensitivity at the expense of a considerable loss in specificity if positive by either test is referred to colposcopy, in comparison with HPV testing only.
Randomised trials and follow-up of cohort studies consistently demonstrate a significantly lower cumulative incidence of CIN3+ and even of cancer, in women aged 30 years or older, who were at enrollment hrHPV DNA negative compared to those who were cytologically negative.
The difference in cumulative risk of CIN3+ or cancer for double negative (cytology & HPV) versus only HPV-negative women is small.
HC2, GP5+/6+ PCR (polymerase chain reaction), cobas(®) 4800 PCR (Roche Molecular Systems Inc., Alameda, CA, USA) and Real Time PCR (Abbott Molecular, Des Plaines, IL, USA) can be considered as clinically validated for use in primary screening.
The loss in specificity associated with primary HPV-based screening can be compensated by appropriate algorithms involving reflex cytology and/or HPV genotyping for HPV16 or 18.
There exists a substantial evidence base to support that HPV testing is advantageous both in triage of women with equivocal abnormal cytology, in surveillance after treatment of CIN lesions and in primary screening of women aged 30 years or older.
However, the possible advantages offered by HPV-based screening require a well organised program with good compliance with screening and triage policies.
This article forms part of a special supplement entitled "Comprehensive Control of HPV Infections and Related Diseases" Vaccine Volume 30, Supplement 5, 2012. | {"verdict": "CONTRADICT", "evidence": ["Primary screening for hrHPV generally detects more CIN2, CIN3 or cancer compared to cytology at cut-off atypical squamous cells of undetermined significance (ASC-US) or LSIL, but is less specific."]} | science.scifact_entailment.625 | [
{
"content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nPrimary cervical cancer screening with HPV detection has lower longitudinal sensitivity than conventional cytology to detect cervical intraepithelial neoplasia grade 2.\n\nResearch Abstract:\nMore than ever, clinicians need regularly updated reviews given the continuously increasing amount of new information regarding innovative cervical cancer prevention methods.\nA summary is given from recent meta-analyses and systematic reviews on 3 possible clinical applications of human papillomavirus (HPV) testing: triage of women with equivocal or low-grade cytologic abnormalities; prediction of the therapeutic outcome after treatment of cervical intraepithelial neoplasia (CIN) lesions, and last not but not least, primary screening for cervical cancer and pre-cancer.\nConsistent evidence is available indicating that HPV-triage with the Hybrid Capture(®) 2 assay (Qiagen Gaithersburg, Inc., MD, USA [previously Digene Corp.] (HC2) is more accurate (higher sensitivity, similar specificity) than repeat cytology to triage women with equivocal Pap smear results.\nSeveral other tests show at least similar accuracy but mRNA testing with the APTIMA(®) (Gen-Probe Inc., San Diego, CA, USA) test is similarly sensitive but more specific compared to HC2.\nIn triage of low-grade squamous intraepithelial lesions (LSIL), HC2 is more sensitive but its specificity is substantially lower compared to repeat cytology.\nThe APTIMA(®) test is more specific than HC2 without showing a loss in sensitivity.\nIdentification of DNA of HPV types 16 and/or 18, or RNA from the five most carcinogenic HPV types allow selecting women at highest risk for CIN3+ but the sensitivity and negative predictive value of these markers are lower than full-range high-risk HPV (hrHPV) testing.\nAfter conservative treatment of cervical pre-cancer, HPV testing picks up more quickly, with higher sensitivity and not lower specificity, residual or recurrent high-grade CIN than follow-up cytology.\nPrimary screening for hrHPV generally detects more CIN2, CIN3 or cancer compared to cytology at cut-off atypical squamous cells of undetermined significance (ASC-US) or LSIL, but is less specific.\nCombined HPV and cytology screening provides a further small gain in sensitivity at the expense of a considerable loss in specificity if positive by either test is referred to colposcopy, in comparison with HPV testing only.\nRandomised trials and follow-up of cohort studies consistently demonstrate a significantly lower cumulative incidence of CIN3+ and even of cancer, in women aged 30 years or older, who were at enrollment hrHPV DNA negative compared to those who were cytologically negative.\nThe difference in cumulative risk of CIN3+ or cancer for double negative (cytology & HPV) versus only HPV-negative women is small.\nHC2, GP5+/6+ PCR (polymerase chain reaction), cobas(®) 4800 PCR (Roche Molecular Systems Inc., Alameda, CA, USA) and Real Time PCR (Abbott Molecular, Des Plaines, IL, USA) can be considered as clinically validated for use in primary screening.\nThe loss in specificity associated with primary HPV-based screening can be compensated by appropriate algorithms involving reflex cytology and/or HPV genotyping for HPV16 or 18.\nThere exists a substantial evidence base to support that HPV testing is advantageous both in triage of women with equivocal abnormal cytology, in surveillance after treatment of CIN lesions and in primary screening of women aged 30 years or older.\nHowever, the possible advantages offered by HPV-based screening require a well organised program with good compliance with screening and triage policies.\nThis article forms part of a special supplement entitled \"Comprehensive Control of HPV Infections and Related Diseases\" Vaccine Volume 30, Supplement 5, 2012.",
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"content": "{\"verdict\": \"CONTRADICT\", \"evidence\": [\"Primary screening for hrHPV generally detects more CIN2, CIN3 or cancer compared to cytology at cut-off atypical squamous cells of undetermined significance (ASC-US) or LSIL, but is less specific.\"]}",
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science.scierc_ner | Analyze the given abstract from a computer science paper. Identify and list entities in a structured JSON format. Focus on capturing distinct elements relevant to the research context.
Input Abstract:
In this paper, we propose a new approach to generate oriented object proposals (OOPs) to reduce the detection error caused by various orientations of the object. To this end, we propose to efficiently locate object regions according to pixelwise object probability, rather than measuring the objectness from a set of sampled windows. We formulate the proposal generation problem as a generative proba-bilistic model such that object proposals of different shapes (i.e., sizes and orientations) can be produced by locating the local maximum likelihoods. The new approach has three main advantages. First, it helps the object detector handle objects of different orientations. Second, as the shapes of the proposals may vary to fit the objects, the resulting proposals are tighter than the sampling windows with fixed sizes. Third, it avoids massive window sampling, and thereby reducing the number of proposals while maintaining a high recall. Experiments on the PASCAL VOC 2007 dataset show that the proposed OOP outperforms the state-of-the-art fast methods. Further experiments show that the rotation invariant property helps a class-specific object detector achieve better performance than the state-of-the-art proposal generation methods in either object rotation scenarios or general scenarios. Generating OOPs is very fast and takes only 0.5 s per image. | Output Entities:
{"Generic": ["approach", "state-of-the-art fast methods"], "Material": ["object rotation scenarios", "general scenarios", "PASCAL VOC 2007 dataset"], "Method": ["proposal generation methods", "object detector", "class-specific object detector", "OOP", "massive window sampling"], "Metric": ["detection error", "recall"], "OtherScientificTerm": ["shapes of the proposals", "objectness", "pixelwise object probability", "rotation invariant property", "orientations of the object", "object regions", "sampling windows", "object proposals", "shapes", "sizes", "number of proposals", "orientations", "local maximum likelihoods"], "Task": ["oriented object proposals (OOPs)", "proposal generation problem"]} | science.scierc_ner.41 | [
{
"content": "Analyze the given abstract from a computer science paper. Identify and list entities in a structured JSON format. Focus on capturing distinct elements relevant to the research context.\n\nInput Abstract:\n\nIn this paper, we propose a new approach to generate oriented object proposals (OOPs) to reduce the detection error caused by various orientations of the object. To this end, we propose to efficiently locate object regions according to pixelwise object probability, rather than measuring the objectness from a set of sampled windows. We formulate the proposal generation problem as a generative proba-bilistic model such that object proposals of different shapes (i.e., sizes and orientations) can be produced by locating the local maximum likelihoods. The new approach has three main advantages. First, it helps the object detector handle objects of different orientations. Second, as the shapes of the proposals may vary to fit the objects, the resulting proposals are tighter than the sampling windows with fixed sizes. Third, it avoids massive window sampling, and thereby reducing the number of proposals while maintaining a high recall. Experiments on the PASCAL VOC 2007 dataset show that the proposed OOP outperforms the state-of-the-art fast methods. Further experiments show that the rotation invariant property helps a class-specific object detector achieve better performance than the state-of-the-art proposal generation methods in either object rotation scenarios or general scenarios. Generating OOPs is very fast and takes only 0.5 s per image.",
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"content": "Output Entities:\n\n{\"Generic\": [\"approach\", \"state-of-the-art fast methods\"], \"Material\": [\"object rotation scenarios\", \"general scenarios\", \"PASCAL VOC 2007 dataset\"], \"Method\": [\"proposal generation methods\", \"object detector\", \"class-specific object detector\", \"OOP\", \"massive window sampling\"], \"Metric\": [\"detection error\", \"recall\"], \"OtherScientificTerm\": [\"shapes of the proposals\", \"objectness\", \"pixelwise object probability\", \"rotation invariant property\", \"orientations of the object\", \"object regions\", \"sampling windows\", \"object proposals\", \"shapes\", \"sizes\", \"number of proposals\", \"orientations\", \"local maximum likelihoods\"], \"Task\": [\"oriented object proposals (OOPs)\", \"proposal generation problem\"]}",
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science.multicite_intent_classification | Examine the text below, which includes a citation marked by "<cite>" and "</cite>". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.
Provide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.
Text with Citation:
Most relevant is similar work which focuses on conditions under which compositional language emerges as deep agents learn to cooperate (Mordatch & Abbeel, 2018;<cite> Kottur et al., 2017)</cite> . | ["Background"] | science.multicite_intent_classification.643 | [
{
"content": "Examine the text below, which includes a citation marked by \"<cite>\" and \"</cite>\". Your objective is to identify the intent behind this citation. Focus on understanding the role the cited work plays in the context of the text.\n\nProvide your analysis as a JSON array. Ensure your response is concise and directly related to the citation's purpose.\n\nText with Citation:\nMost relevant is similar work which focuses on conditions under which compositional language emerges as deep agents learn to cooperate (Mordatch & Abbeel, 2018;<cite> Kottur et al., 2017)</cite> .",
"role": "user"
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"content": "[\"Background\"]",
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science.mup_single_document_summarization | Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.
Title of the Paper:
Do Transformers Understand Polynomial Simplification?
Main Content:
Recently researchers have demonstrated that Transformers can be trained to learn symbolic tasks such as solving integration and differential equations in an end-toend fashion . In these setups , for an input symbolic expression , the Transformer predicts the final solution in a single step . Since such tasks may consist of a sequence of logical steps , question remains whether such networks have understood and learnt individual steps to reach the solution . To take a deeper look , we consider the task of polynomial simplification . Polynomials can be written in a simple normal form as a sum of monomials which are ordered in a lexicographic order . For a polynomial which is not necessarily in this normal form , a sequence of simplification steps is applied to reach the fully simplified ( i.e. , in the normal form ) polynomial . For this task , we describe a synthetic Polynomial dataset generation algorithm which generates polynomials with unique proof steps . Then , we conduct an extensive analysis of the Transformer ’ s abilities to learn the polynomial simplification task along different dimensions . 1 INTRODUCTION . With the state-of-the-art performance of Deep Neural Nets ( DNNs ) in perceptual tasks , researchers have started to explore their logical reasoning capabilities , in particular within the domain of Automated Theorem Proving ( ATP ) . In these domains ( LEAN ( de Moura et al. , 2015 ) , HOL Light and Mizar ( miz , 2020 ) ) , many recent works ( Paliwal et al. , 2020 ; Aygün et al. , 2020 ; Hahn et al. , 2020 ) have shown that Graph Neural Networks ( Gori et al. , 2005 ; Veličković et al. , 2018 ) and Transformers ( Vaswani et al. , 2017 ) can be trained to perform impressively on the theorem-proving task as part of a neuro-symbolic system . In a related but different development , recently Lample & Charton ( 2019 ) showed that for symbolic integration and differential equations , a large amount of synthetic end-to-end examples can be generated using symbolic systems . In these tasks , the authors show that Transformer networks can be trained to produce the final solution from an input integral ( or differential equation ) in a single step . This points to the exciting possibility of using deep neural nets to learn end-to-end theorem provers , and can be beneficial for formal mathematics ( Szegedy , 2020 ) . However , the setup combines multiple reasoning steps in a single shot . Additionally , integration ( or differential equation solving ) is a complex task requiring understanding of the integral symbols , functions , variables , and the basic concepts of arithmetic . As the system in Lample & Charton ( 2019 ) is simply trained to output the top solution ( s ) and a corresponding confidence score ( s ) , it is unclear what internal mechanisms enable these models to solve these problems . This lack of transparency has been noted in this context ( Davis , 2019 ) . An earlier work by Piotrowski et al . ( 2019 ) showed similar results for certain symbolic manipulation tasks and their work shares the same limitation . In this paper we ask if instead of only producing the end-result of symbolic manipulation or integral , can we have the model produce a human-readable proof as well . While we do not know if these models reason in the way humans do , one way to produce proofs would be to “ extract ” a proof from the models of the above type by “ probing ” them in some mannner . The problem of unraveling the inner workings of Transformers by probing is an active area of research ; however , at present our understanding is still evolving ( Rogers et al. , 2020 ) . Hence taking a detour , we instead train the model to produce the full proof . Inspired by Piotrowski et al . ( 2019 ) , we explore a novel but simpler setting of polynomial simplification . We illustrate the task with an example . We begin with a polynomial which is a sum of product of factors , where each factor is again a sum of monomials ( including constants ) , as shown below : P0 = ( 2 ∗ x22 ) ∗ factor︷ ︸︸ ︷ ( 3 ∗ x12︸ ︷︷ ︸ term +4 ) + product︷ ︸︸ ︷ ( 5 ∗ x21 + x11 ∗ x12 ) ∗ ( 3 ∗ x11 ) ∗ ( 2 ) , / * Initial * / To construct unique simplification steps , first each term in a factor is simplified . Once all factors are simplified ( facstep ) ; then within a product , all factors are multiplied ( mulstep ) . Lastly simplified products are summed ( sumstep ) . P0 = ( 2 ∗ x22 ) ∗ ( 3 ∗ x12 + 4 ) + ( 5 ∗ x21 + x11 ∗ x12 ) ∗ ( 3 ∗ x11 ) ∗ ( 2 ) , / * FACSTEP * / = ( 2 ∗ x22 ) ∗ ( 3 ∗ x2 + 4 ) + ( 5 ∗ x21 + x11 ∗ x12 ) ∗ ( 3 ∗ x11 ) ∗ ( 2 ) , ( P1 ) , / * FACSTEP * / = ( 2 ∗ x22 ) ∗ ( 3 ∗ x2 + 4 ) + ( 5 ∗ x21 + x1 ∗ x2 ) ∗ ( 3 ∗ x1 ) ∗ ( 2 ) , ( P2 ) , / * MULSTEP * / = ( 6 ∗ x32 + 8 ∗ x22 ) + ( 5 ∗ x21 + x1 ∗ x2 ) ∗ ( 3 ∗ x1 ) ∗ ( 2 ) , ( P3 ) , / * MULSTEP * / = ( 6 ∗ x32 + 8 ∗ x22 ) + ( 30 ∗ x31 + 6 ∗ x21 ∗ x2 ) , ( P4 ) , / * SUMSTEP * / = 30 ∗ x31 + 6 ∗ x32 + 6 ∗ x21 ∗ x2 + 8 ∗ x22 . ( P5 ) , / * ENDPOINT * / . Piotrowski et al . ( 2019 ) explores the task of learning symbolic re-write of an entire expression . In contrast , in our setting , for step-wise prediction , at each step the system needs to find the candidate sub-expression and a relevant simplification type to perform the simplification . This setup resembles the traditional ATP setup where a system needs to learn and execute symbolic steps to reach a final solution . But it is simpler as for each step only one type of simplification is applicable . By proof for an initial polynomial ( P0 ) we mean the sequence of simplification steps ( P1 to P5 ) . A model trained on step-wise prediction task , can be used to generate a full proof . Essentially , we start with an initial polynomial , and recursively feed the model output to itself , till it generates the final simplified polynomial ( in normal form ) . A proof is correct when all steps are correct . In the above setting ( termed COARSE ) , all terms in a factor are simplified at once in a facstep , and similarly all factors in a product are simplified at once in a mulstep . Additionally , we define another setting FINER , where a facstep involves simplification of a single term , and a mulstep involves multiplications of only two factors at once , illustrated below with an example ( for facstep ) : P0 = ( 5 ∗ x21 + x11 ∗ x12 ) ∗ ( 3 ∗ x11 ) ∗ ( 2 ) , / * FACSTEP * / = ( 5 ∗ x21 + x1 ∗ x2 ) ∗ ( 3 ∗ x11 ) ∗ ( 2 ) , / * FACSTEP * / = ( 5 ∗ x21 + x1 ∗ x2 ) ∗ ( 3 ∗ x1 ) ∗ ( 2 ) . As a state-of-the-art model , we explore Transformers . While both Graph Neural Networks and Transformers have been used for single-step representation learning of symbolic theorems and single step goal-theorem scoring , Transformer-based sequence-to-sequence networks have shown superiority in end-to-end tasks in integration , differential equations ( Lample & Charton , 2019 ) and temporal logic ( Hahn et al. , 2020 ) domains . Hence for the aforementioned tasks of step-wise polynomial simplification , we explore the Transformer ’ s ability along several dimensions . Our contributions are the following : 1 ) we propose polynomial simplification tasks requiring multiple steps of symbolic manipulation , 2 ) we show how datasets of different configurations can be generated synthetically for the task , 3 ) we propose an array of metrics to dissect the performance of Transformers , and 4 ) lastly through extensive experiments we show the performance of the Transformer on this task , establishing a strong baseline for future endeavors . Results Summary By varying over coefficient size , proof granularity and input representation ( in Tables 1 , 2 , Appendix Table 6 ) we observe that 1 ) full proof accuracy is only slightly lower than single-shot endpoint prediction accuracy in many 1-variable configurations , 2 ) coarse granular proofs help learn somewhat more accurate proofs , 3 ) prefix representation helps in most cases but infix sometimes provides higher accuracy . More than 80 % errors ( Tab . 7 and 8 in Appendix ) occur in multiplication steps , and we observe ( through independent experiments ) Transformer ’ s struggle to learn how to multiply numeric coefficients . By letting the system annotate the candidate subexpression , we observe that the system can understand candidate sub-expressions and which next step to perform explicitly ( Tables 3 , and Appendix Tables 9 , 10 , 11 ) . Also , through visualization we observe similar effects ( Figures 1 , 2 Appendix ) . We see systems trained for 2-variable outperform corresponding 1-variable systems on 1-variable test sets . For 1 variable , we observe steady and significant higher gains ( till 10 % for full proof ) using curriculum learning ( Table 17 Appendix ) . 2 RELATED WORK AND DISCUSSION . Unlike the problems dealt with by the aforementioned automatic theorem provers and related neuralbased systems , polynomial simplification does not involve any search . Our problem is simpler and tests a specific ability , namely certain kinds of symbol manipulations . This simplicity affords certain advantages ( shared by Piotrowski et al . ( 2019 ) and Lample & Charton ( 2019 ) ) : ( 1 ) We can generate artificial data to train models without limitations on the size . ( 2 ) It is easier to test the abilities of the models more thoroughly along multiple axes . ( 3 ) Accuracy achieved is much higher than for harder tasks , suggesting that fully solving such tasks may be possible in the near future . To compare with symbolic manipulation systems we note that in more detail the ability tested by our task is the following : the model must be able to identify smallest parts of the polynomial that can be simplified : ( 1 ) simplification of a factor , ( 2 ) multiplication of two factors , ( 3 ) addition of two sub-polynomials . Having identified what simplification to apply the model must produce a new polynomial with just that simplification . This ability is not tested by previous neural-based symbolic manipulation systems such as Piotrowski et al . ( 2019 ) and Lample & Charton ( 2019 ) and related works such as Saxton et al . ( 2019 ) and Hahn et al . ( 2020 ) . Several recent works have produced synthetic datasets for theorem proving tasks ( Aygün et al. , 2020 ; Wu et al. , 2020 ; Polu & Sutskever , 2020 ) , however , their focus remains more on search-based proofs . | The authors analyze the performance of Transformer models on simplifying polynomials and - importantly - generating proofs at the same time. This is a very nice idea that allows to study the performance of Transformers in depth and at the same time in an important setting where verification is performed as part of running the model. And the authors show a strong baseline, with models performing very well in a number of settings. A few areas seem to have been neglected though. For one, the authors only train a 4-layer 4-head model, which is quite small as far as Transformers go. Maybe it's irrelevant for this problem - but having at least one bigger model as a point of comparison would be good. Next, the out-of-distribution question warrants more experiments. Can the Transformers simplify polynomials with way more factors than trained on? With a higher number of variables? Higher degrees? The authors also show that one main problem for Transformers is learning to multiply the coefficients. But - assuming this reviewer understood correctly - the authors do not apply the proof requirement to multiplication. E.g., for "12*3" the model has to immediately output "36" rather than "10*3 + 2*3 = 30 + 6 = 36". Maybe this could help the Transformer learn and be more resilient to coefficient size? So while the current version of the paper is ok, there are a few areas for improvement which prevent it from being a clear accept. | science.mup_single_document_summarization.1084 | [
{
"content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nDo Transformers Understand Polynomial Simplification? \n\nMain Content:\nRecently researchers have demonstrated that Transformers can be trained to learn symbolic tasks such as solving integration and differential equations in an end-toend fashion . In these setups , for an input symbolic expression , the Transformer predicts the final solution in a single step . Since such tasks may consist of a sequence of logical steps , question remains whether such networks have understood and learnt individual steps to reach the solution . To take a deeper look , we consider the task of polynomial simplification . Polynomials can be written in a simple normal form as a sum of monomials which are ordered in a lexicographic order . For a polynomial which is not necessarily in this normal form , a sequence of simplification steps is applied to reach the fully simplified ( i.e. , in the normal form ) polynomial . For this task , we describe a synthetic Polynomial dataset generation algorithm which generates polynomials with unique proof steps . Then , we conduct an extensive analysis of the Transformer ’ s abilities to learn the polynomial simplification task along different dimensions . 1 INTRODUCTION . With the state-of-the-art performance of Deep Neural Nets ( DNNs ) in perceptual tasks , researchers have started to explore their logical reasoning capabilities , in particular within the domain of Automated Theorem Proving ( ATP ) . In these domains ( LEAN ( de Moura et al. , 2015 ) , HOL Light and Mizar ( miz , 2020 ) ) , many recent works ( Paliwal et al. , 2020 ; Aygün et al. , 2020 ; Hahn et al. , 2020 ) have shown that Graph Neural Networks ( Gori et al. , 2005 ; Veličković et al. , 2018 ) and Transformers ( Vaswani et al. , 2017 ) can be trained to perform impressively on the theorem-proving task as part of a neuro-symbolic system . In a related but different development , recently Lample & Charton ( 2019 ) showed that for symbolic integration and differential equations , a large amount of synthetic end-to-end examples can be generated using symbolic systems . In these tasks , the authors show that Transformer networks can be trained to produce the final solution from an input integral ( or differential equation ) in a single step . This points to the exciting possibility of using deep neural nets to learn end-to-end theorem provers , and can be beneficial for formal mathematics ( Szegedy , 2020 ) . However , the setup combines multiple reasoning steps in a single shot . Additionally , integration ( or differential equation solving ) is a complex task requiring understanding of the integral symbols , functions , variables , and the basic concepts of arithmetic . As the system in Lample & Charton ( 2019 ) is simply trained to output the top solution ( s ) and a corresponding confidence score ( s ) , it is unclear what internal mechanisms enable these models to solve these problems . This lack of transparency has been noted in this context ( Davis , 2019 ) . An earlier work by Piotrowski et al . ( 2019 ) showed similar results for certain symbolic manipulation tasks and their work shares the same limitation . In this paper we ask if instead of only producing the end-result of symbolic manipulation or integral , can we have the model produce a human-readable proof as well . While we do not know if these models reason in the way humans do , one way to produce proofs would be to “ extract ” a proof from the models of the above type by “ probing ” them in some mannner . The problem of unraveling the inner workings of Transformers by probing is an active area of research ; however , at present our understanding is still evolving ( Rogers et al. , 2020 ) . Hence taking a detour , we instead train the model to produce the full proof . Inspired by Piotrowski et al . ( 2019 ) , we explore a novel but simpler setting of polynomial simplification . We illustrate the task with an example . We begin with a polynomial which is a sum of product of factors , where each factor is again a sum of monomials ( including constants ) , as shown below : P0 = ( 2 ∗ x22 ) ∗ factor︷ ︸︸ ︷ ( 3 ∗ x12︸ ︷︷ ︸ term +4 ) + product︷ ︸︸ ︷ ( 5 ∗ x21 + x11 ∗ x12 ) ∗ ( 3 ∗ x11 ) ∗ ( 2 ) , / * Initial * / To construct unique simplification steps , first each term in a factor is simplified . Once all factors are simplified ( facstep ) ; then within a product , all factors are multiplied ( mulstep ) . Lastly simplified products are summed ( sumstep ) . P0 = ( 2 ∗ x22 ) ∗ ( 3 ∗ x12 + 4 ) + ( 5 ∗ x21 + x11 ∗ x12 ) ∗ ( 3 ∗ x11 ) ∗ ( 2 ) , / * FACSTEP * / = ( 2 ∗ x22 ) ∗ ( 3 ∗ x2 + 4 ) + ( 5 ∗ x21 + x11 ∗ x12 ) ∗ ( 3 ∗ x11 ) ∗ ( 2 ) , ( P1 ) , / * FACSTEP * / = ( 2 ∗ x22 ) ∗ ( 3 ∗ x2 + 4 ) + ( 5 ∗ x21 + x1 ∗ x2 ) ∗ ( 3 ∗ x1 ) ∗ ( 2 ) , ( P2 ) , / * MULSTEP * / = ( 6 ∗ x32 + 8 ∗ x22 ) + ( 5 ∗ x21 + x1 ∗ x2 ) ∗ ( 3 ∗ x1 ) ∗ ( 2 ) , ( P3 ) , / * MULSTEP * / = ( 6 ∗ x32 + 8 ∗ x22 ) + ( 30 ∗ x31 + 6 ∗ x21 ∗ x2 ) , ( P4 ) , / * SUMSTEP * / = 30 ∗ x31 + 6 ∗ x32 + 6 ∗ x21 ∗ x2 + 8 ∗ x22 . ( P5 ) , / * ENDPOINT * / . Piotrowski et al . ( 2019 ) explores the task of learning symbolic re-write of an entire expression . In contrast , in our setting , for step-wise prediction , at each step the system needs to find the candidate sub-expression and a relevant simplification type to perform the simplification . This setup resembles the traditional ATP setup where a system needs to learn and execute symbolic steps to reach a final solution . But it is simpler as for each step only one type of simplification is applicable . By proof for an initial polynomial ( P0 ) we mean the sequence of simplification steps ( P1 to P5 ) . A model trained on step-wise prediction task , can be used to generate a full proof . Essentially , we start with an initial polynomial , and recursively feed the model output to itself , till it generates the final simplified polynomial ( in normal form ) . A proof is correct when all steps are correct . In the above setting ( termed COARSE ) , all terms in a factor are simplified at once in a facstep , and similarly all factors in a product are simplified at once in a mulstep . Additionally , we define another setting FINER , where a facstep involves simplification of a single term , and a mulstep involves multiplications of only two factors at once , illustrated below with an example ( for facstep ) : P0 = ( 5 ∗ x21 + x11 ∗ x12 ) ∗ ( 3 ∗ x11 ) ∗ ( 2 ) , / * FACSTEP * / = ( 5 ∗ x21 + x1 ∗ x2 ) ∗ ( 3 ∗ x11 ) ∗ ( 2 ) , / * FACSTEP * / = ( 5 ∗ x21 + x1 ∗ x2 ) ∗ ( 3 ∗ x1 ) ∗ ( 2 ) . As a state-of-the-art model , we explore Transformers . While both Graph Neural Networks and Transformers have been used for single-step representation learning of symbolic theorems and single step goal-theorem scoring , Transformer-based sequence-to-sequence networks have shown superiority in end-to-end tasks in integration , differential equations ( Lample & Charton , 2019 ) and temporal logic ( Hahn et al. , 2020 ) domains . Hence for the aforementioned tasks of step-wise polynomial simplification , we explore the Transformer ’ s ability along several dimensions . Our contributions are the following : 1 ) we propose polynomial simplification tasks requiring multiple steps of symbolic manipulation , 2 ) we show how datasets of different configurations can be generated synthetically for the task , 3 ) we propose an array of metrics to dissect the performance of Transformers , and 4 ) lastly through extensive experiments we show the performance of the Transformer on this task , establishing a strong baseline for future endeavors . Results Summary By varying over coefficient size , proof granularity and input representation ( in Tables 1 , 2 , Appendix Table 6 ) we observe that 1 ) full proof accuracy is only slightly lower than single-shot endpoint prediction accuracy in many 1-variable configurations , 2 ) coarse granular proofs help learn somewhat more accurate proofs , 3 ) prefix representation helps in most cases but infix sometimes provides higher accuracy . More than 80 % errors ( Tab . 7 and 8 in Appendix ) occur in multiplication steps , and we observe ( through independent experiments ) Transformer ’ s struggle to learn how to multiply numeric coefficients . By letting the system annotate the candidate subexpression , we observe that the system can understand candidate sub-expressions and which next step to perform explicitly ( Tables 3 , and Appendix Tables 9 , 10 , 11 ) . Also , through visualization we observe similar effects ( Figures 1 , 2 Appendix ) . We see systems trained for 2-variable outperform corresponding 1-variable systems on 1-variable test sets . For 1 variable , we observe steady and significant higher gains ( till 10 % for full proof ) using curriculum learning ( Table 17 Appendix ) . 2 RELATED WORK AND DISCUSSION . Unlike the problems dealt with by the aforementioned automatic theorem provers and related neuralbased systems , polynomial simplification does not involve any search . Our problem is simpler and tests a specific ability , namely certain kinds of symbol manipulations . This simplicity affords certain advantages ( shared by Piotrowski et al . ( 2019 ) and Lample & Charton ( 2019 ) ) : ( 1 ) We can generate artificial data to train models without limitations on the size . ( 2 ) It is easier to test the abilities of the models more thoroughly along multiple axes . ( 3 ) Accuracy achieved is much higher than for harder tasks , suggesting that fully solving such tasks may be possible in the near future . To compare with symbolic manipulation systems we note that in more detail the ability tested by our task is the following : the model must be able to identify smallest parts of the polynomial that can be simplified : ( 1 ) simplification of a factor , ( 2 ) multiplication of two factors , ( 3 ) addition of two sub-polynomials . Having identified what simplification to apply the model must produce a new polynomial with just that simplification . This ability is not tested by previous neural-based symbolic manipulation systems such as Piotrowski et al . ( 2019 ) and Lample & Charton ( 2019 ) and related works such as Saxton et al . ( 2019 ) and Hahn et al . ( 2020 ) . Several recent works have produced synthetic datasets for theorem proving tasks ( Aygün et al. , 2020 ; Wu et al. , 2020 ; Polu & Sutskever , 2020 ) , however , their focus remains more on search-based proofs .",
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"content": "The authors analyze the performance of Transformer models on simplifying polynomials and - importantly - generating proofs at the same time. This is a very nice idea that allows to study the performance of Transformers in depth and at the same time in an important setting where verification is performed as part of running the model. And the authors show a strong baseline, with models performing very well in a number of settings. A few areas seem to have been neglected though. For one, the authors only train a 4-layer 4-head model, which is quite small as far as Transformers go. Maybe it's irrelevant for this problem - but having at least one bigger model as a point of comparison would be good. Next, the out-of-distribution question warrants more experiments. Can the Transformers simplify polynomials with way more factors than trained on? With a higher number of variables? Higher degrees? The authors also show that one main problem for Transformers is learning to multiply the coefficients. But - assuming this reviewer understood correctly - the authors do not apply the proof requirement to multiplication. E.g., for \"12*3\" the model has to immediately output \"36\" rather than \"10*3 + 2*3 = 30 + 6 = 36\". Maybe this could help the Transformer learn and be more resilient to coefficient size? So while the current version of the paper is ok, there are a few areas for improvement which prevent it from being a clear accept.",
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science.mup_single_document_summarization | Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.
Title of the Paper:
Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret
Main Content:
√ SAK ) , where K is the number of episodes , S is the number of states , A is the number of actions , and B ? bounds the expected cumulative cost of the optimal policy from any state , thus closing the gap with the lower bound . Interestingly , EB-SSP obtains this result while being parameter-free , i.e. , it does not require any prior knowledge of B ? , nor of T ? , which bounds the expected time-to-goal of the optimal policy from any state . Furthermore , we illustrate various cases ( e.g. , positive costs , or general costs when an order-accurate estimate of T ? is available ) where the regret only contains a logarithmic dependence on T ? , thus yielding the first ( nearly ) horizon-free regret bound beyond the finite-horizon MDP setting . 1 Introduction . Stochastic shortest path ( SSP ) is a goal-oriented reinforcement learning ( RL ) setting where the agent aims to reach a predefined goal state while minimizing its total expected cost [ Bertsekas , 1995 ] . In particular , the interaction between the agent and the environment ends only when ( and if ) the goal state is reached , so the length of an episode is not predetermined ( nor bounded ) and it is influenced by the agent ’ s behavior . SSP includes both finite-horizon and discounted Markov Decision Processes ( MDPs ) as special cases . Moreover , many common RL problems can be cast under the SSP formulation , such as game playing ( e.g. , Atari games ) or navigation ( e.g. , Mujoco mazes ) . We study the online learning problem in the SSP setting ( online SSP in short ) , where both the transition dynamics and the cost function are initially unknown and the agent interacts with the environment through multiple episodes . The learning objective is to achieve a performance as close ∗equal contribution 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . as possible to the optimal policy π ? , that is , the agent should achieve low regret ( i.e. , the cumulative difference between the total cost accumulated across episodes by the agent and by the optimal policy ) . We identify three desirable properties for a learning algorithm in online SSP . • Desired property 1 : Minimax . The information-theoretic lower bound on the regret is Ω ( B ? √ SAK ) [ Rosenberg et al. , 2020 ] , where K is the number of episodes , S is the number of states , A is the number of actions , and B ? bounds the total expected cost of the optimal policy starting from any state ( assuming for simplicity that B ? ≥ 1 ) . An algorithm for online SSP is ( nearly ) minimax optimal if its regret is bounded by Õ ( B ? √ SAK ) , up to logarithmic factors and lower-order terms . • Desired property 2 : Parameter-free . Another relevant dimension is the amount of prior knowledge required by the algorithm . While the knowledge of S , A , and the cost ( or reward ) range [ 0 , 1 ] is standard across regret-minimization settings ( e.g. , finite-horizon , discounted , average-reward ) , the complexity of learning in SSP problems may be linked to SSP-specific quantities such as B ? and T ? , which denotes the expected time-to-goal of the optimal policy from any state . An algorithm for online SSP is parameter-free if it relies neither on T ? nor B ? prior knowledge . • Desired property 3 : Horizon-free . A core challenge in SSP is to trade off between minimizing costs and quickly reaching the goal state . This is accentuated when the instantaneous costs are small , i.e. , when there is a mismatch between B ? and T ? . Indeed , while B ? ≤ T ? always holds since the cost range is [ 0 , 1 ] , the gap between the two may be arbitrarily large ( see e.g. , the simple example of App . A ) . The lower bound stipulates that the regret does depend on B ? , while the “ time horizon ” of the problem , i.e. , T ? should a priori not impact the regret , even as a lower-order term . An algorithm for online SSP is ( nearly ) horizon-free if its regret depends only logarithmically on T ? . Our definition extends the property of so-called horizon-free bounds recently uncovered in finitehorizon MDPs with total reward bounded by 1 [ Wang et al. , 2020a , Zhang et al. , 2021a , b ] . These bounds depend only logarithmically on the horizon H , which is the number of time steps by which any policy terminates . Such notion of horizon would clearly be too strong in the more general class of SSP , where some ( even most ) policies may never reach the goal , thus having unbounded time horizon . A more adequate notion of horizon in SSP is T ? , which bounds the expected time of the optimal policy to terminate the episode starting from any state . Finally , while the previous properties focus on the learning aspects of the algorithm , another important consideration is computational efficiency . It is desirable that a learning algorithm has run-time complexity at most polynomial in K , S , A , B ? , and T ? . All existing algorithms for online SSP , including the one proposed in this paper , meet such requirement . Related Work . Table 1 reviews the existing work on online learning in SSP . The setting was first studied by Tarbouriech et al . [ 2020a ] who gave a parameter-free algorithm with a Õ ( K3/2 ) regret guarantee . Rosenberg et al . [ 2020 ] then improved this result by deriving the first order-optimal algorithm with regret Õ ( B3/2 ? S √ AK ) in the parameter-free case and Õ ( B ? S √ AK ) if B ? is known ( to tune cost perturbation appropriately ) . Both approaches are model-optimistic,2 drawing inspiration from the ideas behind the UCRL2 algorithm [ Jaksch et al. , 2010 ] for average-reward MDPs . Concurrently to our work , Cohen et al . [ 2021 ] propose an algorithm for online SSP based on a blackbox reduction from SSP to finite-horizon MDPs . It successively tackles finite-horizon problems with horizon set to H = Ω ( T ? ) and costs augmented by a terminal cost set to cH ( s ) = Ω ( B ? I ( s 6= g ) ) , where g denotes the goal state . This finite-horizon construction guarantees that its optimal policy has a similar value function to the optimal policy in the original SSP instance up to a lower-order bias . Their algorithm comes with a regret bound of O ( B ? √ SAKL+ T 4 ? S 2AL5 ) , with L = log ( KT ? SAδ−1 ) ( with probability at least 1 − δ ) . It achieves a nearly minimax-optimal rate , however it relies on both T ? and B ? prior knowledge to tune the horizon and terminal cost in the reduction , respectively.3 2We refer the reader to Neu and Pike-Burke [ 2020 ] for details on the differences and interplay between model-optimistic and value-optimistic approaches . 3As mentioned by Cohen et al . [ 2021 , Remark 2 ] , in the case of positive costs lower bounded by cmin > 0 , their knowledge of T ? can be bypassed by replacing it with the upper bound T ? ≤ B ? /cmin . However , when generalizing from the cmin case to general costs with a perturbation argument , their regret guarantee worsens from Õ ( √ K + c−4min ) to Õ ( K 4/5 ) , because of the poor additive dependence on c−1min . Finally , all existing bounds contain lower-order dependencies either on T ? in the case of general costs , or on B ? /cmin in the case of positive costs lower bounded by cmin > 0 ( note that T ? ≤ B ? /cmin , which is one of the reasons why cmin can show up in existing bounds ) . As such , no existing analysis satisfies horizon-free properties for online SSP . Contributions . We summarize our main contributions as follows ( see also Table 1 ) : • We propose EB-SSP ( Exploration Bonus for SSP ) , a new algorithm for online SSP . It introduces a value-optimistic scheme to efficiently compute optimistic policies for SSP , by both perturbing the empirical costs with an exploration bonus and slightly biasing the empirical transitions towards reaching the goal from each state-action pair with positive probability . Under these biased transitions , all policies are in fact proper ( i.e. , they eventually reach the goal with probability 1 starting from any state ) . We decay the bias over time in a way that it only contributes to a lower-order regret term . See Sect . 3 for an overview of our algorithm and analysis . Note that EB-SSP is not based on a model-optimistic approach2 [ Tarbouriech et al. , 2020a , Rosenberg et al. , 2020 ] , and it does not rely on a reduction from SSP to finite-horizon [ Cohen et al. , 2021 ] ( i.e. , we operate at the level of the non-truncated SSP model ) ; • EB-SSP is the first algorithm to achieve the minimax regret rate of Õ ( B ? √ SAK ) while simulta- neously being parameter-free : it does not require to know nor estimate T ? , and it is able to bypass the knowledge of B ? at the cost of only logarithmic and lower-order contributions to the regret ; • EB-SSP is the first algorithm to achieve horizon-free regret for SSP in various cases : i ) positive costs , ii ) no almost-sure zero-cost cycles , and iii ) the general cost case when an order-accurate estimate of T ? is available ( i.e. , a value T ? such that T ? υ ≤ T ? ≤ λT ζ ? for some unknown constants υ , λ , ζ ≥ 1 is available ) . This property is especially relevant if T ? is much larger than B ? , which can occur in SSP models with very small instantaneous costs . Moreover , EB-SSP achieves its horizon-free guarantees while maintaining the minimax rate . For instance , under general costs when relying on T ? andB ? , its regret is Õ ( B ? √ SAK+B ? S 2A ) .4 To the best of our knowledge , EB-SSP yields the first set of ( nearly ) horizon-free bounds beyond the setting of finite-horizon MDPs . Additional Related Work . Planning in SSP : Early work by Bertsekas and Tsitsiklis [ 1991 ] , followed by [ e.g. , Bertsekas , 1995 , Bonet , 2007 , Kolobov et al. , 2011 , Bertsekas and Yu , 2013 , Guillot and Stauffer , 2020 ] , examine the planning problem in SSP , i.e. , how to compute an optimal policy when all parameters of the SSP model are known . Under mild assumptions , the optimal policy is deterministic and stationary and can be computed efficiently using standard planning techniques , e.g. , value iteration , policy iteration or linear programming . Regret minimization in MDPs : The exploration-exploitation dilemma in tabular MDPs has been extensively studied in finite-horizon [ e.g. , Azar et al. , 2017 , Jin et al. , 2018 , Zanette and Brunskill , 2019 , Efroni et al. , 2019 , Simchowitz and Jamieson , 2019 , Zhang et al. , 2020 , Neu and Pike-Burke , 2020 , Xu et al. , 2021 , Menard et al. , 2021 ] and infinite-horizon [ e.g. , Jaksch et al. , 2010 , Bartlett and Tewari , 2012 , Fruit et al. , 2018 , Wang et al. , 2020b , Qian et al. , 2019 , Wei et al. , 2020 ] . Other SSP-based settings : SSP with adversarial costs was investigated by Rosenberg and Mansour [ 2021 ] , Chen et al . [ 2021 ] , Chen and Luo [ 2021 ] .5 Tarbouriech et al . [ 2021 ] study the sample complexity of SSP with a generative model , as a standard regret-to-PAC conversion may not hold in SSP ( as opposed to finite-horizon ) . Exploration problems involving multiple goal states ( i.e. , multigoal SSP or goal-conditioned RL ) were analyzed by Lim and Auer [ 2012 ] , Tarbouriech et al . [ 2020b ] . | This paper investigates the problem of regret minimization of the stochastic shortest path, which is actively studied in the recent two years. The main contribution is a novel algorithm that enjoys nearly minimax optimal regret, matching the existing lower bound up to some log factors. Furthermore, the algorithm, as well as its variant, enjoy other desired properties, including parameter-free (does not require the knowledge of $B_\star$ or $T_\star$) and horizon-free (does not depend on $T_\star$) | science.mup_single_document_summarization.13293 | [
{
"content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nStochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret\n\nMain Content:\n√ SAK ) , where K is the number of episodes , S is the number of states , A is the number of actions , and B ? bounds the expected cumulative cost of the optimal policy from any state , thus closing the gap with the lower bound . Interestingly , EB-SSP obtains this result while being parameter-free , i.e. , it does not require any prior knowledge of B ? , nor of T ? , which bounds the expected time-to-goal of the optimal policy from any state . Furthermore , we illustrate various cases ( e.g. , positive costs , or general costs when an order-accurate estimate of T ? is available ) where the regret only contains a logarithmic dependence on T ? , thus yielding the first ( nearly ) horizon-free regret bound beyond the finite-horizon MDP setting . 1 Introduction . Stochastic shortest path ( SSP ) is a goal-oriented reinforcement learning ( RL ) setting where the agent aims to reach a predefined goal state while minimizing its total expected cost [ Bertsekas , 1995 ] . In particular , the interaction between the agent and the environment ends only when ( and if ) the goal state is reached , so the length of an episode is not predetermined ( nor bounded ) and it is influenced by the agent ’ s behavior . SSP includes both finite-horizon and discounted Markov Decision Processes ( MDPs ) as special cases . Moreover , many common RL problems can be cast under the SSP formulation , such as game playing ( e.g. , Atari games ) or navigation ( e.g. , Mujoco mazes ) . We study the online learning problem in the SSP setting ( online SSP in short ) , where both the transition dynamics and the cost function are initially unknown and the agent interacts with the environment through multiple episodes . The learning objective is to achieve a performance as close ∗equal contribution 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . as possible to the optimal policy π ? , that is , the agent should achieve low regret ( i.e. , the cumulative difference between the total cost accumulated across episodes by the agent and by the optimal policy ) . We identify three desirable properties for a learning algorithm in online SSP . • Desired property 1 : Minimax . The information-theoretic lower bound on the regret is Ω ( B ? √ SAK ) [ Rosenberg et al. , 2020 ] , where K is the number of episodes , S is the number of states , A is the number of actions , and B ? bounds the total expected cost of the optimal policy starting from any state ( assuming for simplicity that B ? ≥ 1 ) . An algorithm for online SSP is ( nearly ) minimax optimal if its regret is bounded by Õ ( B ? √ SAK ) , up to logarithmic factors and lower-order terms . • Desired property 2 : Parameter-free . Another relevant dimension is the amount of prior knowledge required by the algorithm . While the knowledge of S , A , and the cost ( or reward ) range [ 0 , 1 ] is standard across regret-minimization settings ( e.g. , finite-horizon , discounted , average-reward ) , the complexity of learning in SSP problems may be linked to SSP-specific quantities such as B ? and T ? , which denotes the expected time-to-goal of the optimal policy from any state . An algorithm for online SSP is parameter-free if it relies neither on T ? nor B ? prior knowledge . • Desired property 3 : Horizon-free . A core challenge in SSP is to trade off between minimizing costs and quickly reaching the goal state . This is accentuated when the instantaneous costs are small , i.e. , when there is a mismatch between B ? and T ? . Indeed , while B ? ≤ T ? always holds since the cost range is [ 0 , 1 ] , the gap between the two may be arbitrarily large ( see e.g. , the simple example of App . A ) . The lower bound stipulates that the regret does depend on B ? , while the “ time horizon ” of the problem , i.e. , T ? should a priori not impact the regret , even as a lower-order term . An algorithm for online SSP is ( nearly ) horizon-free if its regret depends only logarithmically on T ? . Our definition extends the property of so-called horizon-free bounds recently uncovered in finitehorizon MDPs with total reward bounded by 1 [ Wang et al. , 2020a , Zhang et al. , 2021a , b ] . These bounds depend only logarithmically on the horizon H , which is the number of time steps by which any policy terminates . Such notion of horizon would clearly be too strong in the more general class of SSP , where some ( even most ) policies may never reach the goal , thus having unbounded time horizon . A more adequate notion of horizon in SSP is T ? , which bounds the expected time of the optimal policy to terminate the episode starting from any state . Finally , while the previous properties focus on the learning aspects of the algorithm , another important consideration is computational efficiency . It is desirable that a learning algorithm has run-time complexity at most polynomial in K , S , A , B ? , and T ? . All existing algorithms for online SSP , including the one proposed in this paper , meet such requirement . Related Work . Table 1 reviews the existing work on online learning in SSP . The setting was first studied by Tarbouriech et al . [ 2020a ] who gave a parameter-free algorithm with a Õ ( K3/2 ) regret guarantee . Rosenberg et al . [ 2020 ] then improved this result by deriving the first order-optimal algorithm with regret Õ ( B3/2 ? S √ AK ) in the parameter-free case and Õ ( B ? S √ AK ) if B ? is known ( to tune cost perturbation appropriately ) . Both approaches are model-optimistic,2 drawing inspiration from the ideas behind the UCRL2 algorithm [ Jaksch et al. , 2010 ] for average-reward MDPs . Concurrently to our work , Cohen et al . [ 2021 ] propose an algorithm for online SSP based on a blackbox reduction from SSP to finite-horizon MDPs . It successively tackles finite-horizon problems with horizon set to H = Ω ( T ? ) and costs augmented by a terminal cost set to cH ( s ) = Ω ( B ? I ( s 6= g ) ) , where g denotes the goal state . This finite-horizon construction guarantees that its optimal policy has a similar value function to the optimal policy in the original SSP instance up to a lower-order bias . Their algorithm comes with a regret bound of O ( B ? √ SAKL+ T 4 ? S 2AL5 ) , with L = log ( KT ? SAδ−1 ) ( with probability at least 1 − δ ) . It achieves a nearly minimax-optimal rate , however it relies on both T ? and B ? prior knowledge to tune the horizon and terminal cost in the reduction , respectively.3 2We refer the reader to Neu and Pike-Burke [ 2020 ] for details on the differences and interplay between model-optimistic and value-optimistic approaches . 3As mentioned by Cohen et al . [ 2021 , Remark 2 ] , in the case of positive costs lower bounded by cmin > 0 , their knowledge of T ? can be bypassed by replacing it with the upper bound T ? ≤ B ? /cmin . However , when generalizing from the cmin case to general costs with a perturbation argument , their regret guarantee worsens from Õ ( √ K + c−4min ) to Õ ( K 4/5 ) , because of the poor additive dependence on c−1min . Finally , all existing bounds contain lower-order dependencies either on T ? in the case of general costs , or on B ? /cmin in the case of positive costs lower bounded by cmin > 0 ( note that T ? ≤ B ? /cmin , which is one of the reasons why cmin can show up in existing bounds ) . As such , no existing analysis satisfies horizon-free properties for online SSP . Contributions . We summarize our main contributions as follows ( see also Table 1 ) : • We propose EB-SSP ( Exploration Bonus for SSP ) , a new algorithm for online SSP . It introduces a value-optimistic scheme to efficiently compute optimistic policies for SSP , by both perturbing the empirical costs with an exploration bonus and slightly biasing the empirical transitions towards reaching the goal from each state-action pair with positive probability . Under these biased transitions , all policies are in fact proper ( i.e. , they eventually reach the goal with probability 1 starting from any state ) . We decay the bias over time in a way that it only contributes to a lower-order regret term . See Sect . 3 for an overview of our algorithm and analysis . Note that EB-SSP is not based on a model-optimistic approach2 [ Tarbouriech et al. , 2020a , Rosenberg et al. , 2020 ] , and it does not rely on a reduction from SSP to finite-horizon [ Cohen et al. , 2021 ] ( i.e. , we operate at the level of the non-truncated SSP model ) ; • EB-SSP is the first algorithm to achieve the minimax regret rate of Õ ( B ? √ SAK ) while simulta- neously being parameter-free : it does not require to know nor estimate T ? , and it is able to bypass the knowledge of B ? at the cost of only logarithmic and lower-order contributions to the regret ; • EB-SSP is the first algorithm to achieve horizon-free regret for SSP in various cases : i ) positive costs , ii ) no almost-sure zero-cost cycles , and iii ) the general cost case when an order-accurate estimate of T ? is available ( i.e. , a value T ? such that T ? υ ≤ T ? ≤ λT ζ ? for some unknown constants υ , λ , ζ ≥ 1 is available ) . This property is especially relevant if T ? is much larger than B ? , which can occur in SSP models with very small instantaneous costs . Moreover , EB-SSP achieves its horizon-free guarantees while maintaining the minimax rate . For instance , under general costs when relying on T ? andB ? , its regret is Õ ( B ? √ SAK+B ? S 2A ) .4 To the best of our knowledge , EB-SSP yields the first set of ( nearly ) horizon-free bounds beyond the setting of finite-horizon MDPs . Additional Related Work . Planning in SSP : Early work by Bertsekas and Tsitsiklis [ 1991 ] , followed by [ e.g. , Bertsekas , 1995 , Bonet , 2007 , Kolobov et al. , 2011 , Bertsekas and Yu , 2013 , Guillot and Stauffer , 2020 ] , examine the planning problem in SSP , i.e. , how to compute an optimal policy when all parameters of the SSP model are known . Under mild assumptions , the optimal policy is deterministic and stationary and can be computed efficiently using standard planning techniques , e.g. , value iteration , policy iteration or linear programming . Regret minimization in MDPs : The exploration-exploitation dilemma in tabular MDPs has been extensively studied in finite-horizon [ e.g. , Azar et al. , 2017 , Jin et al. , 2018 , Zanette and Brunskill , 2019 , Efroni et al. , 2019 , Simchowitz and Jamieson , 2019 , Zhang et al. , 2020 , Neu and Pike-Burke , 2020 , Xu et al. , 2021 , Menard et al. , 2021 ] and infinite-horizon [ e.g. , Jaksch et al. , 2010 , Bartlett and Tewari , 2012 , Fruit et al. , 2018 , Wang et al. , 2020b , Qian et al. , 2019 , Wei et al. , 2020 ] . Other SSP-based settings : SSP with adversarial costs was investigated by Rosenberg and Mansour [ 2021 ] , Chen et al . [ 2021 ] , Chen and Luo [ 2021 ] .5 Tarbouriech et al . [ 2021 ] study the sample complexity of SSP with a generative model , as a standard regret-to-PAC conversion may not hold in SSP ( as opposed to finite-horizon ) . Exploration problems involving multiple goal states ( i.e. , multigoal SSP or goal-conditioned RL ) were analyzed by Lim and Auer [ 2012 ] , Tarbouriech et al . [ 2020b ] .",
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"content": "This paper investigates the problem of regret minimization of the stochastic shortest path, which is actively studied in the recent two years. The main contribution is a novel algorithm that enjoys nearly minimax optimal regret, matching the existing lower bound up to some log factors. Furthermore, the algorithm, as well as its variant, enjoy other desired properties, including parameter-free (does not require the knowledge of $B_\\star$ or $T_\\star$) and horizon-free (does not depend on $T_\\star$)",
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science.discomat_te | | Heating rate (a)Kmin-1 | Tg (K) | Tc1 (K) | Tc2 (K) |
| 3 | 585 | 633 | 673 |
| 5 | 587 | 637 | 678 |
| 10 | 591 | 643 | 686 |
| 15 | 593 | 647 | 689 |
| 20 | 595 | 650 | 692 |
Caption: Thermal parameters (Tg, Tc1, Tc2) as obtained from the non-isothermal DSC scans (Fig. 1) at various heating rates for 66.7Li2O-33.3P2O5 mol% glass.
Examine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:
{"value": "xx", "type": "Other"}
{"value": "xx", "type": "Glass_Compound_Amount", "constituent": "xx", "unit": "xx", "material": "xx"}
Process each cell sequentially, and for any unknown attributes, use "xx" as a placeholder. Each JSON should be on a new line.
Output: | {"value": "3", "type": "Other"}
{"value": "585", "type": "Other"}
{"value": "633", "type": "Other"}
{"value": "673", "type": "Other"}
{"value": "5", "type": "Other"}
{"value": "587", "type": "Other"}
{"value": "637", "type": "Other"}
{"value": "678", "type": "Other"}
{"value": "10", "type": "Other"}
{"value": "591", "type": "Other"}
{"value": "643", "type": "Other"}
{"value": "686", "type": "Other"}
{"value": "15", "type": "Other"}
{"value": "593", "type": "Other"}
{"value": "647", "type": "Other"}
{"value": "689", "type": "Other"}
{"value": "20", "type": "Other"}
{"value": "595", "type": "Other"}
{"value": "650", "type": "Other"}
{"value": "692", "type": "Other"} | science.discomat_te.197 | [
{
"content": "| Heating rate (a)Kmin-1 | Tg (K) | Tc1 (K) | Tc2 (K) |\n| 3 | 585 | 633 | 673 |\n| 5 | 587 | 637 | 678 |\n| 10 | 591 | 643 | 686 |\n| 15 | 593 | 647 | 689 |\n| 20 | 595 | 650 | 692 |\n\nCaption: Thermal parameters (Tg, Tc1, Tc2) as obtained from the non-isothermal DSC scans (Fig. 1) at various heating rates for 66.7Li2O-33.3P2O5 mol% glass.\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:",
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{
"content": "{\"value\": \"3\", \"type\": \"Other\"}\n{\"value\": \"585\", \"type\": \"Other\"}\n{\"value\": \"633\", \"type\": \"Other\"}\n{\"value\": \"673\", \"type\": \"Other\"}\n{\"value\": \"5\", \"type\": \"Other\"}\n{\"value\": \"587\", \"type\": \"Other\"}\n{\"value\": \"637\", \"type\": \"Other\"}\n{\"value\": \"678\", \"type\": \"Other\"}\n{\"value\": \"10\", \"type\": \"Other\"}\n{\"value\": \"591\", \"type\": \"Other\"}\n{\"value\": \"643\", \"type\": \"Other\"}\n{\"value\": \"686\", \"type\": \"Other\"}\n{\"value\": \"15\", \"type\": \"Other\"}\n{\"value\": \"593\", \"type\": \"Other\"}\n{\"value\": \"647\", \"type\": \"Other\"}\n{\"value\": \"689\", \"type\": \"Other\"}\n{\"value\": \"20\", \"type\": \"Other\"}\n{\"value\": \"595\", \"type\": \"Other\"}\n{\"value\": \"650\", \"type\": \"Other\"}\n{\"value\": \"692\", \"type\": \"Other\"}",
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science.evidence_inference | Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:
- Treatment: The specific medical procedure or drug being evaluated.
- Control: The standard or placebo used for comparison. If absent, use `null`.
- Result: The health outcome being assessed.
- Impact: The observed effect of the treatment on the result, categorized as: ("increased", "decreased", "no change").
- Justification: The supporting details for the observed impact.
Format the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].
Abstract:
ABSTRACT.BACKGROUND: Metformin has been used for the treatment of type 2 diabetes by suppressing hepatic gluconeogenesis. It has been shown that the subclinical inflammatory responses play important roles in the pathogenesis of type 2 diabetes. In the present study, we determined the effects of metformin on the levels of pro-inflammatory cytokines (i.e., IL-6, TNF-α, and MCP-1) and anti-inflammatory mediator IL-10 in blood and urine of patients with type 2 diabetes. There were 210 patients with type 2 diabetes, which were randomized into metformin (n = 112) and non-metformin (gliclazide, acarbose, and repaglinide, n = 98) groups. The levels of cytokines were measured by the ELISA.
ABSTRACT.RESULTS: We found that metformin reduced the levels of IL-6 in blood and MCP-1 in urine, but increased IL-10 levels in blood of patients with type 2 diabetes. There were no significant differences of TNF-α between metformin and non-metformin groups. Furthermore, compared to individual drug treatment, metformin significantly reduced the levels of serum IL-6 and TNF-α, as well as urine MCP-1. When the patients were stratified based on the durations and doses of metformin, we found that there was only change (i.e., increase) in serum IL-10 levels in patients with metformin for more than 1 year compared to treatment for less than 1 year. Metformin (1.5 g) treatment reduced the urinary levels of MCP-1 as compared with dose of 1.0 g in patients with type 2 diabetes.
ABSTRACT.CONCLUSION: Metformin reduces inflammatory responses without influence on renal function in type 2 diabetic patients.
Extracted Data: | [["Metformin", "Control", "Monocyte chemoattractant protein-1", "significantly decreased", "We found that metformin reduced the levels of IL-6 in blood and MCP-1 in urine, but increased IL-10 levels in blood of patients with type 2 diabetes."], ["Metformin", "Control", "Monocyte chemoattractant protein-1", "significantly decreased", "Furthermore, compared to individual drug treatment, metformin significantly reduced the levels of serum IL-6 and TNF-\u03b1, as well as urine MCP-1."]] | science.evidence_inference.114 | [
{
"content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.BACKGROUND: Metformin has been used for the treatment of type 2 diabetes by suppressing hepatic gluconeogenesis. It has been shown that the subclinical inflammatory responses play important roles in the pathogenesis of type 2 diabetes. In the present study, we determined the effects of metformin on the levels of pro-inflammatory cytokines (i.e., IL-6, TNF-α, and MCP-1) and anti-inflammatory mediator IL-10 in blood and urine of patients with type 2 diabetes. There were 210 patients with type 2 diabetes, which were randomized into metformin (n = 112) and non-metformin (gliclazide, acarbose, and repaglinide, n = 98) groups. The levels of cytokines were measured by the ELISA.\n\nABSTRACT.RESULTS: We found that metformin reduced the levels of IL-6 in blood and MCP-1 in urine, but increased IL-10 levels in blood of patients with type 2 diabetes. There were no significant differences of TNF-α between metformin and non-metformin groups. Furthermore, compared to individual drug treatment, metformin significantly reduced the levels of serum IL-6 and TNF-α, as well as urine MCP-1. When the patients were stratified based on the durations and doses of metformin, we found that there was only change (i.e., increase) in serum IL-10 levels in patients with metformin for more than 1 year compared to treatment for less than 1 year. Metformin (1.5 g) treatment reduced the urinary levels of MCP-1 as compared with dose of 1.0 g in patients with type 2 diabetes.\n\nABSTRACT.CONCLUSION: Metformin reduces inflammatory responses without influence on renal function in type 2 diabetic patients.\n\nExtracted Data:",
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"content": "[[\"Metformin\", \"Control\", \"Monocyte chemoattractant protein-1\", \"significantly decreased\", \"We found that metformin reduced the levels of IL-6 in blood and MCP-1 in urine, but increased IL-10 levels in blood of patients with type 2 diabetes.\"], [\"Metformin\", \"Control\", \"Monocyte chemoattractant protein-1\", \"significantly decreased\", \"Furthermore, compared to individual drug treatment, metformin significantly reduced the levels of serum IL-6 and TNF-\\u03b1, as well as urine MCP-1.\"]]",
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science.evidence_inference | Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:
- Treatment: The specific medical procedure or drug being evaluated.
- Control: The standard or placebo used for comparison. If absent, use `null`.
- Result: The health outcome being assessed.
- Impact: The observed effect of the treatment on the result, categorized as: ("increased", "decreased", "no change").
- Justification: The supporting details for the observed impact.
Format the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].
Abstract:
ABSTRACT: People with trichotillomania often have persistent negative beliefs about giving into one's habit. Central in the present study was the hypothesis that the follow-up effects of cognitive therapy (CT), in which these negative beliefs are directly addressed, are better compared to the follow-up effects of behaviour therapy (BT). Fifty-six trichotillomania patients were randomly assigned to either six sessions CT or BT. Forty-eight completed their treatment. Follow-up measurements took place after a 3 months treatment-free period, and at 12 and 24 months. CT and BT both resulted in clear reductions of trichotillomania symptoms (severity, urge, inability to resist, and negative beliefs) immediately after treatment. There were no differences between the groups. Following the treatment-free period, there was a reoccurrence of symptoms. In contrast to our expectation, we failed to show that CT compared to BT resulted in lower relapse rates after the treatment-free period.
Extracted Data: | [["six sessions of cognitive therapy (CT)", "six sessions of behaviour therapy (BT)", "MGHHS Massachusetts General Hospital Hair pulling Scale", "no significant difference", "CT and BT both resulted in clear reductions of trichotillomania symptoms (severity, urge, inability to resist, and negative beliefs) immediately after treatment. There were no differences between the groups."], ["six sessions of cognitive therapy (CT)", "six sessions of behaviour therapy (BT)", "SURF-urge", "no significant difference", "CT and BT both resulted in clear reductions of trichotillomania symptoms (severity, urge, inability to resist, and negative beliefs) immediately after treatment. There were no differences between the groups."]] | science.evidence_inference.203 | [
{
"content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT: People with trichotillomania often have persistent negative beliefs about giving into one's habit. Central in the present study was the hypothesis that the follow-up effects of cognitive therapy (CT), in which these negative beliefs are directly addressed, are better compared to the follow-up effects of behaviour therapy (BT). Fifty-six trichotillomania patients were randomly assigned to either six sessions CT or BT. Forty-eight completed their treatment. Follow-up measurements took place after a 3 months treatment-free period, and at 12 and 24 months. CT and BT both resulted in clear reductions of trichotillomania symptoms (severity, urge, inability to resist, and negative beliefs) immediately after treatment. There were no differences between the groups. Following the treatment-free period, there was a reoccurrence of symptoms. In contrast to our expectation, we failed to show that CT compared to BT resulted in lower relapse rates after the treatment-free period.\n\nExtracted Data:",
"role": "user"
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"content": "[[\"six sessions of cognitive therapy (CT)\", \"six sessions of behaviour therapy (BT)\", \"MGHHS Massachusetts General Hospital Hair pulling Scale\", \"no significant difference\", \"CT and BT both resulted in clear reductions of trichotillomania symptoms (severity, urge, inability to resist, and negative beliefs) immediately after treatment. There were no differences between the groups.\"], [\"six sessions of cognitive therapy (CT)\", \"six sessions of behaviour therapy (BT)\", \"SURF-urge\", \"no significant difference\", \"CT and BT both resulted in clear reductions of trichotillomania symptoms (severity, urge, inability to resist, and negative beliefs) immediately after treatment. There were no differences between the groups.\"]]",
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science.scifact_entailment | Determine the relationship between them and generate a JSON response. Scientific Claim:
The tip of the inner tube of the toxic type VI secretion system (T6SS) antibacterial effector in Escherichia coli (E. coli) is decorated with VgrG and PAAR proteins.
Research Abstract:
The bacterial type VI secretion system (T6SS) is an organelle that is structurally and mechanistically analogous to an intracellular membrane-attached contractile phage tail.
Recent studies determined that a rapid conformational change in the structure of a sheath protein complex propels T6SS spike and tube components along with antibacterial and antieukaryotic effectors out of predatory T6SS(+) cells and into prey cells.
The contracted organelle is then recycled in an ATP-dependent process.
T6SS is regulated at transcriptional and posttranslational levels, the latter involving detection of membrane perturbation in some species.
In addition to directly targeting eukaryotic cells, the T6SS can also target other bacteria coinfecting a mammalian host, highlighting the importance of the T6SS not only for bacterial survival in environmental ecosystems, but also in the context of infection and disease.
This review highlights these and other advances in our understanding of the structure, mechanical function, assembly, and regulation of the T6SS. | {"verdict": "NEI", "evidence": []} | science.scifact_entailment.827 | [
{
"content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nThe tip of the inner tube of the toxic type VI secretion system (T6SS) antibacterial effector in Escherichia coli (E. coli) is decorated with VgrG and PAAR proteins.\n\nResearch Abstract:\nThe bacterial type VI secretion system (T6SS) is an organelle that is structurally and mechanistically analogous to an intracellular membrane-attached contractile phage tail.\nRecent studies determined that a rapid conformational change in the structure of a sheath protein complex propels T6SS spike and tube components along with antibacterial and antieukaryotic effectors out of predatory T6SS(+) cells and into prey cells.\nThe contracted organelle is then recycled in an ATP-dependent process.\nT6SS is regulated at transcriptional and posttranslational levels, the latter involving detection of membrane perturbation in some species.\nIn addition to directly targeting eukaryotic cells, the T6SS can also target other bacteria coinfecting a mammalian host, highlighting the importance of the T6SS not only for bacterial survival in environmental ecosystems, but also in the context of infection and disease.\nThis review highlights these and other advances in our understanding of the structure, mechanical function, assembly, and regulation of the T6SS.",
"role": "user"
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{
"content": "{\"verdict\": \"NEI\", \"evidence\": []}",
"role": "assistant"
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science.bioasq_list_qa | Examine the following excerpts from biomedical literature. Your objective is to extract relevant information to answer the posed question. Format your answer as a JSON array.
Context: Thulium laser transurethral vaporesection of the prostate versus transurethral resection of the prostate for men with lower urinary tract symptoms or urinary retention (UNBLOCS): a randomised controlled trial.
BACKGROUND: Transurethral resection of the prostate (TURP) is the standard operation for benign prostatic obstruction. Thulium laser transurethral vaporesection of the prostate (ThuVARP) is a technique with suggested advantages over TURP, including reduced complications and hospital stay. We aimed to investigate TURP versus ThuVARP in men with lower urinary tract symptoms or urinary retention secondary to benign prostatic obstruction.METHODS: In this randomised, blinded, parallel-group, pragmatic equivalence trial, men in seven UK hospitals with bothersome lower urinary tract symptoms or urinary retention secondary to benign prostatic obstruction were randomly assigned (1:1) at the point of surgery to receive ThuVARP or TURP.
INTERPRETATION: TURP and ThuVARP were equivalent for urinary symptom improvement (IPSS) 12-months post-surgery, and TURP was superior for Qmax.
The cost-effectiveness of transurethral resection of the prostate vs thulium laser transurethral vaporesection of the prostate in the UNBLOCS randomised controlled trial for benign prostatic obstruction.
OBJECTIVE: To determine the cost-effectiveness of the current 'gold standard' operation of transurethral resection of the prostate (TURP) compared to the new laser technique of thulium laser transurethral vaporesection of the prostate (ThuVARP) in men with benign prostatic obstruction (BPO) within the UK National Health Service (NHS).
Thulium laser transurethral vaporesection versus transurethral resection of the prostate for benign prostatic obstruction: the UNBLOCS RCT.
A randomised controlled trial to determine the clinical and cost effectiveness of thulium laser transurethral vaporesection of the prostate (ThuVARP) versus transurethral resection of the prostate (TURP) in the National Health Service (NHS) - the UNBLOCS trial: a study protocol for a randomised controlled trial.
The cost-effectiveness of transurethral resection of the prostate vs thulium laser transurethral vaporesection of the prostate in the UNBLOCS randomised controlled trial for benign prostatic obstruction
Question: Which treatments were compared in the UNBLOCS trial? | ["thulium laser transurethral vaporesection of the prostate", "transurethral resection of the prostate"] | science.bioasq_list_qa.787 | [
{
"content": "Examine the following excerpts from biomedical literature. Your objective is to extract relevant information to answer the posed question. Format your answer as a JSON array.\n\nContext: Thulium laser transurethral vaporesection of the prostate versus transurethral resection of the prostate for men with lower urinary tract symptoms or urinary retention (UNBLOCS): a randomised controlled trial.\nBACKGROUND: Transurethral resection of the prostate (TURP) is the standard operation for benign prostatic obstruction. Thulium laser transurethral vaporesection of the prostate (ThuVARP) is a technique with suggested advantages over TURP, including reduced complications and hospital stay. We aimed to investigate TURP versus ThuVARP in men with lower urinary tract symptoms or urinary retention secondary to benign prostatic obstruction.METHODS: In this randomised, blinded, parallel-group, pragmatic equivalence trial, men in seven UK hospitals with bothersome lower urinary tract symptoms or urinary retention secondary to benign prostatic obstruction were randomly assigned (1:1) at the point of surgery to receive ThuVARP or TURP.\nINTERPRETATION: TURP and ThuVARP were equivalent for urinary symptom improvement (IPSS) 12-months post-surgery, and TURP was superior for Qmax.\nThe cost-effectiveness of transurethral resection of the prostate vs thulium laser transurethral vaporesection of the prostate in the UNBLOCS randomised controlled trial for benign prostatic obstruction.\nOBJECTIVE: To determine the cost-effectiveness of the current 'gold standard' operation of transurethral resection of the prostate (TURP) compared to the new laser technique of thulium laser transurethral vaporesection of the prostate (ThuVARP) in men with benign prostatic obstruction (BPO) within the UK National Health Service (NHS).\nThulium laser transurethral vaporesection versus transurethral resection of the prostate for benign prostatic obstruction: the UNBLOCS RCT.\nA randomised controlled trial to determine the clinical and cost effectiveness of thulium laser transurethral vaporesection of the prostate (ThuVARP) versus transurethral resection of the prostate (TURP) in the National Health Service (NHS) - the UNBLOCS trial: a study protocol for a randomised controlled trial.\nThe cost-effectiveness of transurethral resection of the prostate vs thulium laser transurethral vaporesection of the prostate in the UNBLOCS randomised controlled trial for benign prostatic obstruction\n\nQuestion: Which treatments were compared in the UNBLOCS trial?",
"role": "user"
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{
"content": "[\"thulium laser transurethral vaporesection of the prostate\", \"transurethral resection of the prostate\"]",
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science.discomat_te | | Melting times (min) | Theoretical molar [F] (x10-3) | Experimental molar [F] (x10-3) (+-3%) | %F kept in final sample after melting |
| 5 | 2.02 | 1.92 | 95 |
| 10 | 2.00 | 1.84 | 92 |
| 15 | 2.04 | 1.63 | 80 |
| 30 | 2.00 | 1.44 | 72 |
Caption: Results obtained from fluorine analyses on 60PbGeO3-20PbF2-20CdF2 glasses melted at 800degC in different melting times
Examine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:
{"value": "xx", "type": "Other"}
{"value": "xx", "type": "Glass_Compound_Amount", "constituent": "xx", "unit": "xx", "material": "xx"}
Process each cell sequentially, and for any unknown attributes, use "xx" as a placeholder. Each JSON should be on a new line.
Output: | {"value": "5", "type": "Other"}
{"value": "2.02", "type": "Other"}
{"value": "1.92", "type": "Other"}
{"value": "95", "type": "Other"}
{"value": "10", "type": "Other"}
{"value": "2.00", "type": "Other"}
{"value": "1.84", "type": "Other"}
{"value": "92", "type": "Other"}
{"value": "15", "type": "Other"}
{"value": "2.04", "type": "Other"}
{"value": "1.63", "type": "Other"}
{"value": "80", "type": "Other"}
{"value": "30", "type": "Other"}
{"value": "2.00", "type": "Other"}
{"value": "1.44", "type": "Other"}
{"value": "72", "type": "Other"} | science.discomat_te.111 | [
{
"content": "| Melting times (min) | Theoretical molar [F] (x10-3) | Experimental molar [F] (x10-3) (+-3%) | %F kept in final sample after melting |\n| 5 | 2.02 | 1.92 | 95 |\n| 10 | 2.00 | 1.84 | 92 |\n| 15 | 2.04 | 1.63 | 80 |\n| 30 | 2.00 | 1.44 | 72 |\n\nCaption: Results obtained from fluorine analyses on 60PbGeO3-20PbF2-20CdF2 glasses melted at 800degC in different melting times\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:",
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"content": "{\"value\": \"5\", \"type\": \"Other\"}\n{\"value\": \"2.02\", \"type\": \"Other\"}\n{\"value\": \"1.92\", \"type\": \"Other\"}\n{\"value\": \"95\", \"type\": \"Other\"}\n{\"value\": \"10\", \"type\": \"Other\"}\n{\"value\": \"2.00\", \"type\": \"Other\"}\n{\"value\": \"1.84\", \"type\": \"Other\"}\n{\"value\": \"92\", \"type\": \"Other\"}\n{\"value\": \"15\", \"type\": \"Other\"}\n{\"value\": \"2.04\", \"type\": \"Other\"}\n{\"value\": \"1.63\", \"type\": \"Other\"}\n{\"value\": \"80\", \"type\": \"Other\"}\n{\"value\": \"30\", \"type\": \"Other\"}\n{\"value\": \"2.00\", \"type\": \"Other\"}\n{\"value\": \"1.44\", \"type\": \"Other\"}\n{\"value\": \"72\", \"type\": \"Other\"}",
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science.evidence_inference | Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:
- Treatment: The specific medical procedure or drug being evaluated.
- Control: The standard or placebo used for comparison. If absent, use `null`.
- Result: The health outcome being assessed.
- Impact: The observed effect of the treatment on the result, categorized as: ("increased", "decreased", "no change").
- Justification: The supporting details for the observed impact.
Format the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].
Abstract:
ABSTRACT.AIM: The aim of this study was to examine the acceptability and efficacy of interactive short message service (SMS) in improving medication adherence in antiretroviral treatment (ART)-naïve individuals living with HIV/AIDS in Hengyang, Hunan, China.
ABSTRACT.BACKGROUND: SMS via mobile phone has emerged as a potential tool for improving ART adherence. However, most studies used SMS only as a medication reminder, with few studies exploring the effect of comprehensive, interactive SMS.
ABSTRACT.PATIENTS AND METHODS: In a randomized controlled trial, 100 HIV-positive patients on ART for <3 months were randomized into control or intervention arm. Participants in the control group received routine standard instruction for ART medication in the HIV clinics, while the intervention group received 6 months of an SMS intervention in addition to the standard care. A total of 124 text messages within 6 modules were edited, preinstalled, and sent to participants according to personalized schedules. Knowledge (of HIV and HIV medications), self-reported antiretroviral adherence (Visual Analog Scale [VAS] and Community Programs for Clinical Research on AIDS [CPCRA] Antiretroviral Medication Self-Report), and CD4 count were assessed at baseline and immediate post-intervention. Intervention participants were interviewed after completion of the study about their satisfaction with and acceptability of the SMS intervention.
ABSTRACT.RESULTS: Baseline assessments were comparable between arms. Repeated-measures analysis showed that both HIV-related and ART medication knowledge of the intervention group showed better improvement over time than those of the control group after the intervention (P<0.0001). For the adherence measures, compared with the control group, participants in the intervention group had significantly higher VAS mean score (Z=2.735, P=0.006) and lower suboptimal adherence rate (Z=2.208, P=0.027) at the end of the study. The intervention had no effect on CD4 cell count. Almost all (96%) intervention participants reported satisfaction or high satisfaction with the SMS intervention, with 74% desiring to continue to receive the SMS intervention. The preferred frequency of messages was 1–2 messages per week.
ABSTRACT.CONCLUSION: An interactive SMS intervention with comprehensive content shows promising efficacy in promoting medication adherence in ART-naïve individuals. Future work might further refine its ability to optimally tailor the intervention for individual preferences.
Extracted Data: | [["interactive short message service (SMS)", "standard care", "HIV-related and ART medication knowledge", "significantly increased", "Repeated-measures analysis showed that both HIV-related and ART medication knowledge of the intervention group showed better improvement over time than those of the control group after the intervention (P\u003c0.0001)."], ["interactive short message service (SMS)", "standard care", "VAS mean score", "significantly increased", "For the adherence measures, compared with the control group, participants in the intervention group had significantly higher VAS mean score (Z=2.735, P=0.006) and lower suboptimal adherence rate (Z=2.208, P=0.027) at the end of the study."]] | science.evidence_inference.76 | [
{
"content": "Analyze the provided abstract from a clinical trial. Extract relevant data points and compile them into a structured JSON format. Focus on identifying key components such as:\n\n- Treatment: The specific medical procedure or drug being evaluated.\n- Control: The standard or placebo used for comparison. If absent, use `null`.\n- Result: The health outcome being assessed.\n- Impact: The observed effect of the treatment on the result, categorized as: (\"increased\", \"decreased\", \"no change\").\n- Justification: The supporting details for the observed impact.\n\nFormat the extracted information as a JSON array, with each entry containing the elements in this sequence: [<treatment>, <control>, <result>, <impact>, <justification>].\n\nAbstract:\nABSTRACT.AIM: The aim of this study was to examine the acceptability and efficacy of interactive short message service (SMS) in improving medication adherence in antiretroviral treatment (ART)-naïve individuals living with HIV/AIDS in Hengyang, Hunan, China.\n\nABSTRACT.BACKGROUND: SMS via mobile phone has emerged as a potential tool for improving ART adherence. However, most studies used SMS only as a medication reminder, with few studies exploring the effect of comprehensive, interactive SMS.\n\nABSTRACT.PATIENTS AND METHODS: In a randomized controlled trial, 100 HIV-positive patients on ART for <3 months were randomized into control or intervention arm. Participants in the control group received routine standard instruction for ART medication in the HIV clinics, while the intervention group received 6 months of an SMS intervention in addition to the standard care. A total of 124 text messages within 6 modules were edited, preinstalled, and sent to participants according to personalized schedules. Knowledge (of HIV and HIV medications), self-reported antiretroviral adherence (Visual Analog Scale [VAS] and Community Programs for Clinical Research on AIDS [CPCRA] Antiretroviral Medication Self-Report), and CD4 count were assessed at baseline and immediate post-intervention. Intervention participants were interviewed after completion of the study about their satisfaction with and acceptability of the SMS intervention.\n\nABSTRACT.RESULTS: Baseline assessments were comparable between arms. Repeated-measures analysis showed that both HIV-related and ART medication knowledge of the intervention group showed better improvement over time than those of the control group after the intervention (P<0.0001). For the adherence measures, compared with the control group, participants in the intervention group had significantly higher VAS mean score (Z=2.735, P=0.006) and lower suboptimal adherence rate (Z=2.208, P=0.027) at the end of the study. The intervention had no effect on CD4 cell count. Almost all (96%) intervention participants reported satisfaction or high satisfaction with the SMS intervention, with 74% desiring to continue to receive the SMS intervention. The preferred frequency of messages was 1–2 messages per week.\n\nABSTRACT.CONCLUSION: An interactive SMS intervention with comprehensive content shows promising efficacy in promoting medication adherence in ART-naïve individuals. Future work might further refine its ability to optimally tailor the intervention for individual preferences.\n\nExtracted Data:",
"role": "user"
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{
"content": "[[\"interactive short message service (SMS)\", \"standard care\", \"HIV-related and ART medication knowledge\", \"significantly increased\", \"Repeated-measures analysis showed that both HIV-related and ART medication knowledge of the intervention group showed better improvement over time than those of the control group after the intervention (P\\u003c0.0001).\"], [\"interactive short message service (SMS)\", \"standard care\", \"VAS mean score\", \"significantly increased\", \"For the adherence measures, compared with the control group, participants in the intervention group had significantly higher VAS mean score (Z=2.735, P=0.006) and lower suboptimal adherence rate (Z=2.208, P=0.027) at the end of the study.\"]]",
"role": "assistant"
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science.mup_single_document_summarization | Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.
Title of the Paper:
SketchODE: Learning neural sketch representation in continuous time
Main Content:
1 INTRODUCTION Drawing-based communications such as sketches , writing , and diagrams come naturally to humans and have been used in some form since ancient times . Modeling such data is becoming an increasingly important and topical challenge area for machine learning systems aiming to interpret and simulate human creative expression . Such chirographic structures are challenging to interpret and generate due to their complex and unstructured nature . However recent progress has been strong , thanks to the advances in learning latent representations for sequences ( Bowman et al. , 2016 ; Graves , 2013 ; Srivastava et al. , 2015 ) . In particular , with the advent of variational sequence-to-sequence generative models ( Srivastava et al. , 2015 ; Bowman et al. , 2016 ) and the collection of largescale datasets ( Gervais et al. , 2020 ; Ha & Eck , 2018 ; Ge et al. , 2021 ) , we have seen a surge of advancements ( Ha & Eck , 2018 ; Aksan et al. , 2020 ) in this direction . Neverthe- less , a key missing link is the fact that drawing is intrinsically continuous in time , as is the resulting drawn artefact , when considered as a sequence rather than a raster image . The dominant approach to model free-hand sketches , popularized by Ha & Eck ( 2018 ) , has been to use a discrete-step recurrent network for capturing the latent process p ( h [ n ] |h [ n− 1 ] ) with discrete step n , and a complex observation model p ( s [ n ] | h [ n ] ) to explain local structure . The large body of approaches built upon this have a critical flaw : they ignore the very nature of the data , i.e . drawn structures are inherently continuous in time ( Refer to Fig . 1 ) . While a few attempts have been made to use continuous-time constructs like Bézier curves ( Das et al. , 2020 ; 2021 ) , their inconvenient mathematical form affect the representational power as well as training dynamics . The reason for such an absence of continuous-time models is the lack of fundamental tools for handling continuous time data . Lately however , the introduction of Neural Ordinary Differential Equations by Chen et al . ( 2018 ) followed by numerous extensions ( Dupont et al. , 2019 ; Yildiz et al. , 2019 ; Kidger et al. , 2020 ) have opened the possibility of building powerful models that can natively represent continuous time data such as handwriting , sketch etc . In this paper , we represent chirographic drawing data like handwriting , sketches etc . as continuoustime function s ( t ) and model the underlying latent process also as continuous-time latent function h ( t ) . Specifically , we propose a framework for capturing the derivative ( potentially higher order ) of the latent process dKh ( t ) dtK with parameterized neural networks . At inference , a solution trajectory ŝ ( t ) is computed from a given Initial Value Problem ( IVP ) that includes a learned ODE dynamics and an initial hidden state h0 , h ( t = 0 ) . An obvious advantage of the functional representation is its arbitrary spatial resolution , i.e . we can retrieve a structure at any spatial resolution by sampling the function at appropriate rate . Moreover , with a functional representation , we can systematically upgrade the representation along time with additional properties ( e.g . line thickness , color etc ) . So far , parameterized ODE dynamics models have largely been treated as a “ replacement for ResNets ” ( Massaroli et al. , 2020 ) where the intermediate states are of little importance . While the capability of ODE models are beginning to be tested in time-series data ( Kidger et al. , 2020 ; Morrill et al. , 2021 ) , they still remain largely unexplored . Following the introduction of Neural ODEs by Chen et al . ( 2018 ) , methods have been proposed in order to regularize the solution trajectory to be as simple as possible ( Kelly et al. , 2020 ; Finlay et al. , 2020 ) . However , time-series data such as chirography are entirely the opposite . The dynamics model , and consequently the solution trajectory should be as flexible as possible in order to learn the high degree of local and global variations with time often exhibited in such data . We increase the flexibility of Neural ODE models by introducing a new class of parameterized dynamics functions . Our experiments show that this is crucial in order to model chirographic data with satisfactory fidelity . We also introduce a new data augmentation method particularly geared towards ODE models and continuous-time sequences . Finally , we propose a deterministic autoencoder with a global latent variable z that exhibits some generative model properties by virtue of inherent continuity . Our final model , SketchODE , is similar to SketchRNN ( Ha & Eck , 2018 ) in terms of high-level design but lifts it to the realm of continuoustime . More generally , our model is the first continuous time Seq2Seq architecture . We explore some of the noteworthy features that differentiate SketchODE from discrete-time Seq2Seq analogoues . An inductive bias for continuity : Discrete-time sequence models ( Ha & Eck , 2018 ) need to use their capacity to model both global structure and temporal continuity in the data . ODE models on the other hand , have a strong intrinsic architectural bias towards temporal continuity . Sequences generated using a well-behaved ODE dynamics function are guaranteed to be continuous . Since they need not learn the continuity bias from scratch , ODE models are more data-efficient and are able to use majority of their capacity for modelling higher level structures . We even demonstrate that our SketchODE supports meaningful 1-shot learning when fine-tuned . Deterministic Autoencoding with Structured Latent Space : A surprising property of Seq2Seq ODE models is the ability to perform latent space interpolation and sampling without the necessity of imposing a prior distribution p ( z ) on the variational posterior qφ ( z|s ) . This property is also a consequence of the latent to output mapping being continuous . 2 RELATED WORK . With the advent of recurrent neural networks , sequential time-series models have seen significant adaptation in a variety of applications . Video ( Srivastava et al. , 2015 ) and natural language ( Bowman et al. , 2016 ) were two early applications to have seen significant success . A different but important time-series modality is chirographic data like handwriting ( Graves , 2013 ) and sketches ( Ha & Eck , 2018 ) which gained traction only recently . Subsequent developments also address data modalities that are not free-flowing , like Fonts ( Lopes et al. , 2019 ) and Icons ( Carlier et al. , 2020 ) which require slightly different models . Mirroring developments in natural language processing ( Vaswani et al. , 2017 ) , Transformers are now replacing ( Carlier et al. , 2020 ) recurrent networks in such models . Following the seminal work of Ha & Eck ( 2018 ) , the primary representation for chirographic data has been discrete sequence of waypoints or tokens from specialized Domain Specific Languages like SVG ( Scalable Vector Graphics ) , with a minority continuing to make use of standard raster graphic architectures ( Ge et al. , 2021 ) . Overall , few studies have attempted to develop better representations using specialized functional forms , with Aksan et al . ( 2020 ) learning stroke embeddings and Das et al . ( 2020 ; 2021 ) directly modeling strokes as parametric Bézier curves . However , to the best of our knowledge , modelling chirographic data as generic functions have not yet been tried so far . The Neural ODE ( Chen et al. , 2018 ) framework provides a credible tool for natively modelling continuous time functions using dynamical systems . Since their inception , Neural ODEs have sparked a wide range of discussions and developments ( Massaroli et al. , 2020 ) , mostly in the theoretical domain . Neural ODEs have been extended to work with latent states ( Dupont et al. , 2019 ) , to define generative models ( Song et al. , 2021 ) , and have external data control ( Kidger et al. , 2020 ) . While practical training of ODE models remain a challenge , significant amounts of work have been dedicated towards developing engineering techniques ( Finlay et al. , 2020 ; Grathwohl et al. , 2019 ; Kelly et al. , 2020 ; Poli et al. , 2020 ) for faster training convergence and better regularization . Despite flourishing theoretical interests , due to high training complexity , applied works have so far been limited to few domains including Reinforcement Learning ( Du et al. , 2020 ) , reasoning in visual question answering ( Kim & Lee , 2019 ) , and physics-inspired system identification ( Lutter et al. , 2019 ; Greydanus et al. , 2019 ; Finzi et al. , 2020 ) . 3 NEURAL ORDINARY DIFFERENTIAL EQUATIONS ( NEURAL ODE ) . Neural ODE proposed by Chen et al . ( 2018 ) provides a framework for modelling an inherently continuous-time process by means of its time derivative . Given a continuous-time process s ( t ) ∈ Rd over a defined time interval [ t0 , t1 ] , we can approximate its underlying true dynamics by learning the parameters of a neural networkFΘ ( · ) acting as a proxy for the same ṡ ( t ) = FΘ ( t , s ) ∈ Rd . ( 1 ) The time derivative of a vector-valued function , i.e . ṡ ( t ) defines a vector field over the state space Rd which guides the evolution of the process over time . Given the initial state s0 : = s ( t = t0 ) and the learned set of parameters Θ∗ , the process can be recovered by solving an Initial Value Problem ( IVP ) using any Differential Equation solver ( e.g . RK4 method ) s ( t ) = s0 + ∫ t t0 FΘ∗ ( t , s ) · dt ∀t ∈ [ t0 , t1 ] Given a set of time-varying functions , one can learn dynamics FΘ capturing high-level concepts . The learning algorithm provided by Chen et al . ( 2018 ) makes it practical to train such models with constant memory cost ( w.r.t time horizon ) both in forward and backward pass . 4 FRAMEWORK OVERVIEW . We borrow the notation of Section 3 and denote chirographic structures as continuous functions of time s ( t ) defined within [ t0 , t1 ] . The exact form of s ( t ) must include the running 2D coordinates over time and may include other properties like pen-state ( whether ink is visible at time t ) etc . In principle , one could directly model ṡ ( t ) with a Neural ODE as in Eq . 1 ( see Fig . 2 ) . However , such a model would possess low representational power resulting in underfitting , and furthermore would be incapable of representing self-overlaps naturally exhibited by chirographic data . 4.1 NEURAL ODE AS DECODER . Augmented ODE . We first augment ( Dupont et al. , 2019 ) the original state s ( t ) with the underlying hidden process h ( t ) in the same time interval . Unlike discrete-time analogues like recurrent networks which model discrete transition of the hidden state followed by an observation model p ( s|h ) , we model the “ co-evolution ” of the hidden process along with data using a dynamical system defined over the joint space a ( t ) : = [ s ( t ) h ( t ) ] T . Augmented ODEs ( Dupont et al. , 2019 ) improve representational power by lifting the vanilla ODE of Eq . 1 to a higher dimension , allowing the data trajectory to self-overlap in time . In addition , an augmented latent state h ( t ) provides a principled way to control the trajectory by simply solving the dynamics with a different initial condition h0 . Higher-Order Dynamics . To further increase representational power , we capture the secondorder derivative ä ( t ) with a parameterized neural network FΘ ( · ) like Yildiz et al . ( 2019 ) . This formulation however , leads to the requirement of an extra initial condition ȧ0 : = ȧ ( t = t0 ) [ a ( t ) ȧ ( t ) ] = [ a0 ȧ0 ] + ∫ t t0 [ ȧ ( t ) FΘ ( · ) ] dt ( 2 ) Traditionally , the quantities a ( t ) and ȧ ( t ) are termed as “ position ” and “ velocity ” respectively of the dynamical system represented by ä ( t ) = FΘ ( · ) . Inspired by SketchRNN ( Ha & Eck , 2018 ) , we simplify the model by dropping the position component a ( t ) as a dependency to the dynamics function , leading to a dynamics of ä ( t ) = FΘ ( ȧ ( t ) , t ) . Conditioning . Our model so far is deterministic given a particular choice of initial condition . In order to generate different samples , we need to condition the decoding on a latent vector . Thus , we introduce the following architectural changes : 1 . We define a global latent vector z for each sample and compute the hidden part of initial conditions by projecting it through Ldec , as H0 : = [ h0 ḣ0 ] T = Ldec ( z ) . Without loss of generality , we fix the visible part of the initial state , i.e . s0 and ṡ0 to be constants . 2 . We further include z directly as a fixed parameter to the dynamics function . This is a specific form of data-controlled dynamics ( Massaroli et al. , 2020 ) . Please see Appendix B for implementation details . The final second order dynamical system represented by our decoder is therefore ä ( t ) = FΘ ( ȧ ( t ) , t , z ) ( 3 ) Computing the forward pass ( Eq . 2 ) requires the knowledge of the exact value of z . The latent code can be computed by employing a parameterized encoder . Similar to discrete-time analogues , we propose to employ a non-causal model as an encoder . | This paper proposes a new model, called SketchODE, for learning representations of sketches using neural ODEs. Specifically, the authors parameterize hand drawn strokes as solutions of ODEs and build an auto encoder like framework for learning the vector fields of these ODEs from data. The decoder is modelled with a second order neural ODE acting on an augmented state (which allows for self overlapping trajectories which is necessary for handwritten data) that includes the stroke itself as well as an underlying hidden state. The decoder is made conditional by passing a latent vector to the model, which is mapped both to the initial state of the ODE as well as the dynamics function of the ODE. Varying this latent vector and solving the ODE then gives rise to different handwritten patterns. The encoder is parameterized by a neural CDE which takes as input some ground truth trajectory and returns a new state which is mapped to the latent vector. The model is then trained end to end using a reconstruction loss between the ground truth and reconstructed trajectories. In addition, the authors show that two additional tricks can further improve performance: using sine activation functions in the MLPs parameterizing the vector fields as well as using perlin noise to create (continuous) augmentations of the data. The authors then discuss and demonstrate various compelling properties of their model. This includes the existence of a latent space (which allows for interpolation of handwritten data) as well as one shot learning capabilities for new classes of data. The main contributions of the paper in my eyes are then: - Introducing an interesting continuous neural model of handwritten data - Demonstrating compelling properties of this continuous model over traditional discrete models - Introducing the VectorMNIST dataset, which I believe could be useful for other researchers - Interesting experiments on various handwritten datasets | science.mup_single_document_summarization.4120 | [
{
"content": "Summarize the key points and contributions of the following computer science research paper. Your summary should be clear and concise.\n\nTitle of the Paper:\nSketchODE: Learning neural sketch representation in continuous time\n\nMain Content:\n1 INTRODUCTION Drawing-based communications such as sketches , writing , and diagrams come naturally to humans and have been used in some form since ancient times . Modeling such data is becoming an increasingly important and topical challenge area for machine learning systems aiming to interpret and simulate human creative expression . Such chirographic structures are challenging to interpret and generate due to their complex and unstructured nature . However recent progress has been strong , thanks to the advances in learning latent representations for sequences ( Bowman et al. , 2016 ; Graves , 2013 ; Srivastava et al. , 2015 ) . In particular , with the advent of variational sequence-to-sequence generative models ( Srivastava et al. , 2015 ; Bowman et al. , 2016 ) and the collection of largescale datasets ( Gervais et al. , 2020 ; Ha & Eck , 2018 ; Ge et al. , 2021 ) , we have seen a surge of advancements ( Ha & Eck , 2018 ; Aksan et al. , 2020 ) in this direction . Neverthe- less , a key missing link is the fact that drawing is intrinsically continuous in time , as is the resulting drawn artefact , when considered as a sequence rather than a raster image . The dominant approach to model free-hand sketches , popularized by Ha & Eck ( 2018 ) , has been to use a discrete-step recurrent network for capturing the latent process p ( h [ n ] |h [ n− 1 ] ) with discrete step n , and a complex observation model p ( s [ n ] | h [ n ] ) to explain local structure . The large body of approaches built upon this have a critical flaw : they ignore the very nature of the data , i.e . drawn structures are inherently continuous in time ( Refer to Fig . 1 ) . While a few attempts have been made to use continuous-time constructs like Bézier curves ( Das et al. , 2020 ; 2021 ) , their inconvenient mathematical form affect the representational power as well as training dynamics . The reason for such an absence of continuous-time models is the lack of fundamental tools for handling continuous time data . Lately however , the introduction of Neural Ordinary Differential Equations by Chen et al . ( 2018 ) followed by numerous extensions ( Dupont et al. , 2019 ; Yildiz et al. , 2019 ; Kidger et al. , 2020 ) have opened the possibility of building powerful models that can natively represent continuous time data such as handwriting , sketch etc . In this paper , we represent chirographic drawing data like handwriting , sketches etc . as continuoustime function s ( t ) and model the underlying latent process also as continuous-time latent function h ( t ) . Specifically , we propose a framework for capturing the derivative ( potentially higher order ) of the latent process dKh ( t ) dtK with parameterized neural networks . At inference , a solution trajectory ŝ ( t ) is computed from a given Initial Value Problem ( IVP ) that includes a learned ODE dynamics and an initial hidden state h0 , h ( t = 0 ) . An obvious advantage of the functional representation is its arbitrary spatial resolution , i.e . we can retrieve a structure at any spatial resolution by sampling the function at appropriate rate . Moreover , with a functional representation , we can systematically upgrade the representation along time with additional properties ( e.g . line thickness , color etc ) . So far , parameterized ODE dynamics models have largely been treated as a “ replacement for ResNets ” ( Massaroli et al. , 2020 ) where the intermediate states are of little importance . While the capability of ODE models are beginning to be tested in time-series data ( Kidger et al. , 2020 ; Morrill et al. , 2021 ) , they still remain largely unexplored . Following the introduction of Neural ODEs by Chen et al . ( 2018 ) , methods have been proposed in order to regularize the solution trajectory to be as simple as possible ( Kelly et al. , 2020 ; Finlay et al. , 2020 ) . However , time-series data such as chirography are entirely the opposite . The dynamics model , and consequently the solution trajectory should be as flexible as possible in order to learn the high degree of local and global variations with time often exhibited in such data . We increase the flexibility of Neural ODE models by introducing a new class of parameterized dynamics functions . Our experiments show that this is crucial in order to model chirographic data with satisfactory fidelity . We also introduce a new data augmentation method particularly geared towards ODE models and continuous-time sequences . Finally , we propose a deterministic autoencoder with a global latent variable z that exhibits some generative model properties by virtue of inherent continuity . Our final model , SketchODE , is similar to SketchRNN ( Ha & Eck , 2018 ) in terms of high-level design but lifts it to the realm of continuoustime . More generally , our model is the first continuous time Seq2Seq architecture . We explore some of the noteworthy features that differentiate SketchODE from discrete-time Seq2Seq analogoues . An inductive bias for continuity : Discrete-time sequence models ( Ha & Eck , 2018 ) need to use their capacity to model both global structure and temporal continuity in the data . ODE models on the other hand , have a strong intrinsic architectural bias towards temporal continuity . Sequences generated using a well-behaved ODE dynamics function are guaranteed to be continuous . Since they need not learn the continuity bias from scratch , ODE models are more data-efficient and are able to use majority of their capacity for modelling higher level structures . We even demonstrate that our SketchODE supports meaningful 1-shot learning when fine-tuned . Deterministic Autoencoding with Structured Latent Space : A surprising property of Seq2Seq ODE models is the ability to perform latent space interpolation and sampling without the necessity of imposing a prior distribution p ( z ) on the variational posterior qφ ( z|s ) . This property is also a consequence of the latent to output mapping being continuous . 2 RELATED WORK . With the advent of recurrent neural networks , sequential time-series models have seen significant adaptation in a variety of applications . Video ( Srivastava et al. , 2015 ) and natural language ( Bowman et al. , 2016 ) were two early applications to have seen significant success . A different but important time-series modality is chirographic data like handwriting ( Graves , 2013 ) and sketches ( Ha & Eck , 2018 ) which gained traction only recently . Subsequent developments also address data modalities that are not free-flowing , like Fonts ( Lopes et al. , 2019 ) and Icons ( Carlier et al. , 2020 ) which require slightly different models . Mirroring developments in natural language processing ( Vaswani et al. , 2017 ) , Transformers are now replacing ( Carlier et al. , 2020 ) recurrent networks in such models . Following the seminal work of Ha & Eck ( 2018 ) , the primary representation for chirographic data has been discrete sequence of waypoints or tokens from specialized Domain Specific Languages like SVG ( Scalable Vector Graphics ) , with a minority continuing to make use of standard raster graphic architectures ( Ge et al. , 2021 ) . Overall , few studies have attempted to develop better representations using specialized functional forms , with Aksan et al . ( 2020 ) learning stroke embeddings and Das et al . ( 2020 ; 2021 ) directly modeling strokes as parametric Bézier curves . However , to the best of our knowledge , modelling chirographic data as generic functions have not yet been tried so far . The Neural ODE ( Chen et al. , 2018 ) framework provides a credible tool for natively modelling continuous time functions using dynamical systems . Since their inception , Neural ODEs have sparked a wide range of discussions and developments ( Massaroli et al. , 2020 ) , mostly in the theoretical domain . Neural ODEs have been extended to work with latent states ( Dupont et al. , 2019 ) , to define generative models ( Song et al. , 2021 ) , and have external data control ( Kidger et al. , 2020 ) . While practical training of ODE models remain a challenge , significant amounts of work have been dedicated towards developing engineering techniques ( Finlay et al. , 2020 ; Grathwohl et al. , 2019 ; Kelly et al. , 2020 ; Poli et al. , 2020 ) for faster training convergence and better regularization . Despite flourishing theoretical interests , due to high training complexity , applied works have so far been limited to few domains including Reinforcement Learning ( Du et al. , 2020 ) , reasoning in visual question answering ( Kim & Lee , 2019 ) , and physics-inspired system identification ( Lutter et al. , 2019 ; Greydanus et al. , 2019 ; Finzi et al. , 2020 ) . 3 NEURAL ORDINARY DIFFERENTIAL EQUATIONS ( NEURAL ODE ) . Neural ODE proposed by Chen et al . ( 2018 ) provides a framework for modelling an inherently continuous-time process by means of its time derivative . Given a continuous-time process s ( t ) ∈ Rd over a defined time interval [ t0 , t1 ] , we can approximate its underlying true dynamics by learning the parameters of a neural networkFΘ ( · ) acting as a proxy for the same ṡ ( t ) = FΘ ( t , s ) ∈ Rd . ( 1 ) The time derivative of a vector-valued function , i.e . ṡ ( t ) defines a vector field over the state space Rd which guides the evolution of the process over time . Given the initial state s0 : = s ( t = t0 ) and the learned set of parameters Θ∗ , the process can be recovered by solving an Initial Value Problem ( IVP ) using any Differential Equation solver ( e.g . RK4 method ) s ( t ) = s0 + ∫ t t0 FΘ∗ ( t , s ) · dt ∀t ∈ [ t0 , t1 ] Given a set of time-varying functions , one can learn dynamics FΘ capturing high-level concepts . The learning algorithm provided by Chen et al . ( 2018 ) makes it practical to train such models with constant memory cost ( w.r.t time horizon ) both in forward and backward pass . 4 FRAMEWORK OVERVIEW . We borrow the notation of Section 3 and denote chirographic structures as continuous functions of time s ( t ) defined within [ t0 , t1 ] . The exact form of s ( t ) must include the running 2D coordinates over time and may include other properties like pen-state ( whether ink is visible at time t ) etc . In principle , one could directly model ṡ ( t ) with a Neural ODE as in Eq . 1 ( see Fig . 2 ) . However , such a model would possess low representational power resulting in underfitting , and furthermore would be incapable of representing self-overlaps naturally exhibited by chirographic data . 4.1 NEURAL ODE AS DECODER . Augmented ODE . We first augment ( Dupont et al. , 2019 ) the original state s ( t ) with the underlying hidden process h ( t ) in the same time interval . Unlike discrete-time analogues like recurrent networks which model discrete transition of the hidden state followed by an observation model p ( s|h ) , we model the “ co-evolution ” of the hidden process along with data using a dynamical system defined over the joint space a ( t ) : = [ s ( t ) h ( t ) ] T . Augmented ODEs ( Dupont et al. , 2019 ) improve representational power by lifting the vanilla ODE of Eq . 1 to a higher dimension , allowing the data trajectory to self-overlap in time . In addition , an augmented latent state h ( t ) provides a principled way to control the trajectory by simply solving the dynamics with a different initial condition h0 . Higher-Order Dynamics . To further increase representational power , we capture the secondorder derivative ä ( t ) with a parameterized neural network FΘ ( · ) like Yildiz et al . ( 2019 ) . This formulation however , leads to the requirement of an extra initial condition ȧ0 : = ȧ ( t = t0 ) [ a ( t ) ȧ ( t ) ] = [ a0 ȧ0 ] + ∫ t t0 [ ȧ ( t ) FΘ ( · ) ] dt ( 2 ) Traditionally , the quantities a ( t ) and ȧ ( t ) are termed as “ position ” and “ velocity ” respectively of the dynamical system represented by ä ( t ) = FΘ ( · ) . Inspired by SketchRNN ( Ha & Eck , 2018 ) , we simplify the model by dropping the position component a ( t ) as a dependency to the dynamics function , leading to a dynamics of ä ( t ) = FΘ ( ȧ ( t ) , t ) . Conditioning . Our model so far is deterministic given a particular choice of initial condition . In order to generate different samples , we need to condition the decoding on a latent vector . Thus , we introduce the following architectural changes : 1 . We define a global latent vector z for each sample and compute the hidden part of initial conditions by projecting it through Ldec , as H0 : = [ h0 ḣ0 ] T = Ldec ( z ) . Without loss of generality , we fix the visible part of the initial state , i.e . s0 and ṡ0 to be constants . 2 . We further include z directly as a fixed parameter to the dynamics function . This is a specific form of data-controlled dynamics ( Massaroli et al. , 2020 ) . Please see Appendix B for implementation details . The final second order dynamical system represented by our decoder is therefore ä ( t ) = FΘ ( ȧ ( t ) , t , z ) ( 3 ) Computing the forward pass ( Eq . 2 ) requires the knowledge of the exact value of z . The latent code can be computed by employing a parameterized encoder . Similar to discrete-time analogues , we propose to employ a non-causal model as an encoder .",
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"content": "This paper proposes a new model, called SketchODE, for learning representations of sketches using neural ODEs. Specifically, the authors parameterize hand drawn strokes as solutions of ODEs and build an auto encoder like framework for learning the vector fields of these ODEs from data. The decoder is modelled with a second order neural ODE acting on an augmented state (which allows for self overlapping trajectories which is necessary for handwritten data) that includes the stroke itself as well as an underlying hidden state. The decoder is made conditional by passing a latent vector to the model, which is mapped both to the initial state of the ODE as well as the dynamics function of the ODE. Varying this latent vector and solving the ODE then gives rise to different handwritten patterns. The encoder is parameterized by a neural CDE which takes as input some ground truth trajectory and returns a new state which is mapped to the latent vector. The model is then trained end to end using a reconstruction loss between the ground truth and reconstructed trajectories. In addition, the authors show that two additional tricks can further improve performance: using sine activation functions in the MLPs parameterizing the vector fields as well as using perlin noise to create (continuous) augmentations of the data. The authors then discuss and demonstrate various compelling properties of their model. This includes the existence of a latent space (which allows for interpolation of handwritten data) as well as one shot learning capabilities for new classes of data. The main contributions of the paper in my eyes are then: - Introducing an interesting continuous neural model of handwritten data - Demonstrating compelling properties of this continuous model over traditional discrete models - Introducing the VectorMNIST dataset, which I believe could be useful for other researchers - Interesting experiments on various handwritten datasets",
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science.qasper_abstractive_qa | Examine the provided sections from a research document and address the accompanying query. The text is divided by newlines.
Document Content:
Revisiting Summarization Evaluation for Scientific Articles
Evaluation of text summarization approaches have been mostly based on metrics that measure similarities of system generated summaries with a set of human written gold-standard summaries. The most widely used metric in summarization evaluation has been the ROUGE family. ROUGE solely relies on lexical overlaps between the terms and phrases in the sentences; therefore, in cases of terminology variations and paraphrasing, ROUGE is not as effective. Scientific article summarization is one such case that is different from general domain summarization (e.g. newswire data). We provide an extensive analysis of ROUGE's effectiveness as an evaluation metric for scientific summarization; we show that, contrary to the common belief, ROUGE is not much reliable in evaluating scientific summaries. We furthermore show how different variants of ROUGE result in very different correlations with the manual Pyramid scores. Finally, we propose an alternative metric for summarization evaluation which is based on the content relevance between a system generated summary and the corresponding human written summaries. We call our metric SERA (Summarization Evaluation by Relevance Analysis). Unlike ROUGE, SERA consistently achieves high correlations with manual scores which shows its effectiveness in evaluation of scientific article summarization.
Summarization evaluation by Rouge
Rouge has been the most widely used family of metrics in summarization evaluation. In the following, we briefly describe the different variants of Rouge:
Rouge-L, Rouge-W, Rouge-S and Rouge-SU were later extended to consider both the recall and precision. In calculating Rouge, stopword removal or stemming can also be considered, resulting in more variants.
In the summarization literature, despite the large number of variants of Rouge, only one or very few of these variants are often chosen (arbitrarily) for evaluation of the quality of the summarization approaches. When Rouge was proposed, the original variants were only recall-oriented and hence the reported correlation results BIBREF1 . The later extension of Rouge family by precision were only reflected in the later versions of the Rouge toolkit and additional evaluation of its effectiveness was not reported. Nevertheless, later published work in summarization adopted this toolkit for its ready implementation and relatively efficient performance.
The original Rouge metrics show high correlations with human judgments of the quality of summaries on the DUC 2001-2003 benchmarks. However, these benchmarks consist of newswire data and are intrinsically very different than other summarization tasks such as summarization of scientific papers. We argue that Rouge is not the best metric for all summarization tasks and we propose an alternative metric for evaluation of scientific summarization. The proposed alternative metric shows much higher and more consistent correlations with manual judgments in comparison with the well-established Rouge.
Summarization Evaluation by Relevance Analysis (Sera)
Rouge functions based on the assumption that in order for a summary to be of high quality, it has to share many words or phrases with a human gold summary. However, different terminology may be used to refer to the same concepts and thus relying only on lexical overlaps may underrate content quality scores. To overcome this problem, we propose an approach based on the premise that concepts take meanings from the context they are in, and that related concepts co-occur frequently.
Our proposed metric is based on analysis of the content relevance between a system generated summary and the corresponding human written gold-standard summaries. On high level, we indirectly evaluate the content relevance between the candidate summary and the human summary using information retrieval. To accomplish this, we use the summaries as search queries and compare the overlaps of the retrieved results. Larger number of overlaps, suggest that the candidate summary has higher content quality with respect to the gold-standard. This method, enables us to also reward for terms that are not lexically equivalent but semantically related. Our method is based on the well established linguistic premise that semantically related words occur in similar contexts BIBREF5 . The context of the words can be considered as surrounding words, sentences in which they appear or the documents. For scientific summarization, we consider the context of the words as the scientific articles in which they appear. Thus, if two concepts appear in identical set of articles, they are semantically related. We consider the two summaries as similar if they refer to same set of articles even if the two summaries do not have high lexical overlaps. To capture if a summary relates to a article, we use information retrieval by considering the summaries as queries and the articles as documents and we rank the articles based on their relatedness to a given summary. For a given pair of system summary and the gold summary, similar rankings of the retrieved articles suggest that the summaries are semantically related, and thus the system summary is of higher quality.
Based on the domain of interest, we first construct an index from a set of articles in the same domain. Since TAC 2014 was focused on summarization in the biomedical domain, our index also comprises of biomedical articles. Given a candidate summary INLINEFORM0 and a set of gold summaries INLINEFORM1 ( INLINEFORM2 ; INLINEFORM3 is the total number of human summaries), we submit the candidate summary and gold summaries to the search engine as queries and compare their ranked results. Let INLINEFORM4 be the entire index which comprises of INLINEFORM5 total documents.
Let INLINEFORM0 be the ranked list of retrieved documents for candidate summary INLINEFORM1 , and INLINEFORM2 the ranked list of results for the gold summary INLINEFORM3 . These lists of results are based on a rank cut-off point INLINEFORM4 that is a parameter of the system. We provide evaluation results on different choices of cut-off point INLINEFORM5 in the Section SECREF5 We consider the following two scores: (i) simple intersection and (ii) discounted intersection by rankings. The simple intersection just considers the overlaps of the results in the two ranked lists and ignores the rankings. The discounted ranked scores, on the other hand, penalizes ranking differences between the two result sets. As an example consider the following list of retrieved documents (denoted by INLINEFORM6 s) for a candidate and a gold summary as queries:
Results for candidate summary: INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , INLINEFORM3
Results for gold summary: INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , INLINEFORM3
These two sets of results consist of identical documents but the ranking of the retrieved documents differ. Therefore, the simple intersection method assigns a score of 1.0 while in the discounted ranked score, the score will be less than 1.0 (due to ranking differences between the result lists).
We now define the metrics more precisely. Using the above notations, without loss of generality, we assume that INLINEFORM0 . Sera is defined as follows: INLINEFORM1
To also account for the ranked position differences, we modify this score to discount rewards based on rank differences. That is, in ideal score, we want search results from candidate summary ( INLINEFORM0 ) to be the same as results for gold-standard summaries ( INLINEFORM1 ) and the rankings of the results also be the same. If the rankings differ, we discount the reward by log of the differences of the ranks. More specifically, the discounted score (Sera-Dis) is defined as: INLINEFORM2
where, as previously defined, INLINEFORM0 , INLINEFORM1 and INLINEFORM2 are total number of human gold summaries, result list for the candidate summary and result list for the human gold summary, respectively. In addition, INLINEFORM3 shows the INLINEFORM4 th results in the ranked list INLINEFORM5 and INLINEFORM6 is the maximum attainable score used as the normalizing factor.
We use elasticsearch[1], an open-source search engine, for indexing and querying the articles. For retrieval model, we use the Language Modeling retrieval model with Dirichlet smoothing BIBREF6 . Since TAC 2014 benchmark is on summarization of biomedical articles, the appropriate index would be the one constructed from articles in the same domain. Therefore, we use the open access subset of Pubmed[2] which consists of published articles in biomedical literature.
[1]https://github.com/elastic/elasticsearch [2]PubMed is a comprehensive resource of articles and abstracts published in life sciences and biomedical literature http://www.ncbi.nlm.nih.gov/pmc/
We also experiment with different query (re)formulation approaches. Query reformulation is a method in Information Retrieval that aims to refine the query for better retrieval of results. Query reformulation methods often consist of removing ineffective terms and expressions from the query (query reduction) or adding terms to the query that help the retrieval (query expansion). Query reduction is specially important when queries are verbose. Since we use the summaries as queries, the queries are usually long and therefore we consider query reductions.
In our experiments, the query reformulation is done by 3 different ways: (i) Plain: The entire summary without stopwords and numeric values; (ii) Noun Phrases (NP): We only keep the noun phrases as informative concepts in the summary and eliminate all other terms; and (iii) Keywords (KW): We only keep the keywords and key phrases in the summary. For extracting the keywords and keyphrases (with length of up to 3 terms), we extract expressions whose idf[1] values is higher than a predefined threshold that is set as a parameter. We set this threshold to the average idf values of all terms except stopwords. idf values are calculated on the same index that is used for the retrieval.
[1]Inverted Document Frequency
We hypothesize that using only informative concepts in the summary prevents query drift and leads to retrieval of more relevant documents. Noun phrases and keywords are two heuristics for identifying the informative concepts.
Data
To the best of our knowledge, the only scientific summarization benchmark is from TAC 2014 summarization track. For evaluating the effectiveness of Rouge variants and our metric (Sera), we use this benchmark, which consists of 20 topics each with a biomedical journal article and 4 gold human written summaries.
Results and Discussion
We calculated all variants of Rouge scores, our proposed metric, Sera, and the Pyramid score on the generated summaries from the summarizers described in Section SECREF13 . We do not report the Rouge, Sera or pyramid scores of individual systems as it is not the focus of this study. Our aim is to analyze the effectiveness of the evaluation metrics, not the summarization approaches. Therefore, we consider the correlations of the automatic evaluation metrics with the manual Pyramid scores to evaluate their effectiveness; the metrics that show higher correlations with manual judgments are more effective.
Table TABREF23 shows the Pearson, Spearman and Kendall correlation of Rouge and Sera, with pyramid scores. Both Rouge and Sera are calculated with stopwords removed and with stemming. Our experiments with inclusion of stopwords and without stemming showed similar results and thus, we do not include those to avoid redundancy.
Sera
The results of our proposed method (Sera) are shown in the bottom part of Table TABREF23 . In general, Sera shows better correlation with pyramid scores in comparison with Rouge. We observe that the Pearson correlation of Sera with cut-off point of 5 (shown by Sera-5) is 0.823 which is higher than most of the Rouge variants. Similarly, the Spearman and Kendall correlations of the Sera evaluation score is 0.941 and 0.857 respectively, which are higher than all Rouge correlation values. This shows the effectiveness of the simple variant of our proposed summarization evaluation metric.
Table TABREF23 also shows the results of other Sera variants including discounting and query reformulation methods. Some of these variants are the result of applying query reformulation in the process of document retrieval which are described in section SECREF3 As illustrated, the Noun Phrases (NP) query reformulation at cut-off point of 5 (shown as Sera-np-5) achieves the highest correlations among all the Sera variants ( INLINEFORM0 = INLINEFORM1 , INLINEFORM2 = INLINEFORM3 = INLINEFORM4 ). In the case of Keywords (KW) query reformulation, without using discounting, we can see that there is no positive gain in correlation. However, keywords when applied on the discounted variant of Sera, result in higher correlations.
Discounting has more positive effect when applied on query reformulation-based Sera than on the simple variant of Sera. In the case of discounting and NP query reformulation (Sera-dis-np), we observe higher correlations in comparison with simple Sera. Similarly, in the case of Keywords (KW), positive correlation gain is obtained in most of correlation coefficients. NP without discounting and at cut-off point of 5 (Sera-np-5) shows the highest non-parametric correlation. In addition, the discounted NP at cut-off point of 10 (Sera-np-dis-10) shows the highest parametric correlations.
In general, using NP and KW as heuristics for finding the informative concepts in the summary effectively increases the correlations with the manual scores. Selecting informative terms from long queries results in more relevant documents and prevents query drift. Therefore, the overall similarity between the two summaries (candidate and the human written gold summary) is better captured.
Acknowledgments
We would like to thank all three anonymous reviewers for their feedback and comments, and Maryam Iranmanesh for helping in annotation. This work was partially supported by National Science Foundation (NSF) through grant CNS-1204347.
Query: In the proposed metric, how is content relevance measured?
JSON Response: | {"answer": "The content relevance between the candidate summary and the human summary is evaluated using information retrieval - using the summaries as search queries and compare the overlaps of the retrieved results. ", "evidence": ["Our proposed metric is based on analysis of the content relevance between a system generated summary and the corresponding human written gold-standard summaries. On high level, we indirectly evaluate the content relevance between the candidate summary and the human summary using information retrieval. To accomplish this, we use the summaries as search queries and compare the overlaps of the retrieved results. Larger number of overlaps, suggest that the candidate summary has higher content quality with respect to the gold-standard. This method, enables us to also reward for terms that are not lexically equivalent but semantically related. Our method is based on the well established linguistic premise that semantically related words occur in similar contexts BIBREF5 . The context of the words can be considered as surrounding words, sentences in which they appear or the documents. For scientific summarization, we consider the context of the words as the scientific articles in which they appear. Thus, if two concepts appear in identical set of articles, they are semantically related. We consider the two summaries as similar if they refer to same set of articles even if the two summaries do not have high lexical overlaps. To capture if a summary relates to a article, we use information retrieval by considering the summaries as queries and the articles as documents and we rank the articles based on their relatedness to a given summary. For a given pair of system summary and the gold summary, similar rankings of the retrieved articles suggest that the summaries are semantically related, and thus the system summary is of higher quality."]} | science.qasper_abstractive_qa.127.0 | [
{
"content": "Examine the provided sections from a research document and address the accompanying query. The text is divided by newlines.\n\nDocument Content:\n\nRevisiting Summarization Evaluation for Scientific Articles\n\nEvaluation of text summarization approaches have been mostly based on metrics that measure similarities of system generated summaries with a set of human written gold-standard summaries. The most widely used metric in summarization evaluation has been the ROUGE family. ROUGE solely relies on lexical overlaps between the terms and phrases in the sentences; therefore, in cases of terminology variations and paraphrasing, ROUGE is not as effective. Scientific article summarization is one such case that is different from general domain summarization (e.g. newswire data). We provide an extensive analysis of ROUGE's effectiveness as an evaluation metric for scientific summarization; we show that, contrary to the common belief, ROUGE is not much reliable in evaluating scientific summaries. We furthermore show how different variants of ROUGE result in very different correlations with the manual Pyramid scores. Finally, we propose an alternative metric for summarization evaluation which is based on the content relevance between a system generated summary and the corresponding human written summaries. We call our metric SERA (Summarization Evaluation by Relevance Analysis). Unlike ROUGE, SERA consistently achieves high correlations with manual scores which shows its effectiveness in evaluation of scientific article summarization.\n\nSummarization evaluation by Rouge\nRouge has been the most widely used family of metrics in summarization evaluation. In the following, we briefly describe the different variants of Rouge:\nRouge-L, Rouge-W, Rouge-S and Rouge-SU were later extended to consider both the recall and precision. In calculating Rouge, stopword removal or stemming can also be considered, resulting in more variants.\nIn the summarization literature, despite the large number of variants of Rouge, only one or very few of these variants are often chosen (arbitrarily) for evaluation of the quality of the summarization approaches. When Rouge was proposed, the original variants were only recall-oriented and hence the reported correlation results BIBREF1 . The later extension of Rouge family by precision were only reflected in the later versions of the Rouge toolkit and additional evaluation of its effectiveness was not reported. Nevertheless, later published work in summarization adopted this toolkit for its ready implementation and relatively efficient performance.\nThe original Rouge metrics show high correlations with human judgments of the quality of summaries on the DUC 2001-2003 benchmarks. However, these benchmarks consist of newswire data and are intrinsically very different than other summarization tasks such as summarization of scientific papers. We argue that Rouge is not the best metric for all summarization tasks and we propose an alternative metric for evaluation of scientific summarization. The proposed alternative metric shows much higher and more consistent correlations with manual judgments in comparison with the well-established Rouge.\n\nSummarization Evaluation by Relevance Analysis (Sera)\nRouge functions based on the assumption that in order for a summary to be of high quality, it has to share many words or phrases with a human gold summary. However, different terminology may be used to refer to the same concepts and thus relying only on lexical overlaps may underrate content quality scores. To overcome this problem, we propose an approach based on the premise that concepts take meanings from the context they are in, and that related concepts co-occur frequently.\nOur proposed metric is based on analysis of the content relevance between a system generated summary and the corresponding human written gold-standard summaries. On high level, we indirectly evaluate the content relevance between the candidate summary and the human summary using information retrieval. To accomplish this, we use the summaries as search queries and compare the overlaps of the retrieved results. Larger number of overlaps, suggest that the candidate summary has higher content quality with respect to the gold-standard. This method, enables us to also reward for terms that are not lexically equivalent but semantically related. Our method is based on the well established linguistic premise that semantically related words occur in similar contexts BIBREF5 . The context of the words can be considered as surrounding words, sentences in which they appear or the documents. For scientific summarization, we consider the context of the words as the scientific articles in which they appear. Thus, if two concepts appear in identical set of articles, they are semantically related. We consider the two summaries as similar if they refer to same set of articles even if the two summaries do not have high lexical overlaps. To capture if a summary relates to a article, we use information retrieval by considering the summaries as queries and the articles as documents and we rank the articles based on their relatedness to a given summary. For a given pair of system summary and the gold summary, similar rankings of the retrieved articles suggest that the summaries are semantically related, and thus the system summary is of higher quality.\nBased on the domain of interest, we first construct an index from a set of articles in the same domain. Since TAC 2014 was focused on summarization in the biomedical domain, our index also comprises of biomedical articles. Given a candidate summary INLINEFORM0 and a set of gold summaries INLINEFORM1 ( INLINEFORM2 ; INLINEFORM3 is the total number of human summaries), we submit the candidate summary and gold summaries to the search engine as queries and compare their ranked results. Let INLINEFORM4 be the entire index which comprises of INLINEFORM5 total documents.\nLet INLINEFORM0 be the ranked list of retrieved documents for candidate summary INLINEFORM1 , and INLINEFORM2 the ranked list of results for the gold summary INLINEFORM3 . These lists of results are based on a rank cut-off point INLINEFORM4 that is a parameter of the system. We provide evaluation results on different choices of cut-off point INLINEFORM5 in the Section SECREF5 We consider the following two scores: (i) simple intersection and (ii) discounted intersection by rankings. The simple intersection just considers the overlaps of the results in the two ranked lists and ignores the rankings. The discounted ranked scores, on the other hand, penalizes ranking differences between the two result sets. As an example consider the following list of retrieved documents (denoted by INLINEFORM6 s) for a candidate and a gold summary as queries:\nResults for candidate summary: INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , INLINEFORM3 \nResults for gold summary: INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , INLINEFORM3 \nThese two sets of results consist of identical documents but the ranking of the retrieved documents differ. Therefore, the simple intersection method assigns a score of 1.0 while in the discounted ranked score, the score will be less than 1.0 (due to ranking differences between the result lists).\nWe now define the metrics more precisely. Using the above notations, without loss of generality, we assume that INLINEFORM0 . Sera is defined as follows: INLINEFORM1 \nTo also account for the ranked position differences, we modify this score to discount rewards based on rank differences. That is, in ideal score, we want search results from candidate summary ( INLINEFORM0 ) to be the same as results for gold-standard summaries ( INLINEFORM1 ) and the rankings of the results also be the same. If the rankings differ, we discount the reward by log of the differences of the ranks. More specifically, the discounted score (Sera-Dis) is defined as: INLINEFORM2 \nwhere, as previously defined, INLINEFORM0 , INLINEFORM1 and INLINEFORM2 are total number of human gold summaries, result list for the candidate summary and result list for the human gold summary, respectively. In addition, INLINEFORM3 shows the INLINEFORM4 th results in the ranked list INLINEFORM5 and INLINEFORM6 is the maximum attainable score used as the normalizing factor.\nWe use elasticsearch[1], an open-source search engine, for indexing and querying the articles. For retrieval model, we use the Language Modeling retrieval model with Dirichlet smoothing BIBREF6 . Since TAC 2014 benchmark is on summarization of biomedical articles, the appropriate index would be the one constructed from articles in the same domain. Therefore, we use the open access subset of Pubmed[2] which consists of published articles in biomedical literature.\n[1]https://github.com/elastic/elasticsearch [2]PubMed is a comprehensive resource of articles and abstracts published in life sciences and biomedical literature http://www.ncbi.nlm.nih.gov/pmc/\nWe also experiment with different query (re)formulation approaches. Query reformulation is a method in Information Retrieval that aims to refine the query for better retrieval of results. Query reformulation methods often consist of removing ineffective terms and expressions from the query (query reduction) or adding terms to the query that help the retrieval (query expansion). Query reduction is specially important when queries are verbose. Since we use the summaries as queries, the queries are usually long and therefore we consider query reductions.\nIn our experiments, the query reformulation is done by 3 different ways: (i) Plain: The entire summary without stopwords and numeric values; (ii) Noun Phrases (NP): We only keep the noun phrases as informative concepts in the summary and eliminate all other terms; and (iii) Keywords (KW): We only keep the keywords and key phrases in the summary. For extracting the keywords and keyphrases (with length of up to 3 terms), we extract expressions whose idf[1] values is higher than a predefined threshold that is set as a parameter. We set this threshold to the average idf values of all terms except stopwords. idf values are calculated on the same index that is used for the retrieval.\n[1]Inverted Document Frequency\nWe hypothesize that using only informative concepts in the summary prevents query drift and leads to retrieval of more relevant documents. Noun phrases and keywords are two heuristics for identifying the informative concepts.\n\nData\nTo the best of our knowledge, the only scientific summarization benchmark is from TAC 2014 summarization track. For evaluating the effectiveness of Rouge variants and our metric (Sera), we use this benchmark, which consists of 20 topics each with a biomedical journal article and 4 gold human written summaries.\n\nResults and Discussion\nWe calculated all variants of Rouge scores, our proposed metric, Sera, and the Pyramid score on the generated summaries from the summarizers described in Section SECREF13 . We do not report the Rouge, Sera or pyramid scores of individual systems as it is not the focus of this study. Our aim is to analyze the effectiveness of the evaluation metrics, not the summarization approaches. Therefore, we consider the correlations of the automatic evaluation metrics with the manual Pyramid scores to evaluate their effectiveness; the metrics that show higher correlations with manual judgments are more effective.\nTable TABREF23 shows the Pearson, Spearman and Kendall correlation of Rouge and Sera, with pyramid scores. Both Rouge and Sera are calculated with stopwords removed and with stemming. Our experiments with inclusion of stopwords and without stemming showed similar results and thus, we do not include those to avoid redundancy.\n\nSera\nThe results of our proposed method (Sera) are shown in the bottom part of Table TABREF23 . In general, Sera shows better correlation with pyramid scores in comparison with Rouge. We observe that the Pearson correlation of Sera with cut-off point of 5 (shown by Sera-5) is 0.823 which is higher than most of the Rouge variants. Similarly, the Spearman and Kendall correlations of the Sera evaluation score is 0.941 and 0.857 respectively, which are higher than all Rouge correlation values. This shows the effectiveness of the simple variant of our proposed summarization evaluation metric.\nTable TABREF23 also shows the results of other Sera variants including discounting and query reformulation methods. Some of these variants are the result of applying query reformulation in the process of document retrieval which are described in section SECREF3 As illustrated, the Noun Phrases (NP) query reformulation at cut-off point of 5 (shown as Sera-np-5) achieves the highest correlations among all the Sera variants ( INLINEFORM0 = INLINEFORM1 , INLINEFORM2 = INLINEFORM3 = INLINEFORM4 ). In the case of Keywords (KW) query reformulation, without using discounting, we can see that there is no positive gain in correlation. However, keywords when applied on the discounted variant of Sera, result in higher correlations.\nDiscounting has more positive effect when applied on query reformulation-based Sera than on the simple variant of Sera. In the case of discounting and NP query reformulation (Sera-dis-np), we observe higher correlations in comparison with simple Sera. Similarly, in the case of Keywords (KW), positive correlation gain is obtained in most of correlation coefficients. NP without discounting and at cut-off point of 5 (Sera-np-5) shows the highest non-parametric correlation. In addition, the discounted NP at cut-off point of 10 (Sera-np-dis-10) shows the highest parametric correlations.\nIn general, using NP and KW as heuristics for finding the informative concepts in the summary effectively increases the correlations with the manual scores. Selecting informative terms from long queries results in more relevant documents and prevents query drift. Therefore, the overall similarity between the two summaries (candidate and the human written gold summary) is better captured.\n\nAcknowledgments\nWe would like to thank all three anonymous reviewers for their feedback and comments, and Maryam Iranmanesh for helping in annotation. This work was partially supported by National Science Foundation (NSF) through grant CNS-1204347.\n\nQuery: In the proposed metric, how is content relevance measured?\n\nJSON Response:",
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"content": "{\"answer\": \"The content relevance between the candidate summary and the human summary is evaluated using information retrieval - using the summaries as search queries and compare the overlaps of the retrieved results. \", \"evidence\": [\"Our proposed metric is based on analysis of the content relevance between a system generated summary and the corresponding human written gold-standard summaries. On high level, we indirectly evaluate the content relevance between the candidate summary and the human summary using information retrieval. To accomplish this, we use the summaries as search queries and compare the overlaps of the retrieved results. Larger number of overlaps, suggest that the candidate summary has higher content quality with respect to the gold-standard. This method, enables us to also reward for terms that are not lexically equivalent but semantically related. Our method is based on the well established linguistic premise that semantically related words occur in similar contexts BIBREF5 . The context of the words can be considered as surrounding words, sentences in which they appear or the documents. For scientific summarization, we consider the context of the words as the scientific articles in which they appear. Thus, if two concepts appear in identical set of articles, they are semantically related. We consider the two summaries as similar if they refer to same set of articles even if the two summaries do not have high lexical overlaps. To capture if a summary relates to a article, we use information retrieval by considering the summaries as queries and the articles as documents and we rank the articles based on their relatedness to a given summary. For a given pair of system summary and the gold summary, similar rankings of the retrieved articles suggest that the summaries are semantically related, and thus the system summary is of higher quality.\"]}",
"role": "assistant"
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|
science.scifact_entailment | Determine the relationship between them and generate a JSON response. Scientific Claim:
Rapamycin delays aging in fruit flies.
Research Abstract:
The target of rapamycin (TOR) pathway is a major nutrient-sensing pathway that, when genetically downregulated, increases life span in evolutionarily diverse organisms including mammals.
The central component of this pathway, TOR kinase, is the target of the inhibitory drug rapamycin, a highly specific and well-described drug approved for human use.
We show here that feeding rapamycin to adult Drosophila produces the life span extension seen in some TOR mutants.
Increase in life span by rapamycin was associated with increased resistance to both starvation and paraquat.
Analysis of the underlying mechanisms revealed that rapamycin increased longevity specifically through the TORC1 branch of the TOR pathway, through alterations to both autophagy and translation.
Rapamycin could increase life span of weak insulin/Igf signaling (IIS) pathway mutants and of flies with life span maximized by dietary restriction, indicating additional mechanisms. | {"verdict": "SUPPORT", "evidence": ["We show here that feeding rapamycin to adult Drosophila produces the life span extension seen in some TOR mutants."]} | science.scifact_entailment.663 | [
{
"content": "Determine the relationship between them and generate a JSON response. Scientific Claim: \nRapamycin delays aging in fruit flies.\n\nResearch Abstract:\nThe target of rapamycin (TOR) pathway is a major nutrient-sensing pathway that, when genetically downregulated, increases life span in evolutionarily diverse organisms including mammals.\nThe central component of this pathway, TOR kinase, is the target of the inhibitory drug rapamycin, a highly specific and well-described drug approved for human use.\nWe show here that feeding rapamycin to adult Drosophila produces the life span extension seen in some TOR mutants.\nIncrease in life span by rapamycin was associated with increased resistance to both starvation and paraquat.\nAnalysis of the underlying mechanisms revealed that rapamycin increased longevity specifically through the TORC1 branch of the TOR pathway, through alterations to both autophagy and translation.\nRapamycin could increase life span of weak insulin/Igf signaling (IIS) pathway mutants and of flies with life span maximized by dietary restriction, indicating additional mechanisms.",
"role": "user"
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"content": "{\"verdict\": \"SUPPORT\", \"evidence\": [\"We show here that feeding rapamycin to adult Drosophila produces the life span extension seen in some TOR mutants.\"]}",
"role": "assistant"
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|
science.discomat_te | | Sample | r-Factor | r (A) | n (atoms) | s 2 (A2) |
| Hardystonite | | | | |
| Fit | 0.015 | 1.932 (0.005) | 3.9 (0.4) | 0.0032 (0.0008) |
| actual [3] | 1.937 | 4.0 | | |
| | | | | |
| Zn series glasses | Zn series glasses | Zn series glasses | Zn series glasses | Zn series glasses |
| Zn4 | 0.010 | 1.952 (0.005) | 4.6 (0.5) | 0.0066 (0.0010) |
| Zn8 | 0.006 | 1.957 (0.005) | 4.5 (0.4) | 0.0067 (0.0008) |
| Bulk glass | 0.010 | 1.957 (0.005) | 4.1 (0.4) | 0.0062 (0.0010) |
| Zn12 Crystal-glass layer | 0.020 | 1.956 (0.006) | 4.0 (0.6) | 0.0061 (0.0013) |
| | | | | |
| CZR series glasses | CZR series glasses | CZR series glasses | CZR series glasses | CZR series glasses |
| CZR17.84 | 0.039 | 1.951 (0.008) | 4.1 (0.8) | 0.0046 (0.0016) |
| CZR17.85 | 0.016 | 1.958 (0.006) | 4.2 (0.5) | 0.0048 (0.0011) |
| CZR19.21 | 0.012 | 1.960 (0.005) | 4.0 (0.4) | 0.0055 (0.0010) |
Caption: First shell Zn-O fitting results for the standard hardystonite and the glasses, using the Zn-O first shell path in hardystonite a
Examine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:
{"value": "xx", "type": "Other"}
{"value": "xx", "type": "Glass_Compound_Amount", "constituent": "xx", "unit": "xx", "material": "xx"}
Process each cell sequentially, and for any unknown attributes, use "xx" as a placeholder. Each JSON should be on a new line.
Output: | {"value": "0.015", "type": "Other"}
{"value": "1.932", "type": "Other"}
{"value": "3.9", "type": "Other"}
{"value": "0.0032", "type": "Other"}
{"value": "1.937", "type": "Other"}
{"value": "4.0", "type": "Other"}
{"value": "0.010", "type": "Other"}
{"value": "1.952", "type": "Other"}
{"value": "4.6", "type": "Other"}
{"value": "0.0066", "type": "Other"}
{"value": "0.006", "type": "Other"}
{"value": "1.957", "type": "Other"}
{"value": "4.5", "type": "Other"}
{"value": "0.0067", "type": "Other"}
{"value": "0.010", "type": "Other"}
{"value": "1.957", "type": "Other"}
{"value": "4.1", "type": "Other"}
{"value": "0.0062", "type": "Other"}
{"value": "0.020", "type": "Other"}
{"value": "1.956", "type": "Other"}
{"value": "4.0", "type": "Other"}
{"value": "0.0061", "type": "Other"}
{"value": "0.039", "type": "Other"}
{"value": "1.951", "type": "Other"}
{"value": "4.1", "type": "Other"}
{"value": "0.0046", "type": "Other"}
{"value": "0.016", "type": "Other"}
{"value": "1.958", "type": "Other"}
{"value": "4.2", "type": "Other"}
{"value": "0.0048", "type": "Other"}
{"value": "0.012", "type": "Other"}
{"value": "1.960", "type": "Other"}
{"value": "4.0", "type": "Other"}
{"value": "0.0055", "type": "Other"} | science.discomat_te.70 | [
{
"content": "| Sample | r-Factor | r (A) | n (atoms) | s 2 (A2) |\n| Hardystonite | | | | |\n| Fit | 0.015 | 1.932 (0.005) | 3.9 (0.4) | 0.0032 (0.0008) |\n| actual [3] | 1.937 | 4.0 | | |\n| | | | | |\n| Zn series glasses | Zn series glasses | Zn series glasses | Zn series glasses | Zn series glasses |\n| Zn4 | 0.010 | 1.952 (0.005) | 4.6 (0.5) | 0.0066 (0.0010) |\n| Zn8 | 0.006 | 1.957 (0.005) | 4.5 (0.4) | 0.0067 (0.0008) |\n| Bulk glass | 0.010 | 1.957 (0.005) | 4.1 (0.4) | 0.0062 (0.0010) |\n| Zn12 Crystal-glass layer | 0.020 | 1.956 (0.006) | 4.0 (0.6) | 0.0061 (0.0013) |\n| | | | | |\n| CZR series glasses | CZR series glasses | CZR series glasses | CZR series glasses | CZR series glasses |\n| CZR17.84 | 0.039 | 1.951 (0.008) | 4.1 (0.8) | 0.0046 (0.0016) |\n| CZR17.85 | 0.016 | 1.958 (0.006) | 4.2 (0.5) | 0.0048 (0.0011) |\n| CZR19.21 | 0.012 | 1.960 (0.005) | 4.0 (0.4) | 0.0055 (0.0010) |\n\nCaption: First shell Zn-O fitting results for the standard hardystonite and the glasses, using the Zn-O first shell path in hardystonite a\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:",
"role": "user"
},
{
"content": "{\"value\": \"0.015\", \"type\": \"Other\"}\n{\"value\": \"1.932\", \"type\": \"Other\"}\n{\"value\": \"3.9\", \"type\": \"Other\"}\n{\"value\": \"0.0032\", \"type\": \"Other\"}\n{\"value\": \"1.937\", \"type\": \"Other\"}\n{\"value\": \"4.0\", \"type\": \"Other\"}\n{\"value\": \"0.010\", \"type\": \"Other\"}\n{\"value\": \"1.952\", \"type\": \"Other\"}\n{\"value\": \"4.6\", \"type\": \"Other\"}\n{\"value\": \"0.0066\", \"type\": \"Other\"}\n{\"value\": \"0.006\", \"type\": \"Other\"}\n{\"value\": \"1.957\", \"type\": \"Other\"}\n{\"value\": \"4.5\", \"type\": \"Other\"}\n{\"value\": \"0.0067\", \"type\": \"Other\"}\n{\"value\": \"0.010\", \"type\": \"Other\"}\n{\"value\": \"1.957\", \"type\": \"Other\"}\n{\"value\": \"4.1\", \"type\": \"Other\"}\n{\"value\": \"0.0062\", \"type\": \"Other\"}\n{\"value\": \"0.020\", \"type\": \"Other\"}\n{\"value\": \"1.956\", \"type\": \"Other\"}\n{\"value\": \"4.0\", \"type\": \"Other\"}\n{\"value\": \"0.0061\", \"type\": \"Other\"}\n{\"value\": \"0.039\", \"type\": \"Other\"}\n{\"value\": \"1.951\", \"type\": \"Other\"}\n{\"value\": \"4.1\", \"type\": \"Other\"}\n{\"value\": \"0.0046\", \"type\": \"Other\"}\n{\"value\": \"0.016\", \"type\": \"Other\"}\n{\"value\": \"1.958\", \"type\": \"Other\"}\n{\"value\": \"4.2\", \"type\": \"Other\"}\n{\"value\": \"0.0048\", \"type\": \"Other\"}\n{\"value\": \"0.012\", \"type\": \"Other\"}\n{\"value\": \"1.960\", \"type\": \"Other\"}\n{\"value\": \"4.0\", \"type\": \"Other\"}\n{\"value\": \"0.0055\", \"type\": \"Other\"}",
"role": "assistant"
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science.qasper_abstractive_qa | Examine the provided sections from a research document and address the accompanying query. The text is divided by newlines.
Document Content:
A Corpus for Automatic Readability Assessment and Text Simplification of German
In this paper, we present a corpus for use in automatic readability assessment and automatic text simplification of German. The corpus is compiled from web sources and consists of approximately 211,000 sentences. As a novel contribution, it contains information on text structure, typography, and images, which can be exploited as part of machine learning approaches to readability assessment and text simplification. The focus of this publication is on representing such information as an extension to an existing corpus standard.
Introduction
Simplified language is a variety of standard language characterized by reduced lexical and syntactic complexity, the addition of explanations for difficult concepts, and clearly structured layout. Among the target groups of simplified language commonly mentioned are persons with cognitive impairment or learning disabilities, prelingually deaf persons, functionally illiterate persons, and foreign language learners BIBREF0.
Two natural language processing tasks deal with the concept of simplified language: automatic readability assessment and automatic text simplification. Readability assessment refers to the process of determining the level of difficulty of a text, e.g., along readability measures, school grades, or levels of the Common European Framework of Reference for Languages (CEFR) BIBREF1. Readability measures, in their traditional form, take into account only surface features. For example, the Flesch Reading Ease Score BIBREF2 measures the length of words (in syllables) and sentences (in words). While readability has been shown to correlate with such features to some extent BIBREF3, a consensus has emerged according to which they are not sufficient to account for all of the complexity inherent in a text. As [p. 2618]kauchak-et-al-2014 state, “the usability of readability formulas is limited and there is little evidence that the output of these tools directly results in improved understanding by readers”. Recently, more sophisticated models employing (deeper) linguistic features such as lexical, semantic, morphological, morphosyntactic, syntactic, pragmatic, discourse, psycholinguistic, and language model features have been proposed BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8.
Automatic text simplification was initiated in the late 1990s BIBREF9, BIBREF10 and since then has been approached by means of rule-based and statistical methods. As part of a rule-based approach, the operations carried out typically include replacing complex lexical and syntactic units by simpler ones. A statistical approach generally conceptualizes the simplification task as one of converting a standard-language into a simplified-language text using machine translation. nisioi-et-al-2017 introduced neural machine translation to automatic text simplification. Research on automatic text simplification is comparatively widespread for languages such as English, Swedish, Spanish, and Brazilian Portuguese. To the authors' knowledge, no productive system exists for German. suter-2015, suter-et-al-2016 presented a prototype of a rule-based system for German.
Machine learning approaches to both readability assessment and text simplification rely on data systematically prepared in the form of corpora. Specifically, for automatic text simplification via machine translation, pairs of standard-language/simplified-language texts aligned at the sentence level (i.e., parallel corpora) are needed.
The paper at hand introduces a corpus developed for use in automatic readability assessment and automatic text simplification of German. The focus of this publication is on representing information that is valuable for these tasks but that hitherto has largely been ignored in machine learning approaches centering around simplified language, specifically, text structure (e.g., paragraphs, lines), typography (e.g., font type, font style), and image (content, position, and dimensions) information. The importance of considering such information has repeatedly been asserted theoretically BIBREF11, BIBREF12, BIBREF0. The remainder of this paper is structured as follows: Section SECREF2 presents previous corpora used for automatic readability assessment and text simplification. Section SECREF3 describes our corpus, introducing its novel aspects and presenting the primary data (Section SECREF7), the metadata (Section SECREF10), the secondary data (Section SECREF28), the profile (Section SECREF35), and the results of machine learning experiments carried out on the corpus (Section SECREF37).
Previous Corpora for Automatic Readability Assessment and Automatic Text Simplification
A number of corpora for use in automatic readability assessment and automatic text simplification exist. The most well-known example is the Parallel Wikipedia Simplification Corpus (PWKP) compiled from parallel articles of the English Wikipedia and Simple English Wikipedia BIBREF13 and consisting of around 108,000 sentence pairs. The corpus profile is shown in Table TABREF2. While the corpus represents the largest dataset involving simplified language to date, its application has been criticized for various reasons BIBREF15, BIBREF14, BIBREF16; among these, the fact that Simple English Wikipedia articles are not necessarily direct translations of articles from the English Wikipedia stands out. hwang-et-al-2015 provided an updated version of the corpus that includes a total of 280,000 full and partial matches between the two Wikipedia versions. Another frequently used data collection for English is the Newsela Corpus BIBREF14 consisting of 1,130 news articles, each simplified into four school grade levels by professional editors. Table TABREF3 shows the profile of the Newsela Corpus. The table obviates that the difference in vocabulary size between the English and the simplified English side of the PWKP Corpus amounts to only 18%, while the corresponding number for the English side and the level representing the highest amount of simplification in the Newsela Corpus (Simple-4) is 50.8%. Vocabulary size as an indicator of lexical richness is generally taken to correlate positively with complexity BIBREF17.
gasperin-et-al-2010 compiled the PorSimples Corpus consisting of Brazilian Portuguese texts (2,116 sentences), each with a natural and a strong simplification, resulting in around 4,500 aligned sentences. drndarevic-saggion-2012, bott-et-al-2012, bott-saggion-2012 produced the Simplext Corpus consisting of 200 Spanish/simplified Spanish document pairs, amounting to a total of 1,149 (Spanish)/1,808 (simplified Spanish) sentences (approximately 1,000 aligned sentences).
klaper-ebling-volk-2013 created the first parallel corpus for German/simplified German, consisting of 256 parallel texts downloaded from the web (approximately 70,000 tokens).
Building a Corpus for Automatic Readability Assessment and Automatic Text Simplification of German
Section SECREF2 demonstrated that the only corpus containing simplified German available is that of klaper-ebling-volk-2013. Since its creation, a number of legal and political developments have spurred the availability of data in simplified German. Among these developments is the introduction of a set of regulations for accessible information technology (Barrierefreie-Informationstechnik-Verordnung, BITV 2.0) in Germany and the ratification of the United Nations Convention on the Rights of Persons with Disabilities (CRPD) in Switzerland. The paper at hand introduces a corpus that represents an enhancement of the corpus of klaper-ebling-volk-2013 in the following ways:
The corpus contains more parallel data.
The corpus additionally contains monolingual-only data (simplified German).
The corpus newly contains information on text structure, typography, and images.
The simplified German side of the parallel data together with the monolingual-only data can be used for automatic readability assessment. The parallel data in the corpus is useful both for deriving rules for a rule-based text simplification system in a data-driven manner and for training a data-driven machine translation system. A data augmentation technique such as back-translation BIBREF18 can be applied to the monolingual-only data to arrive at additional (synthetic) parallel data.
Building a Corpus for Automatic Readability Assessment and Automatic Text Simplification of German ::: Primary Data
The corpus contains PDFs and webpages collected from web sources in Germany, Austria, and Switzerland at the end of 2018/beginning of 2019. The web sources mostly consist of websites of governments, specialised institutions, translation agencies, and non-profit organisations (92 different domains). The documents cover a range of topics, such as politics (e.g., instructions for voting), health (e.g., what to do in case of pregnancy), and culture (e.g., introduction to art museums).
For the webpages, a static dump of all documents was created. Following this, the documents were manually checked to verify the language. The main content was subsequently extracted, i.e., HTML markup and boilerplate removed using the Beautiful Soup library for Python. Information on text structure (e.g., paragraphs, lines) and typography (e.g., boldface, italics) was retained. Similarly, image information (content, position, and dimensions of an image) was preserved.
For PDFs, the PDFlib Text and Image Extraction Toolkit (TET) was used to extract the plain text and record information on text structure, typography, and images. The toolkit produces output in an XML format (TETML).
Building a Corpus for Automatic Readability Assessment and Automatic Text Simplification of German ::: Secondary Data
Annotations were added in the Text Corpus Format by WebLicht (TCF) developed as part of CLARIN. TCF supports standoff annotation, which allows for representation of annotations with conflicting hierarchies. TCF does not assign a separate file for each annotation layer; instead, the source text and all annotation layers are stored jointly in a single file. A token layer acts as the key element to which all other annotation layers are linked.
The following types of annotations were added: text structure, fonts, images, tokens, parts of speech, morphological units, lemmas, sentences, and dependency parses. TCF does not readily accommodate the incorporation of all of these types of information. We therefore extended the format in the following ways:
Information on the font type and font style (e.g., italics, bold print) of a token and its position on the physical page (for PDFs only) was specified as attributes to the token elements of the tokens layer (cf. Figure FIGREF34 for an example)
Information on physical page segmentation (for PDFs only), paragraph segmentation, and line segmentation was added as part of a textspan element in the textstructure layer
A separate images layer was introduced to hold image elements that take as attributes the x and y coordinates of the images, their dimensions (width and height), and the number of the page on which they occur
A separate fonts layer was introduced to preserve detailed information on the font configurations referenced in the tokens layer
Linguistic annotation was added automatically using the ParZu dependency parser for German BIBREF19 (for tokens and dependency parses), the NLTK toolkit BIBREF20 (for sentences), the TreeTagger BIBREF21 (for part-of-speech tags and lemmas), and Zmorge BIBREF22 (for morphological units). Figure FIGREF34 shows a sample corpus annotation. Together, the metadata shown in Figure FIGREF11 and the annotations presented in Figure FIGREF34 constitute a complete TCF file.
Building a Corpus for Automatic Readability Assessment and Automatic Text Simplification of German ::: Corpus Profile
The resulting corpus contains 6,217 documents (5,461 monolingual documents plus 378 documents for each side of the parallel data). Table TABREF36 shows the corpus profile. The monolingual-only documents on average contain fewer sentences than the simplified German side of the parallel data (average document length in sentences 31.64 vs. 55.75). The average sentence length is almost equal (approx. 11 tokens). Hence, the monolingual-only texts are shorter than the simplified German texts in the parallel data. Compared to their German counterparts, the simplified German texts in the parallel data have clearly undergone a process of lexical simplification: The vocabulary is smaller by 51% (33,384 vs. 16,352 types), which is comparable to the rate of reduction reported in Section SECREF2 for the Newsela Corpus (50.8%).
Conclusion and Outlook
We have introduced a corpus compiled for use in automatic readability assessment and automatic text simplification of German. While such tasks have been addressed for other languages, research on German is still scarce. The features exploited as part of machine learning approaches to readability assessment so far typically include surface and/or (deeper) linguistic features. The corpus presented in this paper additionally contains information on text structure, typography, and images. These features have been shown to be indicative of simple vs. complex texts both theoretically and, using the corpus described in this paper, empirically.
Information on text structure, typography, and images can also be leveraged as part of a neural machine translation approach to text simplification. A set of parallel documents used in machine translation additionally requires sentence alignments, which are still missing from our corpus. Hence, as a next step, we will include such information using the Customized Alignment for Text Simplification (CATS) tool BIBREF23.
Table 1: Parallel Wikipedia Simplification Corpus (PWKP) (Zhu et al., 2010): Profile (from Xu et al. (2015))
Table 3: Corpus profile
Query: Which information about text structure is included in the corpus?
JSON Response: | {"answer": "paragraph, lines, textspan element (paragraph segmentation, line segmentation, Information on physical page segmentation(for PDF only))", "evidence": ["The paper at hand introduces a corpus developed for use in automatic readability assessment and automatic text simplification of German. The focus of this publication is on representing information that is valuable for these tasks but that hitherto has largely been ignored in machine learning approaches centering around simplified language, specifically, text structure (e.g., paragraphs, lines), typography (e.g., font type, font style), and image (content, position, and dimensions) information. The importance of considering such information has repeatedly been asserted theoretically BIBREF11, BIBREF12, BIBREF0. The remainder of this paper is structured as follows: Section SECREF2 presents previous corpora used for automatic readability assessment and text simplification. Section SECREF3 describes our corpus, introducing its novel aspects and presenting the primary data (Section SECREF7), the metadata (Section SECREF10), the secondary data (Section SECREF28), the profile (Section SECREF35), and the results of machine learning experiments carried out on the corpus (Section SECREF37).", "Information on physical page segmentation (for PDFs only), paragraph segmentation, and line segmentation was added as part of a textspan element in the textstructure layer"]} | science.qasper_abstractive_qa.5.0 | [
{
"content": "Examine the provided sections from a research document and address the accompanying query. The text is divided by newlines.\n\nDocument Content:\n\nA Corpus for Automatic Readability Assessment and Text Simplification of German\n\nIn this paper, we present a corpus for use in automatic readability assessment and automatic text simplification of German. The corpus is compiled from web sources and consists of approximately 211,000 sentences. As a novel contribution, it contains information on text structure, typography, and images, which can be exploited as part of machine learning approaches to readability assessment and text simplification. The focus of this publication is on representing such information as an extension to an existing corpus standard.\n\nIntroduction\nSimplified language is a variety of standard language characterized by reduced lexical and syntactic complexity, the addition of explanations for difficult concepts, and clearly structured layout. Among the target groups of simplified language commonly mentioned are persons with cognitive impairment or learning disabilities, prelingually deaf persons, functionally illiterate persons, and foreign language learners BIBREF0.\nTwo natural language processing tasks deal with the concept of simplified language: automatic readability assessment and automatic text simplification. Readability assessment refers to the process of determining the level of difficulty of a text, e.g., along readability measures, school grades, or levels of the Common European Framework of Reference for Languages (CEFR) BIBREF1. Readability measures, in their traditional form, take into account only surface features. For example, the Flesch Reading Ease Score BIBREF2 measures the length of words (in syllables) and sentences (in words). While readability has been shown to correlate with such features to some extent BIBREF3, a consensus has emerged according to which they are not sufficient to account for all of the complexity inherent in a text. As [p. 2618]kauchak-et-al-2014 state, “the usability of readability formulas is limited and there is little evidence that the output of these tools directly results in improved understanding by readers”. Recently, more sophisticated models employing (deeper) linguistic features such as lexical, semantic, morphological, morphosyntactic, syntactic, pragmatic, discourse, psycholinguistic, and language model features have been proposed BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8.\nAutomatic text simplification was initiated in the late 1990s BIBREF9, BIBREF10 and since then has been approached by means of rule-based and statistical methods. As part of a rule-based approach, the operations carried out typically include replacing complex lexical and syntactic units by simpler ones. A statistical approach generally conceptualizes the simplification task as one of converting a standard-language into a simplified-language text using machine translation. nisioi-et-al-2017 introduced neural machine translation to automatic text simplification. Research on automatic text simplification is comparatively widespread for languages such as English, Swedish, Spanish, and Brazilian Portuguese. To the authors' knowledge, no productive system exists for German. suter-2015, suter-et-al-2016 presented a prototype of a rule-based system for German.\nMachine learning approaches to both readability assessment and text simplification rely on data systematically prepared in the form of corpora. Specifically, for automatic text simplification via machine translation, pairs of standard-language/simplified-language texts aligned at the sentence level (i.e., parallel corpora) are needed.\nThe paper at hand introduces a corpus developed for use in automatic readability assessment and automatic text simplification of German. The focus of this publication is on representing information that is valuable for these tasks but that hitherto has largely been ignored in machine learning approaches centering around simplified language, specifically, text structure (e.g., paragraphs, lines), typography (e.g., font type, font style), and image (content, position, and dimensions) information. The importance of considering such information has repeatedly been asserted theoretically BIBREF11, BIBREF12, BIBREF0. The remainder of this paper is structured as follows: Section SECREF2 presents previous corpora used for automatic readability assessment and text simplification. Section SECREF3 describes our corpus, introducing its novel aspects and presenting the primary data (Section SECREF7), the metadata (Section SECREF10), the secondary data (Section SECREF28), the profile (Section SECREF35), and the results of machine learning experiments carried out on the corpus (Section SECREF37).\n\nPrevious Corpora for Automatic Readability Assessment and Automatic Text Simplification\nA number of corpora for use in automatic readability assessment and automatic text simplification exist. The most well-known example is the Parallel Wikipedia Simplification Corpus (PWKP) compiled from parallel articles of the English Wikipedia and Simple English Wikipedia BIBREF13 and consisting of around 108,000 sentence pairs. The corpus profile is shown in Table TABREF2. While the corpus represents the largest dataset involving simplified language to date, its application has been criticized for various reasons BIBREF15, BIBREF14, BIBREF16; among these, the fact that Simple English Wikipedia articles are not necessarily direct translations of articles from the English Wikipedia stands out. hwang-et-al-2015 provided an updated version of the corpus that includes a total of 280,000 full and partial matches between the two Wikipedia versions. Another frequently used data collection for English is the Newsela Corpus BIBREF14 consisting of 1,130 news articles, each simplified into four school grade levels by professional editors. Table TABREF3 shows the profile of the Newsela Corpus. The table obviates that the difference in vocabulary size between the English and the simplified English side of the PWKP Corpus amounts to only 18%, while the corresponding number for the English side and the level representing the highest amount of simplification in the Newsela Corpus (Simple-4) is 50.8%. Vocabulary size as an indicator of lexical richness is generally taken to correlate positively with complexity BIBREF17.\ngasperin-et-al-2010 compiled the PorSimples Corpus consisting of Brazilian Portuguese texts (2,116 sentences), each with a natural and a strong simplification, resulting in around 4,500 aligned sentences. drndarevic-saggion-2012, bott-et-al-2012, bott-saggion-2012 produced the Simplext Corpus consisting of 200 Spanish/simplified Spanish document pairs, amounting to a total of 1,149 (Spanish)/1,808 (simplified Spanish) sentences (approximately 1,000 aligned sentences).\nklaper-ebling-volk-2013 created the first parallel corpus for German/simplified German, consisting of 256 parallel texts downloaded from the web (approximately 70,000 tokens).\n\nBuilding a Corpus for Automatic Readability Assessment and Automatic Text Simplification of German\nSection SECREF2 demonstrated that the only corpus containing simplified German available is that of klaper-ebling-volk-2013. Since its creation, a number of legal and political developments have spurred the availability of data in simplified German. Among these developments is the introduction of a set of regulations for accessible information technology (Barrierefreie-Informationstechnik-Verordnung, BITV 2.0) in Germany and the ratification of the United Nations Convention on the Rights of Persons with Disabilities (CRPD) in Switzerland. The paper at hand introduces a corpus that represents an enhancement of the corpus of klaper-ebling-volk-2013 in the following ways:\nThe corpus contains more parallel data.\nThe corpus additionally contains monolingual-only data (simplified German).\nThe corpus newly contains information on text structure, typography, and images.\nThe simplified German side of the parallel data together with the monolingual-only data can be used for automatic readability assessment. The parallel data in the corpus is useful both for deriving rules for a rule-based text simplification system in a data-driven manner and for training a data-driven machine translation system. A data augmentation technique such as back-translation BIBREF18 can be applied to the monolingual-only data to arrive at additional (synthetic) parallel data.\n\nBuilding a Corpus for Automatic Readability Assessment and Automatic Text Simplification of German ::: Primary Data\nThe corpus contains PDFs and webpages collected from web sources in Germany, Austria, and Switzerland at the end of 2018/beginning of 2019. The web sources mostly consist of websites of governments, specialised institutions, translation agencies, and non-profit organisations (92 different domains). The documents cover a range of topics, such as politics (e.g., instructions for voting), health (e.g., what to do in case of pregnancy), and culture (e.g., introduction to art museums).\nFor the webpages, a static dump of all documents was created. Following this, the documents were manually checked to verify the language. The main content was subsequently extracted, i.e., HTML markup and boilerplate removed using the Beautiful Soup library for Python. Information on text structure (e.g., paragraphs, lines) and typography (e.g., boldface, italics) was retained. Similarly, image information (content, position, and dimensions of an image) was preserved.\nFor PDFs, the PDFlib Text and Image Extraction Toolkit (TET) was used to extract the plain text and record information on text structure, typography, and images. The toolkit produces output in an XML format (TETML).\n\nBuilding a Corpus for Automatic Readability Assessment and Automatic Text Simplification of German ::: Secondary Data\nAnnotations were added in the Text Corpus Format by WebLicht (TCF) developed as part of CLARIN. TCF supports standoff annotation, which allows for representation of annotations with conflicting hierarchies. TCF does not assign a separate file for each annotation layer; instead, the source text and all annotation layers are stored jointly in a single file. A token layer acts as the key element to which all other annotation layers are linked.\nThe following types of annotations were added: text structure, fonts, images, tokens, parts of speech, morphological units, lemmas, sentences, and dependency parses. TCF does not readily accommodate the incorporation of all of these types of information. We therefore extended the format in the following ways:\nInformation on the font type and font style (e.g., italics, bold print) of a token and its position on the physical page (for PDFs only) was specified as attributes to the token elements of the tokens layer (cf. Figure FIGREF34 for an example)\nInformation on physical page segmentation (for PDFs only), paragraph segmentation, and line segmentation was added as part of a textspan element in the textstructure layer\nA separate images layer was introduced to hold image elements that take as attributes the x and y coordinates of the images, their dimensions (width and height), and the number of the page on which they occur\nA separate fonts layer was introduced to preserve detailed information on the font configurations referenced in the tokens layer\nLinguistic annotation was added automatically using the ParZu dependency parser for German BIBREF19 (for tokens and dependency parses), the NLTK toolkit BIBREF20 (for sentences), the TreeTagger BIBREF21 (for part-of-speech tags and lemmas), and Zmorge BIBREF22 (for morphological units). Figure FIGREF34 shows a sample corpus annotation. Together, the metadata shown in Figure FIGREF11 and the annotations presented in Figure FIGREF34 constitute a complete TCF file.\n\nBuilding a Corpus for Automatic Readability Assessment and Automatic Text Simplification of German ::: Corpus Profile\nThe resulting corpus contains 6,217 documents (5,461 monolingual documents plus 378 documents for each side of the parallel data). Table TABREF36 shows the corpus profile. The monolingual-only documents on average contain fewer sentences than the simplified German side of the parallel data (average document length in sentences 31.64 vs. 55.75). The average sentence length is almost equal (approx. 11 tokens). Hence, the monolingual-only texts are shorter than the simplified German texts in the parallel data. Compared to their German counterparts, the simplified German texts in the parallel data have clearly undergone a process of lexical simplification: The vocabulary is smaller by 51% (33,384 vs. 16,352 types), which is comparable to the rate of reduction reported in Section SECREF2 for the Newsela Corpus (50.8%).\n\nConclusion and Outlook\nWe have introduced a corpus compiled for use in automatic readability assessment and automatic text simplification of German. While such tasks have been addressed for other languages, research on German is still scarce. The features exploited as part of machine learning approaches to readability assessment so far typically include surface and/or (deeper) linguistic features. The corpus presented in this paper additionally contains information on text structure, typography, and images. These features have been shown to be indicative of simple vs. complex texts both theoretically and, using the corpus described in this paper, empirically.\nInformation on text structure, typography, and images can also be leveraged as part of a neural machine translation approach to text simplification. A set of parallel documents used in machine translation additionally requires sentence alignments, which are still missing from our corpus. Hence, as a next step, we will include such information using the Customized Alignment for Text Simplification (CATS) tool BIBREF23.\n\nTable 1: Parallel Wikipedia Simplification Corpus (PWKP) (Zhu et al., 2010): Profile (from Xu et al. (2015))\n\nTable 3: Corpus profile\n\nQuery: Which information about text structure is included in the corpus?\n\nJSON Response:",
"role": "user"
},
{
"content": "{\"answer\": \"paragraph, lines, textspan element (paragraph segmentation, line segmentation, Information on physical page segmentation(for PDF only))\", \"evidence\": [\"The paper at hand introduces a corpus developed for use in automatic readability assessment and automatic text simplification of German. The focus of this publication is on representing information that is valuable for these tasks but that hitherto has largely been ignored in machine learning approaches centering around simplified language, specifically, text structure (e.g., paragraphs, lines), typography (e.g., font type, font style), and image (content, position, and dimensions) information. The importance of considering such information has repeatedly been asserted theoretically BIBREF11, BIBREF12, BIBREF0. The remainder of this paper is structured as follows: Section SECREF2 presents previous corpora used for automatic readability assessment and text simplification. Section SECREF3 describes our corpus, introducing its novel aspects and presenting the primary data (Section SECREF7), the metadata (Section SECREF10), the secondary data (Section SECREF28), the profile (Section SECREF35), and the results of machine learning experiments carried out on the corpus (Section SECREF37).\", \"Information on physical page segmentation (for PDFs only), paragraph segmentation, and line segmentation was added as part of a textspan element in the textstructure layer\"]}",
"role": "assistant"
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|
science.discomat_te | | Sample no. | V m (cm3/mol) | R m | a O 2 - (A3) | L (n o ) | L th | A(n o ) (A-3) | A th (A-3) |
| 1 | 32.52 | 14.18 | 1.683 | 0.678 | 0.716 | 0.143 | 0.147 |
| 2 | 33.82 | 14.84 | 1.768 | 0.725 | 0.747 | 0.130 | 0.137 |
| 3 | 34.46 | 15.38 | 1.837 | 0.761 | 0.778 | 0.121 | 0.128 |
| 4 | 35.04 | 15.81 | 1.891 | 0.787 | 0.809 | 0.114 | 0.118 |
| 5 | 35.08 | 16.12 | 1.930 | 0.805 | 0.840 | 0.109 | 0.109 |
| 6 | 35.40 | 16.48 | 1.976 | 0.825 | 0.871 | 0.104 | 0.100 |
| 7 | 35.76 | 16.81 | 2.017 | 0.842 | 0.902 | 0.099 | 0.090 |
| 8 | 36.30 | 17.22 | 2.068 | 0.863 | 0.933 | 0.093 | 0.081 |
| 9 | 36.66 | 17.59 | 2.114 | 0.880 | 0.964 | 0.089 | 0.071 |
Caption: Molar volume (V m ), molar refraction (R m ), electronic polarizability of oxide ions a O 2 - , optical basicity (L(n o )), and interaction parameter (A(n o )) for 25Gd2O3-(75-x)WO3-75B2O3 glasses with x=25-65. The values of theoretical optical basicity (L th) and interaction parameter (A th) estimated from Eqs. (6) and (7) are also shown. The uncertainties of V m , R m , a O 2 - , L(n o ), and A(n o ) are +-0.02cm3/mol, +-0.02, +-0.005A3, +-0.003, and +-0.003A-3, respectively.
Examine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:
{"value": "xx", "type": "Other"}
{"value": "xx", "type": "Glass_Compound_Amount", "constituent": "xx", "unit": "xx", "material": "xx"}
Process each cell sequentially, and for any unknown attributes, use "xx" as a placeholder. Each JSON should be on a new line.
Output: | {"value": "1", "type": "Other"}
{"value": "32.52", "type": "Other"}
{"value": "14.18", "type": "Other"}
{"value": "1.683", "type": "Other"}
{"value": "0.678", "type": "Other"}
{"value": "0.716", "type": "Other"}
{"value": "0.143", "type": "Other"}
{"value": "0.147", "type": "Other"}
{"value": "2", "type": "Other"}
{"value": "33.82", "type": "Other"}
{"value": "14.84", "type": "Other"}
{"value": "1.768", "type": "Other"}
{"value": "0.725", "type": "Other"}
{"value": "0.747", "type": "Other"}
{"value": "0.130", "type": "Other"}
{"value": "0.137", "type": "Other"}
{"value": "3", "type": "Other"}
{"value": "34.46", "type": "Other"}
{"value": "15.38", "type": "Other"}
{"value": "1.837", "type": "Other"}
{"value": "0.761", "type": "Other"}
{"value": "0.778", "type": "Other"}
{"value": "0.121", "type": "Other"}
{"value": "0.128", "type": "Other"}
{"value": "4", "type": "Other"}
{"value": "35.04", "type": "Other"}
{"value": "15.81", "type": "Other"}
{"value": "1.891", "type": "Other"}
{"value": "0.787", "type": "Other"}
{"value": "0.809", "type": "Other"}
{"value": "0.114", "type": "Other"}
{"value": "0.118", "type": "Other"}
{"value": "5", "type": "Other"}
{"value": "35.08", "type": "Other"}
{"value": "16.12", "type": "Other"}
{"value": "1.930", "type": "Other"}
{"value": "0.805", "type": "Other"}
{"value": "0.840", "type": "Other"}
{"value": "0.109", "type": "Other"}
{"value": "0.109", "type": "Other"}
{"value": "6", "type": "Other"}
{"value": "35.40", "type": "Other"}
{"value": "16.48", "type": "Other"}
{"value": "1.976", "type": "Other"}
{"value": "0.825", "type": "Other"}
{"value": "0.871", "type": "Other"}
{"value": "0.104", "type": "Other"}
{"value": "0.100", "type": "Other"}
{"value": "7", "type": "Other"}
{"value": "35.76", "type": "Other"}
{"value": "16.81", "type": "Other"}
{"value": "2.017", "type": "Other"}
{"value": "0.842", "type": "Other"}
{"value": "0.902", "type": "Other"}
{"value": "0.099", "type": "Other"}
{"value": "0.090", "type": "Other"}
{"value": "8", "type": "Other"}
{"value": "36.30", "type": "Other"}
{"value": "17.22", "type": "Other"}
{"value": "2.068", "type": "Other"}
{"value": "0.863", "type": "Other"}
{"value": "0.933", "type": "Other"}
{"value": "0.093", "type": "Other"}
{"value": "0.081", "type": "Other"}
{"value": "9", "type": "Other"}
{"value": "36.66", "type": "Other"}
{"value": "17.59", "type": "Other"}
{"value": "2.114", "type": "Other"}
{"value": "0.880", "type": "Other"}
{"value": "0.964", "type": "Other"}
{"value": "0.089", "type": "Other"}
{"value": "0.071", "type": "Other"} | science.discomat_te.494 | [
{
"content": "| Sample no. | V m (cm3/mol) | R m | a O 2 - (A3) | L (n o ) | L th | A(n o ) (A-3) | A th (A-3) |\n| 1 | 32.52 | 14.18 | 1.683 | 0.678 | 0.716 | 0.143 | 0.147 |\n| 2 | 33.82 | 14.84 | 1.768 | 0.725 | 0.747 | 0.130 | 0.137 |\n| 3 | 34.46 | 15.38 | 1.837 | 0.761 | 0.778 | 0.121 | 0.128 |\n| 4 | 35.04 | 15.81 | 1.891 | 0.787 | 0.809 | 0.114 | 0.118 |\n| 5 | 35.08 | 16.12 | 1.930 | 0.805 | 0.840 | 0.109 | 0.109 |\n| 6 | 35.40 | 16.48 | 1.976 | 0.825 | 0.871 | 0.104 | 0.100 |\n| 7 | 35.76 | 16.81 | 2.017 | 0.842 | 0.902 | 0.099 | 0.090 |\n| 8 | 36.30 | 17.22 | 2.068 | 0.863 | 0.933 | 0.093 | 0.081 |\n| 9 | 36.66 | 17.59 | 2.114 | 0.880 | 0.964 | 0.089 | 0.071 |\n\nCaption: Molar volume (V m ), molar refraction (R m ), electronic polarizability of oxide ions a O 2 - , optical basicity (L(n o )), and interaction parameter (A(n o )) for 25Gd2O3-(75-x)WO3-75B2O3 glasses with x=25-65. The values of theoretical optical basicity (L th) and interaction parameter (A th) estimated from Eqs. (6) and (7) are also shown. The uncertainties of V m , R m , a O 2 - , L(n o ), and A(n o ) are +-0.02cm3/mol, +-0.02, +-0.005A3, +-0.003, and +-0.003A-3, respectively.\n\nExamine the table provided from a materials science study. Your task is to convert each numeric cell into a JSON format. Use the following structures for guidance:\n{\"value\": \"xx\", \"type\": \"Other\"}\n{\"value\": \"xx\", \"type\": \"Glass_Compound_Amount\", \"constituent\": \"xx\", \"unit\": \"xx\", \"material\": \"xx\"}\n\nProcess each cell sequentially, and for any unknown attributes, use \"xx\" as a placeholder. Each JSON should be on a new line.\n\nOutput:",
"role": "user"
},
{
"content": "{\"value\": \"1\", \"type\": \"Other\"}\n{\"value\": \"32.52\", \"type\": \"Other\"}\n{\"value\": \"14.18\", \"type\": \"Other\"}\n{\"value\": \"1.683\", \"type\": \"Other\"}\n{\"value\": \"0.678\", \"type\": \"Other\"}\n{\"value\": \"0.716\", \"type\": \"Other\"}\n{\"value\": \"0.143\", \"type\": \"Other\"}\n{\"value\": \"0.147\", \"type\": \"Other\"}\n{\"value\": \"2\", \"type\": \"Other\"}\n{\"value\": \"33.82\", \"type\": \"Other\"}\n{\"value\": \"14.84\", \"type\": \"Other\"}\n{\"value\": \"1.768\", \"type\": \"Other\"}\n{\"value\": \"0.725\", \"type\": \"Other\"}\n{\"value\": \"0.747\", \"type\": \"Other\"}\n{\"value\": \"0.130\", \"type\": \"Other\"}\n{\"value\": \"0.137\", \"type\": \"Other\"}\n{\"value\": \"3\", \"type\": \"Other\"}\n{\"value\": \"34.46\", \"type\": \"Other\"}\n{\"value\": \"15.38\", \"type\": \"Other\"}\n{\"value\": \"1.837\", \"type\": \"Other\"}\n{\"value\": \"0.761\", \"type\": \"Other\"}\n{\"value\": \"0.778\", \"type\": \"Other\"}\n{\"value\": \"0.121\", \"type\": \"Other\"}\n{\"value\": \"0.128\", \"type\": \"Other\"}\n{\"value\": \"4\", \"type\": \"Other\"}\n{\"value\": \"35.04\", \"type\": \"Other\"}\n{\"value\": \"15.81\", \"type\": \"Other\"}\n{\"value\": \"1.891\", \"type\": \"Other\"}\n{\"value\": \"0.787\", \"type\": \"Other\"}\n{\"value\": \"0.809\", \"type\": \"Other\"}\n{\"value\": \"0.114\", \"type\": \"Other\"}\n{\"value\": \"0.118\", \"type\": \"Other\"}\n{\"value\": \"5\", \"type\": \"Other\"}\n{\"value\": \"35.08\", \"type\": \"Other\"}\n{\"value\": \"16.12\", \"type\": \"Other\"}\n{\"value\": \"1.930\", \"type\": \"Other\"}\n{\"value\": \"0.805\", \"type\": \"Other\"}\n{\"value\": \"0.840\", \"type\": \"Other\"}\n{\"value\": \"0.109\", \"type\": \"Other\"}\n{\"value\": \"0.109\", \"type\": \"Other\"}\n{\"value\": \"6\", \"type\": \"Other\"}\n{\"value\": \"35.40\", \"type\": \"Other\"}\n{\"value\": \"16.48\", \"type\": \"Other\"}\n{\"value\": \"1.976\", \"type\": \"Other\"}\n{\"value\": \"0.825\", \"type\": \"Other\"}\n{\"value\": \"0.871\", \"type\": \"Other\"}\n{\"value\": \"0.104\", \"type\": \"Other\"}\n{\"value\": \"0.100\", \"type\": \"Other\"}\n{\"value\": \"7\", \"type\": \"Other\"}\n{\"value\": \"35.76\", \"type\": \"Other\"}\n{\"value\": \"16.81\", \"type\": \"Other\"}\n{\"value\": \"2.017\", \"type\": \"Other\"}\n{\"value\": \"0.842\", \"type\": \"Other\"}\n{\"value\": \"0.902\", \"type\": \"Other\"}\n{\"value\": \"0.099\", \"type\": \"Other\"}\n{\"value\": \"0.090\", \"type\": \"Other\"}\n{\"value\": \"8\", \"type\": \"Other\"}\n{\"value\": \"36.30\", \"type\": \"Other\"}\n{\"value\": \"17.22\", \"type\": \"Other\"}\n{\"value\": \"2.068\", \"type\": \"Other\"}\n{\"value\": \"0.863\", \"type\": \"Other\"}\n{\"value\": \"0.933\", \"type\": \"Other\"}\n{\"value\": \"0.093\", \"type\": \"Other\"}\n{\"value\": \"0.081\", \"type\": \"Other\"}\n{\"value\": \"9\", \"type\": \"Other\"}\n{\"value\": \"36.66\", \"type\": \"Other\"}\n{\"value\": \"17.59\", \"type\": \"Other\"}\n{\"value\": \"2.114\", \"type\": \"Other\"}\n{\"value\": \"0.880\", \"type\": \"Other\"}\n{\"value\": \"0.964\", \"type\": \"Other\"}\n{\"value\": \"0.089\", \"type\": \"Other\"}\n{\"value\": \"0.071\", \"type\": \"Other\"}",
"role": "assistant"
}
]
|
science.qasper_abstractive_qa | Examine the provided sections from a research document and address the accompanying query. The text is divided by newlines.
Document Content:
A Multi Purpose and Large Scale Speech Corpus in Persian and English for Speaker and Speech Recognition: The Deepmine Database
DeepMine is a speech database in Persian and English designed to build and evaluate text-dependent, text-prompted, and text-independent speaker verification, as well as Persian speech recognition systems. It contains more than 1850 speakers and 540 thousand recordings overall, more than 480 hours of speech are transcribed. It is the first public large-scale speaker verification database in Persian, the largest public text-dependent and text-prompted speaker verification database in English, and the largest public evaluation dataset for text-independent speaker verification. It has a good coverage of age, gender, and accents. We provide several evaluation protocols for each part of the database to allow for research on different aspects of speaker verification. We also provide the results of several experiments that can be considered as baselines: HMM-based i-vectors for text-dependent speaker verification, and HMM-based as well as state-of-the-art deep neural network based ASR. We demonstrate that the database can serve for training robust ASR models.
Introduction
Nowadays deep learning techniques outperform the other conventional methods in most of the speech-related tasks. Training robust deep neural networks for each task depends on the availability of powerful processing GPUs, as well as standard and large scale datasets. In text-independent speaker verification, large-scale datasets are available, thanks to the NIST SRE evaluations and other data collection projects such as VoxCeleb BIBREF0.
In text-dependent speaker recognition, experiments with end-to-end architectures conducted on large proprietary databases have demonstrated their superiority over traditional approaches BIBREF1. Yet, contrary to text-independent speaker recognition, text-dependent speaker recognition lacks large-scale publicly available databases. The two most well-known datasets are probably RSR2015 BIBREF2 and RedDots BIBREF3. The former contains speech data collected from 300 individuals in a controlled manner, while the latter is used primarily for evaluation rather than training, due to its small number of speakers (only 64). Motivated by this lack of large-scale dataset for text-dependent speaker verification, we chose to proceed with the collection of the DeepMine dataset, which we expect to become a standard benchmark for the task.
Apart from speaker recognition, large amounts of training data are required also for training automatic speech recognition (ASR) systems. Such datasets should not only be large in size, they should also be characterized by high variability with respect to speakers, age and dialects. While several datasets with these properties are available for languages like English, Mandarin, French, this is not the case for several other languages, such as Persian. To this end, we proceeded with collecting a large-scale dataset, suitable for building robust ASR models in Persian.
The main goal of the DeepMine project was to collect speech from at least a few thousand speakers, enabling research and development of deep learning methods. The project started at the beginning of 2017, and after designing the database and the developing Android and server applications, the data collection began in the middle of 2017. The project finished at the end of 2018 and the cleaned-up and final version of the database was released at the beginning of 2019. In BIBREF4, the running project and its data collection scenarios were described, alongside with some preliminary results and statistics. In this paper, we announce the final and cleaned-up version of the database, describe its different parts and provide various evaluation setups for each part. Finally, since the database was designed mainly for text-dependent speaker verification purposes, some baseline results are reported for this task on the official evaluation setups. Additional baseline results are also reported for Persian speech recognition. However, due to the space limitation in this paper, the baseline results are not reported for all the database parts and conditions. They will be defined and reported in the database technical documentation and in a future journal paper.
Data Collection
DeepMine is publicly available for everybody with a variety of licenses for different users. It was collected using crowdsourcing BIBREF4. The data collection was done using an Android application. Each respondent installed the application on his/her personal device and recorded several phrases in different sessions. The Android application did various checks on each utterance and if it passed all of them, the respondent was directed to the next phrase. For more information about data collection scenario, please refer to BIBREF4.
Data Collection ::: Statistics
After processing the database and removing problematic respondents and utterances, 1969 respondents remained in the database, with 1149 of them being male and 820 female. 297 of the respondents could not read English and have therefore read only the Persian prompts. About 13200 sessions were recorded by females and similarly, about 9500 sessions by males, i.e. women are over-represented in terms of sessions, even though their number is 17% smaller than that of males. Other useful statistics related to the database are shown in Table TABREF4.
The last status of the database, as well as other related and useful information about its availability can be found on its website, together with a limited number of samples.
DeepMine Database Parts ::: Part1 - Text-dependent (TD)
This part contains a set of fixed phrases which are used to verify speakers in text-dependent mode. Each speaker utters 5 Persian phrases, and if the speaker can read English, 5 phrases selected from Part1 of the RedDots database are also recorded.
We have created three experimental setups with different numbers of speakers in the evaluation set. For each setup, speakers with more recording sessions are included in the evaluation set and the rest of the speakers are used for training in the background set (in the database, all background sets are basically training data). The rows in Table TABREF13 corresponds to the different experimental setups and shows the numbers of speakers in each set. Note that, for English, we have filtered the (Persian native) speakers by the ability to read English. Therefore, there are fewer speakers in each set for English than for Persian. There is a small “dev” set in each setup which can be used for parameter tuning to prevent over-tuning on the evaluation set.
For each experimental setup, we have defined several official trial lists with different numbers of enrollment utterances per trial in order to investigate the effects of having different amounts of enrollment data. All trials in one trial list have the same number of enrollment utterances (3 to 6) and only one test utterance. All enrollment utterances in a trial are taken from different consecutive sessions and the test utterance is taken from yet another session. From all the setups and conditions, the 100-spk with 3-session enrollment (3-sess) is considered as the main evaluation condition. In Table TABREF14, the number of trials for Persian 3-sess are shown for the different types of trial in the text-dependent speaker verification (SV). Note that for Imposter-Wrong (IW) trials (i.e. imposter speaker pronouncing wrong phrase), we merely create one wrong trial for each Imposter-Correct (IC) trial to limit the huge number of possible trials for this case. So, the number of trials for IC and IW cases are the same.
DeepMine Database Parts ::: Part2 - Text-prompted (TP)
For this part, in each session, 3 random sequences of Persian month names are shown to the respondent in two modes: In the first mode, the sequence consists of all 12 months, which will be used for speaker enrollment. The second mode contains a sequence of 3 month names that will be used as a test utterance. In each 8 sessions received by a respondent from the server, there are 3 enrollment phrases of all 12 months (all in just one session), and $7 \times 3$ other test phrases, containing fewer words. For a respondent who can read English, 3 random sequences of English digits are also recorded in each session. In one of the sessions, these sequences contain all digits and the remaining ones contain only 4 digits.
Similar to the text-dependent case, three experimental setups with different number of speaker in the evaluation set are defined (corresponding to the rows in Table TABREF16). However, different strategy is used for defining trials: Depending on the enrollment condition (1- to 3-sess), trials are enrolled on utterances of all words from 1 to 3 different sessions (i.e. 3 to 9 utterances). Further, we consider two conditions for test utterances: seq test utterance with only 3 or 4 words and full test utterances with all words (i.e. same words as in enrollment but in different order). From all setups an all conditions, the 100-spk with 1-session enrolment (1-sess) is considered as the main evaluation condition for the text-prompted case. In Table TABREF16, the numbers of trials (sum for both seq and full conditions) for Persian 1-sess are shown for the different types of trials in the text-prompted SV. Again, we just create one IW trial for each IC trial.
DeepMine Database Parts ::: Part3 - Text-independent (TI)
In this part, 8 Persian phrases that have already been transcribed on the phone level are displayed to the respondent. These phrases are chosen mostly from news and Persian Wikipedia. If the respondent is unable to read English, instead of 5 fixed phrases and 3 random digit strings, 8 other Persian phrases are also prompted to the respondent to have exactly 24 phrases in each recording session.
This part can be useful at least for three potential applications. First, it can be used for text-independent speaker verification. The second application of this part (same as Part4 of RedDots) is text-prompted speaker verification using random text (instead of a random sequence of words). Finally, the third application is large vocabulary speech recognition in Persian (explained in the next sub-section).
Based on the recording sessions, we created two experimental setups for speaker verification. In the first one, respondents with at least 17 recording sessions are included to the evaluation set, respondents with 16 sessions to the development and the rest of respondents to the background set (can be used as training data). In the second setup, respondents with at least 8 sessions are included to the evaluation set, respondents with 6 or 7 sessions to the development and the rest of respondents to the background set. Table TABREF18 shows numbers of speakers in each set of the database for text-independent SV case.
For text-independent SV, we have considered 4 scenarios for enrollment and 4 scenarios for test. The speaker can be enrolled using utterances from 1, 2 or 3 consecutive sessions (1sess to 3sess) or using 8 utterances from 8 different sessions. The test speech can be one utterance (1utt) for short duration scenario or all utterances in one session (1sess) for long duration case. In addition, test speech can be selected from 5 English phrases for cross-language testing (enrollment using Persian utterances and test using English utterances). From all setups, 1sess-1utt and 1sess-1sess for 438-spk set are considered as the main evaluation setups for text-independent case. Table TABREF19 shows number of trials for these setups.
For text-prompted SV with random text, the same setup as text-independent case together with corresponding utterance transcriptions can be used.
DeepMine Database Parts ::: Part3 - Speech Recognition
As explained before, Part3 of the DeepMine database can be used for Persian read speech recognition. There are only a few databases for speech recognition in Persian BIBREF6, BIBREF7. Hence, this part can at least partly address this problem and enable robust speech recognition applications in Persian. Additionally, it can be used for speaker recognition applications, such as training deep neural networks (DNNs) for extracting bottleneck features BIBREF8, or for collecting sufficient statistics using DNNs for i-vector training.
We have randomly selected 50 speakers (25 for each gender) from the all speakers in the database which have net speech (without silence parts) between 25 minutes to 50 minutes as test speakers. For each speaker, the utterances in the first 5 sessions are included to (small) test-set and the other utterances of test speakers are considered as a large-test-set. The remaining utterances of the other speakers are included in the training set. The test-set, large-test-set and train-set contain 5.9, 28.5 and 450 hours of speech respectively.
There are about 8300 utterances in Part3 which contain only Persian full names (i.e. first and family name pairs). Each phrase consists of several full names and their phoneme transcriptions were extracted automatically using a trained Grapheme-to-Phoneme (G2P). These utterances can be used to evaluate the performance of a systems for name recognition, which is usually more difficult than the normal speech recognition because of the lack of a reliable language model.
Experiments and Results
Due to the space limitation, we present results only for the Persian text-dependent speaker verification and speech recognition.
Acknowledgments
The data collection project was mainly supported by Sharif DeepMine company. The work on the paper was supported by Czech National Science Foundation (GACR) project "NEUREM3" No. 19-26934X and the National Programme of Sustainability (NPU II) project "IT4Innovations excellence in science - LQ1602".
Table 1. Statistics of the DeepMine database.
Table 3. Number of speakers in different evaluation sets of textdependent part.
Table 4. Numbers of trials in each different evaluation set for all possible trial types in text-dependent SV for 3-sess enrollment. TC: Target-Correct, TW: Target-Wrong, IC: Imposter-Correct, IW: Imposter-Wrong. K stands for thousand and M stands for million trials.
Table 5. Numbers of trials in each different evaluation sets for all possible trial types in text-prompted SV for 1-sess enrollment.
Table 6. Different evaluation sets of text-independent part of the DeepMine database.
Table 7. Numbers of trials in each different evaluation sets for all possible trial types in text-dependent SV for 3-sess enrollment. “cross” means enrollment using Persian utterances and test using 5 English utterances. K stands for thousand and M stands for million trials.
Table 9. i-vector/HMM results on text-dependent part (i.e. Part1) on 100-spk, 3-sess setup of Persian language. The results are only for correct trials (i.e. Target-Correct vs Imposter-Corrects). The first column shows filtering of the trials in target:imposter format. The first Y letter on each side of the colon shows the condition where the same mobile brand and the second Y shows exactly the same device models were used for recording the enrollment and test utterances.
Table 11. WER [%] obtained with different ASR systemss trained on DeepMine database. Test-set has 5.9 hours of speech and LargeTest-set contains 28.5 hours of speech.
Query: how was the speech collected?
JSON Response: | {"answer": "The speech was collected from respondents using an android application.", "evidence": ["DeepMine is publicly available for everybody with a variety of licenses for different users. It was collected using crowdsourcing BIBREF4. The data collection was done using an Android application. Each respondent installed the application on his/her personal device and recorded several phrases in different sessions. The Android application did various checks on each utterance and if it passed all of them, the respondent was directed to the next phrase. For more information about data collection scenario, please refer to BIBREF4."]} | science.qasper_abstractive_qa.174.0 | [
{
"content": "Examine the provided sections from a research document and address the accompanying query. The text is divided by newlines.\n\nDocument Content:\n\nA Multi Purpose and Large Scale Speech Corpus in Persian and English for Speaker and Speech Recognition: The Deepmine Database\n\nDeepMine is a speech database in Persian and English designed to build and evaluate text-dependent, text-prompted, and text-independent speaker verification, as well as Persian speech recognition systems. It contains more than 1850 speakers and 540 thousand recordings overall, more than 480 hours of speech are transcribed. It is the first public large-scale speaker verification database in Persian, the largest public text-dependent and text-prompted speaker verification database in English, and the largest public evaluation dataset for text-independent speaker verification. It has a good coverage of age, gender, and accents. We provide several evaluation protocols for each part of the database to allow for research on different aspects of speaker verification. We also provide the results of several experiments that can be considered as baselines: HMM-based i-vectors for text-dependent speaker verification, and HMM-based as well as state-of-the-art deep neural network based ASR. We demonstrate that the database can serve for training robust ASR models.\n\nIntroduction\nNowadays deep learning techniques outperform the other conventional methods in most of the speech-related tasks. Training robust deep neural networks for each task depends on the availability of powerful processing GPUs, as well as standard and large scale datasets. In text-independent speaker verification, large-scale datasets are available, thanks to the NIST SRE evaluations and other data collection projects such as VoxCeleb BIBREF0.\nIn text-dependent speaker recognition, experiments with end-to-end architectures conducted on large proprietary databases have demonstrated their superiority over traditional approaches BIBREF1. Yet, contrary to text-independent speaker recognition, text-dependent speaker recognition lacks large-scale publicly available databases. The two most well-known datasets are probably RSR2015 BIBREF2 and RedDots BIBREF3. The former contains speech data collected from 300 individuals in a controlled manner, while the latter is used primarily for evaluation rather than training, due to its small number of speakers (only 64). Motivated by this lack of large-scale dataset for text-dependent speaker verification, we chose to proceed with the collection of the DeepMine dataset, which we expect to become a standard benchmark for the task.\nApart from speaker recognition, large amounts of training data are required also for training automatic speech recognition (ASR) systems. Such datasets should not only be large in size, they should also be characterized by high variability with respect to speakers, age and dialects. While several datasets with these properties are available for languages like English, Mandarin, French, this is not the case for several other languages, such as Persian. To this end, we proceeded with collecting a large-scale dataset, suitable for building robust ASR models in Persian.\nThe main goal of the DeepMine project was to collect speech from at least a few thousand speakers, enabling research and development of deep learning methods. The project started at the beginning of 2017, and after designing the database and the developing Android and server applications, the data collection began in the middle of 2017. The project finished at the end of 2018 and the cleaned-up and final version of the database was released at the beginning of 2019. In BIBREF4, the running project and its data collection scenarios were described, alongside with some preliminary results and statistics. In this paper, we announce the final and cleaned-up version of the database, describe its different parts and provide various evaluation setups for each part. Finally, since the database was designed mainly for text-dependent speaker verification purposes, some baseline results are reported for this task on the official evaluation setups. Additional baseline results are also reported for Persian speech recognition. However, due to the space limitation in this paper, the baseline results are not reported for all the database parts and conditions. They will be defined and reported in the database technical documentation and in a future journal paper.\n\nData Collection\nDeepMine is publicly available for everybody with a variety of licenses for different users. It was collected using crowdsourcing BIBREF4. The data collection was done using an Android application. Each respondent installed the application on his/her personal device and recorded several phrases in different sessions. The Android application did various checks on each utterance and if it passed all of them, the respondent was directed to the next phrase. For more information about data collection scenario, please refer to BIBREF4.\n\nData Collection ::: Statistics\nAfter processing the database and removing problematic respondents and utterances, 1969 respondents remained in the database, with 1149 of them being male and 820 female. 297 of the respondents could not read English and have therefore read only the Persian prompts. About 13200 sessions were recorded by females and similarly, about 9500 sessions by males, i.e. women are over-represented in terms of sessions, even though their number is 17% smaller than that of males. Other useful statistics related to the database are shown in Table TABREF4.\nThe last status of the database, as well as other related and useful information about its availability can be found on its website, together with a limited number of samples.\n\nDeepMine Database Parts ::: Part1 - Text-dependent (TD)\nThis part contains a set of fixed phrases which are used to verify speakers in text-dependent mode. Each speaker utters 5 Persian phrases, and if the speaker can read English, 5 phrases selected from Part1 of the RedDots database are also recorded.\nWe have created three experimental setups with different numbers of speakers in the evaluation set. For each setup, speakers with more recording sessions are included in the evaluation set and the rest of the speakers are used for training in the background set (in the database, all background sets are basically training data). The rows in Table TABREF13 corresponds to the different experimental setups and shows the numbers of speakers in each set. Note that, for English, we have filtered the (Persian native) speakers by the ability to read English. Therefore, there are fewer speakers in each set for English than for Persian. There is a small “dev” set in each setup which can be used for parameter tuning to prevent over-tuning on the evaluation set.\nFor each experimental setup, we have defined several official trial lists with different numbers of enrollment utterances per trial in order to investigate the effects of having different amounts of enrollment data. All trials in one trial list have the same number of enrollment utterances (3 to 6) and only one test utterance. All enrollment utterances in a trial are taken from different consecutive sessions and the test utterance is taken from yet another session. From all the setups and conditions, the 100-spk with 3-session enrollment (3-sess) is considered as the main evaluation condition. In Table TABREF14, the number of trials for Persian 3-sess are shown for the different types of trial in the text-dependent speaker verification (SV). Note that for Imposter-Wrong (IW) trials (i.e. imposter speaker pronouncing wrong phrase), we merely create one wrong trial for each Imposter-Correct (IC) trial to limit the huge number of possible trials for this case. So, the number of trials for IC and IW cases are the same.\n\nDeepMine Database Parts ::: Part2 - Text-prompted (TP)\nFor this part, in each session, 3 random sequences of Persian month names are shown to the respondent in two modes: In the first mode, the sequence consists of all 12 months, which will be used for speaker enrollment. The second mode contains a sequence of 3 month names that will be used as a test utterance. In each 8 sessions received by a respondent from the server, there are 3 enrollment phrases of all 12 months (all in just one session), and $7 \\times 3$ other test phrases, containing fewer words. For a respondent who can read English, 3 random sequences of English digits are also recorded in each session. In one of the sessions, these sequences contain all digits and the remaining ones contain only 4 digits.\nSimilar to the text-dependent case, three experimental setups with different number of speaker in the evaluation set are defined (corresponding to the rows in Table TABREF16). However, different strategy is used for defining trials: Depending on the enrollment condition (1- to 3-sess), trials are enrolled on utterances of all words from 1 to 3 different sessions (i.e. 3 to 9 utterances). Further, we consider two conditions for test utterances: seq test utterance with only 3 or 4 words and full test utterances with all words (i.e. same words as in enrollment but in different order). From all setups an all conditions, the 100-spk with 1-session enrolment (1-sess) is considered as the main evaluation condition for the text-prompted case. In Table TABREF16, the numbers of trials (sum for both seq and full conditions) for Persian 1-sess are shown for the different types of trials in the text-prompted SV. Again, we just create one IW trial for each IC trial.\n\nDeepMine Database Parts ::: Part3 - Text-independent (TI)\nIn this part, 8 Persian phrases that have already been transcribed on the phone level are displayed to the respondent. These phrases are chosen mostly from news and Persian Wikipedia. If the respondent is unable to read English, instead of 5 fixed phrases and 3 random digit strings, 8 other Persian phrases are also prompted to the respondent to have exactly 24 phrases in each recording session.\nThis part can be useful at least for three potential applications. First, it can be used for text-independent speaker verification. The second application of this part (same as Part4 of RedDots) is text-prompted speaker verification using random text (instead of a random sequence of words). Finally, the third application is large vocabulary speech recognition in Persian (explained in the next sub-section).\nBased on the recording sessions, we created two experimental setups for speaker verification. In the first one, respondents with at least 17 recording sessions are included to the evaluation set, respondents with 16 sessions to the development and the rest of respondents to the background set (can be used as training data). In the second setup, respondents with at least 8 sessions are included to the evaluation set, respondents with 6 or 7 sessions to the development and the rest of respondents to the background set. Table TABREF18 shows numbers of speakers in each set of the database for text-independent SV case.\nFor text-independent SV, we have considered 4 scenarios for enrollment and 4 scenarios for test. The speaker can be enrolled using utterances from 1, 2 or 3 consecutive sessions (1sess to 3sess) or using 8 utterances from 8 different sessions. The test speech can be one utterance (1utt) for short duration scenario or all utterances in one session (1sess) for long duration case. In addition, test speech can be selected from 5 English phrases for cross-language testing (enrollment using Persian utterances and test using English utterances). From all setups, 1sess-1utt and 1sess-1sess for 438-spk set are considered as the main evaluation setups for text-independent case. Table TABREF19 shows number of trials for these setups.\nFor text-prompted SV with random text, the same setup as text-independent case together with corresponding utterance transcriptions can be used.\n\nDeepMine Database Parts ::: Part3 - Speech Recognition\nAs explained before, Part3 of the DeepMine database can be used for Persian read speech recognition. There are only a few databases for speech recognition in Persian BIBREF6, BIBREF7. Hence, this part can at least partly address this problem and enable robust speech recognition applications in Persian. Additionally, it can be used for speaker recognition applications, such as training deep neural networks (DNNs) for extracting bottleneck features BIBREF8, or for collecting sufficient statistics using DNNs for i-vector training.\nWe have randomly selected 50 speakers (25 for each gender) from the all speakers in the database which have net speech (without silence parts) between 25 minutes to 50 minutes as test speakers. For each speaker, the utterances in the first 5 sessions are included to (small) test-set and the other utterances of test speakers are considered as a large-test-set. The remaining utterances of the other speakers are included in the training set. The test-set, large-test-set and train-set contain 5.9, 28.5 and 450 hours of speech respectively.\nThere are about 8300 utterances in Part3 which contain only Persian full names (i.e. first and family name pairs). Each phrase consists of several full names and their phoneme transcriptions were extracted automatically using a trained Grapheme-to-Phoneme (G2P). These utterances can be used to evaluate the performance of a systems for name recognition, which is usually more difficult than the normal speech recognition because of the lack of a reliable language model.\n\nExperiments and Results\nDue to the space limitation, we present results only for the Persian text-dependent speaker verification and speech recognition.\n\nAcknowledgments\nThe data collection project was mainly supported by Sharif DeepMine company. The work on the paper was supported by Czech National Science Foundation (GACR) project \"NEUREM3\" No. 19-26934X and the National Programme of Sustainability (NPU II) project \"IT4Innovations excellence in science - LQ1602\".\n\nTable 1. Statistics of the DeepMine database.\n\nTable 3. Number of speakers in different evaluation sets of textdependent part.\n\nTable 4. Numbers of trials in each different evaluation set for all possible trial types in text-dependent SV for 3-sess enrollment. TC: Target-Correct, TW: Target-Wrong, IC: Imposter-Correct, IW: Imposter-Wrong. K stands for thousand and M stands for million trials.\n\nTable 5. Numbers of trials in each different evaluation sets for all possible trial types in text-prompted SV for 1-sess enrollment.\n\nTable 6. Different evaluation sets of text-independent part of the DeepMine database.\n\nTable 7. Numbers of trials in each different evaluation sets for all possible trial types in text-dependent SV for 3-sess enrollment. “cross” means enrollment using Persian utterances and test using 5 English utterances. K stands for thousand and M stands for million trials.\n\nTable 9. i-vector/HMM results on text-dependent part (i.e. Part1) on 100-spk, 3-sess setup of Persian language. The results are only for correct trials (i.e. Target-Correct vs Imposter-Corrects). The first column shows filtering of the trials in target:imposter format. The first Y letter on each side of the colon shows the condition where the same mobile brand and the second Y shows exactly the same device models were used for recording the enrollment and test utterances.\n\nTable 11. WER [%] obtained with different ASR systemss trained on DeepMine database. Test-set has 5.9 hours of speech and LargeTest-set contains 28.5 hours of speech.\n\nQuery: how was the speech collected?\n\nJSON Response:",
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"content": "{\"answer\": \"The speech was collected from respondents using an android application.\", \"evidence\": [\"DeepMine is publicly available for everybody with a variety of licenses for different users. It was collected using crowdsourcing BIBREF4. The data collection was done using an Android application. Each respondent installed the application on his/her personal device and recorded several phrases in different sessions. The Android application did various checks on each utterance and if it passed all of them, the respondent was directed to the next phrase. For more information about data collection scenario, please refer to BIBREF4.\"]}",
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science.bioasq_list_qa | Examine the following excerpts from biomedical literature. Your objective is to extract relevant information to answer the posed question. Format your answer as a JSON array.
Context: achieved by selectively inhibiting sodium-glucose co-transporter 2 (SGLT2).
After a decade-long effort to improve the pharmacokinetics of GLP1, a number of GLP1 analogues are currently available on the market
short-acting glucagon-like peptide-1 receptor agonists exenatide and lixisenatide.
Sulphonylurea (SU) drugs have a history stretching back over 60 years, and have traditionally been the mainstay choice as second-line agents to be added to metformin once glycaemic control with metformin monotherapy deteriorates;
In 2013, sulfonylureas were the most common second-line treatment after metformin i
this position was taken by DPP-4 inhibitors
[Studies on resorption of orally administered antibiotics and chemotherapeutic agents in children and its modification.
The drugs used to treat diabetes mellitus are diverse and include several classes.
Many of the therapeutic agents used to treat diabetes mellitus have the ability to lower blood glucose to dangerous concentrations; these include the sulfonylurea, meglitinide, and thiazolidinedione drug classes.
The most common therapeutic classes of drugs used in all participating regions, in the previous week, were cardiovascular, gastrointestinal, metabolic (including drugs to treat diabetes) and nervous system.
Use of oral antidiabetic agents, by drug class, did not differ significantly by race/ethnicity (p = 0.33 for TZDs, p = 0.43 for metformin, p = 0.38 for sulfonylureas).
The use of these drugs was also common among insulin treated diabetics but did not differ significantly from among non-diabetics.
Drug use among those treated with insulin and those treated orally was substantially higher than among non-diabetics while the difference between diabetics on dietary regimen and non-diabetics was much smaller.
UNLABELLED: The aims of the study were to evaluate differences in dietary, oral hygiene habits and social class in children with Type I diabetes mellitus (DM), compared to non-diabetics, and to investigate relationship between selected caries-risk factors and caries experience in diabetics.
Vildagliptin is a potent and selective inhibitor of dipeptidyl peptidase-IV (DPP-4), orally active, that improves glycemic control in patients with type 2 diabetes (T2DM) primarily by enhancing pancreatic (alpha and beta) islet function.
The literature covered includes original studies and meta-analyses identified in PubMed, recent abstracts presented at major diabetes scientific conferences, and clinical trials registered at ClinicalTrials.gov.Although there are some differences in the pharmacokinetic and pharmacodynamic profiles of the different DPP-4 inhibitors, all are small orally active compounds with broadly similar HbA1c-lowering efficacy.
The authors conclude that rosiglitazone can be safely administered with metformin and, due to the different mechanisms of action of these agents, may offer a therapeutic advantage in patients with type 2 diabetes mellitus.
There are different classes of anti-diabetic drugs reported to treat diabetes.
There are now four different classes of oral medications which are available to treat diabetes-sulfonylureas, biguanides, thiazolidinediones, and alpha-glucosidase inhibitors.
During followup, incident treated diabetes was defined as a self-report of a new physician diagnosis of diabetes treated with insulin or oral drugs.
This systematic review will compare adherence and persistence rates across the different classes of medication available to people with type 2 diabetes.
Five classes of oral hypoglycaemic drugs and two trace minerals used to treat diabetes mellitus in humans are reviewed and current knowledge on the use of these drugs in diabetic dogs and cats is presented.
Sulfonylureas, biguanides, alpha-glucosidase, meglitinides, DPP-4 inhibitors and thiazolidinediones are among the classes of oral hypoglycemic drugs available to treat Type II diabetes, but concerns exist regarding safety and efficacy of these drugs.
Currently, four classes of orally administered antidiabetic agents are available for use in patients with type 2 diabetes: insulin secretagogues, biguanides, a-glucosidase inhibitors, and thiazolidinediones.
Question: What are the different classes of orally administered drugs used to treat diabetes | ["Biguanides", "Sulfonylureas", "Meglitinides", "D-Phenylalanine Derivatives", "glitazones", "DPP-4 Inhibitors", "Alpha-glucosidase inhibitors", "bile acid sequestrants"] | science.bioasq_list_qa.416 | [
{
"content": "Examine the following excerpts from biomedical literature. Your objective is to extract relevant information to answer the posed question. Format your answer as a JSON array.\n\nContext: achieved by selectively inhibiting sodium-glucose co-transporter 2 (SGLT2).\nAfter a decade-long effort to improve the pharmacokinetics of GLP1, a number of GLP1 analogues are currently available on the market\nshort-acting glucagon-like peptide-1 receptor agonists exenatide and lixisenatide.\nSulphonylurea (SU) drugs have a history stretching back over 60 years, and have traditionally been the mainstay choice as second-line agents to be added to metformin once glycaemic control with metformin monotherapy deteriorates;\nIn 2013, sulfonylureas were the most common second-line treatment after metformin i\nthis position was taken by DPP-4 inhibitors\n[Studies on resorption of orally administered antibiotics and chemotherapeutic agents in children and its modification.\nThe drugs used to treat diabetes mellitus are diverse and include several classes.\nMany of the therapeutic agents used to treat diabetes mellitus have the ability to lower blood glucose to dangerous concentrations; these include the sulfonylurea, meglitinide, and thiazolidinedione drug classes.\nThe most common therapeutic classes of drugs used in all participating regions, in the previous week, were cardiovascular, gastrointestinal, metabolic (including drugs to treat diabetes) and nervous system.\nUse of oral antidiabetic agents, by drug class, did not differ significantly by race/ethnicity (p = 0.33 for TZDs, p = 0.43 for metformin, p = 0.38 for sulfonylureas).\nThe use of these drugs was also common among insulin treated diabetics but did not differ significantly from among non-diabetics.\nDrug use among those treated with insulin and those treated orally was substantially higher than among non-diabetics while the difference between diabetics on dietary regimen and non-diabetics was much smaller.\nUNLABELLED: The aims of the study were to evaluate differences in dietary, oral hygiene habits and social class in children with Type I diabetes mellitus (DM), compared to non-diabetics, and to investigate relationship between selected caries-risk factors and caries experience in diabetics.\nVildagliptin is a potent and selective inhibitor of dipeptidyl peptidase-IV (DPP-4), orally active, that improves glycemic control in patients with type 2 diabetes (T2DM) primarily by enhancing pancreatic (alpha and beta) islet function.\nThe literature covered includes original studies and meta-analyses identified in PubMed, recent abstracts presented at major diabetes scientific conferences, and clinical trials registered at ClinicalTrials.gov.Although there are some differences in the pharmacokinetic and pharmacodynamic profiles of the different DPP-4 inhibitors, all are small orally active compounds with broadly similar HbA1c-lowering efficacy.\nThe authors conclude that rosiglitazone can be safely administered with metformin and, due to the different mechanisms of action of these agents, may offer a therapeutic advantage in patients with type 2 diabetes mellitus.\nThere are different classes of anti-diabetic drugs reported to treat diabetes.\nThere are now four different classes of oral medications which are available to treat diabetes-sulfonylureas, biguanides, thiazolidinediones, and alpha-glucosidase inhibitors.\nDuring followup, incident treated diabetes was defined as a self-report of a new physician diagnosis of diabetes treated with insulin or oral drugs.\nThis systematic review will compare adherence and persistence rates across the different classes of medication available to people with type 2 diabetes.\nFive classes of oral hypoglycaemic drugs and two trace minerals used to treat diabetes mellitus in humans are reviewed and current knowledge on the use of these drugs in diabetic dogs and cats is presented.\nSulfonylureas, biguanides, alpha-glucosidase, meglitinides, DPP-4 inhibitors and thiazolidinediones are among the classes of oral hypoglycemic drugs available to treat Type II diabetes, but concerns exist regarding safety and efficacy of these drugs.\nCurrently, four classes of orally administered antidiabetic agents are available for use in patients with type 2 diabetes: insulin secretagogues, biguanides, a-glucosidase inhibitors, and thiazolidinediones.\n\nQuestion: What are the different classes of orally administered drugs used to treat diabetes",
"role": "user"
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"content": "[\"Biguanides\", \"Sulfonylureas\", \"Meglitinides\", \"D-Phenylalanine Derivatives\", \"glitazones\", \"DPP-4 Inhibitors\", \"Alpha-glucosidase inhibitors\", \"bile acid sequestrants\"]",
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