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Difference between revisions of "Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms"
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This paper presents an approach on detecting heart disease from ECG signals by fine-tuning the deep learning neural network, ConvNetQuake. For context, ConvNetQuake is a convolutional neural network, used by Perol, Gharbi, and Denolle [4], for Earthquake detection and location from a single waveform. A deep learning approach was used due to the model's ability to be trained using multiple GPUs and terabyte-sized datasets. This, in turn, creates a model that is robust against noise. The purpose of this paper is to provide detailed analyses of the contributions of the ECG leads on identifying heart disease, to show the use of multiple channels in ConvNetQuake enhances prediction accuracy, and to show that feature engineering is not necessary for any of the training, validation, or testing processes. In this area, the combination of data fusion and machine learning techniques exhibits great promise to healthcare innovation, and the analyses in this paper help further this realization. The benefits of translating knowledge between deep learning and its real-world applications in health are also illustrated.
This paper presents ConvNetQuake, an approach to detecting heart disease from ECG signals by fine-tuning the deep learning neural network. For context, ConvNetQuake is a convolutional neural network, used by Perol, Gharbi, and Denolle [4], for Earthquake detection and location from a single waveform. A deep learning approach was used due to the model's ability to be trained using multiple GPUs and terabyte-sized datasets. This, in turn, creates a model that is robust against noise. The purpose of this paper is to provide detailed analyses of the contributions of the ECG leads on identifying heart disease, to show the use of multiple channels in ConvNetQuake enhances prediction accuracy, and to show that feature engineering is not necessary for any of the training, validation, or testing processes. In this area, the combination of data fusion and machine learning techniques exhibits great promise to healthcare innovation, and the analyses in this paper help further this realization. The benefits of translating knowledge between deep learning and its real-world applications in health are also illustrated.
== Previous Work and Motivation ==
The database used in previous works is the Physikalisch-Technische Bundesanstalt (PTB) database, which consists of ECG records. Previous papers used techniques, such as CNN, SVM, K-nearest neighbors, naïve Bayes classification, and ANN. From these instances, the paper observes several faults in the previous papers. The first being the issue that most papers use feature selection on the raw ECG data before training the model. Dabanloo and Attarodi [2] used various techniques such as ANN, K-nearest neighbors, and Naïve Bayes. However, they extracted two features, the T-wave integral and the total integral, to aid in localizing and detecting heart disease. Sharma and Sunkaria [3] used SVM and K-nearest neighbors as their classifier, but extracted various features using stationary wavelet transforms to decompose the ECG signal into sub-bands. The second issue is that papers that do not use feature selection would arbitrarily pick ECG leads for classification without rationale. For example, Liu et al. [1] used a deep CNN that uses 3 seconds of ECG signal from lead II at a time as input. The decision for using lead II compared to the other leads was not explained.
The database used in previous works is the Physikalisch-Technische Bundesanstalt (PTB) database, which consists of ECG records. Previous papers used techniques, such as CNN, SVM, K-nearest neighbors, naïve Bayes classification, and ANN. From these instances, the paper observes several shortcomings in the previous papers. The first being the issue that most papers use feature selection on the raw ECG data before training the model. Dabanloo and Attarodi [2] used various techniques, such as ANN, K-nearest neighbors, and Naïve Bayes. However, they extracted two features, the T-wave integral and the total integral, to help localize and detect heart disease. Sharma and Sunkaria [3] used SVM and K-nearest neighbors as their classifiers but extracted various features using stationary wavelet transforms to decompose the ECG signal into sub-bands. The second issue is that papers that do not use feature selection would arbitrarily pick ECG leads for classification without rationale. For example, Liu et al. [1] used a deep CNN that uses 3 seconds of ECG signal from lead II at a time as input. The decision to use lead II compared to the other leads was not explained.
The issue with feature selection is that it can be time-consuming and impractical with large volumes of data. The second issue with the arbitrary selection of leads is that it does not offer insight into why the lead was chosen and the contributions of each lead in the identification of heart disease. Thus, this paper addresses these two issues through implementing a deep learning model that does not rely on feature selection of ECG data and to quantify the contributions of each ECG and Frank lead in identifying heart disease.
The issue with feature selection is that it can be time-consuming and impractical with large volumes of data. The second issue with the arbitrary selection of leads is that it does not offer insight into why the lead was chosen and each lead's contributions in the identification of heart disease. Thus, this paper addresses these two issues through implementing a deep learning model that does not rely on feature selection of ECG data and to quantify the contributions of each ECG and Frank lead in identifying heart disease.
== Model Architecture ==
The input layer is a 10-second long ECG signal. There are 8 hidden layers in this model, each of which consists of a 1D convolution layer with the ReLu activation function followed by a batch normalization layer. The output layer is a one-dimensional layer that uses the Sigmoid activation function.
This model is trained by using batches of size 10. The learning rate is 10^-4. The ADAM optimizer is used. In training the model, the dataset is split into a train set, validation set, and test set with ratios 80-10-10.
This model is trained by using batches of size 10. The learning rate is <math>10^{-4}</math>. The ADAM optimizer is used. The ADAM (adaptive moment estimation) optimizer is a stochastic gradient optimization method that uses adaptive learning rates for the parameters used in the estimating the gradient's first and second moments [5]. In training the model, the dataset is split into a train set, validation set, and test set with ratios 80-10-10.
During the training process, the model was trained from scratch numerous times to avoid inserting unintended variation into the model by randomly initializing weights.
==Results==
The paper first uses quantification of accuracies for single channels with 20-fold cross-validation, resulting in the highest individual accuracies: v5, v6, vx, vz, and ii. The researchers further investigated the accuracies for pairs of the top 5 highest individual channels using 20-fold cross-validation. They arrived at the conclusion of highest pairs accuracies to fed into a neural network is lead v6 and lead vz. They then use 100-fold cross validation on v6 and vz pair of channels, then compare outliers based on top 20, top 50 and total 100 performing models, finding that standard deviation is non-trivial and there are few models performed very poorly.
The paper first uses quantification of accuracies for single channels with 20-fold cross-validation, resulting in the highest individual accuracies: v5, v6, vx, vz, and ii. The researchers further investigated the accuracies for pairs of the top 5 highest individual channels using 20-fold cross-validation. They arrived at the conclusion that the highest pairs accuracies to feed into a neural network are lead v6 and lead vz. They then use 100-fold cross validation on v6 and vz pair of channels, compare outliers based on top 20, top 50 and total 100 performing models, finding that standard deviation is non-trivial and there are few models performed very poorly.
Next, they discussed 2 factors affecting model performance evaluation: 1) Random train-val-test split might have effects on the performance of the model, but it can be improved by access with a larger data set and further discussion; and 2) random initialization of the weights of the neural network shows little effects on the performance of the model performance evaluation, because of showing high average results with a fixed train-val-test split.
Comparing with other models in the other 12 papers, the model in this article has the highest accuracy, specificity, and precision. With concerns of patients' records affecting the training accuracy, they used 290 fold patient-wise split, resulting in the same highest accuracy of the pair v6 and vz same as record-wise split. The second best pair was ii and vz, which also contains the vz channel. Combining the two best pair channels into v6, vz, vii ultimately gave the best results over 10 trials which has an average of 97.83% in patient-wise split. Even though the patient-wise split might result in lower accuracy evaluation, however, it still maintains a very high average.
Comparing with other models in the other 12 papers, the model in this article has the highest accuracy, specificity, and precision. The dataset contained 549 records from 290 unique patients. In order to ensure that the model did not overfit specific patient profiles, they performed a patient-wise split, where all records associated with a given patient are either in test data or train data (but not both). They tested the 290 fold patient-wise split, resulting in the same highest accuracy of the pair v6 and vz same as record-wise split. The second best pair was ii and vz, which also contains the vz channel. Combining the two best pair channels into v6, vz, vii ultimately gave the best results over 10 trials which has an average of 97.83% in patient-wise split. Even though the patient-wise split might result in lower accuracy evaluation, however, it still maintains a very high average.
==Conclusion & Discussion==
The paper introduced a new architecture for heart condition classification based on raw ECG signals using multiple leads. It outperformed the state-of-art model by a large margin of 1 percent. This study finds that out of the 15 ECG channels(12 conventional ECG leads and 3 Frank Leads), channel v6, vz, and ii contain the most meaningful information for detecting myocardial infraction. Also, recent advances in machine learning can be leveraged to produce a model capable of classifying myocardial infraction with a cardiologist-level success rate. To further improve the performance of the models, access to a larger labeled data set is needed. The PTB database is small. It is difficult to test the true robustness of the model with a relatively small test set. If a larger data set can be found to help correctly identify other heart conditions beyond myocardial infraction, the research group plans to share the deep learning models and develop an open-source, computationally efficient app that can be readily used by cardiologists.
The paper introduced a new architecture for heart condition classification based on raw ECG signals using multiple leads. It outperformed the state-of-art model by a large margin of 1 percent. This study finds that out of the 15 ECG channels(12 conventional ECG leads and 3 Frank Leads), channel v6, vz, and ii contain the most meaningful information for detecting myocardial infarction. Also, recent advances in machine learning can be leveraged to produce a model capable of classifying myocardial infraction with a cardiologist-level success rate. To further improve the performance of the models, access to a larger labeled data set is needed. The PTB database is small. It is difficult to test the true robustness of the model with a relatively small test set. If a larger data set can be found to help correctly identify other heart conditions beyond myocardial infraction, the research group plans to share the deep learning models and develop an open-source, computationally efficient app that can be readily used by cardiologists.
A detailed analysis of the relative importance of each of the 15 ECG channels indicates that deep learning can identify myocardial infraction by processing only ten seconds of raw ECG data from the v6, vz and ii leads and reaches a cardiologist-level success rate. Deep learning algorithms may be readily used as commodity software. The neural network model that was originally designed to identify earthquakes may be re-designed and tuned to identify myocardial infraction. Feature engineering of ECG data is not required to identify myocardial infraction in the PTB database. This model only required ten seconds of raw ECG data to identify this heart condition with cardiologist-level performance. Access to a larger database should be provided to deep learning researchers so they can work on detecting different types of heart conditions. Deep learning researchers and the cardiology community can work together to develop deep learning algorithms that provide trustworthy, real-time information regarding heart conditions with minimal computational resources.
A detailed analysis of the relative importance of each of the 15 ECG channels indicates that deep learning can identify myocardial infraction by processing only ten seconds of raw ECG data from the v6, vz, and ii leads and reaches a cardiologist-level success rate. Deep learning algorithms may be readily used as commodity software. The neural network model that was originally designed to identify earthquakes may be re-designed and tuned to identify myocardial infarction. Feature engineering of ECG data is not required to identify myocardial infraction in the PTB database. This model only required ten seconds of raw ECG data to identify this heart condition with cardiologist-level performance. Access to a larger database should be provided to deep learning researchers so they can work on detecting different types of heart conditions. Deep learning researchers and the cardiology community can work together to develop deep learning algorithms that provide trustworthy, real-time information regarding heart conditions with minimal computational resources.
Fourier Transform(such as FFT) can be helpful when dealing with ECG signals. It transforms signals from time domain to frequency domain, which means some hidden features in frequency may be discovered.
Fourier Transform (such as FFT) can be helpful when dealing with ECG signals. It transforms signals from the time domain to the frequency domain, which means some hidden features in frequency may be discovered.
A limitation specified by the authors is the lack of labeled data. The use of a small dataset such as PTB makes it difficult to determine the robustness of the model due to the small size of the test set. Given a larger dataset, the model could be tested to see if it generalizes to identify heart conditions other than myocardial infarction.
==Critiques==
- As mentioned above, the dataset is comparably small in the context of machine learning. While on the other hand, each record has a length of roughly 100 seconds, which is significantly large as a single input. Therefore, it might be helpful to apply data augmentation algorithms during data preprocessing sections so that there will be a more reasonable dataset than what we currently have so far, which has a high chance of being biased or overfitted.
- There are several points from the Model Architecture section that can be improved. It mentions that both 1d batch normalization layers and label smoothing are used to improve the accuracy of the models, based on empirical experiment results. Yet, there's no breakdown of how each of these two method improves the accuracy. So it's left unclear whether each method is significant on its own, or the model simultaneously requires both methods in order to achieve improved accuracy. Some more data can be provided about this. It's mentioned that "models are trained from scratch numerous times." How many times is numerous times? Can we get the exact number? Training time about the models should also be provided. This is because if these models take a long time to train, then training them from scratch every time may cause issues with respect to runtime.
- The authors should have indicated how much the accuracy has been improved by what method. It is a little unclear that how can we define "better results". Also, this paper could be more clear if they included the details about the Model Architecture such as how it was performed and how long was the training time for the model.
- The summary is lacking several components such as explanation of model, data-preprocessing, result visualization and such. It is hard to understand how the result improved since there is no comparison. Information about dataset is unclear too, it is not explained well what they are and how they are populated.
- The authors didn't specify how many epochs the model ran for. A common practice when dealing with small datasets is to run more epochs at the risk of overfitting. However the use of batch normalization (and perhaps the introduction of Dropout layers) aid in preventing the model to overfitting the data or affirming the bias of the dataset so more epochs may have improved performance in this case.
- It is difficult to justify the effectiveness of deep learning for detecting myocardial infarction in EKG due to the lack of information available on the deep learning structure. Meanwhile, false negatives and false positives must be as close to 0 as possible, therefore the authors should test their algorithm on a variety of datasets before determining if deep learning is effective.
- The authors do not motivate the use of ConvNetQuake as their baseline model for deep transfer learning. There are likely several other model candidates that perform similar signal processing related tasks such as CNN models for gravitational wave detection.
- Further, there is very limited mention of the ECG data used, and what features are of interest. For someone who has limited domain-knowledge about Myocardial Infarctions and ECGs, it is hard to interpret and relate the information both in the original paper and the summary. There is a large use of medical terminology that the average student is not likely to know. The absence of concrete data and results leads to a lot of confusion for someone trying to understand the relevance of the model to ECGs.
- Although the application prospects mentioned by the author are exciting, the model still faces many improvements. First, misdiagnosis in a medical examination is a very serious medical malpractice, thus a confusion matrix should be added in the model robustness. Either false positive and false negative testing results should be considered. Second, the data set is still facing the issue of small size. It is recommended that the author carry out long-term tracking and supplementation of the data on different heart diseases in order to form more robust conclusions in the future.
- The large amount of medical jargon mixed within the mathematical definition is not justified, instead of giving names of diseases, we should have seen clear training labels of the database. It also does not fully justify the lack of data augmentation (amplify the intensity for example). Though the wave-related network structure was chosen, the paper later does not build upon the network but rather structure the model entirely. Within the training stage, the use of ADAM optimizer is a standard protocol but why not explore further and if not, why did they forfeit the pursuit. The training of each iteration (across all 100 iterations) is said to be done in one randomly 80-10-10 split; however, for better accuracy and further examination into the model, it is a good practice to mix up the model to allow a better fit against overfitting. The lack of an ensemble network to combat overfitting is also not presented in the paper.
- The table 9 in section 4.1 record-wise split of the paper shows a list of results compared with other studies in the literature that also use the PTB database. It was revealed that the model implemented in the paper has the highest precision, accuracy and specificity, not the best in sensitivity. It would be interesting to investigate how Huang's model got the best sensitivity, and what he was doing differently than the above model. Also, there could have been a section to compare the difference between record-wise split and patient-wise split. That is because the three statistical attributes: standard deviation, median and average of the resulting models using different split vary quite a lot.
[4] Perol Thibaut, Gharbi Michaël, and Denolle Marin. "Convolutional neural network for earthquake detection and location". In: Science Advances (Feb. 2018). doi: 10.1126/sciadv.1700578
[5] Kingma, D. and Ba, J., 2015. Adam: A Method for Stochastic Optimization. In: International Conference for Learning Representations. [online] San Diego: 3rd International Conference for Learning Representations, p.1. Available at: <https://arxiv.org/pdf/1412.6980.pdf> [Accessed 3 December 2020].
3 Previous Work and Motivation
4 Model Architecture
6 Conclusion & Discussion
Zihui (Betty) Qin, Wenqi (Maggie) Zhao, Muyuan Yang, Amartya (Marty) Mukherjee
Previous Work and Motivation
Model Architecture
The dataset, which was used to train, validate, and test the neural network models, consists of 549 ECG records taken from 290 unique patients. Each ECG record has a mean length of over 100 seconds.
This Deep Neural Network model was created by modifying the ConvNetQuake model by adding 1D batch normalization layers; this addition helps to combat overfitting. A second modification that was made was to introduce the use of label smoothing, which can help by discouraging the model from making overconfident predictions. Label smoothing refers to the method of relaxing the confidence on the model's prediction labels. The authors' experiments demonstrated that both of these modifications helped to increase model accuracy.
During the training stage, a 10-second long two-channel input was fed into the neural network. In order to ensure that the two channels were weighted equally, both channels were normalized. Besides, time invariance was incorporated by selecting the 10-second long segment randomly from the entire signal.
This model is trained by using batches of size 10. The learning rate is [math]10^{-4}[/math]. The ADAM optimizer is used. The ADAM (adaptive moment estimation) optimizer is a stochastic gradient optimization method that uses adaptive learning rates for the parameters used in the estimating the gradient's first and second moments [5]. In training the model, the dataset is split into a train set, validation set, and test set with ratios 80-10-10.
The following images gives a visual representation of the model.
Model Architecture (Gupta et al., 2019)
Conclusion & Discussion
- The lack of large, labelled data sets is often a common problem in most applied deep learning studies. Since the PTB database is as small as you describe it to be, the robustness of the model which may be hard to gauge. There are very likely various other physical factors that may play a role in the study which the deep neural network may not be able to adjust for as well, since health data can be somewhat subjective at times and/or may be somewhat inaccurate, especially if machines are used to measurement. This might mean error was propagated forward in the study.
- Additionally, there is a risk of confirmation bias, which may occur when a model is self-training, especially given the fact that the training set is small.
- I feel that the results of deep learning models in medical settings where the consequences of misclassification can be severe should be evaluated by assigning weights to classification. In case if the misclassification can lead to severe consequences, then the network should be trained in such a way that it errs towards safety. For example, in case if heart disease, the consequences will be very high if the system says that there is no heart disease when in fact there is. So, the evaluation metric must be selected carefully.
- This is a useful and meaningful application topic in machine learning. Using Deep Learning to detect heart disease can be very helpful if it is difficult to detect disease by looking at ECG by humans eys. This model also useful for doing statistics, such as calculating the percentage of people get heart disease. But I think the doctor should not 100% trust the result from the model, it is almost impossible to get 100% accuracy from a model. So, I think double-checking by human eyes is necessary if the result is weird. What is more, I think it will be interesting to discuss more applications in mediccal by using this method, such as detecting the Brainwave diagram to predict a person's mood and to diagnose mental diseases.
- Compared to the dataset for other topics such as object recognition, the PTB database is pretty small with only 549 ECG records. And these are highly unbiased (Table 1) with 4 records for myocarditis and 148 for myocardial infarction. Medical datasets can only be labeled by specialists. This is why these datasets are related small. It would be great if there will be a larger, more comprehensive dataset.
- Only results using 20-fold cross validation were presented. It should be shown that the results could be reproduced using a more common number of folds like 5 or 10
- There are potential issues with the inclusion of Frank leads. From a practitioner standpoint, ECGs taken with Frank leads are less common. This could prevent the use of this technique. Additionally, Frank leads are expressible as a linear combinations of the 12 traditional leads. The authors are not adding any fundamentally new information by including them and their inclusion could be viewed as a form of feature selection (going against the authors' original intentions).
- It will better if we can see how the model in this paper outperformed those methods that used feature selections. The details of the results are not enough.
- A new extended dataset for PTB dubbed PTB-XL, has 21837 records. Using this dataset could yield a more accurate result, since the original PTB's small dataset posed limitations on the deep learning model.
- The paper mentions that it has better results, but by how much? what accuracy did the methods you compared to have? Also, what methods did you compare to? (Authors mentioned feature engineering methods but this is vague) Also how much were the labels smoothed? (i.e. 1 -> 0.99 or 1-> 0.95 for example) How much of a difference did the label smoothing make?
- It is nice to see that the authors also considered training and testing the model on data via a patient-wise split, which gives more insights towards the cases when a patient has multiple records of diagnosis. Obviously and similar to what other critiques suggested, using a patient-wise split might disadvantage from the lack of training data, given that there are only 290 unique patients in the PTB database. Also, acquiring prior knowledge from professionals about correlations, such as causal relationships, between different diagnoses might be helpful for improving the model.
[1] Na Liu et al. "A Simple and Effective Method for Detecting Myocardial Infarction Based on Deep Convolutional Neural Network". In: Journal of Medical Imaging and Health Informatics (Sept. 2018). doi: 10.1166/jmihi.2018.2463.
[2] Naser Safdarian, N.J. Dabanloo, and Gholamreza Attarodi. "A New Pattern Recognition Method for Detection and Localization of Myocardial Infarction Using T-Wave Integral and Total Integral as Extracted Features from One Cycle of ECG Signal". In: J. Biomedical Science and Engineering (Aug. 2014). doi: http://dx.doi.org/10.4236/jbise.2014.710081.
[3] L.D. Sharma and R.K. Sunkaria. "Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach." In: Signal, Image and Video Processing (July 2017). doi: https://doi.org/10.1007/s11760-017-1146-z.
Retrieved from "http://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Learning_for_Cardiologist-level_Myocardial_Infarction_Detection_in_Electrocardiograms&oldid=49889"


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It's cooling off, but we're cooking!!!
Two GREAT events this week: Thursday (10/9) we're at Club 6 in in Old Town Scottsdale for a Benefit for the Lost Boys, it's FREE and should be a lot of fun, bring your dance shoes.
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•The law of the United States punishes only culpable behavior and thus the legal principles of mens rea (i.e., guilty mind) and actus rea (i.e., the forbidden act or omission) form the basis of a criminal prosecution. Compare and contrast these two (2) concepts then specify whether the concurrent occurrence of these elements is more beneficial to the prosecution or the defense. Justify your response.
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HomeData AnalyticsDenmark-based UniQcus to Offer Altair's Data Analytics Solutions in Nordic Region
Data AnalyticsDesign News
Denmark-based UniQcus to Offer Altair's Data Analytics Solutions in Nordic Region
By Nitin Patil
TROY, MI, USA, Nov 15, 2022 – Altair (Nasdaq: ALTR), a global leader in computational science and artificial intelligence (AI), announced that UniQcus has joined its growing channel partner network. Located in Odense, Denmark, UniQcus will offer Altair's data analytics solutions to its customers in the Nordic region. UniQcus delivers core experience with SAS programming and has more than a decade of experience in SAS language and World Programming (acquired by Altair) projects.
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"We are proud to have been a World Programming partner since 2008 and see our new partnership with Altair give customers increased flexibility to leverage their decades of investment in the SAS language alongside modern, open-source coding languages, giving them the true power of a hybrid approach," said Torben Christensen, chief executive officer, UniQcus. "We are impressed with Altair's robust technology solutions, unique licensing model, and customer-centered approach. We look forward to the exciting times ahead and, most importantly, to offering our customers best-in-class data analytics solutions."
Altair works with a global network of channel and technology partners. To learn more or to become a partner, visit: https://www.altair.com/partners/.
About Altair
Altair is a global leader in computational science and artificial intelligence (AI) that provides software and cloud solutions in simulation, high-performance computing (HPC), data analytics, and AI. Altair enables organizations across all industries to compete more effectively and drive smarter decisions in an increasingly connected world – all while creating a greener, more sustainable future. For more information, visit https://www.altair.com/.
Altair Channel Partner Network
UniQcus
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This recipe started out as a desperate craving for my Eggplant Parmesan that just wouldn't go away. But it also happened to be 96ºF out, so there was no way I was going to turn on the stove to panfry the eggplant before baking it in a 450ºF oven!
Instead I brushed the eggplant slices with garlic herb olive oil and grilled them. Then I stacked the eggplant slices with fresh mozzarella, and let the grill melt it all into cheesy deliciousness. Top that all off with some homemade pesto and vine ripe tomatoes, and you've got the best of summer going on right here.
Something wonderful happens to eggplant when you grill it, it becomes slightly smoky and really addictive. Couple that with a garlic herb olive oil brushed over the top, and you've got a great side dish as is on it's own that you could serve with grilled meats and a green salad, (my current favorite is my Farmer's Market Chopped Salad with Basil Honey Mustard Dressing).
Anything less in the blender is hard to blend, and the pesto tends to get stuck a lot in smaller batches.
You can use the leftover pesto the next day by tossing it with freshly cooked pasta and chopped grilled veggies for a super quick and easy meal.
I like to slice the ball of fresh mozzarella into round slices, and then halve them so that there's enough mozzarella to place a little slice in between every slice of grilled eggplant.
Budget Tip: Basil is usually expensive when you buy it from a regular grocery store, especially when you need a full 2 cups. I purchase mine at the farmer's market in big bags which they usually label as 'pesto basil', and it's even cheaper than buying a small amount by the ounce.
Preheat a grill over high heat for about 10 minutes, or until it reaches 450ºF.
While the grill heats, cut the ends off the eggplants, and slice them into 1/2″ slices. Mix together the olive oil, garlic, chopped herbs, and sea salt. Lightly brush both sides of the eggplant with the garlic herb olive oil.
Slice the mozzarella into 1/4″ rounds, and then cut each slice in half. Slice the cherry tomatoes in half or quarters if they're large, before setting them aside.
Place the eggplant on the grill and immediately lower the heat to medium high, maintaining a temperature of around 400ºF. Grill for 4-6 minutes per side, or until the eggplant slices are completely tender with nicely browned grill marks.
When the eggplant is tender, remove it from the grill and layer it together with the sliced mozzarella in a 9″ cast iron pan. Place the pan back on the grill and grill for another 10 minutes, or until the cheese is molten and browning on the edges.
While the eggplant is finishing on the grill, make the pesto by placing all the pesto ingredients into a blender in the order listed. Start with 1/4 cup olive oil and add a bit more if needed while blending. Blend starting on low, and using the tamper keep lightly pressing the basil downwards, increasing the speed as you go. Keep blending until a chunky pesto forms. Remove the pesto from the blender, and place half of it into a sealed container with the least amount of air space possible. This recipe makes 2 cups, so I use 1 cup for the eggplant, and place the remaining 1 cup into a 1 cup/8 ounce mason jar with a lid. A brown layer will form on the remaining pesto which is due to oxidization, just scrape it off and use the bright green pesto underneath within 2-3 days.
When the eggplant and cheese have finished cooking, serve it immediately with the halved cherry tomatoes and the fresh pesto.
Absolutely delicious, I cooked this in the oven, added zucchini and served with angel hair tossed with pesto. Very delicious!
Oh my goodness, I love the sound of that, and I'm so glad to hear you enjoyed it!
OMG look at that melted mozzarella!!! I just had lunch but I could definitely make room for a serving of those eggplant stacks, they look superb!
Any kind of cheesy goodness makes me immediately hungry too!


3-storey visitor gallery with large scaled models of the entire country (ground floor) as well as the city centre (incredibly life-like), which provide good orientation of the country for first-timers.
3-storey visitor gallery with large scaled models of the entire country (ground floor) as well as the city centre (incredibly life-like), which provide good orientation of the country for first-timers. The gallery tells the history of Singapore's urban planning, various planning, design, and conservation strategies adopted to create a good living environment, sustainable development, and many others. Learn the story of Singapore's transformation from 3rd to 1st world, play games on land planning, and the expanse of land reclamation done on the island country. There are also wonderful images of old-new Singapore to browse, free walking maps to unique districts like Joo Chiat to pick up. It is situated within an office building. Just walk in and take the escalator up to 2nd floor for permanent exhibits.


text:
Home > Videos > Mary Kay Letourneau
Vili Fualaau's Car Crash Victim Considers Slamming Him With Lawsuit
Mary Kay Letourneau's husband was arrested for a DUI in February after crashing into two cars – and now he could be in even more legal hot water as a result of allegedly driving while intoxicated behind the wheel.
That's because one of the victims is considering filing a lawsuit against him, RadarOnline.com has learned exclusively.
Vili Fualaau, 34, rammed into two cars stopped at a traffic light in Burien, Washington, and the victims immediately called authorities while the infamous sex teacher's estranged hubby remained in the car, exhibiting what they described as unusual behavior, according to 911 audio previously published by RadarOnline.com.
One of the crash victims, Terrence Young, has retained an attorney who told RadarOnline.com that her client plans to file a civil suit, if necessary.
"He has made a claim with Vili's insurance," said Cheryl Farrish. "I would think it would be something Vili's insurance would want to settle. It's ideal if they admit that the driver is at fault, especially in a DUI case."
It may be too early to tell, but he is keeping his options open, Farrish said.
According to the calls made to police after the scary accident, one of the victims said, "We're both standing. We're all a little bit shook up. The cars are destroyed."
He described Fualaau's odd behavior to the operator.
"He's still sitting at the turn light. His door is open. I'm standing with the other gentlemen that was hit. He's on the phone calling 911 too. He's sitting in line as if he's waiting for the light to turn green. As if he's normal. Just sitting there at the light. I think his car is dead, he smacked me in the rear."
Mary Kay Letourneau
Late Convicted Sex Offender Mary Kay Letourneau's Ex-Teen Lover Vili Fualaau Welcomes Third Daughter
Convicted Sex Offender Mary Kay Letourneau Wrote Close To 30 Letters To Loved Ones Before Her Death: 'She Had Wrongs To Right'
Radar Told You First: Mary Kay Letourneau & Vili Fualaau Officially Separating
As RadarOnline.com previously reported, Fualaau, who's reportedly going through a separation from Letourneau, was arrested after police arrived on the scene and believed he was "impaired by alcohol."
Cops reportedly observed his bloodshot eyes, dazed expression and slurred speech as he agreed to take a breathalyzer test. But just before the test was administered his wife, Letourneau, arrived on the scene and told him to wait for their attorney.
A blood test was administered at the police station two hours later, and his blood alcohol level was reportedly .08, which is the legal limit. He also had THC in his system.
According to the King County District Court clerk's office, Fualaau has officially been charged with DUI, but he hasn't yet entered a plea.
The next hearing is scheduled for May 8 where he will enter a plea or the case will go to trial.
DUIs, Drugs & More! Superbowl 51's Biggest Scandals Uncovered


text:
module Invitation
  VERSION = "0.0.4"
end


World War II pilot and Democratic politician who served four terms as a U.S. senator from Nevada. He lost his 1982 bid for reelection after Teamster officials were convicted of offering him a bribe. Cannon was never indicted in the scandal.


-Multi-purposes designed, you can use it as Backpack,Handbag,Shoulder Bag.
-External USB port, offers a convenient charging for your electronic devices.
-Large capacity,you carry a lot of baby things,like diaper pad,feeding-bottle,baby clothes,baby toys,tissue, and so on.In addition,this mummy bag is very suitable for shopping, traveling when you with baby.
-Perfect for traveling, weekend getaways, shopping and other outdoor activities in daily life.


text:
You are here: Home / Uncategorized / DIANA HOROWITZ
DIANA HOROWITZ
www.dianahorowitz.com
Brooklyn College, MFA January 1987
Tyler School of Art, Rome Graduate Program 1983-84
SUNY Purchase, BFA June 1980
AWARDS AND RESIDENCIES
2015,'13,'11 Professional Staff Congress Grant, City University of New York
2014 Award for Excellence in Teaching for Part time Faculty
2010 Brooklyn Art:Work 2010 artist honoree, BRIC Arts|Media|Bklyn
2006 National Academy, elected member
2004 Edwin Palmer Memorial Prize, National Academy
1999 Ballinglen Fellowship, County Mayo, Ireland
1997-98 Lower Manhattan Cultural Council, World Views Residency
1996 American Academy & Institute of Arts and Letters, Rosenthal Foundation Award
1993,'89 Pollock-Krasner Foundation Grant
1991 MacDowell Colony Residency
1991 Yaddo Residency
1988 Ingram Merrill Foundation Grant
ONE PERSON SHOWS
2016 Lori Bookstein Fine Art, New York
2012 ,'08, '03 Hirschl & Adler Modern, New York
2004 P.P.O.W., New York
2000, 1999 MB Modern Gallery, New York
1996,'94,'91,'89 Hackett Freedman Gallery; San Francisco
1993 Sazama Gallery; Chicago
1993 Suffolk County Community College; Brentwood NY
1987,'85 Hell's Kitchen Gallery; Provincetown MA
SELECTED GROUP EXHIBITS
2017 "Creative Mischief" National Academy Museum, New York City
"Painted Landscapes: Contemporary Views" Heritage Museum, Sandwich MA
"Life on the Canal Then by Artists Now" Erie Canal Museum, Syracuse NY
"No Man's Land" The College of William and Mary, Williamsburg, VA
2016 "Summer Show" Hirschl & Adler Modern, New York
2015 "Staten Island SEEN: Witnessing Change" Staten Island Museum at Snug Harbor, New York City
"Summer Selections" Hirschl & Adler Modern, New York City
"The Annual 2015: The Depth of Surface" National Academy Museum, New York City
"Group Show" Schoolhouse Gallery, Provincetown, MA
"Hirschl & Adler Modern at Art Southampton" Southhampton, NY
2014 "City as Subject" curated by Xico Greenwald, Westbeth Galleries, New York City
"Derbes, Kahn & Selesnick, Horowitz, Murray, O'Malley" Schoolhouse Gallery, Provincetown, MA
"Our American Life" Hirschl & Adler Modern, New York City
2013 "On the Horizon", Adelphi University, Garden City, NY
"Duets", Hirschl & Adler Modern, New York City
"Spring Show," Hirschl & Adler Modern, New York City
2012 "Summer Selections," Hirschl & Adler Modern, New York City
"The Armory Show: Piers 92 and 94," New York City
"From Protest to Process," National Academy Museum, New York City
"187th Annual Exhibition of Contemporary American Art," National Academy of Design, New York City
2011 "MasterWorks: The Best of Hirschl & Adler," New York City
"Pathways to Landscape," Ridgefield Guild of Artists, Ridgefield CT
"Generations," Provincetown Art Association & Museum, Provincetown MA
2009 "184th Annual Exhibition of Contemporary American Art," National Academy of Design, New York City
"Landscape Today: Five Perspectives" University Gallery, University of Delaware, Newark, DE
"Urban Structures" Adelphi University, Garden City NY
2008 "Artist Installation," MacDowell Colony Benefit, New York City
"Winter Selections," Hirschl & Adler Modern, New York City
"Recent Acquisitions," National Academy Museum, New York City
"Painting in the Park," Lori Bookstein Fine Art, New York City
"Women Artists," Hirschl & Adler Modern, New York City
"Summer Selections," Hirschl & Adler Modern, New York City
2007 "Winter Selections," Hirschl & Adler Modern, New York City
"182nd Annual Exhibition of Contemporary American Art," National Academy of Design, New York City
"The Brooklyn Bridge and the Brooklyn Museum: Spanning Art and History"
on-line digital exhibition of works in the Brooklyn Museum collection
2006 "American Artists in Rural Ireland: The Ballinglen Experience," Concord Art Association, Concord MA
"Trade," Museum of the City of New York, New York City
"Collectors Collect," Provincetown Art Association & Museum, Provincetown MA
2005 "Three Generations of Figurative Artists: Between Perception and Invention," Sharon Arts Center, Peterborough NH
"Brooklyn College Alumni," Brooklyn NY
"Images from North Mayo," Courthouse Gallery, Ballycastle, County Mayo, Ireland
2004 "Small Works / Big Spaces," Lori Bookstein Fine Art, New York City
"American Artists in Ireland," Residence of the U.S. Ambassador, Phoenix Park, Dublin
"Industrial Beauty," George Billis Gallery, New York City
2003 "Brooklyn Waterfront," Fordham University, Marymount College, Tarrytown NY
"Plein Air," Gross McCleaf Gallery, Philadelphia
"Ballinglen: The Artist in Rural Ireland," Castlebar, County Mayo, Ireland
2002 "Colours," Ballinglen Arts Foundation, Ballycastle, County Mayo, Ireland
2001 "Group Show," Adam Baumgold Gallery, New York City
"DЬchas National Heritage Exhibition," CОide Fields, County Mayo, Ireland
"Judith Belzer, Stanley Bielen, Diana Horowitz," Pepper Gallery, Boston
"Bridges of Brooklyn," Brooklyn College Galleries, Brooklyn NY
2000 "New York Perspectives," MB Modern Gallery, New York City
"Landscapes," Pepper Gallery, Boston
"175th Annual Exhibition," National Academy of Design, New York City
"Hackett-Freedman Gallery Artists," Shasta College Art Gallery, Redding CA
1999 "The Italian Landscape: Paintings & Drawings," Gallery West, Suffolk County Community College, Brentwood NY
"Central Park," MB Modern Gallery, New York City
"Enduring Vision: Contemporary Painters in the Tradition of the Hudson River School," Union College, Schenectady NY
"Bridges," three-person show, Pfizer Inc., Brooklyn NY
"Spirit of the City: Horowitz, Larko, Metcalf," Walker Kornbluth Art Gallery, Fair Lawn NJ
1998 "Vertigo: Works from the World Views Project," New York Studio School, New York City
"Brooklyn Salon Show," Rotunda Gallery, Brooklyn NY
"Intimate Visions: Diana Horowitz, Michael Norton, Marjorie Portnow, Stuart Shils, John Smalley"
List Gallery, Swarthmore College, PA
"New York Perspectives," MB Modern Gallery, New York City
"Artists Choose Artists," The Century Association, New York City, chosen by David Levine
1997 "Three Languages of Landscape: Jonathan Fabricant, Diana Horowitz, Lisa Sigal,"
The Painting Center, New York City
"10th Anniversary Group Show," Hackett Freedman Gallery, San Francisco
"The Press of My Foot to the Earth," Rotunda Gallery, Brooklyn NY, guest curator, Byron Kim
"Interpretations of the Colorado Landscape," The Forbes Magazine Galleries, New York City
"Colleagues in Landscape," Paramount Center for the Arts, Peekskill NY
"Tyler Faculty, Past & Present," GallerТa Temple, Rome, Italy
1996 "25 Years of Visual Arts: 32 Visual Artists at Purchase College," Neuberger Museum of Art, Purchase NY
"Invitational Exhibition of Painting & Sculpture," American Academy & Institute of Arts and Letters, New York City
"Americans in Italy," Gallery North, Setauket NY
"20th Annual Small Works," New York University, New York City, juror: Edward Thorp
1995 "Art Initiatives/Mass Exposure," Curators' Award, Tribeca 148 Gallery, New York City
"Made in Italy" two-person show, Slover-McCutcheon Gallery, Houston TX
1994 "Selected Gallery Artists," Hackett Freedman Gallery, San Francisco
"Art Initiatives/Mass Exposure," Curators' Award, Tribeca 148 Gallery, New York City
1994 "18th Annual Small Works," New York University, New York City, juror: Stephen Haller
"Gallery Artists," Sazama Gallery, Chicago
1992 "April Funcke, Diana Horowitz, Jan Knipe, Langdon Quinn," Univ. of Northern Iowa Gallery of Art, Cedar Falls IA
"Generations in the Arts: Provincetown," Berta Walker Gallery, Provincetown MA
"Invitational: Artists Select Artists," Trenton City Museum, Trenton NJ
1991 Hackett-Freedman Gallery Invitational Group Show, New York Academy, New York City
"Contemporary Landscape," Santa Rosa College, Santa Rosa CA
"Gabriel Laderman Selects," First Street Gallery, New York City
1990 "Italian Landscape," Hackett Freedman Gallery, San Francisco
"Lennart Anderson Selects," First Street Gallery, New York City
"Landscape Painting 1960-1990: The Italian Tradition in American Art"
Gibbes Museum of Art, Charleston, SC and Bayly Museum of Art, Charlottesville VA
"Tyler Faculty," GallerТa Temple, Rome, Italy
"Landscape," Sazama Gallery, Chicago
1989 "Group Show," Sazama Gallery, Chicago
"20/20: Twenty Artists Choose Twenty Artists," Provincetown Art Association & Museum, Provincetown MA
1987 "Night Light/Night Life," Sherry French Gallery, New York City
1986 "Summer Show: Gallery Artists," Sherry French Gallery, New York City
1984 American-Italian Historical Association, Washington DC, two-person show
1983 Swansborough Gallery, Wellfleet MA, two-person show
1983, '82 "Young Artists Exhbition;" juried show, Provincetown Art Association & Museum, Provincetown MA
1982 "Third Annual Drawing Competition," Boson Center for Fine Arts, Boston MA
1982 "Directors Invitational," Group Gallery, Provincetown MA
SELECTED PUBLIC COLLECTIONS
Brooklyn Museum of Art, Brooklyn, NY
New-York Historical Society, New York City
Museum of the City of New York, New York City
National Academy Museum, New York City
Staten Island Museum, Staten Island NY
Hunter Museum, Chattanooga TN
Kings County Hospital, Brooklyn NY
Mississippi Museum of Art, Jackson, MS
Ballinglen Foundation, Ballycastle, County Mayo, Ireland
Bellevue Hospital, New York City
Marriott Hotel, Brooklyn NY
Brooklyn Union Gas Company, Brooklyn NY
Sheldon Swope Art Museum, Terre Haute IN
CATALOGS, PUBLICATIONS & REVIEWS:
2017 "Creative Mischief" 6th annual exhibition catalog, National Academy Museum, New York City
Lauren Della Monica, "Painted Landscapes: Contemporary Views" (New York, Schiffer)
Marina Davalos, "Looking at Landscapes in a Whole New Light" Cape Cod ART 2017 Annual
2016 "One World Trade Center: Biography of a Building" Judith Dupre (Little,Brown,& Company)
2013: Lauren P. Della Monica: Painted Landscapes: Contemporary Views, (New York, Schiffer)
Larry Groff: Interview with Diana Horowitz, paintingperceptions.com, December 2013
2012: The New Yorker, October 22, November 22, 2012
Brooklyn Daily Eagle, October 19, 2012
Antiques & The Arts Weekly, October 19, 2012.
2011: Judith Fairly: "The Height of Abstraction," The Artists Magazine, September 2011
The Labletter, 2011, pages 27-30, and cover (guest editor Rachel Youens)
G. Flynn, "Artists bear witness to the rebirth of WTC site," The Villager (81:15), September 8-14, 2011, p. 42
Shepherd Smith, "The Rise of Freedom: An unusual artists' colony," Fox News, September 16, 2010
(liveshots.blogs.foxnews.com/2010/09/16/rise-of-freedom-an-unusual-artists-colony/)
Bobby Cuza, "9/11 A Decade Later: From High Above, Artists Paint Rebuilding Effort," New York 1, May 20
(ny1.com/content/139479/9-11-a-decade-later–from-high-above–artists-paint-rebuilding-effort)
2008: Maureen Mullarkey: "Evaporated Images," The New York Sun, February 28, 2008.
The New Yorker, March 3, 2008, p. 14.
Amy Crawford, "Beauty in Brooklyn's Industrial Neighborhoods," Brooklyn Daily Eagle, March 14, 2008, p.1.
New York Living, "Design News," March 2008, p. 54
2007: James Gardner: "Hooray for Humbug," The New York Sun, May 31, 2007, p. 25.
Martha Schwendener: "182nd Annual Exhibition," The New York Times, June 1, 2007 p. E-27.
2006: Three Generations of Figurative Artists: Between Perception and Invention
Sharon Arts Center, Peterborough, NH, exhibition catalog
American Artists in Rural Ireland: The Ballinglen Experience, Concord Art Ass'n, Concord MA, exhibition catalog
2004: Site Matters: LMCC's World Trade Center Artists Residency, 1997-2001, (New York, LMCC/DAP).
Maureen Mullarkey: "Gallery Going," The New York Sun, July 9, 2004.
Daniel Kunitz: "Institutional Exhibitionism," The New York Sun, Thursday, May 6, 2004.
2003: Ballinglen: The Artist in Rural Ireland, Castlebar, County Mayo, Ireland, exhibition catalog.
The New Yorker, March 10 and 17, 2003, p. 18.
Mario Naves: "Touched by Grace," The New York Observer, March 10, 2003.
2001 Cate McQuaid: "Galleries," The Boston Globe, June 28, 2001, p.E-5.
"The Gallery,"Antiques and the Art Weekly, June 8, 2001, p.53.
2000 Mario Naves: "Currently Hanging," The New York Observer, May 22, 2000.
Ken Johnson: "175th Annual Exhibition," The New York Times, February 25, 2000.
Michael P. Kinch: "Italy: An Artist's Delight, " American Artist (February 2000).
1999 The New Yorker, April 19, 1999, p. 12.
New American Painting (Open Studios Press).
Ken Johnson: "Vertigo, " The New York Times, January 8, 1999, p. E-42.
1998 Lavender Lake: documentary film about the clean-up of Brooklyn's Gowanus Canal,
directed by Alison Prete, Rough on Rats Productions
1997 Brooklyn: Two Centuries of Artists' Views, 1997 Calendar (cover), The Fund for Brooklyn/Rotunda Gallery.
M. Stephen Doherty: The Watson-Guptill Handbook of Landscape Painting (New York, Watson-Guptill).
1996 M. Stephen Doherty: "Interpretations of the Colorado Landscape, " American Artist (November 1996).
25 Years of Visual Arts: Purchase College 1971-1996, exhibition catalog.
Mark Arnest: "Creative Differences," Gazette Telegraph (Colorado Springs) Sunday, July 21.
1994 Lois Martin: "Classical Landscapes," American Artist (Nov. 1994; rep. Sept. 1995 in Oil Highlights: Landscapes).
Laurie S. Hurwitz: "Reexamining the Oil Sketch," American Artist (November 1994).
1990 Landscape Painting 1960-1990: The Italian Tradition in American Art.
Gibbes Museum of Art, Charleston, South Carolina and travelling, catalog essay by John Hollander.
1992-'93, '03- '17 Adjunct Associate Professor of Painting, Brooklyn College; Brooklyn, NY
2012 Visiting Critic, New York Academy of Art; New York City
2008 Jerusalem Studio School Summer Program, Corciano, Italy
2008 Visiting Critic, The Johns Hopkins University. Baltimore, MD
2008 Visiting Critic, School of Design (graduate), University of Pennsylvania, Philadelphia PA
2007 Visiting Critic, National Academy of Art, New York City
2006 Jerusalem Studio School, Jerusalem
2001-03, 2005 Instructor of Painting, International School of Art, Umbria, Italy
2005 Visiting Artist, Massachusetts College of Art, Graduate School, Boston MA
1998 Adjunct Professor of Painting, SUNY Purchase; Purchase, NY
1994 Visiting Critic, Boston University
1991 Visiting Critic, Graduate School of Painting, New York Academy
1991 Visiting Critic, Rhode Island School of Design; Rome, Italy
1983-84, 89-90 Instructor of Painting & Drawing, Tyler School of Art; Rome, Italy
1987-89 Instructor of Painting & Drawing, School of the Art Institute of Chicago


Workplace Insanity: Adapt or Die!
You recently left a prior position of your own accord, because you didn't find it a fit, or the work environment was unacceptable, or for whatever reason (see Know When to Fold 'Em, and When It's Time to 'Walk Away', Don't Turn Back!). Prior to leaving, you looked around for a good opportunity that would be a good match to your background and interests, and that would provide good opportunities for growth. You've just accepted a position in what you believe to be a promising company. You've been told what your job will be by your hiring manager, by the folks you interviewed with, and by Human Resources. You believe that job description to be a perfect match to your experience and interests, and you're really looking forward to getting started. But when you start, your first assignment is actually fairly far removed from what you were told. But you do it well anyway. Then your second job builds upon your first, and is also far removed from what you had been told. As you move forward in your new job you find that, while there may be a passing resemblance to what you were told you would be doing, the reality is actually pretty far removed from that job description. What's going on, and what does this mean for you going forward?
The mismatch of actual job assignment to what you were told it was going to be is typically not due to any ill intent on the part of anybody involved, but more due to the concept of 'sh!t happens', and what has happened, totally outside of your control, requires you to get deeply involved in these job assignments somewhat outside of what you expected (see The Best Laid Plans … and Then Life Happens!). You need to decide whether you want to adapt, or find something elsewhere that is more in keeping with what you expected (see Take the Time to Think!).
A reality of life, at least for knowledge workers at almost every level of a company, is that the job you expect to be doing often bears fairly little resemblance to the job you will actually be doing. This can be true for a wide variety of reasons.
Your company hits a rough patch, and needs all hands on deck to get through it. This means that everyone needs to work on what is most critical to company survival, whether that is related to your 'job description' or not (see Like Trying to Change Tires on a Fast Moving Car! and The Sky is Falling!). Far from being a distraction from what you thought you'd be doing, this can actually be a great opportunity to demonstrate what you can really offer when the chips are down, and to stand out among the crowd and show people what you're made of. Be grateful you're not one of the people with the potential of being laid off and show your potential. Seize the opportunity!
Your company hits the jackpot and sales rocket through the roof. It's all hands on deck for an entirely different reason, where higher production needs must be supported, where new projects must quickly be launched, where hiring new people to fill new positions is critical, where product and/or service changes to support new features or changes are critical, and everyone needs to work overtime to build on the company's success. In situations like this, your 'job description' is likely to go out the window, and you have an opportunity to chart your own course, to success if you respond positively, or to failure if you don't. Seize the opportunity!
Other actions, even some totally unrelated to your job, cause directions in the company to change, and alter what you were told you would be doing (see The Butterfly Effect in the Workplace). Again, you may not be driving the train, but its far better to enjoy the ride than to cause it to derail. Show people what you can do! Seize the opportunity!
Even in a relatively stable environment, the job is seldom what you were told. Problems arise that must be quickly addressed, pulling you and others off the work you were planning to do. A crucial trade show is coming up and special needs must be addressed in order to meet commitments made, or to counteract competitors features (see Showing Progress vs. Making Progress Syndrome). Performance problems among your group or peers may necessarily pull you away from your expected path (see Learn from Good Role Models; Learn More from Bad!). Some of these may be hard to accept; some may be serendipitous (see Serendipity Can Change Your Life!). You may well not be certain which case it is at the time, but you still need to adapt, and quickly!
What can you do about it? Probably not much. Sh!t happens, and everyone must deal with it. To the extent you can, you should keep your antenna up to try to anticipate unexpected direction changes, people or group or organizational problems, competitive problems, etc. Get ahead of the situation, if you can, and volunteer to take the lead. Demonstrating leadership and initiative in most circumstances will be recognized and valued, but only if such leadership and initiative is real and properly motivated (see Say What Your Mean, Mean What You Say, and Do What You Say You'll Do!). Knowledge workers are hired for their brainpower and their ability to think on their feet and adapt to changing situations (see Knowledge Is Power!). Seize the opportunity!
Life is full of surprises, where twists and turns abound. And every twist causes a new assessment, and every turn takes you further away from your 'job description' or what you were told you'd be doing. But every twist and turn is the reality of life at that time, and you need to adapt and excel, or you will fail and fall by the wayside. Life is what you make of it. These changes are part of your job, and you need to own you job, whatever that entails (see Own Your Job! All of It!). Adapt or die!
As a maturing electrical engineer I remind younger engineering students that "nothing they sell today at Best Buy was yet invented when I was in college." Engineers work in fields that advance rapidly, and part of our job is to advance with the technologies.


text:
Q: Защита ELF от LD_PRELOAD Имеется исполняемый ELF файл «glinkd». Мне необходимо дополнить реализацию некоторых функций, посредством их перехвата. Попался под руку удобный инструмент LD_PRELOAD. Он прекрасно отрабатывает на любых elf файлах, но к несчастью, необходимый для меня файл патчить он отказался. Это выражается в том, что при подгрузки моей библиотеки в «glinkd», попросту ничего не происходит, как будто на этом файле имеется какая то защита.
В гугле ничего про защиту от LD_PRELOAD я не нашел.
Как я патчу elf файлы:

*

*testso.cpp:
#include <cstdio>

void __attribute__ ((constructor)) module_load(void);
void __attribute__ ((destructor)) module_unload(void);

void module_load(void)
{
    printf("hello from .so!\n");
}

void module_unload(void)
{
}

Компилирую:
g++ -fPIC -c testso.cpp -o testso.o -m32 -std=c++0x
  g++ -shared -Wl,-soname,libtestso.so -o libtestso.so testso.o -m32 -std=c++0x

*test_prog.cpp:
#include <cstdio>

int main()
{
    printf("Hello from executable!\n");
}

Компилирую:
g++ -o test_prog test_prog.cpp -m32

*Патчим test_prog:
root@ubuntu:/home/glinkd# LD_PRELOAD=/home/glinkd/libtestso.so ./test_prog
hello from .so!
Hello from executable!
root@ubuntu:/home/glinkd#

*Патчим glinkd:
root@ubuntu:/home/glinkd# LD_PRELOAD=/home/glinkd/libtestso.so ./glinkd
Usage: ./glinkd configurefile section_num
root@ubuntu:/home/glinkd#

Как видим, наша .so просто игнорируется. Есть идеи что это за защита, и как ее можно снять?

A: Из комментария пользователя @Mike:
Это невозможно. Чтобы LD_PRELOAD сработал, надо, чтобы его кто то задействовал, а его задействует динамический загрузчик. Но он работает только с динамически собранными исполняемыми файлами. Работа при статической компоновке возможна только самостоятельной загрузкой требуемого ELF в свое адресное пространство.

redpajama_set_name: RedPajamaStackExchange


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This magic money wallet was introduced to the living by the great ancestors and is meant to bring in money on a daily basis .This wallet has helped many people not to live a life without money, because you will always have money to spend whenever you want too. NOTE; Rituals are performed in the names of the person who is going to use this wallet in other words you cannot borrow this wallet to anyone.


In 1943 the Navy accomplished the teleportation of a warship from Phila to Norfolk by successfully applying Einstein's Unified Field Theory. The experiment also caused the crew and officers of the ship to become invisible during which time they were sent to a time space warm. One survivor tells his amazing experience. Alfred Bielek has appeared along with the author on the Art Bell show many times.


Most of us have some sort of trauma or injury to our bodies. Whether on a physical or emotional level, we cannot begin to start the healing process if we are in a constant state of tension and agitation. This workshop will help you learn the power of your own healing touch, and show you the benefits of certain essential oils. This is not a Reiki attunement class, but you will receive Reiki from a Level 2 certified Reiki practitioner.
Comfortable clothing recommended. Note that use of essential oils can cause irritation or allergic reaction in some people. 5 attendees minimum required.


The cold winter days are over, and with the end of March, we have finally arrived in April, where we can witness the awakening of spring. During this month, the weather in Turkey gets warmer, grasses, and trees bloom … In different parts of the world, there will be disasters due to monsoon rains … What about other countries witnessing the awakening of nature just like our country in April? Of course, there is! Here are 10 different locations to visit in April!
Kyoto, the former imperial capital of Japan, is one of the places you can visit in April. This is because Kyoto, in this season, has a pink blanket with the blossoming of the famous cherry trees. You can also enjoy the romantic scenery of Kyoto this April by staying in a riverside villa, and you can have unique nature walks in the heart of this landscape.
Barcelona is another location that can be travelled in the spring. In this season, you can visit the unique historical beauties in the region accompanied by the magnificent Mediterranean weather, and you can walk calmly through the city streets away from the summer crowd. Moreover, as you can guess, Barcelona is more economical than other times this season.
The Virgin Islands, which is a full Caribbean paradise, is exceptionally ideal for visiting this season. Because there was no hurricane season yet. White sand beaches, first-class water sports with desirable features of the Virgin Islands, in April is quite calm and economic. You can also turn your holiday route here to have fun and enjoy the sea and nature.
Casablanca, Morocco, in April; It has a warm and dry air away from desert heat of the summer. Moreover, this beautiful city is definitely worth a visit with its French colonial architecture and white sandy beach! To experience the unique tastes of Mediterranean and Moroccan cuisine and witness the different atmosphere of Casablanca, you can turn your route to Morocco.
The best time to visit Cairo with its charming atmosphere can be April. Because in those months the overwhelming humidity and heat of the region have not yet begun. Egyptian pyramids, the Nile and the surrounding restaurants are some of the places to visit. You may also consider going to Cairo for a different holiday experience in April.
Amsterdam is also one of the places you can visit in April. King's Day, one of the largest festivals in the Netherlands, is celebrated this month. Free concerts, open marketplaces, fairs, festivals and so on are also starting in this period. Therefore, you can witness this magnificent period of the city and plan your vacation to Amsterdam.
Copenhagen, which lived between May and November, is extremely quiet and economical in April. Europe's favourite cultural art city is among the cheapest overseas locations this season. By taking this opportunity, you can visit here in April when Tivoli Gardens, one of the oldest amusement parks in Europe, will open and you can watch the magnificent architecture of the city on the wooden roller-coaster.
Another nice place in April should be Rome. With three thousand years of history and a charming atmosphere, this delightful city has numerous historical beauties. Moreover, in April the weather is excellent! Therefore, you can choose to visit this beautiful city in April, where you will find another surprise in every street.
One of its overseas locations, which must be visited in April, is Dubai. Because in this period, you can comfortably enjoy the sea, sand and sun in Dubai. Of course, by the time you get there, don't come back without shopping!
Los Angeles, the world's art, music and film industry capital, is one of the places to visit in April. Because this beautiful city also has sea, sand, sun richness. Major attractions include Santa Monika, Venice Beach, Malibu Beach, Universal Studios, Six Flags, Hollywood, Beverly Hills, Westwood Village, Griffith Observatory, and Chinatown. You can turn your route to America in April for a different holiday experience.


The Tokyo Geographical Society was established in 1879, after the Meiji Restoration, as one of the pioneering modern societies of the sciences. Following the establishment of the European Royal Geographical Societies, successive presidents of the society were appointed from among members of Japan's imperial family. Since the 1970s, presidents have been elected from among member scientists.
The Journal of Geography (Chigaku Zasshi) was first published in 1889. Its main editorial scope was geography, which included human science. With advances in the natural and human sciences, particularly after the establishment of plate tectonics in Earth science in the 1960s, geology and geophysics were also included. Our society has been flexible to change over time in adopting disciplines required to clarify phenomena related to the Earth. Recently, environmental sciences, which include urban disasters, civilization, infrastructure, eco-system, and saving energy, have been covered in the journal. More recently, with the inclusion of biospheric subjects, we have been offering extensive coverage of sciences that describe phenomena related to the Earth. Our journal presents subjects that not only bring together natural and human sciences, but also focuses on the history of the Earth and life both today and in the future. However, the central core of the journal is always based on solid Earth, environmental, and human sciences.


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Cette page a pour objet de présenter un arbre phylogénétique des Trilobita (Trilobites), c'est-à-dire un cladogramme mettant en lumière les relations de parenté existant entre leurs différents groupes (ou taxa), telles que les dernières analyses reconnues les proposent. Ce n'est qu'une possibilité, et les principaux débats qui subsistent au sein de la communauté scientifique sont brièvement présentés ci-dessous, avant la bibliographie.

Arbre phylogénétique

 ▲
 └─o Trilobita (éteint)
   ├─o Agnostida (éteint)
   └─o Polymerida (éteint)
     ├─o Olenellina (éteint)
     └─o
       ├─o Redlichiina (éteint)
       ├─o
       │ ├─o
       │ │ ├─o Lichida (éteint)
       │ │ └─o Odontopleurida (éteint)
       │ └─o Corynexochida (éteint)
       ├─? Phacopida (éteint)
       └─o Librostoma (éteint)
         ├─o Harpetida (éteint)
         ├─o Proetida (éteint)
         ├─o Asaphida (éteint)
         └─o Ptychopariida (éteint)

 ▲
 └─o Trilobita (éteint)
   ├─? Missisquoiidae (éteint)
   ├─? Isocolidae (éteint)
   ├─? Granulariidae (éteint)
   ├─? Sarkiidae (éteint)
   ├─? Otarionidae (éteint)
   ├─? Plethopeltidae (éteint)
   ├─? Monorakidae (éteint)
   ├─? Bathynotidae (éteint)
   ├─? Hicksiidae (éteint)
   ├─? Endymioniidae (éteint)
   ├─? Hypermecaspididae (éteint)
   ├─? Avoninidae (éteint)
   ├─? Catillicephalidae (éteint)
   ├─? Yunnanocephalidae (éteint)
   ├─? Tricrepicephalidae (éteint)
   ├─? Andrarinidae (éteint)
   ├─? Burlingiidae (éteint)
   ├─? Raymondinidae (éteint)
   ├─o Agnostida (éteint)
   │ ├─o Tsunyidiscidae (éteint)
   │ ├─o Hebediscidae (éteint)
   │ ├─o Calodiscidae (éteint)
   │ ├─o Eodiscidae (éteint)
   │ ├─o Yukoniidae (éteint)
   │ └─o
   │   ├─o Weymouthiidae (éteint)
   │   └─o Agnostina (éteint)
   │     ├─? Phalacromidae (éteint)
   │     ├─? Sphaeragnostidae (éteint)
   │     ├─o Condylopygidae (éteint)
   │     └─o Agnostoidea (éteint)
   │       ├─o Agnostidae (éteint)
   │       │ ├─o Agnostinae (éteint)
   │       │ ├─o Ammaghostinae (éteint)
   │       │ └─o Glyptagnostinae (éteint)
   │       ├─o Spinagnostidae (éteint)
   │       │ ├─o Doryagostinae (éteint)
   │       │ └─o Spinagnostinae (éteint)
   │       ├─o Clavagnostidae (éteint)
   │       │ ├─o Clavagnostinae (éteint)
   │       │ └─o Aspidagnostinae (éteint)
   │       ├─o Metagnostidae (éteint)
   │       ├─o Peronopsidae (éteint)
   │       ├─o Ptychagnostidae (éteint)
   │       └─o Diplagnostidae (éteint)
   │         ├─o Diplagostinae (éteint)
   │         ├─o Oidalagostinae (éteint)
   │         └─o Pseudagnostinae (éteint)
   └─o Polymerida ou Redlichiida (éteint) (paraphylétique)
     ├─? Dipharus (éteint)
     └─o
       ├─o Olenellina (éteint)
       │ ├─? Poletaevella (éteint)
       │ ├─? Postfallotaspis (éteint)
       │ └─o Fallotaspidoidea (éteint)
       │   ├─? Archaeaspis (éteint)
       │   ├─? Fallotaspidella (éteint)
       │   ├─o Fallotaspididae (éteint)
       │   │ ├─o Fallotaspidinae (éteint)
       │   │ └─o Daguinaspidinae (éteint)
       │   └─o
       │     ├─o Pseudojudomia (éteint)
       │     └─o
       │       ├─o Plesionevadia (éteint)
       │       └─o Nevadioidea (éteint)
       │         ├─o Nevadia (éteint)
       │         └─o
       │           ├─o Nevadella (éteint)
       │           └─o
       │             ├─o Cirquella (éteint)
       │             └─o Judomioidea (éteint)
       │               ├─o
       │               │ ├─o Bradyfallotaspis (éteint)
       │               │ └─o Paranevadella (éteint)
       │               └─o
       │                 ├─o Cambroinyoella (éteint)
       │                 └─o
       │                   ├─o Gerardiella (éteint)
       │                   └─o
       │                     ├─o Holmiinae (éteint)
       │                     └─o
       │                       ├─o
       │                       │ ├─o Gabriellinae (éteint)
       │                       │ └─o Olenellidae (éteint)
       │                       └─o
       │                         ├─o Callaviinae (éteint)
       │                         └─o
       │                           ├─o Callavalonia (éteint)
       │                           └─o
       │                             ├─o Sdzuyomia (éteint)
       │                             └─o
       │                               ├─o Judomia (éteint)
       │                               └─o Neltneriidae (éteint)
       └─o
         ├─o Redlichiina (éteint)
         │ ├─o Emuelloidea ou Emuellidae (éteint)
         │ ├─o Paradoxidoidea (éteint)
         │ │ ├─o Paradoxididae (éteint)
         │ │ ├─o Centropleuridae (éteint)
         │ │ └─o Xystriduridae (éteint)
         │ └─o Redlichioidea (éteint)
         │   ├─o Dolerolenidae (éteint)
         │   │ ├─o Doleroleninae (éteint)
         │   │ └─? Paramalungiinae (éteint)
         │   ├─o Yinitidae (éteint)
         │   ├─o Mayiellidae (éteint)
         │   ├─o Gigantopygidae (éteint)
         │   │ ├─o Gigantopyginae (éteint)
         │   │ └─o Yiliangellinae (éteint)
         │   ├─o Saukinandidae (éteint)
         │   │ ├─o Saukiandinae (éteint)
         │   │ ├─o Despujolsiinae (éteint)
         │   │ └─o Resseropinae (éteint)
         │   ├─o Metadoxididae (éteint)
         │   ├─o Abadiellidae (éteint)
         │   ├─o Kueichowiidae (éteint)
         │   ├─o Menneraspididae (éteint)
         │   ├─o Redlichinidae (éteint)
         │   ├─o Chengkouaspidae (éteint)
         │   └─o Redlichiidae (éteint)
         │     ├─o Redlichiinae (éteint)
         │     ├─o Metaredlichiinae (éteint)
         │     ├─o Neoredlichiinae (éteint)
         │     ├─o Pararedlichiinae (éteint)
         │     └─o Wutingaspinae (éteint)
         ├─o
         │ ├─o
         │ │ ├─o Lichida (éteint)
         │ │ │ ├─o Lichakelaphidae (éteint)
         │ │ │ └─o Lichidae (éteint)
         │ │ │   ├─o Lichinae (éteint)
         │ │ │   └─o Trochurinae (éteint)
         │ │ └─o Odontopleurida (éteint)
         │ │   ├─o Damesellidae (éteint)
         │ │   └─o Odontopleuroidea (éteint)
         │ │     ├─o Eoacidaspididae (éteint)
         │ │     ├─o Odontopleuridae (éteint)
         │ │     └─o Solenopeltidae (éteint)
         │ └─o Corynexochida (éteint)
         │   ├─o Leiostegioidea (éteint)
         │   │ ├─o Leiostegiidae (éteint)
         │   │ ├─o Pagodiidae (éteint)
         │   │ ├─o Kaolishaniidae (éteint)
         │   │ ├─o Cheilocephalidae (éteint)
         │   │ ├─o Lecanopygidae (éteint)
         │   │ └─o Shirakellidae (éteint)
         │   ├─o Illaenoidea (éteint)
         │   │ ├─o Thysanopeltidae (éteint)
         │   │ ├─o Styginidae (éteint)
         │   │ ├─o Phillipsinellidae (éteint)
         │   │ ├─o Tsinaniidae (éteint)
         │   │ └─o Illaenidae (éteint)
         │   │   ├─o Illaeninae (éteint)
         │   │   └─o Panderiinae (éteint)
         │   └─o Corynexochoidea (éteint)
         │     ├─o Corynexochidae (éteint)
         │     ├─o Cheiruroididae (éteint)
         │     ├─o Chenghuiidae (éteint)
         │     ├─o Dorypygidae (éteint)
         │     ├─o Ogygopsidae (éteint)
         │     ├─o Oryctocephalidae (éteint)
         │     ├─o Dolichometopidae (éteint)
         │     ├─o Edelsteinaspidae (éteint)
         │     ├─o Jakutidae (éteint)
         │     ├─o Longduiidae (éteint)
         │     ├─o Zacanthoididae (éteint)
         │     └─o Dinesidae (éteint)
         ├─? Phacopida (éteint)
         │ ├─o Calymenoidea (éteint)
         │ │ ├─o Calymenidae (éteint)
         │ │ ├─o Homalonotidae (éteint)
         │ │ ├─o Carmonidae (éteint)
         │ │ └─o Bathycheilidae (éteint)
         │ ├─o Cheiruroidea (éteint)
         │ │ ├─o Cheiruridae (éteint)
         │ │ ├─o Pliomeridae (éteint)
         │ │ ├─o Pilekiidae (éteint)
         │ │ ├─o Encrinuridae (éteint)
         │ │ ├─o Staurocephalidae (éteint)
         │ │ └─o Hammatocnemidae (éteint)
         │ └─o Phacopina (éteint)
         │   ├─o Phacopoidea (éteint)
         │   │ ├─o Phacopidae (éteint)
         │   │ └─o Pterygometopidae (éteint)
         │   ├─o Acastoidea (éteint)
         │   │ ├─o Acastidae (éteint)
         │   │ └─o Calmoniidae (éteint)
         │   └─o Dalmanitoidea (éteint)
         │     ├─o Dalmanitidae (éteint)
         │     ├─o Prosopiscidae (éteint)
         │     └─o Diaphanometopidae (éteint)
         └─o Librostoma (éteint)
           ├─o Harpetida (éteint)
           │ ├─o Harpetidae (éteint)
           │ ├─o Harpididae (éteint)
           │ └─o Entomaspididae (éteint)
           ├─o Proetida (éteint)
           │ ├─o Proetoidea (éteint)
           │ │ ├─o Proetidae (éteint)
           │ │ └─o Phillipsiidae (éteint)
           │ ├─o Aulacopleuroidea (éteint)
           │ │ ├─o Aulacopleuridae (éteint)
           │ │ ├─o Brachymetopidae (éteint)
           │ │ └─o Rorringtoniidae (éteint)
           │ └─o Bathyuroidea (éteint)
           │   ├─o Bathyuridae (éteint)
           │   ├─o Dimeropygidae (éteint)
           │   ├─o Tornquistiidae (éteint)
           │   ├─o Celmidae (éteint)
           │   ├─o Glaphuridae (éteint)
           │   ├─o Holotrachelidae (éteint)
           │   └─o Telephinidae (éteint)
           ├─o Asaphida (éteint)
           │ ├─? Rhyssometopidae (éteint)
           │ ├─? Monkaspididae (éteint)
           │ ├─o Anomocaroidea (éteint)
           │ │ ├─o Anomocaridae (éteint)
           │ │ ├─o Pterocephalidae (éteint)
           │ │ ├─o Parabolinoididae (éteint)
           │ │ └─o Dikelokephalinidae (éteint)
           │ ├─o Asaphoidea (éteint)
           │ │ ├─o Asaphidae (éteint)
           │ │ └─o Ceratopygidae (éteint)
           │ ├─o Dikelokephaloidea (éteint)
           │ │ ├─o Dikelokephalidae (éteint)
           │ │ ├─o Saukiidae (éteint)
           │ │ ├─o Ptychaspididae (éteint)
           │ │ ├─o Eurekiidae (éteint)
           │ │ └─o Loganellidae (éteint)
           │ ├─o Cyclopygoidea (éteint)
           │ │ ├─o Cyclopygidae (éteint)
           │ │ ├─o Taihungshaniidae (éteint)
           │ │ └─o Nileidae (éteint)
           │ ├─o Trinucleoidea (éteint)
           │ │ ├─o Trinucleidae (éteint)
           │ │ ├─o Dionididae (éteint)
           │ │ ├─o Orometopidae (éteint)
           │ │ ├─o Raphiophoridae (éteint)
           │ │ ├─o Alsataspididae (éteint)
           │ │ └─o Liostracinidae (éteint)
           │ └─o Remopleuridoidea (éteint)
           │   ├─o Remopleurididae (éteint)
           │   ├─o Kainellidae (éteint)
           │   ├─o Opipeuteridae (éteint)
           │   ├─o Bohemillidae (éteint)
           │   ├─o Auritamiidae (éteint)
           │   ├─o Idahoiidae (éteint)
           │   └─o Hungaiidae (éteint)
           └─o Ptychopariida (éteint)
             ├─? Ithyophoridae (éteint)
             ├─o Olenina ou Olenidae (éteint)
             └─o Ptychopariina (éteint)
               ├─o Ellipsocephaloidea (éteint)
               │ ├─o Ellipsocephalidae (éteint)
               │ ├─o Protolenidae (éteint)
               │ └─o Agraulidae (éteint)
               └─o Ptychoparioidea (éteint)
                 ├─o Ptychopariidae (éteint)
                 ├─o Marjumiidae (éteint)
                 ├─o Solenopleuridae (éteint)
                 ├─o Conocoryphidae (éteint)
                 ├─o Dokimocephalidae (éteint)
                 ├─o Onchonotopsidae (éteint)
                 ├─o Nepeiidae (éteint)
                 ├─o Crepicephalidae (éteint)
                 ├─o Lonchcephalidae (éteint)
                 ├─o Kingstoniidae (éteint)
                 ├─o Shumardiidae (éteint)
                 ├─o Asaphiscidae (éteint)
                 ├─o Coosellidae (éteint)
                 ├─o Elviniidae (éteint)
                 ├─o Cedariidae (éteint)
                 ├─o Norwoodiidae (éteint)
                 ├─o Menomoniidae (éteint)
                 ├─o Bolaspididae (éteint)
                 ├─o Papyriaspididae (éteint)
                 ├─o Emmrichellidae (éteint)
                 ├─o Diceratocephalidae (éteint)
                 ├─o Tengfengiidae (éteint)
                 ├─o Lisaniidae (éteint)
                 ├─o Inouyiidae (éteint)
                 ├─o Wuaniidae (éteint)
                 ├─o Lorenzellidae (éteint)
                 ├─o Proasaphiscidae (éteint)
                 ├─o Ignotogregatidae (éteint)
                 ├─o Holanshaniidae (éteint)
                 ├─o Mapaniidae (éteint)
                 └─o Acrocephalidae (éteint)

Débat scientifique relatif à la phylogénie des Trilobita

En savoir plus

Sources bibliographiques de référence

Sources internet

Liens internes
 Arbre phylogénétique
 Le vivant (classification phylogénétique)
 Eukaryota (classification phylogénétique)
 Opisthokonta (classification phylogénétique)
 Metazoa (classification phylogénétique)
 Bilateria (classification phylogénétique)
 Arthropoda (classification phylogénétique)
 Orientation bibliographique en zoologie (taxinomie)
 Photo-guide taxinomique du monde animal
 Répertoire général des ressources en zoologie
 Trilobita

Arthropode (classification phylogénétique)
Trilobita


text:
Merle Wood Sells Superyacht April Fool
Merle Wood & Associates has today announced the successful sale of the 61m (200') Feadship superyacht April Fool.
April Fool was launched in 2006 by the world renowned Dutch yacht builder Feadship and after a long, successful career on the water and an extensive refit in 2009, has now been sold to her new owner.
Designed by De Voogt Naval Architect, this sleek and sophisticated superyacht holds an interior design from Larvor Janet Leroy and space for eight guests on board.
Before her sale, April Fool was originally listed with Merle Wood & Associates at an asking price of $59,950,000.


text:
Q: while loop until empty string in online interpreter Why does my program work in PyCharm but in online interpreter gives this error:

Traceback (most recent call last):   File "Solution.py", line 4, in

      s = input() EOFError: EOF when reading a line

Here's the part of code that matters:
i = 0
while True:
    s = input()
    if s == '':
        break
    else:
        ...

I'm trying to input strings until empty string occurs but it always gets stuck on line with empty string.
Thanks in advance and sorry if I'm sloppy with my question (my 1st question).

A: Perhaps you can handle there exception with try and except:
while True:
    try:
        s = input()
        ...
    except EOFError:
        break
...


text:
Today: 19 Jul. 2019
Global Book Marketing
Tullula
Home » New Books
Author: AUTHOR Refiloe Moahloli ILLUSTRATOR Simon Mahlo
Pub. Date: 01 Feb 2019
Publisher: JACANA
Download more information...
Tullula is a majestic bird born into royalty - the ndunas of the herd.
Being a nduna means having to guard the skies at night (although its not entirely clear what the ndunas are guarding against).
Tullula longs for something different, so she sneaks off during the day (while the rest of the ndunas are sleeping) to explore what life is like living in sunlight.
An inspirational story in rhyming verse about the rewards of challenging the status quo; not only for the individual who dares to do it, but for everyone else around them as well.
Includes a bonus CD featuring the Tullula songs and a reading of the story by the author.
Colour throughout
"Allow yourself to be taken on the journey with the adventurous Tullala to a brighter side of life – Dr Gcina Mhlophe
Refiloe Moahloli is a commerce graduate who spent the early part of her career navigating the dynamic corporate world, she decided to pursue her passion for writing after an eye-opening assignment to Mumbai, India. She is the bestselling author of How Many Ways Can You Say Hello?, published in 2017 by Penguin Random House
Abena-Saah Mophatlane exposure to different cultures and ethnicities gives her an advantage in identifying talent with a widespread appeal. One such identification led to the creation of Tullula. She first came across Simon Mahlo's hand-embroidery through a cushion that was sold to her. She was immediately impressed with the quality of work and felt she had to meet the artist behind it. After meeting with Mahlo, she was inspired to create a children's book with hand-embroidered illustrations – and so Tullula was born.
Bookseller News
Thompson wins Arthur C Clarke Prize for Rosewater
Tade Thompson has won the Arthur C Clarke Award for science fiction with his novel Rosewater (Orbit).
Mostly Lit's Derek Owusu signs two-book deal with Stormzy imprint
Stormzy's Penguin Random House imprint #Merky Books has signed a two-book deal with "Mostly Lit" podcaster Derek Owusu.
Harry Potter and the Cursed Child behind the scenes book cover revealed
Little, Brown has revealed the cover for its behind the scenes book on hit play Harry Potter and the Cursed Child.
SPCK accused of 'cashing in' on Eddo-Lodge success in book cover row
SPCK has been accused of "cashing in" on the success of Reni Eddo-Lodge by launching a book with a similar title and cover to Why I'm No Longer Talking to White People About Race (Bloomsbury).
Flame Tree signs deal for CWA crime anthology
Flame Tree Publishing has signed a deal with the Crime Writers' Association (CWA) to publish the latest anthology of stories by CWA members.
A Dark Iris
Elizabeth J. Jones
BLUE BANYAN BOOKS
£10.99 B is for Breadfruit
Staysean Daley
£5.99 The Dark of the Sea
Imam Baksh
Global Book Marketing provides representaion and marketing services for publishers.
For further information visit the representation page, or contact:
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London, RM8 1RX
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One of the most frequent concerns for Surrey residents is inconsiderate parking. Park SMART aims to inform motorists about current parking regulations and encourages sensible parking. By parking correctly you can help reduce accidents, improve the flow of traffic and limit the damage to the environment caused by congestion.
What type of parking is inconsiderate?
How does inconsiderate parking affect others?
accessibility problems for larger vehicles i.e. refuse lorries.
Who's responsible for parking in Surrey?
Amendments to the Traffic Management Act mean that councils now have the option to crack down on bad parking; including double parking and parking next to dropped kerbs. Surrey's district and borough councils have now taken on the new parking powers on the county council's behalf. Drivers will now be liable to a penalty charge notice for these acts.


text:
Home Blog Presentations from the Leadership Forum Available Online
Presentations from the Leadership Forum Available Online
by Catherine Jhee
Lots of people have asked us when speaker presentations from last month's Leadership Forum would be available online. It's taking us awhile to get the video edited down, but we are pleased to say that many of our speakers' presentation slides are now available online. You can see them now on slideshare.net. If you're wondering whether a speaker had a presentation or not, simply check out the agenda and concurrent panels on the Learning from Hollywood site; if we have the slides, we've added a little icon ( ) next to his or her name.
We've got slides from Cooney Center Executive Director Michael Levine, Scholastic's Chief Academic Officer Francie Levine, Fred Rogers Center Executive Director Rita Catalano, and Senior Fellow at FrameWorks Institute and Dean of UCLA Luskin School of Public Affairs Frank Gilliam, among many others.
We'll be adding to our Slideshare collection with new presentations from Cooney Center events. We hope that you'll find them helpful as you reflect on talks that you've seen – or perhaps missed but wish you'd been able to attend.
If you have any questions or comments, please let us know below. We welcome your feedback!
TAGS: conference, leadership forum, powerpoint, presentations, slides
Catherine Jhee
Catherine is Director of Web and Strategic Communications at the Joan Ganz Cooney Center, where she leads its digital media and publication efforts. She is a writer, editor, and interactive producer with more than 10 years of experience creating and distributing educational and documentary content for organizations including POV Interactive, the award-winning website for the independent documentary series on PBS, the Columbia Center for New Media Teaching and Learning, Science Bulletins at the American Museum of Natural History, and Picture Projects. Catherine's interest in children's media research began as an undergraduate at Duke University. She received an MAH from the University at Buffalo in Comparative Literature and Media Studies and an MS from the Columbia University Graduate School of Journalism. She has taught courses on documentary video production and filmmaking at the University at Buffalo and interactive design at the Cooper Union School of Art.
Action! An update from TASC on its Action Group.
by Susan Brenna | February 2012
by Gary Knell | June 2011
Reflections on the Learning from Hollywood Forum: Storytelling and Collaboration
by Makeda Mays Green | June 2011


Last edited by cooke24; 07-22-2011, 09:29 PM.
Last edited by cooke24; 07-20-2011, 12:39 PM.
i'll be in. might not drive straight there though, have to catch some buddies across the border.
Same thing here. Wat time is Better for you to meet ?
Last edited by cooke24; 07-22-2011, 03:21 AM.
still waiting for reply on the other line. ill let you know asap.
Now why would two best friends fight? I'd say they'd just make a cookie pie, tell each other bed time stories and have a sleepover.
Yeah I might drop by before you guys head to the border.