text:
Why Canadian Solar Shares Plunged 18.7% Today
Travis Hoium, The Motley Fool
Motley Fool March 21, 2019
Shares of Canadian Solar (NASDAQ: CSIQ) fell as much as 18.7% in trading Thursday after the company reported fourth-quarter 2018 earnings. At 3:15 p.m. EDT, shares were still down 18.8% on the day.
Fourth-quarter results weren't particularly startling for Canadian Solar. Module shipments were 1.95 gigawatts (GW) versus guidance of 1.9 GW to 1.95 GW. Revenue was $901 million and earnings were $99.5 million, or $1.61 per share on a non-GAAP basis.
Solar farm with a sunset in the background.
Where investors really saw weakness was in guidance for 2019. Management said it expects to ship 1.3 GW to 1.4 GW in the first quarter (which is almost over) and report $450 million to $480 million of revenue, and gross margin of 16% to 18%. For the full year, the company expects 7.4 GW to 7.8 GW of shipments and $3.5 billion to $3.8 billion of revenue. Net income is also expected to be down from the $237.1 million reported in 2018. For context, analysts were expecting $3.94 billion in revenue.
Canadian Solar is up against falling prices for solar panels, which is hurting both revenue and income. But that's a long-term trend solar companies have been dealing with for a decade. Investors were just surprised at how quickly the decline is going to hit business, and that's why shares are down big today.
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Travis Hoium has no position in any of the stocks mentioned. The Motley Fool has no position in any of the stocks mentioned. The Motley Fool has a disclosure policy.
Qatar signs $470 mn solar deal
Stratasys (NASDAQ:SSYS) Is In A Strong Position To Grow Its Business


DETROIT – The Ross Initiative in Sports for Equality (RISE), headquartered in New York, has opened its first regional office, a Detroit location serving the Midwest.RISE is an unprecedented alliance of professional sports leagues, organizations, athletes, educators, media networks and sports professionals using sports to bring people together to promote understanding, respect and equality. Through panel discussions with professional athletes, law enforcement and elected officials; public awareness campaigns; and educational programming at the high school and university levels, RISE aims to improve race relations.
The new office is downtown in Suite 600 of the Chrysler House building, 719 Griswold St. Initially, five staff members will be based there. The Midwest office will serve the states of Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota and Wisconsin.
RISE has sponsored programs with the Detroit Lions, Pistons, Red Wings and Tigers. RISE also has partnerships with Michigan State University and the University of Michigan. Through its leadership training program, RISE has worked with these Michigan high schools: Cass Technical High School, Cesar Chavez Academy High School and Jalen Rose Leadership Academy, all in Detroit; Plymouth High School; Saline High School; Southfield High School for the Arts and Technology; and West Bloomfield High School. RISE also has worked with the Detroit Police Athletic League Team Up Program.
Additionally, RISE has been active in Arizona, California, Florida, Georgia, Indiana, Illinois, Massachusetts, Missouri, New York, North Carolina and Washington, D.C.
Real estate developer and philanthropist Stephen M. Ross, a Detroit native and owner of the Miami Dolphins, founded RISE in 2015. The nonprofit organization is dedicated to harnessing the unifying power of sports to improve race relations and drive social progress.


text:
Outbreak of Klebsiella pneumoniae Carbapenemase-Producing Citrobacter freundii at a Tertiary Acute Care Facility in Miami, Florida
Adriana Jiménez, José G. Castro, L. Silvia Munoz-Price, Dennise De Pascale, Luis Shimose, Mustapha M. Mustapha, Caressa N. Spychala, Roberta T. Mettus, Vaughn S. Cooper, Yohei Doi
OBJECTIVE To describe the investigation and control of a rare cluster of Klebsiella pneumoniae carbapenemase-producing Citrobacter freundii in a hospital in southern Florida. METHODS An epidemiologic investigation, review of infection prevention procedures, and molecular studies including whole genome sequencing were conducted. RESULTS An outbreak of K. pneumoniae carbapenemase-3-producing C. freundii was identified at a tertiary hospital in Florida in 2014. Of the 6 cases identified, 3 occurred in the same intensive care unit and were caused by the same clone. For 2 of the 3 remaining cases, the isolates had low carbapenem minimum inhibitory concentrations and were unrelated by whole genome sequencing. As a response to the outbreak, supplementary environmental cleaning was implemented, including closure and terminal cleaning of the unit where the 3 cases clustered, in addition to the infection control bundle already in place at the time. No further cases were identified after these additional interventions. CONCLUSIONS Although C. freundii is not a species that commonly demonstrates carbapenem resistance, our findings suggest that carbapenemase-producing C. freundii may be underdetected even when active surveillance is in place and has a potential to cause hospital outbreak.
Infection Control and Hospital Epidemiology
https://doi.org/10.1017/ice.2016.273
Carbapenems
Microbial Sensitivity Tests
Intensive Care Units
Clone Cells
carbapenemase
Jiménez, A., Castro, J. G., Munoz-Price, L. S., De Pascale, D., Shimose, L., Mustapha, M. M., ... Doi, Y. (2017). Outbreak of Klebsiella pneumoniae Carbapenemase-Producing Citrobacter freundii at a Tertiary Acute Care Facility in Miami, Florida. Infection Control and Hospital Epidemiology, 38(3), 320-326. https://doi.org/10.1017/ice.2016.273
Jiménez, Adriana ; Castro, José G. ; Munoz-Price, L. Silvia ; De Pascale, Dennise ; Shimose, Luis ; Mustapha, Mustapha M. ; Spychala, Caressa N. ; Mettus, Roberta T. ; Cooper, Vaughn S. ; Doi, Yohei. / Outbreak of Klebsiella pneumoniae Carbapenemase-Producing Citrobacter freundii at a Tertiary Acute Care Facility in Miami, Florida. In: Infection Control and Hospital Epidemiology. 2017 ; Vol. 38, No. 3. pp. 320-326.
@article{34ca5697ed02428a90dc121f3b784d60,
title = "Outbreak of Klebsiella pneumoniae Carbapenemase-Producing Citrobacter freundii at a Tertiary Acute Care Facility in Miami, Florida",
abstract = "OBJECTIVE To describe the investigation and control of a rare cluster of Klebsiella pneumoniae carbapenemase-producing Citrobacter freundii in a hospital in southern Florida. METHODS An epidemiologic investigation, review of infection prevention procedures, and molecular studies including whole genome sequencing were conducted. RESULTS An outbreak of K. pneumoniae carbapenemase-3-producing C. freundii was identified at a tertiary hospital in Florida in 2014. Of the 6 cases identified, 3 occurred in the same intensive care unit and were caused by the same clone. For 2 of the 3 remaining cases, the isolates had low carbapenem minimum inhibitory concentrations and were unrelated by whole genome sequencing. As a response to the outbreak, supplementary environmental cleaning was implemented, including closure and terminal cleaning of the unit where the 3 cases clustered, in addition to the infection control bundle already in place at the time. No further cases were identified after these additional interventions. CONCLUSIONS Although C. freundii is not a species that commonly demonstrates carbapenem resistance, our findings suggest that carbapenemase-producing C. freundii may be underdetected even when active surveillance is in place and has a potential to cause hospital outbreak.",
author = "Adriana Jim{\'e}nez and Castro, {Jos{\'e} G.} and Munoz-Price, {L. Silvia} and {De Pascale}, Dennise and Luis Shimose and Mustapha, {Mustapha M.} and Spychala, {Caressa N.} and Mettus, {Roberta T.} and Cooper, {Vaughn S.} and Yohei Doi",
doi = "10.1017/ice.2016.273",
journal = "Infection Control and Hospital Epidemiology",
publisher = "University of Chicago Press",
Jiménez, A, Castro, JG, Munoz-Price, LS, De Pascale, D, Shimose, L, Mustapha, MM, Spychala, CN, Mettus, RT, Cooper, VS & Doi, Y 2017, 'Outbreak of Klebsiella pneumoniae Carbapenemase-Producing Citrobacter freundii at a Tertiary Acute Care Facility in Miami, Florida', Infection Control and Hospital Epidemiology, vol. 38, no. 3, pp. 320-326. https://doi.org/10.1017/ice.2016.273
Outbreak of Klebsiella pneumoniae Carbapenemase-Producing Citrobacter freundii at a Tertiary Acute Care Facility in Miami, Florida. / Jiménez, Adriana; Castro, José G.; Munoz-Price, L. Silvia; De Pascale, Dennise; Shimose, Luis; Mustapha, Mustapha M.; Spychala, Caressa N.; Mettus, Roberta T.; Cooper, Vaughn S.; Doi, Yohei.
In: Infection Control and Hospital Epidemiology, Vol. 38, No. 3, 01.03.2017, p. 320-326.
T1 - Outbreak of Klebsiella pneumoniae Carbapenemase-Producing Citrobacter freundii at a Tertiary Acute Care Facility in Miami, Florida
AU - Jiménez, Adriana
AU - Castro, José G.
AU - Munoz-Price, L. Silvia
AU - De Pascale, Dennise
AU - Shimose, Luis
AU - Mustapha, Mustapha M.
AU - Spychala, Caressa N.
AU - Mettus, Roberta T.
AU - Cooper, Vaughn S.
AU - Doi, Yohei
N2 - OBJECTIVE To describe the investigation and control of a rare cluster of Klebsiella pneumoniae carbapenemase-producing Citrobacter freundii in a hospital in southern Florida. METHODS An epidemiologic investigation, review of infection prevention procedures, and molecular studies including whole genome sequencing were conducted. RESULTS An outbreak of K. pneumoniae carbapenemase-3-producing C. freundii was identified at a tertiary hospital in Florida in 2014. Of the 6 cases identified, 3 occurred in the same intensive care unit and were caused by the same clone. For 2 of the 3 remaining cases, the isolates had low carbapenem minimum inhibitory concentrations and were unrelated by whole genome sequencing. As a response to the outbreak, supplementary environmental cleaning was implemented, including closure and terminal cleaning of the unit where the 3 cases clustered, in addition to the infection control bundle already in place at the time. No further cases were identified after these additional interventions. CONCLUSIONS Although C. freundii is not a species that commonly demonstrates carbapenem resistance, our findings suggest that carbapenemase-producing C. freundii may be underdetected even when active surveillance is in place and has a potential to cause hospital outbreak.
AB - OBJECTIVE To describe the investigation and control of a rare cluster of Klebsiella pneumoniae carbapenemase-producing Citrobacter freundii in a hospital in southern Florida. METHODS An epidemiologic investigation, review of infection prevention procedures, and molecular studies including whole genome sequencing were conducted. RESULTS An outbreak of K. pneumoniae carbapenemase-3-producing C. freundii was identified at a tertiary hospital in Florida in 2014. Of the 6 cases identified, 3 occurred in the same intensive care unit and were caused by the same clone. For 2 of the 3 remaining cases, the isolates had low carbapenem minimum inhibitory concentrations and were unrelated by whole genome sequencing. As a response to the outbreak, supplementary environmental cleaning was implemented, including closure and terminal cleaning of the unit where the 3 cases clustered, in addition to the infection control bundle already in place at the time. No further cases were identified after these additional interventions. CONCLUSIONS Although C. freundii is not a species that commonly demonstrates carbapenem resistance, our findings suggest that carbapenemase-producing C. freundii may be underdetected even when active surveillance is in place and has a potential to cause hospital outbreak.
U2 - 10.1017/ice.2016.273
DO - 10.1017/ice.2016.273
JO - Infection Control and Hospital Epidemiology
JF - Infection Control and Hospital Epidemiology
Jiménez A, Castro JG, Munoz-Price LS, De Pascale D, Shimose L, Mustapha MM et al. Outbreak of Klebsiella pneumoniae Carbapenemase-Producing Citrobacter freundii at a Tertiary Acute Care Facility in Miami, Florida. Infection Control and Hospital Epidemiology. 2017 Mar 1;38(3):320-326. https://doi.org/10.1017/ice.2016.273
10.1017/ice.2016.273

redpajama_set_name: RedPajamaCommonCrawl


text:
Pretty as a Picture: A Novel
by Elizabeth LittleElizabeth Little
Available for Pre-Order. This item will be available on February 25, 2020
An egomaniacal movie director, an isolated island, and a decades-old murder—the addictive new novel from the bestselling author of Dear Daughter
Marissa Dahl, a shy but successful film editor, travels to a small island off the coast of Delaware to work with the legendary—and legendarily demanding—director Tony Rees on a feature film with a familiar logline.
Some girl dies.
It's not much to go on, but the specifics don't concern Marissa. Whatever the script is, her job is the same. She'll spend her days in the editing room, doing what she does best: turning pictures into stories.
But she soon discovers that on this set, nothing is as it's supposed to be—or as it seems. There are rumors of accidents and indiscretions, of burgeoning scandals and perilous schemes. Half the crew has been fired. The other half wants to quit. Even the actors have figured out something is wrong. And no one seems to know what happened to the editor she was hired to replace.
Then she meets the intrepid and incorrigible teenage girls who are determined to solve the real-life murder that is the movie's central subject, and before long, Marissa is drawn into the investigation herself.
The only problem is, the killer may still be on the loose. And he might not be finished.
A wickedly funny exploration of our cultural addiction to tales of murder and mayhem and a thrilling, behind-the-scenes whodunit, Pretty as a Picture is a captivating page-turner from one of the most distinctive voices in crime fiction.
Elizabeth Little is the author of Dear Daughter, which won the Strand Critics Award for Best First Novel, and two works of nonfiction, Biting the Wax Tadpole and Trip of the Tongue. She lives in Los Angeles with her family.
Praise for Pretty as a Picture
"Elizabeth Little is part of an exciting new generation of crime writers who have been bending this sturdy genre into new, unexpected shapes. Pretty as a Picture is a glorious buffet—a twisty story, a cinephile's delight, a knockout of a heroine. I loved it."
—Laura Lippman, New York Times bestselling author of Lady in the Lake and Sunburn
"Engaging and irresistible from the very first page, Pretty as a Picture is a smart and compelling thriller filled with surprises. Little writes with such a clear, confident, and witty voice, I found myself smiling as I read."
—Alafair Burke, New York Times bestselling author of The Better Sister
"Pretty as a Picture is a remarkable thriller that succeeds on almost too many levels. It's sharp and stylish, witty and fierce, not to mention extremely intelligent. Little perfectly and expertly conjures the unglamorous side of filmmaking—the gruesome grind of a movie shoot—in meticulous detail. This is no mere crime novel, but a must read for anyone with a passion for movies or the industry."
—Ivy Pochoda, author of Wonder Valley
"Pretty as a Picture is an atmospheric thriller that sparkles with intelligence and irrepressible wit, anchored by the singular voice of Marissa Dahl, a mega-competent female film editor plagued by anxiety. Elizabeth Little has an impeccable ear for dialogue and a sharp eye for detail, and she's created a page-turner that is as well written as it is captivating. I enjoyed every scene and sentence of this glorious book."
—Steph Cha, author of Your House Will Pay
Praise for Dear Daughter
"Quick-witted and fast-paced, this debut mystery should be a hit with Gone Girl fans."
"When former It Girl Janie Jenkins is released from prison, she embarks on a mission to discover if it was really she who murdered her mother. The debut novel's twists will easily hook you, but it's the narrator's dark wit and sharp observations that make this a truly fun read."
"Little keeps you guessing until the end—and then closes her book with a final, twisted flourish."
—Daneet Steffens, The Boston Globe
"Compelling. . . . This novel's engrossing suspense comes from its unreliable (and not especially likable) narrator who pursues answers with relentless fervor, regardless of the painful truths she turns up about herself. . . . Excellent."
—Stephanie Klose, Oprah.com
"Engrossing. . . The unlikable protagonist with a biting personality and outrageous actions, but who is fascinating at the same time, has never been more popular. Just think of Gone Girl. In her confident fiction debut, Elizabeth Little puts a fresh spin on this character in the form of Jane Jenkins, a young woman famous for being famous until she was sent to prison for the murder of her wealthy socialite mother. Little also makes Dear Daughter a parable about the cult of the celebrity stoked by a relentless press and a ruthless public's thirst for details of a woman it loves to hate."
"This is not your mother's mystery. The clever, prickly and profane heroine is, after all, a former It Girl whose aim as a teen was to be the next Paris Hilton, only better. . . . Sassy and lively. . . . The book's satisfying conclusion somehow manages to tie things up while also providing a cliffhanger, a pretty neat trick for a debut novel."
—Colleen Kelly, Minneapolis Star Tribune
"The best debut crime novel of 2014, a spiky, voicey, jolting, surprising story of a celebutante convicted of murdering her mother. . . . Little also produces one of the best endings of 2014, too."
—Sarah Weinman, National Post (Canada)
"A former It Girl hunts down her mom's murderer in this can't-put-down thriller."
"In prison for her mother's murder, L.A. socialite Jane Jenkins is released on a technicality. To track down the real killer Jane gets plain, goes underground and stirs up dangerous amounts of dirt in her mom's South Dakota hometown."
"[A] fun and riveting debut mystery."
—The San Diego Union Tribune
"Little makes a thrilling debut with this gripping read. Fans of Tana French and Gillian Flynn are going to enjoy the smart narrator and the twists and turns in the case."
—Library Journal (starred review)
"Stunning and chilling. . . . A harrowing story that will keep readers on the edge of their seat. . . . A novel you should not miss."
—Bookreporter.com
"Clever. . . . This is a killer debut, in every sense of the word!"
"A really gutsy, clever, energetic read, often unexpected, always entertaining. I loved Janie Jenkins's sassy voice and Elizabeth Little's too. In the world of crime novels, Dear Daughter is a breath of fresh air."
—Kate Atkinson, New York Times bestselling author of Life After Life
"Dear Daughter has three of my favorite things in a book: a smart, damaged, unstoppable narrator with a slicing sense of humor; needle-sharp writing that brings characters and atmosphere leaping off the page; and a vivid, original plot full of satisfying twists. This is an all-nighter, and the best debut mystery I've read in a long time."
—Tana French, New York Times bestselling author of Broken Harbor and In the Woods


text:
\section{Conclusions and Future Work}
In this paper, we have proposed attentive knowledge tracing, a new method for knowledge tracing that relies fully on attention networks. Our method improves upon existing knowledge tracing methods by building context-aware representations of questions and responses, using a monotonic attention mechanism to summarize past learner performance in the right time scale, and by using the Rasch model to capture individual differences among questions covering the same concept. Experimental results on a series of benchmark real-world learner response datasets show that our method outperforms state-of-the-art KT methods and exhibit excellent interpretability.
Avenues of future work include i) incorporating question text to further enhance the interpretability of question and concept embeddings and ii) testing whether our method can improve prediction performance on language learning datasets where memory decay occurs \cite{reddy}.
\section{Experimental Results}
\label{sec:experiments}
In this section, we detail a series of experiments we conducted to test on several real-world datasets. We evaluate AKT both quantitatively through predicting future learner responses and qualitatively through a series of visualizations and case studies.

 \begin{table}[t]\centering

     \scalebox{.9}{
     \begin{tabular}{llllll }\toprule
         Dataset & Learners & Concepts & Questions & Responses \\
         \midrule
         Statics2011 & $333$ & $1,223$ & - & $189,297$ \\
         ASSISTments2009 & $4,151$ & $110$ & $16,891$ & $325,637$ \\
         ASSISTments2015 & $19,840$ & $100$ & - & $683,801$ \\
         ASSISTments2017 & $1,709$ & $102$ & $3,162$ & $942,816$ \\

         \bottomrule
     \end{tabular}
     }

     \caption{Dataset statistics.}
     \label{tab:main}

 \end{table}

\begin{table*}[t]\centering

    \scalebox{0.97}{

        \begin{tabular}{l lllllll }\toprule
            Dataset & $\mbox{BKT+}$&   DKT & DKT+ & DKVMN & SAKT & AKT-NR & AKT-R  \\
            \midrule
            Statics2011 & $\sim 0.75$ & $ 0.8233\pm 0.0039$ & ${\bf 0.8301\pm 0.0039}$ & $ 0.8195\pm 0.0041$ & $ 0.8029\pm 0.0032$ & ${\it 0.8265\pm 0.0049}$ & \\
ASSISTments2009 & $\sim 0.69$ & ${\it 0.817\pm 0.0043}$ & $ 0.8024\pm 0.0045$ & $ 0.8093\pm 0.0044$ & $ 0.752\pm 0.004$ & $ 0.8169\pm 0.0045$ & ${\bf 0.8346\pm 0.0036}$ \\
ASSISTments2015 & & $ 0.731\pm 0.0018$ & ${\it 0.7313\pm 0.0018}$ & $ 0.7276\pm 0.0017$ & $ 0.7212\pm 0.002$ & ${\bf 0.7828\pm 0.0019}$ & \\
ASSISTments2017 & & $ 0.7263\pm 0.0054$ & $ 0.7124\pm 0.0041$ & $ 0.7073\pm 0.0044$ & $ 0.6569\pm 0.0027$ & ${\it 0.7282\pm 0.0037}$ & ${\bf 0.7702\pm 0.0026}$ \\

            \bottomrule
        \end{tabular}

    \caption{Performance of all KT methods on all datasets in predicting future learner responses. AKT (sometimes significantly) outperforms all baseline methods on all datasets. Best models are bold, second best models are italic.}
    \label{tab:bestversion}
    \vspace{-0.3cm}
\end{table*}

\subsection{Experimental Setup}
\sloppy
\textbf{Datasets.}
We evaluate the performance of AKT and several baselines on predicting future learner responses using four benchmark datasets: ASSISTments2009,
ASSISTments2015,
 ASSISTments2017\footnote{The ASSISTments datasets are retrieved from \\ \href{https://sites.google.com/site/assistmentsdata/home}{https://sites.google.com/site/assistmentsdata/home} and \\ \href{https://sites.google.com/view/assistmentsdatamining/}{https://sites.google.com/view/assistmentsdatamining/}.},
and Statics2011.\footnote{The Statics2011 dataset is retrieved from \\ \href{https://pslcdatashop.web.cmu.edu/DatasetInfo?datasetId=507}{https://pslcdatashop.web.cmu.edu/DatasetInfo?datasetId=507}.}
The ASSISTments datasets were collected from an online tutoring platform; in particular, the ASSISTments2009 dataset has been the standard benchmark for KT methods over the last decade.
The Statics2011 dataset was collected from a college-level engineering course on statics.
On all these datasets, we follow a series of standard pre-processing steps in the literature.
For the ASSISTments2009 dataset, we remove all interactions that are not associated to a named concept.
For the ASSISTments2015 dataset, we remove all interactions where the ``isCorrect'' field is not 0 or 1.
We list the numbers of learners, concepts, questions, and question-response pairs in Table~\ref{tab:main}.
Out of these datasets, only the ASSISTments2009 and ASSISTments2017 datasets contain question IDs; therefore, the Rasch model-based embeddings are only applicable to these two datasets.

\textbf{Baseline methods and evaluation metric.}
We compare AKT against several baseline KT methods, including BKT+ \cite{bkt}, DKT, DKT+ (which is an improved version of DKT with regularization on prediction consistency \cite{dktplus}), DKVMN \cite{dkvmn}, and the recently proposed self-attentive KT (SAKT) method \cite{sakt}, which uses an attention mechanism that can be viewed as a special case of AKT without context-aware representations of questions and responses and the monotonic attention mechanism.
We use the area under the receiver operating characteristics curve (AUC) as the metric to evaluate the performance of all KT methods on predicting binary-valued future learner responses to questions.

\textbf{Training and testing.}
For evaluation purposes, we perform standard k-fold cross-validation (with $k=5$) for all models and all datasets. Thus, for each fold, $20\%$ learners are used as the test set, $20\%$ are used as the validation set, and $60\%$ are used as the training set. For each fold, we use the validation set to perform early stopping and tune the parameters for every KT method.

We truncate learner response sequences that are longer than $200$, following \cite{dkt,dkvmn}, for computational efficiency reasons. If a learner has more than $200$ responses, we break up their entire sequence into multiple shorter sequences.
We use the Adam optimizer to train all models \cite{adam} with a batch size of $24$ learners to ensure that an entire batch can fit into the memory of our machine (equipped with one NVIDIA Titan X GPU).
We implement all versions of AKT in \texttt{PyTorch}; We also re-implement DKT, DKT+, and SAKT, since including question IDs requires new dataset partitions and leads to new experimental results.
We use the Xavier parameter initialization method \cite{xavier} for AKT, DKT, DKT+, and SAKT; for DKVMN, we follow their work and use samples from normal distributions to initialize the parameters \cite{dkvmn}.
We do not re-implement BKT+; its performance on various datasets is taken from \cite{dkvmn}.
For most datasets and most algorithms, one training epoch takes less than 10 seconds. We set the maximum number of epochs to $300$.

\begin{table}[t]\centering

    \scalebox{0.9}{

        \begin{tabular}{l ll|ll}\toprule
            Dataset & $\mbox{AKT}^{\mbox{raw}}$-NR & $\mbox{AKT-NR}$ & $\mbox{AKT}^{\mbox{raw}}$-R  & AKT-R \\
            \midrule
Statics2011  & $ 0.8253$& ${\bf 0.8265}$&  & \\
ASSISTments2009  & $ 0.8082$& ${\bf 0.8169}$& $ 0.8267$& ${\bf 0.8346}$\\
ASSISTments2015  & $ 0.7332$& ${\bf 0.7828}$&  & \\
ASSISTments2017  & $ 0.7066$& ${\bf 0.7282}$& $ 0.7552$& ${\bf 0.7702}$\\
            \bottomrule
        \end{tabular}

    \caption{AKT outperforms its variants that do not use contextual-aware question and response representations.}
    \label{tab:raw}

\end{table}

\begin{table}[t]\centering

    \scalebox{0.9}{

        \begin{tabular}{l llllllll}\toprule
            Dataset & SAKT & $\mbox{AKT-NR}^{\mbox{pos}}$ &  $\mbox{AKT-NR}^{\mbox{fixed}}$ & AKT-NR \\
            \midrule
Statics2011  & $ 0.8029$& $ 0.8196$& $ 0.8196$& ${\bf 0.8265}$\\
ASSISTments2009  & $ 0.752$& $ 0.7706$& $ 0.7708$& ${\bf 0.8169}$\\
ASSISTments2015  & $ 0.7212$& $ 0.7271$& $ 0.7272$& ${\bf 0.7828}$\\
ASSISTments2017 & $ 0.6569$& $ 0.672$& $ 0.6722$& ${\bf 0.7282}$\\
            \bottomrule
        \end{tabular}

    \caption{AKT significantly outperforms its variants that do not use monotonic attention.}
    \label{tab:monotonic}

\end{table}

\begin{table*}[t]\centering

    \scalebox{1}{

        \begin{tabular}{l llllllllll}\toprule
            Dataset & {\small DKT}&   {\small DKT-R} & {\small DKT+}&   {\small DKT+-R}& {\small DKVMN} & {\small DKVMN-R} & {\small SAKT}  &{\small SAKT-R} &{\small AKT-NR}  &{\small AKT-R}\\
            \midrule
{\small ASSISTments2009}  & $ 0.817$& $ 0.8179$& $ 0.8024$& $ 0.8033$& $ 0.8093$& $ 0.8235$& $ 0.752$& $ 0.7784$& $ 0.8169$& ${\bf 0.8346}$\\
{\small ASSISTments2017}  & $ 0.7263$& $ 0.7543$& $ 0.7124$& $ 0.7382$& $ 0.7073$& $ 0.7628$& $ 0.6569$& $ 0.7137$& $ 0.7282$& ${\bf 0.7702}$\\
            \bottomrule
        \end{tabular}

    \caption{The Rasch model-based embeddings (sometimes significantly) improve the performance of KT methods.
    \label{tab:rasch}

\end{table*}

\subsection{Results and Discussion}
Table~\ref{tab:bestversion} lists the performance of all KT methods across all datasets on predicting future learner responses; we report the averages as well as the standard deviations across five test folds.  AKT-R and AKT-NR represent variants of the AKT model with and without the Rasch model-based embeddings, respectively.
We see that AKT (sometimes significantly) outperforms other KT methods on the ASSISTments datasets while DKT+ marginally outperforms AKT on the smallest Statics2011 dataset.
In general, AKT performs better on larger datasets; this result suggests that attention mechanisms are more flexible than recurrent neural networks and are thus more capable of capturing the rich information contained in large-scale real-world learner response datasets.
On the ASSISTments2015 and ASSISTments2017 datasets, AKT-NR improves the AUC by $6\%$ and $1\%$ over the closest baseline. It performs on-par with the best-performing baseline on the Statics2011 and ASSISTments2009 datasets.
More importantly, on the ASSISTments2009 and 2017 datasets with question IDs, AKT-R significantly outperforms other KT methods, by $2\%$ and $6\%$ over the closest baseline, respectively.
We note that DKT outperforms the more advanced DKVMN method in our implementation. While we are able to replicate the performance of DKVMN using the same experimental setting \cite{dkvmn}, we found that DKT performs much better than previously reported in that work. DKT+ performs on-par with DKT, with minor improvements on the Statics2011 dataset.
We also observe that the RNN-based model, DKT, outperforms SAKT on all datasets.

\textbf{Ablation study.}
In order to justify the three key innovations in the AKT method, context-aware representations of questions and responses, the monotonic attention mechanism, and the Rasch model-based embeddings, we perform three additional ablation experiments comparing several variants of the AKT method.
The first experiment compares AKT-NR and AKT-R using context-aware question and response representations (with the question and knowledge encoders) with two variants $\mbox{AKT}^{\mbox{raw}}$-NR and $\mbox{AKT}^{\mbox{raw}}$-R; In these variants, we use raw question and response embeddings as their representations instead of the context-aware representations (i.e., without passing them through the encoders).
The second experiment compares AKT-NR against several variants without the monotonic attention mechanism. These variants include $\mbox{AKT-NR}^{\mbox{pos}}$, which uses (learnable) positional encoding to capture temporal dependencies in learner response data and $\mbox{AKT-NR}^{\mbox{fixed}}$, which uses (fixed) positional encoding using different frequencies of sine and cosine functions \cite{transformer}.
The third experiment compares AKT-R with AKT-NR, DKT, DKT-R, DKT+, DKT+-R, DKVMN, DKVMN-R, SAKT, and SAKT-R on the ASSISTments2009 and 2017 datasets where question IDs are available; DKT-R, DKT+-R, DKVMN-R, and SAKT-R refer to the DKT, DKT+, DKVMN, and SAKT methods augmented with the Rasch model-based embeddings as input, respectively.

Table~\ref{tab:raw} shows the results (only averages and not standard deviations across test folds, due to spatial constraints) of the first ablation experiment for the context-aware representations (i.e., the question and knowledge encoders). On all datasets
AKT-R and AKT-NR outperform their counterparts, $\mbox{AKT}^{\mbox{raw}}$-NR and $\mbox{AKT}^{\mbox{raw}}$-R, which use only a single self-attention mechanism with exponential decay (i.e., the knowledge retriever).
These results suggest that our context-aware representations of questions and responses are effective at summarizing each learner's  practice history.

Table~\ref{tab:monotonic} shows the results of the second ablation experiment for the monotonic attention mechanism. We see that AKT-NR significantly outperforms other attention mechanisms using positional embeddings, including SAKT, by about $1\%$ to $6\%$ on all datasets.
We postulate that the reason for this result is that unlike in language tasks where strong long-distance dependencies between words are more common, the dependence of future learner performance on the past is restricted to a much shorter time window.
Therefore, using multi-head attention with different exponential decay rates in the attention weights can effectively capture short-term dependencies on the past at different time scales.

Table~\ref{tab:rasch} shows the results of the third ablation experiment for the Rasch model-based embeddings on the two ASSISTments datasets where question IDs are available. All baseline KT methods with the added Rasch model-based embeddings outperform their regular versions, especially on the ASSISTments2017 dataset.
These results confirm our intuition that treating all questions covering the same concept as a single question is problematic; individual differences among these questions should not be ignored as long as overparameterization can be avoided.

\textbf{Remark.}
Our standard experimental setting follows that used in \cite{dkvmn,dkt}. In this setting, for questions tagged with multiple concepts (in the ASSISTments2009 dataset), a single learner response is repeated multiple times, one for each concept. Other works used different experimental settings for these questions; In \cite{notdeep}, the authors removed such questions and as a result, DKT's performance dropped to 0.71. In \cite{goingdeeper}, the authors built new concepts for each combination of co-occurring single concepts and as a result, DKT's performance dropped to 0.73. Therefore, we also use an alternative experimental setting on the ASSISTments2009 dataset.
For a question tagged with multiple concepts, we average the corresponding concept embeddings and use them as both input embeddings and for response prediction. Table~\ref{tab:alternate-skills} lists the performance of all KT methods on the ASSISTments2009 dataset under this setting.
DKT's performance dropped to 0.76 using average embeddings, faring better than settings under \cite{goingdeeper,notdeep}. We observe similar performance drops compared to our standard experimental setting for all KT methods, while AKT-R still comfortably outperforms all baselines.

\begin{table}[t]\centering

    \scalebox{0.9}{

        \begin{tabular}{l llllllll}\toprule
DKT & DKT+ & DKVMN & SAKT & AKT-NR & AKT-R\\
\midrule
$ 0.7616$& $ 0.7552$& $ 0.7556$& $ 0.7432$& ${\it 0.7627}$& ${\bf 0.7866}$ \\
\bottomrule
\end{tabular}

    \caption{AKT still outperforms other KT methods on the ASSISTments2009 dataset under an alternative experimental setting for questions tagged with multiple concepts.}
    \label{tab:alternate-skills}

\end{table}

\subsection{Visualizing Learned AKT Parameters}

\begin{figure}[t]
\vspace{-0.5cm}
\centering
\subfigure[]{

\includegraphics[width=1\columnwidth]{fig/response_attention_score_heatmap_kdd.pdf}
\label{fig:heatmapviz}
}
\subfigure[]{
\includegraphics[width=1\columnwidth]{fig/aaai_tracking_1}
\label{fig:ktviz}
}
 %
\caption{Visualizations of \subref{fig:heatmapviz} attention weights in the decoder of AKT for three attention heads and \subref{fig:ktviz} attention weights for three consecutive practice questions for a learner. Concept similarity and recency are key factor that control the attention weights.}
\label{fig:attn}
\vspace{-0.2cm}
\end{figure}

\textbf{Monotonic attention.}
Figure~\ref{fig:attn} shows the interpretability offered by AKT's monotonic attention mechanism using the ASSISTments2009 dataset.
Figure~\ref{fig:heatmapviz} visualizes the attention weights in the knowledge retriever for one learner as an example; we plot the attention weights used to predict their performance on 20 consecutive practice questions across three attention heads.
We see that each attention head operates on its own time scale: they all have attention windows of different widths. For example, the second head is capable of attending to the entire past, up to 20 time steps (in this example); on the contrary, the third head can only attend to the immediate past and focuses primarily on the last 3-5 time steps.
This observation suggests that some questions and responses in the past contain information that is highly predictive of the learner's response to the current question; this information can be effectively captured by multiple attention heads with different decay rates.

Figure~\ref{fig:ktviz} visualizes the normalized attention weights in the knowledge retriever for a single learner for three consecutive time steps. In the top row, the learner is responding to a question on Concept 30 at time $T$ after practicing this concept from $T-10$ to $T-5$, then taking a break to practice on Concept 42, before coming back to Concept 30 at time $T-1$. We see that AKT predicts their response to the current question by focusing more on previous practices on this concept (both in the immediate past and further back) than practices on another concept also in the immediate past.
In the middle row, the learner switches to practicing on Concept 42 again. Again, AKT learns to focus on past practices on the same concept rather than the immediate past on a different concept at times $T-2$ and $T-1$.
In the bottom row, the learner practices on Concept 42 for the second consecutive time, and AKT shows a similar focus pattern to that in the top row, with the roles of Concepts 30 and 42 swapped.
These observations suggest that AKT's monotonic attention mechanism has the potential to provide feedback to teachers by linking a learner's current response to their responses in the past; this information may enable teachers to select certain questions that they have practiced for them to re-practice and clear misconceptions before moving on. We also note that AKT, using a data-driven approach, learns these attention patterns that match hand-crafted features in existing KT methods (e.g., the number of total attempts and correct attempts on this concept) \cite{pfa,dash}.

\textbf{Rasch model-based embeddings.}
Figure~\ref{fig:rasch} visualizes the learned Rasch model-based question embeddings for several concepts using t-SNE \cite{tsne} using the ASSISTments2009 dataset, together with their empirical difficulties for selected questions (portions of correct responses across learners). We also highlight the hardest and easiest question for each concept based on their empirical difficulties. We see that questions on the same concept form a curve and are ordered by their difficulty levels: for the majority of concepts, questions on one end of the line segment are easy while questions on the other end are hard.
This result confirms our intuition that questions from the same concept are not identical but closely related to each other; this relationship can be well-captured by the Rasch model using its difficulty parameter.
\begin{figure}[t]

    \centering

        \includegraphics[width=0.98\columnwidth]{fig/rasch1.pdf}
    \caption{Visualization of learned question embeddings with fraction of correct responses among learners for selected concepts.
    }
    \label{fig:rasch}
\end{figure}

\begin{table}[h]\centering

    \scalebox{0.9}{

        \begin{tabular}{L{1.75cm}|C{5.25cm}|C{1.1cm}}\toprule
            Concept &Question & $\mu_q$\\ \hline
    & From the list of numbers below, which number is the largest?
            6.7,        6.4,         3.4,        5.1 & -0.0397\\ \cline{2-3}
\multirow{3}{1.5cm}{Ordering Positive Decimals} &            Which of following decimals is the smallest?
            0.107,           0.1889,               0.12,         0.11582 & 0.0090\\ \cline{2-3}

            & Arrange these numbers from least to greatest.
            -1/4, 12.1, -1.4, -4/4 & 0.0279\\ \hline
        &    Steve has a marble jar, that he likes to randomly select marbles from it to play with. The jar has 5 orange marbles and 5 purple marbles. What is the probability that Steve gets an orange marble from the jar?  & $-0.0515$\\ \cline{2-3}
\multirow{3}{1.5cm}{Probability of a Single Event} &    A bag contains 8 red, 5 green, and 7 blue popsicles. John is going to draw out a popsicle without looking in the bag. What is the probability that he will draw either a green or a blue popsicle? &  $0.0088$ \\ \cline{2-3}
&            A card is selected at random from a standard deck  of 52 cards.  Find the probability of choosing a club or an ace card. Enter your answer as a fraction. & $0.0548$ \\\hline

&            Convert     7/7    into a percent.   & -0.0540\\ \cline{2-3}

\multirow{3}{2cm}{Conversion of Fractions} &            Convert     8/4    into a percent. & 0.0038 \\ \cline{2-3}

&            Convert     9/8    into a percent. & 0.0529 \\ [.4cm]

            \bottomrule
        \end{tabular}

    \caption{Question text and learned difficulty parameters ($\mu_q$) for selected questions on three concepts. Learned difficulty levels match our intuition on the difficulty of these questions.}
    \label{tab:raschq}
    \vspace{-0.3cm}
\end{table}
Table~\ref{tab:raschq} lists sample questions each for three different concepts, ``Ordering Positive Decimals'', ``Probability of a Single Event'', and ``Conversion of Fractions to Percents'', and their learned difficulty parameters. We show three questions for each concept: an easy one, an average one, and a hard one. Using the ``Probability of a Single Event'' concept as an example, the learned difficulty parameter values ($\mu_q$) are $-0.0515$  for the easy one, $0.0088$ for the average one, and $0.0548$ for the hard one. These learned difficulty levels match our understanding about the difficulty levels of these questions.

These results suggest that AKT has the potential to be applied in real-world educational settings. Using the estimated difficulty parameters, a computerized learning platform can either i) automatically select questions with appropriate difficulty levels for each learner given their past responses, or ii) support teachers to adjust course plans by providing them feedback on question difficulty levels learned from real data. Therefore, AKT improves over existing KT methods by not only providing state-of-the-art predictive performance but also exhibiting interpretability and potential for personalized learning.

\section{Introduction}
\label{sec:intro}
Recent advances in data analytics and intelligent tutoring systems \cite{bevbook} have enabled the collection and analysis of large-scale learner data; these advances hint at the potential of \emph{personalized} learning at large scale, by automatically providing personalized feedback \cite{piechfeedback} and learning activity recommendations \cite{banditslan} to each learner by analyzing data from their learning history.

A key problem in learner data analysis is to predict future learner performance (their responses to assessment questions), given their past performance, which is referred to as the knowledge tracing (KT) problem \cite{kt}. Over the last 30 years, numerous methods for solving the KT problem were developed based on two common assumptions: i) a learner's past performance can be summarized by a set of variables representing their current latent \emph{knowledge level} on a set of concepts/skills/knowledge components, and ii) a learner's future performance can be predicted using their current latent concept knowledge levels. Concretely, let $t$ denote a set of discrete time indices, we have the following generic model for a learner's knowledge and performance
\begin{align*}
r_t \sim f(h_t), \quad h_t \sim g(h_{t-1}),
\end{align*}
where $r_t \in \{0,1\}$ denotes the learner's graded response to an assessment question at time step~$t$, which is usually binary-valued ($1$ corresponds to a correct response and $0$ corresponds to an incorrect one) and is observed. The latent variable $h_t$ denotes the learner's current knowledge level and is not observed. $f(\cdot)$ and $g(\cdot)$ are functions that characterize how learner knowledge dictate their responses and how it evolves; they are sometimes referred to as the response model and the knowledge evolution model, respectively.

Earlier developments in KT methods before 2010 can be divided into two classes.
The first class centered around the Bayesian knowledge tracing (BKT) method \cite{pardoskt,bkt} where knowledge ($h_t$) is a binary-valued scalar that characterizes whether or not a learner masters the (single) concept covered by a question. Since the response ($r_t$) is also binary-valued, the response and knowledge evolution models are simply noisy binary channels, parameterized by the guessing, slipping, learning, and forgetting probabilities.
The second class centered around item response theory (IRT) models \cite{lordirt} and use these models (especially sigmoidal link functions) as the response model $f(\cdot)$; learner knowledge level is then modeled as real-valued vectors ($\mathbf{h}_t$) for questions that cover multiple concepts. Among these methods, the SPARFA-Trace method \cite{tracekdd} used a simple affine transformation model as the explicit knowledge evolution model $g(\cdot)$. Other methods, e.g., additive factor models \cite{lfa}, performance factor analysis \cite{pfa}, the difficulty, ability, and student history (DASH) model \cite{dash}, and a few recent methods including knowledge factorization machines \cite{kfm} and an extension to the DASH model, the DAS3H model \cite{das3h}, used \emph{hand-crafted features} such as the number of previous attempts, successes, and failures on each concept in their knowledge evolution model.
Methods in both classes rely on expert labels to associate questions to concepts, resulting in excellent interpretability since they can effectively estimate the knowledge level of each learner on expert-defined concepts.

Recent developments in KT centered around using more sophisticated and flexible models to fully exploit the information contained in large-scale learner response datasets.
The deep knowledge tracing (DKT) method \cite{dkt} was the first method to explore the use of (possibly deep) neural networks for KT by using long short-term memory (LSTM) networks \cite{lstm} as the knowledge evolution model $g(\cdot)$. Since LSTM units are nonlinear, complex functions, they are more flexible than affine transformations and more capable of capturing nuances in real data.

The dynamic key-value memory networks (DKVMN) method extended DKT by using an external memory matrix ($\mathbf{H}_t$) to characterize learner knowledge \cite{dkvmn}. The matrix is separated into two parts: a static, ``key'' matrix that contains the fixed representation of each concept, and a dynamic, ``value'' matrix that contains the evolving knowledge levels of each learner on each concept. DKVMN also uses separate ``read'' and ``write'' processes on this external matrix for the response and knowledge evolution models; these processes make it even more flexible than DKT.
DKT and KVMN reported state-of-the-art performance in predicting future learner performance \cite{howdeep} and have been the benchmark for new KT methods.

The self-attentive knowledge tracing (SAKT) method \cite{sakt} is the first method to use attention mechanisms in the context of KT. Attention mechanisms are more flexible than recurrent and memory-based neural networks and have demonstrated superior performance in natural language processing tasks.
The basic setup of SAKT has many similarities to the Transformer model \cite{transformer}, an effective model for many sequence-to-sequence prediction tasks.
However, we observe that SAKT does not outperform DKT and DKVMN in our experiments; see Section~\ref{sec:experiments} for details.
Possible reasons for this include i) unlike in language tasks where strong long-distance dependencies between words are more common, the dependence of future learner performance on the past is likely restricted to a much shorter window, and ii) the sizes of learner response datasets are several magnitudes lower than natural language datasets and are less likely to benefit from highly flexible and large-scale attention models.

More importantly, no existing KT method truly excels at both future performance prediction and interpretability. Early KT methods exhibit excellent interpretability but do not provide state-of-the-art performance on future learner performance prediction. Recent KT methods based on deep learning excel at that but offer limited interpretability.  Therefore, these KT methods do not fully meet the needs of personalized learning, which requires not only accurate performance prediction but also the ability to provide automated, interpretable feedback and actionable recommendations to help learners achieve better learning outcomes.

\subsection{Contributions}
For the task of predicting the learner's response to the current question, we propose the attentive knowledge tracing (AKT) method, which uses a series of attention networks to draw connections between this question and every question the learner has responded to in the past. We summarize our key innovations below:
\begin{enumerate}[leftmargin=*]
\item Contrary to existing attention methods that use raw question and response embeddings, we put raw embeddings into context and use \emph{context-aware representations} of past questions and responses by taking a learner's entire practice history into account.
\item Inspired by cognitive science findings on the mechanisms of forgetting, we propose a novel \emph{monotonic attention mechanism} that uses an exponential decay curve to down weight the importance of questions in the distant past. We also develop a context-aware measure to characterize the time distance between questions a learner has responded to in the past.
\item Leveraging the Rasch model, a simple and interpretable IRT model, we use a series of \emph{Rasch model-based embeddings} to capture individual differences among questions without introducing an excessive amount of model parameters.
\end{enumerate}
We conduct a series of experiments on several benchmark real-world educational datasets comparing AKT to state-of-the-art KT methods. Our results show that AKT (sometimes significantly) outperforms other KT methods in predicting future learner performance. Further, we perform ablation studies on each of the key AKT model components to demonstrate their value.
We also perform several case studies to show that AKT exhibits excellent interpretability and has the potential for automated feedback and practice question recommendation, both key requirements of personalized learning\footnote{Source code and datasets will be available at \href{https://github.com/arghosh/AKT}{https://github.com/arghosh/AKT}}.

\section*{References}

	\bibliographystyle{ACM-Reference-Format}

\section{Knowledge Tracing Problem Setup}
Each learner's performance record consists of a sequence of questions and responses at each discrete time step.
For learner $i$ at time step $t$, we denote the combination of the question that they answered, the concept this question covers, and their graded response as a tuple, $(q_t^i, c_t^i,  r_t^i)$, where $q_t^i\in \mathbb{N}^{+}$ is the question index, $c_t^i\in \mathbb{N}^{+}$ is the concept index, and $r_t^i\in \{0,1\}$ is the response. Under this notation, $(q_t^i, c_t^i, 1)$ means learner $i$ responded to question $q_t^i$ on concept $c_t^i$ correctly at time $t$.
We note that this setup is different from some prior works on deep knowledge tracing that often ignore the question index and summarize learner performance as $(c_t^i,  r_t^i)$. This choice was made to avoid overparameterization; see Section~\ref{sec:rasch} for a detailed analysis.
In the following discussions, we omit the superscript $i$ as we discuss how to predict future performance for a single learner.
Given their past history up to time $t-1$ as $\{(q_1, c_1, r_1), \ldots, (q_{t-1}, c_{t-1}, r_{t-1})\}$, our goal is to predict their response $r_t$ to question $q_t$ on concept $c_t$ at the current time step, $t$.

\subsection{Question and Response Embeddings}
\label{sec:emb}
Following previous work \cite{dkvmn},
we use real-valued embedding vectors $\mathbf{x}_t \in \mathbb{R}^{D}$ and $\mathbf{y}_t \in \mathbb{R}^{D}$ to represent each question and each question-response pair ($q_t, r_t$), respectively. $\mathbf{x}_t$ characterizes information about questions, and $\mathbf{y}_t$ characterizes the knowledge learners acquire by responding to questions, with two separate embeddings for correct and incorrect responses, respectively. $D$ denotes the dimension of these embeddings.
Therefore, let $Q$ denote the number of questions, there are a total of $Q$ question embedding vectors and $2Q$ question-response embedding vectors.
In most real-world educational settings, the question bank is considerably larger than the set of concepts and many questions are assigned to very few learners. Therefore, the majority of existing KT methods use concepts to index questions to avoid overparameterization; all questions covering the same concept are treated as a single question. In this case, $q_t = c_t$ and $Q=C$.

\section{The AKT Method}
The AKT method consists of four components: two self-attentive encoders, one for questions and one for knowledge acquisition, a single attention-based knowledge retriever, and a feed-forward response prediction model; Figure~\ref{fig:main} visualizes the AKT method and its connected components.

We use the two self-attentive encoders to learn context-aware representations of the questions and responses.
We refer to the first encoder as the \emph{question encoder}, which produces modified, contextualized representations of each question, given the sequence of questions the learner has previously practiced on.
Similarly, we refer to the second encoder as the \emph{knowledge encoder}, which produces modified, contextualized representations of the knowledge the learner acquired while responding to past questions. Alternatively, we could use raw embeddings of questions and responses similar to prior work. We found that the context-aware representation performs better in most datasets.
We refer to the knowledge evolution model as the \emph{knowledge retriever}, which retrieves knowledge acquired in the past that is relevant to the current question using an attention mechanism.
Finally, the response prediction model predicts the learner's response to the current question using the retrieved knowledge.
The AKT method is motivated by three intuitions rooted in cognitive science and psychometrics; we will detail these intuitions in what follows.

\begin{figure*}[tp]
    \centering
    \includegraphics[width=1.9\columnwidth]{fig/akt.pdf}
    \caption{Overview of the AKT method. We use the Rasch model-based embeddings as raw embeddings for questions and responses. The question and knowledge encoders compute the context-aware representations of questions and responses pairs. The knowledge retriever uses these representations as input and computes the knowledge state of the learner. For simplicity, we do not show the monotonic attention mechanism in the encoders. We do not show sublayers either.}
    \label{fig:main}
\end{figure*}

\subsection{Context-aware Representations and The Knowledge Retriever}
As introduced above, we use two encoders in our model. The question encoder takes raw question embeddings $\{\mathbf{x}_1, \ldots, \mathbf{x}_{t}\}$ as input and outputs a sequence of context-aware question embeddings $\{\hat{\mathbf{x}}_1,\ldots$, $ \hat{\mathbf{x}}_{t} \}$ using a monotonic attention mechanism (detailed in the next subsection). The context-aware embedding of each question depends on both itself and the past questions, i.e., $\hat{\mathbf{x}}_{t}  =  f_{\mbox{enc}_1}$ $(\mathbf{x}_{1},\ldots,\mathbf{x}_{t})$.
Similarly, the knowledge encoder takes raw question-response embeddings $\{\mathbf{y}_1, \ldots, \mathbf{y}_{t-1}\}$ as input and outputs a sequence of actual knowledge acquired $\{\hat{\mathbf{y}}_1,\ldots, \hat{\mathbf{y}}_{t-1} \}$ using the same monotonic attention mechanism. The context-aware embedding of acquired knowledge depend on the learner's response to both the current question and past questions, i.e., $\hat{\mathbf{y}}_{t-1}=  f_{\mbox{enc}_2}(\mathbf{y}_{1},\ldots,\mathbf{y}_{t-1})$.

The choice of using context-aware embeddings rather than raw embeddings reflects our first intuition: \emph{the way a learner comprehends and learns while responding to a question depends on the learner}. These modified representations reflect each learner's actual comprehension of the question and the knowledge they actually acquire, given their personal response history. This model choice is motivated by the intuition that for two learners with different past response sequences, the way they understand the same question and the knowledge they acquire from practicing on it can differ.

The knowledge retriever takes the context-aware question and question-response pair embeddings $\hat{\mathbf{x}}_{1:t}$ and $\hat{\mathbf{y}}_{1:t-1}$ as input and outputs a retrieved knowledge state $\mathbf{h}_t$ for the current question. We note that in AKT, the learner's current knowledge state is also context-aware since it depends on the current question they are responding to; this model choice is different from that in most existing methods, including DKT. We also note that the knowledge retriever can only use information on the past questions, the learner's responses to them, and the representation of the current question, but not the learner's response to the current question, i.e., $\mathbf{h}_{t} = f_{\mbox{kr}}(\hat{\mathbf{x}}_1,\ldots, \hat{\mathbf{x}}_{t}, \hat{\mathbf{y}}_{1},\ldots,\hat{\mathbf{y}}_{t-1})$. The response prediction model uses the retrieved knowledge  to predict the current response.

\subsection{The Monotonic Attention Mechanism}
\label{sec:ma}
We use a modified, monotonic version of the scaled dot-product attention mechanism for the encoders and the knowledge retriever. We start by briefly summarizing the original scaled dot-product attention mechanism. Under this framework, each encoder and the knowledge retriever has a key, query, and value embedding layer that maps the input into output queries, keys, and values of dimension $D_q = D_k$, $D_k$, and $D_v$, respectively.
Let $\mathbf{q}_t \in \mathbb{R}^{D_k \times 1}$ denote the query corresponding to the question the learner responds to at time $t$, the scaled dot-product attention values $\alpha_{t,\tau}$ are computed using the softmax function \cite{dlbook} as
\begin{align*}
\alpha_{t,\tau} = \mbox{Softmax}(\frac{\mathbf{q}_t^{\intercal} \mathbf{k}_\tau}{\sqrt{D_k}}) = \frac{\exp(\frac{\mathbf{q}_t^{\intercal} \mathbf{k}_\tau}{\sqrt{D_k}})}{\sum_{\tau'} \exp(\frac{\mathbf{q}_t^{\intercal} \mathbf{k}_\tau}{\sqrt{D_k}})} \in [0,1].
\end{align*}
The output of the scaled dot-product attention mechanism is then given by $\textstyle\sum_{\tau} \alpha_{t,\tau} \mathbf{v}_\tau \in \mathbb{R}^{D_v\times 1}$. $\mathbf{k}_\tau \in \mathbb{R}^{D_k\times 1}$ and $\mathbf{v}_\tau \in \mathbb{R}^{D_v\times 1}$ denote the key and value for the question at time step $\tau$, respectively.
Depending on the specific component, the output depends either on both the past and the current ($\tau \leq t$ for the question and knowledge encoders) or only the past ($\tau < t$ for the knowledge retriever).

\sloppy
Both encoders employ the self-attention mechanism, i.e., $\mathbf{q}_t$, $\mathbf{k}_t$, and $\mathbf{v}_t$ are computed using the same input; the question encoder uses $\{\mathbf{x}_1, \ldots,\mathbf{x}_t\}$ while the knowledge encoder uses $\{\mathbf{y}_1, \ldots,\mathbf{y}_{t-1}\}$.
The knowledge retriever, on the other hand, does not use self-attention. As shown in Fig.~\ref{fig:main}, at time step $t$, it uses $\hat{\mathbf{x}}_{t}$ (the modified embedding of the current question), $\{\hat{\mathbf{x}}_1,\ldots,\hat{\mathbf{x}}_{t-1}\}$ (the context-aware embeddings of past questions), and $\{\hat{\mathbf{y}}_1,\ldots,\hat{\mathbf{y}}_{t-1}\}$ (the context-aware embeddings of past question-response pairs) as input to generate the query, keys, and values, respectively. We note that SAKT uses question embeddings for mapping queries whereas response embeddings for key and value mapping.
In our experiments, we have found that using question embeddings for mapping both queries and keys is much more effective.

However, this basic scaled dot-product attention mechanism is not likely going to be sufficient for KT. The reason is that learning is temporal and memories decay \cite{mcm}; a learner's performance in the distant past is not as informative as recent performance when we are predicting their response to the current question.
Therefore, we develop a new monotonic attention mechanism that reflects our second intuition: \emph{when a learner faces a new question, past experiences i) on unrelated concepts and ii) that are from too long ago are not likely to be highly relevant.}
Specifically, we add a multiplicative exponential decay term to the attention scores as:
\begin{align*}
\alpha_{t,\tau} = \frac{\exp{(s_{t,\tau})}}{\sum_{\tau'} \exp{(s_{t,\tau'})}},
\end{align*}
with
\begin{align}
s_{t,\tau} &=  \frac{\exp \left(- \theta\cdot  d(t,\tau) \right) \cdot \mathbf{q}_t^{\intercal} \mathbf{k}_\tau }{\sqrt{D_k}},
\end{align}
where $\theta > 0$ is a learnable decay rate parameter and $d(t,\tau) $ is temporal distance measure between time steps $t$ and $\tau$.
In other words, the attention weights for the current question on a past question depends not only on the similarity between the corresponding query and key, but also on the relative number of time steps between them. In summary, our monotonic attention mechanism takes the basic form of an exponential decay curve over time with possible spikes at time steps when the past question is highly similar to the current question. We note that we apply exponential decay to the attention weights rather than latent knowledge, which is the common approach in existing learner models (see e.g., \cite{reddy,moz}).

We note that there are many other possible ways to characterize the temporal dynamics of attention. First, in language tasks that attention networks excel at, the temporal dynamics can be modeled using additive positional embeddings or learnable embeddings \cite{transformer}.
Second, in our monotonic attention mechanism, we can also parameterize the exponential decay as $s_{t,\tau} = \frac{\mathbf{q}_t^{\intercal} \mathbf{k}_\tau}{\sqrt{D_k}} - \theta\cdot d(t ,\tau)$.
However, neither of these changes lead to comparable performance to our chosen model setup; we will compare AKT against its variants using positional encoding rather than monotonic attention in our experiments.

\textbf{A context-aware distance measure.}
The exponential decay function decides the rate at which the attention weights decay as the distance between the current time index and the previous time indices increase.
A straightforward way to define the distance between two time indices is their absolute value difference, i.e., $d(t,\tau)= {|t - \tau|}$. However, this distance is not context-aware and ignores the practice history of each learner. For example, consider the two following sequences of concepts a learner practiced on:
\begin{eqnarray*}
{\mbox{Venn Diagram (VD)}}_1,{\mbox{VD}}_2,\cdots,{\mbox{VD}}_8, {\mbox{Prime Numbers (PN)}}_9, {\mbox{PN}}_{10}
\end{eqnarray*}
and
\begin{eqnarray*}
{\mbox{PN}}_1, {\mbox{VD}}_2,{\mbox{VD}}_3, \cdots, {\mbox{VD}}_9,
{\mbox{PN}}_{10},
\end{eqnarray*}
where the notation ``$VD_2$'' means that the learner practiced the concept of Venn Diagram at time step~$2$. In this example, the learner answers a question on Prime Numbers at $t=10$, i.e., the current time index, in both of these sequences, but the most recent past practice on Prime Numbers comes at different time indices. Since the concepts of Venn Diagram and Prime Numbers are not closely related, the learner's previous practices on Prime Numbers is more relevant to us when predicting their answer to the current practice question than recent practices on Venn Diagram. In this case, with the straightforward absolute value difference, an exponential decay curve will significantly reduce the attention weight assigned to the practice on Prime Numbers at $t=1$.

Therefore, we propose the following context-aware distance measure between time steps $d(t,\tau)$ with $\tau \leq t$ for the exponential decay mechanism (in the encoders):
\begin{align*}
d(t, \tau) &= |t-\tau| \cdot \sum_{t'=\tau+1}^{t}\gamma_{t, t'}, \\ \gamma_{t,t'} &= \frac{\exp{ (\frac{\mathbf{q}_t^{\intercal} \mathbf{k}_{t'}}{\sqrt{D_k}} )}}{\sum_{1 \leq \tau'\leq t} \exp{ (\frac{\mathbf{q}_t^{\intercal} \mathbf{k}_{\tau'}}{\sqrt{D_k}})}}, \, \forall t'\leq t.
\end{align*}
For the knowledge retriever, we replace $\tau'\leq t$ with $\tau<t$ and $t' \leq t$ with $t' <t$ correspondingly.
In other words, this context-aware distance measure uses another softmax function to adjust the distance between consecutive time indices according to how the concept practiced in the past is related to the current concept.
In practice, in each iteration during model training, we use the current AKT model parameters to compute the modified distance measure and fix it; we do not pass gradients through the distance measure.

\textbf{Multi-head attention and sub-layers.}
We also incorporate multi-head attention which is effective in attending to past positions at multiple time scales \cite{transformer}. Therefore, we use $H$ independent attention heads where every head has its own decay rate $\theta$, concatenate the final output into a $(D_v \cdot H) \times 1$ vector and pass it to the next layer.
This model design enables AKT to summarize past learner performance at multiple time scales, which bears some similarities to the multiple time windows in the multiscale context, DASH, and DAS3H models \cite{mcm,dash,das3h}.
We also use several sub-layers, including one for layer normalization \cite{layer}, one for dropout \cite{dropout}, a fully-connected feedforward layer, and a residual connection layer \cite{resnet} in each encoder and the knowledge retriever.

\subsection{Response Prediction}
\label{sec:rasch}

The last component of the AKT method predicts the learner's response to the current question. The input to the prediction model is a vector that concatenates both the retrieved knowledge (the knowledge retriever output $\mathbf{h}_t$) and the current question embedding ${\mathbf{x}}_t$; this input goes through another fully-connected network
before finally going through the sigmoid function \cite{dlbook} to generate the predicted probability $\hat{r}_t \in [0,1]$ that the learner answers the current question correctly. All learnable parameters in the entire AKT method are trained in end-to-end fashion by minimizing the binary cross-entropy loss of all learner responses, i.e., \[\ell =\textstyle\sum_{i} \textstyle\sum_t -(r_t^i \log \hat{r}_t^i + (1- r_t^i) \log (1- \hat{r}_t^i)).\]

\subsection{Rasch Model-Based Embeddings}
As we discussed above, existing KT methods use concepts to index questions, i.e., setting $q_t = c_t$.
This setup is necessary due to data sparsity.
Let $Q$ denote the total number of questions and $L$ denote the number of learners. In most real-world learner response datasets, the number of learner responses is comparable to $CL$ and much less than $QL$ since many questions are assigned to few learners.
Therefore, using concepts to index questions is effective in avoiding overparameterization and overfitting.
However, this basic setup ignores the individual differences among questions covering the same concept, thus limiting the flexibility of KT methods and their potential for personalization.

We use a classic yet powerful model in psychometrics, the Rasch model (which is also known as the 1PL IRT model) \cite{lordirt,rasch}, to construct raw question and knowledge embeddings. The Rasch model characterizes the probability that a learner answers a question correctly using two scalars: the question's difficulty, and the learner's ability. Despite its simplicity, it has shown to achieve comparable performance to more sophisticated models on learner performance prediction in formal assessments when knowledge is static \cite{db,notdeep}. Specifically,
we construct the embedding of the question $q_t$ from concept $c_t$ at time step $t$ as
\begin{align*}
\mathbf{x}_t = \mathbf{c}_{c_t} + \mu_{q_t} \cdot \mathbf{d}_{c_t},
\end{align*}
where $\mathbf{c}_{c_t} \in \mathbb{R}^{D}$ is the embedding of the concept this question covers, and $\mathbf{d}_{c_t} \in \mathbb{R}^{D}$ is a vector that summarizes the variation in questions covering this concept, and $\mu_{q_t} \in \mathbb{R}$ is a scalar \emph{difficulty} parameter that controls how far this question deviates from the concept it covers. The question-response pairs $(q_t, r_t)$ from concept $c_t$ are extended similarly using the scalar difficulty parameter for each pair:
\begin{align*}
\mathbf{y}_t = \mathbf{e}_{(c_t,r_t)} + \mu_{q_t} \cdot \mathbf{f}_{(c_t, r_t)},
\end{align*}
where $\mathbf{e}_{(c_t,r_t)} \in \mathbb{R}^{D}$ and $\mathbf{f}_{(c_t,r_t)} \in \mathbb{R}^{D}$ are concept-response embedding and variation vectors.
This model choice reflects our third intuition: \emph{questions labeled as covering the same concept are closely related but have important individual differences that should not be ignored.} This model choice is partly inspired by another work in fusing KT and IRT models \cite{mozerfuse}.

These Rasch model-based embeddings strike the right balance between modeling individual question differences and avoiding overparameterization. For the question embeddings, the total number of embedding parameters in this model is $2CD + Q$, which is slightly more than that in a model that uses concepts to index questions ($CD$), but much less than that in a model where each question is parameterized individually ($QD$), since $C \ll Q$ and $D \gg 1$. We further define the concept-response embeddings as $ \mathbf{e}_{(c_t,r_t)}=\mathbf{c}_{c_t}+\mathbf{g}_{r_t}$, where $\mathbf{g}_1$ and $\mathbf{g}_0$ denote the embeddings for correct and incorrect responses (regardless of the concept), respectively. Therefore, we only introduce a total of $(C+2)D+Q$ new embedding parameters instead of $2CD+Q$ new parameters for the concept-response embeddings. We note that our question and question-response embeddings share a group of parameters ($\mathbf{c}_{c_t}$); this setting is different from existing neural network-based KT methods where the two are independent of each other.
These compact embedding representations significantly reduce the number of parameters in not only AKT but also some other KT methods, leading to improved performance on future learner performance prediction; see Table~\ref{tab:rasch} for details.

redpajama_set_name: RedPajamaArXiv


The Oregon State football program will be one of many that break out some throwback uniforms for homecoming weekend. The Beavers unveiled their new look this week ahead of their return to the field on Saturday, Oct. 20 when they take on the Cal Golden Bears.
For their throwback look, Oregon State will be bringing back the iconic Benny Beaver logo.
Along with the Benny Beaver logo, the throwback uniforms will feature old school shoulder stripes, a Benny Beaver logo on the neck, and "Oregon State" written in script on the pants.
You didn't think we were stopping at just a helmet, did you?
THE RETRO BENNY MAKES AN OFFICIAL RETURN HOMECOMING WEEKEND OCT. 20 WHEN THE OSU FOOTBALL TEAM WILL WEAR A SPECIALLY DESIGNED NIKE UNIFORM WHEN IT HOSTS THE CALIFORNIA GOLDEN BEARS AT RESER STADIUM. IN ADDITION, RETRO BENNY WILL APPEAR ON THE TEAM'S HELMETS FOR ONLY THE SECOND TIME IN HISTORY; INITIALLY WAS PART OF THE 1973 TEAM'S HELMETS UNDER THE LEADERSHIP OF OSU HALL OF FAME COACH, THE LATE DEE ANDROS.
The uniforms will also feature retro lettering on the front and back of the jerseys.


text:
K-State Slate: 9.30.14-Tyler Lockett breaking records, winning awards, while Matt Green wins at golf
Monday saw a slew of wins for Kansas State, as Tyler Lockett was named the Big 12 special teams player of the week, Matt Green won the Jack Nicklaus invitational and the women's golf team is poised to overtake Missouri to win their golf tournament.
By KSUEMAW!@KStated2010 Sep 30, 2014, 7:02am CDT
Share All sharing options for: K-State Slate: 9.30.14-Tyler Lockett breaking records, winning awards, while Matt Green wins at golf
This guy is fast, just ask number 19.
Scott Sewell-USA TODAY Sports
If you haven't already, go check out yesterday's slate. Jon does an excellent job of concisely detailing why Brady Hoke should have been relieved of his coaching duties immediately.
Morse also unboxes the polls for our benefit, and basically finds a four way tie for first place. Something has to give soon right? RIGHT? We are a month out from the committee's first official poll release, so it will be interesting to see how much movement occurs in the AP and the Coaches leading up to the "official" release
As fanshotted by Morse and developed by Ryan Patrick, Denzel McDaniel A.K.A. Freak Nasty utterly destroys Aaron Jones. As Wildcat fans it is your civic duty to go watch this as many times as possible.
Lastly, the best named staff member (PervisPascosTravellingCircus) brings us the crowd-sourcing for the BroTech game.
Tyler Lockett was a bad, bad man Saturday where, as a returner, he scored a 58-yard touchdown, and almost had another punt return for a touchdown if not for a holding penalty. This led to Lockett winning the Big 12 Special Teams Player of the Week. It is the second week in a row that a Kansas State player has won a weekly award. Also on Saturday, Lockett passed his Uncle Aaron Lockett and moved into third place in receiving yards trailing Jordy Nelson and, of course, his dad Kevin Lockett.
Jake Trotter rewinds the Big 12 weekend, while Max Olsen breaks down what we learned over a busy Week Five in the Big 12. noting that the Wildcats are looking mighty stout against the run.
From Monday's teleconference, Kellis Robinett has the other Big 12 coaches reaction to Kansas firing Charlie Weis, noticeably absent from his quotes on Weis' firing is one Bill Snyder.
On the recruiting front, K-State commit Denzel Goolsby is tearing up the Wichita City League, rushing for over 100 yards in all four games, while only carrying the ball more than 20 times once. This performance earned Goolsby the third spot on catchitkansas.com's top 10 September performances list.
While he's only a sophomore, Marcus Foster will be looked too as the leader during this upcoming basketball season. He proved himself as an All-Conference talent a year ago, and people are starting to take notice, including Ricky O'Donnell, who previews Marcus Foster and the Wildcats as SBNation begins their preview of the Top 25 basketball teams.
Meanwhile, Scott Gleeson has been breaking down/projecting the field of 68 way to early and has Kansas State in as the 28th ranked team in the tournament.
As anyone who has lived in Manhattan can tell you, the sound of thunder on a clear day isn't actually thunder, but rather artillery being fired from Fort Riley. Last week that artillery may have been fired by your Kansas State Men's Basketball team, who took a trip to train with their partner unit the 1-7 Field Artillery.
Matt Green became K-State's 30th player to win an individual golf tournament, and the first under first year head coach Grant Robbins. Green finished 3 under par to win the Jack Nicklaus Invitational. Overall, the Wildcats finished ninth our of a twelve team tournament.
In Women's Golf news, the women's team sits in second place, only three shots back of Missouri at the Johine Imes Invitational. Carly Ragains also sits in second place only two shots back of Missouri Tiger, Alina Rogers.
After a disappointing five set loss to Baylor, the volleyball team travels down the river to the flaw on the kaw Kansas to take on a Jayhawk team fresh off their own disappointing conference opening loss to Oklahoma. After last season's disappointing Big 12 play, the Wildcats need to come out strong and take it to the Jayhawks.
The Iowa State Daily has the only Big 12 Volleyball power rankings that I've seen, and has Kansas State ranked at number 4, which seems about right even though the 'Cats need to prove they belong towards to top of the Big 12 by winning matches against teams who they are clearly better than, *cough* Baylor *cough*.


How soon after the referendum did you realise that Brexit would impact on your business?
We have 37 employees and straight after the referendum I lost two of my best foreign staff to Germany, because they were looking for certainty. Since then I've probably had to replace half my workforce as a result of Brexit. My foreign employees are not interested in people saying 'it will be alright.' They want a document stamped by the Home Office that says you have the right to remain. To make up for staff losses, I'm now having to employ a lot of agency staff and it's costing me an awful lot of money, plus they're often not nearly as reliable, motivated or as loyal as my old workforce. It's difficult to find permanent staff now. We used to get people knocking on the door the whole time, albeit they were generally from Eastern Europe, but now we don't get anyone knocking on the door at all and we pay well over the living wage for assembly and packing work.
Has Brexit impacted your export business?
Yes. We export about 30% of what we make and our German distributor, my biggest single customer, is saying, why should I market your product when I don't have certainty of supply? It's worrying. I've also been hit by the exchange rate as all of my cocoa is quoted in dollars or euros. This has contributed to my overall costs rising by about 18% post Brexit. Not all of this is down to Brexit, I've had changes to business rates and wages as well, but a lot of it is down to Brexit and I can't pass those price rises on.
We've heard that Brexit will impact particularly on food regulations, does this concern you?
Yes. For us the regulations, the labeling, the compliance, the allergen portrayal and the manufacturing standards are key. As soon as they change something by a decimal point, etc. and we are not compliant, that will cause us more damage than any additional tariffs we may have to pay.
Do you see any opportunities from Brexit?
I hate to sound so pessimistic, but no. I don't get the idea of pushing new markets. The products that I sell are heavy. It costs about £500 to send a pallet to Australasia, £300 to North America, £120 to the EU and £55 within the UK. So, although I am exporting to America, Canada, Australia and New Zealand already, they are not key markets for me because they have to incorporate the cost of freight into the product sale price, which makes it uncompetitive.
What do you think the government could do to help steer SMEs through Brexit?
The problem is that the ideology is getting in the way of the reality. My little company is paying a quarter of a million pounds in VAT, PAYE and National Insurance a year and it seems to be Brexit at all costs. Any inference that we should align ourselves with the EU in terms of regulations, which is what I would personally like a commitment on, is seen as letting down the people who voted to leave. The one thing that I would ask for would be complete compliance with EU packaging and food manufacturing standards.
Have you taken any steps to ready yourself for a 'No Deal' Brexit?
Yes. I've had a serious look at relocating to Slovakia, because we export so much to the EU. Then we could invoice in euros and manufacture in euros and in Slovakia wage rates and rentals are much cheaper too, I've been over there on a site visit. We're almost at capacity at our Harlow site so we have to move anyway. Our investment in the next stage is worth £2 million. That's a big commitment for a company my size to invest £2 million when you don't know where you're going to be in five years' time.
I've also had a conversation with the Welsh inward investment people who have informally agreed to fund the whole project if we move to Bridgend, but again without knowing how it's going to be pan out it could be foolish to go and move there only to find that the 30% of turnover which comes from the EU no longer exists or is unviable.
It makes me sad. I'm so proud to be a UK manufacturer, I might be a tiddler, but when those pallets go off to foreign countries I get quite emotional. It will be annoying for me to have to relocate to Slovakia and then import back into the UK, but at the end of the day, I will do whatever I have to do to ensure that the business survives.


text:
Q: handle https file response errors How would I handle a situation where the file does not exist on the server, or is unable to connect with the server to get the file?
As of the code below, the file download still occurs, and the file is empty.
var https = require('https');
var fs = require('fs');
var exec = require('child_process').exec;
var file = fs.createWriteStream('mediaserver_setup.exe');
var len = 0;
var req = https.get(url + 'appdata/update_setup.exe', function (res) {
  if (res.statusCode === 404) {
    alert('problem with request:');
  }
  res.on('data', function (chunk) {
    file.write(chunk);
    len += chunk.length;
    var percent = (len / res.headers['content-length']) * 100;
    progressLoading.val(percent);
  });
  res.on('end', function () {
    setTimeout(function () {
      file.close();
    }, 500);
  });
  file.on('close', function () {
    win.close();
    exec('update_setup.exe');
  });
});
req.on('error', function (err) {
  alert('problem with request: ');
});

A: Use the error event for requests, or check the status code of the response:
var https = require('https');
var req = https.get(options, function(res) {
  // use res.statusCode
  if (res.statusCode === 404) {
    // example for checking 404 errors
  }
});

req.on('error', function(err) {
  // an error has occurred
});

redpajama_set_name: RedPajamaStackExchange


text:
Q: text replacement & seo I've been having a chat with a designer friend about text replacement and seo. I suspect I've got the wrong end of the stick, but I was under the impression that hiding content from users was bad practice. I'm aware that text is king and of separation of content & presentation. But to me this is a bit of a grey area. Is one considered best practice (with an seo slant)? I don't see one being more semantic than another, I suspect I will stand corrected.
Delete

with
background: image(pic.jpg);
text-indent: -9999px;

or

A: Good SEO tips:

*

*provide content valuable for users

*always be honest

If you make src, alt and title the same, they do not add value at all.
I have always used position relative and negative result with good results. For icon buttons there is no difference. Do users consider image icons are more valuable than text links?
Also you should avoid href="#", this is more important in this case than the rest.
BTW, If you make delete buttons crawlable by search engines, they will delete the content ;)


我可以放心地假设,不是每个人都喜欢去俱乐部。Sweaty bodies all cramped into a small space,people who thought they were having the time of their lives for a few hours,overdosing on overpriced drinks with the hopes of giving you mindless chances to flirt with random people.有时候你只想喝杯冷饮,listen to raw live music and be in trance to the purity of experience.
音乐是我日常生活的一部分。Nothing beats seeing your favorite bands perform live.OPM was revived 2002 due to an emergence of bands which gave record franchises to capitalize on the emerging markets.过度资本主义的线性大胆之处在于它扼杀了音乐的纯粹性和实质性。After 2006,the scene slowly dwindled down and killed most of its following.But there are still these artists that never backed down and they've to continue to emerge and create music regardless if they're losing money.他们能够传承和激励这些对创作和分享音乐有着同样热情的新一代人。There are only a number of venues that really houses performances of these artists regardless if they're signed or not.这些场馆是这些艺术家和音乐迷们在大会上聚会一晚的场所,每个人都将分享一个共同的快乐;To be in a trance with Music.
We have curated these places based on their role in local music,the type of artist they welcome,the crowd and the overall community they continue to support.金沙开户Fat Girls Day Out's15 recommended spots to witness the local independent music scene in Metro Manila.
They're open Mondays-Saturdays as early as 6 pm and open til' late.一周的开始是低调的。点冰镇啤酒,listen to live music,轻轻地调整凹槽,舒适地坐着。During big nights,你会出汗的,jump,你会再坐一次,be in trance,站在离艺术家几英寸远的地方。你会在凌晨2-3点结束你的夜晚,也许你对刚才的乐趣感到困惑,and buy fishballs from outside.
点他们签名的饮料:伊莎帕!425php/pitcher.Hits you hard when you want to.saGuijo is Metro Manila's definitive bar for live music.
Designed with red leather couches,砖墙,还有闪亮的吊灯。They have excellent acoustics,good air conditioning,还有非常宽敞的地板。精选的酒和美味的食物。Expect to draw more wad of cash as it's relatively more expensive to hang out here.If you can recall Bagaberde in Pasig,this place reminds me of it only posher.
在当地独立制作地下波浪。Intimate hang out and cheap beer.Nothing beats their very cheap beer.They sometimes hose beer book reading sessions as well.
As it names precedes it- 'Tago' it is quite hidden.Make sure to find it on Waze as you'll likely get lost on the back street of Cubao to find this signless bar.
This is a long-standing character-filled venue attracting an older,sit-down crowd for more acoustic-oriented gigs.The garden is a good place to chill with a dark San Miguel.Gigs start around 10 pm.Host to Ebe Dancel,Jahnoy Danao,Noel Cabangon just to name a few.当地歌手/菲律宾民间艺术家。在口语表演成为主流之前,他们一直在主持它。这是一座60年代或70年代的老房子,改建成了酒吧。The place is airy and eclectic.
What I love about this venue is that regardless our countless conversations happening all around,有一次,这位艺术家弹吉他,唱他的歌。Everyone stops and focus.你可以听到吉他即兴演奏和艺术家的声音。Your world will stop and your heart will wrench.Unjustified melancholic state.
An old timer.70's Bistro was once in competition with Mayric's in Espana.Mostly popular OPM bands come play here.他们有良好的空调,虽然相对陈旧的家具,and good sound acoustics.You have to come in early though if you want to get the seats near the stage.It can get pretty packed so AGAIN.Come early.
Off-radar artist to Spotify famous bands.The likes of Aia de Leon and Terno Recording artist sets foot to perform on their stage.
This bar conducts film viewing nights.music video launches and benefit hosted gigs.
Minokaua is a two-story building,餐厅所在的一楼,可以翻译为合作空间,当它的二楼是你可以观看演出现场。他们的上层阁楼可供贵宾预订/活动使用。如果你喜欢爵士乐,some blues and a bunch of synthesized music-muses wrapped in Malate's charm then this place is for you.I can personally vouch for this place.
Ultimate college vibe.大多数上大学的学生都到这里来,或者那些喜欢上大学的人。我还是更喜欢原来的tk,但是这个新的地点一点也不坏,不管它是不是前者的苍白反映。Indie college bands play here and even tenure bands like Pedicab still grace its location.
A chill and comfortable place to watch live bands while enjoying your cold beer towers and chicken wings.They have a mighty selection of good grubs prepared by their in-house chef. They do weekly promos for their alcoholic making it more affordable.
金属头音乐之家。Watch off the radar artist still giving homage to raw slashed down guitar rifts with heavy set bass.Mosh and trash through the crowd.
理由:新的邻居乐队,signed and unsigned artist,and local art makers.
Did we miss anything or have something that you'd want us to check out?Feel free to connect with the金沙开户crew on different social channels listed below.
Find us atKlook PHshould you want to join us during our #日出.Adult Fieldtrip,yes?
1 thought on "Watch Raw and Live OPM Music: Let Us Show You Where To Go"
Small and homey Route is your best bet if you want to catch your favorite local act but can't convince any of your friends to ditch the latest The Walking Dead episode to hang out with you — bartenders know regulars by names,and where not seeing a familiar face is next to impossible.
Our mission is to provide unbiased reviews,raw,为我们的读者提供诚实和明智的建议。We will not compromise our judgment based on paid ads or placements as compared with other websites.


Using temperature stand-offs can replace expensive high temperature pressure sensors.
When selecting a pressure transducer for monitoring high temperature liquids or gases, be sure to note the operating temperature range of the transducer. This specification is included under environmental data on most manufacturers' data sheets. The temperature compensated range specification of a transducer is the error introduced as temperature increases or decreases from 70°F. This thermal error affects both the span and zero setting of the transducers analog output and is generally listed as a percent of full scale per degree (% FS/°F). The static accuracy (accuracy at 70°F) of the pressure transducer plus the thermal error introduced by a high temperature pressure source is the total possible error for a specific high temperature pressure measurement.
The expense of a special high temperature pressure transducer can be avoided by using a temperature stand-off (short length of tubing) to isolate the transducer from the high temperature pressure media. The chart below shows various lengths and diameters of tubing for 303 stainless steel and brass that will limit the media temperature at the transducer to 0-200°F.
This chart assumes the ambient temperature around the transducer is 100°F and the major source of thermal input is via the connecting tubing. As an example, a 6 inch length of 1/4" O.D. 303 stainless steel tubing will protect a transducer from an 1100°F pressure media.
CLICK HERE to learn how to select an intrinsically safe sensor.


text:
package org.epnoi.uia.informationstore.dao.cassandra;

public interface ExternalResourceCassandraHelper  {
	public static final String DESCRIPTION = "DESCRIPTION";
	public static final String COLUMN_FAMILY="ExternalResource";
	public static final String URI = "URI";

}

redpajama_set_name: RedPajamaGithub


text:
The seven best tips from the seven worst CEOs of all-time
The Daily Ticker
Daily Ticker• April 29, 2014
Editor's note: This blog was written by Zac Bissonnette, author of the new book Good Advice from Bad People: Selected Wisdom from Murderers, Stock Swindlers, and Lance Armstrong. The Daily Ticker's Aaron Task interviews Zac in the video above.
Complacency in the C-suite is dangerous, and when a CEO seems to be taking a victory lap, investors should get nervous. A 2007 New York University study found that shareholder returns tend to decline after a CEO builds his trophy home and other research suggests a similar decline in performance after companies build lavish new headquarters.
While researching my new book Good Advice from Bad People: Selected Wisdom from Murderers, Stock Swindlers, and Lance Armstrong, I noticed another trend: There are an awful lot of management guides, self-help books, and speeches delivered by CEOs at the apex of their power — right before their kingdoms turned into dumpsters, and they traded the keys to their Ferraris in for prison jumpsuits.
Fortune 500 CEOs, even crooked and/or inept ones, are generally well-spoken oracles of advice. Here are the top seven tips from seven of the worst CEOs of all-time:
"The only barriers in your career are self-imposed." – Al Dunlap
Chainsaw Al Dunlap was, at one time, one of the most respected CEOs in America. His tough, take-no-prisoners approach to cost-cutting made him an icon of the 1990s, and his book Mean Business: How I Save Bad Companies and Make Good Companies Great was an instant bestseller. When he was hired as CEO of Sunbeam in 1996, the stock surged close to 50% in a single day. But in 1998, the whole thing collapsed under the weight of management mistakes and aggressive accounting. Dunlap was fired, the company went into bankruptcy and in 2002, Dunlap settled SEC fraud charges by paying a $500,000 fine and an agreement not to serve as an officer or director of a publicly-traded company for the rest of his life. Not that anyone was asking him to.
"As an impresario, I encourage and elicit contrarian views and contrasts. . . It's important to place tension between points of view to extract the best from people. Dissent stimulates discussion, prompting others to make more perceptive observations." – John Sculley
As CEO of Apple (AAPL) from 1983 until 1993, John Sculley worked hard to brand himself as a thought leader and management icon. His 1987 book was a bestseller, even though The New York Times called it "severely overwritten."
But when Sculley placed tension between himself and Steve Jobs, arguably the most creative man in the history of the world, he extracted the worst from the situation. The two were locked in a power struggle and when Sculley learned that Jobs was plotting to have him ousted, he went to the company's board of directors. The board stripped Jobs of his management responsibilities in response, and Jobs left the company in 1985. Sculley was fired in 1993.
"Leaders must have a strong sense of renewal—an eagerness to create new opportunities through an entrepreneurial approach." – Kay R. Whitmore
As CEO of Kodak from 1990 until 1993, Kay Whitmore was slow to respond to changes in the industry.
"They didn't believe the American public would buy another film," Alecia Swasy wrote in "Changing Focus: Kodak and the Battle to Save a Great American Company," explaining the company's blasé attitude about competition. Whitmore's complacency descended into caricature: He fell asleep during a meeting with Bill Gates that was supposed to focus on ways to win back the market share Kodak was losing to Polaroid. Kodak never did regain its edge and it filed for bankruptcy in 2012.
"[G]reed is not good. . . Every deal is a series of negotiations, and negotiation is not about taking or winning; it's more often about sharing and compromise." – Lou Pearlman
Lou Pearlman was a career conman who stumbled on a spectacularly successful career as packager of boy bands in the late 90s and early 2000s. He was the man behind NSYNC and Backstreet Boys—both of which later sued him.
Pearlman's greed knew no bounds; in 2006, he was exposed as having run one of the largest Ponzi schemes in U.S. history. Investors lost $447 million and Pearlman fled the country. He was arrested in 2007 and in 2008, he was sentenced to 25 years in prison.
"When you know what you are talking about, others will follow you because it's safe to follow you." – Richard Fuld
On May 12, 2006, Lehman Brothers CEO Richard Fuld returned to his alma mater, The University of Colorado at Boulder, to deliver a commencement address—in which he told students about the leadership opportunities that would come with knowing what you were talking about. Less than two-and-a-half years later, Lehman Brothers became the largest bankruptcy filing in U.S. history. Those who'd followed Fuld because they thought he knew what he was talking about lost everything.
"[T]aking a problem head on is better than hiding from it, even when it hurts. Not doing the right thing because it's too hard or too uncomfortable is not acceptable. Not today, and not ever." – Jon Corzine
Jon Corzine wrote that was when he was governor of New Jersey—but a few years later, following his loss to Chris Christie, he was back in the private sector as CEO of MF Global.
There, a series of bad trades made at Corzine's behest led the company to a spectacular financial collapse—and when regulators descended on the futures trading firm, they found that money was missing from customer accounts. In June 2013, the Commodities Futures Trading Commission filed a civil case against Corzine, accusing him of illegally using customer funds in a desperate effort to keep the firm afloat. Corzine has denied the allegations but having led MF Global into a spot on the list of the ten largest bankruptcies in U.S. history was enough to ruin his reputation either way.
"When you let your imagination win, there's no limit to what you can accomplish." – Ron Johnson
As senior vice president of Retail Operations at Apple, Ron Johnson ran the roll-out of the company's hugely successful stores.
J.C. Penney (JCP) lured him away with a huge pay package to serve as CEO, and the stock soared when his hiring was announced. At J.C. Penney, Johnson let his imagination run wild. Without any testing, he tried to move the stores away from discounting and into a higher-end model. Sales plunged, the company chalked up a nine-digit loss in a single quarter, and Johnson was ousted after a year and half on the job.
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