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101. Aspirin and Statin Therapy for Nonobstructive Coronary Artery Disease: Five-year Outcomes from the CONFIRM Registry

Author

Indraratna, Praveen, Naoum, Christopher, Ben Zekry, Sagit, Gransar, Heidi, Blanke, Philipp, Sellers, Stephanie, Achenbach, Stephan, Al-Mallah, Mouaz H, Andreini, Daniele, Berman, Daniel S, Budoff, Matthew J, Cademartiri, Filippo, Callister, Tracy Q, Chang, Hyuk-Jae, Chinnaiyan, Kavitha, Chow, Benjamin JW, Cury, Ricardo C, DeLago, Augustin, Feuchtner, Gudrun, Hadamitzky, Martin, Hausleiter, Joerg, Kaufmann, Philipp A, Kim, Yong-Jin, Maffei, Erica, Marques, Hugo, Gonçalves, Pedro de Araújo, Pontone, Gianluca, Raff, Gilbert L, Rubinshtein, Ronen, Villines, Todd C, Lin, Fay Y, Shaw, Leslee J, Narula, Jagat, Bax, Jeroen J, and Leipsic, Jonathon A

Subjects
Epidemiology, Biomedical and Clinical Sciences, Health Sciences, Clinical Research, Cardiovascular, Heart Disease - Coronary Heart Disease, Heart Disease, Clinical Trials and Supportive Activities, Atherosclerosis, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Good Health and Well Being, Aspirin, CT Angiography, Coronary Artery Disease, Nonobstructive Coronary Artery Disease, Statin
Abstract

PurposeIn this cohort study, 5-year data from the Coronary CT Angiography Evaluation for Clinical Outcomes: An International Multicenter Registry (ie, CONFIRM) were examined to identify associations of baseline aspirin and statin use with mortality, major adverse cardiovascular events (MACE), and myocardial infarction (MI) in individuals without substantial (≥50%) stenosis.Materials and methodsIn this prospective cohort study, all participants in the registry underwent coronary CT angiography and were classified as having no detectable coronary plaque or having nonobstructive coronary artery disease (CAD) (1%-49% stenosis). Participants with obstructive (≥50%) stenosis were excluded from analysis. The study commenced in June 2003 and was completed in March 2016. All unadjusted and risk-adjusted analyses utilized the Cox proportional hazard model with hospital sites modeled using shared frailty.ResultsA total of 6386 participants with no detectable plaque or with nonobstructive CAD were included (mean age, 56.0 years ± 13.3 [SD], 52% men). The mean follow-up period was 5.66 years ± 1.10. Nonobstructive CAD (n = 2815, 44% of all participants included in the study) was associated with a greater risk of all-cause mortality (10.6% [298 of 2815] vs 4.8% [170 of 3571], P < .001) compared to those without CAD (n = 3571, 56%). Baseline aspirin and statin use was documented for 1415 and 1429 participants, respectively, with nonobstructive CAD, and for 1560 and 1565 participants without detectable plaque, respectively. In individuals with nonobstructive CAD, baseline aspirin use was not associated with a reduction in MACE (10.9% [102 of 936] vs 14.7% [52 of 355], P = .06), all-cause mortality (9.6% [95 of 991] vs 10.9% [46 of 424], P = .468), or MI (4.4% [41 of 936] vs 6.2% [22 of 355], P = .18). On multivariate risk-adjusted analysis, baseline statin use was associated with a lower rate of MACE (hazard ratio, 0.59; 95% CI: 0.40, 0.87; P = .007). Neither therapy improved clinical outcomes for participants with no detectable plaque.ConclusionIn participants with nonobstructive CAD, baseline use of statins, but not of aspirin, was associated with improved clinical outcomes. Neither therapy was associated with benefit in participants without plaque.Keywords: Aspirin, Statin, Coronary Artery Disease, CT Angiography, Nonobstructive Coronary Artery DiseaseClinical trial registration no. NCT01443637 Supplemental material is available for this article. © RSNA, 2022See also the commentary by Canan and Navar in this issue.

Published
2022

104. Leveraging Uncertainty for Improved Static Malware Detection Under Extreme False Positive Constraints

Author

Nguyen, Andre T., Raff, Edward, Nicholas, Charles, and Holt, James

Subjects
Computer Science - Machine Learning, Computer Science - Cryptography and Security
Abstract

The detection of malware is a critical task for the protection of computing environments. This task often requires extremely low false positive rates (FPR) of 0.01% or even lower, for which modern machine learning has no readily available tools. We introduce the first broad investigation of the use of uncertainty for malware detection across multiple datasets, models, and feature types. We show how ensembling and Bayesian treatments of machine learning methods for static malware detection allow for improved identification of model errors, uncovering of new malware families, and predictive performance under extreme false positive constraints. In particular, we improve the true positive rate (TPR) at an actual realized FPR of 1e-5 from an expected 0.69 for previous methods to 0.80 on the best performing model class on the Sophos industry scale dataset. We additionally demonstrate how previous works have used an evaluation protocol that can lead to misleading results.

Published
2021

105. The language of race, ethnicity, and ancestry in human genetic research

Author

Birney, Ewan, Inouye, Michael, Raff, Jennifer, Rutherford, Adam, and Scally, Aylwyn

Subjects
Quantitative Biology - Populations and Evolution
Abstract

The language commonly used in human genetics can inadvertently pose problems for multiple reasons. Terms like "ancestry", "ethnicity", and other ways of grouping people can have complex, often poorly understood, or multiple meanings within the various fields of genetics, between different domains of biological sciences and medicine, and between scientists and the general public. Furthermore, some categories in frequently used datasets carry scientifically misleading, outmoded or even racist perspectives derived from the history of science. Here, we discuss examples of problematic lexicon in genetics, and how commonly used statistical practices to control for the non-genetic environment may exacerbate difficulties in our terminology, and therefore understanding. Our intention is to stimulate a much-needed discussion about the language of genetics, to begin a process to clarify existing terminology, and in some cases adopt a new lexicon that both serves scientific insight, and cuts us loose from various aspects of a pernicious past.

Published
2021

106. Generating Thermal Human Faces for Physiological Assessment Using Thermal Sensor Auxiliary Labels

Author

Ordun, Catherine, Raff, Edward, and Purushotham, Sanjay

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Thermal images reveal medically important physiological information about human stress, signs of inflammation, and emotional mood that cannot be seen on visible images. Providing a method to generate thermal faces from visible images would be highly valuable for the telemedicine community in order to show this medical information. To the best of our knowledge, there are limited works on visible-to-thermal (VT) face translation, and many current works go the opposite direction to generate visible faces from thermal surveillance images (TV) for law enforcement applications. As a result, we introduce favtGAN, a VT GAN which uses the pix2pix image translation model with an auxiliary sensor label prediction network for generating thermal faces from visible images. Since most TV methods are trained on only one data source drawn from one thermal sensor, we combine datasets from faces and cityscapes. These combined data are captured from similar sensors in order to bootstrap the training and transfer learning task, especially valuable because visible-thermal face datasets are limited. Experiments on these combined datasets show that favtGAN demonstrates an increase in SSIM and PSNR scores of generated thermal faces, compared to training on a single face dataset alone.

Published
2021

107. Evading Malware Classifiers via Monte Carlo Mutant Feature Discovery

Author

Boutsikas, John, Eren, Maksim E., Varga, Charles, Raff, Edward, Matuszek, Cynthia, and Nicholas, Charles

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract

The use of Machine Learning has become a significant part of malware detection efforts due to the influx of new malware, an ever changing threat landscape, and the ability of Machine Learning methods to discover meaningful distinctions between malicious and benign software. Antivirus vendors have also begun to widely utilize malware classifiers based on dynamic and static malware analysis features. Therefore, a malware author might make evasive binary modifications against Machine Learning models as part of the malware development life cycle to execute an attack successfully. This makes the studying of possible classifier evasion strategies an essential part of cyber defense against malice. To this extent, we stage a grey box setup to analyze a scenario where the malware author does not know the target classifier algorithm, and does not have access to decisions made by the classifier, but knows the features used in training. In this experiment, a malicious actor trains a surrogate model using the EMBER-2018 dataset to discover binary mutations that cause an instance to be misclassified via a Monte Carlo tree search. Then, mutated malware is sent to the victim model that takes the place of an antivirus API to test whether it can evade detection., Comment: Presented at the Malware Technical Exchange Meeting, Online,2021. Copyright 2021 by the author(s)

Published
2021

108. FORBIDDEN PURPOSES: A NEW PATH FOR LIMITING CRIMINALIZATION.

Author

Donelson, Raff

Subjects
Criminal law -- Evaluation, Constitutional law -- Evaluation, Criticism, Textual -- Analysis, Equality before the law -- Laws, regulations and rules, Illegality -- Laws, regulations and rules, Due process of law -- Analysis, Liberalism -- Analysis, Democracy -- Analysis, Government regulation
Abstract

Activists and scholars have often complained that the American criminal justice system makes choices about criminalization and sentences based on nefarious reasons. For instance, critics have claimed that criminalization and [...]

Published
2023

109. Associations between dyspnoea, coronary atherosclerosis, and cardiovascular outcomes: results from the long-term follow-up CONFIRM registry.

Author

van Rosendael, Alexander R, Bax, A Maxim, van den Hoogen, Inge J, Smit, Jeff M, Al'Aref, Subhi J, Achenbach, Stephan, Al-Mallah, Mouaz H, Andreini, Daniele, Berman, Daniel S, Budoff, Matthew J, Cademartiri, Filippo, Callister, Tracy Q, Chang, Hyuk-Jae, Chinnaiyan, Kavitha, Chow, Benjamin JW, Cury, Ricardo C, DeLago, Augustin, Feuchtner, Gudrun, Hadamitzky, Martin, Hausleiter, Joerg, Kaufmann, Philipp A, Kim, Yong-Jin, Leipsic, Jonathon A, Maffei, Erica, Marques, Hugo, de Araújo Gonçalves, Pedro, Pontone, Gianluca, Raff, Gilbert L, Rubinshtein, Ronen, Villines, Todd C, Gransar, Heidi, Lu, Yao, Peña, Jessica M, Lin, Fay Y, Shaw, Leslee J, Narula, Jagat, Min, James K, and Bax, Jeroen J

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Atherosclerosis, Cardiovascular, Clinical Research, Biomedical Imaging, Heart Disease, Heart Disease - Coronary Heart Disease, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Aged, Coronary Angiography, Coronary Artery Disease, Dyspnea, Follow-Up Studies, Humans, Male, Middle Aged, Predictive Value of Tests, Prognosis, Prospective Studies, Registries, Risk Factors, coronary artery disease, coronary computed tomography angiography, dyspnoea, prognosis, Cardiovascular medicine and haematology
Abstract

AimsThe relationship between dyspnoea, coronary artery disease (CAD), and major cardiovascular events (MACE) is poorly understood. This study evaluated (i) the association of dyspnoea with the severity of anatomical CAD by coronary computed tomography angiography (CCTA) and (ii) to which extent CAD explains MACE in patients with dyspnoea.Methods and resultsFrom the international COronary CT Angiography EvaluatioN for Clinical Outcomes: An InteRnational Multicenter (CONFIRM) registry, 4425 patients (750 with dyspnoea) with suspected but without known CAD were included and prospectively followed for ≥5 years. First, the association of dyspnoea with CAD severity was assessed using logistic regression analysis. Second, the prognostic value of dyspnoea for MACE (myocardial infarction and death), and specifically, the interaction between dyspnoea and CAD severity was investigated using Cox proportional-hazard analysis. Mean patient age was 60.3 ± 11.9 years, 63% of patients were male and 592 MACE events occurred during a median follow-up duration of 5.4 (IQR 5.1-6.0) years. On uni- and multivariable analysis (adjusting for age, sex, body mass index, chest pain typicality, and risk factors), dyspnoea was associated with two- and three-vessel/left main (LM) obstructive CAD. The presence of dyspnoea increased the risk for MACE [hazard ratio (HR) 1.57, 95% confidence interval (CI): 1.29-1.90], which was modified after adjusting for clinical predictors and CAD severity (HR 1.26, 95% CI: 1.02-1.55). Conversely, when stratified by CAD severity, dyspnoea did not provide incremental prognostic value in one-, two-, or three-vessel/LM obstructive CAD, but dyspnoea did provide incremental prognostic value in non-obstructive CAD.ConclusionIn patients with suspected CAD, dyspnoea was independently associated with severe obstructive CAD on CCTA. The severity of obstructive CAD explained the elevated MACE rates in patients presenting with dyspnoea, but in patients with non-obstructive CAD, dyspnoea portended additional risk.

Published
2022

110. Exact Acceleration of K-Means++ and K-Means$\|$

Author

Raff, Edward

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Mathematical Software, Statistics - Machine Learning
Abstract

K-Means++ and its distributed variant K-Means$\|$ have become de facto tools for selecting the initial seeds of K-means. While alternatives have been developed, the effectiveness, ease of implementation, and theoretical grounding of the K-means++ and $\|$ methods have made them difficult to "best" from a holistic perspective. By considering the limited opportunities within seed selection to perform pruning, we develop specialized triangle inequality pruning strategies and a dynamic priority queue to show the first acceleration of K-Means++ and K-Means$\|$ that is faster in run-time while being algorithmicly equivalent. For both algorithms we are able to reduce distance computations by over $500\times$. For K-means++ this results in up to a 17$\times$ speedup in run-time and a $551\times$ speedup for K-means$\|$. We achieve this with simple, but carefully chosen, modifications to known techniques which makes it easy to integrate our approach into existing implementations of these algorithms., Comment: to appear in the 30th International Joint Conference on Artificial Intelligence (IJCAI-21)

Published
2021

111. GPU Semiring Primitives for Sparse Neighborhood Methods

Author

Nolet, Corey J., Gala, Divye, Raff, Edward, Eaton, Joe, Rees, Brad, Zedlewski, John, and Oates, Tim

Subjects
Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing, Mathematics - Rings and Algebras
Abstract

High-performance primitives for mathematical operations on sparse vectors must deal with the challenges of skewed degree distributions and limits on memory consumption that are typically not issues in dense operations. We demonstrate that a sparse semiring primitive can be flexible enough to support a wide range of critical distance measures while maintaining performance and memory efficiency on the GPU. We further show that this primitive is a foundational component for enabling many neighborhood-based information retrieval and machine learning algorithms to accept sparse input. To our knowledge, this is the first work aiming to unify the computation of several critical distance measures on the GPU under a single flexible design paradigm and we hope that it provides a good baseline for future research in this area. Our implementation is fully open source and publicly available as part of the RAFT library of GPU-accelerated machine learning primitives (https://github.com/rapidsai/raft).

Published
2021

112. Accounting for Variance in Machine Learning Benchmarks

Author

Bouthillier, Xavier, Delaunay, Pierre, Bronzi, Mirko, Trofimov, Assya, Nichyporuk, Brennan, Szeto, Justin, Sepah, Naz, Raff, Edward, Madan, Kanika, Voleti, Vikram, Kahou, Samira Ebrahimi, Michalski, Vincent, Serdyuk, Dmitriy, Arbel, Tal, Pal, Chris, Varoquaux, Gaël, and Vincent, Pascal

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Strong empirical evidence that one machine-learning algorithm A outperforms another one B ideally calls for multiple trials optimizing the learning pipeline over sources of variation such as data sampling, data augmentation, parameter initialization, and hyperparameters choices. This is prohibitively expensive, and corners are cut to reach conclusions. We model the whole benchmarking process, revealing that variance due to data sampling, parameter initialization and hyperparameter choice impact markedly the results. We analyze the predominant comparison methods used today in the light of this variance. We show a counter-intuitive result that adding more sources of variation to an imperfect estimator approaches better the ideal estimator at a 51 times reduction in compute cost. Building on these results, we study the error rate of detecting improvements, on five different deep-learning tasks/architectures. This study leads us to propose recommendations for performance comparisons., Comment: Submitted to MLSys2021

Published
2021

114. Research Reproducibility as a Survival Analysis

Author

Raff, Edward

Subjects
Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Applications
Abstract

There has been increasing concern within the machine learning community that we are in a reproducibility crisis. As many have begun to work on this problem, all work we are aware of treat the issue of reproducibility as an intrinsic binary property: a paper is or is not reproducible. Instead, we consider modeling the reproducibility of a paper as a survival analysis problem. We argue that this perspective represents a more accurate model of the underlying meta-science question of reproducible research, and we show how a survival analysis allows us to draw new insights that better explain prior longitudinal data. The data and code can be found at https://github.com/EdwardRaff/Research-Reproducibility-Survival-Analysis, Comment: To appear in AAAI 2021

Published
2020

115. Classifying Sequences of Extreme Length with Constant Memory Applied to Malware Detection

Author

Raff, Edward, Fleshman, William, Zak, Richard, Anderson, Hyrum S., Filar, Bobby, and McLean, Mark

Subjects
Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Recent works within machine learning have been tackling inputs of ever-increasing size, with cybersecurity presenting sequence classification problems of particularly extreme lengths. In the case of Windows executable malware detection, inputs may exceed $100$ MB, which corresponds to a time series with $T=100,000,000$ steps. To date, the closest approach to handling such a task is MalConv, a convolutional neural network capable of processing up to $T=2,000,000$ steps. The $\mathcal{O}(T)$ memory of CNNs has prevented further application of CNNs to malware. In this work, we develop a new approach to temporal max pooling that makes the required memory invariant to the sequence length $T$. This makes MalConv $116\times$ more memory efficient, and up to $25.8\times$ faster to train on its original dataset, while removing the input length restrictions to MalConv. We re-invest these gains into improving the MalConv architecture by developing a new Global Channel Gating design, giving us an attention mechanism capable of learning feature interactions across 100 million time steps in an efficient manner, a capability lacked by the original MalConv CNN. Our implementation can be found at https://github.com/NeuromorphicComputationResearchProgram/MalConv2, Comment: To appear in AAAI 2021

Published
2020

125. Crosslingual Generalization through Multitask Finetuning.

Author

Niklas Muennighoff, Thomas Wang, Lintang Sutawika, Adam Roberts, Stella Biderman, Teven Le Scao, M. Saiful Bari, Sheng Shen, Zheng Xin Yong, Hailey Schoelkopf, Xiangru Tang, Dragomir Radev, Alham Fikri Aji, Khalid Almubarak, Samuel Albanie, Zaid Alyafeai, Albert Webson, Edward Raff, and Colin Raffel

Published
2023
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138. Marvolo: Programmatic Data Augmentation for Deep Malware Detection

Author

Wong, Mike, Raff, Edward, Holt, James, Netravali, Ravi, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Koutra, Danai, editor, Plant, Claudia, editor, Gomez Rodriguez, Manuel, editor, Baralis, Elena, editor, and Bonchi, Francesco, editor

Published
2023
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140. Wearing Austen

Author

Raff, Sarah, Sinanan, Kerry, editor, Bautz, Annika, editor, and Cook, Daniel, editor

Published
2023
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142. Impact of primary kidney disease on the effects of empagliflozin in patients with chronic kidney disease: secondary analyses of the EMPA-KIDNEY trial

Author

Judge, PK, Staplin, N, Mayne, KJ, Wanner, C, Green, JB, Hauske, SJ, Emberson, JR, Preiss, D, Ng, SYA, Roddick, AJ, Sammons, E, Zhu, D, Hill, M, Stevens, W, Wallendszus, K, Brenner, S, Cheung, AK, Liu, ZH, Li, J, Hooi, LS, Liu, WJ, Kadowaki, T, Nangaku, M, Levin, A, Cherney, D, Maggioni, AP, Pontremoli, R, Deo, R, Goto, S, Rossello, X, Tuttle, KR, Steubl, D, Massey, D, Landray, MJ, Baigent, C, Haynes, R, Herrington, WG, Abat, S, Abd Rahman, R, Abdul Cader, R, Abdul Hafidz, MI, Abdul Wahab, MZ, Abdullah, NK, Abdul-Samad, T, Abe, M, Abraham, N, Acheampong, S, Achiri, P, Acosta, JA, Adeleke, A, Adell, V, Adewuyi-Dalton, R, Adnan, N, Africano, A, Agharazii, M, Aguilar, F, Aguilera, A, Ahmad, M, Ahmad, MK, Ahmad, NA, Ahmad, NH, Ahmad, NI, Ahmad Miswan, N, Ahmad Rosdi, H, Ahmed, I, Ahmed, S, Aiello, J, Aitken, A, AitSadi, R, Aker, S, Akimoto, S, Akinfolarin, A, Akram, S, Alberici, F, Albert, C, Aldrich, L, Alegata, M, Alexander, L, Alfaress, S, Alhadj Ali, M, Ali, A, Alicic, R, Aliu, A, Almaraz, R, Almasarwah, R, Almeida, J, Aloisi, A, Al-Rabadi, L, Alscher, D, Alvarez, P, Al-Zeer, B, Amat, M, Ambrose, C, Ammar, H, An, Y, Andriaccio, L, Ansu, K, Apostolidi, A, Arai, N, Araki, H, Araki, S, Arbi, A, Arechiga, O, Armstrong, S, Arnold, T, Aronoff, S, Arriaga, W, Arroyo, J, Arteaga, D, Asahara, S, Asai, A, Asai, N, Asano, S, Asawa, M, Asmee, MF, Aucella, F, Augustin, M, Avery, A, Awad, A, Awang, IY, Awazawa, M, Axler, A, Ayub, W, Azhari, Z, Baccaro, R, Badin, C, Bagwell, B, Bahlmann-Kroll, E, Bahtar, AZ, Bains, D, Bajaj, H, Baker, R, Baldini, E, Banas, B, Banerjee, D, Banno, S, Bansal, S, Barberi, S, Barnes, S, Barnini, C, Barot, C, Barrett, K, Barrios, R, Bartolomei Mecatti, B, Barton, I, Barton, J, Basily, W, Bavanandan, S, Baxter, A, Becker, L, Beddhu, S, Beige, J, Beigh, S, Bell, S, Benck, U, Beneat, A, Bennett, A, Bennett, D, Benyon, S, Berdeprado, J, Bergler, T, Bergner, A, Berry, M, Bevilacqua, M, Bhairoo, J, Bhandari, S, Bhandary, N, Bhatt, A, Bhattarai, M, Bhavsar, M, Bian, W, Bianchini, F, Bianco, S, Bilous, R, Bilton, J, Bilucaglia, D, Bird, C, Birudaraju, D, Biscoveanu, M, Blake, C, Bleakley, N, Bocchicchia, K, Bodine, S, Bodington, R, Boedecker, S, Bolduc, M, Bolton, S, Bond, C, Boreky, F, Boren, K, Bouchi, R, Bough, L, Bovan, D, Bowler, C, Bowman, L, Brar, N, Braun, C, Breach, A, Breitenfeldt, M, Brettschneider, B, Brewer, A, Brewer, G, Brindle, V, Brioni, E, Brown, C, Brown, H, Brown, L, Brown, R, Brown, S, Browne, D, Bruce, K, Brueckmann, M, Brunskill, N, Bryant, M, Brzoska, M, Bu, Y, Buckman, C, Budoff, M, Bullen, M, Burke, A, Burnette, S, Burston, C, Busch, M, Bushnell, J, Butler, S, Büttner, C, Byrne, C, Caamano, A, Cadorna, J, Cafiero, C, Cagle, M, Cai, J, Calabrese, K, Calvi, C, Camilleri, B, Camp, S, Campbell, D, Campbell, R, Cao, H, Capelli, I, Caple, M, Caplin, B, Cardone, A, Carle, J, Carnall, V, Caroppo, M, Carr, S, Carraro, G, Carson, M, Casares, P, Castillo, C, Castro, C, Caudill, B, Cejka, V, Ceseri, M, Cham, L, Chamberlain, A, Chambers, J, Chan, CBT, Chan, JYM, Chan, YC, Chang, E, Chant, T, Chavagnon, T, Chellamuthu, P, Chen, F, Chen, J, Chen, P, Chen, TM, Chen, Y, Cheng, C, Cheng, H, Cheng, MC, Ching, CH, Chitalia, N, Choksi, R, Chukwu, C, Chung, K, Cianciolo, G, Cipressa, L, Clark, S, Clarke, H, Clarke, R, Clarke, S, Cleveland, B, Cole, E, Coles, H, Condurache, L, Connor, A, Convery, K, Cooper, A, Cooper, N, Cooper, Z, Cooperman, L, Cosgrove, L, Coutts, P, Cowley, A, Craik, R, Cui, G, Cummins, T, Dahl, N, Dai, H, Dajani, L, D'Amelio, A, Damian, E, Damianik, K, Danel, L, Daniels, C, Daniels, T, Darbeau, S, Darius, H, Dasgupta, T, Davies, J, Davies, L, Davis, A, Davis, J, Davis, L, Dayanandan, R, Dayi, S, Dayrell, R, De Nicola, L, Debnath, S, Deeb, W, Degenhardt, S, DeGoursey, K, Delaney, M, DeRaad, R, Derebail, V, Dev, D, Devaux, M, Dhall, P, Dhillon, G, Dienes, J, Dobre, M, Doctolero, E, Dodds, V, Domingo, D, Donaldson, D, Donaldson, P, Donhauser, C, Donley, V, Dorestin, S, Dorey, S, Doulton, T, Draganova, D, Draxlbauer, K, Driver, F, Du, H, Dube, F, Duck, T, Dugal, T, Dugas, J, Dukka, H, Dumann, H, Durham, W, Dursch, M, Dykas, R, Easow, R, Eckrich, E, Eden, G, Edmerson, E, Edwards, H, Ee, LW, Eguchi, J, Ehrl, Y, Eichstadt, K, Eid, W, Eilerman, B, Ejima, Y, Eldon, H, Ellam, T, Elliott, L, Ellison, R, Emberson, J, Epp, R, Er, A, Espino-Obrero, M, Estcourt, S, Estienne, L, Evans, G, Evans, J, Evans, S, Fabbri, G, Fajardo-Moser, M, Falcone, C, Fani, F, Faria-Shayler, P, Farnia, F, Farrugia, D, Fechter, M, Fellowes, D, Feng, F, Fernandez, J, Ferraro, P, Field, A, Fikry, S, Finch, J, Finn, H, Fioretto, P, Fish, R, Fleischer, A, Fleming-Brown, D, Fletcher, L, Flora, R, Foellinger, C, Foligno, N, Forest, S, Forghani, Z, Forsyth, K, Fottrell-Gould, D, Fox, P, Frankel, A, Fraser, D, Frazier, R, Frederick, K, Freking, N, French, H, Froment, A, Fuchs, B, Fuessl, L, Fujii, H, Fujimoto, A, Fujita, A, Fujita, K, Fujita, Y, Fukagawa, M, Fukao, Y, Fukasawa, A, Fuller, T, Funayama, T, Fung, E, Furukawa, M, Furukawa, Y, Furusho, M, Gabel, S, Gaidu, J, Gaiser, S, Gallo, K, Galloway, C, Gambaro, G, Gan, CC, Gangemi, C, Gao, M, Garcia, K, Garcia, M, Garofalo, C, Garrity, M, Garza, A, Gasko, S, Gavrila, M, Gebeyehu, B, Geddes, A, Gentile, G, George, A, George, J, Gesualdo, L, Ghalli, F, Ghanem, A, Ghate, T, Ghavampour, S, Ghazi, A, Gherman, A, Giebeln-Hudnell, U, Gill, B, Gillham, S, Girakossyan, I, Girndt, M, Giuffrida, A, Glenwright, M, Glider, T, Gloria, R, Glowski, D, Goh, BL, Goh, CB, Gohda, T, Goldenberg, R, Goldfaden, R, Goldsmith, C, Golson, B, Gonce, V, Gong, Q, Goodenough, B, Goodwin, N, Goonasekera, M, Gordon, A, Gordon, J, Gore, A, Goto, H, Gowen, D, Grace, A, Graham, J, Grandaliano, G, Gray, M, Greene, T, Greenwood, G, Grewal, B, Grifa, R, Griffin, D, Griffin, S, Grimmer, P, Grobovaite, E, Grotjahn, S, Guerini, A, Guest, C, Gunda, S, Guo, B, Guo, Q, Haack, S, Haase, M, Haaser, K, Habuki, K, Hadley, A, Hagan, S, Hagge, S, Haller, H, Ham, S, Hamal, S, 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2024
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143. Effects of empagliflozin on progression of chronic kidney disease: a prespecified secondary analysis from the empa-kidney trial

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Staplin, N, Haynes, R, Judge, PK, Wanner, C, Green, JB, Emberson, J, Preiss, D, Mayne, KJ, Ng, SYA, Sammons, E, Zhu, D, Hill, M, Stevens, W, Wallendszus, K, Brenner, S, Cheung, AK, Liu, ZH, Li, J, Hooi, LS, Liu, WJ, Kadowaki, T, Nangaku, M, Levin, A, Cherney, D, Maggioni, AP, Pontremoli, R, Deo, R, Goto, S, Rossello, X, Tuttle, KR, Steubl, D, Petrini, M, Seidi, S, Landray, MJ, Baigent, C, Herrington, WG, Abat, S, Abd Rahman, R, Abdul Cader, R, Abdul Hafidz, MI, Abdul Wahab, MZ, Abdullah, NK, Abdul-Samad, T, Abe, M, Abraham, N, Acheampong, S, Achiri, P, Acosta, JA, Adeleke, A, Adell, V, Adewuyi-Dalton, R, Adnan, N, Africano, A, Agharazii, M, Aguilar, F, Aguilera, A, Ahmad, M, Ahmad, MK, Ahmad, NA, Ahmad, NH, Ahmad, NI, Ahmad Miswan, N, Ahmad Rosdi, H, Ahmed, I, Ahmed, S, Aiello, J, Aitken, A, AitSadi, R, Aker, S, Akimoto, S, Akinfolarin, A, Akram, S, Alberici, F, Albert, C, Aldrich, L, Alegata, M, Alexander, L, Alfaress, S, Alhadj Ali, M, Ali, A, Alicic, R, Aliu, A, Almaraz, R, 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Simmons, K, Sinha, S, Sitter, T, Sivanandam, S, Skipper, M, Sloan, K, Sloan, L, Smith, R, Smyth, J, Sobande, T, Sobata, M, Somalanka, S, Song, X, Sonntag, F, Sood, B, Sor, SY, Soufer, J, Sparks, H, Spatoliatore, G, Spinola, T, Squyres, S, Srivastava, A, Stanfield, J, Staylor, K, Steele, A, Steen, O, Steffl, D, Stegbauer, J, Stellbrink, C, Stellbrink, E, Stevenson, A, Stewart-Ray, V, Stickley, J, Stoffler, D, Stratmann, B, Streitenberger, S, Strutz, F, Stubbs, J, Stumpf, J, Suazo, N, Suchinda, P, Suckling, R, Sudin, A, Sugamori, K, Sugawara, H, Sugawara, K, Sugimoto, D, Sugiyama, H, Sugiyama, T, Sullivan, M, Sumi, M, Suresh, N, Sutton, D, Suzuki, H, Suzuki, R, Suzuki, Y, Swanson, E, Swift, P, Syed, S, Szerlip, H, Taal, M, Taddeo, M, Tailor, C, Tajima, K, Takagi, M, Takahashi, K, Takahashi, M, Takahashi, T, Takahira, E, Takai, T, Takaoka, M, Takeoka, J, Takesada, A, Takezawa, M, Talbot, M, Taliercio, J, Talsania, T, Tamori, Y, Tamura, R, Tamura, Y, Tan, CHH, Tan, EZZ, Tanabe, A, Tanabe, K, Tanaka, A, Tanaka, N, Tang, S, Tang, Z, Tanigaki, K, Tarlac, M, Tatsuzawa, A, Tay, JF, Tay, LL, Taylor, J, Taylor, K, Te, A, Tenbusch, L, Teng, KS, Terakawa, A, Terry, J, Tham, ZD, Tholl, S, Thomas, G, Thong, KM, Tietjen, D, Timadjer, A, Tindall, H, Tipper, S, Tobin, K, Toda, N, Tokuyama, A, Tolibas, M, Tomita, A, Tomita, T, Tomlinson, J, Tonks, L, Topf, J, Topping, S, Torp, A, Torres, A, Totaro, F, Toth, P, Toyonaga, Y, Tripodi, F, Trivedi, K, Tropman, E, Tschope, D, Tse, J, Tsuji, K, Tsunekawa, S, Tsunoda, R, Tucky, B, Tufail, S, Tuffaha, A, Turan, E, Turner, H, Turner, J, Turner, M, Tye, YL, Tyler, A, Tyler, J, Uchi, H, Uchida, H, Uchida, T, Udagawa, T, Ueda, S, Ueda, Y, Ueki, K, Ugni, S, Ugwu, E, Umeno, R, Unekawa, C, Uozumi, K, Urquia, K, Valleteau, A, Valletta, C, van Erp, R, Vanhoy, C, Varad, V, Varma, R, Varughese, A, Vasquez, P, Vasseur, A, Veelken, R, Velagapudi, C, Verdel, K, Vettoretti, S, Vezzoli, G, Vielhauer, V, Viera, R, Vilar, E, Villaruel, S, Vinall, L, Vinathan, J, Visnjic, M, Voigt, E, von-Eynatten, M, Vourvou, M, Wada, J, Wada, T, Wada, Y, Wakayama, K, Wakita, Y, Walters, T, Wan Mohamad, WH, Wang, L, Wang, W, Wang, X, Wang, Y, Wanninayake, S, Watada, H, Watanabe, K, Watanabe, M, Waterfall, H, Watkins, D, Watson, S, Weaving, L, Weber, B, Webley, Y, Webster, A, Webster, M, Weetman, M, Wei, W, Weihprecht, H, Weiland, L, Weinmann-Menke, J, Weinreich, T, Wendt, R, Weng, Y, Whalen, M, Whalley, G, Wheatley, R, Wheeler, A, Wheeler, J, Whelton, P, White, K, Whitmore, B, Whittaker, S, Wiebel, J, Wiley, J, Wilkinson, L, Willett, M, Williams, A, Williams, E, Williams, K, Williams, T, Wilson, A, Wilson, P, Wincott, L, Wines, E, Winkelmann, B, Winkler, M, Winter-Goodwin, B, Witczak, J, Wittes, J, Wittmann, M, Wolf, G, Wolf, L, Wolfling, R, Wong, C, Wong, E, Wong, HS, Wong, LW, Wong, YH, Wonnacott, A, Wood, A, Wood, L, Woodhouse, H, Wooding, N, Woodman, A, Wren, K, Wu, J, Wu, P, Xia, S, Xiao, H, Xiao, X, Xie, Y, Xu, C, Xu, Y, Xue, H, Yahaya, H, Yalamanchili, H, Yamada, A, Yamada, N, Yamagata, K, Yamaguchi, M, Yamaji, Y, Yamamoto, A, Yamamoto, S, Yamamoto, T, Yamanaka, A, Yamano, T, Yamanouchi, Y, Yamasaki, N, Yamasaki, Y, Yamashita, C, Yamauchi, T, Yan, Q, Yanagisawa, E, Yang, F, Yang, L, Yano, S, Yao, S, Yao, Y, Yarlagadda, S, Yasuda, Y, Yiu, V, Yokoyama, T, Yoshida, S, Yoshidome, E, Yoshikawa, H, Young, A, Young, T, Yousif, V, Yu, H, Yu, Y, Yuasa, K, Yusof, N, Zalunardo, N, Zander, B, Zani, R, Zappulo, F, Zayed, M, Zemann, B, Zettergren, P, Zhang, H, Zhang, L, Zhang, N, Zhang, X, Zhao, J, Zhao, L, Zhao, S, Zhao, Z, Zhong, H, Zhou, N, Zhou, S, Zhu, L, Zhu, S, Zietz, M, Zippo, M, Zirino, F, and Zulkipli, FH

Published
2024
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147. Sampling Approach Matters: Active Learning for Robotic Language Acquisition

Author

Pillai, Nisha, Raff, Edward, Ferraro, Francis, and Matuszek, Cynthia

Subjects
Computer Science - Robotics, Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

Ordering the selection of training data using active learning can lead to improvements in learning efficiently from smaller corpora. We present an exploration of active learning approaches applied to three grounded language problems of varying complexity in order to analyze what methods are suitable for improving data efficiency in learning. We present a method for analyzing the complexity of data in this joint problem space, and report on how characteristics of the underlying task, along with design decisions such as feature selection and classification model, drive the results. We observe that representativeness, along with diversity, is crucial in selecting data samples., Comment: To appear in IEEE Big Data 2020

Published
2020

148. Getting Passive Aggressive About False Positives: Patching Deployed Malware Detectors

Author

Raff, Edward, Filar, Bobby, and Holt, James

Subjects
Computer Science - Machine Learning, Computer Science - Cryptography and Security
Abstract

False positives (FPs) have been an issue of extreme importance for anti-virus (AV) systems for decades. As more security vendors turn to machine learning, alert deluge has hit critical mass with over 20% of all alerts resulting in FPs and, in some organizations, the number reaches half of all alerts. This increase has resulted in fatigue, frustration, and, worst of all, neglect from security workers on SOC teams. A foundational cause for FPs is that vendors must build one global system to try and satisfy all customers, but have no method to adjust to individual local environments. This leads to outrageous, albeit technically correct, characterization of their platforms being 99.9% effective. Once these systems are deployed the idiosyncrasies of individual, local environments expose blind spots that lead to FPs and uncertainty. We propose a strategy for fixing false positives in production after a model has already been deployed. For too long the industry has tried to combat these problems with inefficient, and at times, dangerous allowlist techniques and excessive model retraining which is no longer enough. We propose using a technique called passive-aggressive learning to alter a malware detection model to an individual's environment, eliminating false positives without sharing any customer sensitive information. We will show how to use passive-aggressive learning to solve a collection of notoriously difficult false positives from a production environment without compromising the malware model's accuracy, reducing the total number of FP alerts by an average of 23x., Comment: to appear in IEEE International Conference on Data Mining Workshop (ICDM) on Deep Learning for Cyber Threat Intelligence (DL-CTI)

Published
2020

149. The Use of AI for Thermal Emotion Recognition: A Review of Problems and Limitations in Standard Design and Data

Author

Ordun, Catherine, Raff, Edward, and Purushotham, Sanjay

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

With the increased attention on thermal imagery for Covid-19 screening, the public sector may believe there are new opportunities to exploit thermal as a modality for computer vision and AI. Thermal physiology research has been ongoing since the late nineties. This research lies at the intersections of medicine, psychology, machine learning, optics, and affective computing. We will review the known factors of thermal vs. RGB imaging for facial emotion recognition. But we also propose that thermal imagery may provide a semi-anonymous modality for computer vision, over RGB, which has been plagued by misuse in facial recognition. However, the transition to adopting thermal imagery as a source for any human-centered AI task is not easy and relies on the availability of high fidelity data sources across multiple demographics and thorough validation. This paper takes the reader on a short review of machine learning in thermal FER and the limitations of collecting and developing thermal FER data for AI training. Our motivation is to provide an introductory overview into recent advances for thermal FER and stimulate conversation about the limitations in current datasets., Comment: Presented at AAAI FSS-20: Artificial Intelligence in Government and Public Sector, Washington, DC, USA

Published
2020

150. Automatic Yara Rule Generation Using Biclustering

Author

Raff, Edward, Zak, Richard, Munoz, Gary Lopez, Fleming, William, Anderson, Hyrum S., Filar, Bobby, Nicholas, Charles, and Holt, James

Subjects
Computer Science - Cryptography and Security, Computer Science - Information Retrieval, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Yara rules are a ubiquitous tool among cybersecurity practitioners and analysts. Developing high-quality Yara rules to detect a malware family of interest can be labor- and time-intensive, even for expert users. Few tools exist and relatively little work has been done on how to automate the generation of Yara rules for specific families. In this paper, we leverage large n-grams ($n \geq 8$) combined with a new biclustering algorithm to construct simple Yara rules more effectively than currently available software. Our method, AutoYara, is fast, allowing for deployment on low-resource equipment for teams that deploy to remote networks. Our results demonstrate that AutoYara can help reduce analyst workload by producing rules with useful true-positive rates while maintaining low false-positive rates, sometimes matching or even outperforming human analysts. In addition, real-world testing by malware analysts indicates AutoYara could reduce analyst time spent constructing Yara rules by 44-86%, allowing them to spend their time on the more advanced malware that current tools can't handle. Code will be made available at https://github.com/NeuromorphicComputationResearchProgram ., Comment: to be published in the 13th ACM Workshop on Artificial Intelligence and Security (AISec)

Published
2020
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