Publications

TrafficVis: Visualizing Organized Activity and Spatio-Temporal Patterns for Detecting and Labeling Human Trafficking
Catalina Vajiac
Duen Horng Chau
Andreas Olligschlaeger
Rebecca Mackenzie
Pratheeksha Nair
Meng-Chieh Lee
Yifei Li
Namyong Park
Reihaneh Rabbany
Christos Faloutsos
Law enforcement and domain experts can detect human trafficking (HT) in online escort websites by analyzing suspicious clusters of connected… (see more) ads. How can we explain clustering results intuitively and interactively, visualizing potential evidence for experts to analyze? We present TrafficVis, the first interface for cluster-level HT detection and labeling. Developed through months of participatory design with domain experts, TrafficVis provides coordinated views in conjunction with carefully chosen backend algorithms to effectively show spatio-temporal and text patterns to a wide variety of anti-HT stakeholders. We build upon state-of-the-art text clustering algorithms by incorporating shared metadata as a signal of connected and possibly suspicious activity, then visualize the results. Domain experts can use TrafficVis to label clusters as HT, or other, suspicious, but non-HT activity such as spam and scam, quickly creating labeled datasets to enable further HT research. Through domain expert feedback and a usage scenario, we demonstrate TRAFFICVIS's efficacy. The feedback was overwhelmingly positive, with repeated high praises for the usability and explainability of our tool, the latter being vital for indicting possible criminals.
2023-01-01
IEEE Transactions on Visualization and Computer Graphics (published)
doi.org
Transposable elements regulate thymus development and function 1
Jean-David Larouche
Céline M. Laumont
Assya Trofimov
Krystel Vincent
Leslie Hesnard
Sylvie Brochu
Caroline Côté
Juliette Humeau
Eric Bonneil
Joël Lanoix
Chantal Durette
Patrick Gendron
Jean-Philippe Laverdure
Ellen Rothman Richie
Sébastien Lemieux
Pierre Thibault
Claude Perreault
21 Transposable elements (TE) are repetitive sequences representing ~45% of the human and mouse genomes 22 and are highly expressed by medul… (see more)lary thymic epithelial cells (mTEC). In this study, we investigated the 23 role of transposable elements (TE), which are highly expressed by medullary thymic epithelial cells 24 (mTEC), on T-cell development in the thymus. We performed multi-omic analyses of TEs in human and 25 mouse thymic cells to elucidate their role in T cell development. We report that TE expression in the 26 human thymus is high and shows extensive ageand cell lineage-related variations. TEs interact with 27 multiple transcription factors in all cell types of the human thymus. Two cell types express particularly 28 broad TE repertoires: mTECs and plasmacytoid dendritic cells (pDC). In mTECs, TEs interact with 29 transcription factors essential for mTEC development and function (e.g., PAX1 and RELB) and generate 30 MHC-I-associated peptides implicated in thymocyte education. Notably, AIRE, FEZF2, and CHD4 31 regulate non-redundant sets of TEs in murine mTECs. Human thymic pDCs homogenously express large 32 numbers of TEs that lead to the formation of dsRNA, triggering RIG-I and MDA5 signaling and 33 explaining why thymic pDCs constitutively secrete IFN ɑ/β. This study illustrates the diversity of 34 interactions between TEs and the adaptive immune system. TEs are genetic parasites, and the two thymic 35 cell types most affected by TEs (mTEcs and pDCs) are essential to establishing central T-cell tolerance. 36 Therefore, we propose that the orchestration of TE expression in thymic cells is critical to prevent 37 autoimmunity in vertebrates. 38
2023-01-01
(published)
www.semanticscholar.org
Transposable elements regulate thymus development and function 1
Jean-David Larouche
Céline M. Laumont
Assya Trofimov
Krystel Vincent
Leslie Hesnard
Sylvie Brochu
Caroline Côté
Juliette Humeau
Eric Bonneil
Joël Lanoix
Chantal Durette
Patrick Gendron
Jean-Philippe Laverdure
Ellen Rothman Richie
Sébastien Lemieux
Pierre Thibault
Claude Perreault
21 Transposable elements (TE) are repetitive sequences representing ~45% of the human and mouse genomes 22 and are highly expressed by medul… (see more)lary thymic epithelial cells (mTEC). In this study, we investigated the 23 role of transposable elements (TE), which are highly expressed by medullary thymic epithelial cells 24 (mTEC), on T-cell development in the thymus. We performed multi-omic analyses of TEs in human and 25 mouse thymic cells to elucidate their role in T cell development. We report that TE expression in the 26 human thymus is high and shows extensive ageand cell lineage-related variations. TEs interact with 27 multiple transcription factors in all cell types of the human thymus. Two cell types express particularly 28 broad TE repertoires: mTECs and plasmacytoid dendritic cells (pDC). In mTECs, TEs interact with 29 transcription factors essential for mTEC development and function (e.g., PAX1 and RELB) and generate 30 MHC-I-associated peptides implicated in thymocyte education. Notably, AIRE, FEZF2, and CHD4 31 regulate non-redundant sets of TEs in murine mTECs. Human thymic pDCs homogenously express large 32 numbers of TEs that lead to the formation of dsRNA, triggering RIG-I and MDA5 signaling and 33 explaining why thymic pDCs constitutively secrete IFN ɑ/β. This study illustrates the diversity of 34 interactions between TEs and the adaptive immune system. TEs are genetic parasites, and the two thymic 35 cell types most affected by TEs (mTEcs and pDCs) are essential to establishing central T-cell tolerance. 36 Therefore, we propose that the orchestration of TE expression in thymic cells is critical to prevent 37 autoimmunity in vertebrates. 38
2023-01-01
(published)
www.semanticscholar.org
Trophic interaction models predict interactions across space, not food webs.
Dominique Caron
Ulrich Brose
Miguel Lurgi
F. Guillaume Blanchet
Dominique Gravel
Laura J. Pollock
Aim: Trophic interactions are central to our understanding of essential ecosystem functions as well as their stability. Predicting these int… (see more)eractions has become increasingly common due to the lack of empirical data on trophic interactions for most taxa in most ecosystems. We aim to determine how far and accurately trophic interaction models extrapolate to new communities both in terms of pairwise predator-prey interactions and higher level food web attributes (i.e., species position, food web-level properties).
2023-01-01
(published)
www.semanticscholar.org
Ultrastructure Analysis of Cardiomyocytes and Their Nuclei
Tabish A Syed
Yanan Wang
Drisya Dileep
Minhajuddin Sirajuddin
Kaleem Siddiqi
2023-01-01
Functional Imaging and Modeling of the Heart (published)
doi.org
Use of machine learning in pediatric surgical clinical prediction tools: A systematic review.
Amanda Bianco
Zaid A.M. Al-Azzawi
Elena Guadagno
Esli Osmanlliu
Jocelyn Gravel
Dan Poenaru
2023-01-01
Journal of Pediatric Surgery (published)
doi.org
Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top
Eduard Gorbunov
Samuel Horváth
Peter Richtárik
Gauthier Gidel
Byzantine-robustness has been gaining a lot of attention due to the growth of the interest in collaborative and federated learning. However,… (see more) many fruitful directions, such as the usage of variance reduction for achieving robustness and communication compression for reducing communication costs, remain weakly explored in the field. This work addresses this gap and proposes Byz-VR-MARINA - a new Byzantine-tolerant method with variance reduction and compression. A key message of our paper is that variance reduction is key to fighting Byzantine workers more effectively. At the same time, communication compression is a bonus that makes the process more communication efficient. We derive theoretical convergence guarantees for Byz-VR-MARINA outperforming previous state-of-the-art for general non-convex and Polyak-Lojasiewicz loss functions. Unlike the concurrent Byzantine-robust methods with variance reduction and/or compression, our complexity results are tight and do not rely on restrictive assumptions such as boundedness of the gradients or limited compression. Moreover, we provide the first analysis of a Byzantine-tolerant method supporting non-uniform sampling of stochastic gradients. Numerical experiments corroborate our theoretical findings.
2023-01-01
International Conference on Learning Representations (published)
doi.org
openreview.net
Versatile Energy-Based Models for High Energy Physics
Taoli Cheng
Aaron Courville
2023-01-01
arXiv.org (preprint)
doi.org
openreview.net
Video Killed the HD-Map: Predicting Multi-Agent Behavior Directly From Aerial Images
Yunpeng Liu
Vasileios Lioutas
Jonathan Wilder Lavington
Matthew Niedoba
Justice Sefas
Setareh Dabiri
Dylan Green
Xiaoxuan Liang
Berend Zwartsenberg
Adam Ścibior
Frank Wood
The development of algorithms that learn multi-agent behavioral models using human demonstrations has led to increasingly realistic simulati… (see more)ons in the field of autonomous driving. In general, such models learn to jointly predict trajectories for all controlled agents by exploiting road context information such as drivable lanes obtained from manually annotated high-definition (HD) maps. Recent studies show that these models can greatly benefit from increasing the amount of human data available for training. However, the manual annotation of HD maps which is necessary for every new location puts a bottleneck on efficiently scaling up human traffic datasets. We propose an aerial image-based map (AIM) representation that requires minimal annotation and provides rich road context information for traffic agents like pedestrians and vehicles. We evaluate multi-agent trajectory prediction using the AIM by incorporating it into a differentiable driving simulator as an image-texture-based differentiable rendering module. Our results demonstrate competitive multi-agent trajectory prediction performance especially for pedestrians in the scene when using our AIM representation as compared to models trained with rasterized HD maps.
2023-01-01
2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) (published)
doi.org
arxiv.org
When Do Graph Neural Networks Help with Node Classification: Investigating the Homophily Principle on Node Distinguishability
Sitao Luan
Chenqing Hua
Minkai Xu
Qincheng Lu
Jiaqi Zhu
Xiao-Wen Chang
Jie Fu
Jure Leskovec
Doina Precup
Homophily principle, i.e., nodes with the same labels are more likely to be connected, was believed to be the main reason for the performanc… (see more)e superiority of Graph Neural Networks (GNNs) over Neural Networks (NNs) on Node Classification (NC) tasks. Recently, people have developed theoretical results arguing that, even though the homophily principle is broken, the advantage of GNNs can still hold as long as nodes from the same class share similar neighborhood patterns [29], which questions the validity of homophily. However, this argument only considers intra-class Node Distinguishability (ND) and ignores inter-class ND, which is insufficient to study the effect of homophily. In this paper, we first demonstrate the aforementioned insufficiency with examples and argue that an ideal situation for ND is to have smaller intra-class ND than inter-class ND. To formulate this idea and have a better understanding of homophily, we propose Contextual Stochastic Block Model for Homophily (CSBM-H) and define two metrics, Probabilistic Bayes Error (PBE) and Expected Negative KL-divergence (ENKL), to quantify ND, through which we can also find how intra- and inter-class ND influence ND together. We visualize the results and give detailed analysis. Through experiments, we verified that the superiority of GNNs is
openreview.net
Willingness to Engage in Shared Decision Making: Impact of an Educational Intervention for Resident Physicians (SDM-FM)
Roland M. Grad
A. Sandhu
Michael Ferrante
Vinita D'souza
Lily Puterman-Salzman
Samira Abbasgholizadeh-Rahimi
Gabrielle Stevens
G. Elwyn
2023-01-01
Education and training (published)
doi.org
Workflow Discovery from Dialogues in the Low Data Regime
Amine El hattami
Stefania Raimondo
Issam Hadj Laradji
David Vazquez
Pau Rodriguez
Chris Pal
Text-based dialogues are now widely used to solve real-world problems. In cases where solution strategies are already known, they can someti… (see more)mes be codified into workflows and used to guide humans or artificial agents through the task of helping clients. We introduce a new problem formulation that we call Workflow Discovery (WD) in which we are interested in the situation where a formal workflow may not yet exist. Still, we wish to discover the set of actions that have been taken to resolve a particular problem. We also examine a sequence-to-sequence (Seq2Seq) approach for this novel task. We present experiments where we extract workflows from dialogues in the Action-Based Conversations Dataset (ABCD). Since the ABCD dialogues follow known workflows to guide agents, we can evaluate our ability to extract such workflows using ground truth sequences of actions. We propose and evaluate an approach that conditions models on the set of possible actions, and we show that using this strategy, we can improve WD performance. Our conditioning approach also improves zero-shot and few-shot WD performance when transferring learned models to unseen domains within and across datasets. Further, on ABCD a modified variant of our Seq2Seq method achieves state-of-the-art performance on related but different problems of Action State Tracking (AST) and Cascading Dialogue Success (CDS) across many evaluation metrics.
2023-01-01
Trans. Mach. Learn. Res. (published)
doi.org
openreview.net