Publications

GIANT: Scalable Creation of a Web-scale Ontology
Bang Liu
Weidong Guo
Di Niu
Jinwen Luo
Chaoyue Wang
Zhen Wen
Yu Xu
2020-05-30
Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (publié)
doi.org
arxiv.org
Current works and future directions on application of machine learning in primary care
S. A. Rahimi
Vera Granikov
Pierre Pluye
In this short paper, we explained current machine learning works in primary care based on a scoping review that we performed. The performed … (voir plus)review was in line with the methodological framework proposed by Colquhoun and colleagues. Lastly, we discussed our observations and gave important directions to the future studies in this fast-growing area.
2020-05-26
International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (publié)
doi.org
Minimisation in Logical Form
Nick Bezhanishvili
Marcello M. Bonsangue
Helle Hvid Hansen
Dexter Kozen
C. Kupke
Prakash Panangaden
Alexandra Silva
2020-05-22
ArXiv (prépublication)
arxiv.org
arxiv.org
CNN to detect differences in cerebral cortical anatomy of left- and right- handers
Lisa Meyer-Baese
Erik Roecher
Lucas Moesch
Danilo Bzdok
Klaus Mathiak
Handedness is one of the most obvious functional asymmetries, but its relation to anatomical asymmetry in the brain has not yet been clearly… (voir plus) demonstrated. However, there is no significant evidence to prove or disprove this structure-function correlation, thus left-handed patients are often excluded from magnetic resonance imaging (MRI) studies. MRI classification of left and right hemispheres is a difficult task on its own due to the complexity of the images and the structural similarities between the two halves. We demonstrate a deep artificial neural network approach in connection with a detailed preprocessing pipeline for the classification of lateralization in T1-weighted MR images of the human brain. Preprocessing includes bias field correction and registration on the MNI template. Our classifier is a convolutional neural network (CNN) that was trained on 287 images. Each image was duplicated and mirrored on the mid-sagittal plane. The best model reached an accuracy of 97.594% with a mean of 95.42% and standard deviation of 1.37%. Additionally, our model’s performance was evaluated on an independent set of 118 images and reached a classification accuracy of 97%. In a larger study we tested the model on grey-matter images of 927 left and 927 right-handed patients from the UK Biobank. Here all right-handed images and all left-handed images were classified as belonging to one class. The results suggest that there is no structural difference in grey-matter between the two hemispheres that can be distinguished by the deep learning classifier.
2020-05-19
Defense + Commercial Sensing (publié)
doi.org
Failure to follow medication changes made at hospital discharge is associated with adverse events in 30 days
Daniala L. Weir
Aude Motulsky
Michal Abrahamowicz
Todd C. Lee
Steven Morgan
David L. Buckeridge
Robyn Tamblyn
To evaluate the hypothesis that nonadherence to medication changes made at hospital discharge is associated with an increased risk of advers… (voir plus)e events in the 30 days postdischarge. Patients admitted to hospitals in Montreal, Quebec, between 2014 and 2016. Prospective cohort study. Nonadherence to medication changes was measured by comparing medications dispensed in the community with those prescribed at hospital discharge. Patient, health system, and drug regimen‐level covariates were measured using medical services and pharmacy claims data as well as data abstracted from the patient's hospital chart. Multivariable Cox models were used to determine the association between nonadherence to medication changes and the risk of adverse events. Among 2655 patients who met our inclusion criteria, mean age was 69.5 years (SD 14.7) and 1581 (60%) were males. Almost half of patients (n = 1161, 44%) were nonadherent to at least one medication change, and 860 (32%) were readmitted to hospital, visited the emergency department, or died in the 30 days postdischarge. Patients who were not adherent to any of their medication changes had a 35% higher risk of adverse events compared to those who were adherent to all medication changes (1.41 vs 1.27 events/100 person‐days, adjusted hazard ratio: 1.35, 95% CI: 1.06‐1.71). Almost half of all patients were not adherent to some or all changes made to their medications at hospital discharge. Nonadherence to all changes was associated with an increased risk of adverse events. Interventions addressing barriers to adherence should be considered moving forward.
2020-05-19
Health Services Research (publié)
doi.org
Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis
Aaron Carass
Snehashis Roy
Adrian Gherman
Jacob C. Reinhold
Andrew Jesson
Tal Arbel
Oskar Maier
Heinz Handels
Mohsen Ghafoorian
Bram Platel
Ariel Birenbaum
Hayit Greenspan
Dzung L. Pham
Ciprian M. Crainiceanu
Peter A. Calabresi
Jerry L. Prince
William R. Gray Roncal
Russell T. Shinohara
Ipek Oguz
2020-05-18
Scientific Reports (publié)
doi.org
Saliency Based Deep Neural Network for Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI
Joshua D. Durso-Finley
Douglas Arnold
Tal Arbel
2020-05-18
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (publié)
doi.org
Depth Prediction for Monocular Direct Visual Odometry
Ran Cheng
Christopher Agia
David Meger
Gregory Dudek
Depth prediction from monocular images with deep CNNs is a topic of increasing interest to the community. Advances have lead to models capab… (voir plus)le of predicting disparity maps with consistent scale, which are an acceptable prior for gradient-based direct methods. With this in consideration, we exploit depth prediction as a candidate prior for the coarse initialization, tracking, and marginalization steps of the direct visual odometry system, enabling the second-order optimizer to converge faster into a precise global minimum. In addition, the given depth prior supports large baseline stereo scenarios, maintaining robust pose estimations against challenging motion states such as in-place rotation. We further refine our pose estimation with semi-online loop closure. The experiments on KITTI demonstrate that our proposed method achieves state- of-the-art performance compared to both traditional direct visual odometry and learning-based counterparts.
2020-05-12
2020 17th Conference on Computer and Robot Vision (CRV) (publié)
doi.org
Story Forest
Bang Liu
Fred X. Han
Di Niu
Linglong Kong
Kunfeng Lai
Yu Xu
Extracting events accurately from vast news corpora and organize events logically is critical for news apps and search engines, which aim to… (voir plus) organize news information collected from the Internet and present it to users in the most sensible forms. Intuitively speaking, an event is a group of news documents that report the same news incident possibly in different ways. In this article, we describe our experience of implementing a news content organization system at Tencent to discover events from vast streams of breaking news and to evolve news story structures in an online fashion. Our real-world system faces unique challenges in contrast to previous studies on topic detection and tracking (TDT) and event timeline or graph generation, in that we (1) need to accurately and quickly extract distinguishable events from massive streams of long text documents, and (2) must develop the structures of event stories in an online manner, in order to guarantee a consistent user viewing experience. In solving these challenges, we propose Story Forest, a set of online schemes that automatically clusters streaming documents into events, while connecting related events in growing trees to tell evolving stories. A core novelty of our Story Forest system is EventX, a semi-supervised scheme to extract events from massive Internet news corpora. EventX relies on a two-layered, graph-based clustering procedure to group documents into fine-grained events. We conducted extensive evaluations based on (1) 60 GB of real-world Chinese news data, (2) a large Chinese Internet news dataset that contains 11,748 news articles with truth event labels, and (3) the 20 News Groups English dataset, through detailed pilot user experience studies. The results demonstrate the superior capabilities of Story Forest to accurately identify events and organize news text into a logical structure that is appealing to human readers.
2020-05-12
ACM Transactions on Knowledge Discovery from Data (publié)
doi.org
Active Domain Randomization
Bhairav Mehta
Manfred Diaz
Florian Golemo
Christopher Pal
Liam Paull
Domain randomization is a popular technique for improving domain transfer, often used in a zero-shot setting when the target domain is unkno… (voir plus)wn or cannot easily be used for training. In this work, we empirically examine the effects of domain randomization on agent generalization. Our experiments show that domain randomization may lead to suboptimal, high-variance policies, which we attribute to the uniform sampling of environment parameters. We propose Active Domain Randomization, a novel algorithm that learns a parameter sampling strategy. Our method looks for the most informative environment variations within the given randomization ranges by leveraging the discrepancies of policy rollouts in randomized and reference environment instances. We find that training more frequently on these instances leads to better overall agent generalization. In addition, when domain randomization and policy transfer fail, Active Domain Randomization offers more insight into the deficiencies of both the chosen parameter ranges and the learned policy, allowing for more focused debugging. Our experiments across various physics-based simulated and a real-robot task show that this enhancement leads to more robust, consistent policies.
2020-05-11
Proceedings of the Conference on Robot Learning (publié)
proceedings.mlr.press
proceedings.mlr.press
Leveraging exploration in off-policy algorithms via normalizing flows
Bogdan Mazoure
Thang Doan
Audrey Durand
R Devon Hjelm
Joelle Pineau
The ability to discover approximately optimal policies in domains with sparse rewards is crucial to applying reinforcement learning (RL) in … (voir plus)many real-world scenarios. Approaches such as neural density models and continuous exploration (e.g., Go-Explore) have been proposed to maintain the high exploration rate necessary to find high performing and generalizable policies. Soft actor-critic(SAC) is another method for improving exploration that aims to combine efficient learning via off-policy updates while maximizing the policy entropy. In this work, we extend SAC to a richer class of probability distributions (e.g., multimodal) through normalizing flows (NF) and show that this significantly improves performance by accelerating the discovery of good policies while using much smaller policy representations. Our approach, which we call SAC-NF, is a simple, efficient,easy-to-implement modification and improvement to SAC on continuous control baselines such as MuJoCo and PyBullet Roboschool domains. Finally, SAC-NF does this while being significantly parameter efficient, using as few as 5.5% the parameters for an equivalent SAC model.
2020-05-11
Proceedings of the Conference on Robot Learning (publié)
doi.org
proceedings.mlr.press
Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments
Martin Weiss
Simon Chamorro
Roger Girgis
Margaux Luck
Samira E. Kahou
Joseph P. Cohen
Derek Nowrouzezahrai
Doina Precup
Florian Golemo
Chris Pal
Millions of blind and visually-impaired (BVI) people navigate urban environments every day, using smartphones for high-level path-planning a… (voir plus)nd white canes or guide dogs for local information. However, many BVI people still struggle to travel to new places. In our endeavor to create a navigation assistant for the BVI, we found that existing Reinforcement Learning (RL) environments were unsuitable for the task. This work introduces SEVN, a sidewalk simulation environment and a neural network-based approach to creating a navigation agent. SEVN contains panoramic images with labels for house numbers, doors, and street name signs, and formulations for several navigation tasks. We study the performance of an RL algorithm (PPO) in this setting. Our policy model fuses multi-modal observations in the form of variable resolution images, visible text, and simulated GPS data to navigate to a goal door. We hope that this dataset, simulator, and experimental results will provide a foundation for further research into the creation of agents that can assist members of the BVI community with outdoor navigation.
2020-05-11
Proceedings of the Conference on Robot Learning (publié)
doi.org
proceedings.mlr.press