Publications

Prediction intervals for travel time on transportation networks
Mohamad Elmasri
Aurélie Labbe
Denis Larocque
Laurent Charlin
Estimating travel-time is essential for making travel decisions in transportation networks. Empirically, single road-segment travel-time is … (see more)well studied, but how to aggregate such information over many edges to arrive at the distribution of travel time over a route is still theoretically challenging. Understanding travel-time distribution can help resolve many fundamental problems in transportation, quantifying travel uncertainty as an example. We develop a novel statistical perspective to specific types of dynamical processes that mimic the behavior of travel time on real-world networks. We show that, under general conditions, travel-time normalized by distance, follows a Gaussian distribution with route-invariant (universal) location and scale parameters. We develop efficient inference methods for such parameters, with which we propose asymptotic universal confidence and prediction intervals of travel time. We further develop our theory to include road-segment level information to construct route-specific location and scale parameter sequences that produce tighter route-specific Gaussian-based prediction intervals. We illustrate our methods with a real-world case study using precollected mobile GPS data, where we show that the route-specific and route-invariant intervals both achieve the 95\% theoretical coverage levels, where the former result in tighter bounds that also outperform competing models.
2020-04-22
(published)
www.semanticscholar.org
To Write Code: The Cultural Fabrication of Programming Notation and Practice
Ian Arawjo
2020-04-22
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (published)
doi.org
Distinct roles of parvalbumin and somatostatin interneurons in gating the synchronization of spike times in the neocortex
Hyun Jae Jang
Hyowon Chung
James M. Rowland
Blake A. Richards
Michael M. Kohl
Jeehyun Kwag
Sensory information–driven spikes are synchronized across cortical layers by distinct subtypes of interneurons.
2020-04-21
Science Advances (published)
doi.org
Meta‐analytic evidence for a joint neural mechanism underlying response inhibition and state anger
Andrei A. Puiu
Olga Wudarczyk
Gregor Kohls
Danilo Bzdok
Beate Herpertz‐Dahlmann
Kerstin Konrad
Although anger may weaken response inhibition (RI) by allowing outbursts to bypass deliberate processing, it is equally likely that RI defic… (see more)its precipitate a state of anger (SA). In adolescents, for instance, anger occurs more frequently and often leads to escalating aggressive behaviors. Even though RI is considered a key component in explaining individual differences in SA expression, the neural overlap between SA and RI remains elusive. Here, we aimed to meta‐analytically revisit and update the neural correlates of motor RI, to determine a consistent neural architecture of SA, and to identify their joint neural network. Considering that inhibitory abilities follow a protracted maturation until early adulthood, we additionally computed RI meta‐analyses in youths and adults. Using activation likelihood estimation, we calculated twelve meta‐analyses across 157 RI and 39 SA experiments on healthy individuals. Consistent with previous findings, RI was associated with a broad frontoparietal network including the anterior insula/inferior frontal gyrus (aI/IFG), premotor and midcingulate cortices, extending into right temporoparietal areas. Youths showed convergent activity in right midcingulate and medial prefrontal areas, left aI/IFG, and the temporal poles. SA, on the other hand, reliably recruited the right aI/IFG and anterior cingulate cortex. Conjunction analyses between RI and SA yielded a single convergence cluster in the right aI/IFG. While frontoparietal networks and bilateral aI are ubiquitously recruited during RI, the right aI/IFG cluster likely represents a node in a dynamically‐adjusting monitoring network that integrates salient information thereby facilitating the execution of goal‐directed behaviors under highly unpredictable scenarios.
2020-04-20
Human Brain Mapping (published)
doi.org
Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus
Bang Liu
Haojie Wei
Di Niu
Haolan Chen
Yancheng He
The ability to ask questions is important in both human and machine intelligence. Learning to ask questions helps knowledge acquisition, imp… (see more)roves question-answering and machine reading comprehension tasks, and helps a chatbot to keep the conversation flowing with a human. Existing question generation models are ineffective at generating a large amount of high-quality question-answer pairs from unstructured text, since given an answer and an input passage, question generation is inherently a one-to-many mapping. In this paper, we propose Answer-Clue-Style-aware Question Generation (ACS-QG), which aims at automatically generating high-quality and diverse question-answer pairs from unlabeled text corpus at scale by imitating the way a human asks questions. Our system consists of: i) an information extractor, which samples from the text multiple types of assistive information to guide question generation; ii) neural question generators, which generate diverse and controllable questions, leveraging the extracted assistive information; and iii) a neural quality controller, which removes low-quality generated data based on text entailment. We compare our question generation models with existing approaches and resort to voluntary human evaluation to assess the quality of the generated question-answer pairs. The evaluation results suggest that our system dramatically outperforms state-of-the-art neural question generation models in terms of the generation quality, while being scalable in the meantime. With models trained on a relatively smaller amount of data, we can generate 2.8 million quality-assured question-answer pairs from a million sentences found in Wikipedia.
2020-04-19
Proceedings of The Web Conference 2020 (published)
doi.org
arxiv.org
A Unifying Framework for Fairness-Aware Influence Maximization
Golnoosh Farnadi
Behrouz Babaki
Michel Gendreau
The problem of selecting a subset of nodes with greatest influence in a graph, commonly known as influence maximization, has been well studi… (see more)ed over the past decade. This problem has real world applications which can potentially affect lives of individuals. Algorithmic decision making in such domains raises concerns about their societal implications. One of these concerns, which surprisingly has only received limited attention so far, is algorithmic bias and fairness. We propose a flexible framework that extends and unifies the existing works in fairness-aware influence maximization. This framework is based on an integer programming formulation of the influence maximization problem. The fairness requirements are enforced by adding linear constraints or modifying the objective function. Contrary to the previous work which designs specific algorithms for each variant, we develop a formalism which is general enough for specifying different notions of fairness. A problem defined in this formalism can be then solved using efficient mixed integer programming solvers. The experimental evaluation indicates that our framework not only is general but also is competitive with existing algorithms.
2020-04-19
Companion Proceedings of the Web Conference 2020 (published)
doi.org
Role-Wise Data Augmentation for Knowledge Distillation
Jie Fu
Xue Geng
Zhijian Duan
Bohan Zhuang
Xingdi Yuan
Adam Trischler
Jie Lin
Vijay Chandrasekhar
Christopher Pal
Hao Dong
2020-04-18
ArXiv (preprint)
openreview.net
openreview.net
Neuropsychiatric copy number variants exert shared effects on human brain structure
Claudia Modenato
Kuldeep Kumar
Clara Moreau
Sandra Martin‐Brevet
Guillaume Huguet
Catherine Schramm
Martineau Jean‐Louis
Charles-Olivier Martin
Nadine Younis
Petra Tamer
Élise Douard
Fanny Thébault‐Dagher
Valérie Côté
Audrey Charlebois
Florence Deguire
Anne Maillard
Borja Rodríguez‐Herreros
Aurélie Pain
Sonia Richetin
Leila Kushan … (see 12 more)
Lester Melie‐García
Ana Isabel Silva
Marianne van den Bree
David E.J. Linden
Michael J. Owen
Jeremy Hall
Sarah Lippé
M. Mallar Chakravarty
Danilo Bzdok
Carrie E. Bearden
Bogdan Draganski
Sébastien Jacquemont
Neuropsychiatric CNVs share neuroanatomical signatures characterized by a parsimonious set of brain dimensions. The latter may underlie the … (see more)risk conferred by CNVs for a similar spectrum of neuropsychiatric conditions.
2020-04-16
medRxiv (preprint)
doi.org
Uncertainty Evaluation Metric for Brain Tumour Segmentation
Raghav Mehta
Angelos Filos
Yarin Gal
Tal Arbel
In this paper, we develop a metric designed to assess and rank uncertainty measures for the task of brain tumour sub-tissue segmentation in … (see more)the BraTS 2019 sub-challenge on uncertainty quantification. The metric is designed to: (1) reward uncertainty measures where high confidence is assigned to correct assertions, and where incorrect assertions are assigned low confidence and (2) penalize measures that have higher percentages of under-confident correct assertions. Here, the workings of the components of the metric are explored based on a number of popular uncertainty measures evaluated on the BraTS 2019 dataset.
2020-04-16
MIDL.io/2020/Conference (accepted)
openreview.net
openreview.net
Clustering for Continuous-Time Hidden Markov Models.
Yu Luo
David A. Stephens
David L Buckeridge
We develop clustering procedures for longitudinal trajectories based on a continuous-time hidden Markov model (CTHMM) and a generalized line… (see more)ar observation model. Specifically in this paper, we carry out infinite mixture model-based clustering for CTHMM and achieve inference using Markov chain Monte Carlo (MCMC). Specifically, for Bayesian nonparametric inference using a Dirichlet process mixture model, we utilize restricted Gibbs sampling split-merge proposals to expedite the MCMC algorithm. We employ the proposed algorithm to the simulated data as well as a large real data example, and the results demonstrate the desired performance of the new sampler.
2020-04-12
(published)
www.semanticscholar.org
Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction
Vishal Jain
William Fedus
Hugo Larochelle
Doina Precup
Bellemare Marc-Emmanuel
Text-based games are a natural challenge domain for deep reinforcement learning algorithms. Their state and action spaces are combinatoriall… (see more)y large, their reward function is sparse, and they are partially observable: the agent is informed of the consequences of its actions through textual feedback. In this paper we emphasize this latter point and consider the design of a deep reinforcement learning agent that can play from feedback alone. Our design recognizes and takes advantage of the structural characteristics of text-based games. We first propose a contextualisation mechanism, based on accumulated reward, which simplifies the learning problem and mitigates partial observability. We then study different methods that rely on the notion that most actions are ineffectual in any given situation, following Zahavy et al.'s idea of an admissible action. We evaluate these techniques in a series of text-based games of increasing difficulty based on the TextWorld framework, as well as the iconic game Zork. Empirically, we find that these techniques improve the performance of a baseline deep reinforcement learning agent applied to text-based games.
2020-04-02
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
CNN Detection of New and Enlarging Multiple Sclerosis Lesions from Longitudinal Mri Using Subtraction Images
Nazanin Mohammadi Sepahvand
Douglas Arnold
Tal Arbel
Accurate detection and segmentation of new lesional activity in longitudinal Magnetic Resonance Images (MRIs) of patients with Multiple Scle… (see more)rosis (MS) is important for monitoring disease activity, as well as for assessing treatment effects. In this work, we present the first deep learning framework to automatically detect and segment new and enlarging (NE) T2w lesions from longitudinal brain MRIs acquired from relapsing-remitting MS (RRMS) patients. The proposed framework is an adapted 3D U-Net [1] which includes as inputs the reference multi-modal MRI and T2-weighted lesion maps, as well an attention mechanism based on the subtraction MRI (between the two timepoints) which serves to assist the network in learning to differentiate between real anatomical change and artifactual change, while constraining the search space for small lesions. Experiments on a large, proprietary, multi -center, multi-modal, clinical trial dataset consisting of 1677 multi-modal scans illustrate that network achieves high overall detection accuracy (detection AUC=.95), outperforming (1) a U-Net without an attention mechanism (de-tection AUC=.93), (2) a framework based on subtracting independent T2-weighted segmentations (detection AUC=.57), and (3) DeepMedic (detection AUC=.84) [2], particularly for small lesions. In addition, the method was able to accurately classify patients as active/inactive with (sensitivities of. 69 and specificities of. 97).
2020-04-02
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) (published)
doi.org