Publications

How Do Open Source Software Contributors Perceive and Address Usability?: Valued Factors, Practices, and Challenges
Wenting Wang
Jinghui Cheng
Jin Guo
Given the recent changes in the open source software (OSS) landscape, we examined OSS contributors’ current valued factors, practices, and… (voir plus) challenges concerning usability. Our survey provides insights for OSS practitioners and tool designers to promote a user-centric mindset and improve usability practice in OSS communities.
2022-02-01
IEEE Software (publié)
doi.org
arxiv.org
Inferring global-scale temporal latent topics from news reports to predict public health interventions for COVID-19
Zhi Wen
Guido Powell
Imane Chafi
David Buckeridge
Y. K. Li
Yue Li
2022-02-01
Patterns (publié)
doi.org
Exploring social inequalities in healthcare trajectories following diagnosis of diabetes: a state sequence analysis of linked survey and administrative data
Rachel Marie Mckay
Laurence Letarte
Alain Gillian Lucie David Manon Catherine Anaïs Benoit A Vanasse Bartlett Blais Buckeridge Choinière Hudon
Alain Vanasse
Gillian M. Bartlett
Lucie Blais
David Buckeridge
Manon. Choinière
Catherine. Hudon
Anaïs Lacasse
Benoit Lamarche
Alexandre Lebel
Amélie Quesnel-Vallée
Pasquale Roberge
Valérie Émond
Marie-Pascale Pomey
Mike Benigeri
Anne-Marie Cloutier
Marc Dorais
Josiane Courteau … (voir 15 de plus)
Mireille Courteau
Stéphanie Plante
Pierre Cambon
Annie Giguère
Isabelle Nogueira Leroux
Danielle St-Laurent
Denis Roy
Jaime Borja
A. Néron
Geneviève Landry
J. Éthier
Roxanne Dault
Marc-Antoine Côté-Marcil
Pier Tremblay
Sonia Quirion
2022-01-31
BMC Health Services Research (published)
doi.org
Exploring social inequalities in healthcare trajectories following diagnosis of diabetes: a state sequence analysis of linked survey and administrative data
Rachel McKay
Laurence Letarte
Alain Gillian Lucie David Manon Catherine Anaïs Benoit A Vanasse Bartlett Blais Buckeridge Choinière Hudon
Alain Gillian Lucie David Manon Catherine Anaïs Benoit Alexandre Amélie Pasquale Valérie Marie-Pascale Mike Anne-Marie Marc Josiane Mireille Stéphanie Pierre Annie Isabelle Danielle Denis Jaime André Geneviève Jean-François Roxanne Marc-Antoine Pier Sonia Vanasse
Alain Vanasse
Gillian Bartlett
Lucie Blais
David Buckeridge
Manon Choinière
Catherine Hudon
Anaïs Lacasse
Benoit Lamarche
Alexandre Lebel
Amélie Quesnel-Vallée
Pasquale Roberge
Valérie Émond
Marie-Pascale Pomey
Mike Benigeri
Anne-Marie Cloutier
Marc Dorais … (voir 16 de plus)
Josiane Courteau
Mireille Courteau
Stéphanie Plante
Pierre Cambon
Annie Giguère
Isabelle Leroux
Danielle St-Laurent
Denis Roy
Jaime Borja
André Néron
Geneviève Landry
Jean-François Ethier
Roxanne Dault
Marc-Antoine Côté-Marcil
Pier Tremblay
Sonia Quirion
2022-01-31
BMC Health Services Research (publié)
doi.org
Memory-Aware Functional IR for Higher-Level Synthesis of Accelerators
Christof Schlaak
Tzung-Han Juang
Christophe Dubach
Specialized accelerators deliver orders of a magnitude of higher performance than general-purpose processors. The ever-changing nature of mo… (voir plus)dern workloads is pushing the adoption of Field Programmable Gate Arrays (FPGAs) as the substrate of choice. However, FPGAs are hard to program directly using Hardware Description Languages (HDLs). Even modern high-level HDLs, e.g., Spatial and Chisel, still require hardware expertise. This article adopts functional programming concepts to provide a hardware-agnostic higher-level programming abstraction. During synthesis, these abstractions are mechanically lowered into a functional Intermediate Representation (IR) that defines a specific hardware design point. This novel IR expresses different forms of parallelism and standard memory features such as asynchronous off-chip memories or synchronous on-chip buffers. Exposing such features at the IR level is essential for achieving high performance. The viability of this approach is demonstrated on two stencil computations and by exploring the optimization space of matrix-matrix multiplication. Starting from a high-level representation for these algorithms, our compiler produces low-level VHSIC Hardware Description Language (VHDL) code automatically. Several design points are evaluated on an Intel Arria 10 FPGA, demonstrating the ability of the IR to exploit different hardware features. This article also shows that the designs produced are competitive with highly tuned OpenCL implementations and outperform hardware-agnostic OpenCL code.
2022-01-31
ACM Transactions on Architecture and Code Optimization (TACO) (published)
doi.org
Memory-Aware Functional IR for Higher-Level Synthesis of Accelerators
Christof Schlaak
Tzung-Han Juang
Christophe Dubach
2022-01-31
ACM Transactions on Architecture and Code Optimization (publié)
doi.org
Fortuitous Forgetting in Connectionist Networks
Hattie Zhou
Ankit Vani
Hugo Larochelle
Aaron Courville
Forgetting is often seen as an unwanted characteristic in both human and machine learning. However, we propose that forgetting can in fact b… (voir plus)e favorable to learning. We introduce"forget-and-relearn"as a powerful paradigm for shaping the learning trajectories of artificial neural networks. In this process, the forgetting step selectively removes undesirable information from the model, and the relearning step reinforces features that are consistently useful under different conditions. The forget-and-relearn framework unifies many existing iterative training algorithms in the image classification and language emergence literature, and allows us to understand the success of these algorithms in terms of the disproportionate forgetting of undesirable information. We leverage this understanding to improve upon existing algorithms by designing more targeted forgetting operations. Insights from our analysis provide a coherent view on the dynamics of iterative training in neural networks and offer a clear path towards performance improvements.
2022-01-28
ICLR.cc/2022/Conference (poster)
openreview.net
openreview.net
Medical Doctors in Health Reforms
Jean-Louis Denis
Sabrina Germain
Catherine Régis
Gianluca Veronesi
Health and legal experts from England and Canada consider the influence of medical doctors on reforms in this comparative study. With reflec… (voir plus)tions on participation since the inception of publicly funded healthcare systems, they show how the status of doctors affects change.
2022-01-28
(publié)
doi.org
New Insights on Reducing Abrupt Representation Change in Online Continual Learning
Lucas Caccia
Rahaf Aljundi
Nader Asadi
Tinne Tuytelaars
Joelle Pineau
Eugene Belilovsky
In the online continual learning paradigm, agents must learn from a changing distribution while respecting memory and compute constraints. E… (voir plus)xperience Replay (ER), where a small subset of past data is stored and replayed alongside new data, has emerged as a simple and effective learning strategy. In this work, we focus on the change in representations of observed data that arises when previously unobserved classes appear in the incoming data stream, and new classes must be distinguished from previous ones. We shed new light on this question by showing that applying ER causes the newly added classes’ representations to overlap significantly with the previous classes, leading to highly disruptive parameter updates. Based on this empirical analysis, we propose a new method which mitigates this issue by shielding the learned representations from drastic adaptation to accommodate new classes. We show that using an asymmetric update rule pushes new classes to adapt to the older ones (rather than the reverse), which is more effective especially at task boundaries, where much of the forgetting typically occurs. Empirical results show significant gains over strong baselines on standard continual learning benchmarks.
2022-01-28
ICLR.cc/2022/Conference (poster)
doi.org
openreview.net
R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning
Shengyao Lu
Bang Liu
Keith G Mills
SHANGLING JUI
Di Niu
Systematicity, i.e., the ability to recombine known parts and rules to form new sequences while reasoning over relational data, is critical … (voir plus)to machine intelligence. A model with strong systematicity is able to train on small-scale tasks and generalize to large-scale tasks. In this paper, we propose R5, a relational reasoning framework based on reinforcement learning that reasons over relational graph data and explicitly mines underlying compositional logical rules from observations. R5 has strong systematicity and being robust to noisy data. It consists of a policy value network equipped with Monte Carlo Tree Search to perform recurrent relational prediction and a backtrack rewriting mechanism for rule mining. By alternately applying the two components, R5 progressively learns a set of explicit rules from data and performs explainable and generalizable relation prediction. We conduct extensive evaluations on multiple datasets. Experimental results show that R5 outperforms various embedding-based and rule induction baselines on relation prediction tasks while achieving a high recall rate in discovering ground truth rules.
2022-01-28
ICLR.cc/2022/Conference (spotlight)
openreview.net
openreview.net
Lacking social support is associated with structural divergences in hippocampus–default network co-variation patterns
Chris Zajner
Nathan Spreng
Danilo Bzdok
2022-01-27
Social Cognitive and Affective Neuroscience (publié)
doi.org
Multilevel development of cognitive abilities in an artificial neural network
Konstantin Volzhenin
J. Changeux
Guillaume Dumas
Several neuronal mechanisms have been proposed to account for the formation of cognitive abilities through postnatal interactions with the p… (voir plus)hysical and socio-cultural environment. Here, we introduce a three-level computational model of information processing and acquisition of cognitive abilities. We propose minimal architectural requirements to build these levels and how the parameters affect their performance and relationships. The first sensorimotor level handles local nonconscious processing, here during a visual classification task. The second level or cognitive level globally integrates the information from multiple local processors via long-ranged connections and synthesizes it in a global, but still nonconscious manner. The third and cognitively highest level handles the information globally and consciously. It is based on the Global Neuronal Workspace (GNW) theory and is referred to as conscious level. We use trace and delay conditioning tasks to, respectively, challenge the second and third levels. Results first highlight the necessity of epigenesis through selection and stabilization of synapses at both local and global scales to allow the network to solve the first two tasks. At the global scale, dopamine appears necessary to properly provide credit assignment despite the temporal delay between perception and reward. At the third level, the presence of interneurons becomes necessary to maintain a self-sustained representation within the GNW in the absence of sensory input. Finally, while balanced spontaneous intrinsic activity facilitates epigenesis at both local and global scales, the balanced excitatory-inhibitory ratio increases performance. Finally, we discuss the plausibility of the model in both neurodevelopmental and artificial intelligence terms.
2022-01-25
bioRxiv (preprint)
doi.org