Publications

From physics to sentience: Deciphering the semantics of the free-energy principle and evaluating its claims: Comment on "Path integrals, particular kinds, and strange things" by Karl Friston et al.

Zahra Sheikhbahaee

Adam Safron

Casper Hesp

Guillaume Dumas

2023-11-30

Physics of Life Reviews (published)

doi.org

arxiv.org

Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results

Cantin Baron

Sarah Cherkaoui

Sandra Therrien-Laperriere

Yann Ilboudo

Raphaël Poujol

Pamela Mehanna

Melanie E. Garrett

Marilyn J. Telen

Allison E. Ashley-Koch

Pablo Bartolucci

John D. Rioux

Guillaume Lettre

Christine Des Rosiers

Matthieu Ruiz

Julie G. Hussin

Studies combining metabolomics and genetics, known as metabolite genome-wide association studies (mGWAS), have provided valuable insights in… (see more)to our understanding of the genetic control of metabolite levels. However, the biological interpretation of these associations remains challenging due to a lack of existing tools to annotate mGWAS gene-metabolite pairs beyond the use of conservative statistical significance threshold. Here, we computed the shortest reactional distance (SRD) based on the curated knowledge of the KEGG database to explore its utility in enhancing the biological interpretation of results from three independent mGWAS, including a case study on sickle cell disease patients. Results show that, in reported mGWAS pairs, there is an excess of small SRD values and that SRD values and p-values significantly correlate, even beyond the standard conservative thresholds. The added-value of SRD annotation is shown for identification of potential false negative hits, exemplified by the finding of gene-metabolite associations with SRD ≤1 that did not reach standard genome-wide significance cut-off. The wider use of this statistic as an mGWAS annotation would prevent the exclusion of biologically relevant associations and can also identify errors or gaps in current metabolic pathway databases. Our findings highlight the SRD metric as an objective, quantitative and easy-to-compute annotation for gene-metabolite pairs that can be used to integrate statistical evidence to biological networks.

2023-11-30

iScience (published)

doi.org

Growth of TiO2 single crystals by the Verneuil method at different gas flow ratio

Xudong Liu

Hanshu Ma

Wei Wang

Yongqi Hu

Xudong Sun

2023-11-30

Journal of Crystal Growth (published)

doi.org

Large language models: What could they do for neurology?

Guillaume Lajoie

2023-11-30

Journal of the Neurological Sciences (published)

doi.org

A large-scale exploratory study of android sports apps in the google play store

Bhagya Chembakottu

Heng Li

Foutse Khomh

2023-11-30

Information and Software Technology (published)

doi.org

arxiv.org

Measuring the Knowledge Acquisition-Utilization Gap in Pretrained Language Models

Amirhossein Kazemnejad

Mehdi Rezagholizadeh

Prasanna Parthasarathi

Sarath Chandar

While pre-trained language models (PLMs) have shown evidence of acquiring vast amounts of knowledge, it remains unclear how much of this par… (see more)ametric knowledge is actually usable in performing downstream tasks. We propose a systematic framework to measure parametric knowledge utilization in PLMs. Our framework first extracts knowledge from a PLM's parameters and subsequently constructs a downstream task around this extracted knowledge. Performance on this task thus depends exclusively on utilizing the model's possessed knowledge, avoiding confounding factors like insufficient signal. As an instantiation, we study factual knowledge of PLMs and measure utilization across 125M to 13B parameter PLMs. We observe that: (1) PLMs exhibit two gaps - in acquired vs. utilized knowledge, (2) they show limited robustness in utilizing knowledge under distribution shifts, and (3) larger models close the acquired knowledge gap but the utilized knowledge gap remains. Overall, our study provides insights into PLMs' capabilities beyond their acquired knowledge.

2023-11-30

Findings of the Association for Computational Linguistics: EMNLP 2023 (published)

doi.org

openreview.net

Predictive inference for travel time on transportation networks

Mohamad Elmasri

Aurélie Labbe

Denis Larocque

Laurent Charlin

2023-11-30

The Annals of Applied Statistics (published)

doi.org

arxiv.org

Qualitative Code Suggestion: A Human-Centric Approach To Qualitative Coding

Cesare Spinoso-Di Piano

Samira Abbasgholizadeh Rahimi

Jackie Chi Kit Cheung

Qualitative coding is a content analysis method in which researchers read through a text corpus and assign descriptive labels or qualitative… (see more) codes to passages. It is an arduous and manual process which human-computer interaction (HCI) studies have shown could greatly benefit from NLP techniques to assist qualitative coders. Yet, previous attempts at leveraging language technologies have set up qualitative coding as a fully automatable classification problem. In this work, we take a more assistive approach by defining the task of qualitative code suggestion (QCS) in which a ranked list of previously assigned qualitative codes is suggested from an identified passage. In addition to being user-motivated, QCS integrates previously ignored properties of qualitative coding such as the sequence in which passages are annotated, the importance of rare codes and the differences in annotation styles between coders. We investigate the QCS task by releasing the first publicly available qualitative coding dataset, CVDQuoding, consisting of interviews conducted with women at risk of cardiovascular disease. In addition, we conduct a human evaluation which shows that our systems consistently make relevant code suggestions.

2023-11-30

Findings of the Association for Computational Linguistics: EMNLP 2023 (published)

doi.org

openreview.net

Rainproof: An Umbrella To Shield Text Generators From Out-Of-Distribution Data

Maxime Darrin

Pablo Piantanida

Pierre Colombo

Implementing effective control mechanisms to ensure the proper functioning and security of deployed NLP models, from translation to chatbots… (see more), is essential. A key ingredient to ensure safe system behaviour is Out-Of-Distribution (OOD) detection, which aims to detect whether an input sample is statistically far from the training distribution. Although OOD detection is a widely covered topic in classification tasks, most methods rely on hidden features output by the encoder. In this work, we focus on leveraging soft-probabilities in a black-box framework, i.e. we can access the soft-predictions but not the internal states of the model. Our contributions include: (i) RAINPROOF a Relative informAItioN Projection OOD detection framework; and (ii) a more operational evaluation setting for OOD detection. Surprisingly, we find that OOD detection is not necessarily aligned with task-specific measures. The OOD detector may filter out samples well processed by the model and keep samples that are not, leading to weaker performance. Our results show that RAINPROOF provides OOD detection methods more aligned with task-specific performance metrics than traditional OOD detectors.

2023-11-30

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (published)

doi.org

arxiv.org

Shape-Based Measures Improve Scene Categorization

Morteza Rezanejad

John Wilder

Dirk B. Walther

Allan D. Jepson

Sven Dickinson

Kaleem Siddiqi

Converging evidence indicates that deep neural network models that are trained on large datasets are biased toward color and texture informa… (see more)tion. Humans, on the other hand, can easily recognize objects and scenes from images as well as from bounding contours. Mid-level vision is characterized by the recombination and organization of simple primary features into more complex ones by a set of so-called Gestalt grouping rules. While described qualitatively in the human literature, a computational implementation of these perceptual grouping rules is so far missing. In this article, we contribute a novel set of algorithms for the detection of contour-based cues in complex scenes. We use the medial axis transform (MAT) to locally score contours according to these grouping rules. We demonstrate the benefit of these cues for scene categorization in two ways: (i) Both human observers and CNN models categorize scenes most accurately when perceptual grouping information is emphasized. (ii) Weighting the contours with these measures boosts performance of a CNN model significantly compared to the use of unweighted contours. Our work suggests that, even though these measures are computed directly from contours in the image, current CNN models do not appear to extract or utilize these grouping cues.

2023-11-30

IEEE Transactions on Pattern Analysis and Machine Intelligence (published)

doi.org

Spectral Temporal Contrastive Learning

Sacha Morin

Somjit Nath

S Ebrahimi Kahou

Guy Wolf

Learning useful data representations without requiring labels is a cornerstone of modern deep learning. Self-supervised learning methods, pa… (see more)rticularly contrastive learning (CL), have proven successful by leveraging data augmentations to define positive pairs. This success has prompted a number of theoretical studies to better understand CL and investigate theoretical bounds for downstream linear probing tasks. This work is concerned with the temporal contrastive learning (TCL) setting where the sequential structure of the data is used instead to define positive pairs, which is more commonly used in RL and robotics contexts. In this paper, we adapt recent work on Spectral CL to formulate Spectral Temporal Contrastive Learning (STCL). We discuss a population loss based on a state graph derived from a time-homogeneous reversible Markov chain with uniform stationary distribution. The STCL loss enables to connect the linear probing performance to the spectral properties of the graph, and can be estimated by considering previously observed data sequences as an ensemble of MCMC chains.

2023-11-30

ArXiv (preprint)

doi.org

arxiv.org

SWEET: Weakly Supervised Person Name Extraction for Fighting Human Trafficking

Javin Liu

Hao Yu

2023-11-30