Publications

Graph embedding and transfer learning can help predict potential species interaction networks despite data limitations

Tanya Strydom

Salomé Bouskila

Francis Banville

Ceres Barros

Dominique Caron

Maxwell J. Farrell

Marie‐Josée Fortin

Benjamin Mercier

Laura J. Pollock

Rogini Runghen

Giulio V. Dalla Riva

Timothée Poisot

Metawebs (networks of potential interactions within a species pool) are a powerful abstraction to understand how large‐scale species inter… (voir plus)action networks are structured. Because metawebs are typically expressed at large spatial and taxonomic scales, assembling them is a tedious and costly process; predictive methods can help circumvent the limitations in data deficiencies, by providing a first approximation of metawebs. One way to improve our ability to predict metawebs is to maximize available information by using graph embeddings, as opposed to an exhaustive list of species interactions. Graph embedding is an emerging field in machine learning that holds great potential for ecological problems. Here, we outline how the challenges associated with inferring metawebs line‐up with the advantages of graph embeddings; followed by a discussion as to how the choice of the species pool has consequences on the reconstructed network, specifically as to the role of human‐made (or arbitrarily assigned) boundaries and how these may influence ecological hypotheses.

2023-10-19

Methods in Ecology and Evolution (publié)

doi.org

Mean-field games among teams

Jayakumar Subramanian

Akshat Kumar

Aditya Mahajan

2023-10-18

ArXiv (prépublication)

doi.org

arxiv.org

Open-Set Multivariate Time-Series Anomaly Detection

Thomas Lai

Thi Kieu Khanh Ho

Narges Armanfard

2023-10-18

ArXiv (prépublication)

doi.org

arxiv.org

A Persuasive Approach to Combating Misinformation

Safwan Hossain

Andjela Mladenovic

Yiling Chen

Gauthier Gidel

Bayesian Persuasion is proposed as a tool for social media platforms to combat the spread of misinformation. Since platforms can use machine… (voir plus) learning to predict the popularity and misinformation features of to-be-shared posts, and users are largely motivated to share popular content, platforms can strategically signal this informational advantage to change user beliefs and persuade them not to share misinformation. We characterize the optimal signaling scheme with imperfect predictions as a linear program and give sufficient and necessary conditions on the classifier to ensure optimal platform utility is non-decreasing and continuous. Next, this interaction is considered under a performative model, wherein platform intervention affects the user's future behaviour. The convergence and stability of optimal signaling under this performative process are fully characterized. Lastly, we experimentally validate that our approach significantly reduces misinformation in both the single round and performative setting and discuss the broader scope of using information design to combat misinformation.

2023-10-18

ArXiv (prépublication)

doi.org

arxiv.org

Studying the characteristics of AIOps projects on GitHub

Roozbeh Aghili

Heng Li

Foutse Khomh

2023-10-18

Empirical Software Engineering (publié)

doi.org

arxiv.org

Interpreting and Controlling Vision Foundation Models via Text Explanations

Haozhe Chen

Junfeng Yang

Carl Vondrick

Chengzhi Mao

2023-10-16

ArXiv (prépublication)

doi.org

arxiv.org

Transparent Anomaly Detection via Concept-based Explanations

Laya Rafiee Sevyeri

Ivaxi Sheth

Farhood Farahnak

Shirin A. Enger

2023-10-16

ArXiv (prépublication)

doi.org

arxiv.org

RelationalUNet for Image Segmentation

Ivaxi Sheth

Pedro H. M. Braga

Shiva Kanth Sujit

Sahar Dastani

Samira Ebrahimi Kahou

2023-10-15

Machine Learning in Medical Imaging (publié)

doi.org

Defining Feasibility as a Criterion for Essential Surgery: A Qualitative Study with Global Children’s Surgery Experts

Alizeh Abbas

Henry E. Rice

Dan Poenaru

Lubna Samad

2023-10-14

World Journal of Surgery (publié)

doi.org

A community effort in SARS-CoV-2 drug discovery.

Johannes Schimunek

Philipp Seidl

Katarina Elez

Tim Hempel

Tuan Le

Frank Noé

Simon Olsson

Lluís Raich

Robin Winter

Hatice Gokcan

Filipp Gusev

Evgeny M. Gutkin

Olexandr Isayev

Maria G. Kurnikova

Chamali H. Narangoda

Roman Zubatyuk

Ivan P. Bosko

Konstantin V. Furs

Anna D. Karpenko

Yury V. Kornoushenko … (voir 133 de plus)

Mikita Shuldau

Artsemi Yushkevich

Mohammed B. Benabderrahmane

Patrick Bousquet‐Melou

Ronan Bureau

Beatrice Charton

Bertrand C. Cirou

Gérard Gil

William J. Allen

Suman Sirimulla

Stanley Watowich

Nick Antonopoulos

Nikolaos Epitropakis

Agamemnon Krasoulis

Vassilis Pitsikalis

Stavros Theodorakis

Igor Kozlovskii

Anton Maliutin

Alexander Medvedev

Petr Popov

Mark Zaretckii

Hamid Eghbal‐Zadeh

Christina Halmich

Sepp Hochreiter

Andreas Mayr

Peter Ruch

Michael Widrich

Francois Berenger

Ashutosh Kumar

Yoshihiro Yamanishi

Kam Y. J. Zhang

Emmanuel Bengio

Yoshua Bengio

Moksh J. Jain

Maksym Korablyov

Cheng-Hao Liu

Gilles Marcou

Marcous Gilles

Enrico Glaab

Kelly Barnsley

Suhasini M. Iyengar

Mary Jo Ondrechen

V. Joachim Haupt

Florian Kaiser

Michael Schroeder

Luisa Pugliese

Simone Albani

Christina Athanasiou

Andrea Beccari

Paolo Carloni

Giulia D'Arrigo

Eleonora Gianquinto

Jonas Goßen

Anton Hanke

Benjamin P. Joseph

Daria B. Kokh

Sandra Kovachka

Candida Manelfi

Goutam Mukherjee

Abraham Muñiz‐Chicharro

Francesco Musiani

Ariane Nunes‐Alves

Giulia Paiardi

Giulia Rossetti

S. Kashif Sadiq

Francesca Spyrakis

Carmine Talarico

Alexandros Tsengenes

Rebecca C. Wade

Conner Copeland

Jeremiah Gaiser

Daniel R. Olson

Amitava Roy

Vishwesh Venkatraman

Travis J. Wheeler

Haribabu Arthanari

Klara Blaschitz

Marco Cespugli

Vedat Durmaz

Konstantin Fackeldey

Patrick D. Fischer

Christoph Gorgulla

Christian Gruber

Karl Gruber

Michael Hetmann

Jamie E. Kinney

Krishna M. Padmanabha Das

Shreya Pandita

Amit Singh

Georg Steinkellner

Guilhem Tesseyre

Gerhard Wagner

Zi‐Fu Wang

Ryan J. Yust

Dmitry S. Druzhilovskiy

Dmitry A. Filimonov

Pavel V. Pogodin

Vladimir Poroikov

Anastassia V. Rudik

Leonid A. Stolbov

Alexander V. Veselovsky

Maria De Rosa

Giada De Simone

Maria R. Gulotta

Jessica Lombino

Nedra Mekni

Ugo Perricone

Arturo Casini

Amanda Embree

D. Benjamin Gordon

David Lei

Katelin Pratt

Christopher A. Voigt

Kuang‐Yu Chen

Yves Jacob

Tim Krischuns

Pierre Lafaye

Agnès Zettor

M. Luis Rodríguez

Kris M. White

Daren Fearon

Frank Von Delft

Martin A. Walsh

Dragos Horvath

Charles L. Brooks

Babak Falsafi

Bryan Ford

Adolfo García‐Sastre

Sang Yup Lee

Nadia Naffakh

Alexandre Varnek

Günter Klambauer

Thomas M. Hermans

The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availabili… (voir plus)ty of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against Covid-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.

2023-10-13

Molecular Informatics (publié)

doi.org

Jointly-Learned Exit and Inference for a Dynamic Neural Network : JEI-DNN

Florence Regol

Joud Chataoui

Mark Coates

2023-10-13

ArXiv (prépublication)

doi.org

arxiv.org

Posterior Sampling of the Initial Conditions of the Universe from Non-linear Large Scale Structures using Score-Based Generative Models