Publications

A comparison of RL-based and PID controllers for 6-DOF swimming robots: hybrid underwater object tracking
Faraz Lotfi
Khalil Virji
Nicholas Dudek
Gregory Dudek
2024-01-28
ArXiv (prépublication)
doi.org
arxiv.org
Protocol for fever control using external cooling in mechanically ventilated patients with septic shock: SEPSISCOOL II randomised controlled trial
Armelle Guénégou-Arnoux
Juliette Murris
Stéphane Bechet
Camille Jung
Johann Auchabie
Julien Dupeyrat
Nadia Anguel
Pierre Asfar
Julio Badie
Dorothée Carpentier
Benjamin Chousterman
Jeremy Bourenne
Agathe Delbove
Jérôme Devaquet
Nicolas Deye
Guillaume Dumas
Anne-Florence Dureau
Jean-Baptiste Lascarrou
Stephane Legriel
Christophe Guitton … (voir 14 de plus)
Caroline Jannière-Nartey
Jean-Pierre Quenot
Jean-Claude Lacherade
Julien Maizel
Armand Mekontso Dessap
Bruno Mourvillier
Philippe Petua
Gaetan Plantefeve
Jean-Christophe Richard
Alexandre Robert
Clément Saccheri
Ly Van Phach Vong
Sandrine Katsahian
Frédérique Schortgen
2024-01-28
BMJ Open (publié)
doi.org
Trait‐matching models predict pairwise interactions across regions, not food web properties
Dominique Caron
Ulrich Brose
Miguel Lurgi
F. Guillaume Blanchet
Dominique Gravel
Laura J. Pollock
2024-01-28
Global Ecology and Biogeography (publié)
doi.org
Efficient Data-Driven MPC for Demand Response of Commercial Buildings
Marie-Christine Par'e
Vasken Dermardiros
Antoine Lesage-Landry
Model predictive control (MPC) has been shown to significantly improve the energy efficiency of buildings while maintaining thermal comfort.… (voir plus) Data-driven approaches based on neural networks have been proposed to facilitate system modelling. However, such approaches are generally nonconvex and result in computationally intractable optimization problems. In this work, we design a readily implementable energy management method for small commercial buildings. We then leverage our approach to formulate a real-time demand bidding strategy. We propose a data-driven and mixed-integer convex MPC which is solved via derivative-free optimization given a limited computational time of 5 minutes to respect operational constraints. We consider rooftop unit heating, ventilation, and air conditioning systems with discrete controls to accurately model the operation of most commercial buildings. Our approach uses an input convex recurrent neural network to model the thermal dynamics. We apply our approach in several demand response (DR) settings, including a demand bidding, a time-of-use, and a critical peak rebate program. Controller performance is evaluated on a state-of-the-art building simulation. The proposed approach improves thermal comfort while reducing energy consumption and cost through DR participation, when compared to other data-driven approaches or a set-point controller.
2024-01-27
ArXiv (prépublication)
doi.org
arxiv.org
Graphylo: A deep learning approach for predicting regulatory DNA and RNA sites from whole-genome multiple alignments
Dongjoon Lim
Changhyun Baek
Mathieu Blanchette
2024-01-25
iScience (publié)
doi.org
Human-Centered AI
Catherine Régis
Jean-Louis Denis
Maria Luciana Axente
Atsuo Kishimoto
2024-01-24
(publié)
doi.org
Neighbor-Aware Calibration of Segmentation Networks with Penalty-Based Constraints
Balamurali Murugesan
Sukesh Adiga Vasudeva
Bingyuan Liu
Hervé Lombaert
Ismail Ben Ayed
Jose Dolz
Ensuring reliable confidence scores from deep neural networks is of paramount significance in critical decision-making systems, particularly… (voir plus) in real-world domains such as healthcare. Recent literature on calibrating deep segmentation networks has resulted in substantial progress. Nevertheless, these approaches are strongly inspired by the advancements in classification tasks, and thus their uncertainty is usually modeled by leveraging the information of individual pixels, disregarding the local structure of the object of interest. Indeed, only the recent Spatially Varying Label Smoothing (SVLS) approach considers pixel spatial relationships across classes, by softening the pixel label assignments with a discrete spatial Gaussian kernel. In this work, we first present a constrained optimization perspective of SVLS and demonstrate that it enforces an implicit constraint on soft class proportions of surrounding pixels. Furthermore, our analysis shows that SVLS lacks a mechanism to balance the contribution of the constraint with the primary objective, potentially hindering the optimization process. Based on these observations, we propose NACL (Neighbor Aware CaLibration), a principled and simple solution based on equality constraints on the logit values, which enables to control explicitly both the enforced constraint and the weight of the penalty, offering more flexibility. Comprehensive experiments on a wide variety of well-known segmentation benchmarks demonstrate the superior calibration performance of the proposed approach, without affecting its discriminative power. Furthermore, ablation studies empirically show the model agnostic nature of our approach, which can be used to train a wide span of deep segmentation networks.
2024-01-24
ArXiv (prépublication)
doi.org
arxiv.org
Marc Bellemare
Bellemare Marc-Emmanuel
2024-01-23
American Journal of Agricultural Economics (publié)
doi.org
Beyond Predictive Algorithms in Child Welfare
Erina Seh-Young Moon
Erin Moon
Devansh Saxena
Tegan Maharaj
Shion Guha
2024-01-22
graphicsinterface.org/Graphics_Interface/2024/Conference (publié)
doi.org
openreview.net
Connectome-based reservoir computing with the conn2res toolbox
Laura E. Suárez
Agoston Mihalik
Filip Milisav
Kenji Marshall
Mingze Li
Petra E. Vértes
Guillaume Lajoie
Bratislav Misic
Brain connectivity patterns shape computational capacity of biological neural networks, however mapping empirically measured connectivity to… (voir plus) artificial networks remains challenging. The authors present a toolbox for implementing biological neural networks as artificial reservoir networks. The toolbox allows for a variety of empirical/measured connectomes and is equipped with various dynamical systems, and cognitive tasks. The connection patterns of neural circuits form a complex network. How signaling in these circuits manifests as complex cognition and adaptive behaviour remains the central question in neuroscience. Concomitant advances in connectomics and artificial intelligence open fundamentally new opportunities to understand how connection patterns shape computational capacity in biological brain networks. Reservoir computing is a versatile paradigm that uses high-dimensional, nonlinear dynamical systems to perform computations and approximate cognitive functions. Here we present conn2res : an open-source Python toolbox for implementing biological neural networks as artificial neural networks. conn2res is modular, allowing arbitrary network architecture and dynamics to be imposed. The toolbox allows researchers to input connectomes reconstructed using multiple techniques, from tract tracing to noninvasive diffusion imaging, and to impose multiple dynamical systems, from spiking neurons to memristive dynamics. The versatility of the conn2res toolbox allows us to ask new questions at the confluence of neuroscience and artificial intelligence. By reconceptualizing function as computation, conn2res sets the stage for a more mechanistic understanding of structure-function relationships in brain networks.
2024-01-21
Nature Communications (publié)
doi.org
RapidBrachyTG43: A Geant4‐based TG‐43 parameter and dose calculation module for brachytherapy dosimetry
Jonathan Kalinowski
S. Enger
2024-01-21
Medical Physics (publié)
doi.org
Transnational conservation to anticipate future plant shifts in Europe
Yohann Chauvier-Mendes
Laura J. Pollock
Peter H. Verburg
Dirk N. Karger
Loïc Pellissier
Sébastien Lavergne
Niklaus E. Zimmermann
Wilfried Thuiller
2024-01-21
Nature Ecology & Evolution (publié)
doi.org