Publications

The use of extended reality in anesthesiology education: a scoping review
Gianluca Bertolizio
Yu Tong Huang
Marta Garbin
Elena Guadagno
Dan Poenaru
2025-02-25
Canadian Journal of Anaesthesia-journal Canadien D Anesthesie (publié)
doi.org
Learning Multi-agent Multi-machine Tending by Mobile Robots
Abdalwhab Abdalwhab
Giovanni Beltrame
S Ebrahimi Kahou
David St-Onge
Robotics can help address the growing worker shortage challenge of the manufacturing industry. As such, machine tending is a task collaborat… (voir plus)ive robots can tackle that can also highly boost productivity. Nevertheless, existing robotics systems deployed in that sector rely on a fixed single-arm setup, whereas mobile robots can provide more flexibility and scalability. In this work, we introduce a multi-agent multi-machine tending learning framework by mobile robots based on Multi-agent Reinforcement Learning (MARL) techniques with the design of a suitable observation and reward. Moreover, an attention-based encoding mechanism is developed and integrated into Multi-agent Proximal Policy Optimization (MAPPO) algorithm to boost its performance for machine tending scenarios. Our model (AB-MAPPO) outperformed MAPPO in this new challenging scenario in terms of task success, safety, and resources utilization. Furthermore, we provided an extensive ablation study to support our various design decisions.
2025-02-24
AAAI.org/2025/Workshop/MARW (publié)
doi.org
openreview.net
The In-Situ Effect of Offensive Ads on Search Engine Users
Elad Yom-Tov
Liat Levontin
A.R. Olteanu
2025-02-24
ACM Transactions on Information Systems (publié)
doi.org
On the Dichotomy Between Privacy and Traceability in $\ell_p$ Stochastic Convex Optimization
Sasha Voitovych
MAHDI HAGHIFAM
Idan Attias
Gintare Karolina Dziugaite
Roi Livni
Daniel M. Roy
In this paper, we investigate the necessity of memorization in stochastic convex optimization (SCO) under …
2025-02-23
ArXiv (prépublication)
doi.org
arxiv.org
On Traceability in $\ell_p$ Stochastic Convex Optimization
Sasha Voitovych
MAHDI HAGHIFAM
Idan Attias
Gintare Karolina Dziugaite
Roi Livni
Daniel M. Roy
In this paper, we investigate the necessity of traceability for accurate learning in stochastic convex optimization (SCO) under …
2025-02-23
ArXiv (prépublication)
doi.org
openreview.net
Harnessing artificial intelligence to fill global shortfalls in biodiversity knowledge
Laura J. Pollock
Justin Kitzes
Sara Beery
Kaitlyn M. Gaynor
Marta A. Jarzyna
Oisin Mac Aodha
Bernd Meyer
David Rolnick
Graham W. Taylor
Devis Tuia
Tanya Berger-Wolf
2025-02-19
Nature Reviews Biodiversity (publié)
doi.org
A neural implementation model of feedback-based motor learning
Barbara Feulner
Matthew G. Perich
Lee E. Miller
Claudia Clopath
Juan A. Gallego
Animals use feedback to rapidly correct ongoing movements in the presence of a perturbation. Repeated exposure to a predictable perturbation… (voir plus) leads to behavioural adaptation that compensates for its effects. Here, we tested the hypothesis that all the processes necessary for motor adaptation may emerge as properties of a controller that adaptively updates its policy. We trained a recurrent neural network to control its own output through an error-based feedback signal, which allowed it to rapidly counteract external perturbations. Implementing a biologically plausible plasticity rule based on this same feedback signal enabled the network to learn to compensate for persistent perturbations through a trial-by-trial process. The network activity changes during learning matched those from populations of neurons from monkey primary motor cortex — known to mediate both movement correction and motor adaptation — during the same task. Furthermore, our model natively reproduced several key aspects of behavioural studies in humans and monkeys. Thus, key features of trial-by-trial motor adaptation can arise from the internal properties of a recurrent neural circuit that adaptively controls its output based on ongoing feedback.
2025-02-19
Nature Communications (publié)
doi.org
OBELiX: a curated dataset of crystal structures and experimentally measured ionic conductivities for lithium solid-state electrolytes
Félix Therrien
Jamal Abou Haibeh
Divya Sharma
Rhiannon Hendley
Leah Wairimu Mungai
Alex Hernandez-Garcia
Sun Sun
Alain Tchagang
Jiang Su
Samuel Huberman
Yoshua Bengio
Hongyu Guo
Homin Shin
OBELiX is a database of 599 synthesized solid electrolyte materials and their experimentally measured room temperature ionic conductivities … (voir plus)gathered from literature and curated by domain experts.
2025-02-19
arXiv (prépublication)
doi.org
openreview.net
AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Shaona Ghosh
Heather Frase
Adina Williams
Sarah Luger
Paul Rottger
Fazl Barez
Sean McGregor
Kenneth Fricklas
Mala Kumar
Quentin Feuillade--Montixi
Kurt Bollacker
Felix Friedrich
Ryan Tsang
Bertie Vidgen
Alicia Parrish
Chris Knotz
Eleonora Presani
Jonathan Bennion
Marisa Ferrara Boston
Mike Kuniavsky … (voir 81 de plus)
Wiebke Hutiri
James Ezick
Malek Ben Salem
Rajat Sahay
Sujata Goswami
Usman Gohar
Ben Huang
Supheakmungkol Sarin
Elie Alhajjar
Canyu Chen
Roman Eng
K. Manjusha
Virendra Mehta
Eileen Peters Long
Murali Krishna Emani
Natan Vidra
Benjamin Rukundo
Abolfazl Shahbazi
Kongtao Chen
Rajat Ghosh
Vithursan Thangarasa
Pierre Peign'e
Abhinav Singh
Max Bartolo
Satyapriya Krishna
Mubashara Akhtar
Rafael Gold
Cody Coleman
Luis Oala
Vassil Tashev
Joseph Marvin Imperial
Amy Russ
Sasidhar Kunapuli
Nicolas Miailhe
Julien Delaunay
Bhaktipriya Radharapu
Rajat Shinde
Tuesday
Debojyoti Dutta
Declan Grabb
Ananya Gangavarapu
Saurav Sahay
Agasthya Gangavarapu
Patrick Schramowski
Stephen Singam
Tom David
Xudong Han
Priyanka Mary Mammen
Tarunima Prabhakar
Venelin Kovatchev
Ahmed M. Ahmed
Kelvin Manyeki
Sandeep Madireddy
Foutse Khomh
Fedor Zhdanov
Joachim Baumann
N. Vasan
Xianjun Yang
Carlos Mougn
Jibin Rajan Varghese
Hussain Chinoy
Seshakrishna Jitendar
Manil Maskey
Claire V. Hardgrove
Tianhao Li
Aakash Gupta
Emil Joswin
Yifan Mai
Shachi H. Kumar
Çigdem Patlak
Kevin Lu
Vincent Alessi
Sree Bhargavi Balija
Chenhe Gu
Robert Sullivan
James Gealy
Matt Lavrisa
James Goel
Peter Mattson
Percy Liang
Joaquin Vanschoren
2025-02-18
ArXiv (prépublication)
doi.org
arxiv.org
Cell type transcriptomics reveal shared genetic mechanisms in Alzheimer’s and Parkinson’s disease
Anwesha Bhattacharya
Edward A. Fon
Alain Dagher
Yasser Iturria-Medina
Jo Anne Stratton
Chloé Savignac
Jack Stanley
Liam Hodgson
Badr Ait Hammou
David A Bennett
Danilo Bzdok
Historically, Alzheimer’s disease (AD) and Parkinson’s disease (PD) have been investigated as two distinct disorders of the brain. Howev… (voir plus)er, a few similarities in neuropathology and clinical symptoms have been documented over the years. Traditional single gene-centric genetic studies, including GWAS and differential gene expression analyses, have struggled to unravel the molecular links between AD and PD. To address this, we tailor a pattern-learning framework to analyze synchronous gene co-expression at sub-cell-type resolution. Utilizing recently published single-nucleus AD (70,634 nuclei) and PD (340,902 nuclei) datasets from postmortem human brains, we systematically extract and juxtapose disease-critical gene modules. Our findings reveal extensive molecular similarities between AD and PD gene cliques. In neurons, disrupted cytoskeletal dynamics and mitochondrial stress highlight convergence in key processes; glial modules share roles in T-cell activation, myelin synthesis, and synapse pruning. This multi-module sub-cell-type approach offers insights into the molecular basis of shared neuropathology in AD and PD.
2025-02-18
bioRxiv (prépublication)
doi.org
Cell type transcriptomics reveal shared genetic mechanisms in Alzheimer’s and Parkinson’s disease
Anwesha Bhattacharya
Edward A. Fon
Alain Dagher
Yasser Iturria-Medina
Jo Anne Stratton
Chloé Savignac
Jack Stanley
Liam Hodgson
Badr Ait Hammou
David A Bennett
Danilo Bzdok
Historically, Alzheimer’s disease (AD) and Parkinson’s disease (PD) have been investigated as two distinct disorders of the brain. Howev… (voir plus)er, a few similarities in neuropathology and clinical symptoms have been documented over the years. Traditional single gene-centric genetic studies, including GWAS and differential gene expression analyses, have struggled to unravel the molecular links between AD and PD. To address this, we tailor a pattern-learning framework to analyze synchronous gene co-expression at sub-cell-type resolution. Utilizing recently published single-nucleus AD (70,634 nuclei) and PD (340,902 nuclei) datasets from postmortem human brains, we systematically extract and juxtapose disease-critical gene modules. Our findings reveal extensive molecular similarities between AD and PD gene cliques. In neurons, disrupted cytoskeletal dynamics and mitochondrial stress highlight convergence in key processes; glial modules share roles in T-cell activation, myelin synthesis, and synapse pruning. This multi-module sub-cell-type approach offers insights into the molecular basis of shared neuropathology in AD and PD.
2025-02-18
bioRxiv : the preprint server for biology (publié)
doi.org
Making the Write Connections: Linking Writing Support Tools with Writer Needs
Zixin Zhao
Damien Masson
Young-Ho Kim
Gerald Penn
Fanny Chevalier
2025-02-17
ArXiv (prépublication)
doi.org
arxiv.org