Publications

Origin of Nonlinear Circular Photocurrent in 2D Semiconductor MoS_{2}.
Yanchong Zhao
Fengyu Chen
Jing Liang
Mohammad Saeed Bahramy
Mingwei Yang
Yao Guang
Xiaomei Li
Zheng Wei
Jian Tang
Jiaojiao Zhao
Mengzhou Liao
Cheng Shen
Qinqin Wang
Rong Yang
Kenji Watanabe
Takashi Taniguchi
Zhiheng Huang
Dongxia Shi
Kaihui Liu
Zhipei Sun … (voir 3 de plus)
Ji Feng
Luojun Du
Guangyu Zhang
2025-02-25
Physical Review Letters (publié)
doi.org
Recommendations and guidelines from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 1 -- In vivo small-animal imaging
Ileana O. Jelescu
Francesco Grussu
Andrada Ianus
Brian Hansen
Rachel L. C. Barrett
Manisha Aggarwal
Stijn Michielse
Fatima Nasrallah
Warda Syeda
Nian Wang
Jelle Veraart
Alard Roebroeck
Andrew F. Bagdasarian
Cornelius Eichner
Farshid Sepehrband
Jan Zimmermann
Lucas Soustelle
Christien Bowman
Benjamin C. Tendler
Andreea Hertanu … (voir 37 de plus)
Ben Jeurissen
Marleen Verhoye
Lucio Frydman
Yohan van de Looij
David Hike
Jeff F. Dunn
Karla Miller
Bennett A. Landman
Noam Shemesh
Adam Anderson
Emilie McKinnon
Shawna Farquharson
Flavio Dell'Acqua
Carlo Pierpaoli
Ivana Drobnjak
Alexander Leemans
Kevin D. Harkins
Maxime Descoteaux
Duan Xu
Hao Huang
Mathieu D. Santin
Samuel C. Grant
Andre Obenaus
Gene S. Kim
Dan Wu
Denis Le Bihan
Stephen J. Blackband
Luisa Ciobanu
Els Fieremans
Ruiliang Bai
Trygve B. Leergaard
Jiangyang Zhang
Tim B. Dyrby
G. Allan Johnson
Julien Cohen‐Adad
Matthew D. Budde
Kurt G. Schilling
The value of in vivo preclinical diffusion MRI (dMRI) is substantial. Small-animal dMRI has been used for methodological development and val… (voir plus)idation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. Many of the influential works in this field were first performed in small animals or ex vivo samples. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the data. This work aims to serve as a reference, presenting selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. In each section, we also highlight areas for which no guidelines exist (and why), and where future work should focus. We first describe the value that small animal imaging adds to the field of dMRI, followed by general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss how they are appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, imaging sequences and data processing, including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.
2025-02-25
Magnetic Resonance in Medicine (publié)
doi.org
arxiv.org
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
Isolating the impact of tissue heterogeneities in high dose rate brachytherapy treatment of the breast
Jules Faucher
Vincent Turgeon
Boris Bahoric
S. Enger
Peter G.F. Watson
2025-02-21
Physics and Imaging in Radiation Oncology (publié)
doi.org
HyperFusion: A Hypernetwork Approach to Multimodal Integration of Tabular and Medical Imaging Data for Predictive Modeling
Daniel Duenias
Brennan Nichyporuk
Tal Arbel
Tammy Riklin Raviv
The integration of diverse clinical modalities such as medical imaging and the tabular data extracted from patients' Electronic Health Recor… (voir plus)ds (EHRs) is a crucial aspect of modern healthcare. Integrative analysis of multiple sources can provide a comprehensive understanding of the clinical condition of a patient, improving diagnosis and treatment decision. Deep Neural Networks (DNNs) consistently demonstrate outstanding performance in a wide range of multimodal tasks in the medical domain. However, the complex endeavor of effectively merging medical imaging with clinical, demographic and genetic information represented as numerical tabular data remains a highly active and ongoing research pursuit. We present a novel framework based on hypernetworks to fuse clinical imaging and tabular data by conditioning the image processing on the EHR's values and measurements. This approach aims to leverage the complementary information present in these modalities to enhance the accuracy of various medical applications. We demonstrate the strength and generality of our method on two different brain Magnetic Resonance Imaging (MRI) analysis tasks, namely, brain age prediction conditioned by subject's sex and multi-class Alzheimer's Disease (AD) classification conditioned by tabular data. We show that our framework outperforms both single-modality models and state-of-the-art MRI tabular data fusion methods. A link to our code can be found at https://github.com/daniel4725/HyperFusion
2025-02-20
Medical Image Analysis (inconnu)
doi.org
arxiv.org
PairBench: Are Vision-Language Models Reliable at Comparing What They See?
Aarash Feizi
Sai Rajeswar
Adriana Romero
Reihaneh Rabbany
Spandana Gella
Valentina Zantedeschi
Joao Monteiro
Understanding how effectively large vision language models (VLMs) compare visual inputs is crucial across numerous applications, yet this fu… (voir plus)ndamental capability remains insufficiently assessed. While VLMs are increasingly deployed for tasks requiring comparative judgment, including automated evaluation, re-ranking, and retrieval-augmented generation, no systematic framework exists to measure their performance in these scenarios. We present PairBench, a simple framework that evaluates VLMs as customizable similarity tools using widely available image datasets. Our approach introduces four key metrics for reliable comparison: alignment with human annotations, consistency across pair ordering, distribution smoothness, and controllability through prompting. Our analysis reveals that no model consistently excels across all metrics, with each demonstrating distinct strengths and weaknesses. Most concerning is the widespread inability of VLMs to maintain symmetric similarity scores. Interestingly, we demonstrate that performance on our benchmark strongly correlates with popular benchmarks used for more complex tasks, while providing additional metrics into controllability, smoothness and ordering. This makes PairBench a unique and comprehensive framework to evaluate the performance of VLMs for automatic evaluation depending on the task.
2025-02-20
ArXiv (prépublication)
doi.org
arxiv.org
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
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