Publications

Adaptive Inference-Time Scaling via Cyclic Diffusion Search
Gyubin Lee
Truong Nhat Nguyen Bao
Jaesik Yoon
Dongwoo Lee
Diffusion models have demonstrated strong generative capabilities across domains ranging from image synthesis to complex reasoning tasks. Ho… (voir plus)wever, most inference-time scaling methods rely on fixed denoising schedules, limiting their ability to allocate computation based on instance difficulty or task-specific demands adaptively. We introduce the challenge of adaptive inference-time scaling-dynamically adjusting computational effort during inference-and propose Adaptive Bi-directional Cyclic Diffusion (ABCD), a flexible, search-based inference framework. ABCD refines outputs through bi-directional diffusion cycles while adaptively controlling exploration depth and termination. It comprises three components: Cyclic Diffusion Search, Automatic Exploration-Exploitation Balancing, and Adaptive Thinking Time. Experiments show that ABCD improves performance across diverse tasks while maintaining computational efficiency.
Cosmic Ray Muon Polarization to Facilitate Atmospheric Neutrino Physics
Mingchen Sun
Shihan Zhao
Rui-Xuan Gao
He-Sheng Liu
Aiyu Bai
Atmospheric neutrinos (ATNs) offer a paradigm for understanding neutrino properties, while it is critical to quantify uncertainties in flux … (voir plus)modeling. Since ATNs are produced simultaneously with cosmic ray muons, precision measurements of cosmic ray muons, including arrival direction, energy spectra, and spin polarization, will help reduce ATN production uncertainties and facilitate atmospheric neutrino physics. This letter proposes using an array strategy to measure the spin polarization of cosmic ray muons, thereby strengthening the emergent synergies between cosmic ray and atmospheric neutrino physics. Constraints on long-standing atmospheric neutrino flux uncertainties at the percentage level in a few-GeV energy range are achievable within one year using a
Determinants of surgical approach to pediatric appendicitis in Brazil.
Ayla Gerk
Paulo Henrique Moreira Melo
Luiza Telles
Justina O. Seyi-Olajide
Dunya Moghul
Gabriel Schnitman
Cristina Camargo
David P. Mooney
Joaquim Bustorff-Silva
Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy
Joshua Greaves
Ekin Dogus Cubuk
Bellemare Marc-Emmanuel
Sergei Kalinin
Igor Mordatch
Kevin M Roccapriore
We introduce a machine learning approach to determine the transition dynamics of silicon atoms on a single layer of carbon atoms, when stimu… (voir plus)lated by the electron beam of a scanning transmission electron microscope (STEM). Our method is data-centric, leveraging data collected on a STEM. The data samples are processed and filtered to produce symbolic representations, which we use to train a neural network to predict transition probabilities. These learned transition dynamics are then leveraged to guide a single silicon atom throughout the lattice to pre-determined target destinations. We present empirical analyses that demonstrate the efficacy and generality of our approach.
Multi‐center benchmarking of cervical spinal cord RF coils for 7 T MRI: A traveling spines study
Eva Alonso‐Ortiz
Daniel Papp
Robert L. Barry
Kyota Poëti
Alan C. Seifert
Kyle M. Gilbert
Nibardo Lopez‐Rios
Jan Paska
Falk Eippert
Nikolaus Weiskopf
Laura Beghini
Nadine N. Graedel
Robert Trampel
Martina F. Callaghan
Christoph S. Aigner
Patrick Freund
Maryam Seif
Aurélien Destruel
Virginie Callot
Johanna Vannesjo … (voir 1 de plus)
Julien Cohen‐Adad
The depth within the body, small diameter, long length, and varying tissue surrounding the spinal cord impose specific considerations when d… (voir plus)esigning RF coils. The optimal coil configuration for 7 T cervical spinal cord MRI is unknown and currently there are very few coil options. The purpose of this work was (1) to establish a quality control protocol for evaluating 7 T cervical spinal cord coils, and (2) to use that protocol to evaluate the performance of four different coil designs. Three healthy volunteers and a custom anthropomorphic phantom (the traveling spines cohort) were scanned at seven 7 T imaging centers using a common protocol and each center's specific cervical spinal cord coil. Four different coil designs were tested (two in‐house, one Rapid Biomedical, and one MRI.TOOLS design). The Rapid Biomedical coil was found to have the highest B1+ efficiency, whereas one of the in‐house designs (NeuroPoly Lab) had the highest SNR and the largest spinal cord coverage. The MRI.TOOLS coil had the most uniform B1+ profile along the cervical spinal cord; however, it was limited in its ability to provide the requested flip angles (especially for larger individuals). The latter was also the case for the second in‐house coil (MSSM). The results of this study serve as a guide for the spinal cord MRI community in selecting the most suitable coil based on specific requirements and offer a standardized protocol for assessing future coils.
Generalizable Imitation Learning Through Pre-Trained Representations
Wei-Di Chang
Francois Hogan
In this paper we leverage self-supervised vision transformer models and their emergent semantic abilities to improve the generalization abil… (voir plus)ities of imitation learning policies. We introduce BC-ViT, an imitation learning algorithm that leverages rich DINO pre-trained Visual Transformer (ViT) patch-level embeddings to obtain better generalization when learning through demonstrations. Our learner sees the world by clustering appearance features into semantic concepts, forming stable keypoints that generalize across a wide range of appearance variations and object types. We show that this representation enables generalized behaviour by evaluating imitation learning across a diverse dataset of object manipulation tasks. Our method, data and evaluation approach are made available to facilitate further study of generalization in Imitation Learners.
PedMedQA: Comparing Large Language Model Accuracy in Pediatric and Adult Medicine
Nikhil Jaiswal
Yuanchao Ma
Bertrand Lebouché
Esli Osmanlliu
Large language models (LLMs) have the potential to revolutionize healthcare, including aiding in clinical decision-making. However, recent w… (voir plus)ork suggests that LLM performance in pediatric cases may be weaker than adult cases. A key limitation in evaluating these differences is the lack of pediatric-specific benchmarks, making it difficult to systematically assess how well LLMs generalize to pediatric scenarios.
RobusTAD: reference panel based annotation of nested topologically associating domains
Yanlin Zhang
Rola Dali
Topologically associating domains (TADs) are fundamental units of 3D genomes and play essential roles in gene regulation. Hi-C data suggests… (voir plus) a hierarchical organization of TADs. Accurately annotating nested TADs from Hi-C data remains challenging, both in terms of the precise identification of boundaries and the correct inference of hierarchies. While domain boundary is relatively well conserved across cells, few approaches have taken advantage of this fact. Here, we present RobusTAD to annotate TAD hierarchies. It incorporates additional Hi-C data to refine boundaries annotated from the study sample. RobusTAD outperforms existing tools at boundary and domain annotation across several benchmarking tasks. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-025-03568-9.
Topological mapping for traversability-aware long-range navigation in off-road terrain
Autonomous robots navigating in off-road terrain like forests open new opportunities for automation. While off-road navigation has been stud… (voir plus)ied, existing work often relies on clearly delineated pathways. We present a method allowing for long-range planning, exploration and low-level control in unknown off-trail forest terrain, using vision and GPS only. We represent outdoor terrain with a topological map, which is a set of panoramic snapshots connected with edges containing traversability information. A novel traversability analysis method is demonstrated, predicting the existence of a safe path towards a target in an image. Navigating between nodes is done using goal-conditioned behavior cloning, leveraging the power of a pretrained vision transformer. An exploration planner is presented, efficiently covering an unknown off-road area with unknown traversability using a frontiers-based approach. The approach is successfully deployed to autonomously explore two 400 m2 forest sites unseen during training, in difficult conditions for navigation.
A multi-ancestry genetic reference for the Quebec population
Peyton McClelland
Georgette Femerling
Rose Laflamme
Alejandro Mejia-Garcia
Mohadese Sayahian Dehkordi
Hongyu Xiao
Alex Diaz-Papkovich
Justin Pelletier
Jean-Christophe Grenier
Ken Sin Lo
Luke Anderson-Trocmé
Justin Bellavance
Vincent Chapdelaine
Genevieve Gagnon
Annelie De Mori
Gerardo Martinez
Kristen Mohler
Thibault de Malliard
Catherine Labbé
Marjorie Labrecque … (voir 14 de plus)
Alexandre Montpetit
Dan Spiegelman
Guy A Rouleau
Jean-François Théroux
Hufeng Zhou
Simon L Girard
Julie G Hussin
Anne-Marie Laberge
Claude Bhérer
Martine Tétreault
Sarah A Gagliano Taliun
Daniel Taliun
Simon Gravel
Guillaume Lettre
While international efforts have characterized genetic variation in millions of individuals, the interplay of environmental, social, cultura… (voir plus)l, and genetic factors is poorly understood for most worldwide populations. The province of Quebec in Canada has been the site of numerous genetic studies, often focusing on individual Mendelian diseases in founder sub-populations. Here, we profiled and analyzed genome-wide genotyped variation in 29,337 Quebec residents from the large population-based cohort CARTaGENE (CaG), including rich phenotype and environmental data. We also sequenced the whole-genome of 2,173 CaG participants, including 163 and 132 individuals with grandparents born in Haiti and Morocco, respectively. We use this genetic information to gain insight into Quebec's demography and to help interpret the potential significance of variants identified in clinically important genes. We built an imputation panel by phasing the CaG whole-genome sequence data and showed, using genome-wide association studies (GWAS), how it improves the discovery of phenotype-genotype associations in this population. We provide allele frequency information and GWAS results through dedicated and publicly available websites. The genetic data, paired with phenotypic and environmental information, is also available for research use upon scientific and ethical review.
Persistent signs of poisoning after massive drug ingestion: move the ultrasound probe to the stomach.
N. Lautrou-cabasson
H. Pirollet
C. Lombois
The CASTOR mission
Patrick Côté
T. Woods
John Hutchings
J. Rhodes
R. Sánchez-Janssen
Alan D. Scott
J. Pazder
Melissa Amenouche
Michael Balogh
Simon Blouin
Alain Cournoyer
M. Drout
Nick Kuzmin
Katherine J. Mack
Laura Ferrarese
Wesley C. Fraser
S. Gallagher
Frederic J. Grandmont
Daryl Haggard
P. Harrison … (voir 160 de plus)
V. Hénault-Brunet
J. Kavelaars
V. Khatu
J. Roediger
J. Rowe
Marcin Sawicki
Jesper Skottfelt
Matt Taylor
L. van Waerbeke
Laurie Amen
Dhananjhay Bansal
Martin Bergeron
Toby Brown
Greg Burley
Hum Chand
Isaac Cheng
Ryan Cloutier
N. Dickson
Oleg Djazovski
Ivana Damjanov
James Doherty
K. Finner
Macarena García Del Valle Espinosa
Jennifer Glover
A. I. Gómez de Castro
Or Graur
Tim Hardy
Michelle Kao
D A Leahy
Deborah Lokhorst
A. I. Malz
Allison Man
Madeline A. Marshall
Sean McGee
Ryan McKenzie
Kai Michaud
Surhud S. More
David Morris
Patrick W. Morris
T. Moutard
Wasi Naqvi
Matthew Nicholl
G. Noirot
M. S. Oey
C. Opitom
Samir Salim
Bryan R. Scott
Charles Shapiro
Daniel Stern
Ashwin Subramaniam
David Thilke
I. Wevers
Dmitri Vorobiev
L. Y. Aaron Yung
Frédéric Zamkotsian
S. Aigrain
A. Alavi
Martin Barstow
Peter Bartosik
H. Bluhm
J. Bovy
Peter Cameron
R. Carlberg
J. Christiansen
Yuyang Chen
P. Crowther
Kristen Dage
Aaron Dotter
Patrick Dufour
Jean Dupuis
B. Dryer
A. Duara
Gwendolyn M. Eadie
Marielle R. Eduardo
V. Estrada-Carpenter
Sébastien Fabbro
A. Faisst
N. M. Ford
M. Fraser
Boris T. Gaensicke
Shashkiran Ganesh
Poshak Gandhi
Melissa L. Graham
R. Hamel
Martin Hellmich
John J. Hennessy
Kaitlyn Hessel
J. Heyl
Catherine Heymans
Renée Hložek
Michael Hoenk
Andrew Holland
Eric Huff
Ian Hutchinson
I. Iwata
April D. Jewell
Doug Johnstone
Maia Jones
Todd J. Jones
D. Lang
J. Lapington
Justin Larivière
C. Lawlor-Forsyth
Denis Laurin
Charles Lee
Ting S. Li
S. Lim
B. Ludwig
Matt Kozun
V. M
Robert Mann
Alan McConnachie
Evan McDonough
S. Metchev
David R. Miller
Takashi Moriya
Cameron Morgan
Julio F. Navarro
Y. Nazé
Shouleh Nikzad
Vivek Oad
N. N.-Q. Ouellette
E. Pass
Will J. Percival
Joe Postma
Nayyer Raza
G. T. Richards
Harvey Richer
Carmelle Robert
Erik Rosolowsky
J. Ruan
Sarah Rugheimer
S. Safi-Harb
Kanak Saha
Vicky Scowcroft
F. Sestito
Himanshu Sharma
James Sikora
G. Sivakoff
T. S. Sivarani
Patrick Smith
Warren Soh
R. Sorba
S. Subramanian
Hossen Teimoorinia
H. Teplitz
Shaylin Thadani
Shavon Thadani
Aaron Tohuvavohu
K. Venn
Nicholas Vieira
Jeremy J. Webb
P. Wiegert
Ryan Wierckx
Yanqin Wu
J. Yeung
S. K. Yi