Publications

ImmunoStruct: a multimodal neural network framework for immunogenicity prediction from peptide-MHC sequence, structure, and biochemical properties
Kevin Bijan Givechian
João Felipe Rocha
Edward Yang
Chen Liu
Kerrie Greene
Rex Ying
Etienne Caron
Akiko Iwasaki
Smita Krishnaswamy
2025-05-20
Research Square (publié)
doi.org
Adaptive Inference-Time Scaling via Cyclic Diffusion Search
Gyubin Lee
Truong Nhat Nguyen Bao
Jaesik Yoon
Dongwoo Lee
Minsu Kim
Yoshua Bengio
Sungjin Ahn
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.
2025-05-19
ArXiv (prépublication)
doi.org
openreview.net
Cosmic Ray Muon Polarization to Facilitate Atmospheric Neutrino Physics
Mingchen Sun
Shihan Zhao
Rui-Xuan Gao
He-Sheng Liu
Aiyu Bai
Jian Tang
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
2025-05-19
ArXiv (prépublication)
arxiv.org
arxiv.org
Determinants of surgical approach to pediatric appendicitis in Brazil.
Ayla Gerk
Paulo Henrique Moreira Melo
Mohsen Amoei
Shreenik Kundu
Luiza Telles
Justina O. Seyi-Olajide
Dunya Moghul
Gabriel Schnitman
Cristina Camargo
David P. Mooney
Joaquim Bustorff-Silva
Dan Poenaru
2025-05-19
Pediatric surgery international (Print) (publié)
doi.org
Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy
Max Schwarzer
Jesse Farebrother
Joshua Greaves
Ekin Dogus Cubuk
Rishabh Agarwal
Aaron Courville
Bellemare Marc-Emmanuel
Sergei Kalinin
Igor Mordatch
Pablo Samuel Castro
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.
2025-05-19
Advanced Materials Interfaces (publié)
doi.org
arxiv.org
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.
2025-05-19
Magnetic Resonance in Medicine (publié)
doi.org
SDLog: A Deep Learning Framework for Detecting Sensitive Information in Software Logs
Roozbeh Aghili
Xingfang Wu
Foutse Khomh
Heng Li
2025-05-19
ArXiv (prépublication)
doi.org
arxiv.org
Self-Evolving Curriculum for LLM Reasoning
Xiaoyin Chen
Jiarui Lu
Minsu Kim
Dinghuai Zhang
Jian Tang
Alexandre Piché
Nicolas Gontier
Yoshua Bengio
Ehsan Kamalloo
2025-05-19
ArXiv (prépublication)
doi.org
arxiv.org
Virtual Cells: Predict, Explain, Discover
Emmanuel Noutahi
Jason Hartford
Prudencio Tossou
Shawn Whitfield
Ali Denton
Cas Wognum
Kristina Ulicna
Michael Craig
Jonathan Hsu
Michael Cuccarese
Emmanuel Bengio
Dominique Beaini
Christopher Gibson
Daniel Cohen
Berton Earnshaw
2025-05-19
ArXiv (prépublication)
doi.org
arxiv.org
Building spatial world models from sparse transitional episodic memories
Zizhan He
Maxime Daigle
Pouya Bashivan
Many animals possess a remarkable capacity to rapidly construct flexible mental models of their environments. These world models are crucial… (voir plus) for ethologically relevant behaviors such as navigation, exploration, and planning. The ability to form episodic memories and make inferences based on these sparse experiences is believed to underpin the efficiency and adaptability of these models in the brain. Here, we ask: Can a neural network learn to construct a spatial model of its surroundings from sparse and disjoint episodic memories? We formulate the problem in a simulated world and propose a novel framework, the Episodic Spatial World Model (ESWM), as a potential answer. We show that ESWM is highly sample-efficient, requiring minimal observations to construct a robust representation of the environment. It is also inherently adaptive, allowing for rapid updates when the environment changes. In addition, we demonstrate that ESWM readily enables near-optimal strategies for exploring novel environments and navigating between arbitrary points, all without the need for additional training.
2025-05-18
ArXiv (prépublication)
doi.org
arxiv.org
Generalizable Imitation Learning Through Pre-Trained Representations
Wei-Di Chang
Francois Hogan
David Meger
Scott Fujimoto
Gregory Dudek
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.
2025-05-18
2025 IEEE International Conference on Robotics and Automation (ICRA) (publié)
doi.org
arxiv.org
Half Search Space is All You Need
Pavel Rumiantsev
Mark J. Coates
2025-05-18
ArXiv (prépublication)
doi.org
arxiv.org