Publications

What Are They Doing? Joint Audio-Speech Co-Reasoning

Yingzhi Wang

In audio and speech processing, tasks usually focus on either the audio or speech modality, even when both sounds and human speech are prese… (voir plus)nt in the same audio clip. Recent Auditory Large Language Models (ALLMs) have made it possible to process audio and speech simultaneously within a single model, leading to further considerations of joint audio-speech tasks. In this paper, we establish a novel benchmark to investigate how well ALLMs can perform joint audio-speech processing. Specifically, we introduce Joint Audio-Speech Co-Reasoning (JASCO), a novel task that unifies audio and speech processing, strictly requiring co-reasoning across both modalities. We also release a scene-reasoning dataset called "What Are They Doing". Additionally, we provide deeper insights into the models' behaviors by analyzing their dependence on each modality.

2025-04-05

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)

doi.org

arxiv.org

Progressive Multi-Source Domain Adaptation for Personalized Facial Expression Recognition

Muhammad Osama Zeeshan

Marco Pedersoli

Alessandro Lameiras Koerich

Eric Grange

2025-04-04

ArXiv (prépublication)

doi.org

arxiv.org

Accelerated learning of a noninvasive human brain-computer interface via manifold geometry

Erica Lindsey Busch

E. Chandra Fincke

Guillaume Lajoie

Smita Krishnaswamy

Nicholas B Turk-Browne

2025-04-02

bioRxiv (prépublication)

doi.org

Evaluating and Enhancing Segmentation Model Robustness with Metamorphic Testing

Seif Mzoughi

Mohamed Elshafeia

Foutse Khomh

2025-04-02

ArXiv (prépublication)

doi.org

arxiv.org

Spinal Cord Tract Integrity in Degenerative Cervical Myelopathy

Newton Cho

Abdul Al-Shawwa

W. Bradley Jacobs

Nathan Evaniew

Jacques Bouchard

Steve Casha

Stephan duPlessis

Peter Lewkonia

Fred Nicholls

Alex Soroceanu

Ganesh Swamy

Kenneth C. Thomas

Michael M. H. Yang

Julien Cohen-Adad

David W. Cadotte

Degenerative cervical myelopathy (DCM) is the most common cause of spinal dysfunction globally. Despite surgical intervention, motor dysfunc… (voir plus)tion may persist in many patients. The purpose of this study was to comprehensively examine specific spinal cord tract changes in patients with DCM, to better understand potential substrates for compensatory recovery of function. Cervical spinal cord MRI scans with diffusion tensor imaging were performed in patients with DCM and in healthy volunteers. Spinal Cord Toolbox was used to register the PAM50 template, which includes a probabilistic atlas of the white matter tracts of the spinal cord, to the imaging data. Fractional anisotropy (FA) was extracted for each tract at C3 above the level of maximal compression and compared between patients with DCM and healthy volunteers and between patients with mild vs moderate to severe DCM. We included 25 patients with DCM (13 mild and 12 moderate to severe) and 6 healthy volunteers. FA was significantly reduced in DCM subjects relative to healthy volunteers for the lateral corticospinal tract (mild DCM vs healthy ∆ = −0.13, P = .018; moderate to severe DCM vs healthy ∆ = −0.11, P = .047), fasciculus gracilis (mild DCM vs healthy ∆ = −0.16, P = .010; moderate to severe DCM vs healthy ∆ = −0.13, P = .039), and fasciculus cuneatus (mild DCM vs healthy ∆ = −0.16, P = .007; moderate to severe DCM vs healthy ∆ = −0.15, P = .012). There were no differences in FA for all tracts between mild and moderate-to-severe DCM subjects. Patients with DCM had altered diffusion tensor imaging signal in their lateral corticospinal tract, fasciculus gracilis, and fasciculus cuneatus in comparison with healthy volunteers. These findings indicate that DCM is characterized by injury to these structures, which suggests that other tracts within the cord could potentially act as substrates for compensatory motor recovery.

2025-04-02

Neurosurgery (inconnu)

doi.org

Towards Assessing Deep Learning Test Input Generators

Seif Mzoughi

Ahmed Haj Yahmed

Mohamed Elshafei

Foutse Khomh

Diego Elias Costa

2025-04-02

ArXiv (prépublication)

doi.org

arxiv.org

Why do LLMs attend to the first token?

Federico Barbero

'Alvaro Arroyo

Xiangming Gu

Christos Perivolaropoulos

Michael M. Bronstein

Petar Veličković

Razvan Pascanu

2025-04-02

ArXiv (prépublication)

doi.org

arxiv.org

DeepSeek-R1 Thoughtology: Let's think about LLM Reasoning

Sara Vera Marjanovi'c

Arkil Patel

Vaibhav Adlakha

Milad Aghajohari

Parishad BehnamGhader

Amirhossein Kazemnejad

Gaurav Kamath

Marius Mosbach

Karolina Stanczak

Siva Reddy

Large Reasoning Models like DeepSeek-R1 mark a fundamental shift in how LLMs approach complex problems. Instead of directly producing an ans… (voir plus)wer for a given input, DeepSeek-R1 creates detailed multi-step reasoning chains, seemingly"thinking"about a problem before providing an answer. This reasoning process is publicly available to the user, creating endless opportunities for studying the reasoning behaviour of the model and opening up the field of Thoughtology. Starting from a taxonomy of DeepSeek-R1's basic building blocks of reasoning, our analyses on DeepSeek-R1 investigate the impact and controllability of thought length, management of long or confusing contexts, cultural and safety concerns, and the status of DeepSeek-R1 vis-\`a-vis cognitive phenomena, such as human-like language processing and world modelling. Our findings paint a nuanced picture. Notably, we show DeepSeek-R1 has a 'sweet spot' of reasoning, where extra inference time can impair model performance. Furthermore, we find a tendency for DeepSeek-R1 to persistently ruminate on previously explored problem formulations, obstructing further exploration. We also note strong safety vulnerabilities of DeepSeek-R1 compared to its non-reasoning counterpart, which can also compromise safety-aligned LLMs.

2025-04-01

ArXiv (prépublication)

arxiv.org

Predicting pathogen evolution and immune evasion in the age of artificial intelligence

D.J. Hamelin

M. Scicluna

I. Saadie

F. Mostefai

J.C. Grenier

C. Baron

E. Caron

J.G. Hussin

The genomic diversification of viral pathogens during viral epidemics and pandemics represents a major adaptive route for infectious agents … (voir plus)to circumvent therapeutic and public health initiatives. Historically, strategies to address viral evolution have relied on responding to emerging variants after their detection, leading to delays in effective public health responses. Because of this, a long-standing yet challenging objective has been to forecast viral evolution by predicting potentially harmful viral mutations prior to their emergence. The promises of artificial intelligence (AI) coupled with the exponential growth of viral data collection infrastructures spurred by the COVID-19 pandemic, have resulted in a research ecosystem highly conducive to this objective. Due to the COVID-19 pandemic accelerating the development of pandemic mitigation and preparedness strategies, many of the methods discussed here were designed in the context of SARS-CoV-2 evolution. However, most of these pipelines were intentionally designed to be adaptable across RNA viruses, with several strategies already applied to multiple viral species. In this review, we explore recent breakthroughs that have facilitated the forecasting of viral evolution in the context of an ongoing pandemic, with particular emphasis on deep learning architectures, including the promising potential of language models (LM). The approaches discussed here employ strategies that leverage genomic, epidemiologic, immunologic and biological information.

2025-04-01

Computational and Structural Biotechnology Journal (publié)

doi.org

A Truncated Newton Method for Optimal Transport

Mete Kemertas

Amir-massoud Farahmand

Allan D. Jepson

2025-04-01

ArXiv (prépublication)

doi.org

arxiv.org

Addressing Missing Modality Challenges in MRI Images: A Comprehensive Review