Publications

A hierarchical Bayesian brain parcellation framework for fusion of functional imaging datasets
Da Zhi
Ladan Shahshahani
Caroline Nettekoven
Ana Lúısa Pinho
Danilo Bzdok
Jörn Diedrichsen
2025-01-01
Imaging Neuroscience (publié)
doi.org
Access Inequality in LEO Satellite Networks: A Case Study of High-Latitude Coverage in Northern Québec
Mohammed Almekhlafi
Antoine Lesage-Landry
Gunes Karabulut Kurt
Low Earth orbit (LEO) satellite networks play a crucial role in bridging the digital divide, particularly in remote and high-latitude region… (voir plus)s. However, access inequality remains a significant challenge, limiting broadband connectivity for communities in northern areas compared to mid-latitude urban regions. This study reviews recent advancements in non-terrestrial networks (NTNs). We conduct a detailed analysis of coverage disparities in LEO satellite networks considering LEO networks, namely Starlink, Telesat-like, Kuiper-like, and OneWeb, with a specific focus on Québec, Canada versus urban centers in New York City, USA. Our findings highlight a significant disparity in the number of visible satellites resulting in increased transmission delays and reduced network reliability in high-latitude regions. Additionally, we observe that higher elevation angles, more accessible in mid-latitude regions especially for Starlink and Kuiper, contribute to superior signal quality and transmission rates. To mitigate this gap, we propose an inter-constellation/orbit roaming mechanism that enables ground users to be served by different LEO constellations—leveraging OneWeb's and Telesat's strong polar coverage along with the high satellite density of Starlink and Kuiper at mid-latitudes. Jointly, terrestrial network (TN) expansion can enhance signal quality and transmission efficiency, particularly in underserved areas where NTNs act as edge computing and backhaul infrastructures. Additionally, the associated challenges—such as roaming handovers, and radio resource and network slicing management are discussed in detail, where designing a unified management and control entity to ensure seamless interoperability is not a trivial task. Furthermore, we envision wireless power transfer through either relay-based (ground-to-satellite-to-ground) or direct (satellite-to-ground) power beaming as a sustainable approach to energize TN components in remote regions. These strategies collectively support the scalability and resilience of NTNs in bridging the global access inequality.
2024-12-31
IEEE Open Journal of Vehicular Technology (publié)
doi.org
Adaptive Local Training in Federated Learning.
Donald Shenaj
Eugene Belilovsky
Pietro Zanuttigh
In federated learning multiple clients collaboratively train a global machine learning model by exchanging their locally trained model weigh… (voir plus)ts instead of raw data. In the standard setting, every client trains its local model for the same number of epochs. We introduce ALT (Adaptive Local Training), a simple yet effective feedback mechanism that can be introduced on top of any federated learning scheme at the client side to limit unnecessary and degrading computations. ALT dynamically adjusts the number of training epochs for each client based on the similarity between the local representation and the global one, ensuring that well-aligned clients can train longer without experiencing client drift while in case of too large drifts the training is stopped earlier. We evaluated ALT on federated partitions of the CIFAR-10 and Tiny-ImageNet datasets, demonstrating its effectiveness in improving both model convergence speed and accuracy. The code is available at https://github.com/LTTM/ALT.
2024-12-31
European Signal Processing Conference (publié)
doi.org
openreview.net
Advocacy for Children With Surgical Diseases in Nigeria: National Policy Status, Gaps, and Solutions
Justina O. Seyi-Olajide
Ayla Gerk
Elena Guadagno
Adesoji Ademuyiwa
Emmanuel A. Ameh
Dan Poenaru
2024-12-31
Journal of Pediatric Surgery (publié)
doi.org
Anticancer Monotherapy and Polytherapy Drug Response Prediction Using Deep Learning: Guidelines and Best Practices
Amin Emad
David Earl Hostallero
2024-12-31
Methods in Molecular Biology (inconnu)
doi.org
Attention as a Hypernetwork
Simon Schug
Seijin Kobayashi
Yassir Akram
João Sacramento
Razvan Pascanu
Transformers can under some circumstances generalize to novel problem instances whose constituent parts might have been encountered during t… (voir plus)raining, but whose compositions have not. What mechanisms underlie this ability for compositional generalization? By reformulating multi-head attention as a hypernetwork, we reveal that a composable, low-dimensional latent code specifies key-query specific operations. We find empirically that this latent code is predictive of the subtasks the network performs on unseen task compositions, revealing that latent codes acquired during training are reused to solve unseen problem instances. To further examine the hypothesis that the intrinsic hypernetwork of multi-head attention supports compositional generalization, we ablate whether making the hypernetwork-generated linear value network nonlinear strengthens compositionality. We find that this modification improves compositional generalization on abstract reasoning tasks. In particular, we introduce a symbolic version of the Raven's Progressive Matrices human intelligence test, which gives us precise control over the problem compositions encountered during training and evaluation. We demonstrate on this task how scaling model size and data enables compositional generalization in transformers and gives rise to a functionally structured latent space.
2024-12-31
ICLR (publié)
doi.org
arxiv.org
Audio Prototypical Network For Controllable Music Recommendation
Fırat Öncel
Emiliano Penaloza
Haolun Wu
Shubham Gupta
Mirco Ravanelli
Laurent Charlin
Cem Subakan
Traditional recommendation systems represent user preferences in dense representations obtained through black-box encoder models. While thes… (voir plus)e models often provide strong recommendation performance, they lack interpretability for users, leaving users unable to understand or control the system's modeling of their preferences. This limitation is especially challenging in music recommendation, where user preferences are highly personal and often evolve based on nuanced qualities like mood, genre, tempo, or instrumentation. In this paper, we propose an audio prototypical network for controllable music recommendation. This network expresses user preferences in terms of prototypes representative of semantically meaningful features pertaining to musical qualities. We show that the model obtains competitive recommendation performance compared to popular baseline models while also providing interpretable and controllable user profiles.
2024-12-31
MLSP (publié)
doi.org
openreview.net
Automated UML Visualization of Software Ecosystems: Tracking Versions, Dependencies, and Security Updates
Vanessa Kan
M. P. Lnu
Solomon Berhe
C. El Kari
Marc Maynard
Foutse Khomh
2024-12-31
ANT/EDI40 (publié)
doi.org
Automatic segmentation of spinal cord lesions in MS: A robust tool for axial T2-weighted MRI scans
Enamundram Naga Karthik
Julian McGinnis
Ricarda Wurm
Sebastian Ruehling
Robert Graf
Jan Valosek
Pierre-Louis Benveniste
Markus Lauerer
Jason Talbott
Rohit Bakshi
Shahamat Tauhid
Timothy Shepherd
Achim Berthele
Claus Zimmer
Bernhard Hemmer
Daniel Rueckert
Benedikt Wiestler
Jan S. Kirschke
Julien Cohen-Adad
Mark Mühlau
Deep learning models have achieved remarkable success in segmenting brain white matter lesions in multiple sclerosis (MS), becoming integral… (voir plus) to both research and clinical workflows. While brain lesions have gained significant attention in MS research, the involvement of spinal cord lesions in MS is relatively understudied. This is largely owed to the variability in spinal cord magnetic resonance imaging (MRI) acquisition protocols, high individual anatomical differences, the complex morphology and size of spinal cord lesions - and lastly, the scarcity of labeled datasets required to develop robust segmentation tools. As a result, automatic segmentation of spinal cord MS lesions remains a significant challenge. Although some segmentation tools exist for spinal cord lesions, most have been developed using sagittal T2-weighted (T2w) sequences primarily focusing on cervical spines. With the growing importance of spinal cord imaging in MS, axial T2w scans are becoming increasingly relevant due to their superior sensitivity in detecting lesions compared to sagittal acquisition protocols. However, most existing segmentation methods struggle to effectively generalize to axial sequences due to differences in image characteristics caused by the highly anisotropic spinal cord scans. To address these challenges, we developed a robust, open-source lesion segmentation tool tailored specifically for axial T2w scans covering the whole spinal cord. We investigated key factors influencing lesion segmentation, including the impact of stitching together individually acquired spinal regions, straightening the spinal cord, and comparing the effectiveness of 2D and 3D convolutional neural networks (CNNs). Drawing on these insights, we trained a multi-center model using an extensive dataset of 582 MS patients, resulting in a dataset comprising an entirety of 2,167 scans. We empirically evaluated the model’s segmentation performance across various spinal segments for lesions with varying sizes. Our model significantly outperforms the current state-of-the-art methods, providing consistent segmentation across cervical, thoracic and lumbar regions. To support the broader research community, we integrate our model into the widely-used Spinal Cord Toolbox (v7.0 and above), making it accessible via the command sct_deepseg lesion_ms_axial_t2 -i <path-to-image.nii.gz>.
2024-12-31
Imaging Neuroscience (publié)
doi.org
Balancing Profit and Fairness in Risk-Based Pricing Markets
Jesse Thibodeau
Hadi Nekoei
Afaf Taïk
Janarthanan Rajendran
Golnoosh Farnadi
Dynamic, risk-based pricing can systematically exclude vulnerable consumer groups from essential resources such as health insurance and cons… (voir plus)umer credit. We show that a regulator can realign private incentives with social objectives through a learned, interpretable tax schedule. First, we provide a formal proposition that bounding each firm's \emph{local} demographic gap implicitly bounds the \emph{global} opt-out disparity, motivating firm-level penalties. Building on this insight we introduce \texttt{MarketSim} -- an open-source, scalable simulator of heterogeneous consumers and profit-maximizing firms -- and train a reinforcement learning (RL) social planner (SP) that selects a bracketed fairness-tax while remaining close to a simple linear prior via an
2024-12-31
arXiv (prépublication)
doi.org
arxiv.org
A Biodiversity Observation Network to support conservation action and mainstream knowledge in Canada
Andrew Gonzalez
Mary I. O'Connor
Amanda E. Bates
Kyle Bobiwash
A. Cole Burton
Paul van Dam-Bates
Isaac Eckert
Dominique Gravel
C. Julián Idrobo
Laura Pollock
Andrew D.F. Simon
Margaret A. Slein
Péter Sólymos
Brian M. Starzomski
Jennifer Sunday
Eden Tekwa
Canada has begun an ambitious project to build an observing system to monitor the changing state of its biodiversity and ecosystems. A Canad… (voir plus)a-wide Biodiversity Observation Network (CAN BON) can support the measurement, mapping, and modelling of biodiversity change—the losses and gains in the diversity of plant, animal, and microbial life—and ecosystem services. This initiative responds to eight challenges presently constraining Canada's capacity to deliver timely and robust knowledge to achieve its biodiversity goals. CAN BON is conceived as a network connecting diverse organizations to support sustained biodiversity monitoring by collaboration among universities, museums, governments, industries, NGOs, community groups, and Indigenous organizations. This inclusive network will “mobilize monitoring data” to (1) combine observation and computing infrastructures and traditional knowledge to track and understand biodiversity losses and gains across the country; and (2) link the accumulated data and knowledge to models to inform the detection and attribution of biodiversity change needed to support biodiversity policy with forecasts from local to national levels. We expect that CAN BON will foster the mainstreaming of biodiversity data and knowledge into other sectors of the economy and society, and thereby support the technical and social innovation in Canada's transition to a nature-positive future.
2024-12-31
Formal Aspects of Component Software (publié)
doi.org
A Blockchain Framework for Equitable and Secure Task Allocation in Robot Swarms
Hanqing Zhao
Alexandre Pacheco
Giovanni Beltrame
Xue Liu
Marco Dorigo
Gregory Dudek
Recent studies demonstrate the potential of blockchain to enable robots in a swarm to achieve secure consensus about the environment, partic… (voir plus)ularly when robots are homogeneous and perform identical tasks. Typically, robots receive rewards for their contributions to consensus achievement, but no studies have yet targeted heterogeneous swarms, in which the robots have distinct physical capabilities suited to different tasks. We present a novel framework that leverages domain knowledge to decompose the swarm mission into a hierarchy of tasks within smart contracts. This allows the robots to reach a consensus about both the environment and the action plan, allocating tasks among robots with diverse capabilities to improve their performance while maintaining security against faults and malicious behaviors. We refer to this concept as equitable and secure task allocation. Validated in Simultaneous Localization and Mapping missions, our approach not only achieves equitable task allocation among robots with varying capabilities, improving mapping accuracy and efficiency, but also shows resilience against malicious attacks.
2024-12-31
IEEE Robotics and Automation Letters (inconnu)
doi.org