A hierarchical Bayesian brain parcellation framework for fusion of functional imaging datasets
Da Zhi
Ladan Shahshahani
Caroline Nettekoven
Ana Lúısa Pinho
Jörn Diedrichsen
Abstract: Würstchen - An Efficient Architecture for Large-scale Text-to-image Diffusion Models
Pablo Pernias
Dominic Rampas
Mats L. Richter
Marc Aubreville
Access Inequality in LEO Satellite Networks: A Case Study of High-Latitude Coverage in Northern Québec
Mohammed Almekhlafi
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.
Advancements in Affective and Behavior Analysis: The 8th ABAW Workshop and Competition
D. Kollias
Panagiotis Tzirakis
Alan Cowen
S. Zafeiriou
I. Kotsia
Eric Granger
Simon Bacon
Alice Baird
Chris Gagne
Chunchang Shao
Guanyu Hu
Soufiane Belharbi
Muhammad Haseeb Aslam
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
Anti-patterns and Code Smells for Multi-language Systems
Mouna Abidi
Manel Grichi
Yann‐Gaël Guéhéneuc
Automated UML Visualization of Software Ecosystems: Tracking Versions, Dependencies, and Security Updates
Vanessa Kan
M. P. Lnu
Solomon Berhe
C. El Kari
Marc Maynard
Beyond Model Collapse: Scaling Up with Synthesized Data Requires Verification
Yunzhen Feng
Pu Yang
Francois Charton
Julia Kempe
Large Language Models (LLM) are increasingly trained on data generated by other LLM, either because generated text and images become part of… (voir plus) the pre-training corpus, or because synthetized data is used as a replacement for expensive human-annotation. This raises concerns about \emph{model collapse}, a drop in model performance when their training sets include generated data. Considering that it is easier for both humans and machines to tell between good and bad examples than to generate high-quality samples, we investigate the use of verification on synthesized data to prevent model collapse. We provide a theoretical characterization using Gaussian mixtures, linear classifiers, and linear verifiers to derive conditions with measurable proxies to assess whether the verifier can effectively select synthesized data that leads to optimal performance. We experiment with two practical tasks -- computing matrix eigenvalues with transformers and news summarization with LLMs -- which both exhibit model collapse when trained on generated data, and show that verifiers, even imperfect ones, can indeed be harnessed to prevent model collapse and that our proposed proxy measure strongly correlates with performance.
Body size and intracranial volume interact with the structure of the central nervous system: A multi-center in vivo neuroimaging study
René Labounek
Monica T. Bondy
Amy L. Paulson
Sandrine Bédard
Mihael Abramovic
Eva Alonso‐Ortiz
Nicole Atcheson
Laura R. Barlow
Robert L. Barry
Markus Barth
Marco Battiston
Christian Büchel
Matthew D. Budde
Virginie Callot
Anna Combes
Benjamin De Leener
Maxime Descoteaux
Paulo Loureiro de Sousa
Marek Dostál
Julien Doyon … (voir 74 de plus)
Adam Dvorak
Falk Eippert
Karla R. Epperson
Kevin S. Epperson
Patrick Freund
Jürgen Finsterbusch
Alexandru Foias
Michela Fratini
Issei Fukunaga
Claudia A. M. Gandini Wheeler-Kingshott
Giancarlo Germani
Guillaume Gilbert
Federico Giove
Francesco Grussu
Akifumi Hagiwara
Pierre-Gilles Henry
Tomáš Horák
Masaaki Hori
James Joers
Kouhei Kamiya
Haleh Karbasforoushan
Miloš Keřkovský
Ali Khatibi
Joo-won Kim
Nawal Kinany
Hagen H. Kitzler
Shannon Kolind
Yazhuo Kong
Petr Kudlička
Paul Kuntke
Nyoman D. Kurniawan
Slawomir Kusmia
Maria Marcella Lagana
Cornelia Laule
Christine S. W. Law
Csw Law
Tobias Leutritz
Yaou Liu
Sara Llufriu
Sean Mackey
Allan R. Martin
Eloy Martinez-Heras
Loan Mattera
Kristin P. O’Grady
Nico Papinutto
Daniel Papp
Deborah Pareto
Todd B. Parrish
Anna Pichiecchio
Ferran Prados
Àlex Rovira
Marc J. Ruitenberg
Rebecca S. Samson
Giovanni Savini
Maryam Seif
Alan C. Seifert
Alex K. Smith
Seth Aaron Smith
Zachary A. Smith
Elisabeth Solana
Yuichi Suzuki
George Tackley
Alexandra Tinnermann
Dimitri Van De Ville
Marios C. Yiannakas
Kenneth A. Weber
Nikolaus Weiskopf
Richard G. Wise
Patrik O. Wyss
Junqian Xu
Christophe Lenglet
Igor Nestrašil
Changer le regard des étudiants sur les métiers de la comptabilité : Les effets de la simulation de gestion
Yann QUÉMÉNER
La comptabilité véhicule souvent injustement, une image terne et ennuyeuse, auprès du grand public et des jeunes étudiants choisissant l… (voir plus)eur orientation. Dans cet article, nous questionnons l’effet de pratiques pédagogiques sur la perception par les étudiants, des soft skills attendues par les employeurs. Pour cela nous réalisons une quasi-expérimentation dans laquelle nous comparons les perceptions des étudiants selon que le cours ait été animé sous un format classique (application des connaissances par le biais d’exercices avec corrigé par l’enseignant) ou sous la forme d’une simulation de gestion (application des connaissances en vue de prendre des décisions et piloter une entreprise fictive). Les résultats de la recherche montrent qu’une simulation de gestion, plus que les travaux dirigés classiques, permettent aux primo-apprenants en comptabilité, d’avoir une meilleure perception des soft skills attendues par les praticiens et les recruteurs. Nos résultats rappellent l’importance de donner une représentation réaliste (éloignée des clichés) de la profession, afin de rendre les filières d’enseignement de la comptabilité plus attractives.
Child- and Proxy-Reported Differences in Patient-Reported Outcome and Experience Measures in Pediatric Surgery: Systematic Review and Meta-Analysis
Zanib Nafees
Siena O’Neill
Alexandra Dimmer
Elena Guadagno
Julia Ferreira
Nancy Mayo
Child- and Proxy-reported Differences in Patient-reported Outcome and Experience Measures in Pediatric Surgery: Systematic Review and Meta-analysis
Zanib Nafees
Siena O'Neill
Alexandra Dimmer
Elena Guadagno
Julia Ferreira
Nancy Mayo