Publications

Advancing Sustainable Maritime Transport: A Machine Learning Approach to Predict and Mitigate Underwater Radiated Noise from Ships

Soukaina Boujdi

Ayoub Atanane

Pierre Cauchy

2025-04-07

Proceedings of the International Conference on Industrial Engineering and Operations Management (publié)

A Comparative Analysis of AI Models for Short-Term Solar Irradiance Forecasting

Saad Benbrahim

Abdelaziz Berrado

Hanane Dagdougui

2025-04-07

Proceedings of the International Conference on Industrial Engineering and Operations Management (publié)

Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-based Likelihoods and Priors

Connor Bottrell

Laurence Perreaul-Levasseur

Examining the detailed structure of galaxy populations provides valuable insights into their formation and evolution mechanisms. Significant… (voir plus) barriers to such analysis are the nontrivial noise properties of real astronomical images and the point-spread function, which blurs structure. Here we present a framework which combines recent advances in score-based likelihood characterization and diffusion model priors to perform a Bayesian analysis of image deconvolution. The method, when applied to minimally processed Hubble Space Telescope data, recovers structures which have otherwise only become visible in next-generation James Webb Space Telescope imaging.

2025-04-07

Astronomical Journal (publié)

Enhancing Hybrid Model for Photovoltaic Power Prediction: A Case Study of Morocco

Samira Abousaid

Abdelaziz Berrado

Hanane Dagdougui

2025-04-07

Proceedings of the International Conference on Industrial Engineering and Operations Management (publié)

InfoGain Wavelets: Furthering the Design of Diffusion Wavelets for Graph-Structured Data

David R. Johnson

Smita Krishnaswamy

Michael Perlmutter

Diffusion wavelets extract information from graph signals at different scales of resolution by utilizing graph diffusion operators raised to… (voir plus) various powers, known as diffusion scales. Traditionally, the diffusion scales are chosen to be dyadic integers,

2025-04-07

ArXiv (prépublication)

Predicting greenhouse gas Emissions in Shipping: A Case Study Of Canada

Abdelhak El Aissi

Abdelaziz Berrado

Stephane Carron

2025-04-07

Proceedings of the International Conference on Industrial Engineering and Operations Management (publié)

TAPNext: Tracking Any Point (TAP) as Next Token Prediction

Artem Zholus

Carl Doersch

Yi Yang

Skanda Koppula

Viorica Patraucean

Xu Owen He

Ignacio Rocco

Mehdi S. M. Sajjadi

A. Chandar

Ross Goroshin

2025-04-07

ArXiv (prépublication)

The role of AI for MRI-analysis in multiple sclerosis—A brief overview

Jean-Pierre R. Falet

Steven Nobile

Aliya Szpindel

Berardino Barile

Amar Kumar

Joshua D. Durso-Finley

Tal Arbel

Douglas Arnold

2025-04-07

Frontiers Artif. Intell. (publié)

ECLARE: multi-teacher contrastive learning via ensemble distillation for diagonal integration of single-cell multi-omic data

Dylan Mann-Krzisnik

Anjali Chawla

Gustavo Turecki

Corina Nagy

Yue Li

Integrating multimodal single-cell data such as scRNA-seq with scATAC-seq is essential for decoding gene regulatory networks, but remains di… (voir plus)fficult due to feature harmonization and limited paired multiome data. We introduce ECLARE, a framework that uses multi-teacher ensemble knowledge distillation with contrastive learning and optimal-transport alignment to integrate unpaired single-cell multi-omic datasets. Across benchmarks, ECLARE achieves competitive performance for multimodal integration and biological structure preservation. We further demonstrate utility in a major depressive disorder case study using unpaired snRNA-seq and snATAC-seq, identifying transcription factor–target gene programs that are differentially regulated with sex- and cell-type specificity. Finally, ECLARE learns continuous representations that capture longitudinal structure, highlighting altered neurodevelopmental programs associated with depression in female subjects. Altogether, ECLARE expands the practical reach of multimodal single-cell analysis by enabling diagonal integration of unpaired data with strong biological preservation, facilitating integrative regulatory studies across diverse cohorts and conditions.

2025-04-06

bioRxiv (prépublication)

Prism: Dynamic and Flexible Benchmarking of LLMs Code Generation with Monte Carlo Tree Search

Vahid Majdinasab

Amin Nikanjam

Foutse Khomh

2025-04-06

ArXiv (prépublication)