Publications

Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation
Shivalika Singh
Angelika Romanou
Cl'ementine Fourrier
David Ifeoluwa Adelani
Jian Gang Ngui
Daniel Vila-Suero
Peerat Limkonchotiwat
Kelly Marchisio
Wei Qi Leong
Yosephine Susanto
Raymond Ng
Shayne Longpre
Wei-Yin Ko
Madeline Smith
Antoine Bosselut
Alice Oh
André F. T. Martins
Leshem Choshen
Daphne Ippolito
Enzo Ferrante … (voir 3 de plus)
Marzieh Fadaee
Beyza Ermis
Sara Hooker
2024-12-03
ArXiv (prépublication)
doi.org
arxiv.org
Beta cells are essential drivers of pancreatic ductal adenocarcinoma development
Cathy C. Garcia
Aarthi Venkat
Daniel C. McQuaid
Sherry Agabiti
Alex Tong
Rebecca L. Cardone
Rebecca Starble
Akin Sogunro
Jeremy B. Jacox
Christian F. Ruiz
Richard G. Kibbey
Smita Krishnaswamy
Mandar Deepak Muzumdar
Pancreatic endocrine-exocrine crosstalk plays a key role in normal physiology and disease. For instance, endocrine islet beta (β) cell secr… (voir plus)etion of insulin or cholecystokinin (CCK) promotes progression of pancreatic adenocarcinoma (PDAC), an exocrine cell-derived tumor. However, the cellular and molecular mechanisms that govern endocrine-exocrine signaling in tumorigenesis remain incompletely understood. We find that β cell ablation impedes PDAC development in mice, arguing that the endocrine pancreas is critical for exocrine tumorigenesis. Conversely, obesity induces β cell hormone dysregulation, alters CCK-dependent peri-islet exocrine cell transcriptional states, and enhances islet proximal tumor formation. Single-cell RNA-sequencing, in silico latent-space archetypal and trajectory analysis, and genetic lineage tracing in vivo reveal that obesity stimulates postnatal immature β cell expansion and adaptation towards a pro-tumorigenic CCK+ state via JNK/cJun stress-responsive signaling. These results define endocrine-exocrine signaling as a driver of PDAC development and uncover new avenues to target the endocrine pancreas to subvert exocrine tumorigenesis.
2024-12-02
bioRxiv (prépublication)
doi.org
MiRGraph: A hybrid deep learning approach to identify microRNA-target interactions by integrating heterogeneous regulatory network and genomic sequences
Pei Liu
Yang Liu
Jiawei Luo
Yuemei Li
2024-12-02
2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (publié)
doi.org
Neural Implicit Reduced Fluid Simulation
Yuanyuan Tao
Ivan Puhachov
Derek Nowrouzezahrai
Paul Kry
High-fidelity simulation of fluid dynamics is challenging because of the high dimensional state data needed to capture fine details and the … (voir plus)large computational cost associated with advancing the system in time. We present neural implicit reduced fluid simulation (NIRFS), a reduced fluid simulation technique that combines an implicit neural representation of fluid shapes and a neural ordinary differential equation to model the dynamics of fluid in the reduced latent space. The latent trajectories are computed at very little cost in comparison to simulations for training, while preserving fine physical details. We show that this approach can work well, capturing the shapes and dynamics involved in a variety of scenarios with constrained initial conditions, e.g., droplet-droplet collisions, crown splashes, and fluid slosh in a container. In each scenario, we learn the latent implicit representation of fluid shapes with a deep-network signed distance function, as well as the energy function and parameters of a damped Hamiltonian system, which helps guarantee desirable properties of the latent dynamics. To ensure that latent shape representations form smooth and physically meaningful trajectories, we simultaneously learn the latent representation and dynamics. We evaluate novel simulations for conservation of volume and momentum conservation, discuss design decisions, and demonstrate an application of our method to fluid control.
2024-12-02
ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (publié)
doi.org
Insect Identification in the Wild: The AMI Dataset
Aditya Jain
F. Cunha
M. J. Bunsen
Juan Sebastián Cañas
L. Pasi
N. Pinoy
Flemming Helsing
JoAnne Russo
Marc Botham
Michael Sabourin
Jonathan Fréchette
Alexandre Anctil
Yacksecari Lopez
Eduardo Navarro
Filonila Perez Pimentel
Ana Cecilia Zamora
José Alejandro Ramirez Silva
Jonathan Gagnon
Tom August
K. Bjerge … (voir 8 de plus)
Alba Gomez Segura
Marc Bélisle
Yves Basset
K. P. McFarland
David Roy
Toke Thomas Høye
Maxim Larrivée
David Rolnick
Insects represent half of all global biodiversity, yet many of the world's insects are disappearing, with severe implications for ecosystems… (voir plus) and agriculture. Despite this crisis, data on insect diversity and abundance remain woefully inadequate, due to the scarcity of human experts and the lack of scalable tools for monitoring. Ecologists have started to adopt camera traps to record and study insects, and have proposed computer vision algorithms as an answer for scalable data processing. However, insect monitoring in the wild poses unique challenges that have not yet been addressed within computer vision, including the combination of long-tailed data, extremely similar classes, and significant distribution shifts. We provide the first large-scale machine learning benchmarks for fine-grained insect recognition, designed to match real-world tasks faced by ecologists. Our contributions include a curated dataset of images from citizen science platforms and museums, and an expert-annotated dataset drawn from automated camera traps across multiple continents, designed to test out-of-distribution generalization under field conditions. We train and evaluate a variety of baseline algorithms and introduce a combination of data augmentation techniques that enhance generalization across geographies and hardware setups.
2024-12-01
Lecture Notes in Computer Science (publié)
doi.org
arxiv.org
ProGRes: Prompted Generative Rescoring on ASR n-Best
Ada Defne Tur
Adel Moumen
Mirco Ravanaelli
2024-12-01
2024 IEEE Spoken Language Technology Workshop (SLT) (publié)
doi.org
arxiv.org
Combining supervised learning and local search for the multicommodity capacitated fixed-charge network design problem
Charly Robinson La Rocca
Jean-François Cordeau
Emma Frejinger
The multicommodity capacitated fixed-charge network design problem has been extensively studied in the literature due to its wide range of a… (voir plus)pplications. Despite the fact that many sophisticated solution methods exist today, finding high-quality solutions to large-scale instances remains challenging. In this paper, we explore how a data-driven approach can help improve upon the state of the art. By leveraging machine learning models, we attempt to reveal patterns hidden in the data that might be difficult to capture with traditional optimization methods. For scalability, we propose a prediction method where the machine learning model is called at the level of each arc of the graph. We take advantage of off-the-shelf models trained via supervised learning to predict near-optimal solutions. Our experimental results include an algorithm design analysis that compares various integration strategies of predictions within local search algorithms. We benchmark the ML-based approach with respect to the state-of-the-art heuristic for this problem. The findings indicate that our method can outperform the leading heuristic on sets of instances sampled from a uniform distribution.
2024-11-30
Transportation Research Part E: Logistics and Transportation Review (publié)
doi.org
arxiv.org
Decomposing the Brain in Autism: Linking Behavioral Domains to Neuroanatomical Variation and Genomic Underpinnings.
Hanna Seelemeyer
Caroline Gurr
Johanna Leyhausen
Lisa M. Berg
Charlotte M. Pretzsch
Tim Schäfer
Bassem Hermila
Christine M. Freitag
Eva Loth
Beth Oakley
Luke Mason
Jan K. Buitelaar
Christian Beckmann
Dorothea L. Floris
Tony Charman
Tobias Banaschewski
Emily Jones
Thomas Bourgeron
Jumana Ahmad
Sara Ambrosino … (voir 58 de plus)
Bonnie Auyeung
Simon Baron-Cohen
Sarah Baumeister
Sven Bölte
Carsten Bours
Michael Brammer
Daniel Brandeis
Claudia Brogna
Yvette de Bruijn
Bhismadev Chakrabarti
Ineke Cornelissen
Daisy Crawley
Flavio Dell’Acqua
Guillaume Dumas
Sarah Durston
Christine Ecker
Jessica Faulkner
Vincent Frouin
Pilar Garcés
David Goyard
Lindsay Ham
Hannah Hayward
Joerg F. Hipp
Rosemary Holt
Mark Johnson
Emily J. H. Jones
Prantik Kundu
Meng-Chuan Lai
Xavier Liogier D’ardhuy
Michael V. Lombardo
David J. Lythgoe
René Mandl
Andre Marquand
Maarten Mennes
Andreas Meyer-Lindenberg
Carolin Moessnang
Nico Bast
Larry O’Dwyer
Marianne Oldehinkel
Bob Oranje
Gahan Pandina
Antonio Persico
Barbara Ruggeri
Declan G.M. Murphy
Amber N. V. Ruigrok
Jessica Sabet
Roberto Sacco
Antonia San José Cáceres
Emily Simonoff
Will Spooren
Julian Tillmann
Roberto Toro
Heike Tost
Jack Waldman
Steve C. R. Williams
Caroline Wooldridge
Marcel P. Zwiers
Declan Murphy
2024-11-30
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging (publié)
doi.org
Effects of gene dosage on cognitive ability: A function-based association study across brain and non-brain processes
Guillaume Huguet
Thomas Renne
Cécile Poulain
Alma Dubuc
Kuldeep Kumar
Sayeh Kazem
Worrawat Engchuan
Omar Shanta
Élise Douard
Catherine Proulx
Martineau Jean-Louis
Zohra Saci
Josephine Mollon
Laura M. Schultz
Emma E.M. Knowles
Simon R. Cox
David Porteous
Gail Davies
Paul Redmond
Sarah E. Harris … (voir 10 de plus)
Gunter Schumann
Guillaume Dumas
Aurélie Labbe
Zdenka Pausova
Tomáš Paus
Stephen W. Scherer
Jonathan Sebat
Laura Almasy
David C. Glahn
Sébastien Jacquemont
Copy-number variants (CNVs) that increase the risk for neurodevelopmental disorders also affect cognitive ability. However, such CNVs remain… (voir plus) challenging to study due to their scarcity, limiting our understanding of gene-dosage-sensitive biological processes linked to cognitive ability. We performed a genome-wide association study (GWAS) in 258,292 individuals, which identified—for the first time—a duplication at 2q12.3 associated with higher cognitive performance. We developed a functional-burden analysis, which tested the association between cognition and CNVs disrupting 6,502 gene sets biologically defined across tissues, cell types, and ontologies. Among those, 864 gene sets were associated with cognition, and effect sizes of deletion and duplication were negatively correlated. The latter suggested that functions across all biological processes were sensitive to either deletions (e.g., subcortical regions, postsynaptic) or duplications (e.g., cerebral cortex, presynaptic). Associations between non-brain tissues and cognition were driven partly by constrained genes, which may shed light on medical comorbidities in neurodevelopmental disorders.
2024-11-30
Cell Genomics (publié)
doi.org
Ensemble machine learning to accelerate industrial decarbonization: Prediction of Hansen solubility parameters for streamlined chemical solvent selection
Eslam G. Al-Sakkari
Ahmed Ragab
Mostafa Amer
Olumoye Ajao
Marzouk Benali
Daria C. Boffito
Hanane Dagdougui
Mouloud Amazouz
2024-11-30
Digital Chemical Engineering (publié)
doi.org
Predicting the Mathematics Literacy of Resilient Students from High‐performing Economies: A Machine Learning Approach
Yimei Zhang
Maria Cutumisu
2024-11-30
Studies in Educational Evaluation (publié)
doi.org
Towards AI-designed genomes using a variational autoencoder
Natasha K. Dudek
Doina Precup
Genomes encode elaborate networks of genes whose products must seamlessly interact to support living organisms. Humans’ capacity to unders… (voir plus)tand these biological systems is limited by their sheer size and complexity. In this article, we develop a proof of concept framework for training a machine learning (ML) algorithm to model bacterial genome composition. To achieve this, we create simplified representations of genomes in the form of binary vectors that indicate the encoded genes, henceforth referred to as genome vectors. A denoising variational autoencoder was trained to accept corrupted genome vectors, in which most genes had been masked, and reconstruct the original. The resulting model, DeepGenomeVector, effectively captures complex dependencies in genomic networks, as evaluated by both qualitative and quantitative metrics. An in-depth functional analysis of a generated genome vector shows that its encoded pathways are interconnected, near complete, and ecologically cohesive. On the test set, where the model’s ability to reconstruct uncorrupted genome vectors was evaluated, Area Under the Receiver Operating Curve (AUROC) and F1 scores of 0.98 and 0.83, respectively, support the model’s strong performance. This article showcases the power of ML approaches for synthetic biology and highlights the possibility that artifical intelligence agents may one day be able to design genomes that animate carbon-based cells.
2024-11-30
Proceedings of the Royal Society B: Biological Sciences (publié)
doi.org