Publications

Laurence Perreault-Levasseur

Strong gravitational lensing as a probe of dark matter

Simona Vegetti

Simon Birrer

Giulia Despali

C. Fassnacht

Daniel A. Gilman

Yashar Hezaveh

L.

J. McKean

D. Powell

Conor M. O'riordan

G.

Vernardos

Dark matter structures within strong gravitational lens galaxies and along their line of sight leave a gravitational imprint on the multiple… (see more) images of lensed sources. Strong gravitational lensing provides, therefore, a key test of different dark matter models in a way that is independent of the baryonic content of matter structures on subgalactic scales. In this chapter, we describe how galaxy-scale strong gravitational lensing observations are sensitive to the physical nature of dark matter. We provide a historical perspective of the field, and review its current status. We discuss the challenges and advances in terms of data, treatment of systematic errors and theoretical predictions, that will enable one to deliver a stringent and robust test of different dark matter models in the near future. With the advent of the next generation of sky surveys, the number of known strong gravitational lens systems is expected to increase by several orders of magnitude. Coupled with high-resolution follow-up observations, these data will provide a key opportunity to constrain the properties of dark matter with strong gravitational lensing.

2024-07-30

Space Science Reviews (published)

TaskEval: Assessing Difficulty of Code Generation Tasks for Large Language Models

Florian Tambon

Amin Nikanjam

Cyrine Zid

Foutse Khomh

Giuliano Antoniol

2024-07-30

ArXiv (preprint)

TaskEval: Assessing Difficulty of Code Generation Tasks for Large Language Models

Florian Tambon

Amin Nikanjam

Cyrine Zid

Foutse Khomh

Giuliano Antoniol

2024-07-30

ArXiv (preprint)

AAPM task group report 288: Recommendations for guiding radiotherapy event narratives

Bruce Thomadsen

Ajay Kapur

Bette Blankenship

Barrett Caldwell

Lindsey Claps

Joanne Cunningham

Jennifer Elee

Suzanne Evans

Eric Ford

Debbie Gilley

Sandra Hayden

Kathleen Hintenlang

Rishabh Kapoor

John Kildea

Linda Kroger

Ksenija Kujundzic

Qing Liang

Sasa Mutic

Anita O'Donovan

Michael O'Hara … (see 6 more)

Zoubir Ouhib

Jatinder Palta

Todd Pawlicki

William Salter

Stacey Schmidt

Sugata Tripathi

2024-07-29

Medical Physics (Lancaster) (published)

Development of AI-assisted microscopy frameworks through realistic simulation with pySTED

Anthony Bilodeau

Albert Michaud-Gagnon

Julia Chabbert

Benoit Turcotte

Jörn Heine

Flavie Lavoie-Cardinal

2024-07-29

bioRxiv (preprint)

Development of AI-assisted microscopy frameworks through realistic simulation with pySTED

Anthony Bilodeau

Albert Michaud-Gagnon

Julia Chabbert

Benoit Turcotte

Jörn Heine

Flavie Lavoie-Cardinal

The integration of artificial intelligence (AI) into microscopy systems significantly enhances performance, optimizing both the image acquis… (see more)ition and analysis phases. Development of AI-assisted super-resolution microscopy is often limited by the access to large biological datasets, as well as by the difficulties to benchmark and compare approaches on heterogeneous samples. We demonstrate the benefits of a realistic STED simulation platform, pySTED, for the development and deployment of AI-strategies for super-resolution microscopy. The simulation environment provided by pySTED allows the augmentation of data for the training of deep neural networks, the development of online optimization strategies, and the training of reinforcement learning models, that can be deployed successfully on a real microscope.

2024-07-29

bioRxiv (preprint)

Development of AI-assisted microscopy frameworks through realistic simulation with pySTED

Anthony Bilodeau

Albert Michaud-Gagnon

Julia Chabbert

Benoit Turcotte

Jörn Heine

Flavie Lavoie-Cardinal

2024-07-29

bioRxiv (preprint)

Development of AI-assisted microscopy frameworks through realistic simulation with pySTED

Anthony Bilodeau

Albert Michaud-Gagnon

Julia Chabbert

Benoit Turcotte

Jörn Heine

Flavie Lavoie-Cardinal

2024-07-29

bioRxiv (preprint)

Dhuruva Priyan Gowri Mariyappan

Implicitly Bayesian Prediction Rules in Deep Learning

Bruno Mlodozeniec

David Scott Krueger

Richard Turner

The Bayesian approach leads to coherent updates of predictions under new data, which makes adhering to Bayesian principles appealing in deci… (see more)sion-making contexts. Traditionally, integrating Bayesian principles into models like deep neural networks involves setting priors on parameters and approximating posteriors. This is done despite the fact that, typically, priors on parameters reflect any prior beliefs only insofar as they dictate function space behaviour. In this paper, we rethink this approach and consider what properties characterise a prediction rule as being Bayesian. Algorithms meeting such criteria can be deemed implicitly Bayesian — they make the same predictions as some Bayesian model, without explicitly manifesting priors and posteriors. We argue this might be a more fruitful approach towards integrating Bayesian principles into deep learning. In this paper, we propose how to measure how close a general prediction rule is to being implicitly Bayesian, and empirically evaluate multiple prediction strategies using our approach. We also show theoretically that agents relying on non-implicitly Bayesian prediction rules can be easily exploited in adversarial betting settings.

2024-07-29

Proceedings of the 6th Symposium on Advances in Approximate Bayesian Inference (published)

proceedings.mlr.press

Long-term plasticity induces sparse and specific synaptic changes in a biophysically detailed cortical model

András Ecker

Daniela Egas Santander

Marwan Abdellah

Jorge Blanco Alonso

Sirio Bolaños-Puchet

Giuseppe Chindemi

James B. Isbister

James King

Pramod Kumbhar

Ioannis Magkanaris

Eilif Benjamin Muller

Michael W. Reimann

2024-07-29

bioRxiv (preprint)

scCross: A Deep Generative Model for Unifying Single-cell Multi-omics with Seamless Integration, Cross-modal Generation, and In-silico Exploration

Xiuhui Yang

Koren K. Mann

Hao Wu

Jun Ding

Single-cell multi-omics illuminate intricate cellular states, yielding transformative insights into cellular dynamics and disease. Yet, whil… (see more)e the potential of this technology is vast, the integration of its multifaceted data presents challenges. Some modalities have not reached the robustness or clarity of established scRNA-seq. Coupled with data scarcity for newer modalities and integration intricacies, these challenges limit our ability to maximize single-cell omics benefits. We introduce scCross: a tool adeptly engineered using variational autoencoder, generative adversarial network principles, and the Mutual Nearest Neighbors (MNN) technique for modality alignment. This synergy ensures seamless integration of varied single-cell multi-omics data. Beyond its foundational prowess in multi-omics data integration, scCross excels in single-cell cross-modal data generation, multi-omics data simulation, and profound in-silico cellular perturbations. Armed with these capabilities, scCross is set to transform the field of single-cell research, establishing itself in the nuanced integration, generation, and simulation of complex multi-omics data.

2024-07-29

Genome Biology (published)