scHiCyclePred: a deep learning framework for predicting cell cycle phases from single-cell Hi-C data using multi-scale interaction information
Yingfu Wu
Zhenqi Shi
Xiangfei Zhou
Pengyu Zhang
Xiuhui Yang
Hao Wu
Assessing Programming Task Difficulty for Efficient Evaluation of Large Language Models
Florian Tambon
Amin Nikanjam
Giuliano Antoniol
Strong gravitational lensing as a probe of dark matter
Simona Vegetti
Simon Birrer
Giulia Despali
C. Fassnacht
Daniel A. Gilman
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… (voir plus) 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.
TaskEval: Assessing Difficulty of Code Generation Tasks for Large Language Models
Florian Tambon
Amin Nikanjam
Cyrine Zid
Giuliano Antoniol
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
Linda Kroger
Ksenija Kujundzic
Qing Liang
Sasa Mutic
Anita O'Donovan
Michael O'Hara … (voir 6 de plus)
Zoubir Ouhib
Jatinder Palta
Todd Pawlicki
William Salter
Stacey Schmidt
Sugata Tripathi
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
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… (voir plus)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.
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
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
Implicitly Bayesian Prediction Rules in Deep Learning
Bruno Mlodozeniec
Richard Turner
The Bayesian approach leads to coherent updates of predictions under new data, which makes adhering to Bayesian principles appealing in deci… (voir plus)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.
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
Dhuruva Priyan Gowri Mariyappan
James B. Isbister
James King
Pramod Kumbhar
Ioannis Magkanaris
Michael W. Reimann
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
Single-cell multi-omics illuminate intricate cellular states, yielding transformative insights into cellular dynamics and disease. Yet, whil… (voir plus)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.