Publications

Molar Pregnancy in a Quadruplet Conception Following IVF: A Case Report
Madhuri A Mehendale
Meenal Shailesh Sarmalkar
Prerna Kailashchand Gupta
Aishwarya Agrawal
Agraj S Doshi
2024-02-22
Journal of South Asian Federation of Obstetrics and Gynaecology (published)
doi.org
The Sample Average Approximation Method for Solving Two-Stage Stochastic Programs with Endogenous Uncertainty
Maria Bazotte
Margarida Carvalho
Thibaut Vidal
Real-world decision-making problems involve Type 1 decision-dependent uncertainty, where the probability distribution of the stochastic proc… (see more)ess depends on the model decisions. However, few studies focus on two-stage stochastic programs with this type of endogenous uncertainty, and those that do lack general methodologies. We thus propose herein a general method for solving a class of these programs based on the transformation of random variables, a technique widely employed in probability and statistics. The proposed method is tailored to large-scale problems with discrete or continuous endogenous random variables. The random variable transformation allows the use of the sample average approximation (SAA) method, which provides optimality convergence guarantees under certain conditions. We show that, for some classical distributions, the proposed method reduces to solving mixed-integer linear or convex programs. Finally, we validate this method by applying it to a network design and facility-protection problem, considering distinct decision-dependent distributions for the random variables. Whereas most distributions result in a nonlinear nonconvex deterministic equivalent program, the proposed method solves mixed-integer linear programs in all cases. In addition, it produces attractive performance estimators for the SAA method in a reasonable computational time and outperforms the case in which the endogenous distribution defines a mixed-integer deterministic equivalent.
2024-02-22
ArXiv (preprint)
arxiv.org
arxiv.org
Matrix Factorization Recommendation Algorithm Based on Attention Interaction
Chengzhi Mao
Zhifeng Wu
Yingjie Liu
Zhiwei Shi
2024-02-21
Symmetry (published)
doi.org
Posterior inference of Hi-C contact frequency through sampling
Yanlin Zhang
Christopher J. F. Cameron
Mathieu Blanchette
Hi-C is one of the most widely used approaches to study three-dimensional genome conformations. Contacts captured by a Hi-C experiment are r… (see more)epresented in a contact frequency matrix. Due to the limited sequencing depth and other factors, Hi-C contact frequency matrices are only approximations of the true interaction frequencies and are further reported without any quantification of uncertainty. Hence, downstream analyses based on Hi-C contact maps (e.g., TAD and loop annotation) are themselves point estimations. Here, we present the Hi-C interaction frequency sampler (HiCSampler) that reliably infers the posterior distribution of the interaction frequency for a given Hi-C contact map by exploiting dependencies between neighboring loci. Posterior predictive checks demonstrate that HiCSampler can infer highly predictive chromosomal interaction frequency. Summary statistics calculated by HiCSampler provide a measurement of the uncertainty for Hi-C experiments, and samples inferred by HiCSampler are ready for use by most downstream analysis tools off the shelf and permit uncertainty measurements in these analyses without modifications.
2024-02-21
Frontiers in Bioinformatics (published)
doi.org
Reinforcement Learning with Elastic Time Steps
Yong Wang
Giovanni Beltrame
2024-02-21
ArXiv (preprint)
doi.org
arxiv.org
Searching for Strong Gravitational Lenses
Cameron Lemon
Frederic Courbin
Anupreeta More
Paul Schechter
Raoul Cañameras
Ludovic Delchambre
Calvin Leung
Yiping Shu
Chiara Spiniello
Yashar Hezaveh
Jonas Klüter
Richard G. McMahon
2024-02-20
Space Science Reviews (published)
doi.org
arxiv.org
Structural covariation between cerebellum and neocortex intrinsic structural covariation links cerebellum subregions to the cerebral cortex
Zilong Wang
Jörn Diedrichsen
Karin Saltoun
Christopher Steele
Sheeba Rani Arnold-Anteraper
B. T. Thomas Yeo
Jeremy D. Schmahmann
Danilo Bzdok
Cerebellum’s association with the entire cerebral cortex has not been holistically studied in a unified way. Here, we conjointly character… (see more)ize the population-level cortical-cerebellar structural covariation patterns leveraging ∼40,000 UK Biobank participants whole brain structural scans and ∼1,000 phenotypes. We revitalize the previous hypothesis of an anticorrelation between the visual-attention system and advanced associative networks within the cerebellum. We also discovered a novel ipsilateral cerebral-cerebellar associations. Phenome-wide association (PheWAS) revealed real-world implications of the structural covariation patterns.
2024-02-19
bioRxiv (preprint)
doi.org
Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks
Sitao Luan
Mingde Zhao
Xiao-Wen Chang
Doina Precup
2024-02-19
Complex Networks & Their Applications XII (published)
doi.org
arxiv.org
Deep Reinforcement Learning in Human Activity Recognition: A Survey and Outlook.
Bahareh Nikpour
Dimitrios Sinodinos
Narges Armanfard
Human activity recognition (HAR) is a popular research field in computer vision that has already been widely studied. However, it is still a… (see more)n active research field since it plays an important role in many current and emerging real-world intelligent systems, like visual surveillance and human-computer interaction. Deep reinforcement learning (DRL) has recently been used to address the activity recognition problem with various purposes, such as finding attention in video data or obtaining the best network structure. DRL-based HAR has only been around for a short time, and it is a challenging, novel field of study. Therefore, to facilitate further research in this area, we have constructed a comprehensive survey on activity recognition methods that incorporate DRL. Throughout the article, we classify these methods according to their shared objectives and delve into how they are ingeniously framed within the DRL framework. As we navigate through the survey, we conclude by shedding light on the prominent challenges and lingering questions that await the attention of future researchers, paving the way for further advancements and breakthroughs in this exciting domain.
2024-02-18
IEEE Transactions on Neural Networks and Learning Systems (published)
doi.org
Virtual Reality for Pediatric Trauma Education - A Face and Content Validation Study
Fabio Botelho
Said Ashkar
Shreenik Kundu
TJ Matthews
Elena Guadgano
Jason Harley
Dan Poenaru
Purpose: Pediatric trauma is a leading cause of death and disability among children. While trauma education can improve these outcomes, it r… (see more)emains expensive and available only to a few providers worldwide. Innovative educational technologies like virtual reality (VR) can be key to democratizing trauma education. This study, therefore, evaluates the face and content validity of a VR platform designed to enhance pediatric trauma skills. Specifically, we seek to determine whether the platform effectively presents an injured child and comprehensively covers the essential tasks to successfully treat them within a trauma team. Methods: Physicians were invited to test a VR platform simulating a child with blunt head and truncal trauma. After the simulation, they filled out surveys assessing the face and content validity of the scenario, including their opinions on the realism, interaction, ease of use, and the educational content of the platform. Additionally, they completed a cybersickness questionnaire. Demographic data were also collected, including age, gender, country of medical education, and previous experience with VR. A descriptive analysis was performed. Results: Eleven physicians graduated from eight different countries tested the VR platform. Most (87%) found it valuable, and 81% preferred using it over high-fidelity mannequins for training purposes. The platform received more favorable evaluations for non-technical skills training (median: 5, IQR: 5.0 to 5.0) than for technical skills (median: 4, IQR: 3.0 to 5.0). Regarding cybersickness, 73% of the participants reported experiencing any or minimal discomfort during the simulation, and none needed to stop the test due to discomfort. Conclusion: Our initial validation of a VR platform designed for pediatric trauma education was positive. Participants endorsed VR and its potential to enhance performance, particularly in non-technical skills. Encouraged by these results, we will proceed with feasibility and implementation studies, comparing VR to high-fidelity mannequins.
2024-02-17
MedRxiv (preprint)
doi.org
Multi-ancestry polygenic risk scores using phylogenetic regularization
Elliot Layne
Shadi Zabad
Yue Li
Mathieu Blanchette
Accurately predicting phenotype using genotype across diverse ancestry groups remains a significant challenge in human genetics. Many state-… (see more)of-the-art polygenic risk score models are known to have difficulty generalizing to genetic ancestries that are not well represented in their training set. To address this issue, we present a novel machine learning method for fitting genetic effect sizes across multiple ancestry groups simultaneously, while leveraging prior knowledge of the evolutionary relationships among them. We introduce DendroPRS, a machine learning model where SNP effect sizes are allowed to evolve along the branches of the phylogenetic tree capturing the relationship among populations. DendroPRS outperforms existing approaches at two important genotype-to-phenotype prediction tasks: expression QTL analysis and polygenic risk scores. We also demonstrate that our method can be useful for multi-ancestry modelling, both by fitting population-specific effect sizes and by more accurately accounting for covariate effects across groups. We additionally find a subset of genes where there is strong evidence that an ancestry-specific approach improves eQTL modelling.
2024-02-16
bioRxiv (preprint)
doi.org
Deep Equilibrium Models For Algorithmic Reasoning
Sophie Xhonneux
Yu He
Andreea Deac
Jian Tang
Gauthier Gidel
In this blogpost we discuss the idea of teaching neural networks to reach fixed points when reasoning. Specifically, on the algorithmic reas… (see more)oning benchmark CLRS the current neural networks are told the number of reasoning steps they need. While a quick fix is to add a termination network that predicts when to stop, a much more salient inductive bias is that the neural network shouldn't change it's answer any further once the answer is correct, i.e. it should reach a fixed point. This is supported by denotational semantics, which tells us that while loops that terminate are the minimum fixed points of a function. We implement this idea with the help of deep equilibrium models and discuss several hurdles one encounters along the way. We show on several algorithms from the CLRS benchmark the partial success of this approach and the difficulty in making it work robustly across all algorithms.
2024-02-15
ICLR.cc/2024/BlogPosts (accepted)
openreview.net
openreview.net