Publications

Implementing a Hierarchical Deep Learning Approach for Simulating multilevel Auction Data
Marcelin Joanis
Andrea Lodi
Igor Sadoune
2024-05-17
Computational Economics (publié)
doi.org
arxiv.org
Automatic segmentation of Organs at Risk in Head and Neck cancer patients from CT and MRI scans
Sébastien Quetin
Andrew Heschl
Mauricio Murillo
Rohit Murali
S. Enger
Farhad Maleki
Background and purpose: Deep Learning (DL) has been widely explored for Organs at Risk (OARs) segmentation; however, most studies have focus… (voir plus)ed on a single modality, either CT or MRI, not both simultaneously. This study presents a high-performing DL pipeline for segmentation of 30 OARs from MRI and CT scans of Head and Neck (H&N) cancer patients. Materials and methods: Paired CT and MRI-T1 images from 42 H&N cancer patients alongside annotation for 30 OARs from the H&N OAR CT&MR segmentation challenge dataset were used to develop a segmentation pipeline. After cropping irrelevant regions, rigid followed by non-rigid registration of CT and MRI volumes was performed. Two versions of the CT volume, representing soft tissues and bone anatomy, were stacked with the MRI volume and used as input to an nnU-Net pipeline. Modality Dropout was used during the training to force the model to learn from the different modalities. Segmentation masks were predicted with the trained model for an independent set of 14 new patients. The mean Dice Score (DS) and Hausdorff Distance (HD) were calculated for each OAR across these patients to evaluate the pipeline. Results: This resulted in an overall mean DS and HD of 0.777 +- 0.118 and 3.455 +- 1.679, respectively, establishing the state-of-the-art (SOTA) for this challenge at the time of submission. Conclusion: The proposed pipeline achieved the best DS and HD among all participants of the H&N OAR CT and MR segmentation challenge and sets a new SOTA for automated segmentation of H&N OARs.
2024-05-16
ArXiv (prépublication)
doi.org
arxiv.org
Bringing together multimodal and multilevel approaches to study the emergence of social bonds between children and improve social AI
Julie Bonnaire
Guillaume Dumas
Justine Cassell
This protocol paper outlines an innovative multimodal and multilevel approach to studying the emergence and evolution of how children build … (voir plus)social bonds with their peers, and its potential application to improving social artificial intelligence (AI). We detail a unique hyperscanning experimental framework utilizing functional near-infrared spectroscopy (fNIRS) to observe inter-brain synchrony in child dyads during collaborative tasks and social interactions. Our proposed longitudinal study spans middle childhood, aiming to capture the dynamic development of social connections and cognitive engagement in naturalistic settings. To do so we bring together four kinds of data: the multimodal conversational behaviors that dyads of children engage in, evidence of their state of interpersonal rapport, collaborative performance on educational tasks, and inter-brain synchrony. Preliminary pilot data provide foundational support for our approach, indicating promising directions for identifying neural patterns associated with productive social interactions. The planned research will explore the neural correlates of social bond formation, informing the creation of a virtual peer learning partner in the field of Social Neuroergonomics. This protocol promises significant contributions to understanding the neural basis of social connectivity in children, while also offering a blueprint for designing empathetic and effective social AI tools, particularly for educational contexts.
2024-05-16
Frontiers in Neuroergonomics (publié)
doi.org
GFETM: Genome Foundation-based Embedded Topic Model for scATAC-seq Modeling
Yimin Fan
Adrien Osakwe
Shi Han
Yu Li
Jun Ding
Yue Li
Single-cell Assay for Transposase-Accessible Chromatin with sequencing (scATAC-seq) has emerged as a powerful technique for investigating op… (voir plus)en chromatin landscapes at single-cell resolution. However, analyzing scATAC-seq data remain challenging due to its sparsity and noise. Genome Foundation Models (GFMs), pre-trained on massive DNA sequences, have proven effective at genome analysis. Given that open chromatin regions (OCRs) harbour salient sequence features, we hypothesize that leveraging GFMs’ sequence embeddings can improve the accuracy and generalizability of scATAC-seq modeling. Here, we introduce the Genome Foundation Embedded Topic Model (GFETM), an interpretable deep learning framework that combines GFMs with the Embedded Topic Model (ETM) for scATAC-seq data analysis. By integrating the DNA sequence embeddings extracted by a GFM from OCRs, GFETM demonstrates superior accuracy and generalizability and captures cell-state specific TF activity both with zero-shot inference and attention mechanism analysis. Finally, the topic mixtures inferred by GFETM reveal biologically meaningful epigenomic signatures of kidney diabetes.
2024-05-16
Conference on Research in Computational Molecular Biology (publié)
doi.org
Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning
Holly Steach
Siddharth Viswanath
Yixuan He
Xitong Zhang
Natalia Ivanova
Matthew Hirn
Michael Perlmutter
Smita Krishnaswamy
2024-05-16
Lecture Notes in Computer Science (publié)
doi.org
Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer’s disease progression
Liam Hodgson
Yue Li
Yasser Iturria-Medina
Jo Anne Stratton
Guy Wolf
Smita Krishnaswamy
David A. Bennett
Danilo Bzdok
Late onset Alzheimer’s disease (AD) is a progressive neurodegenerative disease, with brain changes beginning years before symptoms surface… (voir plus). AD is characterized by neuronal loss, the classic feature of the disease that underlies brain atrophy. However, GWAS reports and recent single-nucleus RNA sequencing (snRNA-seq) efforts have highlighted that glial cells, particularly microglia, claim a central role in AD pathophysiology. Here, we tailor pattern-learning algorithms to explore distinct gene programs by integrating the entire transcriptome, yielding distributed AD-predictive modules within the brain’s major cell-types. We show that these learned modules are biologically meaningful through the identification of new and relevant enriched signaling cascades. The predictive nature of our modules, especially in microglia, allows us to infer each subject’s progression along a disease pseudo-trajectory, confirmed by post-mortem pathological brain tissue markers. Additionally, we quantify the interplay between pairs of cell-type modules in the AD brain, and localized known AD risk genes to enriched module gene programs. Our collective findings advocate for a transition from cell-type-specificity to gene modules specificity to unlock the potential of unique gene programs, recasting the roles of recently reported genome-wide AD risk loci. Designing a supervised latent factor framework for snRNA-seq human brain, the authors find distinct Alzheimer’s-predictive gene modules across celltypes, suggesting subcelltype disease progression trajectories.
2024-05-16
Communications Biology (publié)
doi.org
Cognitive, interpersonal, and intrapersonal deeper learning domains: A systematic review of computational thinking
Hao-Yue Jin
Maria Cutumisu
2024-05-15
Education and Information Technologies : Official Journal of the IFIP technical committee on Education (publié)
doi.org
Data Selection for Transfer Unlearning
Nazanin Mohammadi Sepahvand
Vincent Dumoulin
Eleni Triantafillou
Gintare Karolina Dziugaite
2024-05-15
arXiv (prépublication)
doi.org
arxiv.org
Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering
Vaibhav Adlakha
Parishad BehnamGhader
Xing Han Lu
Nicholas Meade
Siva Reddy
Retriever-augmented instruction-following models are attractive alternatives to fine-tuned approaches for information-seeking tasks such as … (voir plus)question answering (QA). By simply prepending retrieved documents in its input along with an instruction, these models can be adapted to various information domains and tasks without additional fine-tuning. While the model responses tend to be natural and fluent, the additional verbosity makes traditional QA evaluation metrics such as exact match (EM) and F1 unreliable for accurately quantifying model performance. In this work, we investigate the performance of instruction-following models across three information-seeking QA tasks. We use both automatic and human evaluation to evaluate these models along two dimensions: 1) how well they satisfy the user's information need (correctness), and 2) whether they produce a response based on the provided knowledge (faithfulness). Guided by human evaluation and analysis, we highlight the shortcomings of traditional metrics for both correctness and faithfulness. We then propose simple token-overlap based and model-based metrics that reflect the true performance of these models. Our analysis reveals that instruction-following models are competitive, and sometimes even outperform fine-tuned models for correctness. However, these models struggle to stick to the provided knowledge and often hallucinate in their responses. We hope our work encourages a more holistic evaluation of instruction-following models for QA. Our code and data is available at https://github.com/McGill-NLP/instruct-qa
2024-05-15
Transactions of the Association for Computational Linguistics (publié)
doi.org
arxiv.org
Sustained IFN signaling is associated with delayed development of SARS-CoV-2-specific immunity
Elsa Brunet-Ratnasingham
Sacha Morin
Haley E. Randolph
Marjorie Labrecque
Justin Bélair
Raphaël Lima-Barbosa
Amélie Pagliuzza
Lorie Marchitto
Michael Hultström
Julia Niessl
Rose Cloutier
Alina M. Sreng Flores
Nathalie Brassard
Mehdi Benlarbi
Jérémie Prévost
Shilei Ding
Sai Priya Anand
Gérémy Sannier
Eric Bareke
Hugo Zeberg … (voir 24 de plus)
Miklos Lipcsey
Robert Frithiof
Anders Larsson
Sirui Zhou
Tomoko Nakanishi
David Morrison
Dani Vezina
Catherine Bourassa
Gabrielle Gendron-Lepage
Halima Medjahed
Floriane Point
Jonathan Richard
Catherine Larochelle
Alexandre Prat
Nathalie Arbour
Madeleine Durand
J Brent Richards
Kevin Moon
Nicolas Chomont
Andrés Finzi
Martine Tétreault
Luis Barreiro
Guy Wolf
Daniel E. Kaufmann
Plasma RNAemia, delayed antibody responses and inflammation predict COVID-19 outcomes, but the mechanisms underlying these immunovirological… (voir plus) patterns are poorly understood. We profile 782 longitudinal plasma samples from 318 hospitalized patients with COVID-19. Integrated analysis using k-means reveals four patient clusters in a discovery cohort: mechanically ventilated critically-ill cases are subdivided into good prognosis and high-fatality clusters (reproduced in a validation cohort), while non-critical survivors segregate into high and low early antibody responders. Only the high-fatality cluster is enriched for transcriptomic signatures associated with COVID-19 severity, and each cluster has distinct RBD-specific antibody elicitation kinetics. Both critical and non-critical clusters with delayed antibody responses exhibit sustained IFN signatures, which negatively correlate with contemporaneous RBD-specific IgG levels and absolute SARS-CoV-2-specific B and CD4+ T cell frequencies. These data suggest that the “Interferon paradox” previously described in murine LCMV models is operative in COVID-19, with excessive IFN signaling delaying development of adaptive virus-specific immunity.
2024-05-15
Nature Communications (publié)
doi.org
Towards a framework selection for assessing the performance of photovoltaic solar power plants: criteria determination
Meryam Chafiq
Loubna Benabbou
Hanane Dagdougui
Ismail Belhaj
Abdelali Djdiaa
Hicham Bouzekri
Abdelaziz Berrado
Mastery of Key Performance Indicators (KPIs) in the realm of photovoltaic solar power plants is pivotal for evaluating their effectiveness a… (voir plus)nd fine-tuning their operational efficiency. The assessment of these plants' performance has con-sistently stood as a focal point in scientific research. Nevertheless, the investigation into the process of selecting a framework for classifying KPIs, particularly through their categorization based on criteria, sub-criteria, or aspects, has been relatively limited in research. This article addresses this gap by conducting a comprehensive literature review on various KPIs and, drawing upon both literature and practical experience, formulating a set of criteria to serve as the foundation for a Multi-Criteria Decision Analysis (MCDA) method. This intricate taxonomic framework enhances the understanding of infrastructure performance for stakeholders in the solar industry. By streamlining decision-making, it simplifies the selection of KPIs tailored to specific requirements, thus mitigating the complexity arising from the abundance of KPIs in the literature. As a result, decision-makers can make well-informed choices regarding the monitoring and evaluation framework that best suits the performance goals of their solar plant.
2024-05-15
2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) (publié)
doi.org
Machine Learning and Information Theory Concepts Towards an AI Mathematician
Yoshua Bengio
Nikolay Malkin
The current state-of-the-art in artificial intelligence is impressive, especially in terms of mastery of language, but not so much in terms … (voir plus)of mathematical reasoning. What could be missing? Can we learn something useful about that gap from how the brains of mathematicians go about their craft? This essay builds on the idea that current deep learning mostly succeeds at system 1 abilities -- which correspond to our intuition and habitual behaviors -- but still lacks something important regarding system 2 abilities -- which include reasoning and robust uncertainty estimation. It takes an information-theoretical posture to ask questions about what constitutes an interesting mathematical statement, which could guide future work in crafting an AI mathematician. The focus is not on proving a given theorem but on discovering new and interesting conjectures. The central hypothesis is that a desirable body of theorems better summarizes the set of all provable statements, for example by having a small description length while at the same time being close (in terms of number of derivation steps) to many provable statements.
2024-05-14
Bulletin of the American Mathematical Society (publié)
doi.org
arxiv.org