Publications

Investigation of dosimetric characteristics of radiochromic film in response to alpha particles emitted from Americium-241.
Victor D. Diaz‐Martinez
Mélodie Cyr
S. Devic
Nada Tomic
David F. Lewis
Shirin A. Enger
BACKGROUND In radiotherapy, it is essential to deliver prescribed doses to tumors while minimizing damage to surrounding healthy tissue. Acc… (voir plus)urate measurements of absorbed dose are required for this purpose. Gafchromic® external beam therapy (EBT) radiochromic films have been widely used in radiotherapy. While the dosimetric characteristics of the EBT3 model film have been extensively studied for photon and charged particle beams (protons, electrons, and carbon ions), little research has been done on α
2024-05-20
Medical Physics (Lancaster) (publié)
doi.org
BitPruning: Learning Bitlengths for Aggressive and Accurate Quantization
Miloš Nikolić
Ghouthi Boukli Hacene
Ciaran Bannon
Alberto Delmas Lascorz
Matthieu Courbariaux
Omar Mohamed Awad
Yoshua Bengio
Isak Edo Vivancos
Vincent Gripon
Andreas Moshovos
Neural networks have demonstrably achieved state-of-the art accuracy using low-bitlength integer quantization, yielding both execution time … (voir plus)and energy benefits on existing hardware designs that support short bitlengths. However, the question of finding the minimum bitlength for a desired accuracy remains open. We introduce a training method for minimizing inference bitlength at any granularity while maintaining accuracy. Namely, we propose a regularizer that penalizes large bitlength representations throughout the architecture and show how it can be modified to minimize other quantifiable criteria, such as number of operations or memory footprint. We demonstrate that our method learns thrifty representations while maintaining accuracy. With ImageNet, the method produces an average per layer bitlength of 4.13, 3.76 and 4.36 bits on AlexNet, ResNet18 and MobileNet V2 respectively, remaining within 2.0%, 0.5% and 0.5% of the base TOP-1 accuracy.
2024-05-19
2024 IEEE International Symposium on Circuits and Systems (ISCAS) (publié)
doi.org
arxiv.org
Coordination among leaf and fine-root traits along a strong natural soil fertility gradient
Xavier Guilbeault-Mayers
Hans Lambers
Étienne Laliberté
2024-05-18
Plant and Soil (publié)
doi.org
Implementing a Hierarchical Deep Learning Approach for Simulating multilevel Auction Data
Marcelin Joanis
Andrea Lodi
Igor Sadoune
2024-05-18
Computational Economics (publié)
doi.org
arxiv.org
Towards Modular LLMs by Building and Reusing a Library of LoRAs
Oleksiy Ostapenko
Zhan Su
Edoardo Ponti
Laurent Charlin
Nicolas Le Roux
Matheus Pereira
Lucas Caccia
Alessandro Sordoni
The growing number of parameter-efficient adaptations of a base large language model (LLM) calls for studying whether we can reuse such trai… (voir plus)ned adapters to improve performance for new tasks. We study how to best build a library of adapters given multi-task data and devise techniques for both zero-shot and supervised task generalization through routing in such library. We benchmark existing approaches to build this library and introduce model-based clustering, MBC, a method that groups tasks based on the similarity of their adapter parameters, indirectly optimizing for transfer across the multi-task dataset. To re-use the library, we present a novel zero-shot routing mechanism, Arrow, which enables dynamic selection of the most relevant adapters for new inputs without the need for retraining. We experiment with several LLMs, such as Phi-2 and Mistral, on a wide array of held-out tasks, verifying that MBC-based adapters and Arrow routing lead to superior generalization to new tasks. We make steps towards creating modular, adaptable LLMs that can match or outperform traditional joint training.
2024-05-18
ArXiv (prépublication)
arxiv.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
Shirin A. 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.
2024-05-17
ArXiv (prépublication)
doi.org
arxiv.org
GFETM: Genome Foundation-based Embedded Topic Model for scATAC-seq Modeling
Yimin Fan
Adrien Osakwe
Yu Li
Jun Ding
Yue Li
2024-05-17
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
2024-05-17
Communications Biology (publié)
doi.org
Data Selection for Transfer Unlearning
Nazanin Mohammadi Sepahvand
Vincent Dumoulin
Eleni Triantafillou
Gintare Karolina Dziugaite
2024-05-16
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
2024-05-16
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
Amanda Marks
Dick Wågsäter … (voir 27 de plus)
Eric Bareke
Hugo Zeberg
Miklos Lipcsey
Robert Frithiof
Anders Larsson
Sirui Zhou
Tomoko Nakanishi
David R. Morrison
Dani Vezina
Catherine Bourassa
Gabrielle Gendron-Lepage
Halima Medjahed
Floriane Point
Jonathan Richard
Catherine Larochelle
Alexandre Prat
Janet L. Cunningham
Nathalie Arbour
Madeleine Durand
J. Brent Richards
Kevin R. 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 COVID-19 patients. Integrated analysis using k-means reveal 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 are delineated by high and low antibody responses. 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.
2024-05-16
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.
2024-05-16
2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) (publié)
doi.org