Publications

Investigation of the Dosimetry Characteristics of the GAFCHROMIC® EBT3 Film Response to Alpha Particle Irradiation

Mélodie Cyr

Victor D. Martinez

S. Devic

Nada Tomic

David F. Lewis

S. Enger

2023-10-31

Journal of Physics: Conference Series (publié)

doi.org

Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

Kashif Rasul

Arjun Ashok

Andrew Robert Williams

Hena Ghonia

Rishika Bhagwatkar

Arian Khorasani

Mohammad Javad Darvishi Bayazi

George Adamopoulos

Roland Riachi

Nadhir Hassen

Marin Biloš

Sahil Garg

Anderson Schneider

Nicolas Chapados

Alexandre Drouin

Valentina Zantedeschi

Yuriy Nevmyvaka

Irina Rish

Over the past years, foundation models have caused a paradigm shift in machine learning due to their unprecedented capabilities for zero-sho… (voir plus)t and few-shot generalization. However, despite the success of foundation models in modalities such as natural language processing and computer vision, the development of foundation models for time series forecasting has lagged behind. We present Lag-Llama, a general-purpose foundation model for univariate probabilistic time series forecasting based on a decoder-only transformer architecture that uses lags as covariates. Lag-Llama is pretrained on a large corpus of diverse time series data from several domains, and demonstrates strong zero-shot generalization capabilities compared to a wide range of forecasting models on downstream datasets across domains. Moreover, when fine-tuned on relatively small fractions of such previously unseen datasets, Lag-Llama achieves state-of-the-art performance, outperforming prior deep learning approaches, emerging as the best general-purpose model on average. Lag-Llama serves as a strong contender to the current state-of-art in time series forecasting and paves the way for future advancements in foundation models tailored to time series data.

2023-10-31

R0-FoMo @ Neural Information Processing Systems (poster)

doi.org

openreview.net

MasakhaNEWS: News Topic Classification for African languages

David Ifeoluwa Adelani

Marek Masiak

Israel Abebe Azime

Jesujoba Alabi

Atnafu Lambebo Tonja

Christine Mwase

Odunayo Ogundepo

Bonaventure F. P. Dossou

Akintunde Oladipo

Doreen Nixdorf

Chris Chinenye Emezue

sana al-azzawi

Blessing Sibanda

Davis David

Lolwethu Ndolela

Jonathan Mukiibi

Tunde Ajayi

Tatiana Moteu

Brian Odhiambo

Abraham Owodunni … (voir 45 de plus)

Nnaemeka Obiefuna

Muhidin Mohamed

Shamsuddeen Hassan Muhammad

Teshome Mulugeta Ababu

Saheed Abdullahi Salahudeen

Mesay Gemeda Yigezu

Tajuddeen Gwadabe

Idris Abdulmumin

Mahlet Taye

Oluwabusayo Awoyomi

Iyanuoluwa Shode

Tolulope Adelani

Habiba Abdulganiyu

Abdul-Hakeem Omotayo

Adetola Adeeko

Abeeb Afolabi

Anuoluwapo Aremu

Olanrewaju Samuel

Clemencia Siro

Wangari Kimotho

Onyekachi Ogbu

Chinedu Mbonu

Chiamaka Chukwuneke

Samuel Fanijo

Jessica Ojo

Oyinkansola Awosan

Tadesse Kebede

Toadoum Sari Sakayo

Pamela Nyatsine

Freedmore Sidume

Oreen Yousuf

Mardiyyah Oduwole

Tshinu Tshinu

Ussen Kimanuka

Thina Diko

Siyanda Nxakama

Sinodos Nigusse

Abdulmejid Johar

Shafie Mohamed

Fuad Mire Hassan

Moges Ahmed Mehamed

Evrard Ngabire

Jules Jules

Ivan Ssenkungu

Pontus Stenetorp

African languages are severely under-represented in NLP research due to lack of datasets covering several NLP tasks. While there are individ… (voir plus)ual language specific datasets that are being expanded to different tasks, only a handful of NLP tasks (e.g. named entity recognition and machine translation) have standardized benchmark datasets covering several geographical and typologically-diverse African languages. In this paper, we develop MasakhaNEWS -- a new benchmark dataset for news topic classification covering 16 languages widely spoken in Africa. We provide an evaluation of baseline models by training classical machine learning models and fine-tuning several language models. Furthermore, we explore several alternatives to full fine-tuning of language models that are better suited for zero-shot and few-shot learning such as cross-lingual parameter-efficient fine-tuning (like MAD-X), pattern exploiting training (PET), prompting language models (like ChatGPT), and prompt-free sentence transformer fine-tuning (SetFit and Cohere Embedding API). Our evaluation in zero-shot setting shows the potential of prompting ChatGPT for news topic classification in low-resource African languages, achieving an average performance of 70 F1 points without leveraging additional supervision like MAD-X. In few-shot setting, we show that with as little as 10 examples per label, we achieved more than 90\% (i.e. 86.0 F1 points) of the performance of full supervised training (92.6 F1 points) leveraging the PET approach.

2023-10-31

International Joint Conference on Natural Language Processing and the Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (publié)

doi.org

openreview.net

Mining Mass Spectra for Peptide Facts

Jeremie Zumer

S. Lemieux

The current mainstream software for peptide-centric tandem mass spectrometry data analysis can be categorized as either database-driven, whi… (voir plus)ch rely on a library of mass spectra to identify the peptide associated with novel query spectra, or de novo sequencing-based, which aim to find the entire peptide sequence by relying only on the query mass spectrum. While the first paradigm currently produces state-of-the-art results in peptide identification tasks, it does not inherently make use of information present in the query mass spectrum itself to refine identifications. Meanwhile, de novo approaches attempt to solve a complex problem in one go, without any search space constraints in the general case, leading to comparatively poor results. In this paper, we decompose the de novo problem into putatively easier subproblems, and we show that peptide identification rates of database-driven methods may be improved in terms of peptide identification rate by solving one such subsproblem without requiring a solution for the complete de novo task. We demonstrate this using a de novo peptide length prediction task as the chosen subproblem. As a first prototype, we show that a deep learning-based length prediction model increases peptide identification rates in the ProteomeTools dataset as part of an Pepid-based identification pipeline. Using the predicted information to better rank the candidates, we show that combining ideas from the two paradigms produces clear benefits in this setting. We propose that the next generation of peptide-centric tandem mass spectrometry identification methods should combine elements of these paradigms by mining facts “de novo; about the peptide represented in a spectrum, while simultaneously limiting the search space with a peptide candidates database.

2023-10-31

bioRxiv (prépublication)

doi.org

Open design of a reproducible videogame controller for MRI and MEG

Yann Harel

André Cyr

Julie Boyle

Basile Pinsard

Jeremy Bernard

Marie-France Fourcade

Himanshu Aggarwal

Ana Fernanda Ponce

Bertrand Thirion

Karim Jerbi

Pierre Bellec

Videogames are emerging as a promising experimental paradigm in neuroimaging. Acquiring gameplay in a scanner remains challenging due to the… (voir plus) lack of a scanner-compatible videogame controller that provides a similar experience to standard, commercial devices. In this paper, we introduce a videogame controller designed for use in the functional magnetic resonance imaging as well as magnetoencephalography. The controller is made exclusively of 3D-printed and commercially available parts. We evaluated the quality of our controller by comparing it to a non-MRI compatible controller that was kept outside the scanner. The comparison of response latencies showed reliable button press accuracies of adequate precision. Comparison of the subjects’ motion during fMRI recordings of various tasks showed that the use of our controller did not increase the amount of motion produced compared to a regular MR compatible button press box. Motion levels during an ecological videogame task were of moderate amplitude. In addition, we found that the controller only had marginal effect on temporal SNR in fMRI, as well as on covariance between sensors in MEG, as expected due to the use of non-magnetic building materials. Finally, the reproducibility of the controller was demonstrated by having team members who were not involved in the design build a reproduction using only the documentation. This new videogame controller opens new avenues for ecological tasks in fMRI, including challenging videogames and more generally tasks with complex responses. The detailed controller documentation and build instructions are released under an Open Source Hardware license to increase accessibility, and reproducibility and enable the neuroimaging research community to improve or modify the controller for future experiments.

2023-10-31

PLOS ONE (publié)

doi.org

SAGE: Smart home Agent with Grounded Execution

Dmitriy Rivkin

Francois Hogan

Amal Feriani

Abhisek Konar

Adam Sigal

Steve Liu

Gregory Dudek

2023-10-31

ArXiv (prépublication)

doi.org

arxiv.org

Spatial Distribution Modeling of Pistacia atlantica using Artificial Neural Network in Khohir National Park

Shahab Bakhtiari

Tymour Rostani Shahraji

Reza Akhavan

Reza Ebrahimi Atani

2023-10-31

Ecology of Iranian Forests (publié)

doi.org

Tuning Minimum-Norm regularization parameters for optimal MEG connectivity estimation

Elisabetta Vallarino

Ana Sofia Hincapié

Karim Jerbi

Richard M. Leahy

Annalisa Pascarella

Alberto Sorrentino

Sara Sommariva

The regularization parameter of the Minimum Norm Estimate of neural activity impacts connectivity estimationWe study empirically the optimal… (voir plus) parameter for connectivity estimation using realistic synthetic datasetsWe find the optimal parameter for connectivity estimation is systematically smaller than the optimal parameter for source imaging; different connectivity metrics yield the same resultCode and data are available open source.

2023-10-31

NeuroImage (publié)

doi.org

Adaptive Resolution Residual Networks

Léa Demeule

Mahtab Sandhu

Glen Berseth

We introduce Adaptive Resolution Residual Networks (ARRNs), a form of neural operator that enables the creation of networks for signal-based… (voir plus) tasks that can be rediscretized to suit any signal resolution. ARRNs are composed of a chain of Laplacian residuals that each contain ordinary layers, which do not need to be rediscretizable for the whole network to be rediscretizable. ARRNs have the property of requiring a lower number of Laplacian residuals for exact evaluation on lower-resolution signals, which greatly reduces computational cost. ARRNs also implement Laplacian dropout, which encourages networks to become robust to low-bandwidth signals. ARRNs can thus be trained once at high-resolution and then be rediscretized on the fly at a suitable resolution with great robustness.

2023-10-30

NeurIPS.cc/2023/Workshop/DLDE (spotlight)

openreview.net

Criticality of resting-state EEG predicts perturbational complexity and level of consciousness during anesthesia.

Charlotte Maschke

Jordan O'Byrne

Michele Angelo Colombo

Melanie Boly

Olivia Gosseries

Steven Laureys

Mario Rosanova

Karim Jerbi

Stefanie Blain-Moraes

Consciousness has been proposed to be supported by electrophysiological patterns poised at criticality, a dynamical regime which exhibits ad… (voir plus)aptive computational properties, maximally complex patterns and divergent sensitivity to perturbation. Here, we investigated dynamical properties of the resting-state electroencephalogram of healthy subjects undergoing general anesthesia with propofol, xenon or ketamine. We then studied the relation of these dynamic properties with the perturbational complexity index (PCI), which has shown remarkably high sensitivity in detecting consciousness independent of behavior. All participants were unresponsive under anesthesia, while consciousness was retained only during ketamine anesthesia (in the form of vivid dreams)., enabling an experimental dissociation between unresponsiveness and unconsciousness. We estimated (i) avalanche criticality, (ii) chaoticity, and (iii) criticality-related measures, and found that states of unconsciousness were characterized by a distancing from both the edge of activity propagation and the edge of chaos. We were then able to predict individual subjects’ PCI (i.e., PCImax) with a mean absolute error below 7%. Our results establish a firm link between the PCI and criticality and provide further evidence for the role of criticality in the emergence of consciousness.

2023-10-30

bioRxiv (prépublication)

doi.org

Deep PDE Solvers for Subgrid Modelling and Out-of-Distribution Generalization

Patrick Chatain

Adam Oberman

2023-10-30

NeurIPS.cc/2023/Workshop/DLDE (poster)

openreview.net