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 (published)

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… (see more)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

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… (see more)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 (preprint)

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… (see more) 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 (published)

doi.org

OpenForest: a data catalog for machine learning in forest monitoring

Arthur Ouaknine

Teja Kattenborn

Étienne Laliberté

David Rolnick

Forests play a crucial role in Earth's system processes and provide a suite of social and economic ecosystem services, but are significantly… (see more) impacted by human activities, leading to a pronounced disruption of the equilibrium within ecosystems. Advancing forest monitoring worldwide offers advantages in mitigating human impacts and enhancing our comprehension of forest composition, alongside the effects of climate change. While statistical modeling has traditionally found applications in forest biology, recent strides in machine learning and computer vision have reached important milestones using remote sensing data, such as tree species identification, tree crown segmentation and forest biomass assessments. For this, the significance of open access data remains essential in enhancing such data-driven algorithms and methodologies. Here, we provide a comprehensive and extensive overview of 86 open access forest datasets across spatial scales, encompassing inventories, ground-based, aerial-based, satellite-based recordings, and country or world maps. These datasets are grouped in OpenForest, a dynamic catalogue open to contributions that strives to reference all available open access forest datasets. Moreover, in the context of these datasets, we aim to inspire research in machine learning applied to forest biology by establishing connections between contemporary topics, perspectives and challenges inherent in both domains. We hope to encourage collaborations among scientists, fostering the sharing and exploration of diverse datasets through the application of machine learning methods for large-scale forest monitoring. OpenForest is available at https://github.com/RolnickLab/OpenForest .

2023-10-31

ArXiv (preprint)

doi.org

arxiv.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 (preprint)

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 (published)

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… (see more) 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 (published)

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… (see more) 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… (see more)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 (preprint)

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