Publications

Author Correction: 30×30 biodiversity gains rely on national coordination
Isaac Eckert
Andrea Brown
Dominique Caron
Federico Riva
Laura J. Pollock
2023-11-27
Nature Communications (published)
doi.org
Exploring the multidimensional nature of repetitive and restricted behaviors and interests (RRBI) in autism: neuroanatomical correlates and clinical implications
Aline Lefebvre
Nicolas Traut
Amandine Pedoux
Anna Maruani
Anita Beggiato
Monique Elmaleh
David Germanaud
Anouck Amestoy
Myriam Ly‐Le Moal
Christopher H. Chatham
Lorraine Murtagh
Manuel Bouvard
Marianne Alisson
Marion Leboyer
Thomas Bourgeron
Roberto Toro
Guillaume Dumas
Clara A. Moreau
Richard Delorme
2023-11-27
Molecular Autism (published)
doi.org
scGeneRythm: Using Neural Networks and Fourier Transformation to Cluster Genes by Time-Frequency Patterns in Single-Cell Data
Yiming Jia
Hao Wu
Jun Ding
2023-11-27
bioRxiv (preprint)
doi.org
The search for the lost attractor
Mario Pasquato
Syphax Haddad
Pierfrancesco Di Cintio
Alexandre Adam
Pablo Lemos
No'e Dia
Mircea Petrache
Ugo Niccolo Di Carlo
Alessandro A. Trani
Laurence Perreault-Levasseur
Yashar Hezaveh
2023-11-27
ArXiv (preprint)
arxiv.org
arxiv.org
Mitigating Biases with Diverse Ensembles and Diffusion Models
Luca Scimeca
Alexander Rubinstein
Damien Teney
Seong Joon Oh
Armand Nicolicioiu
Yoshua Bengio
Spurious correlations in the data, where multiple cues are predictive of the target labels, often lead to a phenomenon known as shortcut lea… (see more)rning, where a model relies on erroneous, easy-to-learn cues while ignoring reliable ones. In this work, we propose an ensemble diversification framework exploiting Diffusion Probabilistic Models (DPMs) to mitigate this form of bias. We show that at particular training intervals, DPMs can generate images with novel feature combinations, even when trained on samples displaying correlated input features. We leverage this crucial property to generate synthetic counterfactuals to increase model diversity via ensemble disagreement. We show that DPM-guided diversification is sufficient to remove dependence on primary shortcut cues, without a need for additional supervised signals. We further empirically quantify its efficacy on several diversification objectives, and finally show improved generalization and diversification performance on par with prior work that relies on auxiliary data collection.
2023-11-23
ArXiv (preprint)
arxiv.org
arxiv.org
Propositional Logics for the Lawvere Quantale
Giorgio Bacci
Radu Mardare
Prakash Panangaden
Gordon Plotkin
2023-11-23
Electronic Notes in Theoretical Informatics and Computer Science (published)
doi.org
arxiv.org
scSniper: Single-cell Deep Neural Network-based Identification of Prominent Biomarkers
Mingyang Li
Yanshuo Chen
Jun Ding
2023-11-23
bioRxiv (preprint)
doi.org
Unveiling the Impact of Arsenic Toxicity on Immune Cells in Atherosclerotic Plaques: Insights from Single-Cell Multi-Omics Profiling
Kiran Makhani
Xiuhui Yang
France Dierick
Nivetha Subramaniam
Natascha Gagnon
Talin Ebrahimian
Hao Wu
Jun Ding
Koren K. Mann
2023-11-23
bioRxiv (preprint)
doi.org
Deep Learning Model for Multi-Step Ahead Prediction of Solar Irradiance: Case of Study of Morocco
Saad Benbrahim
Loubna Benabbou
Hanane Dagdougui
Ismail Belhaj
Abdelali Djdiaa
Hicham Bouzekri
Abdelaziz Berrado
Accurate solar irradiance forecasting is crucial for managing energy generation and consumption in the rapidly evolving landscape of renewab… (see more)le energy. It enables renewable energy operators to make informed decisions and maximize their output. This study employs deep learning-based forecasting models to predict the Global Horizontal Irradiance (GHI) of the R&D platform situated in Ouarzazate, Morocco. A sensitivity analysis was conducted on multiple scenarios for a one day-ahead horizon. Moreover, a forecasting technique that encompasses numerous horizons, ranging from one day to three days in advance, was evaluated. The study's findings suggest that the encoder-decoder model we proposed exhibited superior performance compared to the other models tested and produced dependable predictions.
2023-11-22
2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA) (published)
doi.org
Towards an Effective Electrical Market Design: Identifying and Defining Key Criteria for Decision-Making
Souhaila Chiguer
Loubna Benabbou
Hanane Dagdougui
Ismail Belhaj
Abdelali Djdiaa
Hicham Bouzekri
Abdelaziz Berrado
In our changing energy landscape, electricity is taking a major role in achieving decarbonization goals. Electricity can be a clean and effi… (see more)cient source of energy, and it is well-suited to help countries meet their climate goals. However, the electrical market is complex and constantly evolving, and it is important to carefully choose the design elements of the market to ensure that it is meeting its objectives. In this context, evaluating an electrical market's effectiveness requires a multifaceted approach that takes into account a range of elements, from environmental impact to economic viability. This paper provides an overview of several evaluation methods for different objectives to finally select the key criteria to consider in assisting decision-makers, regulators, and stakeholders in developing an electricity market that is not only effective but also reliable and sustainable.
2023-11-22
2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA) (published)
doi.org
Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy
Max Schwarzer
Jesse Farebrother
Joshua Greaves
Ekin Dogus Cubuk
Rishabh Agarwal
Aaron Courville
Marc Gendron-Bellemare
Sergei V. Kalinin
Igor Mordatch
Pablo Samuel Castro
Kevin M Roccapriore
We introduce a machine learning approach to determine the transition dynamics of silicon atoms on a single layer of carbon atoms, when stimu… (see more)lated by the electron beam of a scanning transmission electron microscope (STEM). Our method is data-centric, leveraging data collected on a STEM. The data samples are processed and filtered to produce symbolic representations, which we use to train a neural network to predict transition probabilities. These learned transition dynamics are then leveraged to guide a single silicon atom throughout the lattice to pre-determined target destinations. We present empirical analyses that demonstrate the efficacy and generality of our approach.
2023-11-21
ArXiv (preprint)
doi.org
arxiv.org
Learning from unexpected events in the neocortical microcircuit
Colleen J Gillon
Jason E. Pina
Jérôme A. Lecoq
Ruweida Ahmed
Yazan N. Billeh
Shiella Caldejon
Peter Groblewski
Timothy M. Henley
India Kato
Eric Lee
Jennifer Luviano
Kyla Mace
Chelsea Nayan
Thuyanh V. Nguyen
Kat North
Jed Perkins
Sam Seid
Matthew T. Valley
Ali Williford
Yoshua Bengio … (see 3 more)
Timothy P. Lillicrap
Blake Richards
Joel Zylberberg
2023-11-21
Journal of Neuroscience (published)
doi.org