Publications

Interpersonal neural synchrony as a mechanism of therapeutic alliance? A systematic review of hyperscanning in clinical encounters
Lena Adel
Lincoln E. Moses
Elisabeth Irvine
Kyle T. Greenway
Guillaume Dumas
Michael Lifshitz
Therapeutic alliance is defined as the collaborative relationship between patient and therapist. Strong therapeutic alliances have been asso… (see more)ciated with positive clinical outcomes, but much remains unknown about alliance and its physiological basis. Interpersonal neural synchrony (INS) – the synchronization of neural signals between interacting individuals – is emerging as a novel lens for studying interactions with relatively objective and time-sensitive neuroimaging techniques. We searched four databases to identify studies of INS in clinical encounters. Our search yielded 161 articles, 46 met criteria for full-text review and 12 were included. The included articles reported INS across a total of 174 dyads, all published since 2018. Despite diverse methodologies, INS was observed in all studies and was often related to therapeutic outcomes. However, results were mixed regarding associations between INS and therapeutic alliance. These results highlight that, while promising, additional research is needed to elucidate the relationship between INS, therapeutic alliance and clinical outcomes. Future studies should aim to standardize methodologies, explore temporal dynamics (e.g., through longitudinal assessments), and include larger sample sizes.
2024-10-21
PsyArXiv (preprint)
doi.org
Dynamic Neural Control Flow Execution: An Agent-Based Deep Equilibrium Approach for Binary Vulnerability Detection
Litao Li
Steven H. H. Ding
Andrew Walenstein
Philippe Charland
Benjamin C. M. Fung
2024-10-20
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (published)
doi.org
arxiv.org
Enhancing Click-through Rate Prediction in Recommendation Domain with Search Query Representation
Yuening Wang
Man Chen
Yaochen Hu
Wei Guo
Yingxue Zhang
Huifeng Guo
Yang Liu
Mark J. Coates
2024-10-20
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (published)
doi.org
arxiv.org
GFlowNets for Hamiltonian decomposition in groups of compatible operators
Isaac L. Huidobro-Meezs
Jun Dai
Guillaume Rabusseau
R. A. Vargas-Hern'andez
Quantum computing presents a promising alternative for the direct simulation of quantum systems with the potential to explore chemical probl… (see more)ems beyond the capabilities of classical methods. However, current quantum algorithms are constrained by hardware limitations and the increased number of measurements required to achieve chemical accuracy. To address the measurement challenge, techniques for grouping commuting and anti-commuting terms, driven by heuristics, have been developed to reduce the number of measurements needed in quantum algorithms on near-term quantum devices. In this work, we propose a probabilistic framework using GFlowNets to group fully (FC) or qubit-wise commuting (QWC) terms within a given Hamiltonian. The significance of this approach is demonstrated by the reduced number of measurements for the found groupings; 51% and 67% reduction factors respectively for FC and QWC partitionings with respect to greedy coloring algorithms, highlighting the potential of GFlowNets for future applications in the measurement problem. Furthermore, the flexibility of our algorithm extends its applicability to other resource optimization problems in Hamiltonian simulation, such as circuit design.
2024-10-20
ArXiv (preprint)
doi.org
arxiv.org
Hitting the High-dimensional notes: an ODE for SGD learning dynamics on GLMs and multi-index models
Elizabeth Collins-Woodfin
Courtney Paquette
Elliot Paquette
INBAR SEROUSSI
We analyze the dynamics of streaming stochastic gradient descent (SGD) in the high-dimensional limit when applied to generalized linear mode… (see more)ls and multi-index models (e.g. logistic regression, phase retrieval) with general data-covariance. In particular, we demonstrate a deterministic equivalent of SGD in the form of a system of ordinary differential equations that describes a wide class of statistics, such as the risk and other measures of sub-optimality. This equivalence holds with overwhelming probability when the model parameter count grows proportionally to the number of data. This framework allows us to obtain learning rate thresholds for stability of SGD as well as convergence guarantees. In addition to the deterministic equivalent, we introduce an SDE with a simplified diffusion coefficient (homogenized SGD) which allows us to analyze the dynamics of general statistics of SGD iterates. Finally, we illustrate this theory on some standard examples and show numerical simulations which give an excellent match to the theory.
2024-10-20
Information and Inference: A Journal of the Institute of Mathematics and its Applications (published)
doi.org
arxiv.org
Innovative transfusion strategies for blood deserts in disaster settings
Shreenik Kundu
Ayla Gerk
Robert Glatter
Dan Poenaru
2024-10-20
BMJ Global Health (published)
doi.org
Generating Tabular Data Using Heterogeneous Sequential Feature Forest Flow Matching
Ange-Cl'ement Akazan
Alexia Jolicoeur-Martineau
Ioannis Mitliagkas
2024-10-19
ArXiv (preprint)
doi.org
arxiv.org
Circulating IL-17F, but not IL-17A, is elevated in severe COVID-19 and leads to an ERK1/2 and p38 MAPK-dependent increase in ICAM-1 cell surface expression and neutrophil adhesion on endothelial cells
Jérôme Bédard-Matteau
Antoine Soulé
Katelyn Yixiu Liu
Lyvia Fourcade
Douglas D. Fraser
Amin Emad
Simon Rousseau
Severe COVID-19 is associated with neutrophilic inflammation and immunothrombosis. Several members of the IL-17 cytokine family have been as… (see more)sociated with neutrophilic inflammation and activation of the endothelium. Therefore, we investigated whether these cytokines were associated with COVID-19. We investigated the association between COVID-19 and circulating plasma levels of IL-17 cytokine family members in participants to the Biobanque québécoise de la COVID-19 (BQC19), a prospective observational cohort and an independent cohort from Western University (London, Ontario). We measured the in vitro impact of IL-17F on intercellular adhesion molecule 1 (ICAM-1) cell surface expression and neutrophil adhesion on endothelial cells in culture. The contribution of two Mitogen Activated Protein Kinase (MAPK) pathways was determined using small molecule inhibitors PD184352 (a MKK1/MKK2 inhibitor) and BIRB0796 (a p38 MAPK inhibitor). We found increased IL-17D and IL-17F plasma levels when comparing SARS-CoV-2-positive vs negative hospitalized participants. Moreover, increased plasma levels of IL-17D, IL-17E and IL-17F were noted when comparing severe versus mild COVID-19. IL-17F, but not IL-17A, was significantly elevated in people with COVID-19 compared to healthy controls and with more severe disease. In vitro work on endothelial cells treated with IL-17F for 24h showed an increase cell surface expression of ICAM-1 accompanied by neutrophil adhesion. The introduction of two MAPK inhibitors significantly reduced the binding of neutrophils while also reducing ICAM-1 expression at the surface level of endothelial cells, but not its intracellular expression. Overall, these results have identified an association between two cytokines of the IL-17 family (IL-17D and IL-17F) with COVID-19 and disease severity. Considering that IL-17F stimulation promotes neutrophil adhesion to the endothelium in a MAPK-dependent manner, it is attractive to speculate that this pathway may contribute to pathogenic immunothrombosis in concert with other molecular effectors.
2024-10-17
Frontiers in Immunology (published)
doi.org
A Complexity-Based Theory of Compositionality
Eric Elmoznino
Thomas Jiralerspong
Yoshua Bengio
Guillaume Lajoie
2024-10-17
ArXiv (preprint)
doi.org
arxiv.org
Convergence of Manifold Filter-Combine Networks
David R. Johnson
Joyce Chew
Siddharth Viswanath
Edward De Brouwer
Deanna Needell
Smita Krishnaswamy
Michael Perlmutter
In order to better understand manifold neural networks (MNNs), we introduce Manifold Filter-Combine Networks (MFCNs). The filter-combine fra… (see more)mework parallels the popular aggregate-combine paradigm for graph neural networks (GNNs) and naturally suggests many interesting families of MNNs which can be interpreted as the manifold analog of various popular GNNs. We then propose a method for implementing MFCNs on high-dimensional point clouds that relies on approximating the manifold by a sparse graph. We prove that our method is consistent in the sense that it converges to a continuum limit as the number of data points tends to infinity.
2024-10-17
ArXiv (preprint)
doi.org
arxiv.org
Assessment of the Climate Trace global powerplant CO2 emissions
Kevin R. Gurney
Bilal Aslam
Pawlok Dass
Lech Gawuc
Toby Dylan Hocking
Jarrett J Barber
Anna Kato
Accurate estimation of planetary greenhouse gas (GHG) emissions at the scale of individual emitting activities is a critical need for both s… (see more)cientific and policy applications. Powerplants represent the single largest and most concentrated form of global GHG emissions. Climate Trace, co-founded and promoted by former U.S. Vice President Al Gore, is a new effort using, in part, artificial intelligence (AI) approaches to estimate asset-scale GHG emissions. Climate Trace recently released a database of global powerplant CO2 emissions at the facility-scale that uses both AI and non-AI estimation approaches. However, no independent peer-reviewed assessment has been made of this important global emissions database. Here, we compare the Climate Trace powerplant CO2 emissions to an atmospherically calibrated, multi-constraint estimate of powerplant CO2 emissions in the United States. The 3.7% (65) of compared facilities that used an AI-based approach show a mean relative difference (MRD) of −1.1% (SD: 46.4%) in the year 2019. The 96.3% (1726) of the facilities that used a non-AI-based approach show a MRD of −50.0% (SD: 117.7%). Of the non-AI estimated facilities, 151 (8.7%) facilities agree to within ±20%. The large differences between Climate Trace and Vulcan-power emission estimates for these facilities is primarily caused by Climate Trace’ use of a national-mean power plant capacity factor (CF) which is a poor representation of the reported power plant CFs of individual US facilities and leads to very large errors at those same 1726 facilities.
2024-10-16
Environmental Research Letters (published)
doi.org
A Simulation System Towards Solving Societal-Scale Manipulation
Maximilian Puelma Touzel
Sneheel Sarangi
Austin Welch
Gayatri K
Dan Zhao
Zachary Yang
Hao Yu
Ethan Kosak-Hine
Tom Gibbs
Andreea Musulan
Camille Thibault
Busra Tugce Gurbuz
Reihaneh Rabbany
Jean-François Godbout
Kellin Pelrine
The rise of AI-driven manipulation poses significant risks to societal trust and democratic processes. Yet, studying these effects in real-w… (see more)orld settings at scale is ethically and logistically impractical, highlighting a need for simulation tools that can model these dynamics in controlled settings to enable experimentation with possible defenses. We present a simulation environment designed to address this. We elaborate upon the Concordia framework that simulates offline, `real life' activity by adding online interactions to the simulation through social media with the integration of a Mastodon server. We improve simulation efficiency and information flow, and add a set of measurement tools, particularly longitudinal surveys. We demonstrate the simulator with a tailored example in which we track agents' political positions and show how partisan manipulation of agents can affect election results.
2024-10-16
ArXiv (preprint)
doi.org
arxiv.org