17,326 results on '"Drouin A"'
Search Results
2. InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation
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Sahu, Gaurav, Puri, Abhay, Rodriguez, Juan, Drouin, Alexandre, Taslakian, Perouz, Zantedeschi, Valentina, Lacoste, Alexandre, Vazquez, David, Chapados, Nicolas, Pal, Christopher, Mudumba, Sai Rajeswar, and Laradji, Issam Hadj
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Computer Science - Artificial Intelligence - Abstract
Data analytics is essential for extracting valuable insights from data that can assist organizations in making effective decisions. We introduce InsightBench, a benchmark dataset with three key features. First, it consists of 31 datasets representing diverse business use cases such as finance and incident management, each accompanied by a carefully curated set of insights planted in the datasets. Second, unlike existing benchmarks focusing on answering single queries, InsightBench evaluates agents based on their ability to perform end-to-end data analytics, including formulating questions, interpreting answers, and generating a summary of insights and actionable steps. Third, we conducted comprehensive quality assurance to ensure that each dataset in the benchmark had clear goals and included relevant and meaningful questions and analysis. Furthermore, we implement a two-way evaluation mechanism using LLaMA-3-Eval as an effective, open-source evaluator method to assess agents' ability to extract insights. We also propose AgentPoirot, our baseline data analysis agent capable of performing end-to-end data analytics. Our evaluation on InsightBench shows that AgentPoirot outperforms existing approaches (such as Pandas Agent) that focus on resolving single queries. We also compare the performance of open- and closed-source LLMs and various evaluation strategies. Overall, this benchmark serves as a testbed to motivate further development in comprehensive data analytics and can be accessed here: https://github.com/ServiceNow/insight-bench.
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- 2024
3. WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks
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Boisvert, Léo, Thakkar, Megh, Gasse, Maxime, Caccia, Massimo, De Chezelles, Thibault Le Sellier, Cappart, Quentin, Chapados, Nicolas, Lacoste, Alexandre, and Drouin, Alexandre
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Computer Science - Artificial Intelligence - Abstract
The ability of large language models (LLMs) to mimic human-like intelligence has led to a surge in LLM-based autonomous agents. Though recent LLMs seem capable of planning and reasoning given user instructions, their effectiveness in applying these capabilities for autonomous task solving remains underexplored. This is especially true in enterprise settings, where automated agents hold the promise of a high impact. To fill this gap, we propose WorkArena++, a novel benchmark consisting of 682 tasks corresponding to realistic workflows routinely performed by knowledge workers. WorkArena++ is designed to evaluate the planning, problem-solving, logical/arithmetic reasoning, retrieval, and contextual understanding abilities of web agents. Our empirical studies across state-of-the-art LLMs and vision-language models (VLMs), as well as human workers, reveal several challenges for such models to serve as useful assistants in the workplace. In addition to the benchmark, we provide a mechanism to effortlessly generate thousands of ground-truth observation/action traces, which can be used for fine-tuning existing models. Overall, we expect this work to serve as a useful resource to help the community progress toward capable autonomous agents. The benchmark can be found at https://github.com/ServiceNow/WorkArena/tree/workarena-plus-plus.
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- 2024
4. Structure and Interactions of HIV-1 gp41 CHR-NHR Reverse Hairpin Constructs Reveal Molecular Determinants of Antiviral Activity
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He, Li, McAndrew, Ryan, Barbu, Razvan, Gifford, Grant, Halacoglu, Cari, Drouin-Allaire, Camille, Weber, Lindsey, Kristensen, Line G, Gupta, Sayan, Chen, Yan, Petzold, Christopher J, Allaire, Marc, Li, Kathy H, Ralston, Corie Y, and Gochin, Miriam
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Biochemistry and Cell Biology ,Biological Sciences ,Sexually Transmitted Infections ,HIV/AIDS ,Infectious Diseases ,5.1 Pharmaceuticals ,HIV Envelope Protein gp41 ,HIV-1 ,Crystallography ,X-Ray ,Humans ,Models ,Molecular ,Protein Conformation ,Protein Folding ,Gp41 derived antiviral ,crystal structure ,covalent ligand ,X-ray footprinting ,lipid altered structure ,Medicinal and Biomolecular Chemistry ,Microbiology ,Biochemistry & Molecular Biology ,Biochemistry and cell biology - Abstract
Engineered reverse hairpin constructs containing a partial C-heptad repeat (CHR) sequence followed by a short loop and full-length N-heptad repeat (NHR) were previously shown to form trimers in solution and to be nanomolar inhibitors of HIV-1 Env mediated fusion. Their target is the in situ gp41 fusion intermediate, and they have similar potency to other previously reported NHR trimers. However, their design implies that the NHR is partially covered by CHR, which would be expected to limit potency. An exposed hydrophobic pocket in the folded structure may be sufficient to confer the observed potency, or they may exist in a partially unfolded state exposing full length NHR. Here we examined their structure by crystallography, CD and fluorescence, establishing that the proteins are folded hairpins both in crystal form and in solution. We examined unfolding in the milieu of the fusion reaction by conducting experiments in the presence of a membrane mimetic solvent and by engineering a disulfide bond into the structure to prevent partial unfolding. We further examined the role of the hydrophobic pocket, using a hairpin-small molecule adduct that occluded the pocket, as confirmed by X-ray footprinting. The results demonstrated that the NHR region nominally covered by CHR in the engineered constructs and the hydrophobic pocket region that is exposed by design were both essential for nanomolar potency and that interaction with membrane is likely to play a role in promoting the required inhibitor structure. The design concepts can be applied to other Class 1 viral fusion proteins.
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- 2024
5. Association between density of food retailers and fitness centers and gestational diabetes mellitus in Eastern Massachusetts, USA: population-based study.
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Shupler, Matthew, Klompmaker, Jochem, Leung, Michael, Petimar, Joshua, Drouin-Chartier, Jean-Philippe, Modest, Anna, Hacker, Michele, Farid, Huma, Hernandez-Diaz, Sonia, Papatheodorou, Stefania, and James, Peter
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Fast-food restaurant ,Fitness center ,Food environment ,Full-service restaurant ,Gestational diabetes mellitus ,Supermarket - Abstract
BACKGROUND: Few studies have investigated the relationship between the food and physical activity environment and odds of gestational diabetes mellitus (GDM). This study quantifies the association between densities of several types of food establishments and fitness centers with the odds of having GDM. METHODS: The density of supermarkets, fast-food restaurants, full-service restaurants, convenience stores and fitness centers at 500, 1000 and 1500 m (m) buffers was counted at residential addresses of 68,779 pregnant individuals from Eastern Massachusetts during 2000-2016. The healthy food index assessed the relative availability of healthy (supermarkets) vs unhealthy (fast-food restaurants, convenience stores) food retailers. Multivariable logistic regression quantified the cross-sectional association between exposure variables and the odds of having GDM, adjusting for individual and area-level characteristics. Effect modification by area-level socioeconomic status (SES) was assessed. FINDINGS: In fully adjusted models, pregnant individuals living in the highest density tertile of fast-food restaurants had higher GDM odds compared to those living in the lowest density tertile (500 m: odds ratio (OR):1.17 95% CI: [1.04, 1.31]; 1000 m: 1.33 95% CI: [1.15, 1.53]); 1500 m: 1.18 95% CI: [1.01, 1.38]). Greater residential density of supermarkets was associated with lower odds of GDM (1000 m: 0.86 95% CI: [0.74, 0.99]; 1500 m: 0.86 95% CI: [0.72, 1.01]). Similarly, living in the highest fitness center density tertile was associated with decreased GDM odds (500 m:0.87 95% CI: [0.76, 0.99]; 1500 m: 0.89 95% CI: [0.79, 1.01]). There was no evidence of effect modification by SES and no association found between the healthy food index and GDM odds. INTERPRETATION: In Eastern Massachusetts, living near a greater density of fast-food establishments was associated with higher GDM odds. Greater residential access to supermarkets and fitness centers was associated with lower the odds of having GDM. FUNDING: NIH.
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- 2024
6. Versatile CMOS Analog LIF Neuron for Memristor-Integrated Neuromorphic Circuits
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Garg, Nikhil, Florini, Davide, Dufour, Patrick, Muhr, Eloir, Faye, Mathieu, Bocquet, Marc, Querlioz, Damien, Beilliard, Yann, Drouin, Dominique, Alibart, Fabien, and Portal, Jean-Michel
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Computer Science - Emerging Technologies - Abstract
Heterogeneous systems with analog CMOS circuits integrated with nanoscale memristive devices enable efficient deployment of neural networks on neuromorphic hardware. CMOS Neuron with low footprint can emulate slow temporal dynamics by operating with extremely low current levels. Nevertheless, the current read from the memristive synapses can be higher by several orders of magnitude, and performing impedance matching between neurons and synapses is mandatory. In this paper, we implement an analog leaky integrate and fire (LIF) neuron with a voltage regulator and current attenuator for interfacing CMOS neurons with memristive synapses. In addition, the neuron design proposes a dual leakage that could enable the implementation of local learning rules such as voltage-dependent synaptic plasticity. We also propose a connection scheme to implement adaptive LIF neurons based on two-neuron interaction. The proposed circuits can be used to interface with a variety of synaptic devices and process signals of diverse temporal dynamics., Comment: Accepted to International Conference on Neuromorphic Systems (ICONS 2024)
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- 2024
7. Robust quantum dots charge autotuning using neural networks uncertainty
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Yon, Victor, Galaup, Bastien, Rohrbacher, Claude, Rivard, Joffrey, Godfrin, Clément, Li, Ruoyu, Kubicek, Stefan, De Greve, Kristiaan, Gaudreau, Louis, Dupont-Ferrier, Eva, Beilliard, Yann, Melko, Roger G., and Drouin, Dominique
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Quantum Physics ,Computer Science - Machine Learning ,68T37 (Primary), 81V65 (Secondary) ,I.2.8 ,I.5.1 - Abstract
This study presents a machine-learning-based procedure to automate the charge tuning of semiconductor spin qubits with minimal human intervention, addressing one of the significant challenges in scaling up quantum dot technologies. This method exploits artificial neural networks to identify noisy transition lines in stability diagrams, guiding a robust exploration strategy leveraging neural networks' uncertainty estimations. Tested across three distinct offline experimental datasets representing different single quantum dot technologies, the approach achieves over 99% tuning success rate in optimal cases, where more than 10% of the success is directly attributable to uncertainty exploitation. The challenging constraints of small training sets containing high diagram-to-diagram variability allowed us to evaluate the capabilities and limits of the proposed procedure., Comment: 12 pages (main) + 13 pages (supplementary)
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- 2024
8. Quasi-two-dimensional Antiferromagnetic Spin Fluctuations in the Spin-triplet Superconductor Candidate CeRh$_2$As$_2$
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Chen, Tong, Siddiquee, Hasan, Rehfuss, Zack, Gao, Shiyuan, Lygouras, Chris, Drouin, Jack, Morano, Vincent, Avers, Keenan E., Schmitt, Christopher J., Podlesnyak, Andrey, Ran, Sheng, Song, Yu, and Broholm, Collin
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Condensed Matter - Strongly Correlated Electrons ,Condensed Matter - Superconductivity - Abstract
The tetragonal heavy-fermion superconductor CeRh$_2$As$_2$ ($T_{\rm c}=0.3$ K) exhibits an exceptionally high critical field of 14 T for $\textbf{B} \parallel \textbf{c}$. It undergoes a field-driven first-order phase transition between superconducting (SC) states, potentially transitioning from spin-singlet to spin-triplet superconductivity. To elucidate the underlying pairing mechanism, we probe spin fluctuations in CeRh$_2$As$_2$ using neutron scattering. We find dynamic $(\pi,\pi)$ antiferromagnetic spin correlations with an anisotropic quasi-two-dimensional correlation volume. Our data place an upper limit of 0.31 $\mu_{\rm B}$ on the staggered magnetization of corresponding N\'{e}el orders at $T=0.08$ K. Density functional theory (DFT) calculations, treating Ce $4f$ electrons as core states, show that the AFM wave vector connects significant areas of the Fermi surface. Our findings show the dominant excitations in CeRh$_2$As$_2$ for $\hbar\omega< 1.2$~meV are magnetic and indicate superconductivity in CeRh$_2$As$_2$ is mediated by AFM spin fluctuations associated with a proximate quantum critical point., Comment: 7+6 pages, 4+4 figures
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- 2024
9. Towards scalable cryogenic quantum dot biasing using memristor-based DC sources
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Mouny, Pierre-Antoine, Dawant, Raphaël, Dufour, Patrick, Valdenaire, Matthieu, Ecoffey, Serge, Pioro-Ladrière, Michel, Beillard, Yann, and Drouin, Dominique
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Computer Science - Emerging Technologies ,Physics - Applied Physics - Abstract
Cryogenic memristor-based DC sources offer a promising avenue for in situ biasing of quantum dot arrays. In this study, we present experimental results and discuss the scaling potential for such DC sources. We first demonstrate the operation of a commercial discrete operational amplifier down to 1.2K which is used on the DC source prototype. Then, the tunability of the memristor-based DC source is validated by performing several 250mV-DC sweeps with a resolution of 10mV at room temperature and at 1.2K. Additionally, the DC source prototype exhibits a limited output drift of $\approx1\mathrm{\mu Vs^{-1}}$ at 1.2K. This showcases the potential of memristor-based DC sources for quantum dot biasing. Limitations in power consumption and voltage resolution using discrete components highlight the need for a fully integrated and scalable complementary metal-oxide-semiconductor-based (CMOS-based) approach. To address this, we propose to monolithically co-integrate emerging non-volatile memories (eNVMs) and 65nm CMOS circuitry. Simulations reveal a reduction in power consumption, down to $\mathrm{10\mu W}$ per DC source and in footprint. This allows for the integration of up to one million eNVM-based DC sources at the 4.2K stage of a dilution fridge, paving the way for near term large-scale quantum computing applications.
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- 2024
10. Evaluating Interventional Reasoning Capabilities of Large Language Models
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Kasetty, Tejas, Mahajan, Divyat, Dziugaite, Gintare Karolina, Drouin, Alexandre, and Sridhar, Dhanya
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Computation and Language ,Statistics - Methodology - Abstract
Numerous decision-making tasks require estimating causal effects under interventions on different parts of a system. As practitioners consider using large language models (LLMs) to automate decisions, studying their causal reasoning capabilities becomes crucial. A recent line of work evaluates LLMs ability to retrieve commonsense causal facts, but these evaluations do not sufficiently assess how LLMs reason about interventions. Motivated by the role that interventions play in causal inference, in this paper, we conduct empirical analyses to evaluate whether LLMs can accurately update their knowledge of a data-generating process in response to an intervention. We create benchmarks that span diverse causal graphs (e.g., confounding, mediation) and variable types, and enable a study of intervention-based reasoning. These benchmarks allow us to isolate the ability of LLMs to accurately predict changes resulting from their ability to memorize facts or find other shortcuts. Our analysis on four LLMs highlights that while GPT- 4 models show promising accuracy at predicting the intervention effects, they remain sensitive to distracting factors in the prompts., Comment: 17 pages
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- 2024
11. WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?
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Drouin, Alexandre, Gasse, Maxime, Caccia, Massimo, Laradji, Issam H., Del Verme, Manuel, Marty, Tom, Boisvert, Léo, Thakkar, Megh, Cappart, Quentin, Vazquez, David, Chapados, Nicolas, and Lacoste, Alexandre
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
We study the use of large language model-based agents for interacting with software via web browsers. Unlike prior work, we focus on measuring the agents' ability to perform tasks that span the typical daily work of knowledge workers utilizing enterprise software systems. To this end, we propose WorkArena, a remote-hosted benchmark of 33 tasks based on the widely-used ServiceNow platform. We also introduce BrowserGym, an environment for the design and evaluation of such agents, offering a rich set of actions as well as multimodal observations. Our empirical evaluation reveals that while current agents show promise on WorkArena, there remains a considerable gap towards achieving full task automation. Notably, our analysis uncovers a significant performance disparity between open and closed-source LLMs, highlighting a critical area for future exploration and development in the field., Comment: 21 pages, 11 figures, preprint
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- 2024
12. Water-Vapor Absorption Database using Dual Comb Spectroscopy from 300-1300 K Part II: Air-Broadened H$_2$O, 6600 to 7650 cm$^{-1}$
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Egbert, Scott C., Sung, Keeyoon, Coburn, Sean C., Drouin, Brian J., and Rieker, Gregory B.
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Physics - Atmospheric and Oceanic Physics - Abstract
We present broadband dual frequency comb laser absorption measurements of 2% H$_2$O (natural isotopic abundance of 99.7% H$_2^{16}$O) in air from 6600-7650 cm$^{-1}$ (1307-1515 nm) with a spectral point spacing of 0.0068 cm$^{-1}$. Twenty-nine datasets were collected at temperatures between 300 and 1300 K ($\pm$0.82% average uncertainty) and pressures ranging from 20 to 600 Torr ($\pm$0.25%) with an average residual absorbance noise of 8.0E-4 across the spectrum for all measurements. We fit measurements using a quadratic speed-dependent Voigt profile to determine 7088 absorption parameters for 3366 individual transitions found in HITRAN2020. These measurements build on the line strength, line center, self-broadening, and self-shift parameters determined in the Part I companion of this work. Here we measure air-broadened width (with temperature- and speed-dependence) and air pressure shift (with temperature dependence) parameters. Various trends are explored for extrapolation to weak transitions that were not covered in this work. Improvements made in this work are predominantly due to the inclusion of air pressure shift temperature dependence values. In aggregate, these updates improved RMS absorbance error by a factor of 4.2 on average, and the remaining residual is predominantly spectral noise. This updated database improves high temperature spectroscopic knowledge across the 6600 7650 cm$^{-1}$ region of H$_2$O absorption., Comment: Database files available upon request. Will be included with published manuscript following review process
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- 2024
13. A new high-resolution Coastal Ice-Ocean Prediction System for the East Coast of Canada
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Paquin, Jean-Philippe, Roy, François, Smith, Gregory C., MacDermid, Sarah, Lei, Ji, Dupont, Frédéric, Lu, Youyu, Taylor, Stephanne, St-Onge-Drouin, Simon, Blanken, Hauke, Dunphy, Michael, and Soontiens, Nancy
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- 2024
- Full Text
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14. Physiological and perceptual response to critical power anchored HIIT: a sex comparison study
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Pacitti, Lauren J., Laberge, Joshua, Shikaze, Kaitlyn E., Drouin, Patrick J., Tschakovsky, Michael E., McGlory, Chris, and Gurd, Brendon J.
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- 2024
- Full Text
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15. Capture the Flag: Uncovering Data Insights with Large Language Models
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Laradji, Issam, Taslakian, Perouz, Rajeswar, Sai, Zantedeschi, Valentina, Lacoste, Alexandre, Chapados, Nicolas, Vazquez, David, Pal, Christopher, and Drouin, Alexandre
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Computer Science - Machine Learning ,Computer Science - Computation and Language ,Statistics - Machine Learning - Abstract
The extraction of a small number of relevant insights from vast amounts of data is a crucial component of data-driven decision-making. However, accomplishing this task requires considerable technical skills, domain expertise, and human labor. This study explores the potential of using Large Language Models (LLMs) to automate the discovery of insights in data, leveraging recent advances in reasoning and code generation techniques. We propose a new evaluation methodology based on a "capture the flag" principle, measuring the ability of such models to recognize meaningful and pertinent information (flags) in a dataset. We further propose two proof-of-concept agents, with different inner workings, and compare their ability to capture such flags in a real-world sales dataset. While the work reported here is preliminary, our results are sufficiently interesting to mandate future exploration by the community., Comment: 14 pages, 1 figure, Foundation Models for Decision Making Workshop at NeurIPS 2023
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- 2023
16. Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
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Rasul, Kashif, Ashok, Arjun, Williams, Andrew Robert, Ghonia, Hena, Bhagwatkar, Rishika, Khorasani, Arian, Bayazi, Mohammad Javad Darvishi, Adamopoulos, George, Riachi, Roland, Hassen, Nadhir, Biloš, Marin, Garg, Sahil, Schneider, Anderson, Chapados, Nicolas, Drouin, Alexandre, Zantedeschi, Valentina, Nevmyvaka, Yuriy, and Rish, Irina
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
Over the past years, foundation models have caused a paradigm shift in machine learning due to their unprecedented capabilities for zero-shot 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., Comment: First two authors contributed equally. All data, models and code used are open-source. GitHub: https://github.com/time-series-foundation-models/lag-llama
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- 2023
17. Simulating the Transverse Field Ising Model on the Kagome Lattice using a Programmable Quantum Annealer
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Narasimhan, Pratyankara, Humeniuk, Stephan, Roy, Ananda, and Drouin-Touchette, Victor
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Condensed Matter - Statistical Mechanics ,Condensed Matter - Strongly Correlated Electrons ,Quantum Physics - Abstract
The presence of competing interactions due to geometry leads to frustration in quantum spin models. As a consequence, the ground state of such systems often displays a large degeneracy that can be lifted due to thermal or quantum effects. One such example is the antiferromagnetic Ising model on the Kagome lattice. It was shown that while the same model on the triangular lattice is ordered at zero temperature for small transverse field due to an order by disorder mechanism, the Kagome lattice resists any such effects and exhibits only short range spin correlations and a trivial paramagnetic phase. We embed this model on the latest architecture of D-Wave's quantum annealer, the Advantage2 prototype, which uses the highly connected Zephyr graph. Using advanced embedding and calibration techniques, we are able to embed a Kagome lattice with mixed open and periodic boundary conditions of 231 sites on the full graph of the currently available prototype. Through forward annealing experiments, we show that under a finite longitudinal field the system exhibits a one-third magnetization plateau, consistent with a classical spin liquid state of reduced entropy. An anneal-pause-quench protocol is then used to extract an experimental ensemble of states resulting from the equilibration of the model at finite transverse and longitudinal field. This allows us to construct a partial phase diagram and confirm that the system exits the constrained Hilbert space of the classical spin liquid when subjected to a transverse field. We connect our results to previous theoretical results and quantum Monte Carlo simulation, which helps us confirm the validity of the quantum simulation realized here, thereby extracting insight into the performance of the D-Wave quantum annealer to simulate non-trivial quantum systems in equilibrium., Comment: 12 + 6 pages, 7 + 8 figures, updated paper to be published in PRB
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- 2023
- Full Text
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18. TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series
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Ashok, Arjun, Marcotte, Étienne, Zantedeschi, Valentina, Chapados, Nicolas, and Drouin, Alexandre
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Statistics - Machine Learning - Abstract
We introduce a new model for multivariate probabilistic time series prediction, designed to flexibly address a range of tasks including forecasting, interpolation, and their combinations. Building on copula theory, we propose a simplified objective for the recently-introduced transformer-based attentional copulas (TACTiS), wherein the number of distributional parameters now scales linearly with the number of variables instead of factorially. The new objective requires the introduction of a training curriculum, which goes hand-in-hand with necessary changes to the original architecture. We show that the resulting model has significantly better training dynamics and achieves state-of-the-art performance across diverse real-world forecasting tasks, while maintaining the flexibility of prior work, such as seamless handling of unaligned and unevenly-sampled time series. Code is made available at https://github.com/ServiceNow/TACTiS., Comment: 28 pages, 15 figures, The Twelfth International Conference on Learning Representations (ICLR 2024)
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- 2023
19. Analog programming of CMOS-compatible Al$_2$O$_3$/TiO$_\textrm{2-x}$ memristor at 4.2 K after metal-insulator transition suppression by cryogenic reforming
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Mouny, Pierre-Antoine, Dawant, Raphaël, Galaup, Bastien, Ecoffey, Serge, Pioro-Ladrière, Michel, Beilliard, Yann, and Drouin, Dominique
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Physics - Applied Physics ,Computer Science - Emerging Technologies - Abstract
The exploration of memristors' behavior at cryogenic temperatures has become crucial due to the growing interest in quantum computing and cryogenic electronics. In this context, our study focuses on the characterization at cryogenic temperatures (4.2 K) of TiO$_\textrm{2-x}$-based memristors fabricated with a CMOS-compatible etch-back process. We demonstrate a so-called cryogenic reforming (CR) technique performed at 4.2 K to overcome the well-known metal-insulator transition (MIT) which limits the analog behavior of memristors at low temperatures. This cryogenic reforming process was found to be reproducible and led to a durable suppression of the MIT. This process allowed to reduce by approximately 20% the voltages required to perform DC resistive switching at 4.2 K. Additionally, conduction mechanism studies of memristors before and after cryogenic reforming from 4.2 K to 300 K revealed different behaviors above 100 K, indicating a potential change in the conductive filament stoichiometry. The reformed devices exhibit a conductance level that is 50 times higher than ambient-formed memristor, and the conduction drop between 300 K and 4.2 K is 100 times smaller, indicating the effectiveness of the reforming process. More importantly, CR enables analog programming at 4.2 K with typical read voltages. Suppressing the MIT improved the analog switching dynamics of the memristor leading to approximately 250% larger on/off ratios during long-term depression (LTD)/long-term potentiation (LTP) resistance tuning. This enhancement opens up the possibility of using TiO$_{\textrm{2-x}}$-based memristors to be used as synapses in neuromorphic computing at cryogenic temperatures.
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- 2023
20. A Cryogenic Memristive Neural Decoder for Fault-tolerant Quantum Error Correction
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Marcotte, Frédéric, Mouny, Pierre-Antoine, Yon, Victor, Dagnew, Gebremedhin A., Kulchytskyy, Bohdan, Rochette, Sophie, Beilliard, Yann, Drouin, Dominique, and Ronagh, Pooya
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Quantum Physics ,Computer Science - Emerging Technologies ,Computer Science - Machine Learning - Abstract
Neural decoders for quantum error correction (QEC) rely on neural networks to classify syndromes extracted from error correction codes and find appropriate recovery operators to protect logical information against errors. Despite the good performance of neural decoders, important practical requirements remain to be achieved, such as minimizing the decoding time to meet typical rates of syndrome generation in repeated error correction schemes, and ensuring the scalability of the decoding approach as the code distance increases. Designing a dedicated integrated circuit to perform the decoding task in co-integration with a quantum processor appears necessary to reach these decoding time and scalability requirements, as routing signals in and out of a cryogenic environment to be processed externally leads to unnecessary delays and an eventual wiring bottleneck. In this work, we report the design and performance analysis of a neural decoder inference accelerator based on an in-memory computing (IMC) architecture, where crossbar arrays of resistive memory devices are employed to both store the synaptic weights of the decoder neural network and perform analog matrix-vector multiplications during inference. In proof-of-concept numerical experiments supported by experimental measurements, we investigate the impact of TiO$_\textrm{x}$-based memristive devices' non-idealities on decoding accuracy. Hardware-aware training methods are developed to mitigate the loss in accuracy, allowing the memristive neural decoders to achieve a pseudo-threshold of $9.23\times 10^{-4}$ for the distance-three surface code, whereas the equivalent digital neural decoder achieves a pseudo-threshold of $1.01\times 10^{-3}$. This work provides a pathway to scalable, fast, and low-power cryogenic IMC hardware for integrated QEC.
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- 2023
21. Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation
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Emezue, Chris Chinenye, Drouin, Alexandre, Deleu, Tristan, Bauer, Stefan, and Bengio, Yoshua
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Computer Science - Machine Learning ,Statistics - Methodology - Abstract
The practical utility of causality in decision-making is widespread and brought about by the intertwining of causal discovery and causal inference. Nevertheless, a notable gap exists in the evaluation of causal discovery methods, where insufficient emphasis is placed on downstream inference. To address this gap, we evaluate seven established baseline causal discovery methods including a newly proposed method based on GFlowNets, on the downstream task of treatment effect estimation. Through the implementation of a distribution-level evaluation, we offer valuable and unique insights into the efficacy of these causal discovery methods for treatment effect estimation, considering both synthetic and real-world scenarios, as well as low-data scenarios. The results of our study demonstrate that some of the algorithms studied are able to effectively capture a wide range of useful and diverse ATE modes, while some tend to learn many low-probability modes which impacts the (unrelaxed) recall and precision., Comment: Peer-reviewed and Accepted to ICML 2023 Workshop on Structured Probabilistic Inference & Generative Modeling
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- 2023
22. Causal Discovery with Language Models as Imperfect Experts
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Long, Stephanie, Piché, Alexandre, Zantedeschi, Valentina, Schuster, Tibor, and Drouin, Alexandre
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Computer Science - Artificial Intelligence ,Computer Science - Computation and Language ,Computer Science - Machine Learning - Abstract
Understanding the causal relationships that underlie a system is a fundamental prerequisite to accurate decision-making. In this work, we explore how expert knowledge can be used to improve the data-driven identification of causal graphs, beyond Markov equivalence classes. In doing so, we consider a setting where we can query an expert about the orientation of causal relationships between variables, but where the expert may provide erroneous information. We propose strategies for amending such expert knowledge based on consistency properties, e.g., acyclicity and conditional independencies in the equivalence class. We then report a case study, on real data, where a large language model is used as an imperfect expert., Comment: Peer reviewed and accepted for presentation at the Structured Probabilistic Inference & Generative Modeling (SPIGM) workshop at ICML 2023, Hawaii, USA
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- 2023
23. Influence of mRNA Covid-19 vaccine dosing interval on the risk of myocarditis
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Stéphane Le Vu, Marion Bertrand, Laura Semenzato, Marie-Joelle Jabagi, Jérémie Botton, Jérôme Drouin, Alain Weill, Rosemary Dray-Spira, and Mahmoud Zureik
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Science - Abstract
Abstract Myocarditis is the most salient serious adverse event following messenger RNA-based Covid-19 vaccines. The highest risk is observed after the second dose compared to the first, whereas the level of risk associated with more distant booster doses seems to lie in between. We aimed to assess the relation between dosing interval and the risk of myocarditis, for both the two-dose primary series and the third dose (first booster). This matched case-control study included 7911 cases of myocarditis aged 12 or more in a period where approximately 130 million vaccine doses were administered. Here we show that longer intervals between each consecutive dose, including booster, may decrease the occurrence of vaccine-associated myocarditis by up to a factor of 4, especially under age 50. These results suggest that a minimum 6-month interval might be required when scheduling additional booster vaccination.
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- 2024
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24. In silico fragment-based discovery of CIB1-directed anti-tumor agents by FRASE-bot
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Yi An, Jiwoong Lim, Marta Glavatskikh, Xiaowen Wang, Jacqueline Norris-Drouin, P. Brian Hardy, Tina M. Leisner, Kenneth H. Pearce, and Dmitri Kireev
- Subjects
Science - Abstract
Abstract Chemical probes are an indispensable tool for translating biological discoveries into new therapies, though are increasingly difficult to identify since novel therapeutic targets are often hard-to-drug proteins. We introduce FRASE-based hit-finding robot (FRASE-bot), to expedite drug discovery for unconventional therapeutic targets. FRASE-bot mines available 3D structures of ligand-protein complexes to create a database of FRAgments in Structural Environments (FRASE). The FRASE database can be screened to identify structural environments similar to those in the target protein and seed the target structure with relevant ligand fragments. A neural network model is used to retain fragments with the highest likelihood of being native binders. The seeded fragments then inform ultra-large-scale virtual screening of commercially available compounds. We apply FRASE-bot to identify ligands for Calcium and Integrin Binding protein 1 (CIB1), a promising drug target implicated in triple negative breast cancer. FRASE-based virtual screening identifies a small-molecule CIB1 ligand (with binding confirmed in a TR-FRET assay) showing specific cell-killing activity in CIB1-dependent cancer cells, but not in CIB1-depletion-insensitive cells.
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- 2024
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25. Fabrication of precast segmental tunnel linings
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Cattelan, Carlo, primary, Drouin, Marc, additional, Esch, Fred, additional, and Chau, Van Tuan, additional
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- 2024
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26. Palimpseste urbain au square Cabot de Montréal : quand l’urbanisme colonial rencontre la résistance autochtone
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Guimont-Marceau, Stéphane, Buckell, Jennifer, Drouin-Gagné, Marie-Ève, Léonard, Naomie, and Ainsley-Vincent, Raphaëlle
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- 2024
27. Invariant Causal Set Covering Machines
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Godon, Thibaud, Bauvin, Baptiste, Germain, Pascal, Corbeil, Jacques, and Drouin, Alexandre
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Computer Science - Machine Learning ,Statistics - Methodology ,Statistics - Machine Learning - Abstract
Rule-based models, such as decision trees, appeal to practitioners due to their interpretable nature. However, the learning algorithms that produce such models are often vulnerable to spurious associations and thus, they are not guaranteed to extract causally-relevant insights. In this work, we build on ideas from the invariant causal prediction literature to propose Invariant Causal Set Covering Machines, an extension of the classical Set Covering Machine algorithm for conjunctions/disjunctions of binary-valued rules that provably avoids spurious associations. We demonstrate both theoretically and empirically that our method can identify the causal parents of a variable of interest in polynomial time.
- Published
- 2023
28. GEO-Bench: Toward Foundation Models for Earth Monitoring
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Lacoste, Alexandre, Lehmann, Nils, Rodriguez, Pau, Sherwin, Evan David, Kerner, Hannah, Lütjens, Björn, Irvin, Jeremy Andrew, Dao, David, Alemohammad, Hamed, Drouin, Alexandre, Gunturkun, Mehmet, Huang, Gabriel, Vazquez, David, Newman, Dava, Bengio, Yoshua, Ermon, Stefano, and Zhu, Xiao Xiang
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Computer Science - Machine Learning ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to substantial increases in generalization to downstream tasks. Such models, recently coined foundation models, have been transformational to the field of natural language processing. Variants have also been proposed for image data, but their applicability to remote sensing tasks is limited. To stimulate the development of foundation models for Earth monitoring, we propose a benchmark comprised of six classification and six segmentation tasks, which were carefully curated and adapted to be both relevant to the field and well-suited for model evaluation. We accompany this benchmark with a robust methodology for evaluating models and reporting aggregated results to enable a reliable assessment of progress. Finally, we report results for 20 baselines to gain information about the performance of existing models. We believe that this benchmark will be a driver of progress across a variety of Earth monitoring tasks., Comment: arXiv admin note: text overlap with arXiv:2112.00570
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- 2023
29. Hardware-aware Training Techniques for Improving Robustness of Ex-Situ Neural Network Transfer onto Passive TiO2 ReRAM Crossbars
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Drolet, Philippe, Dawant, Raphaël, Yon, Victor, Mouny, Pierre-Antoine, Valdenaire, Matthieu, Zapata, Javier Arias, Gliech, Pierre, Wood, Sean U. N., Ecoffey, Serge, Alibart, Fabien, Beilliard, Yann, and Drouin, Dominique
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Computer Science - Hardware Architecture ,Computer Science - Machine Learning - Abstract
Passive resistive random access memory (ReRAM) crossbar arrays, a promising emerging technology used for analog matrix-vector multiplications, are far superior to their active (1T1R) counterparts in terms of the integration density. However, current transfers of neural network weights into the conductance state of the memory devices in the crossbar architecture are accompanied by significant losses in precision due to hardware variabilities such as sneak path currents, biasing scheme effects and conductance tuning imprecision. In this work, training approaches that adapt techniques such as dropout, the reparametrization trick and regularization to TiO2 crossbar variabilities are proposed in order to generate models that are better adapted to their hardware transfers. The viability of this approach is demonstrated by comparing the outputs and precision of the proposed hardware-aware network with those of a regular fully connected network over a few thousand weight transfers using the half moons dataset in a simulation based on experimental data. For the neural network trained using the proposed hardware-aware method, 79.5% of the test set's data points can be classified with an accuracy of 95% or higher, while only 18.5% of the test set's data points can be classified with this accuracy by the regularly trained neural network., Comment: 15 pages, 11 figures
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- 2023
30. A tunable and versatile 28nm FD-SOI crossbar output circuit for low power analog SNN inference with eNVM synapses
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Palhares, Joao Henrique Quintino, Beilliard, Yann, Sandrini, Jury, Arnaud, Franck, Garello, Kevin, Prenat, Guillaume, Anghel, Lorena, Alibart, Fabien, Drouin, Dominique, and Galy, Philippe
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Electrical Engineering and Systems Science - Systems and Control ,Computer Science - Emerging Technologies - Abstract
In this work we report a study and a co-design methodology of an analog SNN crossbar output circuit designed in a 28nm FD-SOI technology node that comprises a tunable current attenuator and a leak-integrate and fire neurons that would enable the integration of emerging non-volatile memories (eNVMs) for synaptic arrays based on various technologies including phase change (PCRAM), oxide-based (OxRAM), spin transfer and spin orbit torque magnetic memories (STT, SOT-MRAM). Circuit SPICE simulation results and eNVM experimental data are used to showcase and estimate the neurons fan-in for each type of eNVM considering the technology constraints and design trade-offs that set its limits such as membrane capacitance and supply voltage, etc., Comment: 7 pages, 6 figures
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- 2023
31. GPS Alignment from Multiple Sources to Extract Aircraft Bearing in Aerial Surveys
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Power, Joshua, Jacoby, Derek, Drouin, Marc-Antoine, Durand, Guillaume, Coady, Yvonne, and Meng, Julian
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Electrical Engineering and Systems Science - Signal Processing - Abstract
Methodical aerial population surveys monitoring critically endangered species in Canadian North Atlantic waters are instrumental in influencing government policies both in economic and conservational efforts. The primary factor hindering the success of these missions is poor visibility caused by glare. This paper builds off our foundational paper [1] and pushes the envelope toward a data-driven glare modelling system. Said data-driven system makes use of meteorological and astronomical data to assist aircraft in navigating in order to mitigate acquisition errors and optimize the quality of acquired data. It is found that reliably extracting aircraft orientation is critical to our approach, to that end, we present a GPS alignment methodology which makes use of the fusion of two GPS signals. Using the complementary strengths and weaknesses of these two signals a synthetic interpolation of fused data is used to generate more reliable flight tracks, substantially improving glare modelling. This methodology could be applied to any other applications with similar signal restrictions., Comment: 12 pages, 11 figures, IEEE/ION Position Location and Navigation Symposium (PLANS 2023)
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- 2023
32. Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts
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Marcotte, Étienne, Zantedeschi, Valentina, Drouin, Alexandre, and Chapados, Nicolas
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Computer Science - Machine Learning ,Statistics - Machine Learning - Abstract
Multivariate probabilistic time series forecasts are commonly evaluated via proper scoring rules, i.e., functions that are minimal in expectation for the ground-truth distribution. However, this property is not sufficient to guarantee good discrimination in the non-asymptotic regime. In this paper, we provide the first systematic finite-sample study of proper scoring rules for time-series forecasting evaluation. Through a power analysis, we identify the "region of reliability" of a scoring rule, i.e., the set of practical conditions where it can be relied on to identify forecasting errors. We carry out our analysis on a comprehensive synthetic benchmark, specifically designed to test several key discrepancies between ground-truth and forecast distributions, and we gauge the generalizability of our findings to real-world tasks with an application to an electricity production problem. Our results reveal critical shortcomings in the evaluation of multivariate probabilistic forecasts as commonly performed in the literature., Comment: 47 pages, 37 figures, camera-ready version, Fortieth International Conference on Machine Learning (ICML 2023)
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- 2023
33. Influence of mRNA Covid-19 vaccine dosing interval on the risk of myocarditis
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Le Vu, Stéphane, Bertrand, Marion, Semenzato, Laura, Jabagi, Marie-Joelle, Botton, Jérémie, Drouin, Jérôme, Weill, Alain, Dray-Spira, Rosemary, and Zureik, Mahmoud
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- 2024
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34. 28 nm FDSOI embedded PCM exhibiting near zero drift at 12 K for cryogenic SNNs
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Quintino Palhares, Joao Henrique, Garg, Nikhil, Mouny, Pierre-Antoine, Beilliard, Yann, Sandrini, J., Arnaud, F., Anghel, Lorena, Alibart, Fabien, Drouin, Dominique, and Galy, Philippe
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- 2024
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35. Simultaneous liver-kidney transplantation: future perspective
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Prudhomme, Thomas, Mesnard, Benoit, Branchereau, Julien, Roumiguié, Mathieu, Maulat, Charlotte, Muscari, Fabrice, Kamar, Nassim, Soulié, Michel, Gamé, Xavier, Sallusto, Federico, Timsit, Marc Olivier, and Drouin, Sarah
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- 2024
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36. In silico fragment-based discovery of CIB1-directed anti-tumor agents by FRASE-bot
- Author
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An, Yi, Lim, Jiwoong, Glavatskikh, Marta, Wang, Xiaowen, Norris-Drouin, Jacqueline, Hardy, P. Brian, Leisner, Tina M., Pearce, Kenneth H., and Kireev, Dmitri
- Published
- 2024
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37. Eye movement function captured via an electronic tablet informs on cognition and disease severity in Parkinson’s disease
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Koch, Nils A., Voss, Patrice, Cisneros-Franco, J. Miguel, Drouin-Picaro, Alexandre, Tounkara, Fama, Ducharme, Simon, Guitton, Daniel, and de Villers-Sidani, Étienne
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- 2024
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38. Who was the first to visualize the malaria parasite?
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Drouin, Emmanuel, Hautecoeur, Patrick, and Markus, Miles
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- 2024
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39. Clinical and laboratory characteristics of patients hospitalized with severe COVID-19 in New Orleans, August 2020 to September 2021
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Drouin, Arnaud, Plumb, Ian D., McCullough, Matthew, James Gist, Jade, Liu, Sharon, Theberge, Marc, Katz, Joshua, Moreida, Matthew, Flaherty, Shelby, Chatwani, Bhoomija, Briggs Hagen, Melissa, Midgley, Claire M., and Fusco, Dahlene
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- 2024
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40. Author Correction: Steam caps in geothermal reservoirs can be monitored using seismic noise interferometry
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Sánchez-Pastor, Pilar, Wu, Sin-Mei, Hokstad, Ketil, Kristjánsson, Bjarni, Drouin, Vincent, Ducrocq, Cécile, Gunnarsson, Gunnar, Rinaldi, Antonio, Wiemer, Stefan, and Obermann, Anne
- Published
- 2024
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41. Membrane processes used to treat scrubber gas desulfuration washwater
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Drouin, Maryse, Nasser, Samy, and Moulin, Philippe
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- 2024
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42. Pioneer factor Pax7 initiates two-step cell-cycle-dependent chromatin opening
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Gouhier, Arthur, Dumoulin-Gagnon, Justine, Lapointe-Roberge, Vincent, Harris, Juliette, Balsalobre, Aurelio, and Drouin, Jacques
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- 2024
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43. Toward Data-Driven Glare Classification and Prediction for Marine Megafauna Survey
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Power, Joshua, Jacoby, Derek, Drouin, Marc-Antoine, Durand, Guillaume, Coady, Yvonne, and Meng, Julian
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
Critically endangered species in Canadian North Atlantic waters are systematically surveyed to estimate species populations which influence governing policies. Due to its impact on policy, population accuracy is important. This paper lays the foundation towards a data-driven glare modelling system, which will allow surveyors to preemptively minimize glare. Surveyors use a detection function to estimate megafauna populations which are not explicitly seen. A goal of the research is to maximize useful imagery collected, to that end we will use our glare model to predict glare and optimize for glare-free data collection. To build this model, we leverage a small labelled dataset to perform semi-supervised learning. The large dataset is labelled with a Cascading Random Forest Model using a na\"ive pseudo-labelling approach. A reflectance model is used, which pinpoints features of interest, to populate our datasets which allows for context-aware machine learning models. The pseudo-labelled dataset is used on two models: a Multilayer Perceptron and a Recurrent Neural Network. With this paper, we lay the foundation for data-driven mission planning; a glare modelling system which allows surveyors to preemptively minimize glare and reduces survey reliance on the detection function as an estimator of whale populations during periods of poor subsurface visibility., Comment: 15 pages, 4 figures, 5th ICPR Workshop on Computer Vison for Automated Analysis of Underwater Imagery (CVAUI 2022)
- Published
- 2023
44. Production of Ochratoxin A and Citrinin and the Expression of Their Biosynthetic Genes from Penicillium verrucosum in Liquid Culture
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Marc Sasseville, Hai D. T. Nguyen, Simon Drouin, and Adilah Bahadoor
- Subjects
Chemistry ,QD1-999 - Published
- 2024
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45. Eye movement function captured via an electronic tablet informs on cognition and disease severity in Parkinson’s disease
- Author
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Nils A. Koch, Patrice Voss, J. Miguel Cisneros-Franco, Alexandre Drouin-Picaro, Fama Tounkara, Simon Ducharme, Daniel Guitton, and Étienne de Villers-Sidani
- Subjects
Medicine ,Science - Abstract
Abstract Studying the oculomotor system provides a unique window to assess brain health and function in various clinical populations. Although the use of detailed oculomotor parameters in clinical research has been limited due to the scalability of the required equipment, the development of novel tablet-based technologies has created opportunities for fast, easy, cost-effective, and reliable eye tracking. Oculomotor measures captured via a mobile tablet-based technology have previously been shown to reliably discriminate between Parkinson’s Disease (PD) patients and healthy controls. Here we further investigate the use of oculomotor measures from tablet-based eye-tracking to inform on various cognitive abilities and disease severity in PD patients. When combined using partial least square regression, the extracted oculomotor parameters can explain up to 71% of the variance in cognitive test scores (e.g. Trail Making Test). Moreover, using a receiver operating characteristics (ROC) analysis we show that eye-tracking parameters can be used in a support vector classifier to discriminate between individuals with mild PD from those with moderate PD (based on UPDRS cut-off scores) with an accuracy of 90%. Taken together, our findings highlight the potential usefulness of mobile tablet-based technology to rapidly scale eye-tracking use and usefulness in both research and clinical settings by informing on disease stage and cognitive outcomes.
- Published
- 2024
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46. Who was the first to visualize the malaria parasite?
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Emmanuel Drouin, Patrick Hautecoeur, and Miles Markus
- Subjects
History ,Klencke ,Lavéran ,Malaria ,Plasmodium ,Infectious and parasitic diseases ,RC109-216 - Abstract
Abstract Human malaria, an ancient tropical disease, is caused by infection with protozoan parasites belonging to the genus Plasmodium and is transmitted by female mosquitoes of the genus Anopheles. Our understanding of human malaria parasites began officially in 1880 with their discovery in the blood of malaria patients by Charles Louis Alphonse Lavéran (1845–1922), a French army officer working in Algeria. A claim for priority was made by Philipp Friedrich Hermann Klencke (1813–1881) in 1843, who wrote a chapter entitled: “Marvellous parallelism between the manifestations of vertigo and the presence of animalcule vacuoles in living blood.” We should not lose sight of this old controversy, which is rarely mentioned in historical reviews on malaria. Graphical Abstract
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- 2024
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47. Tracing the evolution of the gender of "COVID-19" in the French of three continents: A traditional and social media study
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Dow, Michael and Drouin, Patrick
- Published
- 2024
48. Evaluating the impact of a standardised intervention for announcing decisions of withholding and withdrawing life-sustaining treatments on the stress of relatives in emergency departments (DISCUSS): protocol for a stepped-wedge randomised controlled trial
- Author
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Karim Tazarourte, Marion Douplat, Laurent Jacquin, Delphine Douillet, Fabien Subtil, Julie Haesebaert, Xavier Dubucs, Anne-Marie Schott-Pethelaz, Damien Viglino, Mathilde Marchal, Manon Verroul, Anne Termoz, Pauline Drouin, Estelle Bravant, Carole Langlois, Bénédicte Clément, Daniel Roux-Boniface, Frédéric Verbois, and Marine Demarquet
- Subjects
Medicine - Abstract
Introduction The decisions of withholding or withdrawing life-sustaining treatments are difficult to make in the context of emergency departments (EDs) because most patients are unable to communicate. Relatives are thus asked to participate in the decision‐making process, although they are unprepared to face such situations. We therefore aimed to develop a standardised intervention for announcing decisions of withholding or withdrawing life-sustaining treatments in EDs and assess the efficacy of the intervention on the stress of relatives.Methods and analysis The DISCUSS trial is a multicentre stepped-wedge cluster randomised study and will be conducted at nine EDs in France. A standardised intervention based on human simulation will be codesigned with partner families and implemented at three levels: the relatives, the healthcare professionals (HCP) and the EDs. The intervention will be compared with a control based on treatment as usual. A total of 538 families are planned to be included: 269 in the intervention group and 269 in the control group. The primary endpoint will be the symptoms of post-traumatic stress disorder (PTSD) at 90 days. The secondary endpoints will be symptoms of PTSD at 7 and 30 days, diagnosis of PTSD at 90 days and anxiety and depression scores at 7, 30 and 90 days. Satisfaction regarding the training, the assertiveness in communication and real-life stress of HCPs will be measured at 90 days.Ethics and dissemination This study was approved by the ethics committee Est III from Nancy and the French national data protection authority. All relatives and HCPs will be informed regarding the study objectives and data confidentiality. Written informed consent will be obtained from participants, as required by French law for this study type. The results from this study will be disseminated at conferences and in a peer-reviewed journal.Trial registration number NCT06071078.
- Published
- 2024
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49. Daily smartphone use predicts parent depressive symptoms, but parents' perceptions of responsiveness to their child moderate this effect
- Author
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Brandon T. McDaniel, Sabrina Uva, Jessica Pater, Victor Cornet, Michelle Drouin, and Jenny Radesky
- Subjects
smartphone use ,parenting ,parent responsiveness ,caregiving ,depression ,technoference ,Psychology ,BF1-990 - Abstract
IntroductionSmartphone use during caregiving has become increasingly common, especially around infants and very young children, and this use around young children has been linked with lower quality and quantity of parent-child interaction, with potential implications for child behavior, and parent-child attachment. To understand drivers and consequences of parent phone use, we were interested in the daily associations between parent phone use and depressed mood, as well as the potential for parent perceptions of their responsiveness toward their infant to alter the association between parent phone use and mood.MethodsIn the present study, we explored associations between day-to-day changes in parent smartphone use (objectively-measured via passive sensing) around their infant, depressed mood, and parent perceptions of their responsiveness to their infants among a sample of 264 parents across 8 days. We utilized multilevel modeling to examine these within-person daily associations.ResultsObjectively-measured parent smartphone use during time around their infant was significantly associated with depressed mood on a daily basis. Interestingly, this was not true on days when parents perceived themselves to be more responsive to their infant.DiscussionThese results suggest that parent judgements and perceptions of their parenting behavior may impact the potential link between parent phone use and parent mood. This is the first study utilizing intensive daily data to examine how parent perceptions may alter the felt effects of phone use on their parenting. Future work examining potential impacts of smartphone use on parenting should consider the effects of both actual use and perceptions about that use.
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- 2024
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50. On the Move: 2023 Observations on Real Time Graben Formation, Grindavík, Iceland
- Author
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Gregory P. DePascale, Tomáš J. Fischer, William Michael Moreland, Halldór Geirsson, Pavla Hrubcová, Vincent Drouin, Danielle Forester, Méline Payet‐‐Clerc, Diana Brum daSilveira, Josef Vlček, Benedikt G. Ófeigsson, Ármann Höskuldsson, Helga Kristín Torfadóttir, Iðunn Kara Valdimarsdóttir, Birta Dís Jónsdóttir Blöndal, Ingibjörg Jónsdóttir, Sigurjón Jónsson, and Thor Thordarson
- Subjects
Iceland ,graben formation ,plate boundary ,tectonics ,reykjanes peninsula ,earthquakes ,Geophysics. Cosmic physics ,QC801-809 - Abstract
Abstract Grabens, or valleys formed during extensional tectonic events, are common but rarely observed during formation. In November 2023, inelastic surface deformation formed abruptly along Iceland's plate boundary in Grindavík. We documented graben formation in real‐time through satellite mapping (InSAR), seismicity, GNSS data, repeated lidar surveys, and field mapping. Five normal faults and ∼12 fissures ruptured the surface delineating two grabens separated by a horst, a context not present in other contemporary case studies. The graben normal faults slipped rapidly (over hours) and maximum surface motions coincided with the occurrence of turbulent seismic swarms in both space and time. Although 3 eruptions took place ∼15 km northeast of Grindavík from 2021 to 2023, attributed to magma intrusions (i.e., dikes), none of these also formed grabens. Thus, the Grindavík grabens shows evidence for tectonic origins. Real‐time monitoring of these phenomena provide insight into graben formation on Earth and potentially on other planets.
- Published
- 2024
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- View/download PDF
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