Publications

Aligning artificial intelligence with climate change mitigation
Lynn H. Kaack
Priya L. Donti
Emma Strubell
George Kamiya
Felix Creutzig
David Rolnick
2022-05-31
Nature Climate Change (inconnu)
doi.org
Biomedical Research and Informatics Living Laboratory for Innovative Advances of New Technologies in Community Mobility Rehabilitation: Protocol for Evaluation and Rehabilitation of Mobility Across Continuums of Care
Sara Ahmed
Philippe Archambault
Claudine Auger
Audrey Durand
Joyce Fung
Eva Kehayia
Anouk Lamontagne
Annette Majnemer
Sylvie Nadeau
Joelle Pineau
Alain Ptito
Bonnie Swaine
Rapid advances in technologies over the past 10 years have enabled large-scale biomedical and psychosocial rehabilitation research to improv… (voir plus)e the function and social integration of persons with physical impairments across the lifespan. The Biomedical Research and Informatics Living Laboratory for Innovative Advances of New Technologies (BRILLIANT) in community mobility rehabilitation aims to generate evidence-based research to improve rehabilitation for individuals with acquired brain injury (ABI). This study aims to (1) identify the factors limiting or enhancing mobility in real-world community environments (public spaces, including the mall, home, and outdoors) and understand their complex interplay in individuals of all ages with ABI and (2) customize community environment mobility training by identifying, on a continuous basis, the specific rehabilitation strategies and interventions that patient subgroups benefit from most. Here, we present the research and technology plan for the BRILLIANT initiative. A cohort of individuals, adults and children, with ABI (N=1500) will be recruited. Patients will be recruited from the acute care and rehabilitation partner centers within 4 health regions (living labs) and followed throughout the continuum of rehabilitation. Participants will also be recruited from the community. Biomedical, clinician-reported, patient-reported, and brain imaging data will be collected. Theme 1 will implement and evaluate the feasibility of collecting data across BRILLIANT living labs and conduct predictive analyses and artificial intelligence (AI) to identify mobility subgroups. Theme 2 will implement, evaluate, and identify community mobility interventions that optimize outcomes for mobility subgroups of patients with ABI. The biomedical infrastructure and equipment have been established across the living labs, and development of the clinician- and patient-reported outcome digital solutions is underway. Recruitment is expected to begin in May 2022. The program will develop and deploy a comprehensive clinical and community-based mobility-monitoring system to evaluate the factors that result in poor mobility, and develop personalized mobility interventions that are optimized for specific patient subgroups. Technology solutions will be designed to support clinicians and patients to deliver cost-effective care and the right intervention to the right person at the right time to optimize long-term functional potential and meaningful participation in the community. PRR1-10.2196/12506
2022-05-31
JMIR Research Protocols (publié)
doi.org
A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex
Giuseppe Chindemi
Marwan Abdellah
Oren Amsalem
Ruth Benavides-Piccione
Vincent Delattre
Michael Doron
András Ecker
Aurélien T. Jaquier
James King
Pramod Kumbhar
Caitlin Monney
Rodrigo Perin
Christian Rössert
Anil M. Tuncel
Werner Van Geit
Javier DeFelipe
Michael Graupner
Idan Segev
Henry Markram
Eilif B. Muller
Pyramidal cells (PCs) form the backbone of the layered structure of the neocortex, and plasticity of their synapses is thought to underlie l… (voir plus)earning in the brain. However, such long-term synaptic changes have been experimentally characterized between only a few types of PCs, posing a significant barrier for studying neocortical learning mechanisms. Here we introduce a model of synaptic plasticity based on data-constrained postsynaptic calcium dynamics, and show in a neocortical microcircuit model that a single parameter set is sufficient to unify the available experimental findings on long-term potentiation (LTP) and long-term depression (LTD) of PC connections. In particular, we find that the diverse plasticity outcomes across the different PC types can be explained by cell-type-specific synaptic physiology, cell morphology and innervation patterns, without requiring type-specific plasticity. Generalizing the model to in vivo extracellular calcium concentrations, we predict qualitatively different plasticity dynamics from those observed in vitro. This work provides a first comprehensive null model for LTP/LTD between neocortical PC types in vivo, and an open framework for further developing models of cortical synaptic plasticity.
2022-05-31
Nature Communications (publié)
doi.org
Conjugate Adder Net (CAddNet) - a Space-Efficient Approximate CNN
Lulan Shen
Maryam Ziaeefard
Brett Meyer
Warren Gross
James J. Clark
The AdderNet was recently developed as a way to implement deep neural networks without needing multiplication operations to combine weights … (voir plus)and inputs. Instead, absolute values of the difference between weights and inputs are used, greatly reducing the gate-level implementation complexity. Training of AdderNets is challenging, however, and the loss curves during training tend to fluctuate significantly. In this paper we propose the Conjugate Adder Network, or CAddNet, which uses the difference between the absolute values of conjugate pairs of inputs and the weights. We show that this can be implemented simply via a single minimum operation, resulting in a roughly 50% reduction in logic gate complexity as compared with AdderNets. The CAddNet method also stabilizes training as compared with AdderNets, yielding training curves similar to standard CNNs.
2022-05-31
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publié)
doi.org
A data-driven approach to include availability of ICU beds in the planning of the operating room
Augustin A
Jouvet P
Lahrichi N
Lodi A
Rousseau LM
2022-05-31
Omega, The International Journal of Management Science (publié)
doi.org
EpiTopics: A dynamic machine learning model to predict and inform non-pharmacological public health interventions from global news reports
Zhi Wen
Jingfu Zhang
Guido Powell
Imane Chafi
David L. Buckeridge
Yue Li
2022-05-31
STAR Protocols (publié)
doi.org
Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion.
Matthew Farrell
Stefano Recanatesi
Timothy Moore
Guillaume Lajoie
Eric Shea-Brown
Neural networks need the right representations of input data to learn. Here we ask how gradient-based learning shapes a fundamental property… (voir plus) of representations in recurrent neural networks (RNNs)—their dimensionality. Through simulations and mathematical analysis, we show how gradient descent can lead RNNs to compress the dimensionality of their representations in a way that matches task demands during training while supporting generalization to unseen examples. This can require an expansion of dimensionality in early timesteps and compression in later ones, and strongly chaotic RNNs appear particularly adept at learning this balance. Beyond helping to elucidate the power of appropriately initialized artificial RNNs, this fact has implications for neurobiology as well. Neural circuits in the brain reveal both high variability associated with chaos and low-dimensional dynamical structures. Taken together, our findings show how simple gradient-based learning rules lead neural networks to solve tasks with robust representations that generalize to new cases. Neural networks in the brain often exhibit chaotic dynamics that can be captured by a small number of dimensions. Farrell et al. find that recurrent neural networks trained with gradient-based learning rules exhibit similar features. This helps form robust but generalizable input representations.
2022-05-31
Nature Machine Intelligence (publié)
doi.org
High-Throughput and Energy-Efficient VLSI Architecture for Ordered Reliability Bits GRAND
Syed Mohsin Abbas
Thibaud Tonnellier
Furkan Ercan
Marwan Jalaleddine
Warren J. Gross
Ultrareliable low-latency communication (URLLC), a major 5G new-radio (NR) use case, is the key enabler for applications with strict reliabi… (voir plus)lity and latency requirements. These applications necessitate the use of short-length and high-rate channel codes. Guessing random additive noise decoding (GRAND) is a recently proposed maximum likelihood (ML) decoding technique for these short-length and high-rate codes. Rather than decoding the received vector, GRAND tries to infer the noise that corrupted the transmitted codeword during transmission through the communication channel. As a result, GRAND can decode any code, structured or unstructured. GRAND has hard-input as well as soft-input variants. Among these variants, ordered reliability bits GRAND (ORBGRAND) is a soft-input variant that outperforms hard-input GRAND and is suitable for parallel hardware implementation. This work reports the first hardware architecture for ORBGRAND, which achieves an average throughput of up to 42.5 Gb/s for a code length of 128 at a target frame error rate (FER) of 10−7. Furthermore, the proposed hardware can be used to decode any code as long as the length and rate constraints are met. In comparison to the GRAND with ABandonment (GRANDAB), a hard-input variant of GRAND, the proposed architecture enhances decoding performance by at least 2 dB. When compared to the state-of-the-art fast dynamic successive cancellation flip decoder (Fast-DSCF) using a 5G polar code (PC) (128, 105), the proposed ORBGRAND VLSI implementation has
2022-05-31
IEEE Transactions on Very Large Scale Integration (VLSI) Systems (publié)
doi.org
arxiv.org
Inhaltsverzeichnis / Table of Contents
Paulina Bravo
Martin Ha
Kirsten Mccaffery
Anik Gigue
Pola Hahlweg
Glyn Elwyn
Karina Dahl Steffensen
Ame ´de ´gogovor
Maya Fakhfakh
Asmaou Dalil
Owolabi Bouba
Gloria Acakpo
Ange Ayivi-Vinz
Mirjam Le Musabyimana
Paula Riganti
Juan Victor
Ariel Franco
Victoria Yanzi
Carolina Carrara
Mariela Barani … (voir 80 de plus)
Karin Kopitowski
Marguerite Tracy
Rachel Thompson
Danielle Muscat
Carissa Bonner
Tammy Hoffmann
Heather Shepherd
France Le ´gare ´
Dawn Stacey
Pierre-Gerlier Forest
Patrick Archambault
Laura Boland
Marie-France Coutu
Annie LeBlanc
Krystina Lewis
Holly Witteman
Angelina Dois
Alejandra Martı ´nez
Marcela Gonza ´lez-Agu ¨ero
Gabriela Soto
Andrea Rioseco
Solange Campos
Claudia Bustamante
Irma Vargas
Bettina Knudsen
Jeanette Finderup
Marlene Willemann
Wu ¨rgler
Karina Olling
Nora Moumjid
Marie-Anne Durand
Julien Carretier
Elodie Charuel
Jocelyne Daumer
Julie Haesebaert
Sandrine Hild
Julien Mancini
Giovanna Marsico
Ce ´dric Rat
Yves Zerbib
Yves-Marie Vincent
François Blot
Christiane Bieber
Anna Levke
Bru ¨tt
Marie-Luise Dierks
Jo ¨rg Dirmaier
Norbert Donner-Banzhoff
Wolfgang Eich
Friedemann Geiger
David Klemperer
Klaus Koch
Mirjam Ko
Isabelle Scholl
O ´scar
Brito Fernandes
Ma ´rta Pe
Samira Abbasgholizadeh-Rahimi
Nam Nguyen
Mahasti Alizadeh
Dan Poenaru
Dan Poenaru
Dulce Alarco n-Yaquetto
Aldo De Ferrari
K Germa n ; Aldona
Joanna Jankowska
Martyna Matjanowska
Borowczyk
Kevin Selby
Marie-Anne Durand
Christiane Bieber
O ´scar
Nikola Biller-Andorno
Anna Levke
Irma Vargas
Angelina Dois
Karina Olling
Marie-Anne Durand
Vikki Entwistle
Sam Finnikin
2022-05-31
Zeitschrift für Evidenz, Fortbildung und Qualität im Gesundheitswesen (publié)
doi.org
Moving shared decision-making forward in Iran
Samira Abbasgholizadeh-Rahimi
Nam Nguyen
Mahasti Alizadeh
Dan Poenaru
2022-05-31
Zeitschrift für Evidenz, Fortbildung und Qualität im Gesundheitswesen (publié)
doi.org
Optimal Control of Network-Coupled Subsystems: Spectral Decomposition and Low-Dimensional Solutions
Shuang Gao
Aditya Mahajan
In this article, we investigate the optimal control of network-coupled subsystems with coupled dynamics and costs. The dynamics coupling may… (voir plus) be represented by the adjacency matrix, the Laplacian matrix, or any other symmetric matrix corresponding to an underlying weighted undirected graph. Cost couplings are represented by two coupling matrices which have the same eigenvectors as the coupling matrix in the dynamics. We use the spectral decomposition of these three coupling matrices to decompose the overall system into
2022-05-31
IEEE Transactions on Control of Network Systems (publié)
doi.org
arxiv.org
OptiMaP: swarm-powered Optimized 3D Mapping Pipeline for emergency response operations
Leandro R. Costa
Daniel Aloise
Luca G. Gianoli
Andrea Lodi
A smart application in sensing is mainly powered by a two-stage process comprising sensing (collect data) and computing (process data). Whil… (voir plus)e the sensing stage is typically performed locally through a dedicated Internet of Things infrastructure, the computing stage may require a powerful infrastructure in the cloud. However, when connectivity is poor and low latency becomes a requirement — as in emergency response and disaster relief operations — edge computing and ad hoc cloud paradigms come in support to keep the computing stage locally. Being local network connectivity and data processing limited, it is vital to properly optimize how the computing workload will be consumed by the local ad hoc cloud. For this purpose, we present and evaluate the swarm-powered Optimized 3D Mapping Pipeline (OptiMaP) for emergency response 3D mapping missions, which is implemented as a collaborative embedded Robot Operating System (ROS) application integrating an ad hoc telecommunication middleware.We simulate — with Software-In-The-Loop — realistic 3D mapping missions comprising up to 5 drones and 363 images covering 0.293km2. We show how the completion times of mapping missions carried out in a typical centralized manner can be dramatically reduced by two versions of the OptiMaP framework powered, respectively, by a variable neighborhood search heuristic and a greedy method.
2022-05-31
2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS) (publié)
doi.org