Publications

Approximate Bayesian Optimisation for Neural Networks
Nadhir Hassen
Irina Rish
2021-08-26
ArXiv (prépublication)
arxiv.org
arxiv.org
Extracting Weighted Automata for Approximate Minimization in Language Modelling
Clara Lacroce
Prakash Panangaden
Guillaume Rabusseau
In this paper we study the approximate minimization problem for language modelling. We assume we are given some language model as a black bo… (voir plus)x. The objective is to obtain a weighted finite automaton (WFA) that fits within a given size constraint and which mimics the behaviour of the original model while minimizing some notion of distance between the black box and the extracted WFA. We provide an algorithm for the approximate minimization of black boxes trained for language modelling of sequential data over a one-letter alphabet. By reformulating the problem in terms of Hankel matrices, we leverage classical results on the approximation of Hankel operators, namely the celebrated Adamyan-Arov-Krein (AAK) theory. This allows us to use the spectral norm to measure the distance between the black box and the WFA. We provide theoretical guarantees to study the potentially infinite-rank Hankel matrix of the black box, without accessing the training data, and we prove that our method returns an asymptotically-optimal approximation.
2021-08-24
Proceedings of the Fifteenth International Conference on Grammatical Inference (publié)
doi.org
proceedings.mlr.press
HAD-Net: A Hierarchical Adversarial Knowledge Distillation Network for Improved Enhanced Tumour Segmentation Without Post-Contrast Images
Saverio Vadacchino
Raghav Mehta
Nazanin Mohammadi Sepahvand
Brennan Nichyporuk
James J Clark
Tal Arbel
Segmentation of enhancing tumours or lesions from MRI is important for detecting new disease activity in many clinical contexts. However, ac… (voir plus)curate segmentation requires the inclusion of medical images (e.g., T1 post contrast MRI) acquired after injecting patients with a contrast agent (e.g., Gadolinium), a process no longer thought to be safe. Although a number of modality-agnostic segmentation networks have been developed over the past few years, they have been met with limited success in the context of enhancing pathology segmentation. In this work, we present HAD-Net, a novel offline adversarial knowledge distillation (KD) technique, whereby a pre-trained teacher segmentation network, with access to all MRI sequences, teaches a student network, via hierarchical adversarial training, to better overcome the large domain shift presented when crucial images are absent during inference. In particular, we apply HAD-Net to the challenging task of enhancing tumour segmentation when access to post-contrast imaging is not available. The proposed network is trained and tested on the BraTS 2019 brain tumour segmentation challenge dataset, where it achieves performance improvements in the ranges of 16% - 26% over (a) recent modality-agnostic segmentation methods (U-HeMIS, U-HVED), (b) KD-Net adapted to this problem, (c) the pre-trained student network and (d) a non-hierarchical version of the network (AD-Net), in terms of Dice scores for enhancing tumour (ET). The network also shows improvements in tumour core (TC) Dice scores. Finally, the network outperforms both the baseline student network and AD-Net in terms of uncertainty quantification for enhancing tumour segmentation based on the BraTs 2019 uncertainty challenge metrics. Our code is publicly available at: https://github.com/SaverioVad/HAD_Net
2021-08-24
Proceedings of the Fourth Conference on Medical Imaging with Deep Learning (publié)
doi.org
proceedings.mlr.press
Monitoring non-pharmaceutical public health interventions during the COVID-19 pandemic
Yannan Shen
Guido Powell
Iris Ganser
Qulu Zheng
Chris Grundy
Anya Okhmatovskaia
David L. Buckeridge
Measuring and monitoring non-pharmaceutical interventions is important yet challenging due to the need to clearly define and encode non-phar… (voir plus)maceutical interventions, to collect geographically and socially representative data, and to accurately document the timing at which interventions are initiated and changed. These challenges highlight the importance of integrating and triangulating across multiple databases and the need to expand and fund the mandate for public health organizations to track interventions systematically.
2021-08-23
Scientific Data (publié)
doi.org
Generating community measures of food purchasing activities using store-level electronic grocery transaction records: an ecological study in Montreal, Canada
Hiroshi Mamiya
Alexandra M. Schmidt
Erica E.M. Moodie
Yu Ma
David L Buckeridge
2021-08-22
Public Health Nutrition (publié)
doi.org
Deep Learning for Detecting Extreme Weather Patterns
Mayur Mudigonda
Mayur Mudigonda, Prabhat Ram
Prabhat Ram
Karthik Kashinath
Evan Racah
Ankur Mahesh
Yunjie Liu
Christopher Beckham
Jim Biard
Thorsten Kurth
Sookyung Kim
S Ebrahimi Kahou
Tegan Maharaj
Burlen Loring
Christopher Pal
Travis O'Brien
K. Kunkel
Kenneth E. Kunkel
M. Wehner
Michael F. Wehner … (voir 2 de plus)
W. Collins
William D. Collins
2021-08-19
Deep Learning for the Earth Sciences (publié)
doi.org
Toward Optimal Solution for the Context-Attentive Bandit Problem
Djallel Bouneffouf
Raphael Feraud
Sohini Upadhyay
Irina Rish
Yasaman Khazaeni
2021-08-18
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (publié)
doi.org
A Two-step Heuristic for the Periodic Demand Estimation Problem
Greta Laage
Emma Frejinger
Gilles Savard
Freight carriers rely on tactical plans to satisfy demand in a cost-effective way. For computational tractability in real large-scale settin… (voir plus)gs, such plans are typically computed by solving deterministic and cyclic formulations. An important input is the periodic demand, i.e., the demand that is expected to repeat in each period of the planning horizon. Motivated by the discrepancy between time series forecasts of demand in each period and the periodic demand, Laage et al. (2021) recently introduced the Periodic Demand Estimation (PDE) problem and showed that it has a high value. However, they made strong assumptions on the solution space so that the problem could be solved by enumeration. In this paper we significantly extend their work. We propose a new PDE formulation that relaxes the strong assumptions on the solution space. We solve large instances of this formulation with a two-step heuristic. The first step reduces the dimension of the feasible space by performing clustering of commodities based on instance-specific information about demand and supply interactions. The formulation along with the first step allow to solve the problem in a second step by either metaheuristics or the state-of-the-art black-box optimization solver NOMAD. In an extensive empirical study using real data from the Canadian National Railway Company, we show that our methodology produces high quality solutions and outperforms existing ones.
2021-08-17
ArXiv (prépublication)
arxiv.org
arxiv.org
Generic acquisition protocol for quantitative MRI of the spinal cord
Julien Cohen‐Adad
Eva Alonso‐Ortiz
Mihael Abramovic
Carina Arneitz
Nicole Atcheson
Laura Barlow
Robert Barry
Markus Barth
Marco Battiston
Christian Büchel
Matthew D. Budde
Virginie Callot
Anna Combes
Benjamin De Leener
Maxime Descoteaux
Paulo Loureiro de Sousa
Marek Dostál
Julien Doyon
Adam Dvorak
Falk Eippert … (voir 71 de plus)
Karla R. Epperson
Kevin Epperson
Patrick Freund
Jürgen Finsterbusch
Alexandru Foias
Michela Fratini
Issei Fukunaga
Claudia A. M. Gandini Wheeler‐Kingshott
Giancarlo Germani
Guillaume Gilbert
Federico Giove
Charley Gros
Francesco Grussu
Akifumi Hagiwara
Pierre-Gilles Henry
Tomáš Horák
Masaaki Hori
James M. Joers
Kouhei Kamiya
Haleh Karbasforoushan
Miloš Keřkovský
Ali Khatibi
Joo-Won Kim
Nawal Kinany
Hagen H. Kitzler
Shannon Kolind
Yazhuo Kong
Petr Kudlička
Paul Kuntke
Nyoman D. Kurniawan
Sławomir Kuśmia
René Labounek
Maria Marcella Laganà
Cornelia Laule
Christine Law
Christophe Lenglet
Tobias Leutritz
Yaou Liu
Sara Llufriú
Sean Mackey
Eloy Martínez‐Heras
Loan Mattera
Igor Nestrašil
Kristin P. O’Grady
Nico Papinutto
Daniel S. Papp
Deborah Pareto
Todd B. Parrish
Anna Pichiecchio
Ferrán Prados
Àlex Rovira
Marc J. Ruitenberg
Rebecca S. Samson
Giovanni Savini
Maryam Seif
Alan C. Seifert
Alex K. Smith
Seth A. Smith
Zachary A. Smith
Elisabeth Solana
Yuichi Suzuki
George Tackley
Alexandra Tinnermann
Jan Valosek
Dimitri Van De Ville
Marios Yiannakas
Kenneth A. Weber
Nikolaus Weiskopf
Richard G. Wise
Patrik O. Wyss
Junqian Xu
Optimized and standardized MRI acquisition protocols for the spinal cord, compatible with Siemens, GE and Philips scanners.
2021-08-15
Nature Protocols (publié)
doi.org
Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers
Julien Cohen-Adad
Eva Alonso-Ortiz
Mihael Abramovic
Carina Arneitz
Nicole Atcheson
Laura Barlow
Robert L. Barry
Markus Barth
Marco Battiston
Christian Büchel
Matthew Budde
Virginie Callot
Anna J. E. Combes
Benjamin De Leener
Maxime Descoteaux
Paulo Loureiro de Sousa
Marek Dostál
Julien Doyon
Adam Dvorak
Falk Eippert … (voir 71 de plus)
Karla R. Epperson
Kevin S. Epperson
Patrick Freund
Jürgen Finsterbusch
Alexandru Foias
Michela Fratini
Issei Fukunaga
Claudia A. M. Gandini Wheeler-Kingshott
Giancarlo Germani
Guillaume Gilbert
Federico Giove
Charley Gros
Francesco Grussu
Akifumi Hagiwara
Pierre-Gilles Henry
Tomáš Horák
Masaaki Hori
James Joers
Kouhei Kamiya
Haleh Karbasforoushan
Miloš Keřkovský
Ali Khatibi
Joo-Won Kim
Nawal Kinany
Hagen H. Kitzler
Shannon Kolind
Yazhuo Kong
Petr Kudlička
Paul Kuntke
Nyoman D. Kurniawan
Slawomir Kusmia
René Labounek
Maria Marcella Laganà
Cornelia Laule
Christine S. Law
Christophe Lenglet
Tobias Leutritz
Yaou Liu
Sara Llufriú
Sean Mackey
Eloy Martinez-Heras
Loan Mattera
Igor Nestrašil
Kristin P. O'Grady
Nico Papinutto
Daniel Papp
Deborah Pareto
Todd B. Parrish
Anna Pichiecchio
Ferrán Prados
Àlex Rovira
Marc J. Ruitenberg
Rebecca S. Samson
Giovanni Savini
Maryam Seif
Alan C. Seifert
Alex K. Smith
Seth A. Smith
Zachary A. Smith
Elisabeth Solana
Y. Suzuki
George Tackley
Alexandra Tinnermann
Jan Valosek
Dimitri Van De Ville
Marios C. Yiannakas
Kenneth A. Weber II
Nikolaus Weiskopf
Richard G. Wise
Patrik O. Wyss
Junqian Xu
In a companion paper by Cohen-Adad et al. we introduce the spine generic quantitative MRI protocol that provides valuable metrics for assess… (voir plus)ing spinal cord macrostructural and microstructural integrity. This protocol was used to acquire a single subject dataset across 19 centers and a multi-subject dataset across 42 centers (for a total of 260 participants), spanning the three main MRI manufacturers: GE, Philips and Siemens. Both datasets are publicly available via git-annex. Data were analysed using the Spinal Cord Toolbox to produce normative values as well as inter/intra-site and inter/intra-manufacturer statistics. Reproducibility for the spine generic protocol was high across sites and manufacturers, with an average inter-site coefficient of variation of less than 5% for all the metrics. Full documentation and results can be found at https://spine-generic.rtfd.io/. The datasets and analysis pipeline will help pave the way towards accessible and reproducible quantitative MRI in the spinal cord.
2021-08-15
Scientific Data (publié)
doi.org
Hybrid Harmony: A Multi-Person Neurofeedback Application for Interpersonal Synchrony
Phoebe Chen
Sophie Hendrikse
Kaia Sargent
Michele Romani
Matthias Oostrik
Tom F. Wilderjans
Sander Koole
Guillaume Dumas
David Medine
Suzanne Dikker
Recent years have seen a dramatic increase in studies measuring brain activity, physiological responses, and/or movement data from multiple … (voir plus)individuals during social interaction. For example, so-called “hyperscanning” research has demonstrated that brain activity may become synchronized across people as a function of a range of factors. Such findings not only underscore the potential of hyperscanning techniques to capture meaningful aspects of naturalistic interactions, but also raise the possibility that hyperscanning can be leveraged as a tool to help improve such naturalistic interactions. Building on our previous work showing that exposing dyads to real-time inter-brain synchrony neurofeedback may help boost their interpersonal connectedness, we describe the biofeedback application Hybrid Harmony, a Brain-Computer Interface (BCI) that supports the simultaneous recording of multiple neurophysiological datastreams and the real-time visualization and sonification of inter-subject synchrony. We report results from 236 dyads experiencing synchrony neurofeedback during naturalistic face-to-face interactions, and show that pairs' social closeness and affective personality traits can be reliably captured with the inter-brain synchrony neurofeedback protocol, which incorporates several different online inter-subject connectivity analyses that can be applied interchangeably. Hybrid Harmony can be used by researchers who wish to study the effects of synchrony biofeedback, and by biofeedback artists and serious game developers who wish to incorporate multiplayer situations into their practice.
2021-08-11
Frontiers in Neuroergonomics (publié)
doi.org
An Advanced Noise Reduction and Edge Enhancement Algorithm
Shih-Chia Huang
Quoc-Viet Hoang
Trung-Hieu Le
Yan-Tsung Peng
Ching-Chun Huang
Cheng Zhang
Benjamin C. M. Fung
Kai-Han Cheng
Sha-Wo Huang
Complementary metal-oxide-semiconductor (CMOS) image sensors can cause noise in images collected or transmitted in unfavorable environments,… (voir plus) especially low-illumination scenarios. Numerous approaches have been developed to solve the problem of image noise removal. However, producing natural and high-quality denoised images remains a crucial challenge. To meet this challenge, we introduce a novel approach for image denoising with the following three main contributions. First, we devise a deep image prior-based module that can produce a noise-reduced image as well as a contrast-enhanced denoised one from a noisy input image. Second, the produced images are passed through a proposed image fusion (IF) module based on Laplacian pyramid decomposition to combine them and prevent noise amplification and color shift. Finally, we introduce a progressive refinement (PR) module, which adopts the summed-area tables to take advantage of spatially correlated information for edge and image quality enhancement. Qualitative and quantitative evaluations demonstrate the efficiency, superiority, and robustness of our proposed method.
2021-08-09
Sensors (Basel, Switzerland) (publié)
doi.org