Publications

Determinants of technology adoption and continued use among cognitively impaired older adults: a qualitative study
Samantha Dequanter
Maaike Fobelets
Iris Steenhout
Marie-Pierre Gagnon
Anne Bourbonnais
Samira Abbasgholizadeh-Rahimi
Ronald Buyl
Ellen Gorus
2022-04-28
BMC Geriatrics (published)
doi.org
Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma
Elizabeth George
Elizabeth Flagg
Kuan-chun Chang
H. Bai
H. Aerts
Martin Vallières
D. Reardon
R.Y. Huang
BACKGROUND AND PURPOSE: Imaging assessment of an immunotherapy response in glioblastoma is challenging due to overlap in the appearance of t… (voir plus)reatment-related changes with tumor progression. Our purpose was to determine whether MR imaging radiomics-based machine learning can predict progression-free survival and overall survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy. MATERIALS AND METHODS: Post hoc analysis was performed of a multicenter trial on the efficacy of durvalumab in glioblastoma (n = 113). Radiomics tumor features on pretreatment and first on-treatment time point MR imaging were extracted. The random survival forest algorithm was applied to clinical and radiomics features from pretreatment and first on-treatment MR imaging from a subset of trial sites (n = 60–74) to train a model to predict long overall survival and progression-free survival and was tested externally on data from the remaining sites (n = 29–43). Model performance was assessed using the concordance index and dynamic area under the curve from different time points. RESULTS: The mean age was 55.2 (SD, 11.5) years, and 69% of patients were male. Pretreatment MR imaging features had a poor predictive value for overall survival and progression-free survival (concordance index  = 0.472–0.524). First on-treatment MR imaging features had high predictive value for overall survival (concordance index = 0.692–0.750) and progression-free survival (concordance index = 0.680–0.715). CONCLUSIONS: A radiomics-based machine learning model from first on-treatment MR imaging predicts survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.
2022-04-28
American Journal of Neuroradiology (publié)
doi.org
Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma
Elizabeth George
Elizabeth Flagg
Kuan-chun Chang
H. Bai
H. Aerts
Martin Vallières
D. Reardon
R.Y. Huang
BACKGROUND AND PURPOSE: Imaging assessment of an immunotherapy response in glioblastoma is challenging due to overlap in the appearance of t… (voir plus)reatment-related changes with tumor progression. Our purpose was to determine whether MR imaging radiomics-based machine learning can predict progression-free survival and overall survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy. MATERIALS AND METHODS: Post hoc analysis was performed of a multicenter trial on the efficacy of durvalumab in glioblastoma (n = 113). Radiomics tumor features on pretreatment and first on-treatment time point MR imaging were extracted. The random survival forest algorithm was applied to clinical and radiomics features from pretreatment and first on-treatment MR imaging from a subset of trial sites (n = 60–74) to train a model to predict long overall survival and progression-free survival and was tested externally on data from the remaining sites (n = 29–43). Model performance was assessed using the concordance index and dynamic area under the curve from different time points. RESULTS: The mean age was 55.2 (SD, 11.5) years, and 69% of patients were male. Pretreatment MR imaging features had a poor predictive value for overall survival and progression-free survival (concordance index  = 0.472–0.524). First on-treatment MR imaging features had high predictive value for overall survival (concordance index = 0.692–0.750) and progression-free survival (concordance index = 0.680–0.715). CONCLUSIONS: A radiomics-based machine learning model from first on-treatment MR imaging predicts survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.
2022-04-28
American Journal of Neuroradiology (published)
doi.org
Two types of human TCR differentially regulate reactivity to self and non-self antigens
Assya Trofimov
Philippe Brouillard
Jean-David Larouche
Jonathan Y. Séguin
Jean-Philippe Laverdure
A. Brasey
Grégory Ehx
D. Roy
Lambert Busque
Silvy Lachance
Sébastien Lemieux
Claude Perreault
2022-04-28
bioRxiv (preprint)
doi.org
Current State and Future Directions for Learning in Biological Recurrent Neural Networks: A Perspective Piece
Luke Y. Prince
Roy Henha Eyono
Ellen Boven
Arna Ghosh
Joseph Pemberton
Franz Scherr
Claudia Clopath
Rui Ponte Costa
Wolfgang Maass
Blake Richards
Cristina Savin
Katharina Wilmes
We provide a brief review of the common assumptions about biological learning with findings from experimental neuroscience and contrast them… (voir plus) with the efficiency of gradient-based learning in recurrent neural networks. The key issues discussed in this review include: synaptic plasticity, neural circuits, theory-experiment divide, and objective functions. We conclude with recommendations for both theoretical and experimental neuroscientists when designing new studies that could help bring clarity to these issues.
2022-04-27
Neurons, Behavior, Data analysis, and Theory (publié)
doi.org
arxiv.org
Accepted Tutorials at The Web Conference 2022
Riccardo Tommasini
Senjuti Basu Roy
Xuan Wang
Hongwei Wang
Heng Ji
Jiawei Han
Preslav Nakov
Giovanni Da San Martino
Firoj Alam
Markus Schedl
Elisabeth Lex
Akash Bharadwaj
Graham Cormode
Milan Dojchinovski
Jan Forberg
Johannes Frey
Pieter Bonte
Marco Balduini
Matteo Belcao
Emanuele Della Valle … (voir 53 de plus)
Junliang Yu
Hongzhi Yin
Tong Chen
Haochen Liu
Yiqi Wang
Wenqi Fan
Xiaorui Liu
Jamell Dacon
Lingjuan Lye
Jiliang Tang
Aristides Gionis
Stefan Neumann
Bruno Ordozgoiti
Simon Razniewski
Hiba Arnaout
Shrestha Ghosh
Fabian Suchanek
Lingfei Wu
Yu Chen
Yunyao Li
Bang Liu
Filip Ilievski
Daniel Garijo
Hans Chalupsky
Pedro Szekely
Ilias Kanellos
Dimitris Sacharidis
Thanasis Vergoulis
Nurendra Choudhary
Nikhil Rao
Karthik Subbian
Srinivasan Sengamedu
Chandan K. Reddy
Friedhelm Victor
Bernhard Haslhofer
George Katsogiannis- Meimarakis
Georgia Koutrika
Shengmin Jin
Danai Koutra
Reza Zafarani
Yulia Tsvetkov
Vidhisha Balachandran
Sachin Kumar
Xiangyu Zhao
Bo Chen
Huifeng Guo
Yejing Wang
Ruiming Tang
Yang Zhang
Wenjie Wang
Peng Wu
Fuli Feng
Xiangnan He
This paper summarizes the content of the 20 tutorials that have been given at The Web Conference 2022: 85% of these tutorials are lecture st… (voir plus)yle, and 15% of these are hands on.
2022-04-25
The Web Conference (publié)
doi.org
Offline Retrieval Evaluation Without Evaluation Metrics
Fernando Diaz
Andres Ferraro
Offline evaluation of information retrieval and recommendation has traditionally focused on distilling the quality of a ranking into a scala… (voir plus)r metric such as average precision or normalized discounted cumulative gain. We can use this metric to compare the performance of multiple systems for the same request. Although evaluation metrics provide a convenient summary of system performance, they also collapse subtle differences across users into a single number and can carry assumptions about user behavior and utility not supported across retrieval scenarios. We propose recall-paired preference (RPP), a metric-free evaluation method based on directly computing a preference between ranked lists. RPP simulates multiple user subpopulations per query and compares systems across these pseudo-populations. Our results across multiple search and recommendation tasks demonstrate that RPP substantially improves discriminative power while correlating well with existing metrics and being equally robust to incomplete data.
2022-04-25
ArXiv (preprint)
doi.org
arxiv.org
QEN: Applicable Taxonomy Completion via Evaluating Full Taxonomic Relations
Suyuchen Wang
Ruihui Zhao
Yefeng Zheng
Bang Liu
Taxonomy is a fundamental type of knowledge graph for a wide range of web applications like searching and recommendation systems. To keep a … (voir plus)taxonomy automatically updated with the latest concepts, the taxonomy completion task matches a pair of proper hypernym and hyponym in the original taxonomy with the new concept as its parent and child. Previous solutions utilize term embeddings as input and only evaluate the parent-child relations between the new concept and the hypernym-hyponym pair. Such methods ignore the important sibling relations, and are not applicable in reality since term embeddings are not available for the latest concepts. They also suffer from the relational noise of the “pseudo-leaf” node, which is a null node acting as a node’s hyponym to enable the new concept to be a leaf node. To tackle the above drawbacks, we propose the Quadruple Evaluation Network (QEN), a novel taxonomy completion framework that utilizes easily accessible term descriptions as input, and applies pretrained language model and code attention for accurate inference while reducing online computation. QEN evaluates both parent-child and sibling relations to both enhance the accuracy and reduce the noise brought by pseudo-leaf. Extensive experiments on three real-world datasets in different domains with different sizes and term description sources prove the effectiveness and robustness of QEN on overall performance and especially the performance for adding non-leaf nodes, which largely surpasses previous methods and achieves the new state-of-the-art of the task.1
2022-04-25
The Web Conference (publié)
doi.org
QEN: Applicable Taxonomy Completion via Evaluating Full Taxonomic Relations
Suyuchen Wang
Ruihui Zhao
Yefeng Zheng
Bang Liu
Taxonomy is a fundamental type of knowledge graph for a wide range of web applications like searching and recommendation systems. To keep a … (voir plus)taxonomy automatically updated with the latest concepts, the taxonomy completion task matches a pair of proper hypernym and hyponym in the original taxonomy with the new concept as its parent and child. Previous solutions utilize term embeddings as input and only evaluate the parent-child relations between the new concept and the hypernym-hyponym pair. Such methods ignore the important sibling relations, and are not applicable in reality since term embeddings are not available for the latest concepts. They also suffer from the relational noise of the “pseudo-leaf” node, which is a null node acting as a node’s hyponym to enable the new concept to be a leaf node. To tackle the above drawbacks, we propose the Quadruple Evaluation Network (QEN), a novel taxonomy completion framework that utilizes easily accessible term descriptions as input, and applies pretrained language model and code attention for accurate inference while reducing online computation. QEN evaluates both parent-child and sibling relations to both enhance the accuracy and reduce the noise brought by pseudo-leaf. Extensive experiments on three real-world datasets in different domains with different sizes and term description sources prove the effectiveness and robustness of QEN on overall performance and especially the performance for adding non-leaf nodes, which largely surpasses previous methods and achieves the new state-of-the-art of the task.1
2022-04-25
The Web Conference (published)
doi.org
Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study
Jianzhong Chen
Angela Tam
Valeria Kebets
Csaba Orban
L.Q.R. Ooi
Christopher L Asplund
Scott A. Marek
N. Dosenbach
Simon B. Eickhoff
Danilo Bzdok
Avram J. Holmes
B.T. Thomas Yeo
2022-04-25
Nature Communications (published)
doi.org
Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study
Jianzhong Chen
Angela Tam
Valeria Kebets
Csaba Orban
L.Q.R. Ooi
Leon Qi Rong Ooi
Christopher L. Asplund
Scott Marek
Nico Dosenbach
Simon B. Eickhoff
Danilo Bzdok
Avram J. Holmes
B.T. Thomas Yeo
2022-04-25
Nature Communications (publié)
doi.org
Staged independent learning: Towards decentralized cooperative multi-agent Reinforcement Learning
Hadi Nekoei
Akilesh Badrinaaraayanan
Amit Sinha
Mohammad Amini
Janarthanan Rajendran
Aditya Mahajan
Sarath Chandar
We empirically show that classic ideas from two-time scale stochastic approximation \citep{borkar1997stochastic} can be combined with sequen… (voir plus)tial iterative best response (SIBR) to solve complex cooperative multi-agent reinforcement learning (MARL) problems. We first start with giving a multi-agent estimation problem as a motivating example where SIBR converges while parallel iterative best response (PIBR) does not. Then we present a general implementation of staged multi-agent RL algorithms based on SIBR and multi-time scale stochastic approximation, and show that our new methods which we call Staged Independent Proximal Policy Optimization (SIPPO) and Staged Independent Q-learning (SIQL) outperform state-of-the-art independent learning on almost all the tasks in the epymarl \citep{papoudakis2020benchmarking} benchmark. This can be seen as a first step towards more decentralized MARL methods based on SIBR and multi-time scale learning.
2022-04-25
ICLR.cc/2022/Workshop/GMS (publié)
openreview.net
openreview.net