Publications

Deep Reinforcement Learning in Human Activity Recognition: A Survey
Bahareh Nikpour
Dimitrios Sinodinos
Narges Armanfard
<p>  Human activity recognition is a popular research field in computer vision that has already been widely studied. However, it is … (see more)still an active research field since it plays an important role in many current and emerging real-world intelligent systems, like visual surveillance and human-computer interaction. Deep Reinforcement Learning (DRL) has recently been employed to address the activity recognition problem with various purposes, such as finding attention in video data or obtaining the best network structure. DRL-based human activity recognition has only been around for a short time, and it is a challenging, novel field of study. Therefore, to facilitate further research in this field, we have constructed a comprehensive survey on activity recognition methods that incorporate deep reinforcement learning. Towards the end of this survey, we summarize key challenges and open problems in this area that can be addressed by researchers in the future.  </p>
2022-08-28
Figshare (preprint)
doi.org
Deep Reinforcement Learning in Human Activity Recognition: A Survey
Bahareh Nikpour
Dimitrios Sinodinos
Narges Armanfard
<p>  Human activity recognition is a popular research field in computer vision that has already been widely studied. However, it is … (see more)still an active research field since it plays an important role in many current and emerging real-world intelligent systems, like visual surveillance and human-computer interaction. Deep Reinforcement Learning (DRL) has recently been employed to address the activity recognition problem with various purposes, such as finding attention in video data or obtaining the best network structure. DRL-based human activity recognition has only been around for a short time, and it is a challenging, novel field of study. Therefore, to facilitate further research in this field, we have constructed a comprehensive survey on activity recognition methods that incorporate deep reinforcement learning. Towards the end of this survey, we summarize key challenges and open problems in this area that can be addressed by researchers in the future.  </p>
2022-08-28
Figshare (preprint)
doi.org
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
Raghav Mehta
Angelos Filos
Ujjwal Baid
Chiharu Sako
Richard McKinley
Michael Rebsamen
Katrin Datwyler
Raphael Meier
Piotr Radojewski
Gowtham Krishnan Murugesan
Sahil Nalawade
Chandan Ganesh
Ben Wagner
Fang F. Yu
Baowei Fei
Ananth J. Madhuranthakam
Joseph A. Maldjian
Laura Daza
Catalina Gomez
Pablo Arbelaez … (see 72 more)
Chengliang Dai
Shuo Wang
Hadrien Reynaud
Yuan-han Mo
Elsa Angelini
Yike Guo
Wenjia Bai
Subhashis Banerjee
Lin-min Pei
Murat AK
Sarahi Rosas-Gonzalez
Ilyess Zemmoura
Clovis Tauber
Minh H. Vu
Tufve Nyholm
Tommy Lofstedt
Laura Mora Ballestar
Veronica Vilaplana
Hugh McHugh
Gonzalo Maso Talou
Alan Wang
Jay Patel
Ken Chang
Katharina Hoebel
Mishka Gidwani
Nishanth Arun
Sharut Gupta
Mehak Aggarwal
Praveer Singh
Elizabeth R. Gerstner
Jayashree Kalpathy-Cramer
Nicolas Boutry
Alexis Huard
Lasitha Vidyaratne
Md Monibor Rahman
Khan M. Iftekharuddin
Joseph Chazalon
Elodie Puybareau
Guillaume Tochon
Jun Ma
Mariano Cabezas
Xavier Llado
Arnau Oliver
Liliana Valencia
Sergi Valverde
Mehdi Amian
Mohammadreza Soltaninejad
Andriy Myronenko
Ali Hatamizadeh
Xue Feng
Quan Dou
Nicholas Tustison
Craig Meyer
Nisarg A. Shah
Sanjay Talbar
Marc-André Weber
Abhishek Mahajan
Andras Jakab
Roland Wiest
Hassan M. Fathallah-Shaykh
Arash Nazeri
Mikhail Milchenko1
Daniel Marcus
Aikaterini Kotrotsou
Rivka Colen
John Freymann
Justin Kirby
Christos Davatzikos
Bjoern Menze
Spyridon Bakas
Yarin Gal
Tal Arbel
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain… (see more) Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS.
2022-08-25
Machine Learning for Biomedical Imaging (published)
doi.org
arxiv.org
Fractal impedance for passive controllers: a framework for interaction robotics
Keyhan Kouhkiloui Babarahmati
Carlo Tiseo
Joshua Smith
Hsiu-Chin Lin
M. S. Erden
Michael Nalin Mistry
2022-08-24
Nonlinear Dynamics (published)
doi.org
arxiv.org
Learning Shared Neural Manifolds from Multi-Subject fMRI Data
Jessie Huang
Erica Busch
Tom Wallenstein
Michal Gerasimiuk
Andrew Benz
Guillaume Lajoie
Guy Wolf
Nicholas Turk-Browne
Smita Krishnaswamy
Functional magnetic resonance imaging (fMRI) is a notoriously noisy measurement of brain activity because of the large variations between in… (see more)dividuals, signals marred by environmental differences during collection, and spatiotemporal averaging required by the measurement resolution. In addition, the data is extremely high dimensional, with the space of the activity typically having much lower intrinsic dimension. In order to understand the connection between stimuli of interest and brain activity, and analyze differences and commonalities between subjects, it becomes important to learn a meaningful embedding of the data that denoises, and reveals its intrinsic structure. Specifically, we assume that while noise varies significantly between individuals, true responses to stimuli will share common, low-dimensional features between subjects which are jointly discoverable. Similar approaches have been exploited previously but they have mainly used linear methods such as PCA and shared response modeling (SRM). In contrast, we propose a neural network called MRMD-AE (manifold-regularized multiple decoder, autoencoder), that learns a common embedding from multiple subjects in an experiment while retaining the ability to decode to individual raw fMRI signals. We show that our learned common space represents an extensible manifold (where new points not seen during training can be mapped), improves the classification accuracy of stimulus features of unseen timepoints, as well as improves cross-subject translation of fMRI signals. We believe this framework can be used for many downstream applications such as guided brain-computer interface (BCI) training in the future.
2022-08-21
2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP) (published)
doi.org
openreview.net
Proteogenomics and Differential Ion Mobility Enable the Exploration of the Mutational Landscape in Colon Cancer Cells
Zhaoguan Wu
Éric Bonneil
Michael Belford
Cornelia Boeser
Maria Virginia Ruiz Cuevas
Sébastien Lemieux
Jean-Jacques Dunyach
Pierre Thibault
The sensitivity and depth of proteomic analyses are limited by isobaric ions and interferences that preclude the identification of low abund… (see more)ance peptides. Extensive sample fractionation is often required to extend proteome coverage when sample amount is not a limitation. Ion mobility devices provide a viable alternate approach to resolve confounding ions and improve peak capacity and mass spectrometry (MS) sensitivity. Here, we report the integration of differential ion mobility with segmented ion fractionation (SIFT) to enhance the comprehensiveness of proteomic analyses. The combination of differential ion mobility and SIFT, where narrow windows of ∼m/z 100 are acquired in turn, is found particularly advantageous in the analysis of protein digests and typically provided more than 60% gain in identification compared to conventional single-shot LC–MS/MS. The application of this approach is further demonstrated for the analysis of tryptic digests from different colorectal cancer cell lines where the enhanced sensitivity enabled the identification of single amino acid variants that were correlated with the corresponding transcriptomic data sets.
2022-08-21
Analytical Chemistry (published)
doi.org
Cascaded Video Generation for Videos In-the-Wild
Lluis Castrejon
Nicolas Ballas
Aaron Courville
Videos can be created by first outlining a global view of the scene and then adding local details. Inspired by this idea we propose a cascad… (see more)ed model for video generation which follows a coarse to fine approach. First our model generates a low resolution video, establishing the global scene structure, which is then refined by subsequent cascade levels operating at larger resolutions. We train each cascade level sequentially on partial views of the videos, which reduces the computational complexity of our model and makes it scalable to high-resolution videos with many frames. We empirically validate our approach on UCF101 and Kinetics-600, for which our model is competitive with the state-of-the-art. We further demonstrate the scaling capabilities of our model and train a three-level model on the BDD100K dataset which generates 256x256 pixels videos with 48 frames.
2022-08-20
2022 26th International Conference on Pattern Recognition (ICPR) (published)
doi.org
arxiv.org
Privacy-aware compression for federated data analysis
Kamalika Chaudhuri
Chuan Guo
Michael G. Rabbat
Federated data analytics is a framework for distributed data analysis where a server compiles noisy responses from a group of distributed lo… (see more)w-bandwidth user devices to estimate aggregate statistics. Two major challenges in this framework are privacy, since user data is often sensitive, and compression, since the user devices have low network bandwidth. Prior work has addressed these challenges separately by combining standard compression algorithms with known privacy mechanisms. In this work, we take a holistic look at the problem and design a family of privacy-aware compression mechanisms that work for any given communication budget. We first propose a mechanism for transmitting a single real number that has optimal variance under certain conditions. We then show how to extend it to metric differential privacy for location privacy use-cases, as well as vectors, for application to federated learning. Our experiments illustrate that our mechanism can lead to better utility vs. compression trade-offs for the same privacy loss in a number of settings.
2022-08-16
Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence (published)
doi.org
proceedings.mlr.press
Probabilistic surrogate networks for simulators with unbounded randomness
Andreas Munk
Berend Zwartsenberg
Adam Ścibior
Atilim Güneş Baydin
Andrew Lawrence Stewart
Goran Fernlund
Anoush Poursartip
Frank N. Wood
We present a framework for automatically structuring and training fast, approximate, deep neural surrogates of stochastic simulators. Unlike… (see more) traditional approaches to surrogate modeling, our surrogates retain the interpretable structure and control flow of the reference simulator. Our surrogates target stochastic simulators where the number of random variables itself can be stochastic and potentially unbounded. Our framework further enables an automatic replacement of the reference simulator with the surrogate when undertaking amortized inference. The fidelity and speed of our surrogates allow for both faster stochastic simulation and accurate and substantially faster posterior inference. Using an illustrative yet non-trivial example we show our surrogates' ability to accurately model a probabilistic program with an unbounded number of random variables. We then proceed with an example that shows our surrogates are able to accurately model a complex structure like an unbounded stack in a program synthesis example. We further demonstrate how our surrogate modeling technique makes amortized inference in complex black-box simulators an order of magnitude faster. Specifically, we do simulator-based materials quality testing, inferring safety-critical latent internal temperature profiles of composite materials undergoing curing.
2022-08-16
Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence (published)
proceedings.mlr.press
proceedings.mlr.press
Stratifying the autistic phenotype using electrophysiological indices of social perception
Luke Mason
Carolin Moessnang
Christopher H. Chatham
Lindsay Ham
Julian Tillmann
Guillaume Dumas
Claire Ellis
Claire Leblond
Freddy Cliquet
Thomas Bourgeron
Christian Beckmann
Tony Charman
Beth Oakley
Tobias Banaschewski
Andreas Meyer-Lindenberg
Simon Baron-Cohen
Sven Bölte
Jan K. Buitelaar
Sarah Durston
Eva Loth … (see 7 more)
Bob Oranje
Antonio Persico
Flavio Dell’Acqua
Christine Ecker
Mark Johnson
Declan Murphy
Emily J. H. Jones
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by difficulties in social communication, but also great heter… (see more)ogeneity. To offer individualized medicine approaches, we need to better target interventions by stratifying autistic people into subgroups with different biological profiles and/or prognoses. We sought to validate neural responses to faces as a potential stratification factor in ASD by measuring neural (electroencephalography) responses to faces (critical in social interaction) in N = 436 children and adults with and without ASD. The speed of early-stage face processing (N170 latency) was on average slower in ASD than in age-matched controls. In addition, N170 latency was associated with responses to faces in the fusiform gyrus, measured with functional magnetic resonance imaging, and polygenic scores for ASD. Within the ASD group, N170 latency predicted change in adaptive socialization skills over an 18-month follow-up period; data-driven clustering identified a subgroup with slower brain responses and poor social prognosis. Use of a distributional data-driven cutoff was associated with predicted improvements of power in simulated clinical trials targeting social functioning. Together, the data provide converging evidence for the utility of the N170 as a stratification factor to identify biologically and prognostically defined subgroups in ASD. Description N170 latency to faces relates to fusiform activity and ASD genetics, predicts social prognosis, and could improve power in clinical trials. Exploiting face processing in patients with ASD The heterogeneity observed in patients with autism spectrum disorder (ASD) highlights the need for better patient stratification methods. Here, Mason et al. evaluated the use of the speed of early-stage face processing (N170 latency) for patient stratification and prognosis in subjects with ASD and age-matched healthy individuals. N170 latency was slower in individuals with ASD and correlated with response to faces measured with fMRI and with polygenic risk score. Among subjects with ASD, the N170 values stratified patients according to socialization prognosis and improved power in a simulated clinical trial. The results suggest that including N170 evaluation in patient stratification might help the design and development of patient-specific therapies for ASD.
2022-08-16
Science Translational Medicine (published)
doi.org
Towards Painless Policy Optimization for Constrained MDPs
Arushi Jain
Sharan Vaswani
Reza Babanezhad Harikandeh
Csaba Szepesvari
Doina Precup
We study policy optimization in an infinite horizon, …
2022-08-16
Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence (published)
doi.org
proceedings.mlr.press
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 … (see 53 more)
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… (see more)yle, and 15% of these are hands on.
2022-08-15
Companion Proceedings of the Web Conference 2022 (published)
doi.org