Publications

Single Allocation Hub Location with Heterogeneous Economies of Scale
Borzou Rostami
Masoud Chitsaz
Okan Arslan
Gilbert Laporte
Andrea Lodi
The economies of scale in hub location is usually modeled by a constant parameter, which captures the benefits companies obtain through cons… (voir plus)olidation. In their article “Single allocation hub location with heterogeneous economies of scale,” Rostami et al. relax this assumption and consider hub-hub connection costs as piecewise linear functions of the flow amounts. This spoils the triangular inequality property of the distance matrix, making the classical flow-based model invalid and further complicates the problem. The authors tackle the challenge by building a mixed-integer quadratically constrained program and by developing a methodology based on constructing Lagrangian function, linear dual functions, and specialized polynomial-time algorithms to generate enhanced cuts. The developed method offers a new strategy in Benders-type decomposition through relaxing a set of complicating constraints in subproblems when such relaxation is tight. The results confirm the efficacy of the solution methods in solving large-scale problem instances.
2021-12-28
Operational Research (published)
doi.org
COVID-19 Seroprevalence in Canada Modelling Waning and Boosting COVID-19 Immunity in Canada a Canadian Immunization Research Network Study
David W. Dick
Lauren Childs
Zhilan Feng
Jing Li
Gergely Röst
David Buckeridge
Nick H. Ogden
Jane Heffernan
2021-12-23
Vaccines (publié)
doi.org
Fall 2021 Resurgence and COVID-19 Seroprevalence in Canada: Modelling waning and boosting COVID-19 immunity in Canada, A Canadian Immunization Research Network Study
David W. Dick
Lauren Childs
Zhilan Feng
Jing Li
Gergely Röst
David Buckeridge
Nick H. Ogden
Jane Heffernan
2021-12-23
Vaccines (publié)
doi.org
Generative Models of Brain Dynamics -- A review
Mahta Ramezanian Panahi
Germán Abrevaya
Jean-Christophe Gagnon-Audet
Vikram Voleti
Irina Rish
Guillaume Dumas
The principled design and discovery of biologically- and physically-informed models of neuronal dynamics has been advancing since the mid-tw… (voir plus)entieth century. Recent developments in artificial intelligence (AI) have accelerated this progress. This review article gives a high-level overview of the approaches across different scales of organization and levels of abstraction. The studies covered in this paper include fundamental models in computational neuroscience, nonlinear dynamics, data-driven methods, as well as emergent practices. While not all of these models span the intersection of neuroscience, AI, and system dynamics, all of them do or can work in tandem as generative models, which, as we argue, provide superior properties for the analysis of neuroscientific data. We discuss the limitations and unique dynamical traits of brain data and the complementary need for hypothesis- and data-driven modeling. By way of conclusion, we present several hybrid generative models from recent literature in scientific machine learning, which can be efficiently deployed to yield interpretable models of neural dynamics.
2021-12-22
ArXiv (prépublication)
arxiv.org
arxiv.org
A Cost-Efficient Metadata Scheme for High-Performance Deduplication Systems
Yuxuan Mo
Yu Hua
Pengfei Li
Qin Cao
Xue (Steve) Liu
Data deduplication has been widely used in backup systems to eliminate redundant data, which speeds up the backup process and reduces the st… (voir plus)orage overhead. Deduplication packs multiple chunks into a large, fixed-size container as a storage unit to maintain the locality and achieve efficient compression. We observe that the traditional containers have low filling ratios due to a large amount of metadata generated by small files. Unfilled containers require more space to store a backup, which decreases the storage efficiency and reduces restore performance. In order to address this problem, we propose a Metadata region Adaptive Container Structure, called MACS. MACS maintains a tag to record the length of metadata region in the container. The boundary between meta-data region and data region is dynamically decided to ensure the maximum space efficiency of the containers. Moreover, we propose a container metadata length-based indexing and cache replacement strategy to allow MACS to be practical in data backup systems. We demonstrate the advantages of MACS with three real world backup datasets. MACS achieves over 95% average container filling ratio, which is significantly higher than existing designs. MACS further achieves better restore performance than the traditional container structure. When combined with existing rewriting method, MACS achieves an efficient trade-off between deduplication ratio and restore performance.
2021-12-20
2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys) (publié)
doi.org
Faults in deep reinforcement learning programs: a taxonomy and a detection approach
Amin Nikanjam
Mohammad Mehdi Morovati
Foutse Khomh
Houssem Ben Braiek
2021-12-20
Automated Software Engineering (publié)
doi.org
arxiv.org
Robustness of Markov perfect equilibrium to model approximations in general-sum dynamic games
Jayakumar Subramanian
Amit Sinha
Aditya Mahajan
Dynamic games (also called stochastic games or Markov games) are an important class of games for modeling multi-agent interactions. In many … (voir plus)situations, the dynamics and reward functions of the game are learnt from past data and are therefore approximate. In this paper, we study the robustness of Markov perfect equilibrium to approximations in reward and transition functions. Using approximation results from Markov decision processes, we show that the Markov perfect equilibrium of an approximate (or perturbed) game is always an approximate Markov perfect equilibrium of the original game. We provide explicit bounds on the approximation error in terms of three quantities: (i) the error in approximating the reward functions, (ii) the error in approximating the transition function, and (iii) a property of the value function of the MPE of the approximate game. The second and third quantities depend on the choice of metric on probability spaces. We also present coarser upper bounds which do not depend on the value function but only depend on the properties of the reward and transition functions of the approximate game. We illustrate the results via a numerical example.
2021-12-20
2021 Seventh Indian Control Conference (ICC) (publié)
doi.org
Does Pre-training Induce Systematic Inference? How Masked Language Models Acquire Commonsense Knowledge
Ian Porada
Alessandro Sordoni
Jackie Cheung
Transformer models pre-trained with a masked-language-modeling objective (e.g., BERT) encode commonsense knowledge as evidenced by behaviora… (voir plus)l probes; however, the extent to which this knowledge is acquired by systematic inference over the semantics of the pre-training corpora is an open question. To answer this question, we selectively inject verbalized knowledge into the pre-training minibatches of BERT and evaluate how well the model generalizes to supported inferences after pre-training on the injected knowledge. We find generalization does not improve over the course of pre-training BERT from scratch, suggesting that commonsense knowledge is acquired from surface-level, co-occurrence patterns rather than induced, systematic reasoning.
2021-12-16
ArXiv (preprint)
doi.org
arxiv.org
Neural Column Generation for Capacitated Vehicle Routing
Behrouz Babaki
Sanjay Dominik Jena
Laurent Charlin
The column generation technique is essential for solving linear programs with an exponential number of variables. Many important application… (voir plus)s such as the vehicle routing problem (VRP) now require it. However, in practice, getting column generation to converge is challenging. It often ends up adding too many columns. In this work, we frame the problem of selecting which columns to add as one of sequential decision-making. We propose a neural column generation architecture that iteratively selects columns to be added to the problem. The architecture, inspired by stabilization techniques, first predicts the optimal duals. These predictions are then used to obtain the columns to add. We show using VRP instances that in this setting several machine learning models yield good performance on the task and that our proposed architecture learned using imitation learning outperforms a modern stabilization technique.
2021-12-16
AAAI.org/2022/Workshop/ML4OR-22 (poster)
openreview.net
openreview.net
Global epidemiology of SARS-CoV-2 infection: a systematic review and meta-analysis of standardized population-based seroprevalence studies, Jan 2020-Oct 2021
Isabel Bergeri
Mairead Whelan
Harriet Ware
Lorenzo Subissi
Anthony Nardone
H. Lewis
Zihan Li
Xiaomeng Ma
Marta Valenciano
Brianna Cheng
Lubna Al Ariqi
Arash Rashidian
Joseph Okeibunor
Tasnim Azim
Pushpa Wijesinghe
Linh-Vi Le
Aisling Vaughan
Richard Pebody
Andrea Vicari
Tingting Yan … (voir 8 de plus)
Mercedes Yanes-Lane
Christian Cao
Matthew P. Cheng
Jesse Papenburg
David Buckeridge
Niklas Bobrovitz
Rahul K. Arora
Maria D Van Kerkhove
Background COVID-19 case data underestimates infection and immunity, especially in low- and middle-income countries (LMICs). We meta-analyze… (voir plus)d standardized SARS-CoV-2 seroprevalence studies to estimate global seroprevalence. Objectives/Methods We conducted a systematic review and meta-analysis, searching MEDLINE, Embase, Web of Science, preprints, and grey literature for SARS-CoV-2 seroprevalence studies aligned with the WHO UNITY protocol published between 2020-01-01 and 2021-10-29. Eligible studies were extracted and critically appraised in duplicate. We meta-analyzed seroprevalence by country and month, pooling to estimate regional and global seroprevalence over time; compared seroprevalence from infection to confirmed cases to estimate under-ascertainment; meta-analyzed differences in seroprevalence between demographic subgroups; and identified national factors associated with seroprevalence using meta-regression. PROSPERO: CRD42020183634. Results We identified 396 full texts reporting 736 distinct seroprevalence studies (41% LMIC), including 355 low/moderate risk of bias studies with national/sub-national scope in further analysis. By April 2021, global SARS-CoV-2 seroprevalence was 26.1%, 95% CI [24.6-27.6%]. Seroprevalence rose steeply in the first half of 2021 due to infection in some regions (e.g., 18.2% to 45.9% in Africa) and vaccination and infection in others (e.g., 11.3% to 57.4% in the Americas high-income countries), but remained low in others (e.g., 0.3% to 1.6% in the Western Pacific). In 2021 Q1, median seroprevalence to case ratios were 1.9:1 in HICs and 61.9:1 in LMICs. Children 0-9 years and adults 60+ were at lower risk of seropositivity than adults 20-29. In a multivariate model using data pre-vaccination, more stringent public health and social measures were associated with lower seroprevalence. Conclusions Global seroprevalence has risen considerably over time and with regional variation, however much of the global population remains susceptible to SARS-CoV-2 infection. True infections far exceed reported COVID-19 cases. Standardized seroprevalence studies are essential to inform COVID-19 control measures, particularly in resource-limited regions.
2021-12-15
medRxiv (prépublication)
doi.org
Preference for biological motion is reduced in ASD: implications for clinical trials and the search for biomarkers
Luke Mason
F. Shic
T. Falck-Ytter
Bhismadev Chakrabarti
Tony Charman
Eva Loth
Julian Tillmann
Tobias Banaschewski
Simon Baron-Cohen
Sven Bölte
J. Buitelaar
Sarah Durston
Bob Oranje
Antonio Persico
C. Beckmann
Thomas Bougeron
Flavio Dell’Acqua
Christine Ecker
Carolin Moessnang
D. Murphy … (voir 49 de plus)
M. H. Johnson
Emily J. H. Jones
Jumana Sara Sarah Carsten Michael Daniel Claudia Yvette Chris Ineke Daisy Guillaume Jessica Vincent Pilar David Lindsay Joerg Rosemary Meng-Chuan Xavier Liogier Michael V. David J. René Andre Maarten Andreas Nico Bethany Laurence Marianne Gahan Barbara Amber Jessica Roberto Antonia San José Emily Will Roberto Heike Jack Steve C. R. Caroline Marcel P. Ahmad
Jumana Sara Sarah Carsten Michael Daniel Claudia Yvette C Ahmad Ambrosino Baumeister Bours Brammer Brandeis
Jumana Ahmad
Sara Ambrosino
Sarah Baumeister
Carsten Bours
Michael Brammer
Daniel Brandeis
Claudia Brogna
Yvette de Bruijn
Christopher H. Chatham
Ineke Cornelissen
Daisy Crawley
Guillaume Dumas
Jessica Faulkner
Vincent Frouin
Pilar Garcés
David Goyard
Lindsay Ham
Joerg F. Hipp
Rosemary Holt
Meng-Chuan Lai
Xavier Liogier D’ardhuy
Michael V. Lombardo
David J. Lythgoe
René Mandl
Andre Marquand
Maarten Mennes
Andreas Meyer-Lindenberg
Nico Bast
Beth Oakley
Laurence O’Dwyer
Marianne Oldehinkel
Gahan Pandina
Barbara Ruggeri
Amber N. V. Ruigrok
Jessica Sabet
Roberto Sacco
Antonia San José Cáceres
Emily Simonoff
Will Spooren
Roberto Toro
Heike Tost
Jack Waldman
Steve C. R. Williams
Caroline Wooldridge
Marcel P. Zwiers
2021-12-15
Molecular Autism (publié)
doi.org
Decision Referrals in Human-Automation Teams
Kesav Kaza
Jerome Le Ny
Aditya Mahajan
We consider a model for optimal decision referrals in human-automation teams performing binary classification tasks. The automation observes… (voir plus) a batch of independent tasks, analyzes them, and has the option to refer a subset of them to a human operator. The human operator performs fresh analysis of the tasks referred to him. Our key modeling assumption is that the human performance degrades with workload (i.e., the number of tasks referred to human). We model the problem as a stochastic optimization problem. We first consider the special case when the workload of the human is pre-specified. We show that in this setting it is optimal to myopically refer tasks which lead to the largest reduction in the conditional expected cost until the desired workload target is met. We next consider the general setting where there is no constraint on the workload. We leverage the solution of the previous step and provide a search algorithm to efficiently find the optimal set of tasks to refer. Finally, we present a numerical study to compare the performance of our algorithm with some baseline allocation policies.
2021-12-14
IEEE Conference on Decision and Control (publié)
doi.org