Publications

Capacity Planning in Stable Matching

Federico Bobbio

Ignacio Rios

Alfredo Torrico

We introduce the problem of jointly increasing school capacities and finding a student-optimal assignment in the expanded market. Due to the… (see more) impossibility of efficiently solving the problem with classical methods, we generalize existent mathematical programming formulations of stability constraints to our setting, most of which result in integer quadratically-constrained programs. In addition, we propose a novel mixed-integer linear programming formulation that is exponentially large on the problem size. We show that its stability constraints can be separated by exploiting the objective function, leading to an effective cutting-plane algorithm. We conclude the theoretical analysis of the problem by discussing some mechanism properties. On the computational side, we evaluate the performance of our approaches in a detailed study, and we find that our cutting-plane method outperforms our generalization of existing mixed-integer approaches. We also propose two heuristics that are effective for large instances of the problem. Finally, we use the Chilean school choice system data to demonstrate the impact of capacity planning under stability conditions. Our results show that each additional seat can benefit multiple students and that we can effectively target the assignment of previously unassigned students or improve the assignment of several students through improvement chains. These insights empower the decision-maker in tuning the matching algorithm to provide a fair application-oriented solution.

2021-10-02

ArXiv (preprint)

arxiv.org

Inter-Brain Synchronization: From Neurobehavioral Correlation to Causal Explanation

Guillaume Dumas

2021-10-01

International Journal of Psychophysiology (published)

doi.org

Small, correlated changes in synaptic connectivity may facilitate rapid motor learning

Barbara Feulner

Matt Perich

Raeed H. Chowdhury

Lee Miller

Juan A. Gallego

Claudia Clopath

2021-10-01

Nature Communications (published)

doi.org

THE EFFECT SIZE OF GENES ON COGNITIVE ABILITIES IS LINKED TO THEIR EXPRESSION ALONG THE MAJOR HIERARCHICAL GRADIENT IN THE HUMAN BRAIN

Sébastien Jacquemont

Guillaume Huguet

Elise Douard

Zohra Saci

Guillaume Dumas

Laura Almasy

David C. Glahn

2021-10-01

European Neuropsychopharmacology (published)

doi.org

Trade-off Between Accuracy and Fairness of Data-driven Building and Indoor Environment Models: A Comparative Study of Pre-processing Methods

Ying Sun

Fariborz Haghighat

Benjamin Fung

2021-10-01

Energy (published)

doi.org

Trade-off Between Accuracy and Fairness of Data-driven Building and Indoor Environment Models: A Comparative Study of Pre-processing Methods

Ying Sun

Fariborz Haghighat

Benjamin Fung

2021-10-01

Energy (published)

doi.org

Transfer functions: learning about a lagged exposure-outcome association in time-series data

Hiroshi Mamiya

Alexandra M. Schmidt

Erica E. M. Moodie

David Buckeridge

Many population exposures in time-series analysis, including food marketing, exhibit a time-lagged association with population health outcom… (see more)es such as food purchasing. A common approach to measuring patterns of associations over different time lags relies on a finite-lag model, which requires correct specification of the maximum duration over which the lagged association extends. However, the maximum lag is frequently unknown due to the lack of substantive knowledge or the geographic variation of lag length. We describe a time-series analytical approach based on an infinite lag specification under a transfer function model that avoids the specification of an arbitrary maximum lag length. We demonstrate its application to estimate the lagged exposure-outcome association in food environmental research: display promotion of sugary beverages with lagged sales.

2021-10-01

medRxiv (preprint)

doi.org

Graph Neural Networks in Natural Language Processing

Bang Liu

Lingfei Wu

Natural language processing (NLP) and understanding aim to read from unformatted text to accomplish different tasks. While word embeddings l… (see more)earned by deep neural networks are widely used, the underlying linguistic and semantic structures of text pieces cannot be fully exploited in these representations. Graph is a natural way to capture the connections between different text pieces, such as entities, sentences, and documents. To overcome the limits in vector space models, researchers combine deep learning models with graph-structured representations for various tasks in NLP and text mining. Such combinations help to make full use of both the structural information in text and the representation learning ability of deep neural networks. In this chapter, we introduce the various graph representations that are extensively used in NLP, and show how different NLP tasks can be tackled from a graph perspective. We summarize recent research works on graph-based NLP, and discuss two case studies related to graph-based text clustering, matching, and multihop machine reading comprehension in detail. Finally, we provide a synthesis about the important open problems of this subfield.

2021-09-30

Deep Learning on Graphs (published)

doi.org

Data-driven approaches for genetic characterization of SARS-CoV-2 lineages

Fatima Mostefai

Isabel Gamache

Jessie Huang

Arnaud N’Guessan

Justin Pelletier

Ahmad Pesaranghader

David J. Hamelin

Carmen Lia Murall

Raphael Poujol

Jean-Christophe Grenier

Martin Smith

Etienne Caron

Morgan Craig

Jesse Shapiro

Guy Wolf

Smita Krishnaswamy

Julie Hussin

The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19)… (see more), has been sequenced at an unprecedented scale, leading to a tremendous amount of viral genome sequencing data. To understand the evolution of this virus in humans, and to assist in tracing infection pathways and designing preventive strategies, we present a set of computational tools that span phylogenomics, population genetics and machine learning approaches. To illustrate the utility of this toolbox, we detail an in depth analysis of the genetic diversity of SARS-CoV-2 in first year of the COVID-19 pandemic, using 329,854 high-quality consensus sequences published in the GISAID database during the pre-vaccination phase. We demonstrate that, compared to standard phylogenetic approaches, haplotype networks can be computed efficiently on much larger datasets, enabling real-time analyses. Furthermore, time series change of Tajima’s D provides a powerful metric of population expansion. Unsupervised learning techniques further highlight key steps in variant detection and facilitate the study of the role of this genomic variation in the context of SARS-CoV-2 infection, with Multiscale PHATE methodology identifying fine-scale structure in the SARS-CoV-2 genetic data that underlies the emergence of key lineages. The computational framework presented here is useful for real-time genomic surveillance of SARS-CoV-2 and could be applied to any pathogen that threatens the health of worldwide populations of humans and other organisms.

2021-09-29

bioRxiv (preprint)

doi.org

Estimating the lagged effect of price discounting: a time-series study using transaction data of sugar sweetened beverages.

Hiroshi Mamiya

Alexandra M. Schmidt

Erica E. M. Moodie

David Buckeridge

2021-09-28

medRxiv (preprint)

doi.org

Guidelines for the Computational Testing of Machine Learning approaches to Vehicle Routing Problems