Publications

Distributional Robustness and Inequity Mitigation in Disaster Preparedness of Humanitarian Operations
Hongming Li
Erick Delage
Ning Zhu
Michael Pinedo
Shoufeng Ma
Problem definition: In this paper, we study a predisaster relief network design problem with uncertain demands. The aim is to determine the … (voir plus)prepositioning and reallocation of relief supplies. Motivated by the call of the International Federation of Red Cross and Red Crescent Societies (IFRC) to leave no one behind, we consider three important practical aspects of humanitarian operations: shortages, equity, and uncertainty. Methodology/results: We first employ a form of robust satisficing measure, which we call the shortage severity measure, to evaluate the severity of the shortage caused by uncertain demand in a context with limited distribution information. Because shortages often raise concerns about equity, we then formulate a mixed-integer lexicographic optimization problem with nonconvex objectives and design a new branch-and-bound algorithm to identify the exact solution. We also propose two approaches for identifying optimal postdisaster adaptable resource reallocation: an exact approach and a conservative approximation that is more computationally efficient. Our case study considers the 2010 Yushu earthquake, which occurred in northwestern China, and demonstrates the value of our methodology in mitigating geographical inequities and reducing shortages. Managerial implications: In our case study, we show that (i) incorporating equity in both predisaster deployment and postdisaster reallocation can produce substantially more equitable shortage prevention strategies while sacrificing only a reasonable amount of total shortage; (ii) increasing donations/budgets may not necessarily alleviate the shortage suffered by the most vulnerable individuals if equity is not fully considered; and (iii) exploiting disaster magnitude information when quantifying uncertainty can help alleviate geographical inequities caused by uncertain relief demands. Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2016-05208], the National Natural Science Foundation of China [Grants 71971154, 72010107004, 72091214, and 72122015], and the Canada Research Chairs [Grant CRC-2018-00105]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1230 .
2023-10-22
Manufacturing & Service Operations Management (publié)
doi.org
Dosimetry of [18F]TRACK, the first PET tracer for imaging of TrkB/C receptors in humans
Alexander Thiel
Alexey Kostikov
Hailey Ahn
Youstina Daoud
Jean-Paul Soucy
Stephan Blinder
Carolin Jaworski
Carmen Wängler
Björn Wängler
Freimut Juengling
Shirin A. Enger
Ralf Schirrmacher
Background Reduced expression or impaired signalling of tropomyosin receptor kinases (Trk receptors) are found in a vast spectrum of CNS dis… (voir plus)orders. [^18F]TRACK is the first PET radioligand for TrkB/C with proven in vivo brain penetration and on-target specific signal. Here we report dosimetry data for [^18F]TRACK in healthy humans. 6 healthy participants (age 22–61 y, 3 female) were scanned on a General Electric Discovery PET/CT 690 scanner. [^18F]TRACK was synthesized with high molar activities (A_m = 250 ± 75 GBq/µmol), and a dynamic series of 12 whole-body scans were acquired after injection of 129 to 147 MBq of the tracer. Images were reconstructed with standard corrections using the manufacturer’s OSEM algorithm. Tracer concentration time-activity curves (TACs) were obtained using CT-derived volumes-of-interest. Organ-specific doses and the total effective dose were estimated using the Committee on Medical Internal Radiation Dose equation for adults and tabulated Source tissue values (S values). Results Average organ absorbed dose was highest for liver and gall bladder with 6.1E−2 (± 1.06E−2) mGy/MBq and 4.6 (± 1.18E−2) mGy/MBq, respectively. Total detriment weighted effective dose E_DW was 1.63E−2 ± 1.68E−3 mSv/MBq. Organ-specific TACs indicated predominantly hepatic tracer elimination. Conclusion Total and organ-specific effective doses for [^18F]TRACK are low and the dosimetry profile is similar to other ^18F-labelled radio tracers currently used in clinical settings.
2023-10-22
EJNMMI Radiopharmacy and Chemistry (publié)
doi.org
Hazards from Increasingly Accessible Fine-Tuning of Downloadable Foundation Models
Alan Chan
Benjamin Bucknall
Herbie Bradley
David M. Krueger
2023-10-22
NeurIPS.cc/2023/Workshop/SoLaR (spotlight)
doi.org
openreview.net
A Novel Information-Theoretic Objective to Disentangle Representations for Fair Classification
Pierre Colombo
Nathan Noiry
Guillaume Staerman
Pablo Piantanida
2023-10-20
ArXiv (prépublication)
doi.org
arxiv.org
Fundamental Limits of Membership Inference Attacks on Machine Learning Models
Eric Aubinais
Elisabeth Gassiat
Pablo Piantanida
Membership inference attacks (MIA) can reveal whether a particular data point was part of the training dataset, potentially exposing sensiti… (voir plus)ve information about individuals. This article provides theoretical guarantees by exploring the fundamental statistical limitations associated with MIAs on machine learning models. More precisely, we first derive the statistical quantity that governs the effectiveness and success of such attacks. We then deduce that in a very general regression setting with overfitting algorithms, attacks may have a high probability of success. Finally, we investigate several situations for which we provide bounds on this quantity of interest. Our results enable us to deduce the accuracy of potential attacks based on the number of samples and other structural parameters of learning models. In certain instances, these parameters can be directly estimated from the dataset.
2023-10-19
ArXiv (prépublication)
doi.org
arxiv.org
Graph embedding and transfer learning can help predict potential species interaction networks despite data limitations
Tanya Strydom
Salomé Bouskila
Francis Banville
Ceres Barros
Dominique Caron
Maxwell J. Farrell
Marie‐Josée Fortin
Benjamin Mercier
Laura J. Pollock
Rogini Runghen
Giulio V. Dalla Riva
Timothée Poisot
Metawebs (networks of potential interactions within a species pool) are a powerful abstraction to understand how large‐scale species inter… (voir plus)action networks are structured. Because metawebs are typically expressed at large spatial and taxonomic scales, assembling them is a tedious and costly process; predictive methods can help circumvent the limitations in data deficiencies, by providing a first approximation of metawebs. One way to improve our ability to predict metawebs is to maximize available information by using graph embeddings, as opposed to an exhaustive list of species interactions. Graph embedding is an emerging field in machine learning that holds great potential for ecological problems. Here, we outline how the challenges associated with inferring metawebs line‐up with the advantages of graph embeddings; followed by a discussion as to how the choice of the species pool has consequences on the reconstructed network, specifically as to the role of human‐made (or arbitrarily assigned) boundaries and how these may influence ecological hypotheses.
2023-10-18
Methods in Ecology and Evolution (publié)
doi.org
Mean-field games among teams
Jayakumar Subramanian
Akshat Kumar
Aditya Mahajan
2023-10-17
ArXiv (prépublication)
doi.org
arxiv.org
Studying the characteristics of AIOps projects on GitHub
Roozbeh Aghili
Heng Li
Foutse Khomh
2023-10-17
Empirical Software Engineering (publié)
doi.org
arxiv.org
Interpreting and Controlling Vision Foundation Models via Text Explanations
Haozhe Chen
Junfeng Yang
Carl Vondrick
Chengzhi Mao
2023-10-15
ArXiv (prépublication)
doi.org
arxiv.org
Transparent Anomaly Detection via Concept-based Explanations
Laya Rafiee Sevyeri
Ivaxi Sheth
Farhood Farahnak
S. Enger
2023-10-15
ArXiv (prépublication)
doi.org
arxiv.org
ConText-GAN: using contextual texture information for realistic and controllable medical image synthesis*
Marc Adrien Hostin
Shahram Attarian
David Bendahan
Bellemare Marc-Emmanuel
This study proposes an enhancement to the ConText-GAN, an image synthesis model using a controllable texture input. The improvement consists… (voir plus) in using a texture feature fusion module to reduce the complexity of the model, and enable the use of the OASIS architecture for image generation.
2023-10-14
2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) (publié)
doi.org
RelationalUNet for Image Segmentation
Ivaxi Sheth
Pedro H. M. Braga
Shiva Kanth Sujit
Sahar Dastani
S Ebrahimi Kahou
2023-10-14
Machine Learning in Medical Imaging (publié)
doi.org