Publications

Machine-learning-based arc selection for constrained shortest path problems in column generation
Mouad Morabit
Guy Desaulniers
Andrea Lodi
Column generation is an iterative method used to solve a variety of optimization problems. It decomposes the problem into two parts: a maste… (voir plus)r problem and one or more pricing problems (PP). The total computing time taken by the method is divided between these two parts. In routing or scheduling applications, the problems are mostly defined on a network, and the PP is usually an NP-hard shortest path problem with resource constraints. In this work, we propose a new heuristic pricing algorithm based on machine learning. By taking advantage of the data collected during previous executions, the objective is to reduce the size of the network and accelerate the PP, keeping only the arcs that have a high chance to be part of the linear relaxation solution. The method has been applied to two specific problems: the vehicle and crew scheduling problem in public transit and the vehicle routing problem with time windows. Reductions in computational time of up to 40% can be obtained.
2023-03-31
INFORMS Journal on Optimization (publié)
doi.org
arxiv.org
SUNMASK: Mask Enhanced Control in Step Unrolled Denoising Autoencoders
Kyle Kastner
Tim Cooijmans
Yusong Wu
Aaron Courville
2023-03-31
Artificial Intelligence in Music, Sound, Art and Design (publié)
doi.org
openreview.net
A Survey of Self-Supervised and Few-Shot Object Detection
Gabriel Huang
Issam Hadj Laradji
David Vázquez
Simon Lacoste-Julien
Pau Rodríguez
Labeling data is often expensive and time-consuming, especially for tasks such as object detection and instance segmentation, which require … (voir plus)dense labeling of the image. While few-shot object detection is about training a model on novel (unseen) object classes with little data, it still requires prior training on many labeled examples of base (seen) classes. On the other hand, self-supervised methods aim at learning representations from unlabeled data which transfer well to downstream tasks such as object detection. Combining few-shot and self-supervised object detection is a promising research direction. In this survey, we review and characterize the most recent approaches on few-shot and self-supervised object detection. Then, we give our main takeaways and discuss future research directions. Project page: https://gabrielhuang.github.io/fsod-survey/.
2023-03-31
IEEE Transactions on Pattern Analysis and Machine Intelligence (publié)
doi.org
arxiv.org
Bayesian stroke modeling details sex biases in the white matter substrates of aphasia
Julius M. Kernbach
Gesa Hartwigsen
Jae-Sung Lim
Hee-Joon Bae
Kyung-Ho Yu
Gottfried Schlaug
Anna Bonkhoff
Natalia S. Rost
Danilo Bzdok
Ischemic cerebrovascular events often lead to aphasia. Previous work provided hints that such strokes may affect women and men in distinct w… (voir plus)ays. Women tend to suffer strokes with more disabling language impairment, even if the lesion size is comparable to men. In 1401 patients, we isolate data-led representations of anatomical lesion patterns and hand-tailor a Bayesian analytical solution to carefully model the degree of sex divergence in predicting language outcomes ~3 months after stroke. We locate lesion-outcome effects in the left-dominant language network that highlight the ventral pathway as a core lesion focus across different tests of language performance. We provide detailed evidence for sex-specific brain-behavior associations in the domain-general networks associated with cortico-subcortical pathways, with unique contributions of the fornix in women and cingular fiber bundles in men. Our collective findings suggest diverging white matter substrates in how stroke causes language deficits in women and men. Clinically acknowledging such sex disparities has the potential to improve personalized treatment for stroke patients worldwide.
2023-03-30
Communications Biology (publié)
doi.org
MLGCN: An Ultra Efficient Graph Convolution Neural Model For 3D Point Cloud Analysis
Mohammad Khodadad
Morteza Rezanejad
Ali Shiraee Kasmaee
Kaleem Siddiqi
Dirk Bernhardt-Walther
Hamidreza Mahyar
2023-03-30
ArXiv (prépublication)
doi.org
arxiv.org
Varepsilon kú mask: Integrating Yorùbá cultural greetings into machine translation
Idris Akinade
Jesujoba Oluwadara Alabi
David Ifeoluwa Adelani
Clement Odoje
Dietrich Klakow
This paper investigates the performance of massively multilingual neural machine translation (NMT) systems in translating Yorùbá greetings… (voir plus) (kú mask), which are a big part of Yorùbá language and culture, into English. To evaluate these models, we present IkiniYorùbá, a Yorùbá-English translation dataset containing some Yorùbá greetings, and sample use cases. We analysed the performance of different multilingual NMT systems including Google and NLLB and show that these models struggle to accurately translate Yorùbá greetings into English. In addition, we trained a Yorùbá-English model by fine-tuning an existing NMT model on the training split of IkiniYorùbá and this achieved better performance when compared to the pre-trained multilingual NMT models, although they were trained on a large volume of data.
2023-03-30
ArXiv (prépublication)
doi.org
arxiv.org
Behavioural equivalences for continuous-time Markov processes
Linan Chen
Florence Clerc
Prakash Panangaden
2023-03-29
Mathematical Structures in Computer Science (publié)
doi.org
DeltaShield: Information Theory for Human- Trafficking Detection
Catalina Vajiac
Meng-Chieh Lee
Aayushi Kulshrestha
Sacha Lévy
Namyong Park
Andreas Olligschlaeger
Cara Jones
Reihaneh Rabbany
Christos Faloutsos
2023-03-29
ACM Transactions on Knowledge Discovery from Data (publié)
doi.org
Machine Learning Application Development: Practitioners' Insights
Md Saidur Rahman
Foutse Khomh
Alaleh Hamidi
Jinghui Cheng
Giuliano Antoniol
Hironori Washizaki
Nowadays, intelligent systems and services are getting increasingly popular as they provide data-driven solutions to diverse real-world prob… (voir plus)lems, thanks to recent breakthroughs in Artificial Intelligence (AI) and Machine Learning (ML). However, machine learning meets software engineering not only with promising potentials but also with some inherent challenges. Despite some recent research efforts, we still do not have a clear understanding of the challenges of developing ML-based applications and the current industry practices. Moreover, it is unclear where software engineering researchers should focus their efforts to better support ML application developers. In this paper, we report about a survey that aimed to understand the challenges and best practices of ML application development. We synthesize the results obtained from 80 practitioners (with diverse skills, experience, and application domains) into 17 findings; outlining challenges and best practices for ML application development. Practitioners involved in the development of ML-based software systems can leverage the summarized best practices to improve the quality of their system. We hope that the reported challenges will inform the research community about topics that need to be investigated to improve the engineering process and the quality of ML-based applications.
2023-03-29
Software Quality Journal (publié)
doi.org
arxiv.org
Cross-sectional and longitudinal neuroanatomical profiles of distinct clinical (adaptive) outcomes in autism
Charlotte M. Pretzsch
Dorothea L. Floris
Tim Schäfer
Anke Bletsch
Caroline Gurr
Michael V. Lombardo
Chris H. Chatham
Julian Tillmann
Tony Charman
Martina Arenella
Emily J. H. Jones
Sara Ambrosino
Thomas Bourgeron
Guillaume Dumas
Freddy Cliquet
Claire Leblond
Eva Loth
Beth Oakley
Jan K. Buitelaar
Simon Baron-Cohen … (voir 7 de plus)
Christian Beckmann
Antonio Persico
Tobias Banaschewski
Sarah Durston
Christine M. Freitag
Declan Murphy
Christine Ecker
2023-03-28
Molecular Psychiatry (publié)
doi.org
Fasting alters the gut microbiome reducing blood pressure and body weight in metabolic syndrome patients
András Maifeld
Hendrik Bartolomaeus
Ulrike Löber
Ellen G. Avery
Nico Steckhan
Lajos Markó
Nicola Wilck
Ibrahim Hamad
Urša Šušnjar
Anja Mähler
Christoph Hohmann
Chia-Yu Chen
Holger Cramer
Gustav Dobos
Till Robin Lesker
Till Strowig
Ralf Dechend
Danilo Bzdok
Markus Kleinewietfeld
Andreas Michalsen … (voir 2 de plus)
Dominik N. Müller
Sofia K. Forslund
Abstract Periods of fasting and refeeding may reduce cardiometabolic risk elevated by Western diet. We show that in hypertensive metabolic s… (voir plus)yndrome (MetS) patients (n=35), a 5-day fast followed by a modified DASH diet (Dietary Approach to Stop Hypertension) reduced systolic blood pressure (SBP), antihypertensive medication need, and body-mass index (BMI) at three months post intervention compared to a modified DASH diet alone (n=36). Fasting altered the gut microbiome, impacting bacterial taxa and gene modules associated with short-chain fatty acid production. Cross-system analyses revealed a positive correlation of circulating mucosa-associated invariant T (MAIT) cells, non-classical monocytes and CD4+ effector T cells with SBP. Furthermore, regulatory T cells (Tregs) positively correlated with BMI and weight. Machine learning could predict sustained SBP-responsiveness within the fasting group from baseline immunome data, identifying CD8+ effector T cells, Th17 cells and Tregs as important contributors to the model. The high-resolution multi-omics data highlights fasting as a promising non-pharmacological intervention in MetS.
2023-03-28
Freie Universität Berlin (publié)
doi.org
FMAS: Fast Multi-Objective SuperNet Architecture Search for Semantic Segmentation
Zhuoran Xiong
Marihan Amein
Olivier Therrien
Warren J. Gross
Brett Meyer
2023-03-28
ArXiv (prépublication)
doi.org
arxiv.org