Publications

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
A Halfspace-Mass Depth-Based Method for Adversarial Attack Detection
Marine Picot
Federica Granese
Guillaume Staerman
Marco Romanelli
Francisco Messina
Pablo Piantanida
Pierre Colombo
2023-03-27
TMLR (accepté)
openreview.net
openreview.net
Green Federated Learning
Ashkan Yousefpour
Sheng Guo
Ashish V. Shenoy
Sayan Ghosh
Pierre Stock
Kiwan Maeng
Schalk-Willem Kruger
Michael G. Rabbat
Carole-Jean Wu
Ilya Mironov
The rapid progress of AI is fueled by increasingly large and computationally intensive machine learning models and datasets. As a consequenc… (voir plus)e, the amount of compute used in training state-of-the-art models is exponentially increasing (doubling every 10 months between 2015 and 2022), resulting in a large carbon footprint. Federated Learning (FL) - a collaborative machine learning technique for training a centralized model using data of decentralized entities - can also be resource-intensive and have a significant carbon footprint, particularly when deployed at scale. Unlike centralized AI that can reliably tap into renewables at strategically placed data centers, cross-device FL may leverage as many as hundreds of millions of globally distributed end-user devices with diverse energy sources. Green AI is a novel and important research area where carbon footprint is regarded as an evaluation criterion for AI, alongside accuracy, convergence speed, and other metrics. In this paper, we propose the concept of Green FL, which involves optimizing FL parameters and making design choices to minimize carbon emissions consistent with competitive performance and training time. The contributions of this work are two-fold. First, we adopt a data-driven approach to quantify the carbon emissions of FL by directly measuring real-world at-scale FL tasks running on millions of phones. Second, we present challenges, guidelines, and lessons learned from studying the trade-off between energy efficiency, performance, and time-to-train in a production FL system. Our findings offer valuable insights into how FL can reduce its carbon footprint, and they provide a foundation for future research in the area of Green AI.
2023-03-25
ArXiv (prépublication)
doi.org
arxiv.org
Evolutionary history explains foliar spectral differences between arbuscular and ectomycorrhizal plant species
Johanna R. Jantzen
Étienne Laliberté
Alexis Carteron
Rosalie Beauchamp‐Rioux
Florence Blanchard
Anna L. Crofts
Alizée Girard
Paul W. Hacker
Juliana Pardo
Anna K. Schweiger
Sabrina Demers‐Thibeault
Nicholas C. Coops
Margaret Kalácska
Mark Vellend
Anne Bruneau
Leaf spectra are integrated foliar phenotypes that capture a range of traits and can provide insight into ecological processes. Leaf traits,… (voir plus) and therefore leaf spectra, may reflect belowground processes such as mycorrhizal associations. However, evidence for the relationship between leaf traits and mycorrhizal association is mixed, and few studies account for shared evolutionary history. We conduct partial least squares discriminant analysis to assess the ability of spectra to predict mycorrhizal type. We model the evolution of leaf spectra for 92 vascular plant species and use phylogenetic comparative methods to assess differences in spectral properties between arbuscular mycorrhizal and ectomycorrhizal plant species. Partial least squares discriminant analysis classified spectra by mycorrhizal type with 90% (arbuscular) and 85% (ectomycorrhizal) accuracy. Univariate models of principal components identified multiple spectral optima corresponding with mycorrhizal type due to the close relationship between mycorrhizal type and phylogeny. Importantly, we found that spectra of arbuscular mycorrhizal and ectomycorrhizal species do not statistically differ from each other after accounting for phylogeny. While mycorrhizal type can be predicted from spectra, enabling the use of spectra to identify belowground traits using remote sensing, this is due to evolutionary history and not because of fundamental differences in leaf spectra due to mycorrhizal type.
2023-03-23
New Phytologist (publié)
doi.org
Catalyzing next-generation Artificial Intelligence through NeuroAI
Anthony Zador
Blake Aaron Richards
Bence Ölveczky
Sean Escola
Yoshua Bengio
Kwabena Boahen
Matthew Botvinick
Dmitri Chklovskii
Anne Churchland
Claudia Clopath
James DiCarlo
Surya Ganguli
Jeff Hawkins
Konrad Paul Kording
Alexei Koulakov
Yann Lecun
Timothy P Lillicrap
Adam Marblestone
Bruno Olshausen
Alexandre Pouget … (voir 7 de plus)
Cristina Savin
Terrence Sejnowski
Eero Simoncelli
Sara Solla
David Sussillo
Andreas S. Tolias
Doris Tsao
2023-03-21
Nature Communications (publié)
doi.org
arxiv.org
A Novel Model for Novelty: Modeling the Emergence of Innovation from Cumulative Culture
Natalie Kastel
Guillaume Dumas
2023-03-21
Active Inference (publié)
doi.org