Publications

Pseudo-random Instance Generators in C++ for Deterministic and Stochastic Multi-commodity Network Design Problems
Eric Larsen
Serge Bisaillon
Jean-François Cordeau
Emma Frejinger
Network design problems constitute an important family of combinatorial optimization problems for which numerous exact and heuristic algorit… (voir plus)hms have been developed over the last few decades. Two central problems in this family are the multi-commodity, capacitated, fixed charge network design problem (MCFNDP) and its stochastic counterpart, the two-stage MCFNDP with recourse. These are standard problems that often serve as work benches for devising and testing models and algorithms in stylized but close-to-realistic settings. The purpose of this paper is to introduce two flexible, high-speed generators capable of simulating a wide range of settings for both the deterministic and stochastic MCFNDPs. We hope that, by facilitating systematic experimentation with new and larger sets of instances, these generators will lead to a more thorough assessment of the performance achieved by exact and heuristic solution methods in both deterministic and stochastic settings. We also hope that making these generators available will promote the reproducibility and comparability of published research.
2023-12-16
ArXiv (prépublication)
arxiv.org
arxiv.org
RescueSpeech: A German Corpus for Speech Recognition in Search and Rescue Domain
Sangeet Sagar
Mirco Ravanelli
Bernd Kiefer
Ivana Kruijff-Korbayová
Josef van Genabith
Despite the recent advancements in speech recognition, there are still difficulties in accurately transcribing conversational and emotional … (voir plus)speech in noisy and reverberant acoustic environments. This poses a particular challenge in the search and rescue (SAR) domain, where transcribing conversations among rescue team members is crucial to support real-time decision-making. The scarcity of speech data and associated background noise in SAR scenarios make it difficult to deploy robust speech recognition systems. To address this issue, we have created and made publicly available a German speech dataset called RescueSpeech. This dataset includes real speech recordings from simulated rescue exercises. Additionally, we have released competitive training recipes and pre-trained models. Our study highlights that the performance attained by state-of-the-art methods in this challenging scenario is still far from reaching an acceptable level.
2023-12-15
2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) (publié)
doi.org
arxiv.org
Speech Emotion Diarization: Which Emotion Appears When?
Yingzhi Wang
Mirco Ravanelli
Alaa Nfissi
Alya Yacoubi
Speech Emotion Recognition (SER) typically relies on utterance-level solutions. However, emotions conveyed through speech should be consider… (voir plus)ed as discrete speech events with definite temporal boundaries, rather than attributes of the entire utterance. To reflect the fine-grained nature of speech emotions, we propose a new task: Speech Emotion Diarization (SED). Just as Speaker Diarization answers the question of "Who speaks when?", Speech Emotion Diarization answers the question of "Which emotion appears when?". To facilitate the evaluation of the performance and establish a common benchmark for researchers, we introduce the Zaion Emotion Dataset (ZED), an openly accessible speech emotion dataset that includes non-acted emotions recorded in real-life conditions, along with manually-annotated boundaries of emotion segments within the utterance. We provide competitive baselines and open-source the code and the pre-trained models.
2023-12-15
2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) (publié)
doi.org
arxiv.org
TorchAudio 2.1: Advancing Speech Recognition, Self-Supervised Learning, and Audio Processing Components for Pytorch
Jeff Hwang
Moto Hira
Caroline Chen
Xiaohui Zhang
Zhaoheng Ni
Guangzhi Sun
Pingchuan Ma
Ruizhe Huang
Vineel Pratap
Yuekai Zhang
Anurag Kumar
Chin-Yun Yu
Chuang Zhu
Chunxi Liu
Jacob Kahn
Mirco Ravanaelli
Peng Sun
Shinji Watanabe
Yangyang Shi
Yumeng Tao … (voir 4 de plus)
Robin Scheibler
Samuele Cornell
Sean Kim
Stavros Petridis
TorchAudio is an open-source audio and speech processing library built for PyTorch. It aims to accelerate the research and development of au… (voir plus)dio and speech technologies by providing well-designed, easy-to-use, and performant PyTorch components. Its contributors routinely engage with users to understand their needs and fulfill them by developing impactful features. Here, we survey TorchAudio’s development principles and contents and highlight key features we include in its latest version (2.1): self-supervised learning pre-trained pipelines and training recipes, high-performance CTC decoders, speech recognition models and training recipes, advanced media I/O capabilities, and tools for performing forced alignment, multi-channel speech enhancement, and reference-less speech assessment. For a selection of these features, through empirical studies, we demonstrate their efficacy and show that they achieve competitive or state-of-the-art performance.
2023-12-15
2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) (publié)
doi.org
arxiv.org
FoMo: Multi-Modal, Multi-Scale and Multi-Task Remote Sensing Foundation Models for Forest Monitoring
Nikolaos Ioannis Bountos
Arthur Ouaknine
Nikolaos Ioannis Bountos
David Rolnick
Forests are vital to ecosystems, supporting biodiversity and essential services, but are rapidly changing due to land use and climate change… (voir plus). Understanding and mitigating negative effects requires parsing data on forests at global scale from a broad array of sensory modalities, and using them in diverse forest monitoring applications. Such diversity in data and applications can be effectively addressed through the development of a large, pre-trained foundation model that serves as a versatile base for various downstream tasks. However, remote sensing modalities, which are an excellent fit for several forest management tasks, are particularly challenging considering the variation in environmental conditions, object scales, image acquisition modes, spatio-temporal resolutions, etc. With that in mind, we present the first unified Forest Monitoring Benchmark (FoMo-Bench), carefully constructed to evaluate foundation models with such flexibility. FoMo-Bench consists of 15 diverse datasets encompassing satellite, aerial, and inventory data, covering a variety of geographical regions, and including multispectral, red-green-blue, synthetic aperture radar and LiDAR data with various temporal, spatial and spectral resolutions. FoMo-Bench includes multiple types of forest-monitoring tasks, spanning classification, segmentation, and object detection. To enhance task and geographic diversity in FoMo-Bench, we introduce TalloS, a global dataset combining satellite imagery with ground-based annotations for tree species classification across 1,000+ categories and hierarchical taxonomic levels. Finally, we propose FoMo-Net, a pre-training framework to develop foundation models with the capacity to process any combination of commonly used modalities and spectral bands in remote sensing.
2023-12-14
ArXiv (prépublication)
doi.org
arxiv.org
Genetic landscape of an<i>in vivo</i>protein interactome
Savandara Besse
Tatsuya Sakaguchi
Louis Gauthier
Zahra Sahaf
Olivier Péloquin
Lidice Gonzalez
Xavier Castellanos-Girouard
Nazli Koçatug
Chloé Matta
Julie G. Hussin
Stephen W. Michnick
Adrian W.R. Serohijos
Protein-interaction quantitative trait locus (“piQTL”) mapping reveals sensitivity ofin vivoPPIs to polymorphisms across the yeast genom… (voir plus)eTrans-piQTLs significantly outnumber and are stronger than cis-piQTLsSNPs in non-coding RNAs and 3’ UTRs have comparable effects to PPI as SNPs in coding regionspiQTL mapping reveals known and novel mechanism of yeast and human drugs
2023-12-14
bioRxiv (prépublication)
doi.org
Temporal encoding in deep reinforcement learning agents
Dongyan Lin
Ann Zixiang Huang
Blake Aaron Richards
Neuroscientists have observed both cells in the brain that fire at specific points in time, known as “time cells”, and cells whose activ… (voir plus)ity steadily increases or decreases over time, known as “ramping cells”. It is speculated that time and ramping cells support temporal computations in the brain and carry mnemonic information. However, due to the limitations in animal experiments, it is difficult to determine how these cells really contribute to behavior. Here, we show that time cells and ramping cells naturally emerge in the recurrent neural networks of deep reinforcement learning models performing simulated interval timing and working memory tasks, which have learned to estimate expected rewards in the future. We show that these cells do indeed carry information about time and items stored in working memory, but they contribute to behavior in large part by providing a dynamic representation on which policy can be computed. Moreover, the information that they do carry depends on both the task demands and the variables provided to the models. Our results suggest that time cells and ramping cells could contribute to temporal and mnemonic calculations, but the way in which they do so may be complex and unintuitive to human observers.
2023-12-14
Scientific Reports (publié)
doi.org
Cone-Traced Supersampling with Subpixel Edge Reconstruction.
Andrei Chubarau
Yangyang Zhao
Ruby Rao
D. Nowrouzezahrai
Paul Kry
While signed distance fields (SDFs) in theory offer infinite level of detail, they are typically rendered using the sphere tracing algorithm… (voir plus) at finite resolutions, which causes the common rasterized image synthesis problem of aliasing. Most existing optimized antialiasing solutions rely on polygon mesh representations; SDF-based geometry can only be directly antialiased with the computationally expensive supersampling or with post-processing filters that may produce undesirable blurriness and ghosting. In this work, we present cone-traced supersampling (CTSS), an efficient and robust spatial antialiasing solution that naturally complements the sphere tracing algorithm, does not require casting additional rays per pixel or offline prefiltering, and can be easily implemented in existing real-time SDF renderers. CTSS performs supersampling along the traced ray near surfaces with partial visibility – object contours – identified by evaluating cone intersections within a pixel's view frustum. We further introduce subpixel edge reconstruction (SER), a technique that extends CTSS to locate and resolve complex pixels with geometric edges in relatively flat regions, which are otherwise undetected by cone intersections. Our combined solution relies on a specialized sampling strategy to minimize the number of shading computations and correlates sample visibility to aggregate the samples. With comparable antialiasing quality at significantly lower computational cost, CTSS is a reliable practical alternative to conventional supersampling.
2023-12-13
IEEE Transactions on Visualization and Computer Graphics (publié)
doi.org
Feasibility of cognitive neuroscience data collection during a speleological expedition
Anita Paas
Hugo R. Jourde
Arnaud Brignol
Marie-Anick Savard
Zseyvfin Eyqvelle
Samuel Bassetto
Giovanni Beltrame
Emily B.J. Coffey
In human cognitive neuroscience and neuropsychology studies, laboratory-based research tasks have been important to establish principles of … (voir plus)brain function and its relationship to behaviour; however, they differ greatly from real-life experiences. Several elements of real-life situations that impact human performance, such as stressors, are difficult or impossible to replicate in the laboratory. Expeditions offer unique possibilities for studying human cognition in complex environments that can transfer to other situations with similar features. For example, as caves share several of the physical and psychological challenges of safety-critical environments such as spaceflight, underground expeditions have been developed as an analogue for astronaut training purposes, suggesting that they might also be suitable for studying aspects of behaviour and cognition that cannot be fully examined under laboratory conditions. While a large range of topics and tools have been proposed for use in such environments, few have been evaluated in the field. We tested the feasibility of collecting human physiological, cognitive, and subjective experience data concerning brain state, sleep, cognitive workload, and fatigue, during a speleological expedition in a remote region. We document our approaches and challenges experienced, and provide recommendations and suggestions to aid future work. The data support the idea that cave expeditions are relevant naturalistic paradigms that offer unique possibilities for cognitive neuroscience to complement laboratory work and help improve human performance and safety in operational environments.
2023-12-13
bioRxiv (prépublication)
doi.org
Asymmetric Actor-Critic with Approximate Information State
Amit Sinha
Aditya Mahajan
Reinforcement learning (RL) for partially observable Markov decision processes (POMDPs) is a challenging problem because decisions need to b… (voir plus)e made based on the entire history of observations and actions. However, in several scenarios, state information is available during the training phase. We are interested in exploiting the availability of this state information during the training phase to efficiently learn a history-based policy using RL. Specifically, we consider actor-critic algorithms, where the actor uses only the history information but the critic uses both history and state. Such algorithms are called asymmetric actor-critic, to highlight the fact that the actor and critic have asymmetric information. Motivated by the recent success of using representation losses in RL for POMDPs [1], we derive similar theoretical results for the asymmetric actor-critic case and evaluate the effectiveness of adding such auxiliary losses in experiments. In particular, we learn a history representation-called an approximate information state (AIS)-and bound the performance loss when acting using AIS.
2023-12-12
IEEE Conference on Decision and Control (publié)
doi.org
Current Practices in Voice Data Collection and Limitations to Voice AI Research: A National Survey.
Emily Evangelista
Rohan Kale
Desiree McCutcheon
Anais Rameau
Alexander Gelbard
Maria Powell
Michael Johns
Anthony Law
Phillip Song
Matthew Naunheim
Stephanie Watts
Paul C. Bryson
Matthew G. Crowson
Jeremy Pinto
Yael Bensoussan
INTRODUCTION Accuracy and validity of voice AI algorithms rely on substantial quality voice data. Although commensurable amounts of voice da… (voir plus)ta are captured daily in voice centers across North America, there is no standardized protocol for acoustic data management, which limits the usability of these datasets for voice artificial intelligence (AI) research. OBJECTIVE The aim was to capture current practices of voice data collection, storage, analysis, and perceived limitations to collaborative voice research. METHODS A 30-question online survey was developed with expert guidance from the voicecollab.ai members, an international collaborative of voice AI researchers. The survey was disseminated via REDCap to an estimated 200 practitioners at North American voice centers. Survey questions assessed respondents' current practices in terms of acoustic data collection, storage, and retrieval as well as limitations to collaborative voice research. RESULTS Seventy-two respondents completed the survey of which 81.7% were laryngologists and 18.3% were speech language pathologists (SLPs). Eighteen percent of respondents reported seeing 40%-60% and 55% reported seeing >60 patients with voice disorders weekly (conservative estimate of over 4000 patients/week). Only 28% of respondents reported utilizing standardized protocols for collection and storage of acoustic data. Although, 87% of respondents conduct voice research, only 38% of respondents report doing so on a multi-institutional level. Perceived limitations to conducting collaborative voice research include lack of standardized methodology for collection (30%) and lack of human resources to prepare and label voice data adequately (55%). CONCLUSION To conduct large-scale multi-institutional voice research with AI, there is a pertinent need for standardization of acoustic data management, as well as an infrastructure for secure and efficient data sharing. LEVEL OF EVIDENCE Level 5 Laryngoscope, 2023.
2023-12-12
The Laryngoscope (publié)
doi.org
Detection and evaluation of bias-inducing features in machine learning
Moses Openja
Gabriel Laberge
Foutse Khomh
2023-12-12
Empirical Software Engineering (publié)
doi.org
arxiv.org