Publications

Online Convex Optimization for On-Board Routing in High-Throughput Satellites
Olivier B'elanger
Jean-Luc Lupien
Olfa Ben Yahia
Stéphane Martel
Antoine Lesage-Landry
Gunes Karabulut Kurt
The rise in low Earth orbit (LEO) satellite Internet services has led to increasing demand, often exceeding available data rates and comprom… (voir plus)ising the quality of service. While deploying more satellites offers a short-term fix, designing higher-performance satellites with enhanced transmission capabilities provides a more sustainable solution. Achieving the necessary high capacity requires interconnecting multiple modem banks within a satellite payload. However, there is a notable gap in research on internal packet routing within extremely high-throughput satellites. To address this, we propose a real-time optimal flow allocation and priority queue scheduling method using online convex optimization-based model predictive control. We model the problem as a multi-commodity flow instance and employ an online interior-point method to solve the routing and scheduling optimization iteratively. This approach minimizes packet loss and supports real-time rerouting with low computational overhead. Our method is tested in simulation on a next-generation extremely high-throughput satellite model, demonstrating its effectiveness compared to a reference batch optimization and to traditional methods.
2024-09-02
ArXiv (prépublication)
doi.org
arxiv.org
Audio Editing with Non-Rigid Text Prompts
Francesco Paissan
Zhepei Wang
Mirco Ravanelli
Paris Smaragdis
Cem Subakan
In this paper, we explore audio-editing with non-rigid text edits. We show that the proposed editing pipeline is able to create audio edits … (voir plus)that remain faithful to the input audio. We explore text prompts that perform addition, style transfer, and in-painting. We quantitatively and qualitatively show that the edits are able to obtain results which outperform Audio-LDM, a recently released text-prompted audio generation model. Qualitative inspection of the results points out that the edits given by our approach remain more faithful to the input audio in terms of keeping the original onsets and offsets of the audio events.
2024-09-01
Interspeech 2024 (publié)
doi.org
arxiv.org
Clinical Care Trajectory Assessment of Children with Congenital Diaphragmatic Hernia and Neurodevelopmental Impairment
Alexandra Dimmer
Gabriel Altit
Sabrina Beauseigle
Elena Guadagno
Louise Koclas
Katryn Paquette
Ana Sant’Anna
Adam Shapiro
Dan Poenaru
Pramod Puligandla
2024-09-01
Journal of Pediatric Surgery (publié)
doi.org
Data Privacy for Record Linkage and Beyond
Shurong Lin
Eric Kolaczyk
In a data-driven world, two prominent research problems are record linkage and data privacy, among others. Record linkage is essential for i… (voir plus)mproving decision-making by integrating information of the same entities from different sources. On the other hand, data privacy research seeks to balance the need to extract accurate insights from data with the imperative to protect the privacy of the entities involved. Inevitably, data privacy issues arise in the context of record linkage. This article identifies two complementary aspects at the intersection of these two fields: (1) how to ensure privacy during record linkage and (2) how to mitigate privacy risks when releasing the analysis results after record linkage. We specifically discuss privacy-preserving record linkage, differentially private regression, and related topics.
2024-09-01
Social Science Research Network (publié)
doi.org
Virtual Reality for Pediatric Trauma Education - A Preliminary Face and Content Validation Study
Fabio Botelho
Said Ashkar
Shreenik Kundu
Tj Matthews
Elena Guadgano
Dan Poenaru
2024-09-01
Journal of Pediatric Surgery (publié)
doi.org
Herbarium collections remain essential in the age of community science
Isaac Eckert
Anne Bruneau
D. Metsger
Simon Joly
T. Dickinson
Laura J. Pollock
2024-08-31
Nature Communications (publié)
doi.org
ProGRes: Prompted Generative Rescoring on ASR n-Best
Ada Defne Tur
Adel Moumen
Mirco Ravanelli
2024-08-30
ArXiv (prépublication)
doi.org
arxiv.org
Learning Multi-agent Multi-machine Tending by Mobile Robots
Abdalwhab Abdalwhab
Giovanni Beltrame
Samira Ebrahimi Kahou
David St-Onge
Robotics can help address the growing worker shortage challenge of the manufacturing industry. As such, machine tending is a task collaborat… (voir plus)ive robots can tackle that can also highly boost productivity. Nevertheless, existing robotics systems deployed in that sector rely on a fixed single-arm setup, whereas mobile robots can provide more flexibility and scalability. In this work, we introduce a multi-agent multi-machine tending learning framework by mobile robots based on Multi-agent Reinforcement Learning (MARL) techniques with the design of a suitable observation and reward. Moreover, an attention-based encoding mechanism is developed and integrated into Multi-agent Proximal Policy Optimization (MAPPO) algorithm to boost its performance for machine tending scenarios. Our model (AB-MAPPO) outperformed MAPPO in this new challenging scenario in terms of task success, safety, and resources utilization. Furthermore, we provided an extensive ablation study to support our various design decisions.
2024-08-29
ArXiv (prépublication)
doi.org
arxiv.org
Active Semantic Mapping and Pose Graph Spectral Analysis for Robot Exploration
Rongge Zhang
Haechan Mark Bong
Giovanni Beltrame
2024-08-27
ArXiv (prépublication)
doi.org
arxiv.org
ARGV: 3D genome structure exploration using augmented reality
Chrisostomos Drogaris
Yanlin Zhang
Éric Zhang
Elena Nazarova
Roman Sarrazin-Gendron
Sélik Wilhelm-Landry
Yan Cyr
Jacek Majewski
Mathieu Blanchette
Jérôme Waldispühl
2024-08-27
BMC Bioinformatics (publié)
doi.org
A long-context RNA foundation model for predicting transcriptome architecture
Ali Saberi
Benedict Choi
Sean Wang
Aldo Hernández-Corchado
Mohsen Naghipourfar
Arsham Mikaeili Namini
Vijay Ramani
Amin Emad
Hamed S. Najafabadi
Hani Goodarzi
Linking DNA sequence to genomic function remains one of the grand challenges in genetics and genomics. Here, we combine large-scale single-m… (voir plus)olecule transcriptome sequencing of diverse cancer cell lines with cutting-edge machine learning to build LoRNASH, an RNA foundation model that learns how the nucleotide sequence of unspliced pre-mRNA dictates transcriptome architecture—the relative abundances and molecular structures of mRNA isoforms. Owing to its use of the StripedHyena architecture, LoRNASH handles extremely long sequence inputs (∼65 kilobase pairs), allowing for quantitative, zero-shot prediction of all aspects of transcriptome architecture, including isoform abundance, isoform structure, and the impact of DNA sequence variants on transcript structure and abundance. We anticipate that our public data release and proof-of-concept model will accelerate varying aspects of RNA biotechnology. More broadly, we envision the use of LoRNASH as a foundation for fine-tuning of any transcriptome-related downstream prediction task, including cell-type specific gene expression, splicing, and general RNA processing.
2024-08-27
bioRxiv (prépublication)
doi.org
MeshUp: Multi-Target Mesh Deformation via Blended Score Distillation
Hyunwoo Kim
Itai Lang
Noam Aigerman
Thibault Groueix
Vladimir Kim
Rana Hanocka
We propose MeshUp, a technique that deforms a 3D mesh towards multiple target concepts, and intuitively controls the region where each conce… (voir plus)pt is expressed. Conveniently, the concepts can be defined as either text queries, e.g.,"a dog"and"a turtle,"or inspirational images, and the local regions can be selected as any number of vertices on the mesh. We can effectively control the influence of the concepts and mix them together using a novel score distillation approach, referred to as the Blended Score Distillation (BSD). BSD operates on each attention layer of the denoising U-Net of a diffusion model as it extracts and injects the per-objective activations into a unified denoising pipeline from which the deformation gradients are calculated. To localize the expression of these activations, we create a probabilistic Region of Interest (ROI) map on the surface of the mesh, and turn it into 3D-consistent masks that we use to control the expression of these activations. We demonstrate the effectiveness of BSD empirically and show that it can deform various meshes towards multiple objectives. Our project page is at https://threedle.github.io/MeshUp.
2024-08-27
ArXiv (prépublication)
doi.org
arxiv.org