Publications

Quality of Service-Constrained Online Routing in High Throughput Satellites

Olivier B'elanger

Olfa Ben Yahia

St'ephane Martel

Gunes Karabulut-kurt

High throughput satellites (HTSs) outpace traditional satellites due to their multi-beam transmission. The rise of low Earth orbit mega cons… (voir plus)tellations amplifies HTS data rate demands to terabits/second with acceptable latency. This surge in data rate necessitates multiple modems, often exceeding single device capabilities. Consequently, satellites employ several processors, forming a complex packet-switch network. This can lead to potential internal congestion and challenges in adhering to strict quality of service (QoS) constraints. While significant research exists on constellation-level routing, a literature gap remains on the internal routing within a single HTS. The intricacy of this internal network architecture presents a significant challenge to achieve high data rates. This paper introduces an online optimal flow allocation and scheduling method for HTSs. The problem is presented as a multi-commodity flow instance with different priority data streams. An initial full time horizon model is proposed as a benchmark. We apply a model predictive control (MPC) approach to enable adaptive routing based on current information and the forecast within the prediction time horizon while allowing for deviation of the latter. Importantly, MPC is inherently suited to handle uncertainty in incoming flows. Our approach minimizes the packet loss by optimally and adaptively managing the priority queue schedulers and flow exchanges between satellite processing modules. Central to our method is a routing model focusing on optimal priority scheduling to enhance data rates and maintain QoS. The model's stages are critically evaluated, and results are compared to traditional methods via numerical simulations. Through simulations, our method demonstrates performance nearly on par with the hindsight optimum, showcasing its efficiency and adaptability in addressing satellite communication challenges.

2023-10-11

ArXiv (prépublication)

doi.org

arxiv.org

Deep Learning Benchmark for First Break Detection from Hardrock Seismic Reflection Data

Pierre-Luc St-Charles

Bruno Rousseau

Joumana Ghosn

Gilles Bellefleur

E. Schetselaar

Deep learning techniques are used to tackle a variety of tasks related to seismic data processing and interpretation. While many works have … (voir plus)shown the benefits of deep learning, assessing the generalization capabilities of proposed methods to data acquired in different conditions and geological environments remains challenging. This is especially true for applications in hardrock environments where seismic surveys are still relatively rare. The primary factors that impede the adoption of machine learning in geosciences include the lack of publicly available and labeled datasets, and the use of inadequate evaluation methodologies. Since machine learning models are prone to overfit and underperform when the data used to train them is site-specific, the applicability of these models on new survey data that could be considered “out-of-distribution” is rarely addressed. This is unfortunate, as evaluating predictive models in out-of-distribution settings can provide a good insight into their usefulness in real-world use cases. To tackle these issues, we propose a simple benchmarking methodology for first break picking to evaluate the transferability of deep learning models that are trained across different environments and acquisition conditions. For this, we consider a reflection seismic survey dataset acquired at five distinct hardrock mining sites combined with annotations for first break picking. We train and evaluate a baseline deep learning solution based on a U-Net for future comparisons, and discuss potential improvements to this approach.

2023-10-10

Geophysics (published)

doi.org

Debiasing Counterfactuals in the Presence of Spurious Correlations

Amar Kumar

Nima Fathi

Raghav Mehta

Brennan Nichyporuk

Jean-Pierre R. Falet

Sotirios A. Tsaftaris

Tal Arbel

Deep learning models can perform well in complex medical imaging classification tasks, even when basing their conclusions on spurious correl… (voir plus)ations (i.e. confounders), should they be prevalent in the training dataset, rather than on the causal image markers of interest. This would thereby limit their ability to generalize across the population. Explainability based on counterfactual image generation can be used to expose the confounders but does not provide a strategy to mitigate the bias. In this work, we introduce the first end-to-end training framework that integrates both (i) popular debiasing classifiers (e.g. distributionally robust optimization (DRO)) to avoid latching onto the spurious correlations and (ii) counterfactual image generation to unveil generalizable imaging markers of relevance to the task. Additionally, we propose a novel metric, Spurious Correlation Latching Score (SCLS), to quantify the extent of the classifier reliance on the spurious correlation as exposed by the counterfactual images. Through comprehensive experiments on two public datasets (with the simulated and real visual artifacts), we demonstrate that the debiasing method: (i) learns generalizable markers across the population, and (ii) successfully ignores spurious correlations and focuses on the underlying disease pathology.

2023-10-09

Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging (publié)

doi.org

openreview.net

On the effectiveness of log representation for log-based anomaly detection

Xingfang Wu

Heng Li

Foutse Khomh

2023-10-09

Empirical Software Engineering (publié)

doi.org

arxiv.org

Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models

Joshua D. Durso-Finley

Jean-Pierre R. Falet

Raghav Mehta

Douglas Arnold

Nick Pawlowski

Tal Arbel

Image-based precision medicine aims to personalize treatment decisions based on an individual's unique imaging features so as to improve the… (voir plus)ir clinical outcome. Machine learning frameworks that integrate uncertainty estimation as part of their treatment recommendations would be safer and more reliable. However, little work has been done in adapting uncertainty estimation techniques and validation metrics for precision medicine. In this paper, we use Bayesian deep learning for estimating the posterior distribution over factual and counterfactual outcomes on several treatments. This allows for estimating the uncertainty for each treatment option and for the individual treatment effects (ITE) between any two treatments. We train and evaluate this model to predict future new and enlarging T2 lesion counts on a large, multi-center dataset of MR brain images of patients with multiple sclerosis, exposed to several treatments during randomized controlled trials. We evaluate the correlation of the uncertainty estimate with the factual error, and, given the lack of ground truth counterfactual outcomes, demonstrate how uncertainty for the ITE prediction relates to bounds on the ITE error. Lastly, we demonstrate how knowledge of uncertainty could modify clinical decision-making to improve individual patient and clinical trial outcomes.

2023-10-08

OpenReview.net/Archive (publié)

doi.org

openreview.net

Mitigating Calibration Bias Without Fixed Attribute Grouping for Improved Fairness in Medical Imaging Analysis

Changjian Shui

Justin Szeto

Raghav Mehta

Douglas Arnold

Tal Arbel

2023-10-08

OpenReview.net/Archive (publié)

doi.org

openreview.net

Better Quality Pre-training Data and T5 Models for African Languages

Akintunde Oladipo

Mofetoluwa Adeyemi

Orevaoghene Ahia

Abraham Toluwase Owodunni

Odunayo Ogundepo

David Ifeoluwa Adelani

Jimmy Lin

In this study, we highlight the importance of enhancing the quality of pretraining data in multilingual language models. Existing web crawl… (voir plus)s have demonstrated quality issues, particularly in the context of low-resource languages. Consequently, we introduce a new multilingual pretraining corpus for

2023-10-07

EMNLP/2023/Conference (accepté)

doi.org

openreview.net

Crystal-GFN: sampling crystals with desirable properties and constraints

Alex Hernandez-Garcia

Alexandre AGM Duval

Alexandra Volokhova

Yoshua Bengio

Divya Sharma

pierre luc carrier

Michał Koziarski

Victor Schmidt

Accelerating material discovery holds the potential to greatly help mitigate the climate crisis. Discovering new solid-state materials such … (voir plus)as electrocatalysts, super-ionic conductors or photovoltaic materials can have a crucial impact, for instance, in improving the efficiency of renewable energy production and storage. In this paper, we introduce Crystal-GFN, a generative model of crystal structures that sequentially samples structural properties of crystalline materials, namely the space group, composition and lattice parameters. This domain-inspired approach enables the flexible incorporation of physical and structural hard constraints, as well as the use of any available predictive model of a desired physicochemical property as an objective function. To design stable materials, one must target the candidates with the lowest formation energy. Here, we use as objective the formation energy per atom of a crystal structure predicted by a new proxy machine learning model trained on MatBench. The results demonstrate that Crystal-GFN is able to sample highly diverse crystals with low (median -3.1 eV/atom) predicted formation energy.

2023-10-07

ArXiv (prépublication)

doi.org

arxiv.org

Efficient Classification of Long Documents via State-Space Models

Peng Lu

Suyuchen Wang

Mehdi Rezagholizadeh

Bang Liu

Ivan Kobyzev

2023-10-07

EMNLP/2023/Conference (accepté)

openreview.net

EpiK-Eval: Evaluation for Language Models as Epistemic Models

Gabriele Prato

Jerry Huang

Prasanna Parthasarathi

Shagun Sodhani

Sarath Chandar Anbil Parthipan

In the age of artificial intelligence, the role of large language models (LLMs) is becoming increasingly central. Despite their growing prev… (voir plus)alence, their capacity to consolidate knowledge from different training documents—a crucial ability in numerous applications—remains unexplored. This paper presents the first study examining the capability of LLMs to effectively combine such information within their parameter space. We introduce EpiK-Eval, a novel question-answering benchmark tailored to evaluate LLMs' proficiency in formulating a coherent and consistent knowledge representation from segmented narratives. Evaluations across various LLMs reveal significant weaknesses in this domain. We contend that these shortcomings stem from the intrinsic nature of prevailing training objectives. Consequently, we advocate for refining the approach towards knowledge consolidation, as it harbors the potential to dramatically improve their overall effectiveness and performance. The findings from this study offer insights for developing more robust and reliable LLMs. Our code and benchmark are available at https://github.com/chandar-lab/EpiK-Eval

2023-10-07

EMNLP/2023/Conference (accepté)

doi.org

openreview.net

HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science

Yu Song

Santiago Miret

Huan Zhang

Bang Liu

2023-10-07

EMNLP/2023/Conference (publié)

openreview.net