Publications

Understanding Web Application Workloads and Their Applications: Systematic Literature Review and Characterization

Roozbeh Aghili

Qiaolin Qin

Heng Li

Web applications, accessible via web browsers over the Internet, facilitate complex functionalities without local software installation. In … (voir plus)the context of web applications, a workload refers to the number of user requests sent by users or applications to the underlying system. Existing studies have leveraged web application workloads to achieve various objectives, such as workload prediction and auto-scaling. However, these studies are conducted in an ad hoc manner, lacking a systematic understanding of the characteristics of web application workloads. In this study, we first conduct a systematic literature review to identify and analyze existing studies leveraging web application workloads. Our analysis sheds light on their workload utilization, analysis techniques, and high-level objectives. We further systematically analyze the characteristics of the web application workloads identified in the literature review. Our analysis centers on characterizing these workloads at two distinct temporal granularities: daily and weekly. We successfully identify and categorize three daily and three weekly patterns within the workloads. By providing a statistical characterization of these workload patterns, our study highlights the uniqueness of each pattern, paving the way for the development of realistic workload generation and resource provisioning techniques that can benefit a range of applications and research areas.

2024-10-05

2024 IEEE International Conference on Software Maintenance and Evolution (ICSME) (publié)

doi.org

arxiv.org

Beyond the lab: 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

2024-10-04

Journal of Environmental Psychology (publié)

doi.org

Multi-Objective Risk Assessment Framework for Exploration Planning Using Terrain and Traversability Analysis

Riana Gagnon Souleiman

Vivek Shankar Vardharajan

Giovanni Beltrame

2024-10-03

ArXiv (prépublication)

doi.org

arxiv.org

DECOLLAGE: 3D Detailization by Controllable, Localized, and Learned Geometry Enhancement

Qimin Chen

Zhiqin Chen

Vladimir Kim

Noam Aigerman

Hao (Richard) Zhang

Hao Zhang 0002

Siddhartha Chaudhuri

2024-10-02

Lecture Notes in Computer Science (publié)

doi.org

arxiv.org

Probabilistic Temporal Prediction of Continuous Disease Trajectories and Treatment Effects Using Neural SDEs

Joshua D. Durso-Finley

Berardino Barile

Jean-Pierre R. Falet

Douglas Arnold

Nick Pawlowski

Tal Arbel

Personalized medicine based on medical images, including predicting future individualized clinical disease progression and treatment respons… (voir plus)e, would have an enormous impact on healthcare and drug development, particularly for diseases (e.g. multiple sclerosis (MS)) with long term, complex, heterogeneous evolutions and no cure. In this work, we present the first stochastic causal temporal framework to model the continuous temporal evolution of disease progression via Neural Stochastic Differential Equations (NSDE). The proposed causal inference model takes as input the patient's high dimensional images (MRI) and tabular data, and predicts both factual and counterfactual progression trajectories on different treatments in latent space. The NSDE permits the estimation of high-confidence personalized trajectories and treatment effects. Extensive experiments were performed on a large, multi-centre, proprietary dataset of patient 3D MRI and clinical data acquired during several randomized clinical trials for MS treatments. Our results present the first successful uncertainty-based causal Deep Learning (DL) model to: (a) accurately predict future patient MS disability evolution (e.g. EDSS) and treatment effects leveraging baseline MRI, and (b) permit the discovery of subgroups of patients for which the model has high confidence in their response to treatment even in clinical trials which did not reach their clinical endpoints.

2024-10-02

Lecture Notes in Computer Science (publié)

doi.org

arxiv.org

Sparse Bayesian Networks: Efficient Uncertainty Quantification in Medical Image Analysis

Zeinab Abboud

Hervé Lombaert

Samuel Kadoury

Efficiently quantifying predictive uncertainty in medical images remains a challenge. While Bayesian neural networks (BNN) offer predictive … (voir plus)uncertainty, they require substantial computational resources to train. Although Bayesian approximations such as ensembles have shown promise, they still suffer from high training and inference costs. Existing approaches mainly address the costs of BNN inference post-training, with little focus on improving training efficiency and reducing parameter complexity. This study introduces a training procedure for a sparse (partial) Bayesian network. Our method selectively assigns a subset of parameters as Bayesian by assessing their deterministic saliency through gradient sensitivity analysis. The resulting network combines deterministic and Bayesian parameters, exploiting the advantages of both representations to achieve high task-specific performance and minimize predictive uncertainty. Demonstrated on multi-label ChestMNIST for classification and ISIC, LIDC-IDRI for segmentation, our approach achieves competitive performance and predictive uncertainty estimation by reducing Bayesian parameters by over 95\%, significantly reducing computational expenses compared to fully Bayesian and ensemble methods.

2024-10-02

Lecture Notes in Computer Science (publié)

doi.org

arxiv.org

TrajGPT: Irregular Time-Series Representation Learning for Health Trajectory Analysis

Ziyang Song

Qincheng Lu

Mike He Zhu

David L Buckeridge

Yuemei Li

2024-10-02

ArXiv (prépublication)

doi.org

openreview.net

Were RNNs All We Needed?

Leo Feng

Frederick Tung

Mohamed Osama Ahmed

Yoshua Bengio

Hossein Hajimirsadeghi

2024-10-01

ArXiv (prépublication)

doi.org

arxiv.org

Were RNNs All We Needed?

Leo Feng

Frederick Tung

Mohamed Osama Ahmed

Yoshua Bengio

Hossein Hajimirsadeghi

The introduction of Transformers in 2017 reshaped the landscape of deep learning. Originally proposed for sequence modelling, Transformers h… (voir plus)ave since achieved widespread success across various domains. However, the scalability limitations of Transformers - particularly with respect to sequence length - have sparked renewed interest in novel recurrent models that are parallelizable during training, offer comparable performance, and scale more effectively. In this work, we revisit sequence modelling from a historical perspective, focusing on Recurrent Neural Networks (RNNs), which dominated the field for two decades before the rise of Transformers. Specifically, we examine LSTMs (1997) and GRUs (2014). We demonstrate that by simplifying these models, we can derive minimal versions (minLSTMs and minGRUs) that (1) use fewer parameters than their traditional counterparts, (2) are fully parallelizable during training, and (3) achieve surprisingly competitive performance on a range of tasks, rivalling recent models including Transformers.

2024-10-01

ArXiv (prépublication)

doi.org

arxiv.org

Challenges for impact evaluation of WHO’s normative output

Catherine Régis

Gaëlle Foucault

Jean-Louis Denis

Pierre Larouche

Miriam Cohen

2024-09-30

Bulletin of the World Health Organization (publié)