activity
20122024
most citedPerformance comparison of Dask and Apache Spark on HPC systems for Neuroimaging

6 citations · 7 across the 7 of their papers we have counts for

collaborators

7 papers

cs.DC20246 cited

Performance comparison of Dask and Apache Spark on HPC systems for Neuroimaging

Mathieu Dugré, Valérie Hayot-Sasson, Tristan Glatard

The general increase in data size and data sharing motivates the adoption of Big Data strategies in several scientific disciplines. However, while several options are available, no…

cs.DC2024

Hierarchical storage management in user space for neuroimaging applications

Valérie Hayot-Sasson, Tristan Glatard

Neuroimaging open-data initiatives have led to increased availability of large scientific datasets. While these datasets are shifting the processing bottleneck from compute-intensi…

cs.LG2024

Scaling up ridge regression for brain encoding in a massive individual fMRI dataset

Sana Ahmadi, Pierre Bellec, Tristan Glatard

Brain encoding with neuroimaging data is an established analysis aimed at predicting human brain activity directly from complex stimuli features such as movie frames. Typically, th…

cs.LG2023

Classification of Anomalies in Telecommunication Network KPI Time Series

Korantin Bordeau-Aubert, Justin Whatley, Sylvain Nadeau +2

The increasing complexity and scale of telecommunication networks have led to a growing interest in automated anomaly detection systems. However, the classification of anomalies de…

eess.IV2023

Numerical Uncertainty of Convolutional Neural Networks Inference for Structural Brain MRI Analysis

Inés Gonzalez Pepe, Vinuyan Sivakolunthu, Hae Lang Park +2

This paper investigates the numerical uncertainty of Convolutional Neural Networks (CNNs) inference for structural brain MRI analysis. It applies Random Rounding -- a stochastic ar…

cs.DC2022

Sea: A lightweight data-placement library for Big Data scientific computing

Valérie Hayot-Sasson, Mathieu Dugré, Tristan Glatard

The recent influx of open scientific data has contributed to the transitioning of scientific computing from compute intensive to data intensive. Whereas many Big Data frameworks ex…