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Institut National de la Recherche Scientifique

Academic institutionnorthamerica · ca
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Research library25linked papers
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Selected work

Representative Papers

CosMAP: Contrastive Manifold Approximation and Projection for Dimensionality Reduction of Omics and Genealogical Data

Aug 10, 2026

This work addresses the challenge of simultaneously preserving local neighborhoods, global structure, and population coherence when reducing the dimensionality of high-dimensional, sparse omics and lineage data. To this end, the authors propose a graph-based unsupervised dimensionality reduction method that constructs an initial neighborhood graph using cosine similarity and optimizes an attraction–repulsion objective in the embedding space via temperature-normalized contrastive affinities. A two-stage optimization strategy is introduced: first, an intermediate high-dimensional representation is used to refine the neighborhood graph and initialize the embedding; second, the final low-dimensional representation is fine-tuned. Evaluated on single-cell RNA-seq, handwritten digit, and large-scale lineage datasets, the method consistently outperforms existing approaches, yielding more coherent visualizations, superior neighborhood preservation, and clearer global structural organization.

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A Flexible Sparsity-Aware FPGA Accelerator with Column-Wise Compression for Efficient CNN Inference

Jul 21, 2026

This work addresses the challenges of efficiently deploying convolutional neural networks on resource-constrained platforms, where irregular sparsity patterns and high hardware overhead hinder performance. To overcome these limitations, the authors propose SparHiXcel-v2, a highly flexible and low-cost FPGA accelerator that leverages column-wise kernel compression, a multi-stage structured pruning-and-recovery algorithm, and sorting-based optimization within a hardware-software co-design framework to effectively exploit irregular sparsity while preserving model accuracy. The accelerator features a scalable two-dimensional MAC array architecture, achieving 2.5 TOPS with 210 GOP/s/W for VGG16 and 1.1 TOPS with 72 GOP/s/W for ResNet18 on an AMD Kintex UltraScale+ FPGA—significantly outperforming existing solutions in both throughput and energy efficiency.

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How Temperature Shapes Ideological Discourse in Retrieval-Augmented Generation?

Jul 13, 2026

This study investigates how retrieval-augmented generation (RAG) systems propagate, amplify, or suppress ideologically charged discourse when incorporating external knowledge with explicit ideological stances, and for the first time systematically reveals the modulating role of sampling temperature in this process. Leveraging a corpus of 1,117 articles on COVID-19 treatments, the research identifies three categories of ideological discourse as retrieval sources and evaluates generative alignment across multiple large language models under varying temperatures using lexical multidimensional analysis (LMDA) and semantic similarity metrics. The findings demonstrate that RAG systems readily transmit ideological discourse from retrieved content into model outputs, with alignment strength significantly influenced by temperature: maximal alignment occurs at moderate temperatures, whereas low temperatures suppress discourse propagation due to excessive output determinism.

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Recent publications

Latest Papers

CosMAP: Contrastive Manifold Approximation and Projection for Dimensionality Reduction of Omics and Genealogical Data

Aug 10, 2026

This work addresses the challenge of simultaneously preserving local neighborhoods, global structure, and population coherence when reducing the dimensionality of high-dimensional, sparse omics and lineage data. To this end, the authors propose a graph-based unsupervised dimensionality reduction method that constructs an initial neighborhood graph using cosine similarity and optimizes an attraction–repulsion objective in the embedding space via temperature-normalized contrastive affinities. A two-stage optimization strategy is introduced: first, an intermediate high-dimensional representation is used to refine the neighborhood graph and initialize the embedding; second, the final low-dimensional representation is fine-tuned. Evaluated on single-cell RNA-seq, handwritten digit, and large-scale lineage datasets, the method consistently outperforms existing approaches, yielding more coherent visualizations, superior neighborhood preservation, and clearer global structural organization.

0 citationsRead paper

A Flexible Sparsity-Aware FPGA Accelerator with Column-Wise Compression for Efficient CNN Inference

Jul 21, 2026

This work addresses the challenges of efficiently deploying convolutional neural networks on resource-constrained platforms, where irregular sparsity patterns and high hardware overhead hinder performance. To overcome these limitations, the authors propose SparHiXcel-v2, a highly flexible and low-cost FPGA accelerator that leverages column-wise kernel compression, a multi-stage structured pruning-and-recovery algorithm, and sorting-based optimization within a hardware-software co-design framework to effectively exploit irregular sparsity while preserving model accuracy. The accelerator features a scalable two-dimensional MAC array architecture, achieving 2.5 TOPS with 210 GOP/s/W for VGG16 and 1.1 TOPS with 72 GOP/s/W for ResNet18 on an AMD Kintex UltraScale+ FPGA—significantly outperforming existing solutions in both throughput and energy efficiency.

0 citationsRead paper

How Temperature Shapes Ideological Discourse in Retrieval-Augmented Generation?

Jul 13, 2026

This study investigates how retrieval-augmented generation (RAG) systems propagate, amplify, or suppress ideologically charged discourse when incorporating external knowledge with explicit ideological stances, and for the first time systematically reveals the modulating role of sampling temperature in this process. Leveraging a corpus of 1,117 articles on COVID-19 treatments, the research identifies three categories of ideological discourse as retrieval sources and evaluates generative alignment across multiple large language models under varying temperatures using lexical multidimensional analysis (LMDA) and semantic similarity metrics. The findings demonstrate that RAG systems readily transmit ideological discourse from retrieved content into model outputs, with alignment strength significantly influenced by temperature: maximal alignment occurs at moderate temperatures, whereas low temperatures suppress discourse propagation due to excessive output determinism.

0 citationsRead paper