arXiv Machine Learning July 21, 2026 By Congrong Ren, Sheng Di, Katrin Heitmann, Franck Cappello, Hanqi Guo

Preserving Clusters in Error-Bounded Lossy Compression of Scientific Particle Data

Read the original on arXiv Machine Learning →

arXiv:2604. 18801v2 Announce Type: replace Abstract: Scientific particle simulations in cosmology, molecular dynamics, and fluid dynamics produce large-scale datasets whose storage, movement, and analysis increasingly rely on lossy compression.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

efficiency

arXiv Machine Learning

Jun 2

Cluster Analysis with Resampling for Validation and Exploration (CARVE)

arXiv:2606. 00327v1 Announce Type: cross Abstract: Clustering is widely used across the sciences as the foundation for downstream data-driven scientific discoveries.

By Kai R. Wycik, Tiffany M. Tang, Tarek M. Zikry, Genevera I. Allen

benchmarks

arXiv Machine Learning

Jun 15

Concatenated Matrix SVD: Compression Bounds, Incremental Approximation, and Error-Constrained Clustering

arXiv:2601. 11626v2 Announce Type: replace-cross Abstract: Large collections of matrices arise throughout modern machine learning, signal processing, and scientific computing, where they are commonly compressed by concatenation followed by truncated singular value decomposition (SVD).

By Maksym Shamrai

arXiv Machine Learning

Jun 8

ScatterPrism: convergence for generative simulation and inverse problems in particle and nuclear physics

arXiv:2604. 01313v2 Announce Type: replace Abstract: High-fidelity simulations and complex inverse problems, such as detector modeling and unfolding, are computationally intensive bottlenecks across subatomic physics, yet essential for accurate physical interpretation.

By Zeyu Xia, Tyler Kim, Trevor Reed, Judy Fox, Geoffrey Fox, Adam Szczepaniak

diffusion benchmarks

arXiv Machine Learning

Jul 10

Persistent Multiscale Density-based Clustering

arXiv:2512. 16558v3 Announce Type: replace Abstract: Clustering is a cornerstone of modern data analysis.

By Dani\"el Bot, Leland McInnes, Jan Aerts

efficiency

arXiv AI

Jul 8

Breaking Structural Isolation: Scalable Graph Clustering via Community-Aware Sampling and Structural Entropy

arXiv:2607. 05469v1 Announce Type: cross Abstract: Unsupervised graph clustering is a fundamental technique for uncovering underlying semantic patterns in large-scale networks.

By Jingyun Zhang, Hao Peng, Jianxin Li, Angsheng Li, Philip S. Yu

benchmarks

arXiv Machine Learning

Jun 16

Scalable Graph Condensation with Evolving Capabilities

arXiv:2502. 17614v3 Announce Type: replace Abstract: The rapid growth of graph data creates significant scalability challenges as most graph algorithms scale quadratically with size.

By Shengbo Gong, Mohammad Hashemi, Juntong Ni, Carl Yang, Wei Jin

benchmarks

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Cluster Analysis with Resampling for Validation and Exploration (CARVE)

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ScatterPrism: convergence for generative simulation and inverse problems in particle and nuclear physics

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Scalable Graph Condensation with Evolving Capabilities