Tootfinder

Efficient GPU-Centered Singular Value Decomposition Using the Divide-and-Conquer Method
Shifang Liu, Huiyuan Li, Hongjiao Sheng, Haoyuan Gui, Xiaoyu Zhang
https://arxiv.org/abs/2508.11467

Efficient GPU-Centered Singular Value Decomposition Using the Divide-and-Conquer Method
Singular Value Decomposition (SVD) is a fundamental matrix factorization technique in linear algebra, widely applied in numerous matrix-related problems. However, traditional SVD approaches are hindered by slow panel factorization and frequent CPU-GPU data transfers in heterogeneous systems, despite advancements in GPU computational capabilities. In this paper, we introduce a GPU-centered SVD algorithm, incorporating a novel GPU-based bidiagonal divide-and-conquer (BDC) method. We reformulate t…

An efficient co-simulation and control approach to tackle complex multi-domain energetic systems: concepts and applications of the PEGASE platform
Mathieu Vallee (DTCH), Roland Baviere (DTCH), Val\'erie Seguin (DTCH), Val\'ery Vuillerme (DTCH), Nicolas Lamaison (DTCH), Michael Nikhil Descamps (DTCH), Antoine Aurousseau (DTCH)
https://

An efficient co-simulation and control approach to tackle complex multi-domain energetic systems: concepts and applications of the PEGASE platform
In this paper, we present a novel research software, called PEGASE, suitable for the design, validation and deployment of advanced control strategies for complex multi-domain energy systems. PEGASE especially features a highly efficient cosimulation engine, together with integrated solutions for defining both rule-based control strategies and Model-Predictive Control (MPC). The main principle behind the PEGASE platform is divide-and-conquer. Indeed, rather than trying to solve a problem as a mo…

MORIC: CSI Delay-Doppler Decomposition for Robust Wi-Fi-based Human Activity Recognition
Navid Hasanzadeh, Shahrokh Valaee
https://arxiv.org/abs/2506.12997

MORIC: CSI Delay-Doppler Decomposition for Robust Wi-Fi-based Human Activity Recognition
The newly established IEEE 802.11bf Task Group aims to amend the WLAN standard to support advanced sensing applications such as human activity recognition (HAR). Although studies have demonstrated the potential of sub-7 GHz Wi-Fi Channel State Information (CSI) for HAR, no method currently performs reliably in real-world scenarios. This work tackles the poor generalization of Wi-Fi-based HAR by introducing an innovative approach to extracting and utilizing movement-related representations, whic…

Landmark-Assisted Monte Carlo Planning
David H. Chan, Mark Roberts, Dana S. Nau
https://arxiv.org/abs/2508.11493 https://arxiv.org/pdf/2508.11493

Landmark-Assisted Monte Carlo Planning
Landmarks$\unicode{x2013}$conditions that must be satisfied at some point in every solution plan$\unicode{x2013}$have contributed to major advancements in classical planning, but they have seldom been used in stochastic domains. We formalize probabilistic landmarks and adapt the UCT algorithm to leverage them as subgoals to decompose MDPs; core to the adaptation is balancing between greedy landmark achievement and final goal achievement. Our results in benchmark domains show that well-chosen la…

Adaptive criterion and modification of wave-particle decomposition in UGKWP method for high-speed flow simulation
Junzhe Cao, Yufeng Wei, Wenpei Long, Chengwen Zhong, Kun Xu
https://arxiv.org/abs/2506.12722

Adaptive criterion and modification of wave-particle decomposition in UGKWP method for high-speed flow simulation
Benefitting from the direct modeling of physical laws in a discretized space and the automatic decomposition of hydrodynamic waves and particles, the unified gas-kinetic wave-particle (UGKWP) method offers notable advantages in various multiscale physics, such as hypersonic flow, plasma transport and radiation transport. Aiming at achieving a more suitable and efficient wave-particle decomposition in high-speed flow simulation and enhancing the performance in the drastic scale variation region,…

D-LiFT: Improving LLM-based Decompiler Backend via Code Quality-driven Fine-tuning
Muqi Zou (Jing), Hongyu Cai (Jing), Hongwei Wu (Jing), Zion Leonahenahe Basque (Jing), Arslan Khan (Jing), Berkay Celik (Jing), Dave (Jing), Tian (Fish), Antonio Bianchi (Fish), Ruoyu (Fish), Wang, Dongyan Xu
https://arxiv.org/abs/2506.10125…

D-LiFT: Improving LLM-based Decompiler Backend via Code Quality-driven Fine-tuning
Decompilers, which reconstruct human-readable source code from binary executables, are vital to many security tasks. Yet, despite recent advances, their output often suffers from syntactic and semantic errors and remains difficult to read. Recently, with the advent of large language models (LLMs), researchers began to explore the potential of LLMs to refine decompiler output. Nevertheless, our study of these approaches reveals significant limitations, such as introducing new errors and relying …

On the Parallel Complexity of Identifying Groups and Quasigroups via Decompositions
Dan Johnson, Michael Levet, Petr Vojt\v{e}chovsk\'y, Brett Widholm
https://arxiv.org/abs/2508.06478

On the Parallel Complexity of Identifying Groups and Quasigroups via Decompositions
In this paper, we investigate the computational complexity of isomorphism testing for finite groups and quasigroups, given by their multiplication tables. We crucially take advantage of their various decompositions to show the following: - We first consider the class $\mathcal{C}$ of groups that admit direct product decompositions, where each indecompsable factor is $O(1)$-generated, and either perfect or centerless. We show any group in $\mathcal{C}$ is identified by the $O(1)$-dimensional c…

Recent Advances and Trends in Research Paper Recommender Systems: A Comprehensive Survey
Iratxe Pinedo, Mikel Larra\~naga, Ana Arruarte
https://arxiv.org/abs/2508.08828 https://…

Recent Advances and Trends in Research Paper Recommender Systems: A Comprehensive Survey
As the volume of scientific publications grows exponentially, researchers increasingly face difficulties in locating relevant literature. Research Paper Recommender Systems have become vital tools to mitigate this information overload by delivering personalized suggestions. This survey provides a comprehensive analysis of Research Paper Recommender Systems developed between November 2021 and December 2024, building upon prior reviews in the field. It presents an extensive overview of the techni…

AMBER: Algorithm for Multiplexing spectrometer Background Estimation with Rotation-independence
Jakob Lass, Victor Cohen, Benjam\'in B\'ejar Haro, Daniel G. Mazzone
https://arxiv.org/abs/2507.11580

AMBER: Algorithm for Multiplexing spectrometer Background Estimation with Rotation-independence
State-of-the art neutron spectrometers enable simultaneous measurements of high-dimensional datasets, allowing for a large collection rate of dynamic material properties. In this paper, we present the Algorithm for Multiplexing spectrometer Background Estimation with Rotation-independence (AMBER), which is a segmentation algorithm designed to decompose measured neutron scattering data into model-agnostic foreground and background contributions. The method takes advantage of the fact that backgr…

A Tight Lower Bound for the Approximation Guarantee of Higher-Order Singular Value Decomposition
Matthew Fahrbach, Mehrdad Ghadiri
https://arxiv.org/abs/2508.06693 https://

A Tight Lower Bound for the Approximation Guarantee of Higher-Order Singular Value Decomposition
We prove that the classic approximation guarantee for the higher-order singular value decomposition (HOSVD) is tight by constructing a tensor for which HOSVD achieves an approximation ratio of $N/(1+\varepsilon)$, for any $\varepsilon > 0$. This matches the upper bound of De Lathauwer et al. (2000a) and shows that the approximation ratio of HOSVD cannot be improved. Using a more advanced construction, we also prove that the approximation guarantees for the ST-HOSVD algorithm of Vannieuwenhoven …