Tootfinder

Yang-Baxter structure of the extended space
Kirill Gubarev, Edvard Musaev
https://arxiv.org/abs/2508.09637 https://arxiv.org/pdf/2508.09637

Yang-Baxter structure of the extended space
We construct an analogue of Yang--Baxter deformations defined by a single Killing vector, that is a solution generating transformation in Einstein--Maxwell dilaton theory. We show that these are nothing but a coordinate transformation in a parent theory related to EMd theory by KK reduction. Similarly (almost-abelian) bi-vector Yang--Baxter deformations are coordinate transformations in the doubled space.

IBEX: Information-Bottleneck-EXplored Coarse-to-Fine Molecular Generation under Limited Data
Dong Xu, Zhangfan Yang, Jenna Xinyi Yao, Shuangbao Song, Zexuan Zhu, Junkai Ji
https://arxiv.org/abs/2508.10775

IBEX: Information-Bottleneck-EXplored Coarse-to-Fine Molecular Generation under Limited Data
Three-dimensional generative models increasingly drive structure-based drug discovery, yet it remains constrained by the scarce publicly available protein-ligand complexes. Under such data scarcity, almost all existing pipelines struggle to learn transferable geometric priors and consequently overfit to training-set biases. As such, we present IBEX, an Information-Bottleneck-EXplored coarse-to-fine pipeline to tackle the chronic shortage of protein-ligand complex data in structure-based drug de…

MirGuard: Towards a Robust Provenance-based Intrusion Detection System Against Graph Manipulation Attacks
Anyuan Sang, Lu Zhou, Li Yang, Junbo Jia, Huipeng Yang, Pengbin Feng, Jianfeng Ma
https://arxiv.org/abs/2508.10639

MirGuard: Towards a Robust Provenance-based Intrusion Detection System Against Graph Manipulation Attacks
Learning-based Provenance-based Intrusion Detection Systems (PIDSes) have become essential tools for anomaly detection in host systems due to their ability to capture rich contextual and structural information, as well as their potential to detect unknown attacks. However, recent studies have shown that these systems are vulnerable to graph manipulation attacks, where attackers manipulate the graph structure to evade detection. While some previous approaches have discussed this type of attack, …

Bills, the NFL's Anti-Cowboys, Continue to Sign Their Top Young Talent Early https://www.foxsports.com/stories/nfl/bills-nfls-anti-cowboys-continue-sign-top-young-talent-early

Structural Similarity-Inspired Unfolding for Lightweight Image Super-Resolution
Zhangkai Ni, Yang Zhang, Wenhan Yang, Hanli Wang, Shiqi Wang, Sam Kwong
https://arxiv.org/abs/2506.11823

Structural Similarity-Inspired Unfolding for Lightweight Image Super-Resolution
Major efforts in data-driven image super-resolution (SR) primarily focus on expanding the receptive field of the model to better capture contextual information. However, these methods are typically implemented by stacking deeper networks or leveraging transformer-based attention mechanisms, which consequently increases model complexity. In contrast, model-driven methods based on the unfolding paradigm show promise in improving performance while effectively maintaining model compactness through …

ADHO (adho.org) updates at the opening of #DH2025 from Diane and Michael - the Italian Digital Humanities association officially joins ADHO, and announces awards including the Zampoli prize to Stylo software, the conference bursaries winners and Fortier prize nominees. Also note the Code of Conduct!

Rethinking LSM-tree based Key-Value Stores: A Survey
Yina Lv, Qiao Li, Quanqing Xu, Congming Gao, Chuanhui Yang, Xiaoli Wang, Chun Jason Xue
https://arxiv.org/abs/2507.09642

Rethinking LSM-tree based Key-Value Stores: A Survey
LSM-tree is a widely adopted data structure in modern key-value store systems that optimizes write performance in write-heavy applications by using append writes to achieve sequential writes. However, the unpredictability of LSM-tree compaction introduces significant challenges, including performance variability during peak workloads and in resource-constrained environments, write amplification caused by data rewriting during compactions, read amplification from multi-level queries, trade-off b…

ChainEdit: Propagating Ripple Effects in LLM Knowledge Editing through Logical Rule-Guided Chains
Zilu Dong, Xiangqing Shen, Zinong Yang, Rui Xia
https://arxiv.org/abs/2507.08427 …

ChainEdit: Propagating Ripple Effects in LLM Knowledge Editing through Logical Rule-Guided Chains
Current knowledge editing methods for large language models (LLMs) struggle to maintain logical consistency when propagating ripple effects to associated facts. We propose ChainEdit, a framework that synergizes knowledge graph-derived logical rules with LLM logical reasoning capabilities to enable systematic chain updates. By automatically extracting logical patterns from structured knowledge bases and aligning them with LLMs' internal logics, ChainEdit dynamically generates and edits logically…

Leveraging Conformational Diversity for Enhanced Structure-Based Virtual Screening: Insights from Molecular Dynamics Simulations of HIV-1 Protease-Ligand Complexes
Pei-Kun Yang
https://arxiv.org/abs/2507.09658

Leveraging Conformational Diversity for Enhanced Structure-Based Virtual Screening: Insights from Molecular Dynamics Simulations of HIV-1 Protease-Ligand Complexes
Structure-based virtual screening aims to identify high-affinity ligands by estimating binding free energies between proteins and small molecules. However, the conformational flexibility of both proteins and ligands challenges conventional rigid docking methods that assume a fixed receptor structure. In this study, we examined the impact of conformational diversity on binding energy calculations using 79 HIV-1 protease-ligand complexes. Molecular dynamics simulations were employed to generate s…

A Survey on Medical Image Compression: From Traditional to Learning-Based
Guofeng Tong, Sixuan Liu, Yang Lv, Hanyu Pei, Feng-Lei Fan
https://arxiv.org/abs/2507.10615

A Survey on Medical Image Compression: From Traditional to Learning-Based
The exponential growth of medical imaging has created significant challenges in data storage, transmission, and management for healthcare systems. In this vein, efficient compression becomes increasingly important. Unlike natural image compression, medical image compression prioritizes preserving diagnostic details and structural integrity, imposing stricter quality requirements and demanding fast, memory-efficient algorithms that balance computational complexity with clinically acceptable reco…