Tootfinder

Utilizing the RAIN method and Graph SAGE Model to Identify Effective Drug Combinations for Gastric Neoplasm Treatment
S. Z. Pirasteh, Ali A. Kiaei, Mahnaz Bush, Sabra Moghadam, Raha Aghaei, Behnaz Sadeghigol
https://arxiv.org/abs/2508.13207

Utilizing the RAIN method and Graph SAGE Model to Identify Effective Drug Combinations for Gastric Neoplasm Treatment
Background: Gastric neoplasm, primarily adenocarcinoma, is an aggressive cancer with high mortality, often diagnosed late, leading to complications like metastasis. Effective drug combinations are vital to address disease heterogeneity, enhance efficacy, reduce resistance, and improve patient outcomes. Methods: The RAIN method integrated Graph SAGE to propose drug combinations, using a graph model with p-value-weighted edges connecting drugs, genes, and proteins. NLP and systematic literature r…

DINOv3 with Test-Time Training for Medical Image Registration
Shansong Wang, Mojtaba Safari, Mingzhe Hu, Qiang Li, Chih-Wei Chang, Richard LJ Qiu, Xiaofeng Yang
https://arxiv.org/abs/2508.14809

DINOv3 with Test-Time Training for Medical Image Registration
Prior medical image registration approaches, particularly learning-based methods, often require large amounts of training data, which constrains clinical adoption. To overcome this limitation, we propose a training-free pipeline that relies on a frozen DINOv3 encoder and test-time optimization of the deformation field in feature space. Across two representative benchmarks, the method is accurate and yields regular deformations. On Abdomen MR-CT, it attained the best mean Dice score (DSC) of 0.7…

The vanishing viscosity process for an eikonal equation in the radially symmetric setting
Fanchen Meng
https://arxiv.org/abs/2508.13230 https://arxiv.org/p…

The vanishing viscosity process for an eikonal equation in the radially symmetric setting
We study the vanishing viscosity method for the eikonal equation $|Du|=V$ in $B(0,1)$ with homogeneous Dirichlet boundary value condition. By assuming $V$ is radially symmetric and restricting attention to radially symmetric solutions, we construct explicit formulas for both the viscous solution $u^ε$ and the limiting solution $u$. We prove $u^ε\rightarrow u$ as $ε\rightarrow 0^+$ qualitatively and quantitatively derive an $ε|\log ε|$ type local convergence rate. Finall…

Knowledge Graph-Infused Fine-Tuning for Structured Reasoning in Large Language Models
Wuyang Zhang, Yexin Tian, Xiandong Meng, Mengjie Wang, Junliang Du
https://arxiv.org/abs/2508.14427

Knowledge Graph-Infused Fine-Tuning for Structured Reasoning in Large Language Models
This paper addresses the problems of missing reasoning chains and insufficient entity-level semantic understanding in large language models when dealing with tasks that require structured knowledge. It proposes a fine-tuning algorithm framework based on knowledge graph injection. The method builds on pretrained language models and introduces structured graph information for auxiliary learning. A graph neural network is used to encode entities and their relations, constructing a graph-based sema…

Improved particle swarm optimization algorithm: multi-target trajectory optimization for swarm drones
Minze Li, Wei Zhao, Ran Chen, Mingqiang Wei
https://arxiv.org/abs/2507.13647 …

Improved particle swarm optimization algorithm: multi-target trajectory optimization for swarm drones
Real-time trajectory planning for unmanned aerial vehicles (UAVs) in dynamic environments remains a key challenge due to high computational demands and the need for fast, adaptive responses. Traditional Particle Swarm Optimization (PSO) methods, while effective for offline planning, often struggle with premature convergence and latency in real-time scenarios. To overcome these limitations, we propose PE-PSO, an enhanced PSO-based online trajectory planner. The method introduces a persistent exp…

A Generalized Genetic Random Field Method for the Genetic Association Analysis of Sequencing Data
Ming Li, Zihuai He, Min Zhang, Xiaowei Zhan, Changshuai Wei, Robert C Elston, Qing Lu
https://arxiv.org/abs/2508.12617

A Generalized Genetic Random Field Method for the Genetic Association Analysis of Sequencing Data
With the advance of high-throughput sequencing technologies, it has become feasible to investigate the influence of the entire spectrum of sequencing variations on complex human diseases. Although association studies utilizing the new sequencing technologies hold great promise to unravel novel genetic variants, especially rare genetic variants that contribute to human diseases, the statistical analysis of high-dimensional sequencing data remains a challenge. Advanced analytical methods are in g…

Semi-supervised classification of Stars, Galaxies and Quasars using K-means and Random Forest
Vahid Asadi, Hosein Haghi, Akram Hasani Zonoozi
https://arxiv.org/abs/2507.14072

Semi-supervised classification of Stars, Galaxies and Quasars using K-means and Random Forest
Classifying stars, galaxies, and quasars is essential for understanding cosmic structure and evolution, but the vast data from modern surveys make manual classification impractical, while supervised learning methods remain constrained by the scarcity of labeled spectroscopic data. We aim to develop a scalable, label-efficient method for astronomical classification by leveraging semi-supervised learning (SSL) to overcome the limitations of fully supervised approaches. We propose a novel SSL fram…

Doppelg\"anger Method: Breaking Role Consistency in LLM Agent via Prompt-based Transferable Adversarial Attack
Daewon Kang, YeongHwan Shin, Doyeon Kim, Kyu-Hwan Jung, Meong Hi Son
https://arxiv.org/abs/2506.14539

Doppelgänger Method: Breaking Role Consistency in LLM Agent via Prompt-based Transferable Adversarial Attack
Since the advent of large language models, prompt engineering now enables the rapid, low-effort creation of diverse autonomous agents that are already in widespread use. Yet this convenience raises urgent concerns about the safety, robustness, and behavioral consistency of the underlying prompts, along with the pressing challenge of preventing those prompts from being exposed to user's attempts. In this paper, we propose the ''Doppelgänger method'' to demonstrate the risk of an agent being hij…

A generalized Hurwitz stability criterion via rectangular block Hankel matrices for nonmonic matrix polynomials
Xuzhou Zhan, Zixiang Ni
https://arxiv.org/abs/2508.14376 https://…

A generalized Hurwitz stability criterion via rectangular block Hankel matrices for nonmonic matrix polynomials
We develop a Hurwitz stability criterion for nonmonic matrix polynomials via column reduction, generalizing existing approaches constrained by the monic assumption, as well as Gantmacher's classical stability criterion via Markov parameters. Starting from redefining the associated Markov parameters through a column-wise adaptive splitting method, our framework constructs two structured matrices whose rectangular Hankel blocks are obtained via the extraction of these parameters. We establish an …

Considering the multi-time scale rolling optimization scheduling method of micro-energy network connected to electric vehicles
Hengyu Liu, Yanhong Luo, Congcong Wu, Yin Guan, Ahmed Lotfy Elrefai, Andreas Elombo, Si Li, Sahban Wael Saeed Alnaser, Mingyu Yan
https://arxiv.org/abs/2506.14112

Considering the multi-time scale rolling optimization scheduling method of micro-energy network connected to electric vehicles
The large-scale access of electric vehicles to the power grid not only provides flexible adjustment resources for the power system, but the temporal uncertainty and distribution complexity of their energy interaction pose significant challenges to the economy and robustness of the micro-energy network. In this paper, we propose a multi-time scale rolling optimization scheduling method for micro-energy networks considering the access of electric vehicles. In order to solve the problem of evaluat…