Tootfinder

Objective clustering protocol for single-molecule data: A lifetime vs. intensity study
Michael Lovemore, Joshua Botha, Gonfa Assefa, Tjaart Kruger
https://arxiv.org/abs/2510.05665

Objective clustering protocol for single-molecule data: A lifetime vs. intensity study
Single-molecule spectroscopy (SMS) is an exceptionally sensitive technique, but its inherently limited photon budget produces noisy data that can readily lead to subjective analyses, fitting errors, and reduced statistical power, obscuring true subpopulations and their dynamics. Here, we present an unbiased, objective method to cluster two-dimensional single-molecule data and demonstrate it on fluorescence lifetime-intensity correlations. The clustering method is based on Gaussian mixture model…

route_views: Route Views AS graphs (1997-1998)
733 daily network snapshots denoting BGP traffic among autonomous systems (ASs) on the Internet, from the Oregon Route Views Project, spanning 8 November 1997 to 2 January 2000. Data collected by NLANR/MOAT.
This network has 4776 nodes and 9866 edges.
Tags: Technological, Communication, Unweighted, Temporal

Generalized Deep Multi-view Clustering via Causal Learning with Partially Aligned Cross-view Correspondence
Xihong Yang, Siwei Wang, Jiaqi Jin, Fangdi Wang, Tianrui Liu, Yueming Jin, Xinwang Liu, En Zhu, Kunlun He
https://arxiv.org/abs/2509.16022

Generalized Deep Multi-view Clustering via Causal Learning with Partially Aligned Cross-view Correspondence
Multi-view clustering (MVC) aims to explore the common clustering structure across multiple views. Many existing MVC methods heavily rely on the assumption of view consistency, where alignments for corresponding samples across different views are ordered in advance. However, real-world scenarios often present a challenge as only partial data is consistently aligned across different views, restricting the overall clustering performance. In this work, we consider the model performance decreasing …

Spirits in the Stacks: A Ghostly Tour at the Congregational Library & Archives
https://ift.tt/V9a4o0c
Rutgers Global Affairs Conference now VIRTUAL - new CFP deadline Rutgers University…
via Input 4 RELCFP

Stable magnetic fields and changing starspots on Vega -- An ultra-deep decadal survey at Pic du Midi and OHP
T. B\"ohm, M. Holschneider, P. Petit, F. Ligni\`eres, F. Paletou, C. P. Folsom, M. Rainer
https://arxiv.org/abs/2508.13348

Stable magnetic fields and changing starspots on Vega -- An ultra-deep decadal survey at Pic du Midi and OHP
Monitoring magnetic and activity variations in A- and B-type stars with ultra-weak magnetic fields is essential to understand the origin and evolution of these fields in this region of the HR diagram, with Vega standing as the prototype of this category. We collected high-resolution spectroscopic and spectropolarimetric data with SOPHIE (OHP, 2018) and with NARVAL/NEO-NARVAL (TBL, 2018, 2023, and 2024), yielding a total of 13108 individual spectra. Magnetic field maps were reconstructed using t…

Generative Diffusion Contrastive Network for Multi-View Clustering
Jian Zhu, Xin Zou, Xi Wang, Ning Zhang, Bian Wu, Yao Yang, Ying Zhou, Lingfang Zeng, Chang Tang, Cheng Luo
https://arxiv.org/abs/2509.09527

Generative Diffusion Contrastive Network for Multi-View Clustering
In recent years, Multi-View Clustering (MVC) has been significantly advanced under the influence of deep learning. By integrating heterogeneous data from multiple views, MVC enhances clustering analysis, making multi-view fusion critical to clustering performance. However, there is a problem of low-quality data in multi-view fusion. This problem primarily arises from two reasons: 1) Certain views are contaminated by noisy data. 2) Some views suffer from missing data. This paper proposes a novel…

Scalable Engine and the Performance of Different LLM Models in a SLURM based HPC architecture
Anderson de Lima Luiz, Shubham Vijay Kurlekar, Munir Georges
https://arxiv.org/abs/2508.17814

Scalable Engine and the Performance of Different LLM Models in a SLURM based HPC architecture
This work elaborates on a High performance computing (HPC) architecture based on Simple Linux Utility for Resource Management (SLURM) [1] for deploying heterogeneous Large Language Models (LLMs) into a scalable inference engine. Dynamic resource scheduling and seamless integration of containerized microservices have been leveraged herein to manage CPU, GPU, and memory allocations efficiently in multi-node clusters. Extensive experiments, using Llama 3.2 (1B and 3B parameters) [2] and Llama 3.1 …

Process-Driven Visual Analysis of Cybersecurity Capture the Flag Exercises
Radek O\v{s}lej\v{s}ek, Radoslav Chudovsk\'y, Martin Macak
https://arxiv.org/abs/2509.15589 https:…

Process-Driven Visual Analysis of Cybersecurity Capture the Flag Exercises
Hands-on training sessions become a standard way to develop and increase knowledge in cybersecurity. As practical cybersecurity exercises are strongly process-oriented with knowledge-intensive processes, process mining techniques and models can help enhance learning analytics tools. The design of our open-source analytical dashboard is backed by guidelines for visualizing multivariate networks complemented with temporal views and clustering. The design aligns with the requirements for post-trai…

Breaking Reward Collapse: Adaptive Reinforcement for Open-ended Medical Reasoning with Enhanced Semantic Discrimination
Yizhou Liu, Jingwei Wei, Zizhi Chen, Minghao Han, Xukun Zhang, Keliang Liu, Lihua Zhang
https://arxiv.org/abs/2508.12957

Breaking Reward Collapse: Adaptive Reinforcement for Open-ended Medical Reasoning with Enhanced Semantic Discrimination
Reinforcement learning (RL) with rule-based rewards has demonstrated strong potential in enhancing the reasoning and generalization capabilities of vision-language models (VLMs) and large language models (LLMs), while reducing computational overhead. However, its application in medical imaging remains underexplored. Existing reinforcement fine-tuning (RFT) approaches in this domain primarily target closed-ended visual question answering (VQA), limiting their applicability to real-world clinical…