Tootfinder

"Motorcyclist on shopping trip arrested amid Labour’s crackdown on undocumented migrants"
https://www.theguardian.com/uk-news/2025/sep/02/motorcyclist-on-shopping-trip-arrested-london-amid-labours-crac…

#RIP Guy Rocher
https://www.lapresse.ca/actualites/2025-09-03/le-sociologue-guy-rocher-n-est-plus.php

Le sociologue Guy Rocher n’est plus
Figure marquante de la vie intellectuelle du Québec depuis la Révolution tranquille, le sociologue québécois Guy Rocher vient de rendre son dernier souffle.

Attributes as Textual Genes: Leveraging LLMs as Genetic Algorithm Simulators for Conditional Synthetic Data Generation
Guangzeng Han, Weisi Liu, Xiaolei Huang
https://arxiv.org/abs/2509.02040

Attributes as Textual Genes: Leveraging LLMs as Genetic Algorithm Simulators for Conditional Synthetic Data Generation
Large Language Models (LLMs) excel at generating synthetic data, but ensuring its quality and diversity remains challenging. We propose Genetic Prompt, a novel framework that combines genetic algorithms with LLMs to augment synthetic data generation. Our approach treats semantic text attributes as gene sequences and leverages the LLM to simulate crossover and mutation operations. This genetic process enhances data quality and diversity by creating novel attribute combinations, yielding syntheti…

Von der Leyen: US-Lieferstopp "Signal" für Europa
Der von der US-Regierung angekündigte Stopp bei der Lieferung einiger wichtiger Waffen für die Ukraine ist nach Worten von EU-Kommissionspräsidentin Ursula eine Aufforderung an Europa. "Das ist eine klare Botschaft, unsere eigene Unterstützung zu verstärken, unsere europäischen Verteidigungskapazitäten auszubauen", sagte die Kommissionschefin vor Jou…
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US-Militärhilfe für die Ukraine: Ein Lieferstopp mit Folgen
In einer Zeit, in der Russland so viele Raketen und Drohnen auf die Ukraine abfeuert wie noch nie, stoppen die USA zugesagte Waffenlieferungen. Kiew reagiert zunächst diplomatisch. Und Russland?

Leveraging SystemC-TLM-based Virtual Prototypes for Embedded Software Fuzzing
Chiara Ghinami, Jonas Winzer, Nils Bosbach, Lennart M. Reimann, Lukas J\"unger, Simon W\"orner, Rainer Leupers
https://arxiv.org/abs/2509.01318

Leveraging SystemC-TLM-based Virtual Prototypes for Embedded Software Fuzzing
SystemC-based virtual prototypes have emerged as widely adopted tools to test software ahead of hardware availability, reducing the time-to-market and improving software reliability. Recently, fuzzing has become a popular method for automated software testing due to its ability to quickly identify corner-case errors. However, its application to embedded software is still limited. Simulator tools can help bridge this gap by providing a more powerful and controlled execution environment for testi…

Unpacking Personal(?!) Health Informatics: An Investigation of Awareness, Understanding, And Leveraged Utility in India
Shyama Sastha Krishnamoorthy Srinivasan, Mohan Kumar, Pushpendra Singh
https://arxiv.org/abs/2509.01231

Unpacking Personal(?!) Health Informatics: An Investigation of Awareness, Understanding, And Leveraged Utility in India
Personal Health Informatics (PHI), which leverages digital tools and information systems to support health assessment and self-care, holds promise for empowering individuals and transforming healthcare delivery. However, barriers to its adoption remain underexplored in the Indian context. This study investigates PHI adoption among Indian users and stakeholders using a multi-method approach. An awareness survey (n = 87) examined the usage of wearables and general PHI engagement, followed by semi…

When wall slip wins over shear flow: A temperature-dependent Eyring slip law and a thermal multiscale model for diamond-like carbon lubricated by a polyalphaolefin oil
Stefan Peeters, Edder J. Garc\'ia, Franziska Stief, Thomas Reichenbach, Kerstin Falk, Gianpietro Moras, Michael Moseler
https://arxiv.org/abs/2509.00028

When wall slip wins over shear flow: A temperature-dependent Eyring slip law and a thermal multiscale model for diamond-like carbon lubricated by a polyalphaolefin oil
The quantitative description of lubricant flow in nanoscale channels is complicated by various finite-size effects that are not taken into account in conventional thermo-elasto-hydrodynamic lubrication (TEHL) models. One of these effects is wall slip, a phenomenon that has been extensively studied both theoretically and experimentally. The relationship between wall slip and thermal effects is intricate, and some authors debate whether the friction reduction observed in their experiments in the …

Leveraging Large Language Models for Spontaneous Speech-Based Suicide Risk Detection
Yifan Gao, Jiao Fu, Long Guo, Hong Liu
https://arxiv.org/abs/2507.00693

Leveraging Large Language Models for Spontaneous Speech-Based Suicide Risk Detection
Early identification of suicide risk is crucial for preventing suicidal behaviors. As a result, the identification and study of patterns and markers related to suicide risk have become a key focus of current research. In this paper, we present the results of our work in the 1st SpeechWellness Challenge (SW1), which aims to explore speech as a non-invasive and easily accessible mental health indicator for identifying adolescents at risk of suicide.Our approach leverages large language model (LLM…

Improving GANs by leveraging the quantum noise from real hardware
Hongni Jin, Kenneth M. Merz Jr
https://arxiv.org/abs/2507.01886 https://

Improving GANs by leveraging the quantum noise from real hardware
We propose a novel approach to generative adversarial networks (GANs) in which the standard i.i.d. Gaussian latent prior is replaced or hybridized with a quantum-correlated prior derived from measurements of a 16-qubit entangling circuit. Each latent sample is generated by grouping repeated shots per qubit into a binary fraction, applying the inverse Gaussian CDF to obtain a 16-dimensional Gaussian vector whose joint copula reflects genuine quantum entanglement, and then projecting into the hig…

An Automated Attack Investigation Approach Leveraging Threat-Knowledge-Augmented Large Language Models
Rujie Dai, Peizhuo Lv, Yujiang Gui, Qiujian Lv, Yuanyuan Qiao, Yan Wang, Degang Sun, Weiqing Huang, Yingjiu Li, XiaoFeng Wang
https://arxiv.org/abs/2509.01271

An Automated Attack Investigation Approach Leveraging Threat-Knowledge-Augmented Large Language Models
Advanced Persistent Threats (APTs) are prolonged, stealthy intrusions by skilled adversaries that compromise high-value systems to steal data or disrupt operations. Reconstructing complete attack chains from massive, heterogeneous logs is essential for effective attack investigation, yet existing methods suffer from poor platform generality, limited generalization to evolving tactics, and an inability to produce analyst-ready reports. Large Language Models (LLMs) offer strong semantic understan…