Tootfinder

Cross-Platform Scaling of Vision-Language-Action Models from Edge to Cloud GPUs
Amir Taherin, Juyi Lin, Arash Akbari, Arman Akbari, Pu Zhao, Weiwei Chen, David Kaeli, Yanzhi Wang
https://arxiv.org/abs/2509.11480

Cross-Platform Scaling of Vision-Language-Action Models from Edge to Cloud GPUs
Vision-Language-Action (VLA) models have emerged as powerful generalist policies for robotic control, yet their performance scaling across model architectures and hardware platforms, as well as their associated power budgets, remain poorly understood. This work presents an evaluation of five representative VLA models -- spanning state-of-the-art baselines and two newly proposed architectures -- targeting edge and datacenter GPU platforms. Using the LIBERO benchmark, we measure accuracy alongsid…

Fraud detection and risk assessment of online payment transactions on e-commerce platforms based on LLM and GCN frameworks
RuiHan Luo, Nanxi Wang, Xiaotong Zhu
https://arxiv.org/abs/2509.09928

Fraud detection and risk assessment of online payment transactions on e-commerce platforms based on LLM and GCN frameworks
With the rapid growth of e-commerce, online payment fraud has become increasingly complex, posing serious threats to financial security and consumer trust. Traditional detection methods often struggle to capture the intricate relational structures inherent in transactional data. This study presents a novel fraud detection framework that combines Large Language Models (LLM) with Graph Convolutional Networks (GCN) to effectively identify fraudulent activities in e-commerce online payment transact…

hello my #fuckaas friends in my phone
you know about mastodon. you may have heard of lemmy, the fedi equivalent of reddit, or pixelfed, the fedi equivalent of Instagram, or bookwyrm, the Goodreads equivalent.
but did you know there's like 8 bajillion other things in the fediverse that you can check out and start using in place of corporate-captured platforms? it's true!

delightful-fediverse-experience
A curated list of server applications supported on the ActivityPub Fediverse and related standards.

Devenez membre de #plaves, la plateforme de planification des transports en Suisse 🇨🇭
🔗 Lien d’inscription: https://docs.google.com/forms/d/e/1FAI

Anmeldeformular «Plattform Verkehrsplanung Schweiz» / Formulaire d’adhésion « Plateforme de planification des transports Suisse»
Die Plattform Verkehrsplanung Schweiz (plaves) ist ein unabhängiger Verein von Fachleuten, die sich für eine zukunftsgerichtete, nachhaltige und gesamtheitliche Verkehrsplanung einsetzen. Er ist aus dem Appell der Autobahn-Abstimmung entstanden. Auch in Zukunft wird die Verkehrsplanung massgeblich von politischen Entscheiden geprägt. Gemeinsam möchten wir deshalb die Mobilitätsdiskussion in der Schweiz mitgestalten und den Austausch zwischen Praxis, Forschung und Politik stärken. Mit Ih…

Cycling Along Euler Road
Dylan Wyrzykowski
https://arxiv.org/abs/2509.10460 https://arxiv.org/pdf/2509.10460

Cycling Along Euler Road
We introduce the notion of $P_λ$ points, which canonically parametrize points on the Euler line. This allows us to show that the Euler line of any inscribed $n$-polytope in $d$-dimensional Euclidean space arises from the Euler lines of its facets, beginning from the Euler line of a point in the plane. Furthermore, we situate $P_λ$ points in the literature of modern triangle centers.

Exploring and Exploiting the Resource Isolation Attack Surface of WebAssembly Containers
Zhaofeng Yu (Harbin Institute of Technology), Dongyang Zhan (Harbin Institute of Technology), Lin Ye (Harbin Institute of Technology), Haining Yu (Harbin Institute of Technology), Hongli Zhang (Harbin Institute of Technology), Zhihong Tian (Guangzhou University)
https://

Exploring and Exploiting the Resource Isolation Attack Surface of WebAssembly Containers
Recently, the WebAssembly (or Wasm) technology has been rapidly evolving, with many runtimes actively under development, providing cross-platform secure sandboxes for Wasm modules to run as portable containers. Compared with Docker, which isolates applications at the operating system level, Wasm runtimes provide more security mechanisms, such as linear memory, type checking, and protected call stacks. Although Wasm is designed with security in mind and considered to be a more secure container r…

Quantum Communication with Quantum Dots Beyond Telecom Wavelengths via Hollow-Core Fibers
Lorenzo Carosini, Francesco Giorgino, Patrik I. Sund, Lena M. Hansen, Rene R. Hamel, Lee A. Rozema, Francesco Poletti, Radan Slav\'ik, Philip Walther, Christopher Hilweg
https://arxiv.org/abs/2509.11889

Quantum Communication with Quantum Dots Beyond Telecom Wavelengths via Hollow-Core Fibers
Quantum dot single-photon sources are promising for quantum communication. Yet, the most advanced devices operate near 900 nm, where standard single-mode fibers experience significant losses. We address this by employing a hollow-core fiber engineered for low-loss transmission at quantum dot wavelengths, with measured loss of 0.65 dB/km and potentially as low as 0.12 dB/km near 934 nm. The fiber also supports strong classical signals at 1550 nm without adding Raman noise. Using this platform, w…

Large Language Model Prompt Datasets: An In-depth Analysis and Insights
Yuanming Zhang, Yan Lin, Arijit Khan, Huaiyu Wan
https://arxiv.org/abs/2510.09316 https://

Large Language Model Prompt Datasets: An In-depth Analysis and Insights
A prompt is a natural language instruction that defines a specific task for a large language model (LLM) and serves as the primary interface for human-LLM interaction. With the growing deployment of LLMs, diverse prompt datasets are emerging from platforms such as GitHub and social media. These datasets span a wide array of applications and content types, facilitating both broader LLM utilization and improved prompt engineering. In this work, we--for the first time--have compiled an extensive l…

Characterizing the Efficiency of Distributed Training: A Power, Performance, and Thermal Perspective
Seokjin Go, Joongun Park, Spandan More, Hanjiang Wu, Irene Wang, Aaron Jezghani, Tushar Krishna, Divya Mahajan
https://arxiv.org/abs/2509.10371

Characterizing the Efficiency of Distributed Training: A Power, Performance, and Thermal Perspective
The rapid scaling of Large Language Models (LLMs) has pushed training workloads far beyond the limits of single-node analysis, demanding a deeper understanding of how these models behave across large-scale, multi-GPU systems. In this paper, we present a comprehensive characterization of LLM training across diverse real-world workloads and hardware platforms, including NVIDIA H100/H200 and AMD MI250 GPUs. We analyze dense and sparse models under various parallelism strategies -- tensor, pipeline…

Human-AI Collaboration Increases Efficiency in Regulatory Writing
Umut Eser, Yael Gozin, L. Jay Stallons, Ari Caroline, Martin Preusse, Brandon Rice, Scott Wright, Andrew Robertson
https://arxiv.org/abs/2509.09738

Human-AI Collaboration Increases Efficiency in Regulatory Writing
Background: Investigational New Drug (IND) application preparation is time-intensive and expertise-dependent, slowing early clinical development. Objective: To evaluate whether a large language model (LLM) platform (AutoIND) can reduce first-draft composition time while maintaining document quality in regulatory submissions. Methods: Drafting times for IND nonclinical written summaries (eCTD modules 2.6.2, 2.6.4, 2.6.6) generated by AutoIND were directly recorded. For comparison, manual draftin…