Tootfinder

( 🧵 1/N: started thinking I would just post a little amusing thing, and...it turned into a mini essay on knowledge, students, teachers, and education. Did not see that coming...)
I may have overestimated my upper-division CS students...
I'm teaching a junior/senior-level course on the foundations of AI. We're using Python, and there's a very simple starter homework assignment to get students to work through the logistics of submitting their code.
Yesterday I was…

Event: CSEE Institute for Teaching World Religions
I was invited to give a talk at a training session of educators on religious literacy and Islam. From the CSEE Institute for Teaching World Religions Website: Knowledge of the world's religious traditions is vitally important for communication and understanding in our current world. CSEE's institute brings together religious studies scholars and world religion teachers for thought-provoking presentations, activity…
https://husseinrashid.com/2026/07/06/event-csee-institute-for-teaching-world-religions.html/

Sometimes I do wonder if the only ethical thing let to do is to delete all your projects, and do your best to have them disappear from public space. Of course, that won't stop people from making your complicit in their acts of planetary destruction entirely, but at least you're trying…
And that's coming from me, a person who considers destroying knowledge and craft an unthinkable crime.
#NoAI #NoLLM

Learning Effective Soliton Dynamics from Scattering Data
Seth Minor, Vanja Dukic, David M. Bortz
https://arxiv.org/abs/2607.01545 https://arxiv.org/pdf/2607.01545 https://arxiv.org/html/2607.01545
arXiv:2607.01545v1 Announce Type: new
Abstract: The inverse scattering transform (IST) provides the standard theoretical framework for deriving soliton dynamics. Traditionally, such derivations have been of an analytical, rather than data-driven, nature. In this paper, we combine the conceptual framework of the IST with weak-form system identification methods to discover effective soliton dynamics directly from observed scattering data, without assuming prior knowledge of the scattering equations. Our method avoids parameterizing solitary waves via ad hoc curve-fitting by working in the scattering domain, yielding interpretable low-dimensional models that remain valid in perturbed and near-integrable regimes. We demonstrate the performance of the proposed approach on synthetic and experimental data governed by shallow-water equations of Korteweg--de Vries-type and recover models that are consistent with canonical IST theory.
toXiv_bot_toot

How Organizations Use AI: Evidence from ChatGPT
Aaron Chatterji, David Holtz, Neel Rakholia, Prasanna Tambe, Gawesha Weeratunga
https://arxiv.org/abs/2608.12236 https://arxiv.org/pdf/2608.12236 https://arxiv.org/html/2608.12236
arXiv:2608.12236v1 Announce Type: new
Abstract: We study how organizations use frontier generative AI by linking ChatGPT Enterprise account records to usage, worker roles, task classifications, and public-company financial data through March 2026. These linked data enable a privacy-preserving analysis of adoption, worker roles, and message-level tasks at scale: for instance, the worker-level sample we analyze at the six-month adoption horizon includes over 1,500 organizations and over 17 million messages. We document four facts about enterprise AI adoption and use. First, ChatGPT Enterprise usage has grown rapidly due to a combination of new firm adoption and growing intensity among existing adopters. Second, U.S.-based public company adoption is concentrated among larger, more valuable, and more R&D- and SG&A-intensive firms. Third, active use within adopting firms spans job functions and seniority levels, with especially high usage intensity among early-career workers. Fourth, ChatGPT Enterprise usage encompasses a broad range of knowledge work tasks, including writing, technical work, communication, and information synthesis. In aggregate, these results suggest that firms differ widely in the speed, breadth and purpose of their enterprise AI adoption, and that they are still actively learning how to integrate AI into organizational workflows.
toXiv_bot_toot

Buzz to Boom: Detecting Message Progression Vulnerabilities in Electron Applications via Segmented Directed Fuzzing
Jianjia Yu, Zhengyu Liu, Ziyang Li, Yu Sun, Yinzhi Cao
https://arxiv.org/abs/2607.20698 https://arxiv.org/pdf/2607.20698 https://arxiv.org/html/2607.20698
arXiv:2607.20698v1 Announce Type: new
Abstract: Electron is a popular framework for building cross-platform desktop applications using web technologies. Such applications consist of multiple processes with different privilege levels that communicate via message passing. When inter-process messages carry attacker-controlled inputs, they can propagate across processes and reach privileged APIs, e.g., command execution. Such a message propagation behavior is characterized as Message Progression Vulnerabilities (MPVs). The exploitation of MPVs is challenging because it often requires multiple steps, e.g., first arbitrary code execution in one process via message passing, and then command injection in another process using another message crafted in the first process. To our knowledge, existing works on Electron security only study unsafe configurations and malicious Document Object Model (DOM) content, i.e., they cannot detect or exploit these vulnerabilities that need to be triggered by complex cross-process exploits via message passing. We present Proton, a segmented directed fuzzing framework for detecting MPVs. Our key insight is to decompose end-to-end fuzzing into per-process segments along message-passing boundaries, where the goals of fuzzing each segment are either: (i) reaching a sink in the current process or (ii) propagating the payload to the next process, to enable the exploration of another process. In the second case, the messages seed the corpus of the next segment. Finally, Proton synthesizes crash inputs from each process to validate end-to-end exploits. We evaluate Proton against 589 real-world Electron applications, resulting in 23 zero-day MPVs. Among them, 22 lead to OS command execution, including projects with over 50k GitHub stars. We responsibly disclosed all findings. To date, we have received 13 acknowledgments, 11 fixes, and 11 CVEs, including a bug bounty from Vercel.
toXiv_bot_toot

RE: https://mastodon.social/@burger_jaap/116919455258692053
British companies can now also access the standard.
https://knowledge.bsigroup.com/products/road-vehicles-vehicle-to-grid-communication-interface-2nd-generation-network-layer-and-application-layer-requirements-3

Climate.us launches independent website for trusted climate information | Climate.us
Climate.us is not an official U.S. government website. It is an independent nonprofit project created to protect public access to climate knowledge and continue the plain-language, science-reviewed communication that made Climate.gov an essential resource for educators, journalists, decision-makers, and communities across the country.
https://www.climate.us/news-features/feed/climateus-launches-independent-website-trusted-climate-information