Tootfinder

This is a really good question from @…. The first thing I keep telling everyone in other cities who wonders how they can help, what they can do:
Get organized NOW. Meet your neighbors NOW. Get your neighbors set up with secure messaging NOW. Form multiple hyper-local neighborhood social groups NOW. Organize a block picnic or community craft night or repair workshop or whatever NOW. Get contact info for the human beings who physically show up for neighborhood events NOW.
Form all those local connections ASAP, so that they’re there when you need them. The hardest lines of communication to establish will be the ones with the people closest to you. If you get local lines open in advance, you’ll be in a far, far better place.
https://infosec.exchange/@mathaetaes/115731104851949153

Contextuality-based quantum key distribution with deterministic single-photon sources
Yu Meng, Debashis Saha, Mikkel Thorbj{\o}rn Mikkelsen, Clara Henke, Ying Wang, Nikolai Bart, Arne Ludwig, Ad\'an Cabello, Leonardo Midolo
https://arxiv.org/abs/2510.12761

Contextuality-based quantum key distribution with deterministic single-photon sources
Photons are central to quantum technologies, with photonic qubits offering a promising platform for quantum communication. Semiconductor quantum dots stand out for their ability to generate single photons on demand, a key capability for enabling long-distance quantum networks. In this work, we utilize high-purity single-photon sources based on self-assembled InAs(Ga)As quantum dots as quantum information carriers. We demonstrate that such on-demand single photons can generate quantum contextual…

DCP: Addressing Input Dynamism In Long-Context Training via Dynamic Context Parallelism
Chenyu Jiang, Zhenkun Cai, Ye Tian, Zhen Jia, Yida Wang, Chuan Wu
https://arxiv.org/abs/2510.10620

DCP: Addressing Input Dynamism In Long-Context Training via Dynamic Context Parallelism
Context parallelism has emerged as a key technique to support long-context training, a growing trend in generative AI for modern large models. However, existing context parallel methods rely on static parallelization configurations that overlook the dynamic nature of training data, specifically, the variability in sequence lengths and token relationships (i.e., attention patterns) across samples. As a result, these methods often suffer from unnecessary communication overhead and imbalanced comp…

Power Cable Radiation: A Novel Approach to Underground Mining Connectivity
Siphiwe Shandu, Thabiso Moropa, Alain R. Ndjiongue
https://arxiv.org/abs/2510.09573 https://

Power Cable Radiation: A Novel Approach to Underground Mining Connectivity
This letter investigates contactless power line communications (CPLC) for underground mining by modeling power wires as long-wire antennas. A system-level framework is developed, comprising a cascade of RF and power line channels. The model accounts for multipath propagation, frequency-dependent attenuation, and Rician fading. Simulations from 1-20 GHz reveal that the length of the wire significantly affects radiation, directivity, and input impedance. The findings show that CPLC transmits elec…

West Coast Health Alliance,
WA Dept of Health,
and Leading National Medical Organizations
Continue to Recommend Hepatitis B Vaccination for Newborns
WCHA disagrees with CDC’s Advisory Committee’s change to decades-long vaccine recommendation
that has reduced pediatric hepatitis B infections by 99 percent

Job insecurity and equilibrium determinacy in a rational expectations, New Keynesian model with asymmetric information. A theoretical analysis
Luca Vota, Luisa Errichiello
https://arxiv.org/abs/2510.11125

Job insecurity and equilibrium determinacy in a rational expectations, New Keynesian model with asymmetric information. A theoretical analysis
Despite the importance of this variable in the macroeconomic context, current research on job insecurity remains mainly confined to its non-systemic dimension. The research aim of this paper is to identify the short-run and long-run macroeconomic determinants of job insecurity in the presence of asymmetric information between public and private agents, informative shocks, and different degrees of institutional communication transparency. To accomplish this goal, a small-scale, rational expectat…

Uncertainty as Feature Gaps: Epistemic Uncertainty Quantification of LLMs in Contextual Question-Answering
Yavuz Bakman, Sungmin Kang, Zhiqi Huang, Duygu Nur Yaldiz, Catarina G. Bel\'em, Chenyang Zhu, Anoop Kumar, Alfy Samuel, Salman Avestimehr, Daben Liu, Sai Praneeth Karimireddy
https://arxiv.org/abs/2510.02671

Uncertainty as Feature Gaps: Epistemic Uncertainty Quantification of LLMs in Contextual Question-Answering
Uncertainty Quantification (UQ) research has primarily focused on closed-book factual question answering (QA), while contextual QA remains unexplored, despite its importance in real-world applications. In this work, we focus on UQ for the contextual QA task and propose a theoretically grounded approach to quantify epistemic uncertainty. We begin by introducing a task-agnostic, token-level uncertainty measure defined as the cross-entropy between the predictive distribution of the given model and…

Orders in Chaos: Enhancing Large-Scale MoE LLM Serving with Data Movement Forecasting
Zhongkai Yu, Yue Guan, Zihao Yu, Chenyang Zhou, Shuyi Pei, Yangwook Kang, Yufei Ding, Po-An Tsai
https://arxiv.org/abs/2510.05497

Orders in Chaos: Enhancing Large-Scale MoE LLM Serving with Data Movement Forecasting
Large Language Models (LLMs) with Mixture of Experts (MoE) architectures achieve remarkable performance improvements, but their random expert selection mechanism introduces significant data movement overhead that becomes the dominant bottleneck in multi-unit serving systems. To forecast the patterns underlying this data movement, we conduct comprehensive data-movement-centric profiling across three state-of-the-art large-scale MoE models (200B- 671B) using over 24,000 requests spanning diverse …

AMAS: Adaptively Determining Communication Topology for LLM-based Multi-Agent System
Hui Yi Leong, Yuheng Li, Yuqing Wu, Wenwen Ouyang, Wei Zhu, Jiechao Gao
https://arxiv.org/abs/2510.01617

AMAS: Adaptively Determining Communication Topology for LLM-based Multi-Agent System
Although large language models (LLMs) have revolutionized natural language processing capabilities, their practical implementation as autonomous multi-agent systems (MAS) for industrial problem-solving encounters persistent barriers. Conventional MAS architectures are fundamentally restricted by inflexible, hand-crafted graph topologies that lack contextual responsiveness, resulting in diminished efficacy across varied academic and commercial workloads. To surmount these constraints, we introdu…

GUI-KV: Efficient GUI Agents via KV Cache with Spatio-Temporal Awareness
Kung-Hsiang Huang, Haoyi Qiu, Yutong Dai, Caiming Xiong, Chien-Sheng Wu
https://arxiv.org/abs/2510.00536

GUI-KV: Efficient GUI Agents via KV Cache with Spatio-Temporal Awareness
Graphical user interface (GUI) agents built on vision-language models have emerged as a promising approach to automate human-computer workflows. However, they also face the inefficiency challenge as they process long sequences of high-resolution screenshots and solving long-horizon tasks, making inference slow, costly and memory-bound. While key-value (KV) caching can mitigate this, storing the full cache is prohibitive for image-heavy contexts. Existing cache-compression methods are sub-optima…