ICE is spending millions on shock gloves designed to overpower subjects through intense, localized pain. With the revelation that ICE will spend up to $20 million by March on gloves that can deliver painful electric shocks to subdue an individual, according to a notice published earlier this week by the Department of Homeland Security, it's worth looking at how this apparel, designated as a nonlethal tool for law enforcement, actually works. These shock gloves go by the unsubtle acronym of GLOVE (Generated Low Output Voltage Emitter) and are manufactured by Compliant Technologies, based in Lexington, Kentucky. The GLOVE, previously used in jails and police departments in the US, looks and functions like a normal pair of patrol gloves until officers press a switch to activate an electrical mode. Classified as a CD3 (conductive distraction and de-escalation device), the gloves don't work like stun guns or Tasers, which shoot out probes or use high voltages to cause neuromuscular incapacitation that overrides the central nervous system.

'Project Hail Mary' may be fiction, but this list could still come in handy. An artist's impression of a theoretical planet orbiting a redder star, which could cause microbes and plants on the planet's surface to reflect very different colors from Earth's green forests. Breakthroughs, discoveries, and DIY tips sent six days a week. Life on Earth is a precious thing, especially given what astronomers know about the visible universe. Although researchers have so far identified over 6,000 exoplanets beyond our solar system, only a handful of them be suitable for human visitors.

Algorithmic stability is a key characteristic of an algorithm regarding its sensitivity to perturbations of input samples. In this paper,we propose an innovativeunsupervised feature selection algorithm attaining this stability with provable guarantees.

Over 11 years, citizen scientists collected billions of data signals for the SETI@home project. Breakthroughs, discoveries, and DIY tips sent six days a week. After reviewing almost 30 years of signals, University of California Berkeley researchers have identified 100 mysterious, deep-space radio blips they want to review for signs of extraterrestrial life . And they couldn't have done it without 11 years of volunteer work from millions of PC owners around the world. Even with today's advanced computers, the world's most complex data problems can't be solved by a single machine.

Time series forecasting has applications across domains and industries, especially in healthcare, but the technical expertise required to analyze data, build models, and interpret results can be a barrier to using these techniques. This article presents a web platform that makes the process of analyzing and plotting data, training forecasting models, and interpreting and viewing results accessible to researchers and clinicians. Users can upload data and generate plots to showcase their variables and the relationships between them. The platform supports multiple forecasting models and training techniques which are highly customizable according to the user's needs. Additionally, recommendations and explanations can be generated from a large language model that can help the user choose appropriate parameters for their data and understand the results for each model. The goal is to integrate this platform into learning health systems for continuous data collection and inference from clinical pipelines.

I went to Sonoma for a NASCAR race and found out heat is the bad guy, fluids are the secret weapon, and Valvoline's engineers are basically mad scientists with pit passes. We may earn revenue from the products available on this page and participate in affiliate programs. A tire is making decent progress coming out of a turn at Sonoma Raceway --except for the fact it's no longer attached to Cody Ware's No. 51 Ford Mustang. Crowds gasp, cars swerve, and the wheel menacingly rolls off, then on, and then off the track again before it finally collapses. I've never related to a tire more.

Correspondence should be addressed to: qiang.cheng@uky.edu. The architecture of our algorithm is shown in Figure 1. For the training based on Eq. (2) of the main text, in each iteration of backpropagation, After training, only the trained selector is used to select features and do reconstruction during testing time. In Eq. (2) of the main text, the second term helps obtain During testing time, only the trained sub-NN is used to select features and do reconstruction. It has 5, 744 samples and 561 features.

Algorithmic stability is a key characteristic of an algorithm regarding its sensitivity to perturbations of input samples. In this paper, we propose an innovative unsupervised feature selection algorithm attaining this stability with provable guarantees.

In this paper, we present a novel framework for enhancing model interpretability by integrating heatmaps produced separately by ResNet and a restructured 2D Transformer with globally weighted input saliency. We address the critical problem of spatial-temporal misalignment in existing interpretability methods, where convolutional networks fail to capture global context and Transformers lack localized precision - a limitation that impedes actionable insights in safety-critical domains like healthcare and industrial monitoring. Our method merges gradient-weighted activation maps (ResNet) and Transformer attention rollout into a unified visualization, achieving full spatial-temporal alignment while preserving real-time performance. Empirical evaluations on clinical (ECG arrhythmia detection) and industrial (energy consumption prediction) datasets demonstrate significant improvements: the hybrid framework achieves 94.1% accuracy (F1 0.93) on the PhysioNet dataset and reduces regression error to RMSE = 0.28 kWh (R2 = 0.95) on the UCI Energy Appliance dataset-outperforming standalone ResNet, Transformer, and InceptionTime baselines by 3.8-12.4%. An NLP module translates fused heatmaps into domain-specific narratives (e.g., "Elevated ST-segment between 2-4 seconds suggests myocardial ischemia"), validated via BLEU-4 (0.586) and ROUGE-L (0.650) scores. By formalizing interpretability as causal fidelity and spatial-temporal alignment, our approach bridges the gap between technical outputs and stakeholder understanding, offering a scalable solution for transparent, time-aware decision-making.