Among the state-of-the-art K-means approximation methods are the algorithms of Kanungo et al. and Ahmadian et al [

Neural Information Processing Systems http://nips.cc/

The company announced the Even G2 smart glasses, sporting a bigger display in a lighter frame, alongside the R1 smart ring, which can control the display on the lenses. As Meta's Ray-Ban smart glasses continue to turn your face into a computer, with a camera and speaker, Even Realities is doubling down on a design that eschews those components. Today, the company announced the Even G2 smart glasses, alongside the Even R1, its first-ever smart ring that controls the display of the G2. The Even G2 glasses don't look wholly different from the original G1 that debuted last summer, which is a good thing, because these are still among the sharpest-looking smart glasses on the market. At a closed-doors briefing a few weeks ago, Even Realities CEO Will Wang said the company didn't advertise its first product much, as it wanted to test it in the market and receive valuable feedback, while also working on expanding its retail presence.

Is a Robot Vacuum Worth It? It's not for everyone, but sometimes my robot vacuum is my only friend. Every single day--weekend, weekday, rain or shine--whichever robot vacuum I'm currently testing starts running at 9 am. I heave a sigh of relief and continue with whatever else I was doing, content that at least f*cking chore in my house is getting done. When I first started testing robot vacuums eight years ago, it sometimes seemed like more trouble than it was worth. I cleaned up the floor .

Meta's messaging app offers free access to the AI chatbot, but only until January 2026. There are plenty of places you can get access to ChatGPT: Not just in the official apps for the web and mobile devices, but also through Copilot from Microsoft, and in Apple's Siri assistant ... and inside the messaging app WhatsApp . WhatsApp, run by Facebook developer Meta, is available free of charge on the web, and on Android and iOS . It's used by billions of people worldwide, which helps to explain why OpenAI has made ChatGPT available here as well as everywhere else. Unfortunately, OpenAI will be pulling free access to its chatbot within WhatsApp on January 15, 2026.

Welcome to Big Tech's'Age of Extraction' In his new book, antitrust scholar and former White House adviser Tim Wu argues that tech giants are bleeding you dry--and lays out a plan to stop them. Growing up in Toronto, Tim Wu had a classmate who was the progeny of Communist parents. His name was Cory Doctorow. Yes, the same guy who just published a book about enshittification . Though they shared a general world view, the boyhood pals also had arguments, with Wu typically taking a less radical stance than his buddy.

The online e-commerce environment is complicated and ever changing, so it requires the coupons allocation policy learning can quickly adapt to the changes of the company's business strategy. Unfortunately, existing studies with a huge computation overhead can hardly satisfy the requirements of real-time and fast-response in the real world.

Large language models (LLMs) have been widely deployed as autonomous agents capable of following user instructions and making decisions in real-world applications. Previous studies have made notable progress in benchmarking the instruction following capabilities of LLMs in general domains, with a primary focus on their inherent commonsense knowledge. Recently, LLMs have been increasingly deployed as domain-oriented agents, which rely on domain-oriented guidelines that may conflict with their commonsense knowledge. These guidelines exhibit two key characteristics: they consist of a wide range of domain-oriented rules and are subject to frequent updates. Despite these challenges, the absence of comprehensive benchmarks for evaluating the domain-oriented guideline following capabilities of LLMs presents a significant obstacle to their effective assessment and further development. In this paper, we introduce GuideBench, a comprehensive benchmark designed to evaluate guideline following performance of LLMs. GuideBench evaluates LLMs on three critical aspects: (i) adherence to diverse rules, (ii) robustness to rule updates, and (iii) alignment with human preferences. Experimental results on a range of LLMs indicate substantial opportunities for improving their ability to follow domain-oriented guidelines.

In the near future, Structural Health Monitoring (SHM) technologies will be capable of overcoming the drawbacks in the current maintenance and life-cycle management paradigms, namely: cost, increased downtime, less-than-optimal safety management paradigm and the limited applicability of fully-autonomous operations. In the context of SHM, one of the most challenging tasks is damage quantification. Current methods face accuracy and/or robustness issues when it comes to varying operating and environmental conditions. In addition, the damage/no-damage paradigm of current frameworks does not offer much information to maintainers on the ground for proper decision-making. In this study, a novel structural damage quantification framework is proposed based on widely-used Damage Indices (DIs) and Gaussian Process Regression Models (GPRMs). The novelty lies in calculating the probability of an incoming test DI point originating from a specific state, which allows for probability-educated decision-making. This framework is applied to three test cases: a Carbon Fiber-Reinforced Plastic (CFRP) coupon with attached weights as simulated damage, an aluminum coupon with a notch, and an aluminum coupon with attached weights as simulated damage under varying loading states. The state prediction method presented herein is applied to single-state quantification in the first two test cases, as well as the third one assuming the loading state is known. Finally, the proposed method is applied to the third test case assuming neither the damage size nor the load is known in order to predict both simultaneously from incoming DI test points. In applying this framework, two forms of GPRMs (standard and variational heteroscedastic) are used in order to critically assess their performances with respect to the three test cases.