We demonstrate that this assumption imposes performance limitations in particular on difficult problem instances.

Humans aren't built for the cold, but have survived frigid temperatures in some amazing cases. Breakthroughs, discoveries, and DIY tips sent six days a week. Winter is not for the faint of heart. In New York City, skyscrapers turn Manhattan into a series of freezing wind tunnels. In Sapporo, Japan, the snowfall is almost 200 inches each winter. Even so, humans have developed plenty of clever ways to wait out the cold. But what would happen if instead of bundling up inside with a hot chocolate, you were left in the frigid cold--just how cold can humans get and recover?

Team Mirai leader Takahiro Anno stumps in Tokyo's Shibuya Ward on Jan. 27, the first day of campaigning for the Lower House election slated for Sunday. A small, 9-month-old party that has only one seat in the Upper House may gain as many seats as decadesold peers such as the Japanese Communist Party (JCP) in Sunday's Lower House election with its unconventional campaign pledges to change politics and the government through digital technology. A weekend poll conducted by the Asahi Shimbun showed Team Mirai could win up to 10 seats under the proportional representation system, more than the JCP's nine seats and Reiwa Shinsengumi's six. The party didn't have any seats in the Lower House before its dissolution. The party's founder and leader is a 35-year-old artificial intelligence engineer behind two AI startups -- Takahiro Anno. He had been working on societal reform through digital transformation when he pivoted from business to politics with the launch of Team Mirai last May.

BERLIN - German technology firm DeepL, known for its artificial intelligence-powered translation software, plans to release a Japanese-language version of its real-time interpretation software by the end of this year, a senior company official has said. The age of machine interpretation has arrived, said Leonardo Doin, head of engineering and research for real-time voice translation service DeepL Voice, in a recent interview. You can just wear an earpiece and ... you can just hear it (foreign-language speech) in your language anytime, Doin said. The interpretation software will integrate DeepL's speech recognition and machine translation technologies, and speech synthesis technology that mimics the tones of the speakers' voices. It will be able to handle multiple languages and speakers, he said, with the software's use in online meetings of multinational companies in mind. DeepL plans to roll out the software on smartphones as well.

The USS Abraham Lincoln aircraft carrier, is seen at Naval Air Station North Island in San Diego last August. President Donald Trump reiterated that the U.S. and Iran are maintaining diplomatic talks, even after an earlier skirmish in the Arabian Sea spooked oil markets amid heightened tensions between the two countries. We are negotiating with them right now" and they'd like to do something," Trump told reporters at the White House on Tuesday. They had a chance to do something a while ago and it didn't work out, and we did Midnight Hammer," he said, referring to the June U.S. military strike in Iran. Earlier Tuesday, a U.S. F-35C warplane shot down a drone in self-defense as the unmanned aircraft aggressively approached" the USS Abraham Lincoln aircraft carrier with unclear intent," U.S. Central Command said in a statement. The command said no American service members were harmed and no U.S. equipment was damaged.

Elon Musk attends the 56th annual World Economic Forum meeting in Davos, Switzerland, on Jan. 22. PARIS - French police raided the offices of Elon Musk's social media network X on Tuesday, and prosecutors ordered the tech billionaire to face questions in a widening investigation, amid growing scrutiny of the platform by authorities across Europe. The raid by the Paris prosecutor's cybercrime unit and Musk's summoning -- which could further increase tensions between Europe and the U.S. over Big Tech and free speech -- are linked to a yearlong investigation into suspected abuse of algorithms and fraudulent data extraction by X or its executives. Britain's privacy watchdog, meanwhile, also kicked off a formal investigation into Musk's artificial-intelligence chatbot Grok over the processing of personal data and its potential to produce harmful sexual images and video content. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

As robots become more adaptable, responsive, and capable of interacting with humans, the design of effective human-robot collaboration becomes critical. Yet, this design process is typically led by monodisciplinary approaches, often overlooking interdisciplinary knowledge and the experiential knowledge of workers who will ultimately share tasks with these systems. To address this gap, we introduce the robotic capabilities framework, a vocabulary that enables transdisciplinary collaborations to meaningfully shape the future of work when robotic systems are integrated into the workplace. Rather than focusing on the internal workings of robots, the framework centers discussion on high-level capabilities, supporting dialogue around which elements of a task should remain human-led and which can be delegated to robots. We developed the framework through reflexive and iterative processes, and applied it in two distinct settings: by engaging roboticists in describing existing commercial robots using its vocabulary, and through a design activity with students working on robotics-related projects. The framework emerges as an intermediate-level knowledge artifact and a boundary object that bridges technical and experiential domains, guiding designers, empowering workers, and contributing to more just and collaborative futures of work.

Abstract--The deployment of autonomous AI agents in Internet of Things (IoT) energy systems requires decision-making mechanisms that remain robust, efficient, and trustworthy under real-time constraints and imperfect monitoring. While reinforcement learning enables adaptive prosumer behaviors, ensuring economic consistency and preventing strategic manipulation remain open challenges, particularly when sensing noise or partial observability degrades the operator's ability to verify actions. This paper introduces a trust-enforcement framework for IoT energy trading that combines an α-approximate Vick-rey-Clarke-Groves (VCG) double auction with an immediate one-shot penalty. Unlike reputation-or history-based approaches, the proposed mechanism restores truthful reporting within a single round, even when allocation accuracy is approximate and monitoring is noisy. We theoretically characterize the incentive gap induced by approximation and derive a penalty threshold that guarantees truthful bidding under bounded sensing errors. T o evaluate learning-enabled prosumers, we embed the mechanism into a multi-agent reinforcement learning environment reflecting stochastic generation, dynamic loads, and heterogeneous trading opportunities. Experiments show that improved allocation accuracy consistently reduces deviation incentives, the required penalty matches analytical predictions, and learned bidding behaviors remain stable and interpretable despite imperfect monitoring. These results demonstrate that lightweight penalty designs can reliably align strategic IoT agents with socially efficient energy-trading outcomes. The rapid expansion of the Internet of Things (IoT) has created large-scale networks of heterogeneous sensors, distributed devices, and autonomous software agents that must jointly perceive, reason, and act in dynamic cyber-physical environments. X. Shao and R. Shimizu are with the Department of Electrical and Electronic Information Engineering, Toyohashi University of Technology, Toyohashi, Aichi 441-8580, Japan (e-mail: xun.shao@tut.jp).

We propose a novel medical image classification method that integrates dual-model weight selection with self-knowledge distillation (SKD). In real-world medical settings, deploying large-scale models is often limited by computational resource constraints, which pose significant challenges for their practical implementation. Thus, developing lightweight models that achieve comparable performance to large-scale models while maintaining computational efficiency is crucial. To address this, we employ a dual-model weight selection strategy that initializes two lightweight models with weights derived from a large pretrained model, enabling effective knowledge transfer. Next, SKD is applied to these selected models, allowing the use of a broad range of initial weight configurations without imposing additional excessive computational cost, followed by fine-tuning for the target classification tasks. By combining dual-model weight selection with self-knowledge distillation, our method overcomes the limitations of conventional approaches, which often fail to retain critical information in compact models. Extensive experiments on publicly available datasets-chest X-ray images, lung computed tomography scans, and brain magnetic resonance imaging scans-demonstrate the superior performance and robustness of our approach compared to existing methods.

Building training-ready multi-hop question answering (QA) datasets that truly stress a model's retrieval and reasoning abilities remains highly challenging recently. While there have been a few recent evaluation datasets that capture the characteristics of hard-to-search but easy-to-verify problems -- requiring the integration of ambiguous, indirect, and cross-domain cues -- these data resources remain scarce and are mostly designed for evaluation, making them unsuitable for supervised fine-tuning (SFT) or reinforcement learning (RL). Meanwhile, manually curating non-trivially retrievable questions -- where answers cannot be found through a single direct query but instead require multi-hop reasoning over oblique and loosely connected evidence -- incurs prohibitive human costs and fails to scale, creating a critical data bottleneck for training high-capability retrieval-and-reasoning agents. To address this, we present BMGQ, a bottom-up automated method for generating high-difficulty, training-ready multi-hop questions from semi-structured knowledge sources. The BMGQ system (i) grows diverse, logically labeled evidence clusters through Natural Language Inference (NLI)-based relation typing and diversity-aware expansion; (ii) applies reverse question construction to compose oblique cues so that isolated signals are underinformative but their combination uniquely identifies the target entity; and (iii) enforces quality with a two-step evaluation pipeline that combines multi-model consensus filtering with structured constraint decomposition and evidence-based matching. The result is a scalable process that yields complex, retrieval-resistant yet verifiable questions suitable for SFT/RL training as well as challenging evaluation, substantially reducing human curation effort while preserving the difficulty profile of strong evaluation benchmarks.