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

This work focuses on the active learning in video captioning.

Sentence scoring aims at measuring the likelihood score of a sentence and is widely usedinnatural language processing scenarios, likereranking, which isto select the best sentence from multiple candidates.

Determinantal point processes (DPPs) offer a popular diversification model in machine learning.

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.

We study online inverse linear optimization, also known as contextual recommendation, where a learner sequentially infers an agent's hidden objective vector from observed optimal actions over feasible sets that change over time. The learner aims to recommend actions that perform well under the agent's true objective, and the performance is measured by the regret, defined as the cumulative gap between the agent's optimal values and those achieved by the learner's recommended actions. Prior work has established a regret bound of $O(d\log T)$, as well as a finite but exponentially large bound of $\exp(O(d\log d))$, where $d$ is the dimension of the optimization problem and $T$ is the time horizon, while a regret lower bound of $Ω(d)$ is known (Gollapudi et al. 2021; Sakaue et al. 2025). Whether a finite regret bound polynomial in $d$ is achievable or not has remained an open question. We partially resolve this by showing that when the feasible sets are M-convex -- a broad class that includes matroids -- a finite regret bound of $O(d\log d)$ is possible. We achieve this by combining a structural characterization of optimal solutions on M-convex sets with a geometric volume argument. Moreover, we extend our approach to adversarially corrupted feedback in up to $C$ rounds. We obtain a regret bound of $O((C+1)d\log d)$ without prior knowledge of $C$, by monitoring directed graphs induced by the observed feedback to detect corruptions adaptively.