Goto

Collaborating Authors

 Industry


Eulerian Neural Network Informed by Chemical Transport for Air Quality Forecasting

Neural Information Processing Systems

Air pollution remains one of the most critical environmental challenges globally, posing severe threats to public health, ecological sustainability, and climate governance. While existing physics-based and data-driven models have made progress in air quality forecasting, they often struggle to jointly capture the complex spatiotemporal dynamics and ensure spatial continuity of pollutant distributions. In this study, we introduce CTENet, a novel chemical transport deep learning model that embeds the Advection-Diffusion-Reaction equation into a Physics-Informed Neural Network (PINN) framework using an Eulerian representation to model the spatiotemporal evolution of pollutants. Extensive experiments on two real-world datasets demonstrate that CTENet consistently outperforms state-of-the-art (SOTA) baselines, achieving a remarkable RMSE improvement of 45.8% on the USA dataset and 21.0% on the China dataset.


Anthropic v. OpenAI: Behind the bitter battle for the future of AI

The Japan Times

The tension between OpenAI CEO Sam Altman and Anthropic CEO Dario Amodei is the driving force in today's biggest technological revolution. SAN FRANCISCO/NEW YORK - If not for the intense rivalry between Anthropic and OpenAI, the generative AI boom might not have arrived so quickly. In late 2022, OpenAI caught wind that Anthropic was working on an AI-powered chatbot. OpenAI CEO Sam Altman immediately directed employees to fast-track a competing product, four people familiar with the matter said. Two weeks later, the company released ChatGPT, sparking a technological revolution that promises to overhaul the global economy and the way humans interact.


What we know about US sea drone used in helicopter crew rescue mission

BBC News

A sea drone was used to save two crew members of a downed US army helicopter off the coast of Oman earlier this week, according to the US military - making it the first publicly known instance of an unmanned vessel being used to conduct a rescue mission. President Donald Trump said the apache helicopter was shot down by Iran near the Strait of Hormuz - the dangerous waterway which has been largely blocked off to shipping since the start of the Iran war. The two soldiers were safely rescued within approximately two hours and are in stable condition, US Central Command (Centcom) said. BBC Verify has examined what we know about the drone boat and how the mission took place. What is the US sea drone?


Memory-Enhanced Neural Solvers for Routing Problems

Neural Information Processing Systems

Routing Problems are central to many real-world applications, yet remain challenging due to their (NP-)hard nature. Amongst existing approaches, heuristics often offer the best trade-off between quality and scalability, making them suitable for industrial use. While Reinforcement Learning (RL) offers a flexible framework for designing heuristics, its adoption over handcrafted heuristics remains incomplete. Existing learned methods still lack the ability to adapt to specific instances and fully leverage the available computational budget. Current best methods either rely on a collection of pre-trained policies, or on RL fine-tuning; hence failing to fully utilize newly available information within the constraints of the budget. In response, we present MEMENTO, an approach that leverages memory to improve the search of neural solvers at inference. MEMENTO updates the action distribution dynamically based on the outcome of previous decisions. We validate its effectiveness on Traveling Salesman and Capacitated Vehicle Routing problems, demonstrating its superiority over tree-search and policy-gradient fine-tuning; and showing that it can be zero-shot combined with diversity-based solvers. We successfully train all RL auto-regressive solvers on large instances, and verify MEMENTO's scalability and data-efficiency: pushing the state-of-the-art on 11 out of 12 evaluated tasks.


OrthoLoC: UAV 6-DoF Localization and Calibration Using Orthographic Geodata

Neural Information Processing Systems

Accurate visual localization from aerial views is a fundamental problem with applications in mapping, large-area inspection, and search-and-rescue operations. In many scenarios, these systems require high-precision localization while operating with limited resources (e.g., no internet connection or GNSS/GPS support), making large image databases or heavy 3D models impractical. Surprisingly, little attention has been given to leveraging orthographic geodata as an alternative paradigm, which is lightweight and increasingly available through free releases by governmental authorities (e.g., the European Union). To fill this gap, we propose OrthoLoC, the first large-scale dataset comprising 16,425 UAV images from Germany and the United States with multiple modalities.


AI sparks alarm in China with call to protect worker rights

The Japan Times

As AI spreads across workplaces, China is also having to contend with chronic weakness in the jobs market. China's rapid adoption of artificial intelligence in the workplace has prompted an unusually blunt call from a state-run newspaper to protect labor rights, as Beijing considers how to contain risks posed by the new technology. In an editorial published on Thursday, the Workers' Daily -- the official mouthpiece of China's umbrella trade union organization -- urged government agencies to mount an active response as new threats emerge to the rights of employees. It called on regulators to improve labor standards and strengthen oversight of AI algorithms, including by giving a greater say to trade unions and workers' representatives. "The benefits of technological advancement should be shared by society as a whole, rather than becoming a tool for a small number of employers to undermine workers' rights," the editorial said.


Google DeepMind is worried about what happens when millions of agents start to interact

MIT Technology Review

Google DeepMind is funding research into the potential dangers of situations where millions of different AI agents interact with each other online. According to Rohin Shah, who directs the company's AGI safety and alignment research, the mass-market arrival of agents that can carry out tasks without human oversight and follow instructions given to them by other agents creates a whole new class of risk . In an effort to address this, Google DeepMind--which made agent-based tools a centerpiece of Google I/O last month --has teamed up with several other organizations to announce a $10 million funding pot for researchers to study the behavior of multi-agent systems and come up with ways to prevent unsafe scenarios. Joining Google DeepMind are Schmidt Sciences, a philanthropic foundation set up by Eric and Wendy Schmidt; ARIA, the UK government's moonshot agency; the Cooperative AI foundation, a UK-based nonprofit research outfit; and Google's charitable arm, Google.org. I asked Shah and James Fox, who leads the Science of Trustworthy AI program at Schmidt Sciences, what they hope to achieve with that $10 million.


Americans Are Trading Billions of Dollars on Polymarket's Banned Offshore Platform

WIRED

Americans Are Trading Billions of Dollars on Polymarket's Banned Offshore Platform It's the first estimate of how many Americans are sneaking onto Polymarket's banned crypto-based platform. Approximately 30 percent of the trading volume on Polymarket comes from the United States, according to a new study--an eye-popping number, considering that none of those people are legally allowed to use the crypto -based platform. The study, conducted by Rutgers University statistician Harry Crane, estimated that people in the US funneled between $10.6 to $26.7 billion through Polymarket. To track the platform's activity, Crane looked at what appeared to be US-based trades on offshore prediction market platforms from May 2025 to the end of April 2026. He found that many of the highest-volume markets on Polymarket were US-centric, including those covering US elections and sporting events.


India's workers are training AI robots to take their jobs

Al Jazeera

India's workers are training AI robots to take their jobs With a smartphone strapped to her head, Indian housewife Nagireddy Sriramyachandra films herself slicing mangoes to train artificial intelligence-powered robots to take on household tasks in the future. Earning 250 rupees ($2.6) for one hour of video, her mundane recordings are invaluable for global tech companies teaching machines how to move like humans in the real world. The 25-year-old is one of a growing army of thousands of AI system trainers in the world's most populous country. "Who else will give you 250 rupees an hour just for doing housework?" "I may get a robot myself in the future," she added.


Japan financial firms to join NEC-Anthropic AI collaboration

The Japan Times

Anthropic CEO Dario Amodei speaks during the World Economic Forum's annual meeting in Davos, Switzerland, in January. Electronics maker NEC said Thursday that major Japanese financial institutions, including Sumitomo Mitsui Financial Group and MS&AD Insurance Group Holdings, will participate in its strategic collaboration with U.S. startup Anthropic in the field of artificial intelligence. The partnership aims to improve the quality of financial services for customers using AI and to strengthen measures against cyberattacks. The other companies are Sumitomo Life Insurance, Daiwa Securities Group, Sumitomo Mitsui Trust Group, Sumitomo Mitsui Trust Bank and Meiji Yasuda Life Insurance. Using Anthropic's AI technology, the partners will work not only on developing new services but also on improving productivity by streamlining business processes at each company.