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.

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 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 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 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.

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.

A fundamental challenge in cognitive neuroscience is understanding how cognition emerges from the interplay between structural connectivity (SC) and dynamic functional connectivity (FC) in the brain. Network neuroscience has emerged as a powerful framework to understand brain function through a holistic perspective on structure-function relationships. In this context, current machine learning approaches typically seek to establish direct mappings between structural connectivity (SC) and functional connectivity (FC) associated with specific cognitive states. However, these state-independent methods often yield inconsistent results due to overlapping brain networks across cognitive states. To address this limitation, we conceptualize to uncover the dendritic coupling mechanism between one static SC and multiple FCs by solving a flow problem that bridges the distribution of SC to a mixed distribution of FCs, conditioned on various cognitive states, along a Riemannian manifold of symmetric positive-definite (SPD) manifold. We further prove the equivalence between flow matching on the SPD manifold and on the computationally efficient Cholesky manifold. Since a spare of functional connections is shared across cognitive states, we introduce the notion of consensus control to promote the shared kinetic structures between multiple FC-to-SC pathways via synchronized coordination, yielding a biologically meaningful underpinning on SC-FC coupling mechanism. Together, we present BrainFlow, a reversible generative model that achieves state-of-the-art performance on not only synthetic data but also large-scale neuroimaging datasets from UK Biobank and Human Connectome Project.

It might not feel all that different from older World Cups--for better or worse. Waymo, the Alphabet subsidiary offering robotaxi rides in 11 US metros right now, says it's ready for the FIFA World Cup . Match attendees can catch driverless rides to six of the 16 North American venues: stadiums in Atlanta, Houston, Los Angeles, Miami, and the San Francisco Bay Area. The sprawling football event, expected to attract some 6.5 million visitors to the continent over more than a month, could prove an exciting close-up for Waymo . The company says it's serving half-a-million paid rides a week--paltry stuff compared to the likes of ride-hail giants Uber and Lyft, but more impressive once you remember that the things don't have drivers.

PanTS is a large-scale, multi-institutional dataset curated to advance research in pancreatic CT analysis. It contains 36,390 CT scans from 145 medical centers, with expert-validated, voxel-wise annotations of over 993,000 anatomical structures, covering pancreatic tumors, pancreas head, body, and tail, and 24 surrounding anatomical structures such as vascular/skeletal structures and abdominal/thoracic organs. Each scan includes metadata such as patient age, sex, diagnosis, contrast phase, in-plane spacing, slice thickness, etc. AI models trained on PanTS achieve significantly better performance in pancreatic tumor detection, localization, and segmentation than those trained on existing public datasets. Our analysis indicates that these gains are directly attributable to the 16 larger-scale tumor annotations and indirectly supported by the 24 additional surrounding anatomical structures. As the largest and most comprehensive resource of its kind, PanTS offers a new benchmark for developing and evaluating AI models in pancreatic CT analysis.

Analysis of noninvasive microvascular blood flow can improve the diagnosis, prognosis, and management of many medical conditions, including cardiovascular, peripheral vascular, and sickle cell disease. This paper introduces SAM2Flow, an interactive optical flow estimation model to analyze long Oblique Back-illumination Microscopy (OBM) videos of *in vivo* microvascular flow. Inspired by the Segment Anything Model (SAM2), SAM2Flow enables users to specify regions of interest through user prompts for focused flow estimation. SAM2Flow also incorporates a dual memory attention mechanism, comprising both motion and context memory, to achieve efficient and stable flow estimations over extended video sequences. According to our experiments, SAM2Flow achieves SOTA accuracy in foreground optical flow estimation on both microvascular flow and public datasets, with a fast inference speed of over $20$ fps on $512\times512$ inputs. Based on the temporally robust flow estimation, SAM2Flow demonstrated superior performance in downstream physiological applications compared to existing models. The code and dataset will be published with this paper.