Police officers in San Bruno observed a Waymo car making an illegal U-turn. The incident highlights a gray area in law enforcement’s ability to cite robot cars when they violate traffic laws.

An entire family - a married couple and their two young sons - have been killed in an overnight Russian drone attack in Ukraine's north-eastern Sumy region, local officials have said. Regional head Oleh Hryhorov said a residential building was hit in the village of Chernechchyna. The bodies of the two children, aged four and six, and their parents were later recovered from the wreckage. Ukraine's air force said its units shot down 46 out of 65 Russian drones across the country - but there were 19 direct hits in six locations. Russia's military has not commented.

The FAA is set to loosen rules to let people fly drones beyond their "line of sight. On Thursday, I published a story about the police-tech giant Flock Safety selling its drones to the private sector to track shoplifters. Keith Kauffman, a former police chief who now leads Flock's drone efforts, described the ideal scenario: A security team at a Home Depot, say, launches a drone from the roof that follows shoplifting suspects to their car. The drone tracks their car through the streets, transmitting its live video feed directly to the police. It's a vision that, unsurprisingly, alarms civil liberties advocates. They say it will expand the surveillance state created by police drones, license-plate readers, and other crime tech, which has allowed law enforcement to collect massive amounts of private data without warrants.

Can Ukraine restore its pre-war borders? Why are Tomahawk missiles for Ukraine a'red line' for Russia? Is Russia testing NATO with aerial incursions in Europe? At least 4 killed in major Russian drone, missile attack on Ukraine's Kyiv Russia's President Vladimir Putin said his forces are prevailing in what he described as a "righteous battle" in Ukraine . "Our fighters and commanders go on the attack, and the entire country, all of Russia, is waging this righteous battle and working hard," he said.

Prime Minister Shigeru Ishiba (right) attends a joint news conference with Sri Lankan President Anura Kumara Dissanayake at the Prime Minister's Office on Monday. Prime Minister Shigeru Ishiba and Sri Lankan President Anura Kumara Dissanayake met in Tokyo on Monday and agreed that Japan will provide about 10 surveillance drones, worth about ¥500 million ($3.36 million), to the South Asian nation's navy. This will be Japan's first provision of defense equipment to Sri Lanka under its official security assistance program. The stability and development of Sri Lanka, which is located at a strategic point in the Indian Ocean, is extremely important, Ishiba said at a joint news conference after the meeting. In response, the president voiced his commitment to creating a peaceful and stable Indo-Pacific region.

San Bruno police posted a photo of a Waymo and a dilemma, writing: 'Since there was no human driver, a ticket couldn't be issued.' San Bruno police posted a photo of a Waymo and a dilemma, writing: 'Since there was no human driver, a ticket couldn't be issued.' San Bruno officers pull over Waymo but say a ticket wasn't issued, as'citation books don't have a box for "robot"' If a driver makes an illegal U-turn, but no one is behind the wheel, does the car still get a ticket? A police department in California grappled with this existential question last week. During a DUI enforcement operation, officers in San Bruno pulled over a car without anyone behind the wheel after the autonomous vehicle made an illegal U-turn at a light.

In this paper, we investigate three cross-facility data streaming architectures, Direct Streaming (DTS), Proxied Streaming (PRS), and Managed Service Streaming (MSS). We examine their architectural variations in data flow paths and deployment feasibility, and detail their implementation using the Data Streaming to HPC (DS2HPC) architectural framework and the SciStream memory-to-memory streaming toolkit on the production-grade Advanced Computing Ecosystem (ACE) infrastructure at Oak Ridge Leadership Computing Facility (OLCF). We present a workflow-specific evaluation of these architectures using three synthetic workloads derived from the streaming characteristics of scientific workflows. Through simulated experiments, we measure streaming throughput, round-trip time, and overhead under work sharing, work sharing with feedback, and broadcast and gather messaging patterns commonly found in AI-HPC communication motifs. Our study shows that DTS offers a minimal-hop path, resulting in higher throughput and lower latency, whereas MSS provides greater deployment feasibility and scalability across multiple users but incurs significant overhead. PRS lies in between, offering a scalable architecture whose performance matches DTS in most cases.

Neural scaling laws underlie many of the recent advances in deep learning, yet their theoretical understanding remains largely confined to linear models. In this work, we present a systematic analysis of scaling laws for quadratic and diagonal neural networks in the feature learning regime. Leveraging connections with matrix compressed sensing and LASSO, we derive a detailed phase diagram for the scaling exponents of the excess risk as a function of sample complexity and weight decay. This analysis uncovers crossovers between distinct scaling regimes and plateau behaviors, mirroring phenomena widely reported in the empirical neural scaling literature. Furthermore, we establish a precise link between these regimes and the spectral properties of the trained network weights, which we characterize in detail. As a consequence, we provide a theoretical validation of recent empirical observations connecting the emergence of power-law tails in the weight spectrum with network generalization performance, yielding an interpretation from first principles.

Graph neural networks (GNNs) are increasingly widely used for community detection in attributed networks. They combine structural topology with node attributes through message passing and pooling. However, their robustness or lack of thereof with respect to different perturbations and targeted attacks in conjunction with community detection tasks is not well understood. To shed light into latent mechanisms behind GNN sensitivity on community detection tasks, we conduct a systematic computational evaluation of six widely adopted GNN architectures: GCN, GAT, Graph-SAGE, DiffPool, MinCUT, and DMoN. The analysis covers three perturbation categories: node attribute manipulations, edge topology distortions, and adversarial attacks. We use element-centric similarity as the evaluation metric on synthetic benchmarks and real-world citation networks. Our findings indicate that supervised GNNs tend to achieve higher baseline accuracy, while unsupervised methods, particularly DMoN, maintain stronger resilience under targeted and adversarial perturbations. Furthermore, robustness appears to be strongly influenced by community strength, with well-defined communities reducing performance loss. Across all models, node attribute perturbations associated with targeted edge deletions and shift in attribute distributions tend to cause the largest degradation in community recovery. These findings highlight important trade-offs between accuracy and robustness in GNN-based community detection and offer new insights into selecting architectures resilient to noise and adversarial attacks.

Deep learning offers new tools for portfolio optimization. We present an end-to-end framework that directly learns portfolio weights by combining Long Short-Term Memory (LSTM) networks to model temporal patterns, Graph Attention Networks (GAT) to capture evolving inter-stock relationships, and sentiment analysis of financial news to reflect market psychology. Unlike prior approaches, our model unifies these elements in a single pipeline that produces daily allocations. It avoids the traditional two-step process of forecasting asset returns and then applying mean--variance optimization (MVO), a sequence that can introduce instability. We evaluate the framework on nine U.S. stocks spanning six sectors, chosen to balance sector diversity and news coverage. In this setting, the model delivers higher cumulative returns and Sharpe ratios than equal-weighted and CAPM-based MVO benchmarks. Although the stock universe is limited, the results underscore the value of integrating price, relational, and sentiment signals for portfolio management and suggest promising directions for scaling the approach to larger, more diverse asset sets.