The BBC has obtained footage and interviews from the Iranian capital Tehran which evoke a city of strained nerves, of constant waiting for the next air strike and relentless fear of the state security apparatus. The identities of the people in this report have been protected. While independent journalists still try to gather testimony that offers a credible alternative view, they run the risk of arrest, torture and possibly worse. Displaced Palestinians were told to secure their tents to prevent them being blown away as a storm swept through the enclave. Video filmed by a witness and verified by the BBC shows a drone crashing close to the airport.

The paper addresses the classical network tomography problem of inferring local traffic given origin-destination observations. Focussing on large complex public transportation systems, we build a scalable model that exploits input-output information to estimate the unobserved link/station loads and the users path preferences. Based on the reconstruction of the users' travel time distribution, the model is flexible enough to capture possible different path-choice strategies and correlations between users travelling on similar paths at similar times. The corresponding likelihood function is intractable for medium or large-scale networks and we propose two distinct strategies, namely the exact maximum-likelihood inference of an approximate but tractable model and the variational inference of the original intractable model. As an application of our approach, we consider the emblematic case of the London Underground network, where a tap-in/tap-out system tracks the start/exit time and location of all journeys in a day. A set of synthetic simulations and real data provided by Transport For London are used to validate and test the model on the predictions of observable and unobservable quantities.

This article was featured in the One Story to Read Today newsletter. T he smell was strange . The concrete wall was too close. One of my kids was standing on the sidewalk next to our car--not crying, just confused. The seat belt had held. The crumple zone had crumpled.

We present an end-to-end framework for solving the Vehicle Routing Problem (VRP) using reinforcement learning. In this approach, we train a single policy model that finds near-optimal solutions for a broad range of problem instances of similar size, only by observing the reward signals and following feasibility rules. We consider a parameterized stochastic policy, and by applying a policy gradient algorithm to optimize its parameters, the trained model produces the solution as a sequence of consecutive actions in real time, without the need to re-train for every new problem instance. On capacitated VRP, our approach outperforms classical heuristics and Google's OR-Tools on medium-sized instances in solution quality with comparable computation time (after training). We demonstrate how our approach can handle problems with split delivery and explore the effect of such deliveries on the solution quality.

Your Out-of-Distribution Detection Method is Not Robust!

The companies haven't yet announced a timeline. At GTC 2026, NVIDIA and Bolt announced what they hope will be a symbiotic partnership. Bolt gets NVIDIA technology that would be costly and impractical to build on its own. Meanwhile, NVIDIA not only gains a major customer but also access to the European rideshare company's driving data. Bolt says its fleet data will build a learning engine for autonomous vehicles (AVs) using NVIDIA tech.

Neural networks are increasingly deployed in real-world safety-critical domains such as autonomous driving, aircraft collision avoidance, and malware detection. However, these networks have been shown to often mispredict on inputs with minor adversarial or even accidental perturbations. Consequences of such errors can be disastrous and even potentially fatal as shown by the recent Tesla autopilot crash. Thus, there is an urgent need for formal analysis systems that can rigorously check neural networks for violations of different safety properties such as robustness against adversarial perturbations within a certain L-norm of a given image. An effective safety analysis system for a neural network must be able to either ensure that a safety property is satisfied by the network or find a counterexample, i.e., an input for which the network will violate the property. Unfortunately, most existing techniques for performing such analysis struggle to scale beyond very small networks and the ones that can scale to larger networks suffer from high false positives and cannot produce concrete counterexamples in case of a property violation. In this paper, we present a new efficient approach for rigorously checking different safety properties of neural networks that significantly outperforms existing approaches by multiple orders of magnitude. Our approach can check different safety properties and find concrete counterexamples for networks that are 10x larger than the ones supported by existing analysis techniques. We believe that our approach to estimating tight output bounds of a network for a given input range can also help improve the explainability of neural networks and guide the training process of more robust neural networks.

Hundreds of passengers flying to Dubai spent 11 hours on a flight to nowhere after their plane was turned back over Egypt. The Emirates flight EK24 set off from Edinburgh at 21:26 on Sunday and was due to land in Dubai at 06:49 on Monday. However, as the plane flew over Egypt, flights at Dubai International Airport were suspended following a fire caused by an Iranian drone hitting a fuel tank. The plane was forced to return to Edinburgh. Travel journalist Simon Calder told the BBC's Radio Scotland Breakfast programme that although Dubai was on the UK Foreign Office's No go list, many people were still taking the risk of flying there. No injuries were reported following the drone strike but officials said they had taken all necessary measures to ensure public safety.

The EV manufacturer is supported by a robust online community. But Elon Musk's politics and overblown hype about Full Self-Driving are turning some loyalists away. This month, Tesla customers erupted in outrage over what some called a " bait and switch " by the electric vehicle manufacturer. Initially, the company had offered to transfer the Full Self-Driving feature, which is now only available through a subscription model but could once be purchased for a "lifetime" fee that ran as high as $15,000, to any new Tesla purchased by March 31. The deal was most tempting for drivers already enticed by a new base Cybertruck model that cost just $59,990, a price that CEO Elon Musk soon clarified would only last for 10 days, leaving potential buyers a very small window to make up their minds. Then Tesla quietly amended the language of the FSD transfer agreement, stipulating that customers would need to take delivery of a Tesla by March 31 in order to swap their FSD from their last vehicle to the next.

In the third season of "The Comeback," Kudrow has brought back her character Valerie Cherish, which had its roots at the Groundlings. A visitor to Stage 24 on the Warner Bros. lot, in Burbank, last November could be forgiven for thinking that the television show being filmed there was a sitcom called "How's That?!" The parking spaces outside were marked with "How's That?!" signs. Inside, director's chairs with the "How's That?!" logo were arranged around video monitors. The set--a New England bed-and-breakfast, with kitschy floral wallpaper--was surrounded by sitcom cameras and buzzing crew members wearing headsets. A studio audience filed into the bleachers, and a warmup comic urged them to "shake those funny bones." Then, with mounting gusto, he introduced the star of "How's That?!": "Here she is . . . the one and only . . . the living legend . . . She emerged to applause, in a potter's smock, wavy red hair under a bandanna, looking like a cross between Lucy Ricardo and Mrs. Garrett ...