It is a significant overhaul of a department that in 40 years has never before been placed so squarely on the chopping block. Here's how today's defense tech companies, which have fostered close connections to the Trump administration, stand to gain, and why safety testing might suffer as a result. The Operational Test and Evaluation office is "the last gate before a technology gets to the field," says Missy Cummings, a former fighter pilot for the US Navy who is now a professor of engineering and computer science at George Mason University. Though the military can do small experiments with new systems without running it by the office, it has to test anything that gets fielded at scale. "In a bipartisan way--up until now--everybody has seen it's working to help reduce waste, fraud, and abuse," she says.

When it comes to autonomous vehicles on city roads, that's been the approach in most of the world's countries. But on Tuesday, the UK announced it would put a cautious foot on the pedal, when the Department of Transport said it would accelerate plans to allow companies to operate self-driving cars on public roads in limited pilot programs starting spring of next year. The British government had initially planned to open up its roads for self-driving vehicles more than a year later, in the second half of 2027. "We can see what a massive economic opportunity this technology presents," Transport secretary Heidi Alexander tells WIRED in an interview. The department estimates the autonomous vehicle industry will create 38,000 jobs and generate 42 billion pounds ( 57 million US) for the country by 2035.

At least six Waymo vehicles across the county have reportedly been the target of vandalism, resulting in the company temporarily suspending operations in the area "out of an abundance of caution." California Gov. Gavin Newsom and Los Angeles Mayor Karen Bass have condemned the violence and destruction, which Newsom attributed to "insurgent groups" and "anarchists" who have infiltrated otherwise peaceful protests. President Donald Trump, who mobilized the National Guard to respond to the situation, has called the demonstrators "troublemakers" and "paid insurrectionists." Waymo is a subsidiary of Alphabet, Google's parent company, and grew out of the Google Self-Driving Car project that began in 2009. It launched its robotaxi business in 2020 in limited markets, which grew to include Los Angeles in 2024.

Russia has launched another large drone attack on Ukraine, striking Kyiv and damaging a maternity ward in the southern port of Odesa, regional officials said early on Tuesday. The overnight attacks follow Russia's biggest drone strike on Ukraine on Monday -- part of intensified operations that Moscow said were retaliatory measures for Kyiv's recent brazen attacks inside Russia. Medics were called to four districts of Kyiv a couple hours after midnight on Tuesday, including the historic Podil neighborhood, Mayor Vitali Klitschko said on the Telegram messaging app. The military said the strikes were still ongoing and urged people to seek bomb shelters. The full scale of the attack was not immediately clear.

The DfE guidance says schools should have clear policies on AI, including when teachers and pupils can and cannot use it, and that manual checks are the best way to spot whether students are using it to cheat. It also says only approved tools should be used and pupils should be taught to recognise deepfakes and other misinformation. Education Secretary Bridget Phillipson said the guidance aimed to "cut workloads". "We're putting cutting-edge AI tools into the hands of our brilliant teachers to enhance how our children learn and develop – freeing teachers from paperwork so they can focus on what parents and pupils need most: inspiring teaching and personalised support," she said. Pepe Di'Iasio, ASCL general secretary, said many schools and colleges were already "safely and effectively using AI" and it had the potential to ease heavy staff workloads and as a result, help recruitment and retention challenges.

As deep learning models are increasingly deployed in high-risk applications, robust defenses against adversarial attacks and reliable performance guarantees become paramount. Moreover, accuracy alone does not provide sufficient assurance or reliable uncertainty estimates for these models. This study advances adversarial training by leveraging principles from Conformal Prediction. Specifically, we develop an adversarial attack method, termed OPSA (OPtimal Size Attack), designed to reduce the efficiency of conformal prediction at any significance level by maximizing model uncertainty without requiring coverage guarantees. Correspondingly, we introduce OPSA-AT (Adversarial Training), a defense strategy that integrates OPSA within a novel conformal training paradigm. Experimental evaluations demonstrate that our OPSA attack method induces greater uncertainty compared to baseline approaches for various defenses. Conversely, our OPSA-AT defensive model significantly enhances robustness not only against OPSA but also other adversarial attacks, and maintains reliable prediction. Our findings highlight the effectiveness of this integrated approach for developing trustworthy and resilient deep learning models for safety-critical domains. Our code is available at https://github.com/bjbbbb/Enhancing-Adversarial-Robustness-with-Conformal-Prediction.

I present a novel approach to estimating the temporal and spatial patterns of violent conflict. I show how we can use highly temporally and spatially disaggregated data on conflict events in tandem with Gaussian processes to estimate temporospatial conflict trends. These trends can be studied to gain insight into conflict traps, diffusion and tempo-spatial conflict exposure in general; they can also be used to control for such phenomenons given other estimation tasks; lastly, the approach allow us to extrapolate the estimated tempo-spatial conflict patterns into future temporal units, thus facilitating powerful, stat-of-the-art, conflict forecasts. Importantly, these results are achieved via a relatively parsimonious framework using only one data source: past conflict patterns.

Graph Neural Networks (GNNs) excel at modeling relational data but face significant challenges in high-stakes domains due to unquantified uncertainty. Conformal prediction (CP) offers statistical coverage guarantees, but existing methods often produce overly conservative prediction intervals that fail to account for graph heteroscedasticity and structural biases. While residual reweighting CP variants address some of these limitations, they neglect graph topology, cluster-specific uncertainties, and risk data leakage by reusing training sets. To address these issues, we propose Residual Reweighted GNN (RR-GNN), a framework designed to generate minimal prediction sets with provable marginal coverage guarantees. RR-GNN introduces three major innovations to enhance prediction performance. First, it employs Graph-Structured Mondrian CP to partition nodes or edges into communities based on topological features, ensuring cluster-conditional coverage that reflects heterogeneity. Second, it uses Residual-Adaptive Nonconformity Scores by training a secondary GNN on a held-out calibration set to estimate task-specific residuals, dynamically adjusting prediction intervals according to node or edge uncertainty. Third, it adopts a Cross-Training Protocol, which alternates the optimization of the primary GNN and the residual predictor to prevent information leakage while maintaining graph dependencies. We validate RR-GNN on 15 real-world graphs across diverse tasks, including node classification, regression, and edge weight prediction. Compared to CP baselines, RR-GNN achieves improved efficiency over state-of-the-art methods, with no loss of coverage.

Crucial to many measurements at the LHC is the use of correlated multi-dimensional information to distinguish rare processes from large backgrounds, which is complicated by the poor modeling of many of the crucial backgrounds in Monte Carlo simulations. In this work, we introduce HI-SIGMA, a method to perform unbinned high-dimensional statistical inference with data-driven background distributions. In contradistinction to many applications of Simulation Based Inference in High Energy Physics, HI-SIGMA relies on generative ML models, rather than classifiers, to learn the signal and background distributions in the high-dimensional space. These ML models allow for efficient, interpretable inference while also incorporating model errors and other sources of systematic uncertainties. We showcase this methodology on a simplified version of a di-Higgs measurement in the $bbγγ$ final state, where the di-photon resonance allows for efficient background interpolation from sidebands into the signal region. We demonstrate that HI-SIGMA provides improved sensitivity as compared to standard classifier-based methods, and that systematic uncertainties can be straightforwardly incorporated by extending methods which have been used for histogram based analyses.

This paper introduces the Difference-in-Differences Bayesian Causal Forest (DiD-BCF), a novel non-parametric model addressing key challenges in DiD estimation, such as staggered adoption and heterogeneous treatment effects. DiD-BCF provides a unified framework for estimating Average (ATE), Group-Average (GATE), and Conditional Average Treatment Effects (CATE). A core innovation, its Parallel Trends Assumption (PTA)-based reparameterization, enhances estimation accuracy and stability in complex panel data settings. Extensive simulations demonstrate DiD-BCF's superior performance over established benchmarks, particularly under non-linearity, selection biases, and effect heterogeneity. Applied to U.S. minimum wage policy, the model uncovers significant conditional treatment effect heterogeneity related to county population, insights obscured by traditional methods. DiD-BCF offers a robust and versatile tool for more nuanced causal inference in modern DiD applications.