Understanding how decision makers balance operational efficiency with environmental and ecological risks is central to vessel navigation. We model vessel speed as a control variable in a constrained optimization framework in which vessel operators balance multiple competing objectives, including transit efficiency, ice related navigational risk, and whale related ecological risk. The underlying risk parameters are estimated using over 14 million Automatic Identification System (AIS) observations from the United States Arctic (2010-2019), together with environmental covariates and spatially explicit whale density estimates. The framework incorporates a nonlinear risk objective, vessel heterogeneity, and regularization to ensure stable and interpretable results.The inferred trade offs reveal distinct decision making patterns across vessel groups and navigational statuses. Vessel types such as Tug Tow and Cargo balance operational speed with environmental and ecological considerations. In contrast, several vessel groups, including Fishing, Passenger, and Unspecified vessels, are strongly influenced by ice related risk, while Pleasure Craft and Tankers exhibit higher sensitivity to whale related risk. Across navigational status categories, similar heterogeneity is observed. The dominant status, under way using engine, displays a clear trade off, whereas other statuses, such as aground and undefined, are strongly shaped by ice related constraints. Statuses including restricted maneuverability and engaged in fishing exhibit higher estimated sensitivity to whale related risk, though with substantial uncertainty.Sensitivity analysis indicates that increasing whale-related risk weighting produces limited changes in model-implied optimal speed, whereas increasing ice-related risk leads to more consistent reductions.

Predicting the effect of interventions with many possible variations, e.g., therapeutic content that affects mental health outcomes or an earnings call transcript that drives movement in share price, is useful across several domains. However, classical causal estimators tend to assume that all possible interventions are observed, which is infeasible when interventions vary widely, for instance, in the space of all text strings. We adapt a well-known approach of recasting causal inference as a learning problem, to address high-dimensional treatment spaces. Specifically, under standard assumptions like no unobserved confounding, we show that causal error decomposes into a series of moment-balancing errors of increasing order, and design objectives that directly improve causal estimation. We also show how to project the effect of a high-dimensional treatment onto lower-dimensional treatment attributes, which allows a single model to answer several causal questions without additional attribute-specific training. We empirically evaluate our estimators in settings with high-dimensional continuous, discrete, and text treatments, the last of which used a semi-synthetic dataset of Amazon Reviews. Our experiments demonstrate the benefit of higher-order balance error optimization and competitive performance of projected causal estimates with attribute-specific estimators.

Privacy auditing aims to empirically assess privacy leakage in machine learning models using membership inference attacks (MIAs), and to derive lower bounds on differential privacy (DP) parameters. Recent one-run auditing methods address the high cost of standard approaches by relying on a single training run with multiple "canary" points whose inclusion or exclusion must be detected by the auditor. In this work, we study the problem of efficiently crafting canaries for one-run privacy auditing. Motivated by recent theoretical insights suggesting that interference between canaries contributes to weaker leakage estimates compared to multi-run methods, we propose to optimize canaries to be both highly detectable and minimally interfering. Our approach combines a greedy initialization based on influence functions with a bilevel optimization procedure that maximizes distinguishability while promoting diversity in embedding space, enabling the use of computationally efficient bilevel algorithms. Experiments show that our method achieves stronger privacy leakage estimates at a lower computational cost than existing canary crafting approaches.

Discrete diffusion models have achieved strong empirical performance in text and other symbolic domains, but, especially for uniform-rate models, they often require many steps to generate a single sample. Existing acceleration methods either rely on training additional quantities or suffer from slow mixing. In this work, we propose a novel Gibbs-based corrector for discrete diffusion models, termed Gibbs-Accelerated Discrete Diffusion (GADD). GADD leverages the structure of the concrete score function to construct Gibbs posterior likelihoods directly, without requiring any additional training beyond standard score estimation. We show that GADD achieves an overall sampling complexity of $\mathcal{O}(\mathrm{polylog} (\varepsilon^{-1}))$, yielding the first such rate for diffusion-based samplers for uniform-rate discrete diffusion models. We also conduct numerical experiments demonstrating the practical advantages of GADD across synthetic data, zero-shot text sampling, and zero-shot conditional music generation. These results corroborate the theory and show that GADD consistently improves sample quality and wall-clock efficiency over standard baselines, including vanilla Euler methods and CTMC correctors. Beyond this, our theoretical analysis introduces a novel framework for analyzing predictor-corrector methods in discrete diffusion models, which may be of independent interest. Unlike existing approaches that rely on the Girsanov change-of-measure technique, our method is based on an induction argument that tracks error propagation across predictor iterations while accounting for inaccuracies in the corrector updates.

Russia'relentlessly targeting' critical infrastructure and democracy, GCHQ says The UK is at a moment of consequence as Russia is relentlessly targeting critical infrastructure, the UK's largest spy agency will warn. GCHQ Director Anne Keast-Butler will set out threats facing the UK and the measures she believes need to be taken to confront them when she makes her inaugural public speech on Wednesday. Russia has been blamed for a string of espionage plots on British soil and, more recently, waging an undeclared'hybrid war' against the UK and other Nato countries. The Kremlin has denied the allegations. Keast-Butler says GCHQ is working tirelessly to fend off cyber attacks and counter what she calls reckless sabotage and assassination attempts.

The Holy Father referenced in his encyclical about AI--an expert (if unintentional) troll of tech billionaires who keep misinterpreting the series. Nobody was surprised that Pope Leo XIV cited well-known saints and previous pontiffs in his first encyclical, or papal letter of spiritual guidance,, released Monday. But the name that immediately jumped out to many readers is one synonymous with high fantasy literature: J.R.R. Tolkien, the Catholic author of . Leo's letter is concerned with "safeguarding the human person in the time of artificial intelligence," a major theme of his first year as leader of the Catholic Church. Drawing from his predecessor, Pope Francis, he warns of "the growing dominance of a technocratic paradigm," one capable of "reducing creation to an object of exploitation and human beings to mere cogs in a system driven toward ever greater efficiency."

Preparations are underway at the White House South Lawn for an upcoming Ultimate Fighting Championship (UFC) event. The cage fight is scheduled to take place 14 June as part of the America 250 celebrations. US President Donald Trump said UFC president Dana White, a longtime ally, will build "a 5,000-seat arena right outside the front door of the White House", along with eight large screens in a nearby park for fans to watch from a distance. Fans rally behind US men's national soccer team as World Cup roster revealed BBC's Carl Nasman was at the event, where he asked enthusiasts about World Cup ticket prices and what the sport means to the US. The plans include permanent human habitation on the Earth's only natural satellite by 2032.

India's communists once ruled millions. For the first time since 1957, India no longer has a single communist-led state government. The defeat of the Communist Party of India (Marxist)-led Left Democratic Front (LDF) in Kerala this month, after a decade in power, marked the end - at least for now - of one of the world's most enduring experiments in democratic communism. At their peak, India's communist parties ruled states stretching from West Bengal to Kerala and Tripura. They impacted the lives of more than 100 million people through trade unions, peasant organisations, student wings and disciplined cadre networks.

Sharrell Shaw's request for strangers to visit her husband's Arlington grave on Memorial Day drew over 6 million views and visits from Trump administration officials.

Paul Anka says Toronto's crime rate has spiked amid the arrival over 400,000 new immigrants, telling Bill Maher that Canada was homogenous until recently.