Things to Do in L.A. Tap to enable a layout that focuses on the article. L.A. teachers union widely expected to announce strike date at massive Wednesday rally Members of the largest unions representing teachers and nonteachers participate in joint rally at Grand Park in March 2023. The scene will be repeated on Wednesday, with union members once again on the verge of a strike. This is read by an automated voice. Please report any issues or inconsistencies here .

Justice Department Says Anthropic Can't Be Trusted With Warfighting Systems In response to Anthropic's lawsuit, the government said it lawfully penalized the company for trying to limit how its Claude AI models could be used by the military. The Trump administration argued in a court filing on Tuesday that it did not violate Anthropic's First Amendment rights by designating the AI developer a supply-chain risk and predicted that the company's lawsuit against the government will fail. "The First Amendment is not a license to unilaterally impose contract terms on the government, and Anthropic cites nothing to support such a radical conclusion," US Department of Justice attorneys wrote. The response was filed in a federal court in San Francisco, one of two venues where Anthropic is challenging the Pentagon's decision to sanction the company with a label that can bar companies from defense contracts over concerns about potential security vulnerabilities. Anthropic argues the Trump administration overstepped its authority in applying the label and preventing the company's technologies from being used inside the department.

Ugly new Nicole Kidman and Keith Urban divorce fight ERUPTS: Her friends share humiliating details of'midlife crisis'... and reveal brutal REAL reason daughter Sunday Rose'snubbed' him Supreme Court's top judge issues chilling warning as Trump targets his own appointees SARAH VINE: How telling that Meghan's joined the ranks of those peddling wellness and fake lifestyles to the gullible I moved my family OFF-GRID after a horrific series of events... now our tiny home saves us thousands each MONTH. We are richer and happier than ever. Here's how you can do it too Furious US troops erupt at CNN's $20m steak and lobster claims as grim photos expose reality Mother of cheating nurse shares horrific way daughter was killed after SUV sex... and shares heartbreaking details of her marriage to doctor Hollywood's top insider makes VERY catty observation about Kaitlan Collins CIA accused of'poisoning the sky' with toxins as files expose secret weather control agenda Mysterious'three-sided pyramid' similar to those in Egypt spotted on Mars in NASA footage Trump says he's'not afraid' of Vietnam-style ground combat in Iran I've always been embarrassed by my spotty skin. I'd tried every lotion and potion, until I found a science-backed plan that restored my skin's health and my confidence Alix Earle stuns in white bikini in first glimpse at 2026 Sports Illustrated Swimsuit edition... after turning heads with Tom Brady and Joe Burrow'We no longer need NATO': Trump sends shockwaves through Europe with ferocious attack on allies Everything JFK Jr told friends about his love affair with'sexual dynamo' Madonna... her unprintable pillow talk... and his perverse incest request that she couldn't go through with What your WALK says about you: Study reveals how your swagger can reveal how you're really feeling READ MORE: 'Tough guy' walk in western movies makes you look powerful A new study has revealed exactly what your walk says about you - whether it's a slow swagger or a peppy stride. Scientists from the Advanced Telecommunications Research Institute International in Japan carried out several experiments as part of their study.

Ad for AI editing app which said it could'remove anything' banned An advert for a video and image editing tool that implied viewers could digitally remove a woman's clothing has been banned by the UK advertising regulator. The YouTube ad for PixVideo - AI Video Maker, seen in January, showed a before and after image of a young women, with red scribble overlaid on her midriff in the former, and parts of her bare skin exposed in the latter. Text across the bottom of the picture stated: Erase anything followed by a heart-eyes emoji. Eight people complained to the Advertising Standards Authority (ASA) that the ad sexualised and objectified women, and was irresponsible, offensive and harmful. It is not clear whether the image in the ad is of a real person or is itself AI-generated, with the ASA telling the BBC making such an assessment had not been part of its investigation.

Global feature effects such as partial dependence (PD) and accumulated local effects (ALE) plots are widely used to interpret black-box models. However, they are only estimates of true underlying effects, and their reliability depends on multiple sources of error. Despite the popularity of global feature effects, these error sources are largely unexplored. In particular, the practically relevant question of whether to use training or holdout data to estimate feature effects remains unanswered. We address this gap by providing a systematic, estimator-level analysis that disentangles sources of bias and variance for PD and ALE. To this end, we derive a mean-squared-error decomposition that separates model bias, estimation bias, model variance, and estimation variance, and analyze their dependence on model characteristics, data selection, and sample size. We validate our theoretical findings through an extensive simulation study across multiple data-generating processes, learners, estimation strategies (training data, validation data, and cross-validation), and sample sizes. Our results reveal that, while using holdout data is theoretically the cleanest, potential biases arising from the training data are empirically negligible and dominated by the impact of the usually higher sample size. The estimation variance depends on both the presence of interactions and the sample size, with ALE being particularly sensitive to the latter. Cross-validation-based estimation is a promising approach that reduces the model variance component, particularly for overfitting models. Our analysis provides a principled explanation of the sources of error in feature effect estimates and offers concrete guidance on choosing estimation strategies when interpreting machine learning models.

Trust and ethical concerns due to the widespread deployment of opaque machine learning (ML) models motivating the need for reliable model explanations. Post-hoc model-agnostic explanation methods addresses this challenge by learning a surrogate model that approximates the behavior of the deployed black-box ML model in the locality of a sample of interest. In post-hoc scenarios, neither the underlying model parameters nor the training are available, and hence, this local neighborhood must be constructed by generating perturbed inputs in the neighborhood of the sample of interest, and its corresponding model predictions. We propose \emph{Expected Active Gain for Local Explanations} (\texttt{EAGLE}), a post-hoc model-agnostic explanation framework that formulates perturbation selection as an information-theoretic active learning problem. By adaptively sampling perturbations that maximize the expected information gain, \texttt{EAGLE} efficiently learns a linear surrogate explainable model while producing feature importance scores along with the uncertainty/confidence estimates. Theoretically, we establish that cumulative information gain scales as $\mathcal{O}(d \log t)$, where $d$ is the feature dimension and $t$ represents the number of samples, and that the sample complexity grows linearly with $d$ and logarithmically with the confidence parameter $1/δ$. Empirical results on tabular and image datasets corroborate our theoretical findings and demonstrate that \texttt{EAGLE} improves explanation reproducibility across runs, achieves higher neighborhood stability, and improves perturbation sample quality as compared to state-of-the-art baselines such as Tilia, US-LIME, GLIME and BayesLIME.

Objective. We establish a principled method for inferring mental health related psychometric variables from neural and behavioral data using the Implicit Association Test (IAT) as the data generation engine, aiming to overcome the limited predictive performance (typically under 0.7 AUC) of the gold-standard D-score method, which relies solely on reaction times. Approach. We propose a sparse hierarchical Bayesian model that leverages multi-modal data to predict experiences related to mental illness symptoms in new participants. The model is a multivariate generalization of the D-score with trainable parameters, engineered for parameter efficiency in the small-cohort regime typical of IAT studies. Data from two IAT variants were analyzed: a suicidality-related E-IAT ($n=39$) and a psychosis-related PSY-IAT ($n=34$). Main Results. Our approach overcomes a high inter-individual variability and low within-session effect size in the dataset, reaching AUCs of 0.73 (E-IAT) and 0.76 (PSY-IAT) in the best modality configurations, though corrected 95% confidence intervals are wide ($\pm 0.18$) and results are marginally significant after FDR correction ($q=0.10$). Restricting the E-IAT to MDD participants improves AUC to 0.79 $[0.62, 0.97]$ (significant at $q=0.05$). Performance is on par with the best reference methods (shrinkage LDA and EEGNet) for each task, even when the latter were adapted to the task, while the proposed method was not. Accuracy was substantially above near-chance D-scores (0.50-0.53 AUC) in both tasks, with more consistent cross-task performance than any single reference method. Significance. Our framework shows promise for enhancing IAT-based assessment of experiences related to entrapment and psychosis, and potentially other mental health conditions, though further validation on larger and independent cohorts will be needed to establish clinical utility.

Benchmarking the performance of complex systems such as rail networks, renewable generation assets and national economies is central to transport planning, regulation and macroeconomic analysis. Classical frontier methods, notably Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA), estimate an efficient frontier in the observed input-output space and define efficiency as distance to this frontier, but rely on restrictive assumptions on the production set and only indirectly address heterogeneity and scale effects. We propose Geometric Manifold Analysis (GeMA), a latent manifold frontier framework implemented via a productivity-manifold variational autoencoder (ProMan-VAE). Instead of specifying a frontier function in the observed space, GeMA represents the production set as the boundary of a low-dimensional manifold embedded in the joint input-output space. A split-head encoder learns latent variables that capture technological structure and operational inefficiency. Efficiency is evaluated with respect to the learned manifold, endogenous peer groups arise as clusters in latent technology space, a quotient construction supports scale-invariant benchmarking, and a local certification radius, derived from the decoder Jacobian and a Lipschitz bound, quantifies the geometric robustness of efficiency scores. We validate GeMA on synthetic data with non-convex frontiers, heterogeneous technologies and scale bias, and on four real-world case studies: global urban rail systems (COMET), British rail operators (ORR), national economies (Penn World Table) and a high-frequency wind-farm dataset. Across these domains GeMA behaves comparably to established methods when classical assumptions hold, and provides additional insight in settings with pronounced heterogeneity, non-convexity or size-related bias.

In this work, we present a new deterministic partition-based global optimization algorithm, HALO (Hybrid Adaptive Lipschitzian Optimization), which uses estimates of the local Lipschitz constants associated with different sub-regions of the objective function's domain to compute lower bounds and guide the search toward global minimizers. These estimates are obtained by adaptively balancing the global and local information collected from the algorithm, based on absolute slopes. HALO is hyperparameter-free, eliminating the need for manual tuning, and it highlights the most important variables to help interpret the optimization problem. We also introduce a coupling strategy with local optimization algorithms, both gradient-based and derivative-free, to accelerate convergence. We compare HALO with popular global optimization algorithms on hundreds of test functions. The numerical results are very promising and demonstrate that HALO can expand our arsenal of efficient procedures of efficient procedures for challenging real-world black-box optimization problems. The Python code of HALO is publicly available on GitHub. https://github.com/dannyzx/HALO

The interception of agile aerial targets using autonomous drones is a challenging and increasingly relevant problem in robotics and security. The increasing presence of unmanned aerial vehicles (UAVs) in unauthorized, restricted airspaces poses significant safety and security risks and has spurred interest in developing effective interception strategies [1] In particular, scenarios such as airspace protection, infrastructure security, and event safety require the ability to capture or neutralize unauthorized drones with high precision and minimal collateral risk. Deploying interceptor drones equipped with nets is apromising approach, but it demandsadvanced control capabilities to match or exceed the agility of evasive targets. Traditional interception methods often rely on accurate models, preplanned strategies, or predictable target behaviour [2]. However, modern quadrotor drones can perform highly dynamic manoeuvres, and will actively evade capture, rendering their trajectories unpredictable and challenging the effectiveness of classical methods [3].