Things to Do in L.A. She thought she talking to her favorite celebrity. This is read by an automated voice. Please report any issues or inconsistencies here . Abigail Ruvalcaba was intrigued when a handsome daytime soap opera actor she'd been watching for years reached out to her in a Facebook message. His rugged exterior softened by his piercing blue eyes and an almost shy smile disarmed her.

How is Russia replenishing its military? What is a'coalition of the willing'? How China forgot promises and'debts' to Ukraine How are Europe, the US pulling apart on Ukraine? In the past week, Russia has ramped up a diplomacy of intimidation in the Baltic Sea using planes, drones and words aimed at Ukraine's European allies. After threats towards Finland earlier in September, Russia violated Estonian airspace on Friday and German airspace on Sunday, days after it had flown two dozen drones into Poland.

A new artificial intelligence tool designed to crack down on fraud has helped the UK government recover almost £500m over the last year, the BBC can reveal. More than a third of the money clawed back related to fraudulent activity during the Covid-19 pandemic, with other cash being recouped from unlawful council tax claims and illegal subletting of social housing. The government will announce later that a new AI tool which has helped to identify the fraud will now be licensed to other countries, including the US and Australia. Civil liberties campaigners have previously criticised the Labour government for its use of AI in trying to counter fraud. The Cabinet Office says the £480m recovered in the 12 months from April 2024 is the largest sum ever reclaimed by government anti-fraud teams in a single year.

Is recognising Palestine a way to'save face' for Western leaders? Organisers of the Global Sumud Flotilla, a Gaza-bound flotilla with pro-Palestinian activists on board carrying aid, reported hearing explosions and seeing multiple drone attacks from their boats situated off Greece from late Tuesday to the early hours of Wednesday. "Multiple drones, unidentified objects dropped, communications jammed and explosions heard from a number of boats," the Global Sumud Flotilla said in a statement, without adding whether there were any casualties. "We are witnessing these psychological operations firsthand, right now, but we will not be intimidated." Suited in a life jacket, Brazilian organiser Tiago Avila updated on his Instagram at midnight on Wednesday that a total of 10 attacks targeted multiple boats with sound bombs and explosive flares.

An escalator used by Donald Trump abruptly stopped because of a safety mechanism that may have been triggered by his videographer, the United Nations has said. The videographer had been travelling backwards up the escalator to capture the US president's arrival with First Lady Melania Trump and may have inadvertently triggered the safety function upon reaching the top, a UN spokesperson said. Trump jokingly referred to the incident during his Tuesday speech at the UN building, saying: If the First Lady wasn't in great shape, she would've fallen. The White House had raised concerns that someone deliberately stopped the escalator as the couple were stepping on. If someone at the U.N. intentionally stopped the escalator as the President and First Lady were stepping on, they need to be fired and investigated immediately, White House press secretary Karoline Leavitt posted on X after the incident.

We study multivariate linear regression under Gaussian covariates in two settings, where data may be erased or corrupted by an adversary under a coordinate-wise budget. In the incomplete data setting, an adversary may inspect the dataset and delete entries in up to an $η$-fraction of samples per coordinate; a strong form of the Missing Not At Random model. In the corrupted data setting, the adversary instead replaces values arbitrarily, and the corruption locations are unknown to the learner. Despite substantial work on missing data, linear regression under such adversarial missingness remains poorly understood, even information-theoretically. Unlike the clean setting, where estimation error vanishes with more samples, here the optimal error remains a positive function of the problem parameters. Our main contribution is to characterize this error up to constant factors across essentially the entire parameter range. Specifically, we establish novel information-theoretic lower bounds on the achievable error that match the error of (computationally efficient) algorithms. A key implication is that, perhaps surprisingly, the optimal error in the missing data setting matches that in the corruption setting-so knowing the corruption locations offers no general advantage.

Residential electricity demand at granular scales is driven by what people do and for how long. Accurately forecasting this demand for applications like microgrid management and demand response therefore requires generative models that can produce realistic daily activity sequences, capturing both the timing and duration of human behavior. This paper develops a generative model of human activity sequences using nationally representative time-use diaries at a 10-minute resolution. We use this model to quantify which demographic factors are most critical for improving predictive performance. We propose a hierarchical semi-Markov framework that addresses two key modeling challenges. First, a time-inhomogeneous Markov \emph{router} learns the patterns of ``which activity comes next." Second, a semi-Markov \emph{hazard} component explicitly models activity durations, capturing ``how long" activities realistically last. To ensure statistical stability when data are sparse, the model pools information across related demographic groups and time blocks. The entire framework is trained and evaluated using survey design weights to ensure our findings are representative of the U.S. population. On a held-out test set, we demonstrate that explicitly modeling durations with the hazard component provides a substantial and statistically significant improvement over purely Markovian models. Furthermore, our analysis reveals a clear hierarchy of demographic factors: Sex, Day-Type, and Household Size provide the largest predictive gains, while Region and Season, though important for energy calculations, contribute little to predicting the activity sequence itself. The result is an interpretable and robust generator of synthetic activity traces, providing a high-fidelity foundation for downstream energy systems modeling.

We present a wall-to - wall map of dominant tree species in Swedish forests accompanied by pixel - level uncertainty estimates. The tree species classification is based on spatiotemporal metrics derived from Sentinel-1 and Sentinel - 2 satellite data, combined with field observations from the Swedish National Forest Inventory and auxiliary data on geomorphometry and canopy height. We apply an extreme gradient boosting model with Bayesian optimization to relate field observations to satellite-derived features and generate the final species map. Classification uncertainty is quantified using Shannon's entropy of the predicted class probabilities, which provide a spatially explicit measure of model confidence. The final model achieved an overall accuracy of 85% (F1 score = 0.82, Matthews correlation coefficient = 0.81), and mapped species distributions showed strong agreement with official forest statistics (r = 0.96). V ariable importance analysis revealed that the most influential predictors were optical bands from Sentinel - 2, particularly those acquired in spring and summer. This study provides scalable, interpretable, and policy-relevant method for tree species mapping with integrated uncertainty that are well-suited to meet emerging legislative and environmental goals.

The 2024 US presidential election is the first major contest to occur in the US since the popularization of large language models (LLMs). Building on lessons from earlier shifts in media (most notably social media's well studied role in targeted messaging and political polarization) this moment raises urgent questions about how LLMs may shape the information ecosystem and influence political discourse. While platforms have announced some election safeguards, how well they work in practice remains unclear. Against this backdrop, we conduct a large-scale, longitudinal study of 12 models, queried using a structured survey with over 12,000 questions on a near-daily cadence from July through November 2024. Our design systematically varies content and format, resulting in a rich dataset that enables analyses of the models' behavior over time (e.g., across model updates), sensitivity to steering, responsiveness to instructions, and election-related knowledge and "beliefs." In the latter half of our work, we perform four analyses of the dataset that (i) study the longitudinal variation of model behavior during election season, (ii) illustrate the sensitivity of election-related responses to demographic steering, (iii) interrogate the models' beliefs about candidates' attributes, and (iv) reveal the models' implicit predictions of the election outcome. To facilitate future evaluations of LLMs in electoral contexts, we detail our methodology, from question generation to the querying pipeline and third-party tooling. We also publicly release our dataset at https://huggingface.co/datasets/sarahcen/llm-election-data-2024

Describing and comparing complex systems requires principled, theoretically grounded tools. Built around the phenomenon of type turbulence, allotaxonographs provide map-and-list visual comparisons of pairs of heavy-tailed distributions. Allotaxonographs are designed to accommodate a wide range of instruments including rank- and probability-turbulence divergences, Jenson-Shannon divergence, and generalized entropy divergences. Here, we describe a suite of programmatic tools for rendering allotaxonographs for rank-turbulence divergence in Matlab, Javascript, and Python, all of which have different use cases.