Due to health and energy concerns, the California Legislature is considering bills to prohibit data centers from being exempted from the state's stringent environmental law and impose new tariffs on new major energy users that strain power supplies.

Things to Do in L.A. With Iran, California military families fear another'forever war' Shalena Critchlow, at the Oceanside Pier, holds a photo of her son Cpl. Saiveon Critchlow, who recently completed his service with the U.S. Marines. This is read by an automated voice. Please report any issues or inconsistencies here .

Shocking new video shows NYC's anti-white renters' tsar sharing her desire to make ALL Americans live in social housing Amy Schumer's friends reveal true meaning of thin bikini pictures and why they're'monitoring her'... as depth of ex Chris Fischer's heartbreak is laid bare The urgent questions for Timothy Busfield's wife Melissa Gilbert that no one dares ask: MAUREEN CALLAHAN analyzes child sex abuse claims spanning 30 years... and uncovers a potential bombshell Chilling final message of doctor's wife gunned down next to her twins, 6, in Arkansas mansion... as her son reveals red flags everyone missed Moment teenager launches bottle attack on'paedophile' is shown to murder trial after 49-year-old'was lured to meeting with girl, 16, and beaten to death with rocks' 'Brazilian Popeye' bodybuilder famed for injecting alcohol and oil into his arms dead at 55 Disney adult sparks outrage with her'trashy' bar crawl through kid-friendly theme park Swimsuit model Aoi Fujino, 27, dies just days after retiring with emotional post: 'Please remember me' I was swimming in shallow waters on my dream holiday when I was attacked by a shark. I lost my hand, leg and two-thirds of my blood. I should be dead... but this is how I was saved by three angels Palm Beach elites break out in civil war over $200m'greed project'... as Don Jr's fiancée furiously intervenes Ellen Greenberg case set to be REOPENED by federal prosecutors after infamous 2011 'suicide' of Philadelphia schoolteacher found with 20 stab wounds RICHARD EDEN: Meghan and Harry'plot' and why Prince William and Kate have REALLY hired a crisis expert. 'The end of the world as we know it': Poland warns of'disaster' if NATO nations turn on each other over Trump's bid to claim Greenland as Danish troops arrive in the region At least 40 earthquakes have shaken Southern California since Wednesday morning, with the largest reaching a magnitude of 4.4. The US Geological Survey recorded the first quake near Holtville at 1:40am PT on Wednesday, with the most recent detected on Thursday morning.

Transitioning to sustainable and resilient energy systems requires navigating complex and interdependent trade-offs across environmental, social, and resource dimensions. Neglecting these trade-offs can lead to unintended consequences across sectors. However, existing assessments often evaluate emerging energy pathways and their impacts in silos, overlooking critical interactions such as regional resource competition and cumulative impacts. We present an integrated modeling framework that couples agent-based modeling and Life Cycle Assessment (LCA) to simulate how energy transition pathways interact with regional resource competition, ecological constraints, and community-level burdens. We apply the model to a case study in Southern California. The results demonstrate how integrated and multiscale decision making can shape energy pathway deployment and reveal spatially explicit trade-offs under scenario-driven constraints. This modeling framework can further support more adaptive and resilient energy transition planning on spatial and institutional scales.

Things to Do in L.A. Tap to enable a layout that focuses on the article. The California Department of Corrections and Rehabilitation operates a reentry facility across the street from MacArthur Park. Two inmates living at the facility were shot, one fatally, on Sept. 2. Voice comes from the use of AI. Please report any issues or inconsistencies here . One man was killed and another wounded outside a facility for state prisoners serving out the remainder of their sentences in the community.

This work tackles the problem of overoptimization in reinforcement learning from human feedback (RLHF), a prevalent technique for aligning models with human preferences. RLHF relies on reward or preference models trained on \emph{fixed preference datasets}, and these models are unreliable when evaluated outside the support of this preference data, leading to the common reward or preference hacking phenomenon. We propose novel, pessimistic objectives for RLHF which are provably robust to overoptimization through the use of pessimism in the face of uncertainty, and design practical algorithms, P3O and PRPO, to optimize these objectives. Our approach is derived for the general preference optimization setting, but can be used with reward models as well. We evaluate P3O and PRPO on the tasks of fine-tuning language models for document summarization and creating helpful assistants, demonstrating remarkable resilience to overoptimization.

Muon Space (Muon) is building a constellation of small satellites, many of which will carry global navigation satellite system-reflectometry (GNSS-R) receivers. In preparation for the launch of this constellation, we have developed a generalized deep learning retrieval pipeline, which now produces operational GNSS-R near-surface soil moisture retrievals using data from NASA's Cyclone GNSS (CYGNSS) mission. In this article, we describe the input datasets, preprocessing methods, model architecture, development methods, and detail the soil moisture products generated from these retrievals. The performance of this product is quantified against in situ measurements and compared to both the target dataset (retrievals from the Soil Moisture Active-Passive (SMAP) satellite) and the v1.0 soil moisture product from the CYGNSS mission. The Muon Space product achieves improvements in spatial resolution over SMAP with comparable performance in many regions. An ubRMSE of 0.032 cm$^3$ cm$^{-3}$ for in situ soil moisture observations from SMAP core validation sites is shown, though performance is lower than SMAP's when comparing in forests and/or mountainous terrain. The Muon Space product outperforms the v1.0 CYGNSS soil moisture product in almost all aspects. This initial release serves as the foundation of our operational soil moisture product, which soon will additionally include data from Muon Space satellites.

With the rapid advancement of neural language models, the deployment of over-parameterized models has surged, increasing the need for interpretable explanations comprehensible to human inspectors. Existing post-hoc interpretability methods, which often focus on unigram features of single input textual instances, fail to capture the models' decision-making process fully. Additionally, many methods do not differentiate between decisions based on spurious correlations and those based on a holistic understanding of the input. Our paper introduces DISCO, a novel method for discovering global, rule-based explanations by identifying causal n-gram associations with model predictions. This method employs a scalable sequence mining technique to extract relevant text spans from training data, associate them with model predictions, and conduct causality checks to distill robust rules that elucidate model behavior. These rules expose potential overfitting and provide insights into misleading feature combinations. We validate DISCO through extensive testing, demonstrating its superiority over existing methods in offering comprehensive insights into complex model behaviors. Our approach successfully identifies all shortcuts manually introduced into the training data (100% detection rate on the MultiRC dataset), resulting in an 18.8% regression in model performance -- a capability unmatched by any other method. Furthermore, DISCO supports interactive explanations, enabling human inspectors to distinguish spurious causes in the rule-based output. This alleviates the burden of abundant instance-wise explanations and helps assess the model's risk when encountering out-of-distribution (OOD) data.

Arabic, with its rich diversity of dialects, remains significantly underrepresented in Large Language Models, particularly in dialectal variations. We address this gap by introducing seven synthetic datasets in dialects alongside Modern Standard Arabic (MSA), created using Machine Translation (MT) combined with human post-editing. We present AraDiCE, a benchmark for Arabic Dialect and Cultural Evaluation. We evaluate LLMs on dialect comprehension and generation, focusing specifically on low-resource Arabic dialects. Additionally, we introduce the first-ever fine-grained benchmark designed to evaluate cultural awareness across the Gulf, Egypt, and Levant regions, providing a novel dimension to LLM evaluation. Our findings demonstrate that while Arabic-specific models like Jais and AceGPT outperform multilingual models on dialectal tasks, significant challenges persist in dialect identification, generation, and translation. This work contributes ~45K post-edited samples, a cultural benchmark, and highlights the importance of tailored training to improve LLM performance in capturing the nuances of diverse Arabic dialects and cultural contexts. We will release the dialectal translation models and benchmarks curated in this study.

We introduce the Concept Bottleneck Large Language Model (CB-LLM), a pioneering approach to creating inherently interpretable Large Language Models (LLMs). Unlike traditional black-box LLMs that rely on post-hoc interpretation methods with limited neuron function insights, CB-LLM sets a new standard with its built-in interpretability, scalability, and ability to provide clear, accurate explanations. This innovation not only advances transparency in language models but also enhances their effectiveness. Our unique Automatic Concept Correction (ACC) strategy successfully narrows the performance gap with conventional black-box LLMs, positioning CB-LLM as a model that combines the high accuracy of traditional LLMs with the added benefit of clear interpretability -- a feature markedly absent in existing LLMs.