Things to Do in L.A. Tap to enable a layout that focuses on the article. L.A.'s defense industry is booming. This is read by an automated voice. Please report any issues or inconsistencies here . L.A. defense-tech startups like Gambit face funding shortfalls as the Small Business Innovation Research program expired in September amid a Capitol Hill dispute.

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.

The purpose of this study is to present and compare three denoising diffusion probabilistic models (DDPMs) that generate 3D $T_1$-weighted MRI human brain images. Three DDPMs were trained using 80,675 image volumes from 42,406 subjects spanning 38 publicly available brain MRI datasets. These images had approximately 1 mm isotropic resolution and were manually inspected by three human experts to exclude those with poor quality, field-of-view issues, and excessive pathology. The images were minimally preprocessed to preserve the visual variability of the data. Furthermore, to enable the DDPMs to produce images with natural orientation variations and inhomogeneity, the images were neither registered to a common coordinate system nor bias field corrected. Evaluations included segmentation, Frechet Inception Distance (FID), and qualitative inspection. Regarding results, all three DDPMs generated coherent MR brain volumes. The velocity and flow prediction models achieved lower FIDs than the sample prediction model. However, all three models had higher FIDs compared to real images across multiple cohorts. In a permutation experiment, the generated brain regional volume distributions differed statistically from real data. However, the velocity and flow prediction models had fewer statistically different volume distributions in the thalamus and putamen. In conclusion this work presents and releases the first 3D non-latent diffusion model for brain data without skullstripping or registration. Despite the negative results in statistical testing, the presented DDPMs are capable of generating high-resolution 3D $T_1$-weighted brain images. All model weights and corresponding inference code are publicly available at https://github.com/piksl-research/medforj .

Things to Do in L.A. Tap to enable a layout that focuses on the article. Man's parents helped him attack his ex and pry their grandson out of her arms, officials say The 1-year-old boy who allegedly was taken from his mother at knifepoint in City of Industry on Sunday was found in Arizona. This is read by an automated voice. Please report any issues or inconsistencies here . A 20-year-old man and his parents allegedly attacked his ex-partner outside a Target store, forcibly taking their baby from her arms.

Ensemble datasets are ever more prevalent in various scientific domains. In climate science, ensemble datasets are used to capture variability in projections under plausible future conditions including greenhouse and aerosol emissions. Each ensemble model run produces projections that are fundamentally similar yet meaningfully distinct. Understanding this variability among ensemble model runs and analyzing its magnitude and patterns is a vital task for climate scientists. In this paper, we present ClimateSOM, a visual analysis workflow that leverages a self-organizing map (SOM) and Large Language Models (LLMs) to support interactive exploration and interpretation of climate ensemble datasets. The workflow abstracts climate ensemble model runs - spatiotemporal time series - into a distribution over a 2D space that captures the variability among the ensemble model runs using a SOM. LLMs are integrated to assist in sensemaking of this SOM-defined 2D space, the basis for the visual analysis tasks. In all, ClimateSOM enables users to explore the variability among ensemble model runs, identify patterns, compare and cluster the ensemble model runs. To demonstrate the utility of ClimateSOM, we apply the workflow to an ensemble dataset of precipitation projections over California and the Northwestern United States. Furthermore, we conduct a short evaluation of our LLM integration, and conduct an expert review of the visual workflow and the insights from the case studies with six domain experts to evaluate our approach and its utility.

Twenty-five years from today, Santa Ana winds will scream through Los Angeles on a dry autumn morning, turning a small hillside campfire into a deadly, fast-moving blaze. At that moment, the city will spring into action. Los Angeles knows how to weather a crisis -- or two or three. Angelenos are tapping into that resilience, striving to build a city for everyone. Satellites will team up with anemometers, pairing live aerial footage with wind patterns to tell firefighters exactly where the fire is going.

The spark becomes a flame, and within seconds, a satellite dish swirling overhead picks up on the anomaly and triggers an alarm. An autonomous helicopter takes flight and zooms toward the fire, using sensors to locate the blaze and artificial intelligence to generate a plan of attack. It measures the wind speed and fire movement, communicating constantly with the unmanned helicopter behind it, and the one behind that. Los Angeles knows how to weather a crisis -- or two or three. Angelenos are tapping into that resilience, striving to build a city for everyone.

In the battle for AI supremacy, Meta has just made a major move. Mark Zuckerberg's firm – which owns Facebook, Instagram and WhatsApp – has lured a renowned AI expert away from rival Apple with an eye-watering pay offer. Over the next'several years', Ruoming Pang, originally from China, will earn more than 200 million ( 147 million) in his new role, a report reveals. The'unusually high' earnings package is among the highest of any corporate job, including CEO roles at the world's major banks, the report adds. Mr Pang becomes a high-ranking member of Meta's mysterious new'superintelligence' lab, thought to be based in California.

Depression is one of the most prevalent mental health disorders worldwide, with estimates indicating that around 5% of the worlds' adult population [1, 2] suffers from this condition. The primary methods for screening and monitoring depression rely on self-reported questionnaires, such as the Patient Health Questionnaire (PHQ-9) [3], Beck's Depression Inventory (BDI) [4] and Hamilton Depression Ratings Scale (HDRS) [5]. While these questionnaires are effective to varying degrees at screening patients for depression, they provide only limited information about the affected underlying neuro-cognitive processes in individuals, limiting the ability to personalize treatments. Given the heterogeneity of depressive symptomatology across patient populations [6, 7], it is crucial to elucidate the underlying neurophysiological mechanisms to support the development of more effective and individualized procedures for screening, monitoring, and treatment. Prior functional imaging studies have identified increased activity in anterior cin-gulate cortex (especially the subgenual anterior cingulate) during presentation of emotional stimuli, altered connectivity in prefrontal cortical areas, and default mode network as potential differentiating markers in depressed participants [8-13].

Misinformation surrounding emerging outbreaks poses a serious societal threat, making robust countermeasures essential. One promising approach is stance detection (SD), which identifies whether social media posts support or oppose misleading claims. In this work, we finetune classifiers on COVID-19 misinformation SD datasets consisting of claims and corresponding tweets. Specifically, we test controllable misinformation generation (CMG) using large language models (LLMs) as a method for data augmentation. While CMG demonstrates the potential for expanding training datasets, our experiments reveal that performance gains over traditional augmentation methods are often minimal and inconsistent, primarily due to built-in safeguards within LLMs. We release our code and datasets to facilitate further research on misinformation detection and generation.