Table I summarizes the datasets used for training and evaluation. Both baseline models and the PV AL framework were fine-tuned on 2,000 annotated tiles from Santa Ana, CA. The large-scale evaluation set includes about 100,000 tiles from Tempe and Santa Ana, while 480 tiles per region were used for cross-domain generalization tests across diverse climates and geographies. B. Multimodal LLM Configuration Configuring the PV AL system for solar panel detection involves a multi-faceted approach that integrates prompt engineering, output standardization, and supervised fine-tuning. This configuration is critical for steering the foundational GPT -4o model towards the specific, high-precision task of geospatial analysis. Prompt Task Decomposition Identify the presence of solar panels in images of residential rooftops, and determine their locations and quantity within the images. You will be provided with images that may contain residential rooftop solar systems. Analyze each image to detect solar panels. Steps: 1. ** Image Analysis **: Examine the entire image to identify any objects that appear to be solar panels.

Judges describe a campaign of firings and interference which threatens the system's independence. On a Thursday morning last month, Patrick O'Brien, a federal immigration judge, walked into his courtroom in downtown San Francisco. He was scheduled for a master-calendar hearing, a roll call, essentially, to get cases ready for trial. O'Brien was wearing a matte-black robe that seemed to absorb the artificial light overhead. He took his seat, scanned the room, and angled himself toward a computer monitor. The court was leanly staffed. There was a judicial clerk but no bailiff or stenographer. Opposite the judge were tables for the prosecution--the Department of Homeland Security--and for the respondent, a succession of immigrants who were applying for asylum. A Spanish interpreter appeared as a faceless box on a big screen. About ten people, all Latino, sat in wooden pews, gripping folders full of esoteric documents.

Accurate detection and localization of solar photovoltaic (PV) panels in satellite imagery is essential for optimizing microgrids and active distribution networks (ADNs), which are critical components of renewable energy systems. Existing methods lack transparency regarding their underlying algorithms or training datasets, rely on large, high-quality PV training data, and struggle to generalize to new geographic regions or varied environmental conditions without extensive re-training. These limitations lead to inconsistent detection outcomes, hindering large-scale deployment and data-driven grid optimization. In this paper, we investigate how large language models (LLMs) can be leveraged to overcome these challenges. Despite their promise, LLMs face several challenges in solar panel detection, including difficulties with multi-step logical processes, inconsistent output formatting, frequent misclassification of visually similar objects (e.g., shadows, parking lots), and low accuracy in complex tasks such as spatial localization and quantification. To overcome these issues, we propose the PV Assessment with LLMs (PVAL) framework, which incorporates task decomposition for more efficient workflows, output standardization for consistent and scalable formatting, few-shot prompting to enhance classification accuracy, and fine-tuning using curated PV datasets with detailed annotations. PVAL ensures transparency, scalability, and adaptability across heterogeneous datasets while minimizing computational overhead. By combining open-source accessibility with robust methodologies, PVAL establishes an automated and reproducible pipeline for solar panel detection, paving the way for large-scale renewable energy integration and optimized grid management.

In the rapidly evolving field of healthcare management, the analysis of medical claims data has become an essential component for improving the quality and equity of healthcare services. The nature of care delivery in the United states is heavily influenced by its fragmentation--care is often spread across multiple disconnected providers (e.g., primary-care physicians, specialists). Settings with greater care fragmentation have been shown to inhibit effective communication and coordination between care team members, thus contributing to higher costs and lower quality of treatment [13,33,21,1,7]. Despite the well-understood impacts of fragmentation, there are still few quantitative tools that can capture the mechanisms of care delivery networks at scale [14]. Standard analyses of local infrastructure features, often executed using tabular data, are limited in their ability to distill complex dynamics between physicians.

We present a method for jointly modeling power generation from a fleet of photovoltaic (PV) systems. We propose a white-box method that finds a function that invertibly maps vector time-series data to independent and identically distributed standard normal variables. The proposed method, based on a novel approach for fitting a smooth, periodic copula transform to data, captures many aspects of the data such as diurnal variation in the distribution of power output, dependencies among different PV systems, and dependencies across time. It consists of interpretable steps and is scalable to many systems. The resulting joint probability model of PV fleet output across systems and time can be used to generate synthetic data, impute missing data, perform anomaly detection, and make forecasts. In this paper, we explain the method and demonstrate these applications.

We provide an integrated and systematic automation approach to spatial object recognition and positional detection using AI machine learning and computer vision algorithms for Orange County, California. We describe a comprehensive methodology for multi-sensor, high-resolution field data acquisition, along with post-field processing and pre-analysis processing tasks. We developed a series of algorithmic formulations and workflows that integrate convolutional deep neural network learning with detected object positioning estimation in 360{\deg} equirectancular photosphere imagery. We provide examples of application processing more than 800 thousand cardinal directions in photosphere images across two areas in Orange County, and present detection results for stop-sign and fire hydrant object recognition. We discuss the efficiency and effectiveness of our approach, along with broader inferences related to the performance and implications of this approach for future technological innovations, including automation of spatial data and public asset inventories, and near real-time AI field data systems.

Tactile afferents such as (RA), and Pacinian (PC) afferents that respond to external stimuli enable complicated actions such as grasping, stroking and identifying an object. To understand the tactile sensation induced by these actions deeply, the activities of the tactile afferents need to be revealed. For this purpose, we develop a computational model for each tactile afferent for vibration stimuli, combining finite element analysis finite element method (FEM) analysis and a leaky integrate-and-fire model that represents the neural characteristics. This computational model can easily estimate the neural activities of the tactile afferents without measuring biological data. Skin deformation calculated using FEM analysis is substituted into the integrate-and-fire model as current input to calculate the membrane potential of each tactile afferent. We optimized parameters in the integrate-and-fire models using reported biological data. Then, we calculated the responses of the numerical models to sinusoidal, diharmonic, and white-noise-like mechanical stimuli to validate the proposed numerical models. From the result, the computational models well reproduced the neural responses to vibration stimuli such as sinusoidal, diharmonic, and noise stimuli and compare favorably with the similar computational models that can simulate the responses to vibration stimuli. Introduction Our tactile senses can perceive not only the shape and material of an object but also the texture of an object, enabling us to perform actions such as grasping, stroking, and identifying an object. Tactile afferents located in the skin that respond to external stimuli enable these complicated actions. Usually, sensory evaluations are performed to interpret the tactile sensation induced by these actions. To understand the perceived tactile sensation quantitatively, it is necessary to reveal the relationship between the skin deformation induced by an object and the activities of tactile afferents in the skin. Of note, there are two possible methods to understand how the tactile afferents are activated: the first is to directly measure the action potential of tactile afferents by inserting electrodes into nerve fibers [1-3].

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IBM Data and AI general manager Rob Thomas discusses AI being incorporated into the workforce. The labor market may be humming right now, but there may be a dark cloud looming ahead. Over the course of the next decade, up to 800 million jobs globally could disappear due to advances in artificial intelligence and robotics, according to research from the McKinsey Global Institute, a top consulting firm. An estimated one-third of the 2030 workforce in the U.S. may need to learn new skills and find work in new occupations. The changes won't hit the country equally.

This complimentary Business Briefing is a 120-minute session designed to bring leaders together to learn about, reflect on and discuss recent research and the key competencies for organizational agility in the context of the massive changes that are anticipated from the widespread implementation of artificial intelligence (AI). Today organizations need to gather and act on information, make decisions quickly and implement them to meet the rapidly-evolving requirements of customers and the business environment. The ability to do so is becoming increasingly important in this era of digital transformation and advances in Artificial Intelligence (AI). We provide a framework for leaders, addressing important considerations for those who want to approach building agility within their organization in a focused, deliberate way.