There have always been two sides to the Meta CEO. But since the beginning of Trump's second term, the nice side has taken a back seat. Ruthlessness is now the name of the game. January 31, 2024, is an uncomfortable day in Washington. An icy wind is whistling around the corners of the Dirksen Senate Office Building, right next to the Capitol. Inside, the atmosphere is not much more welcoming. Indeed, it feels downright hostile. In the large hall, women and men are holding up signs - silent, in mourning and protest. On them are pictures of girls and boys, 12, 13, 14, 15 years old. Harassed, sexually abused, mistreated on social networks on the internet. Many of the children have died from the consequences. And the man primarily to blame is said to be the one sitting in a blue suit in the front row: Mark Zuckerberg, 39 years old at the time. His usually radiant boyish face is expressionless.

Repetitive laboratory testing unlikely to yield clinically useful information is a common practice that burdens patients and increases healthcare costs. Education and feedback interventions have limited success, while general test ordering restrictions and electronic alerts impede appropriate clinical care. We introduce and evaluate SmartAlert, a machine learning (ML)-driven clinical decision support (CDS) system integrated into the electronic health record that predicts stable laboratory results to reduce unnecessary repeat testing. This case study describes the implementation process, challenges, and lessons learned from deploying SmartAlert targeting complete blood count (CBC) utilization in a randomized controlled pilot across 9270 admissions in eight acute care units across two hospitals between August 15, 2024, and March 15, 2025. Results show significant decrease in number of CBC results within 52 hours of SmartAlert display (1.54 vs 1.82, p <0.01) without adverse effect on secondary safety outcomes, representing a 15% relative reduction in repetitive testing. Implementation lessons learned include interpretation of probabilistic model predictions in clinical contexts, stakeholder engagement to define acceptable model behavior, governance processes for deploying a complex model in a clinical environment, user interface design considerations, alignment with clinical operational priorities, and the value of qualitative feedback from end users. In conclusion, a machine learning-driven CDS system backed by a deliberate implementation and governance process can provide precision guidance on inpatient laboratory testing to safely reduce unnecessary repetitive testing.

Accurate electric vehicle (EV) charging demand forecasting is essential for stable grid operation and proactive EV participation in electricity market. Existing forecasting methods, particularly those based on graph neural networks, are often limited to modeling pairwise relationships between stations, failing to capture the complex, group-wise dynamics inherent in urban charging networks. To address this gap, we develop a novel forecasting framework namely HyperCast, leveraging the expressive power of hypergraphs to model the higher-order spatiotemporal dependencies hidden in EV charging patterns. HyperCast integrates multi-view hypergraphs, which capture both static geographical proximity and dynamic demand-based functional similarities, along with multi-timescale inputs to differentiate between recent trends and weekly periodicities. The framework employs specialized hyper-spatiotemporal blocks and tailored cross-attention mechanisms to effectively fuse information from these diverse sources: views and timescales. Extensive experiments on four public datasets demonstrate that HyperCast significantly outperforms a wide array of state-of-the-art baselines, demonstrating the effectiveness of explicitly modeling collective charging behaviors for more accurate forecasting.

The detailed description of each data point's entries is as follows. "query": "What is the weather in Palo Alto?", In this example, the query asks about the current weather in Palo Alto. Here's an example JSON data for the parallel function-calling category, i.e., the user's query contains "query": "Find the sum of all the multiples of 3 and 5 "description": "The numbers to find multiples of.", "description": "Find the product of the first n prime This step helps to filter out poorly formatted or incomplete data points.

Neighbors complained about noise, security guards, and hordes of traffic. An unlicensed school named after the Zuckerbergs' pet chicken tipped them over the edge. The Crescent Park neighborhood of Palo Alto, California, has some of the best real estate in the country, with a charming hodgepodge of homes ranging in style from Tudor revival to modern farmhouse and contemporary Mediterranean. It also has a gigantic compound that is home to Mark Zuckerberg, his wife Priscilla Chan, and their daughters Maxima, August, and Aurelia. Their land has expanded to include 11 previously separate properties, five of which are connected by at least one property line. The Zuckerberg compound's expansion first became a concern for Crescent Park neighbours as early as 2016, due to fears that his purchases were driving up the market. Then, about five years later, neighbors noticed that a school appeared to be operating out of the Zuckerberg compound. This would be illegal under the area's residential zoning code without a permit.

Analyzing financial transactions is crucial for ensuring regulatory compliance, detecting fraud, and supporting decisions. The complexity of financial transaction data necessitates advanced techniques to extract meaningful insights and ensure accurate analysis. Since Transformer-based models have shown outstanding performance across multiple domains, this paper seeks to explore their potential in understanding financial transactions. This paper conducts extensive experiments to evaluate three types of Transformer models: Encoder-Only, Decoder-Only, and Encoder-Decoder models. For each type, we explore three options: pretrained LLMs, fine-tuned LLMs, and small proprietary models developed from scratch. Our analysis reveals that while LLMs, such as LLaMA3-8b, Flan-T5, and SBERT, demonstrate impressive capabilities in various natural language processing tasks, they do not significantly outperform small proprietary models in the specific context of financial transaction understanding. This phenomenon is particularly evident in terms of speed and cost efficiency. Proprietary models, tailored to the unique requirements of transaction data, exhibit faster processing times and lower operational costs, making them more suitable for real-time applications in the financial sector. Our findings highlight the importance of model selection based on domain-specific needs and underscore the potential advantages of customized proprietary models over general-purpose LLMs in specialized applications. Ultimately, we chose to implement a proprietary decoder-only model to handle the complex transactions that we previously couldn't manage. This model can help us to improve 14% transaction coverage, and save more than \$13 million annual cost.

This paper addresses the critical challenge of optimizing electric vehicle charging station placement through a novel data-driven methodology employing causal discovery techniques. While traditional approaches prioritize economic factors or power grid constraints, they often neglect empirical charging patterns that ultimately determine station utilization. We analyze extensive charging data from Palo Alto and Boulder (337,344 events across 100 stations) to uncover latent relationships between station characteristics and utilization. Applying structural learning algorithms (NOTEARS and DAGMA) to this data reveals that charging demand is primarily determined by three factors: proximity to amenities, EV registration density, and adjacency to high-traffic routes. These findings, consistent across multiple algorithms and urban contexts, challenge conventional infrastructure distribution strategies. We develop an optimization framework that translates these insights into actionable placement recommendations, identifying locations likely to experience high utilization based on the discovered dependency structures. The resulting site selection model prioritizes strategic clustering in high-amenity areas with substantial EV populations rather than uniform spatial distribution. Our approach contributes a framework that integrates empirical charging behavior into infrastructure planning, potentially enhancing both station utilization and user convenience. By focusing on data-driven insights instead of theoretical distribution models, we provide a more effective strategy for expanding charging networks that can adjust to various stages of EV market development.

Tesla has been granted a permit to operate a taxi service in California, a spokesperson for the California Public Utilities Commission, a state regulator, said Tuesday. It marks the first step towards Tesla's and CEO Elon Musk's vision of operating a driverless taxi service in the state. One day, Musk has said, Tesla owners should be able to rent out their cars as sort of self-driving Ubers while they're not using them. He has said current owners should be able to operate their Models 3 and Y autonomously in the state later this year--a plan that faces both technological and regulatory hurdles. But despite the permit, Tesla's driverless taxi future still seems far off in California, which has the perfect climate for self-driving cars but some of the strictest regulatory requirements in the US for testing and operating them.

Billionaire mogul and Tesla CEO Elon Musk announced that his company's long-rumored, fully automated taxi service will arrive late this summer'Tesla Robotaxi unveil on 8/8,' Musk posted to his social media platform X. But Musk made similar promises five years ago this month, at Tesla's Autonomy Day with investors at the electric vehicle-maker's Palo Alto, California headquarters. Musk told investors that was'very confident' then, in April 2019, that Tesla will have autonomous robo-taxis on the road as soon as next year, 2020. Then billionaire tech mogul showed off a Tesla ride-sharing app. 'Tesla Robotaxi unveil on 8/8,' Tesla mogul Elon Musk posted to his social media platform X But Musk made similar promises five years ago this month, at Tesla's Autonomy Day with investors at the electric vehicle-maker's Palo Alto, California headquarters In April 2019, Musk said he was'very confident' Tesla would have autonomous robo-taxis on the road as soon as 2020. Musk said Tesla began developing full self-driving technology in 2016 with the idea of launching a ride-share service.