And in so many ways, it kinda sucks. A new graphics card costs more than a mortgage payment because billionaires are sucking up all the GPUs to boil the planet and make Hayao Miyazaki cry at the same time, and I still don't have a Marty McFly hoverboard. But at least I can order fast food that literally flies to my door. In fact, I could order a flying curry delivery if I lived in Charlotte, North Carolina--specifically, within four miles of the Arboretum Shopping Center--where DoorDash is now offering food deliveries via drone. You can choose from a limited selection of local eateries, including Panera Bread, Matcha Cafe Maiko, and Joa Korean.

Kolmogorov-Arnold networks (KANs) have attracted attention recently as an alternative to multilayer perceptrons (MLPs) for scientific machine learning. However, KANs can be expensive to train, even for relatively small networks. Inspired by finite basis physics-informed neural networks (FBPINNs), in this work, we develop a domain decomposition method for KANs that allows for several small KANs to be trained in parallel to give accurate solutions for multiscale problems. We show that finite basis KANs (FBKANs) can provide accurate results with noisy data and for physics-informed training.

This article presents an AI-enabled Smart Video Surveillance (SVS) designed to enhance safety in community spaces such as educational and recreational areas, and small businesses. The proposed system innovatively integrates with existing CCTV and wired camera networks, simplifying its adoption across various community cases to leverage recent AI advancements. Our SVS system, focusing on privacy, uses metadata instead of pixel data for activity recognition, aligning with ethical standards. It features cloud-based infrastructure and a mobile app for real-time, privacy-conscious alerts in communities. This article notably pioneers a comprehensive real-world evaluation of the SVS system, covering AI-driven visual processing, statistical analysis, database management, cloud communication, and user notifications. It's also the first to assess an end-to-end anomaly detection system's performance, vital for identifying potential public safety incidents. For our evaluation, we implemented the system in a community college, serving as an ideal model to exemplify the proposed system's capabilities. Our findings in this setting demonstrate the system's robustness, with throughput, latency, and scalability effectively managing 16 CCTV cameras. The system maintained a consistent 16.5 frames per second (FPS) over a 21-hour operation. The average end-to-end latency for detecting behavioral anomalies and alerting users was 26.76 seconds.

In data-driven systems, data exploration is imperative for making real-time decisions. However, big data is stored in massive databases that are difficult to retrieve. Approximate Query Processing (AQP) is a technique for providing approximate answers to aggregate queries based on a summary of the data (synopsis) that closely replicates the behavior of the actual data, which can be useful where an approximate answer to the queries would be acceptable in a fraction of the real execution time. This study explores the novel utilization of Generative Adversarial Networks (GANs) in the generation of tabular data that can be employed in AQP for synopsis construction. We thoroughly investigate the unique challenges posed by the synopsis construction process, including maintaining data distribution characteristics, handling bounded continuous and categorical data, and preserving semantic relationships and then introduce the advancement of tabular GAN architectures that overcome these challenges. Furthermore, we propose and validate a suite of statistical metrics tailored for assessing the reliability of the GAN-generated synopses. Our findings demonstrate that advanced GAN variations exhibit a promising capacity to generate high-fidelity synopses, potentially transforming the efficiency and effectiveness of AQP in data-driven systems.

Anomaly detection is a crucial task in complex distributed systems. A thorough understanding of the requirements and challenges of anomaly detection is pivotal to the security of such systems, especially for real-world deployment. While there are many works and application domains that deal with this problem, few have attempted to provide an in-depth look at such systems. In this survey, we explore the potentials of graph-based algorithms to identify anomalies in distributed systems. These systems can be heterogeneous or homogeneous, which can result in distinct requirements. One of our objectives is to provide an in-depth look at graph-based approaches to conceptually analyze their capability to handle real-world challenges such as heterogeneity and dynamic structure. This study gives an overview of the State-of-the-Art (SotA) research articles in the field and compare and contrast their characteristics. To facilitate a more comprehensive understanding, we present three systems with varying abstractions as use cases. We examine the specific challenges involved in anomaly detection within such systems. Subsequently, we elucidate the efficacy of graphs in such systems and explicate their advantages. We then delve into the SotA methods and highlight their strength and weaknesses, pointing out the areas for possible improvements and future works.

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Check out what's clicking on Foxnews.com. Vice President Kamala Harris is being considered a "close contact" to President Biden, who tested positive for COVID on Thursday morning, according to a White House official. A White House official told Fox News there are no changes being made to Harris' schedule. She tested negative for COVID Thursday morning. Harris was at the 2022 international meeting of the Omega Psi Phi fraternity in Charlotte, North Carolina, on Thursday.

Charlotte, North Carolina is one of the most prominent banking cities in the U.S., hosts NASCAR's home track, and is widely regarded as one of the South's most diverse and vibrant major cities. But did you know that it is also a top tech hub? Tech jobs in Charlotte are easy to come by, well-paid, and span many sectors. What's more, they can help you build an exciting life in a culturally-diverse and affordable major city! Read on to learn more about tech jobs in Charlotte in terms of top employers, pay, and job opportunities.

A Tesla Model 3 driving on'autopilot' smacked into a Florida Highway Patrol cruiser on Saturday morning, narrowly missing the driver of the cruiser who had stopped in order to help a disabled vehicle. The incident is the 12th such smash involving a Tesla on autopilot mode and an emergency vehicle. All the cars which have been struck had their lights flashing, or had deployed an emergency flare, illuminated warning sign or cones, raising questions about whether they may have confused the Tesla's sensors. Saturday's smash happened after when the 28-year-old trooper, who has not been named, stopped shortly after 5 am on August 28 on I-4 near downtown Orlando while responding to a broken down car. He put his emergency lights and was walking over to a disabled vehicle when the Tesla hit the cruiser's left side, according to a copy of the police report seen by DailyMail.com.