Abstract Neural architecture search (NAS) has emerged as a promising approach for automating deep learning model design. However, its application in sports analytics faces unique challenges due to the complex interplay between biomechanical patterns, physiological adaptations, and coaching expertise. Traditional NAS methods need help to effectively capture the multifaceted nature of athletic performance, often failing to integrate qualitative coaching insights with quantitative measurements. We introduce ChampionNet, a framework incorporating NAS and large language models to enhance accuracy in predicting athletic performance and tailoring training regimens. Our approach offers three primary contributions: integrating hyperdimensional embedding to capture fine-grained biomechanical features and physiological parameters with exceptional detail, a structure-preserving graph encoding leverages to maintain crucial spatiotemporal relationships in athletic movements, and the novel comprehensiveness of the training graph that models forward performance prediction and backward physiological adaptation pathways. Our experiments on various sports demonstrate that ChampionNet outperforms other models by 2.5% in accuracy and over 61.9% in computational cost. Further insights illustrate the framework's performance with complex patterns and multi-modal data, especially for sports with advanced biomechanical needs. These findings support ChampionNet's effectiveness as an integrative athletic performance optimization solution, highlighting the need for automated architecture search tailored to sports.

Summary Recently, the mainstream hardware vendors such as Intel and AMD have made significant efforts to integrate the central processing unit (CPU) and the graphics processing unit (GPU) into a single chip, which forms a coupled CPU‐GPU architecture. Data transfer between the CPU and the GPU through a Peripheral Component Interconnect Express bus is eliminated on this architecture, which provides new opportunities for database community to optimize query processing. Because of the lack of comprehensive evaluation of database systems on coupled CPU‐GPU platforms, it is difficult for academic and industry researchers to make appropriate decisions on improvement and optimization directions. In this paper, we conduct an extensive experimental study to evaluate an online analytical processing system on Intel and AMD machines. The performance difference is measured and analyzed when executing queries on integrated GPUs and multicore CPUs. The impacts of various parameters, data sizes, and optimization techniques on performance are also investigated. The results provide preliminary insights into database query and operator behaviors on state‐of‐the‐art coupled CPU‐GPU architectures.

HAWQ, developed at Pivotal, is a massively parallel processing SQL engine sitting on top of HDFS. As a hybrid of MPP database and Hadoop, it inherits the merits from both parties. It adopts a layered architecture and relies on the distributed file system for data replication and fault tolerance. In addition, it is standard SQL compliant, and unlike other SQL engines on Hadoop, it is fully transactional. This paper presents the novel design of HAWQ, including query processing, the scalable software interconnect based on UDP protocol, transaction management, fault tolerance, read optimized storage, the extensible framework for supporting various popular Hadoop based data stores and formats, and various optimization choices we considered to enhance the query performance. The extensive performance study shows that HAWQ is about 40x faster than Stinger, which is reported 35x-45x faster than the original Hive.

In the cloud computing era, the goal of data center network is not only to interconnect a large number of servers, but also to provide low latency and high bandwidth. A new 2-layer architecture, C-tree, is proposed for cloud computing in this paper. It is a flat topology, in which the horizontal traffic's latency is obviously lower than in traditional three-layer architectures. Also, C-tree can provide high bisection bandwidth which is important for bandwidth-intensive applications. We analyze C-tree theoretically and compare it with other architectures in scalability, accommodation capability, network diameter, bisection bandwidth, path diversity and regularity. We have developed a load balancing routing mechanism to balance the traffic on the parallel links. A network simulation platform is set up to verify the performance of C-tree.

Previous studies have shown mining closed patterns provides more benefits than mining the complete set of frequent patterns, since closed pattern mining leads to more compact results and more efficient algorithms. It is quite useful in a data stream environment where memory and computation power are major concerns. This paper studies the problem of mining closed sequential patterns over data stream sliding windows. A synopsis structure IST (Inverse Closed Sequence Tree) is designed to keep inverse closed sequential patterns in current window. An efficient algorithm SeqStream is developed to mine closed sequential patterns in stream windows incrementally, and various novel strategies are adopted in SeqStream to prune search space aggressively. Extensive experiments on both real and synthetic data sets show that SeqStream outperforms PrefixSpan, CloSpan and BIDE by a factor of about one to two orders of magnitude.