The convergence of virtual reality live streaming and AI-driven avatars has emerged as a significant technological trend. However, current integration attempts remain in the proof-of-concept stage, with the primary challenge of automatic interaction system establishment. To build interactive intelligence anime avatars within VR frameworks, we have developed a multimodal interaction architecture centered on dialogue agents, realizing comprehensive understanding, reasoning, and response. Our approach 1).proposes high granularity explicit-implicit understanding and a dual-center switchable reasoning mechanism to support flexible responses. 2).innovates a dual-source animation mechanism for co-speech face-body visualization and a textual command module for supervising crossmodal animation, and 3).enhances expressiveness through mapping persona, content, voice, and motion to anime style. Experimental results demonstrate the state-of-the-art performance of VRtalk, highlighting its practical significance and future potential.

Real-time data warehouses are essential for modern applications. Extract-Transform-Load (ETL) as a fundamental component of offline data warehouses also provides crucial support within realtime data warehouses. Among various traditional ETL approaches, Lambda and Kappa have emerged as classic real-time data processing solutions due to their freshness and query performance, which best meet business demands. However, both of them often require the integration of external stream processing engines, introducing challenges related to complexity, efficiency, and consistency. ZeroETL has emerged as an approach to address these issues. Nevertheless, existing ZeroETL-based solutions primarily emphasize the implementation of extraction and loading, resulting in limitations in handling transformation. Incremental View Maintenance (IVM) offers an alternative that can enhance ZeroETL. However, existing IVM implementations often focus on query acceleration rather than supporting high-throughput, complex real-time workloads. To address these challenges, we propose Streaming View, an efficient real-time data processing engine integrated within AnalyticDB of Alibaba Cloud. Unlike existing solutions, Streaming View supports high-throughput, complex data processing for realtime streaming ETL workloads. Furthermore, it can be leveraged to optimize ZeroETL-based approaches by enhancing transformation capabilities. We design tailored algorithms and optimizations for diverse syntaxes and high-throughput scenarios, ensuring the system meets complex application needs. By integrating incremental computation into the data warehouse, Streaming View reduces complexity, ensures data consistency, and boosts performance, offering a robust solution for real-world applications. Experiments show Streaming View improves processing performance by up to 7x and 20x over traditional ETL and IVM methods, respectively, and addresses complex scenarios unsolved by existing solutions.

Abstract Few‐shot action recognition aims to learn a classification model with good generalisation ability when trained with only a few labelled videos. However, it is difficult to learn discriminative feature representations for videos in such a setting. The Elastic Temporal Alignment (ETA) for few‐shot action recognition is proposed. First, a convolutional neural network is employed to extract feature representations of video frames sparsely sampled from videos. In order to obtain the similarity of two videos, a temporal alignment estimation function is utilised to estimate the matching score between each pair of frames from the two videos through an elastic alignment mechanism. The analysis shows that when we judge whether two frames from respective videos are matched, multiple adjacent frames in the videos should be considered, so as to embody the temporal information. Thus, before feeding per‐frame feature vectors of videos into the temporal alignment estimation function, a temporal message passing function is leveraged to propagate the information of per‐frame features in the temporal domain. The method has been evaluated on four action recognition datasets, including Kinetics, Something‐Something V2, HMDB51, and UCF101. The experimental results verify the effectiveness of ETA and show its superiority over state‐of‐the‐art methods.

With the rapid development of Machine Learning (ML), Machine-Learning-as-a-Service (MLaaS) clusters appear in large numbers to support cloud platforms services, which adopt virtual machine (VM) to improve the availability, resilience and security. However, low energy efficiency is a major problem in such clusters. Previous work focused on reducing the number of physical machines by centralizing resources migration. Nevertheless, for ML tasks with frequent memory switching, blind migration is not worth the cost because the remaining time is less than the migration time, since the migration time can not be ignore due to the memory intensive of ML tasks. Therefore, this paper explores how the remaining time and memory replacement states in ML tasks, which we summarize as residue, affect migration, and proposes an online residue-aware migration algorithm based on Lyapunov optimization. Through rigorous proof, the gap between the algorithm and the optimal solution is ensured. Extensive simulations show that the proposed algorithm is better than the previous migration.

A new optimization model is established in this paper to build power system emergency load shedding scheme adaptive to multi operation modes. Particle swarm optimization (PSO) is used to solve the model heuristically. To improve performance of PSO, the concept of belief space in cultural algorithm is introduced to form the cultural particle swarm optimization (CPSO). A new solution is generated in the belief space to replace the inferior solution in CPSO, and motion parameters of CPSO are adjusted by crowding distance to make the algorithm escape from local optimum. The proposed model and algorithm are validated with a provincial power grid model.