To address the challenges of manual dependency and complex management in railway data governance, and to promote data value realization, we propose DGMAS, a Data Governance Multi-Agent System based on Large Language Models (LLMs). The architectural design adopts a system comprising a mother system and three subsystems. The DGMAS comprises four critical phases: initialization, execution, re-planning, and failure attribution. Through this system, the process of railway data governance can be simulated, to help stakeholders anticipate potential issues and develop solutions, thereby supporting improved railway data governance.

Existing LLM-based motion models fail to fully leverage large models' planning capabilities for motion-related tasks, exhibiting poor generalization, limited text-motion alignment, and an inability to perform multimodal condition joint driven motion generation. We argue that these issues arise from the modality gap and the highly coupled nature of motion tokens. To address this, we proposed the hybrid motion sentence, which is consistant of fine-grained motion decription and atomic body-part motion token that can bridge the gap between motion and text. To obtain a large corpus of hybrid motion sentences, we introduced a novel motion-to-text generation method that combines atomic motion operators with GPT-4o, resulting in 68.2 million fine-grained textual descriptions across diverse modalities. To reconstruct high-quality motion from hybrid sentences and make better motion-text alignment, we introduce Semantic-Aware Decoupled Motion Tokenization. Furthermore, we propose MotionUPG based on LLaMA, leveraging MotionWords dataset for both pretraining and instruction tuning. Our method achieves strong fine-grained text-motion alignment, impressive zero-shot motion generation, and is the first to support multimodal condition joint driven motion generation tasks.

Cloud computing, a delivery of computing as a service mainly implying how to use utilities in our context, can be provided either at infrastructure, platform or software levels. The Desktop-as-a-Service (DaaS) paradigm, derives from the software level Software-as-a-Service (SaaS) paradigm, is drawing increasing interest because of its transformation from desktops into a cost-efficient, scalable and comfortable subscription service. Unlike most existing solutions delivering service with various protocols based on image transmitting in PC dominating environment, we present a DaaS with cloud server technologies on FPGA to address the problem of high power consumption and heavy network traffic. With the booming of mobile cloud computing, users can access the service on demand with smart phones or other portable devices like iPad or Amazon kindle as well as PC. Our system provides virtual desktop web pages written in HTML/JavaScript to avoid frequent image transmissions and reduce network traffic. To build the cloud prototype system, we combine Lightweight TCP/IP stack (LwIP) and Java Optimized Processor (JOP) to build a web server enabling dynamic web page interactions. Our system significantly saves volumes of data in transmission and network bandwidth. Analytical performance evaluation shows that on average, our system suffers only 25% transmitting latency and saves 46% of energy efficiency in comparison to other solutions. Our efficient DaaS based on FPGA explores new application of embedded web server in green cloud computing as well as new service paradigm of mobile cloud computing.