Graph database systems play a vital role in graph structure analysis across a wide range of application domains. Queries with set-level constraints on community structures are increasingly demanded in real-world applications. However, existing graph databases lack native support for both efficient monadic second-order logic (MSOL) query processing and fast community retrieval, hindering their applicability to such analytical tasks. In this paper, we present Monac- Graph, a graph database system that enables practical MSOL queries. MonacGraph features an efficient two-phase execution engine that minimizes redundant first-order clause evaluations. We propose SO-Gremlin, an extension of the Gremlin graph traversal language with intuitive syntax for set quantification. The system adopts LSM-Community as its storage backend, enabling efficient queries over precomputed graph structures. Additionally, MonacGraph provides a user-friendlyWeb interface for composing complex set-level queries and visualizing results in real time. A demonstration video can be found at https://www.youtube.com/watch?v=Eezdq9tzbJE.

The speech-driven facial animation technology is generally categorized into two main types: 3D and 2D talking face. Both of these have garnered considerable research attention in recent years. However, to our knowledge, the research into 3D talking face has not progressed as deeply as that of 2D talking face, particularly in terms of lip-sync and perceptual mouth movements. The lip-sync necessitates an impeccable synchronization between mouth motion and speech audio. The speech perception derived from the perceptual mouth movements should resemble that of the driving audio. To mind the gap between the two sub-fields, we propose Learn2Talk, a learning framework that enhances 3D talking face network by integrating two key insights from the field of 2D talking face. First, drawing inspiration from the audio-video sync network, we develop a 3D sync-lip expert model for the pursuit of lip-sync between audio and 3D facial motions. Second, we utilize a teacher model, carefully chosen from among 2D talking face methods, to guide the training of the audio-to-3D motions regression network, thereby increasing the accuracy of 3D vertex movements. Extensive experiments demonstrate the superiority of our proposed framework over state-of-the-art methods in terms of lip-sync, vertex accuracy and perceptual movements. Finally, we showcase two applications of our framework: audio-visual speech recognition and speech-driven 3D Gaussian Splatting-based avatar animation.

The state of energy (SOE) serves as an essential parameter in power batteries for new energy vehicles, significantly influencing the energy management strategy of battery management systems (BMS). This study presents a model-based strong tracking filter algorithm to enhance the accuracy and adaptability of SOE estimation amidst changing operating conditions. The second-RC networks model is utilized to identify battery parameters via the hybrid power pulse test. Additionally, for scenarios with randomly varying working conditions, an online SOE estimation approach utilizing the strong tracking extended Kalman filter algorithm is introduced, ensuring robust tracking performance, especially under sudden changes in conditions. The algorithm's validity is confirmed through simulation across various working conditions, demonstrating superior accuracy and robustness compared to the conventional Kalman filter algorithm. Overall, the proposed algorithm meets the requirements of SOE estimation in online scenarios, offering enhanced tracking performance and reliability.