Blendshapes have been widely adopted as a key method for generating facial animation on avatars due to their ease of manipulation, flexibility in capturing diverse facial expressions, and compatibility with real-time rendering. However, current frameworks lack efficient methods for compressing blendshape (BS) animation parameters, which are critical for optimizing data transmission. This study introduces a pioneer compression scheme leveraging the amount of BS to be transmitted, their quantization as well as their transmission frequency. Subjective evaluation using the ITU-R BT.500-15 recommendation demonstrates that the proposed method significantly reduces the amount of data to transmit while preserving acceptable visual quality. This approach extends prior findings on reduced BS sets[kang2023effects] and addresses a significant gap in avatar media coding[avril2023morgan]. This work establishes a baseline for efficient facial animation and, serves as a foundation for further exploration on adaptive rate-allocation strategies and advanced compression strategies tailored for diverse avatar animation scenarios.

Audio- and Video-Driven Talking Head Generation methods have attracted considerable research interest due to recent advances in Artificial Intelligence Generated Content (AIGC) technologies. In such approaches, a single image is artificially animated by leveraging audio and/or motion features extracted from video sources. Despite notable progress, current performance assessments rely primarily on traditional objective metrics, often neglecting subjective evaluation aspects. To address this issue, we propose in this paper a subjective fidelity assessment of recent Audio- and/or Video-Driven Talking Head Generation methods. This study aims to assess how accurately and convincingly the generated video reproduces the visual and behavioral characteristics of a real human face, as well as how closely the video aligns with expected natural human expressions, movements, and/or audio synchronization. In order to provide a detailed assessment of the fidelity in the context of talking heads, our study focuses on six key criteria: Overall Fidelity, Gaze Fidelity, Audio-Video Sync Fidelity, Head Pose Fidelity, Expression Fidelity, and Overall Visual Quality. Experiments results reveal a nuanced picture of the fidelity in this context, where the performance varies significantly depending on the video content itself as well as how the animation is generated, highlighting the needs for further research. This research represents an initial step towards the evaluation of Audio- and Video-Driven generative image animation methods for Talking heads while offering insights for improving the accuracy and realism of those techniques. The dataset and corresponding results are available at https://github.com/a-trioux/Subjective-Fidelity-Assessment-Talking-Head.

Virtual reality (VR) conference, as a typical social VR application, has gained popularity in recent years. It offers users located at different locations a fully immersive experience and a sense of togetherness. However, the remote communication also introduces inevitable latencies, which may adversely affect the so-called social presence. There is still a lack of research on the effect of latency on social presence. To fill the gap, this paper aims to examine the impact of latency on social presence of VR conference and contrast it with that of traditional video conference. Here, the social presence is measured using the Networked Minds Social Presence Inventory (NMSPI). We design and conduct two conversation-based subjective tests for both types of conference and compare the impact of the latency based on the test results. The conclusions of these studies can be used as guidelines for VR service providers to optimize their conference systems.

The autonomous database of the next generation aims to apply the reinforcement learning (RL) on tasks like query optimization and performance tuning with little or no human DBAs’ intervention. Despite the promise, to obtain a decent policy model in the domain of database optimization is still challenging — primarily due to the inherent computational overhead involved in the data hungry RL frameworks — in particular on large databases. In the line of mitigating this adverse effect, we propose Mirror in this work. The core to Mirror is a sampling process built in an RL framework together with a transferring process of the policy model from the sampled database to its original counterpart. While being conceptually simple, we identify that the policy transfer between databases involves heavy noise and prediction drifting that cannot be neglectable. Thereby we build a theoretical-guided sampling algorithm in Mirror assisted by a continuous fine-tuning module. The experiments on the PostgreSQL and an industry database PolarDB validate that Mirror has effectively reduced the computational cost while maintaining a satisfactory performance.

With the prevalence of cloud computing, more and more enterprises are migrating applications to cloud infrastructures. Logs are the key to helping customers understand the status of their applications running on the cloud. They are vital for various scenarios, such as service stability assessment, root cause analysis and user activity profiling. Therefore, it is essential to manage the massive amount of logs collected on the cloud and tap their value. Although various log storages have been widely used in the past few decades, it is still a non-trivial problem to design a cost-effective log storage for cloud applications. It faces challenges of heavy write throughput of tens of millions of log records per second, retrieval on PB-level logs and massive hundreds of thousands of tenants. Traditional log processing systems cannot satisfy all these requirements. To address these challenges, we propose the cloud-native log database LogStore. It combines shared-nothing and shared-data architecture, and utilizes highly scalable and low-cost cloud object storage, while overcoming the bandwidth limitations and high latency of using remote storage when writing a large number of logs. We also propose a multi-tenant management method that physically isolates tenant data to ensure compliance and flexible data expiration policies, and uses a novel traffic scheduling algorithm to mitigate the impact of traffic skew and hotspots among tenants. In addition, we design an efficient column index structure LogBlock to support queries with full-text search, and combined several query optimization techniques to reduce query latency on cloud object storage. LogStore has been deployed in Alibaba Cloud on a large scale (more than 500 machines), processing logs of more than 100 GB per second, and has been running stably for more than two years.

\beginabstract The trend in the DBMS market is to migrate to the cloud for elasticity, high availability, and lower costs. The traditional, monolithic database architecture is difficult to meet these requirements. With the development of high-speed network and new memory technologies, disaggregated data center has become a reality: it decouples various components from monolithic servers into separated resource pools (e.g., compute, memory, and storage) and connects them through a high-speed network. The next generation cloud native databases should be designed for disaggregated data centers. In this paper, we describe the novel architecture of \name, which follows thedisaggregation design paradigm: the CPU resource on compute nodes is decoupled from remote memory pool and storage pool. Each resource pool grows or shrinks independently, providing \revon-demand provisoning at multiple dimensions while improving reliability. We also design our system to mitigate the inherent penalty brought by resource disaggregation, and introduce optimizations such as optimistic locking and index awared prefetching. Compared to the architecture that uses local resources, \name achieves better dynamic resource provisioning capabilities and 5.3 times faster failure recovery speed, while achieving comparable performance. \endabstract

Traffic forecasting is of great importance to transportation management and public safety, and very challenging due to the complicated spatial-temporal dependency and essential uncertainty brought about by the road network and traffic conditions. Latest studies mainly focus on modeling the spatial dependency by utilizing graph convolutional networks (GCNs) throughout a fixed weighted graph. However, edges, i.e., the correlations between pair-wise nodes, are much more complicated and interact with each other. In this paper, we propose the Multi-Range Attentive Bicomponent GCN (MRA-BGCN), a novel deep learning model for traffic forecasting. We first build the node-wise graph according to the road network distance and the edge-wise graph according to various edge interaction patterns. Then, we implement the interactions of both nodes and edges using bicomponent graph convolution. The multi-range attention mechanism is introduced to aggregate information in different neighborhood ranges and automatically learn the importance of different ranges. Extensive experiments on two real-world road network traffic datasets, METR-LA and PEMS-BAY, show that our MRA-BGCN achieves the state-of-the-art results.

With the increasing demand for real-time system monitoring and tracking in various contexts, the amount of time-stamped event data grows at an astonishing rate. Analytics on time-stamped events must be real time and the aggregated results need to be accurate even when data arrives out of order. Unfortunately, frequent occurrences of out-of-order data will significantly slow down the processing, and cause a large delay in the query response. Timon is a timestamped event database that aims to support aggregations and handle late arrivals both correctly (i.e., upholding the exactly-once semantics) and efficiently. Our insight is that a broad range of applications can be implemented with data structures and corresponding operators that satisfy associative and commutative properties. Records arriving after the low watermark are appended to Timon directly, allowing aggregations to be performed lazily. To improve query efficiency, Timon maintains a TS-LSM-Tree, which keeps the most recent data in memory and contains a time-partitioning tree on disk for high-volume data accumulated over long time span. Besides, Timon supports materialized aggregation views and correlation analysis across multiple streams. Timon has been successfully deployed at Alibaba Cloud and is a critical building block for Alibaba cloud's continuous monitoring and anomaly analysis infrastructure.

Many new memory indexing structures have been proposed and outperform current in-memory skip-list index adopted by LevelDB, RocksDB and other key-value systems. However, those new indexes cannot be easily intergrated with key-value systems, because most of them do not consider how the data can be efficiently flushed to disk. Some assumptions, such as fixed size key and value, are unrealistic for real applications. In this paper, we present S3, a scalable in-memory skip-list index for the customized version of RocksDB in Alibaba Cloud. S3 adopts a two-layer structure. In the top layer, a cache-sensitive structure is used to maintain a few guard entries to facilitate the search over the skip-list. In the bottom layer, a semi-ordered skip-list index is built to support highly concurrent insertions and fast lookup and range query. To further improve the performance, we train a neural model to select guard entries intelligently according to the data distribution and query distribution. Experiments on multiple datasets show that S3 achieves a comparable performance to other new memory indexing schemes, and can replace current in-memory skip-list of LevelDB and RocksDB to support huge volume of data.

Smooth end-to-end performance of mission-critical database system is essential to the stability of applications deployed on the cloud. It's a challenge for cloud database vendors to detect any performance degradation in real-time and locate the root cause quickly in sophisticated network environment. Cloud databases vendors tend to favor a multi-tier distributed architecture to achieve multi-tenant management, scalability and high-availability, which may further complicate the problem.