arXiv subjects

Wanying Lu

Publications and source records attributed to Wanying Lu.

3 recordsLinked to original sources

arXiv2025

Step-Audio 2 Technical Report

This paper presents Step-Audio 2, an end-to-end multi-modal large language model designed for industry-strength audio understanding and speech conversation. By integrating a latent audio encoder and reasoning-centric reinforcement learning (RL), Step-Audio 2 achieves promising performance in automatic speech recognition (ASR) and audio understanding. To facilitate genuine end-to-end speech conversation, Step-Audio 2 incorporates the generation of discrete audio tokens into language modeling, significantly enhancing its responsiveness to paralinguistic information such as speaking styles and emotions. To effectively leverage the rich textual and acoustic knowledge in real-world data, Step-Audio 2 integrates retrieval-augmented generation (RAG) and is able to call external tools such as web search to mitigate hallucination and audio search to switch timbres. Trained on millions of hours of speech and audio data, Step-Audio 2 delivers intelligence and expressiveness across diverse conversational scenarios. Evaluation results demonstrate that Step-Audio 2 achieves state-of-the-art performance on various audio understanding and conversational benchmarks compared to other open-source and commercial solutions. Please visit https://github.com/stepfun-ai/Step-Audio2 for more information.

cs.CL↗

arXiv2023

ESTA: An Efficient Spatial-Temporal Range Aggregation Query Processing Algorithm for UAV Networks

Unmanned Aerial Vehicle (UAV) networks are increasingly deployed in military and civilian applications, serving as critical platforms for data collection. Users frequently require aggregated statistical information derived from historical sensory data within specific spatial and temporal boundaries. To address this, users submit aggregation query requests with spatial-temporal constraints to target UAVs that store the relevant data. These UAVs process and return the query results, which can be aggregated within the network during transmission to conserve energy and bandwidth-resources that are inherently limited in UAV networks. However,the dynamic topology caused by UAV mobility, coupled with these resource constraints, makes efficient in-network aggregation challenging without compromising user query delay. To the best of our knowledge, existing research has yet to adequately explore spatial-temporal range aggregation queries in the context of UAV networks. In this paper, we propose ESTA, an Efficient Spatial-Temporal range Aggregation query processing algorithm tailored for UAV networks. ESTA leverages pre-planned UAV trajectories to construct a topology change graph that models the network's evolving connectivity. It then employs an efficient shortest path algorithm to determine the minimum query response delay. Subsequently, while adhering to user-specified delay constraints, ESTA transforms the in-network aggregation process into a series of set cover problems, which are solved recursively to build a Spatial-Temporal Aggregation Tree (STAT). This tree enables the identification of an energy-efficient routing path for aggregating and delivering query results. Extensive simulations demonstrate that ESTA reduces energy consumption by more than 50% compared to a baseline algorithm, all while satisfying the required query delay.

cs.DC↗

arXiv2023

Federated Semi-Supervised and Semi-Asynchronous Learning for Anomaly Detection in IoT Networks

Existing FL-based approaches are based on the unrealistic assumption that the data on the client-side is fully annotated with ground truths. Furthermore, it is a great challenge how to improve the training efficiency while ensuring the detection accuracy in the highly heterogeneous and resource-constrained IoT networks. Meanwhile, the communication cost between clients and the server is also a problem that can not be ignored. Therefore, in this paper, we propose a Federated Semi-Supervised and Semi-Asynchronous (FedS3A) learning for anomaly detection in IoT networks. First, we consider a more realistic assumption that labeled data is only available at the server, and pseudo-labeling is utilized to implement federated semi-supervised learning, in which a dynamic weight of supervised learning is exploited to balance the supervised learning at the server and unsupervised learning at clients. Then, we propose a semi-asynchronous model update and staleness tolerant distribution scheme to achieve a trade-off between the round efficiency and detection accuracy. Meanwhile, the staleness of local models and the participation frequency of clients are considered to adjust their contributions to the global model. In addition, a group-based aggregation function is proposed to deal with the non-IID distribution of the data. Finally, the difference transmission based on the sparse matrix is adopted to reduce the communication cost. Extensive experimental results show that FedS3A can achieve greater than 98% accuracy even when the data is non-IID and is superior to the classic FL-based algorithms in terms of both detection performance and round efficiency, achieving a win-win situation. Meanwhile, FedS3A successfully reduces the communication cost by higher than 50%.

cs.DC↗