Millimeter-wave (mmWave) radar has emerged as a powerful sensing modality for human activity recognition (HAR) owing to its capability to capture 3D point cloud sequences without privacy concerns. However, effectively modeling the sparse, irregular, and non-uniform nature of radar data remains a major challenge. Existing approaches often rely on highly complex network architectures to improve accuracy, which leads to excessive computational overhead and poor scalability. To overcome these limitations, this paper proposes a compact hybrid feature extraction network for voxelized radar point cloud classification, which performs early-stage spatio-temporal fusion and is termed STFusionNet (Spatial-Temporal Fusion Network). The STFusionNet comprises (i) a lightweight 3D convolutional front-end, which treats consecutive temporal frames as input channels to encode motion dynamics into a compact volumetric representation and further aggregates features along the depth axis; (ii) a minimalist 2D convolutional backbone after a Depth-to-Channel Folding operation, which captures spatial features with minimal computational cost. Extensive experiments on the public MMActivity and MiliPoint datasets demonstrate that our model achieves competitive accuracy (e.g., 92.20% on MMActivity and 72.86% on MiliPoint) with only about 50K parameters and 46 MMac, outperforming or matching representative spatio-temporal baselines under a much lower computational budget. Extensive ablation experiments verify the contribution of key components, while statistical significance tests confirm the reliability of the performance improvements. These results confirm that STFusionNet offers a robust, efficient, and generalizable solution for mmWave radar-based HAR in Internet of Things (IoT) applications.

For autonomous aerial vehicles (AAV)-assisted integrated sensing and communication (ISAC) systems, a semantic-based channel state information (CSI) feedback scheme is proposed in this article. Unlike traditional full CSI feedback, the proposed scheme minimizes feedback burden by utilizing predefined semantic databases at both the transmitter and receiver. First, a deep-learning-based clustering method is developed to construct the semantic database from measured CSI samples. Then, an incremental clustering-based identification method is proposed, enabling dynamic updates and adjustments to semantic databases as new CSI is continuously acquired. Finally, the proposed CSI feedback scheme is validated through scenario identification, and extensive channel measurements are conducted in three typical campus scenarios: 1) playground; 2) lake; and 3) buildings. The results show that the accuracy of the semantic feedback-based scenario identification reaches 97.5%, which is 0.6% higher than the accuracy of the full-CSI feedback-based scenario identification. Specifically, the CSI is fed back through semantic database labels, requiring only a few bytes. This significantly reduces feedback burden while maintaining high accuracy of ISAC tasks. Furthermore, the proposed feedback scheme can also be extended to other AAV-assisted applications, such as the Internet of Things and emergency response.

Large volumes of data generated by scientific simulations, genome sequencing, and other applications need to be moved among clusters for data collection/analysis. Data compression techniques have effectively reduced data storage and transfer costs. However, users' requirements on interactively controlling both data quality and compression ratios are non-trivial to fulfill. We propose a novel Compression-as-a-Service (CaaS) platform called Ocelot with four important contributions: (1) It offers real-time visualization, interactive compression, and transfer of scientific datasets. (2) It incorporates new strategies for compressing diverse types of datasets more effectively than traditional methods. (3) It provides an effective method for estimating the compression ratio and execution time of compression tasks. (4) Experiments on multiple real-world datasets on geographically distributed computers show that Ocelot can significantly improve data transfer efficiency with a performance gain of more than 10x in computing clusters with relatively slow networks.

The increasing volume and velocity of science data necessitate the frequent movement of enormous data volumes as part of routine research activities. As a result, limited wide-area bandwidth often leads to bottlenecks in research progress. However, in many cases, consuming applications (e.g., for analysis, visualization, and machine learning) can achieve acceptable performance on reduced-precision data, and thus researchers may wish to compromise on data precision to reduce transfer and storage costs. Error-bounded lossy compression presents a promising approach as it can significantly reduce data volumes while preserving data integrity based on user-specified error bounds. In this paper, we propose a novel data transfer framework called Ocelot that integrates error-bounded lossy compression into the Globus data transfer infrastructure. We note four key contributions: (1) Ocelot is the first integration of lossy compression in Globus to significantly improve scientific data transfer performance over wide area network (WAN). (2) We propose an effective machine-learning based lossy compression quality estimation model that can predict the quality of error-bounded lossy compressors, which is fundamental to ensure that transferred data are acceptable to users. (3) We develop optimized strategies to reduce the compression time overhead, counter the compute-node waiting time, and improve transfer speed for compressed files. (4) We perform evaluations using many real-world scientific applications across different domains and distributed Globus endpoints. Our experiments show that Ocelot can improve dataset transfer performance substantially, and the quality of lossy compression (time, ratio and data distortion) can be predicted accurately for the purpose of quality assurance.

Vast volumes of data are produced by today's scientific simulations and advanced instruments. These data cannot be stored and transferred efficiently because of limited I/O bandwidth, network speed, and storage capacity. Error-bounded lossy compression can be an effective method for addressing these issues: not only can it significantly reduce data size, but it can also control the data distortion based on user-defined error bounds. In practice, many scientific applications have specific requirements or constraints for lossy compression, in order to guarantee that the reconstructed data are valid for post hoc analysis. For example, some datasets contain irrelevant data that should be isolated in particular and users often have intuition regarding value ranges, geospatial regions, and other data subsets that are crucial for subsequent analysis. Existing state-of-the-art error-bounded lossy compressors, however, do not consider these constraints during compression, resulting in inferior compression ratios with respect to user's post hoc analysis, due to the fact that the data itself provides little or no value for post hoc analysis. In this work we address this issue by proposing an optimized framework that can preserve diverse constraints during the error-bounded lossy compression, e.g., cleaning the irrelevant data, efficiently preserving different precision for multiple value intervals, and allowing users to set diverse precision over both regular and irregular regions. We perform our evaluation on a supercomputer with up to 2,100 cores. Experiments with six real-world applications show that our proposed diverse constraints based error-bounded lossy compressor can obtain a higher visual quality or data fidelity on reconstructed data with the same or even higher compression ratios compared with the traditional state-of-the-art compressor SZ. Our experiments also demonstrate very good scalability in compression performance compared with the I/O throughput of the parallel file system.

Vast volumes of scientific data cannot be stored and transferred efficiently because of limited I/O bandwidth, network bandwidth, and storage capacity. Error-bounded lossy compression can be an effective method for resolving these big data issues, since not only can it significantly reduce the data size but it can also control the data distortion based on user-defined error bounds. In practice, many scientific applications have specific data fidelity requirements across different value ranges/intervals of the dataset for the lossy compression, in order to guarantee that the reconstructed data are valid for post hoc analysis. Existing state-of-the-art error-bounded lossy compressors, however, do not support multi-range based error-bounds in the lossy compression, leaving a critical gap that hampers their effective use in practice. In this work, we address this issue by proposing a multi-range based error-bounded lossy compressor based on the state-of-the-art SZ lossy compressor. Our approach allows users to set different error bounds in different value ranges for a compressoin task. We evaluate our approach on several real-world datasets and show that it can obtain a higher visual quality or data fidelity on reconstructed data with the same or even higher compression ratios achieved by SZ.