Volumetric videos are essential for immersive applications due to their engaging and realistic experiences. However, streaming them in real time over constrained, fluctuating networks remains challenging. Progressive streaming is an effective method to mitigate this issue by gradually enhancing video quality through incremental data transmission. However, existing progressive volumetric streaming solutions often rely on specific compression algorithms or require codec modifications, leading to poor compatibility with standard codecs. In this paper, we propose P2VS, a progressive partition-based volumetric video streaming framework, to achieve codec-independent progressive streaming. Specifically, P2VS leverages the unique structure of point cloud-based volumetric video to incrementally enhance video quality without being constrained by specific compression algorithms. Moreover, we propose adaptive streaming algorithms under this framework to enhance the quality of experience (QoE). Extensive simulations demonstrate that P2VS improves QoE by 21% on average compared to non-progressive streaming schemes. It also achieves better bandwidth efficiency and full compatibility with standard codecs. A prototype is built to verify the feasibility of P2VS.

Protein-lipid interactions are essential for many cellular processes, as proteins associate with diverse lipid molecules to exert distinct functions. However, existing approaches are limited in discriminating among lipid categories, leaving a gap in our understanding of lipid-binding specificity. Recent advances in protein language models have opened new possibilities for discovering novel sequence insights. Here, we introduce PLiCat (Protein-Lipid interaction Categorization tool), a sequence-based framework designed to predict the categories of lipids that interact with proteins. PLiCat employs a hybrid deep learning architecture that integrates ESMC with BERT, enabling accurate and interpretable classification across eight major lipid categories. Through attribution analysis, PLiCat uncovers the sequence-encoded lipid-binding signatures and highlights residues contributing to binding specificity. Notably, PLiCat shows potential for identifying lipid-binding sites hidden in protein sequences. Furthermore, PLiCat can be used to assess the impact of pathogenic mutations on lipid-binding events. Collectively, PLiCat provides the first computational tool for lipid category prediction from protein sequence alone, offering valuable insights into lipid recognition mechanisms, and with promising applications in guiding rational protein design. The PLiCat source code and processed datasets are available at https://github.com/Noora68/PLiCat.

Real-time video analytics demand intensive computing resources, often exceeding device capabilities. Heterogeneous computing resources like CPU and GPU, usually work collaboratively to ensure real-time performance. GPU manage data-intensive computing, while CPU handle instruction-intensive tasks. However, analytics accuracy can degrade because of the imbalance between CPU-GPU workloads and resources, and static resources in most systems limit adaptability to dynamic workloads. In addition to CPU-GPU resource control, accuracy is highly dependent on video analytics configuration including resolution, frame rate, and model selection. In this paper, we propose Gemini+, a hardware-accelerated video analytics pipeline empowered by dual-image FPGAs. It enables flexible CPU-GPU resource adaptation by providing near-instantaneous switching between two pre-configured images. We investigate CPU-GPU resource control and video analytics configuration adaptation in a dual-image FPGA-based video analytics pipeline. We study and formulate two problems of practical importance, a single-camera and multi-camera dual computing resource control problem, i.e., SC-DCRC and MC-DCRC. We analyze the problem complexity and develop optimal and sub-optimal algorithms. We evaluate our algorithms through simulation and build a prototype for verification. The results show that Gemini+ can improve analytic accuracy by 25% compared to fixed CPU-GPU resource systems and 88% compared to GPU-dominant systems.

With the proliferation of human-carried mobile devices, spatial crowdsourcing has emerged as a transformative system, where requesters outsource their spatiotemporal tasks to a set of workers who are willing to perform the tasks at the specified locations. However, in order to make efficient assignments, the existing spatial crowdsourcing system usually requires workers and/or tasks to expose their locations, which raises a significant concern of compromising location privacy. In addition, traditional spatial crowdsourcing systems employ a centralized server to manage the information of workers and tasks. Such a centralized design does not scale to a large number of workers/tasks, making the server easily a bottleneck. In this article, we present an online framework for assigning tasks to workers without compromising the location privacy in a fully distributed manner. Our system protects the location privacy of both workers and tasks through homomorphic encryption. We further propose a novel wait-and-decide mechanism and a proportional-backoff mechanism to increase the number of assigned tasks. Extensive experiments on real-world data sets illustrate that our proposed system achieves a large number of task assignments in an efficient and privacy-preserving manner.