Precise and objective quantification of non-human primates (NHPs) actions is vital for neuroscience research and for the phenotypic analysis of neuropsychiatric disorder models. However, existing computational ethology methods primarily focus on coarse-grained action recognition at the video-clip level and lack frame-level temporal segmentation tools for analyzing continuous action streams. This severely restricts the in-depth exploration of action dynamics, including duration, transition patterns, and rhythmicity. To address this issue, this study proposes a multi-modal, multi-stage action segmentation framework. The framework synergistically leverages the complementary strengths of different modalities by deeply fusing kinematic information from skeleton data, appearance features from RGB video, and dense motion information from optical flow, enabling fine-grained frame-wise action analysis of NHPs. To facilitate this task, MonActSeg was introduced as the first fine-grained, frame-by-frame annotated benchmark specifically designed for NHP temporal action segmentation. It encompasses a total of 1.37 million labeled frames across seven core actions. Evaluated on the MonActSeg benchmark under the video-level protocol, the proposed framework demonstrated superior performance, achieving a frame-wise accuracy (Acc) of 91.08%, an edit distance (Edit) of 85.94%, and a segmental F1@50 of 81.23%. This work provides an analytical tool and a standardized benchmark for computational ethology, enabling fine-grained quantification of NHPs behavioral phenotypes.

Nearest neighbor search (NN search) plays a fundamental role in many disciplines. According to recent studies, graph-based search methods show superior performance over other types of methods. In order to accommodate the high dimensionality as well as the growing data-scale, the disk-based NN search in which the index graph and the full-precision vectors are kept in SSD has become a promising direction. This paper optimizes the disk-based NN search from three perspectives. Firstly, an eXtended Neighborhood Graph (XN-Graph) structure is proposed. In contrast to the existing index graphs, the out-edges of the graph neighborhood are collected from much wider coverage of the data space. It therefore reduces the number of hops during NN search, which in turn reduces the search latency. Additionally, a dataset partitioning method called Boundary-adaptive Balanced Partition is proposed to facilitate the graph construction in cases where the system cannot handle large datasets in a single round. Moreover, an efficient hybrid NN search method called In-Memory First Search is proposed. Compared to the existing methods, it considerably reduces the CPU idle times. With the support of XN-Graph, it shows 1.5-3 times lower search latency than SOTA methods. On billion-scale datasets, its QPS is still above 4000 when Recall@10 is as high as 0.9.

With the trend of 5G-A (5G-Advanced) harmonized network communications and sensing harmonized communication and sensing, network capability exposure technology will help operators, business providers and 3rd business parties to realize harmonized network communications and sensing harmonized communication and sensing business and applications. This paper will focus on capability exposure technology based on 5G-A harmonized communication and sensing. Initially, this paper discusses the capability exposure hierarchical architecture based on harmonized communication and sensing, secondly proposes the harmonized communication and sensing network architecture and basic network signaling process combined with capability exposure technology. Then, this paper discusses capability exposure application scenarios based on communication sensing. This paper can provide relevant reference for the technological evolution, network deployment and application discussion of the harmonized communication and sensing capability in the operator network.