Point cloud classification is a fundamental problem in 3-D point cloud analysis. However, most existing methods are supervised, which requires costly and laborious annotations of large-scale point cloud datasets. This severely limits the practical applicability of point clouds. Therefore, exploring point cloud clustering methods, which can group point clouds into semantically meaningful clusters in an unsupervised manner, is of great importance. However, this remains a formidable challenge for humans. Here, we present PointCluster, a novel framework for deep clustering of 3-D point clouds. To enable accurate and reliable self-supervision for the clustering process, the framework introduces two semantic pseudo-labeling algorithms: prototype pseudo-labeling and reliable pseudo-labeling. We devise a three-step training process for the clustering network. First, we adopt a cross-modal representation learning approach to optimize the feature model. Second, we freeze the network parameters of the feature model and apply the prototype pseudo-labeling algorithm to optimize the clustering heads separately. Third, we use the reliable pseudo-labeling algorithm to jointly train the feature model and the clustering head in a semi-supervised manner, which enhances the overall clustering performance. The experimental results demonstrate that PointCluster achieves the state-of-the-art clustering results on public datasets such as ShapeNet. Moreover, our method narrows the gap between unsupervised point cloud clustering and supervised point cloud classification, offering a new perspective for the point cloud classification task.

Summary Data caching is an effective method to reduce traffic and improve the quality of service in network. Traditionally, users' requests are offloaded to the cloud for centralized computing. However, due to security and privacy, these tasks are executed in the nearest server, so that the data and service needed by the task are also essential. After the task is completed, in case the next arriving request needs the same data, resulting in transmission cost, the data need to be stored for a period of time, because we know nothing about the coming request information under an online request stream. In this article, we study data caching problem by extending single data item to multiple data items among servers. About the homogeneous model and the submodular model with constraint, we propose a data caching strategy minimizing the total transfer and caching costs of the system. Moreover, we also solve the semiheterogeneous model by the anticipatory caching (AC) algorithm in Reference 21. Meanwhile we find it is more efficient for our three models in this article to improve the performance.

In this paper, we studied a data caching problem in edge-based CDNs to facilitate the content delivery to serve a sequence of requests, off-line and online, with minimum costs as a goal based on a semi-homo cost model. To this end, we first designed an O(mn \log(mn)) time and space optimal proactive off-line algorithm,called pro-caching, by reducing the problem to a simple shortest path problem in a directed weighted network graph, and then extended the idea of anticipatory caching to develop an 2-competitive reactive online algorithm, called re-caching, for this problem and showed its tightness by proving that no deterministic online algorithm can do better than 2-o(1) in its worst case. Finally, to combine the advantages of both algorithms, we also presented a hybrid algorithm, called hy-caching, to fully utilize the power and benefits of edge-based CDNs while reducing their service costs. Our results improve the previous results not only in the cost model being used but also in the time complexity, competitive ratio, and the quality of the solutions. We provably achieve these results with our deep insights into the problem and the careful analysis, together with an empirical evaluation.

Data caching in the cloud is an efficient way to improve the QoS of diverse data applications. However, this benefit is not freely available, given monetary cost to manage the caches in the cloud. In this paper, we study the data caching problem in the cloud that is driven by the monetary cost reduction, instead of the hit rate under limited capacity as in traditional cases. In particular, given a stream of requestsRto a shared data item, we present a shortest-path based optimal algorithm that can minimize the total transfer and caching costs within O(mn) time for off-line case, here m represents the number of nodes in the network, while n is the length of the request stream. The cost model in this computation is semi-homo, which indicates that all pairs of nodes have the same transfer cost, but each cache server node has its own caching cost rate. Our off-line algorithm improves the previous results not only in reducing the time complexity from O(m2n) to O(mn), but also in relaxing the cost model to be semi-homogeneous, rendering the algorithm more practical in reality. Furthermore, we also study this problem in its online form, and by extending the anticipatory caching idea, we propose a 2-competitive online algorithm based on the same cost model and show its tightness by giving a lower bound of the competitive ratio as 2 - o(1) for any deterministic online algorithm. We provably achieve these results with our deep insights into the problem and careful analysis of the solution algorithms, together with a trace-based study to evaluate their performance in reality.