The anchor-based multi-view clustering method has recently attracted considerable attention due to its superior efficiency. However, most existing methods construct a consensus anchor graph based solely on view-level contributions, overlooking the varying importance of individual samples across different views. Moreover, these methods fail to ensure that anchors are evenly distributed across clusters. Thus, we propose a novel and scalable multi-view clustering method, called Sample-Level Weighted and Structure-Enhanced Anchor Graph Learning for Scalable Multi-View Clustering (SLWSE-AGL). Specifically, we introduce a sample-level weighting mechanism based on anchor self-representation learning, enabling the constructed consensus anchor graph to capture the varying importance of samples in different views. Additionally, we incorporate a structure-enhancement constraint to encourage the learned anchors to be more evenly distributed among clusters, leading to more balanced and meaningful cluster partitions. Furthermore, we employ an anchor-to-sample label propagation mechanism that directly yields the final clustering results, thereby avoiding the information loss associated with the two-stage clustering processes. Extensive experiments demonstrate the superior performance of our method compared to state-of-the-art multi-view clustering approaches. The code of SLWSE-AGL is publicly available at https://github.com/tangchuan2000/SLWSE-AGL.

Tensor-based multi-view clustering has been widely studied to capture high-order correlations among multiple views. Nevertheless, existing tensorial methods still exhibit several limitations. First, many approaches rely on full similarity graphs, leading to quadratic or cubic complexity in the number of samples and poor scalability. Second, view-specific anchor graphs are often tensorized without cross-view anchor alignment, yielding structurally inconsistent tensor representations and reduced cross-view comparability. Third, low-rank regularization is typically imposed via the tensor nuclear norm (TNN), which uniformly shrinks singular values and may bias the estimation of the intrinsic tensor rank. To this end, we propose a novel framework, named Align then Tensorize: Multi-level Consistent Anchor Graph Learning for Scalable Multi-View Clustering (ATTMVC). It adopts an anchor-based graph learning framework in which each view is reconstructed from a small set of anchors with sample-wise sparse noise, substantially reducing computational complexity. Unlike existing tensor-based methods that directly tensorize unaligned view-wise anchor graphs, ATTMVC first aligns view-specific anchor graphs into a shared latent space, thereby enforcing structural consistency across views and enabling more reliable modeling of cross-view higher-order correlations. Furthermore, we introduce a Threshold Tensor Rank (TTR) surrogate on the aligned anchor graph tensor, which effectively promotes low-rank structure while mitigating the over-shrinking effect commonly caused by TNN-based regularization. Finally, extensive experiments demonstrate that ATTMVC outperforms state-of-the-art multi-view clustering methods. The code is publicly available at https://github.com/tangchuan2000/ATTMVC.

Most existing multi-view clustering methods aim to generate a consensus partition across all views, based on the assumption that all views share the same sample arrangement. However, in real-world scenarios, the collected data across different views is often unsynchronized, making it difficult to ensure consistent sample correspondence between views. To address this issue, we propose a scalable sample-alignment-based multi-view clustering method, referred to as SSA-MVC. Specifically, we first employ a cluster-label matching (CLM) algorithm to select the view whose clustering labels best match those of the others as the benchmark view. Then, for each of the remaining views, we construct representations of non-aligned samples by computing their similarities with aligned samples. Based on these representations, we build a similarity graph between the non-aligned samples of each view and those in the benchmark view, which serves as the alignment criterion. This alignment criterion is then integrated into a late-fusion framework to enable clustering without requiring aligned samples. Notably, the learned sample alignment matrix can be used to enhance existing multi-view clustering methods in scenarios where sample correspondence is unavailable. The effectiveness of the proposed SSA-MVC algorithm is validated through extensive experiments conducted on eight real-world multi-view datasets.

Graph-based multi-view clustering methods have demonstrated satisfying performance by effectively capturing relationships among data samples. However, most existing methods primarily emphasize direct pairwise relationships, neglecting the exploration of high-order correlations present within each view. To this end, a novel approach, called multiview clustering via high-order bipartite graph learning and tensor low-rank representation (HBGTLRR), is proposed. Specifically, we first construct high-order bipartite graphs to capture latent relationships and concatenate them into a tensor. By applying tensor nuclear norm (TNN) minimization, we obtain a low-rank representation that reduces noise and preserves high-order consistency. Subsequently, a consensus graph is constructed by adaptively fusing the high-order bipartite graphs with corresponding weights, and then a Laplacian low-rank constraint is imposed on it to effectively capture the intrinsic data structure. Finally, extensive experimental results show that HBGTLRR significantly outperforms existing methods, thereby validating the effectiveness of our proposed method.

In graph based multiview clustering methods, the ultimate partition result is usually achieved by spectral embedding of the consistent graph using some traditional clustering methods, such as -means. However, optimal performance will be reduced by this multistep procedure since it cannot unify graph learning with partition generation closely. In this article, we propose a one-step multiview clustering method through adaptive graph learning and spectral rotation (AGLSR). For every view, AGLSR adaptively learns affinity graphs to capture similar relationships of samples. Then, a spectral embedding is designed to take advantage of the potential feature space shared by different views. In addition, AGLSR utilizes a spectral rotation strategy to obtain the discrete clustering labels from the learned spectral embeddings directly. An effective updating algorithm with proven convergence is derived to optimize the optimization problem. Sufficient experiments on benchmark datasets have clearly demonstrated the effectiveness of the proposed method in six metrics. The code of AGLSR is uploaded at https://github.com/tangchuan2000/AGLSR.

Shape-Text matching is an important task of high-level shape understanding. Current methods mainly represent a 3D shape as multiple 2D rendered views, which obviously can not be understood well due to the structural ambiguity caused by self-occlusion in the limited number of views. To resolve this issue, we directly represent 3D shapes as point clouds, and propose to learn joint embedding of point clouds and texts by bidirectional matching between parts from shapes and words from texts. Specifically, we first segment the point clouds into parts, and then leverage optimal transport method to match parts and words in an optimized feature space, where each part is represented by aggregating features of all points within it and each word is abstracted by its contextual information. We optimize the feature space in order to enlarge the similarities between the paired training samples, while simultaneously maximizing the margin between the unpaired ones. Experiments demonstrate that our method achieves a significant improvement in accuracy over the SOTAs on multi-modal retrieval tasks under the Text2Shape dataset. Codes are available at here.

Finding correspondences between a pair of images is the key ingredient for many applications such as localization and panorama. However, due to a variety of challenges between multi-view images in practice, the results of using a single kind of descriptor may vary significantly across different scenes. In this paper, we treat the assignment task as a clustering problem and propose an image matching method that fuses multiple descriptors to tackle the above difficulties. First, we extract multiple descriptors at the keypoints on two images. Then, we compute a pairwise similarity matrix for each kind of descriptor. Afterwards, we compute a weighted combination of these similarity matrices, and use it to build correspondences via a modified multi-kernel clustering module. The proposed method is tested on three public image datasets: two ground image sets and an Unmanned Aerial Vehicle (UAV) image set. Experiments show that the proposed method can adapt to different number of descriptors. It significantly improves the matching accuracy in a variety of scenarios and downstream tasks.

The surprisingly popular decision in social science fields is a wisdom of the crowd technique that taps into the expert minority opinion within a crowd, which has been demonstrated to be remarkably effective for multiple questions. Most of the existing PSO variants construct the exemplars by solely using fitness, which could be viewed as the democratic approaches or methods. However, the democratic methods tend to highlight the most popular opinion, not necessarily the most correct, which might lead the population into a local trapping region in the scenarios of swarm intelligent computing and evolutionary computation. This paper proposes a method to implement the surprisingly popular decision in PSO to facilitate the exemplar construction, cooperating with the dynamic topology maintenance. The proposed PSO variant is called the Surprisingly Popular Algorithm-based Comprehensive Adaptive Topology Learning Particle Swarm Optimization (SPA-CatlePSO). By using the dynamic topological connection and surprisingly popular decision strategy, the proposed SPA-CatlePSO could adjust the degree of small world topology, mimicking the mechanism of knowledge conversion in the crowd, and guide the direction of the exploitation by constructing exemplars with the largest surprisingly popular degree. We evaluate the proposed SPA-CatlePSO on the full CEC2014 benchmark suite and compare its validity with OLPSO, TSLPSO, ASDPSO, HCLPSO, OptBees and L-shade. The experimental results show that the SPA-CatlePSO algorithm is competitive with the most advanced swarm-based intelligent algorithms.

As we approach the exascale era in supercomputing, designing a balanced computer system with a powerful computing ability and low power requirements has becoming increasingly important. The graphics processing unit (GPU) is an accelerator used widely in most of recent supercomputers. It adopts a large number of threads to hide a long latency with a high energy efficiency. In contrast to their powerful computing ability, GPUs have only a few megabytes of fast on-chip memory storage per streaming multiprocessor (SM). The GPU cache is inefficient due to a mismatch between the throughput-oriented execution model and cache hierarchy design. At the same time, current GPUs fail to handle burst-mode long-access latency due to GPU’s poor warp scheduling method. Thus, benefits of GPU’s high computing ability are reduced dramatically by the poor cache management and warp scheduling methods, which limit the system performance and energy efficiency. In this paper, we put forward a coordinated warp scheduling and locality-protected (CWLP) cache allocation scheme to make full use of data locality and hide latency. We first present a locality-protected cache allocation method based on the instruction program counter (LPC) to promote cache performance. Specifically, we use a PC-based locality detector to collect the reuse information of each cache line and employ a prioritised cache allocation unit (PCAU) which coordinates the data reuse information with the time-stamp information to evict the lines with the least reuse possibility. Moreover, the locality information is used by the warp scheduler to create an intelligent warp reordering scheme to capture locality and hide latency. Simulation results show that CWLP provides a speedup up to 19.8% and an average improvement of 8.8% over the baseline methods.

To improve energy efficiency and spectral efficiency, massive multiple‐input–multiple‐output (MIMO) is proposed and becomes a promising technology in the next generation mobile communication. However, massive MIMO systems equip with scores of or hundreds of antennas which induce large‐scale matrix computations with tremendous complexity, especially for matrix inversion in data detection. Thus, many detection methods have been proposed using approximate matrix inversion algorithms, which satisfy the demand of precision with low complexity. In this study, the authors focus on the approximate detection method based on Newton iteration (NI), and propose upgraded methods named NI method with iterative refinement (NIIR) and diagonal band NIIR (DBNIIR) which combine NI method and DBNI method with iterative refinement (IR). The results show that their proposals provide about 2 dB improvement on bit error rate (BER) for 16‐quadrature amplitude modulation (QAM), and could even break the error floor existing in NI and DBNI methods for 64‐QAM modulation. Furthermore, the BER of their proposals could provide almost the same performance as the exact method. Moreover, in contrast with NI and DBNI methods, NIIR and DBNIIR methods require quite few extra complexity cost and no extra hardware resource which is quite suitable for data detection in massive MIMO.

QR decomposition (QRD) has been a vital component in the transceiver processor of future multiple-input multiple-output (MIMO) systems, in which antenna configuration will be more and more flexible. Therefore, the QRD hardware architecture in the future MIMO systems should be more flexible to meet various antenna configurations. Unfortunately, the existing QRD hardware architectures mainly focus on the matrix of one or several fixed sizes. This paper presents a new triangular systolic array QRD hardware architecture based on parallel tiled QRD algorithm to decompose an 8 × 8 real matrix. The designed hardware architecture is flexible and can be used in various MIMO systems, in which the number of antennas is smaller than 4. This paper also proposes a modified algorithm for the bottleneck operations of parallel tiled QRD algorithm to reduce the hardware overhead. To further reduce the hardware overhead, the Newton-Raphson algorithm is adopted in the proposed algorithm. The implementation results show that the normalized processing latency performance and the normalized processing efficiency performance of the designed QRD hardware architecture both are better than most of the existing QRD hardware architectures. To the best of our knowledge, the hardware architecture presented in this paper achieves the superior normalized QRD rate performance to the existing QRD hardware architectures.

The fast-evolving standards of the wireless communication systems drive the demand for flexible baseband processing platforms. However, with the proliferation of MIMO technologies, traditional single-core-based solutions are hardly able to fulfill requirements with acceptable power and area cost. The reliance on multi-/many-core system is increasing. Different from the computation-limited single-core-based solutions, multi/many-core systems are often communication-limited. In this paper, aiming at MIMO interference cancellation algorithms, we propose a flexible master-slave-based multiprocessor system-on-chiparchitecture based on a systematically divide-and-conquer approach to optimize the communication problems from the application-, architecture- and programming-levels. First, a comprehensively analysis of several typical applications in terms of parallelism, communication patterns and computation patterns is presented. According to the analysis results, a low-complexity and flexible ad hoc point-to-point interconnected fine-grained programmable-element (f -PE) is proposed to execute the arithmetic calculation. In order to reduce the communication traffic, an f-PE-based slave-node is constructed to exploit the data and instruction localities of applications, and a master node that is used to schedule and serve data for the slave nodes is also integrated. Furthermore, to improve the ease of use of the architecture, a multiple instruction multiple data like programming model is adopted and an optimizing mapping strategy is developed. In order to show its flexibility potential, seven linear and nonlinear IC algorithms with distinct computation natures are implemented on the proposed architecture. Finally, the gate-level synthesis and postlayout results are presented to demonstrate the strength and weaknesses of our design.

Peer-to-peer (P2P) botnets outperform the traditional Internet relay chat (IRC) botnets in evading detection and they have become a prevailing type of threat to the Internet nowadays. Current methods for detecting P2P botnets, such as similarity analysis of network behavior and machine-learning based classification, cannot handle the challenges brought about by different network scenarios and botnet variants. We noticed that one important but neglected characteristic of P2P bots is that they periodically send requests to update their peer lists or receive commands from botmasters in the command-and-control (C&C) phase. In this paper, we propose a novel detection model named detection by mining regional periodicity (DMRP), including capturing the event time series, mining the hidden periodicity of host behaviors, and evaluating the mined periodic patterns to identify P2P bot traffic. As our detection model is built based on the basic properties of P2P protocols, it is difficult for P2P bots to avoid being detected as long as P2P protocols are employed in their C&C. For hidden periodicity mining, we introduce the so-called regional periodic pattern mining in a time series and present our algorithms to solve the mining problem. The experimental evaluation on public datasets demonstrates that the algorithms are promising for efficient P2P bot detection in the C&C phase.

Geometric constraint problem can be transformed to an optimization problem. We can solve the problem with niche improved particle swarm. Classical particle swarm optimization is likely to be trapped into local minima as well as premature. A niche improved particle swarm optimization (NIPSO) based on niche theory was developed. After the update of the particle velocity and position, the outlier particle was identified in the NIPSO by comparing the niche number of every particle, with which the crossover and selection operators were employed sequent for those particles, whose personal best values were less than that of the outlier particle. The experiment shows that it can improve the geometric constraint solving efficiency and possess better convergence property than the compared algorithms.