Random walks on graphs are essential for various applications such as network analysis, recommendation systems, and graph embedding. However, existing frameworks for random walks face challenges in efficiently managing and updating walkers. These challenges arise from inefficient walker updating due to the subgraph-based iterative updating strategy, which makes walkers update only one step per iteration, and the high management and storage costs associated with the walker array-based management strategy. This paper presents a novel cache-efficient random walk framework called PTWalker, which sets out to have the majority of walkers updated at least two steps per iteration. This is achieved through an alternating dual-subgraph walk updating scheme, which involves dividing the graph into two sets of subgraphs with maximized edge cuts between them. These subgraph sets are then loaded into the cache for walk updating in an alternating manner, prompting most walkers to transition to the other subgraph set and update additional steps. Besides, a thread-level walker pool management strategy is designed to reduce management and storage costs for walkers. Experimental results demonstrate that PTWalker outperforms state-of-the-art random walk systems, achieving a speedup ranging from 0.18 × to 8.65 ×.

Evolving graph processing has become a critical component in various applications and is gaining increasing attention. However, existing evolving graph systems suffer from cache contention and workload imbalance between threads, which leads to poor scalability and performance degradation on modern multi-core computers.

The metric learning paradigm has achieved notable success in few-shot action recognition; however, it faces unaddressed challenges. Specifically,(1)limited training data could impede the exploration of temporal action relations, and(2)precision would decline from the presence of outliers during the frame-level feature alignment. To address the challenges, we propose a two-stream temporal feature aggregation method based on clustering, incorporating a temporal augmentation module (TAM) and a feature aggregation module (FAM). The TAM adeptly integrates three consecutive grayscale frames into the original RGB frame through weighted summation, thereby addressing the color-related misguidance and enhancing the temporal information extraction. Meanwhile, the FAM employs clustering to aggregate the frame-level features into high semantic sub-actions and replaces the original features with cluster centers to mitigate the adverse impact of outliers on the model performance. Experimental results on benchmark datasets demonstrate the effectiveness of our method in few-shot action recognition. We validate our proposed approach by conducting comprehensive ablation experiments.

The blast furnace is a highly energy-intensive, highly polluting, and extremely complex reactor in the ironmaking process. Soft sensors are a key technology for predicting molten iron quality indices reflecting blast furnace energy consumption and operation stability, and play an important role in saving energy, reducing emissions, improving product quality, and producing economic benefits. With the advancement of the Internet of Things, big data, and artificial intelligence, data-driven soft sensors in blast furnace ironmaking processes have attracted increasing attention from researchers, but there has been no systematic review of the data-driven soft sensors in the blast furnace ironmaking process. This review covers the state-of-the-art studies of data-driven soft sensors technologies in the blast furnace ironmaking process. Specifically, we first conduct a comprehensive overview of various data-driven soft sensor modeling methods (multiscale methods, adaptive methods, deep learning, etc.) used in blast furnace ironmaking. Second, the important applications of data-driven soft sensors in blast furnace ironmaking (silicon content, molten iron temperature, gas utilization rate, etc.) are classified. Finally, the potential challenges and future development trends of data-driven soft sensors in blast furnace ironmaking applications are discussed, including digital twin, multi-source data fusion, and carbon peaking and carbon neutrality.

With the growth in the consumer electronics industry, it is vital to develop an algorithm for ultrahigh definition products that is more effective and has lower time complexity. Image interpolation, which is based on an autoregressive model, has achieved significant improvements compared with the traditional algorithm with respect to image reconstruction, including a better peak signal-to-noise ratio (PSNR) and improved subjective visual quality of the reconstructed image. However, the time-consuming computation involved has become a bottleneck in those autoregressive algorithms. Because of the high time cost, image autoregressive-based interpolation algorithms are rarely used in industry for actual production. In this study, in order to meet the requirements of real-time reconstruction, we use diverse compute unified device architecture (CUDA) optimization strategies to make full use of the graphics processing unit (GPU) (NVIDIA Tesla K80), including a shared memory and register and multi-GPU optimization. To be more suitable for the GPU-parallel optimization, we modify the training window to obtain a more concise matrix operation. Experimental results show that, while maintaining a high PSNR and subjective visual quality and taking into account the I/O transfer time, our algorithm achieves a high speedup of 147.3 times for a Lena image and 174.8 times for a 720p video, compared to the original single-threaded C CPU code with -O2 compiling optimization.