In environments where RGB images are inadequate, pressure maps is a viable alternative, garnering scholarly attention. This study introduces a novel self-supervised pressure map keypoint detection (SPMKD) method, addressing the current gap in specialized designs for human keypoint extraction from pressure maps. Central to our contribution is the Encoder-Fuser-Decoder (EFD) model, which is a robust framework that integrates a lightweight encoder for precise human keypoint detection, a fuser for efficient gradient propagation, and a decoder that transforms human keypoints into reconstructed pressure maps. This structure is further enhanced by the Classification-to-Regression Weight Transfer (CRWT) method, which fine-tunes accuracy through initial classification task training. This innovation not only enhances human keypoint generalization without manual annotations but also showcases remarkable efficiency and generalization, evidenced by a reduction to only 5.96% in FLOPs and 1.11% in parameter count compared to the baseline methods. Code is accessible at SPMKD-52CB.

The tremendous computing capacity of GPU offers significant potential in processing hierarchically compressed text data without decompression. However, current GPU techniques offer only traversal-based text data analytics; random access is exceedingly inefficient, limiting their utility significantly. To address this issue, we develop a novel and widely applicable solution that prompts random access to hierarchically compressed text data without decompression in GPU memory. We address three main challenges for enabling efficient random access to compressed text data on GPUs. The first challenge is designing GPU data structures that facilitate random access. The second challenge is efficiently generating data structures on GPU. The CPU is inefficient when generating data structures for random access, and this inefficiency increases considerably when PCIe transmission is incorporated. The third challenge is query processing on compressed text data in GPU memory. Random accesses, such as data updates, cause massive conflicts among countless threads. In order to address the first challenge, we develop several compressed GPU data structures, including indexing within the intricate GPU memory hierarchy. To handle the second challenge, we propose a two-phase process for producing these data structures on GPU. For the third challenge, a double-parsing design is proposed as a solution to avoid conflicts. We evaluate our solution on three platforms, two server-grade GPU platforms and one edge-grade GPU platform, using five real-world datasets. Experimental results show that random access operations on GPU achieve an average speedup of 52.98× compared to the state-of-the-art solution.

GPU's powerful computational capacity holds great potentials for processing hierarchically-compressed data without decompression in data science domain. Unfortunately, existing GPU approaches offer only traversal-based data analytics; random access is extremely inefficient, substantially limiting their utility. To solve this problem, we develop a novel and broadly applicable optimization that enables efficient random access to hierarchically-compressed data without decompression in GPU memory. We address three major challenges for enabling efficient random access to compressed data on GPUs. The first challenge is designing GPU data structures that support random access. The second challenge is efficiently generating data structures on GPU. Generating data structures for random access is costly on the CPU, and the inefficiency increases dramatically when PCIe data transmission is incorporated. The third challenge is query processing on compressed data in GPU memory. Random accesses, including data updates, result in significant conflicts between massive threads. To solve the first challenge, we propose and modify a number of compressed data structures, including indexing within the complicated GPU memory hierarchy. To address the second challenge, we develop a two-phase process for generating these data structures on the GPU. To handle the third challenge, we propose a double-parsing design to avoid data conflicts. We evaluate our solution on two GPU platforms using five real-world datasets. Experiments show that the random access operations on GPU can achieve 65.04x average speedup compared to the state-of-the-art method.

Malocclusion has a high prevalence in the population, which seriously affects patients’ oral and mental health. Angle’s classification is a widely accepted diagnostic standard for malocclusion, either requiring professional intervention and complicated procedures, or increasing radiation risks. This paper proposes a new method of Angle’s classification based on occlusal contact information to realize the automatic Angle’s classification. Firstly, a novel bite force measurement device is used to record the occlusal data of subjects with different occlusal categories, Meta-analysis evaluated several occlusion quantitative evaluation indicators. Then, the imbalance of the data set is improved by oversampling and popular machine learning models are used for training and performance evaluation. The result shows that the accuracy of the random forest model combined with occlusal contact information reaches 87.83%, and the performance of other evaluation indexes is good. It is demonstrated that machine learning models can be applied to Angle’s classification and shows the great potential of occlusal contact information in the aided diagnosis of oral diseases.

Butterfly counting is an important and costly operation for large bipartite graphs. GPUs are popular parallel heterogeneous devices and can bring significant performance improvement for data science applications. Unfortunately, no work enables efficient butterfly counting on GPU currently. To fill this gap, we propose a GPU-based butterfly counting, called G-BFC. G-BFC addresses three main technical challenges. First, butterfly counting involves massive serial operations, which leads to severe synchronization overheads and performance degradation. We unlock the serial region and utilize the shared memory on GPU to efficiently handle it. Second, butterfly counting on GPU faces the workload imbalance problem. We develop a novel adaptive strategy to balance the workload among threads for efficiency. Third, butterfly counting in parallel suffers from the traversal of the huge amount of two-hop paths, also called wedges, in bipartite graphs. We develop a novel preprocessing strategy, which can effectively reduce the number of wedges to be traversed. Experiments show that G-BFC brings significant performance benefits. On eleven real datasets, G-BFC achieves 19.8X performance speedup over the state-of-the-art solution.

With the development of the parameterized hand model (e.g. MANO), it is possible to reconstruct the 3D hand mesh from a single 2D hand image by learning a few hand model parameters, rather than estimating hundreds of vertices on the mesh. However, it is highly non-linear to learn these parameters from the 2D hand image, as there is no explicit spatial correspondence between these parameters and image pixels. In this paper, we successfully leverage the graph convolutional network (GCN) to capture implicit spatial cues for fitting the well-known MANO hand model, thus greatly improving the performance of monocular 3D hand reconstruction. Our proposed MANO-GCN establishes the spatial hand mesh and hand joints graph for learning MANO parameters, with node features propagated along edges to utilize the spatial information. Among all monocular 3D hand reconstruction methods with MANO hand model, MANO-GCN achieves state-of-the-art accuracy on public FreiHAND and HO-3D benchmarks, without any bells and whistles. Code is available at https://github.com/ChenJoya/manogcn.