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Publications (150)
In the current data-intensive landscape, B+trees are crucial data structures utilized across various fields like databases and web indexing. With the rise of data explosion, the demand for high-performance real-time query processing in database systems has surged. For instance, Alibaba’s PolarDB and AnalyticDB systems handle massive query volumes a...
Mesh generation is a critical but time-consuming process for stable and accurate numerical simulations. Although multi-layer perceptron-based meshing methods can be effective, they suffer from slow training convergence and heavy reliance on prior datasets. To overcome these problems, we propose the Kolmogorov–Arnold Network-based meshing network, a...
Password-based recovery is a widely used method for regaining access to applications or services when passwords are lost or forgotten. It is commonly used in electronic forensics by law enforcement agencies, information acquisition in the commercial sector, and data recovery for individuals. However, as encryption algorithms and complex passwords b...
Mesh smoothing methods can enhance mesh quality by eliminating distorted elements, leading to improved convergence in simulations. To balance the efficiency and robustness of traditional mesh smoothing process, previous approaches have employed supervised learning and reinforcement learning to train intelligent smoothing models. However, these meth...
The marine magnetotelluric (MMT) method is a significant tool extensively utilized in offshore studies, including the understanding of the Earth’s tectonics and hydrocarbon exploration. Conductive anisotropy and non-zero magnetic susceptibility are common phenomena observed in the Earth’s subsurface, and MMT forward modeling is the basis of practic...
The rapid development of artificial intelligence has promoted the emergence of new flow field prediction methods. These methods address challenges posed by nonlinear problems and significantly reduce computational time and cost compared to traditional numerical simulations. However, they often struggle to capture the dynamic sparse characteristics...
The reconstruction of high-fidelity flow fields from low-fidelity data has attracted considerable attention in fluid dynamics but poses many challenges to existing deep learning methods due to the spatiotemporal complexity of flows and the lack of standardized benchmark datasets. In this study, we generate a low- and high-fidelity dataset containin...
Existing methods for video super-resolution (VSR) and video frame interpolation (VFI) primarily concentrate on devising a general pipeline suitable for open-domain videos. However, these approaches tend to overlook the inherent distinctions in animation data. Specifically, animation often features lines and smooth areas that lack textures, thereby...
Sparse matrix-vector multiplication (SpMV) is extensively used in scientific computing and often accounts for a significant portion of the overall computational overhead. Therefore, improving the performance of SpMV is crucial. However, sparse matrices exhibit a sporadic and irregular distribution of non-zero elements, resulting in workload imbalan...
Pointwise convolutions are widely used in various convolutional neural networks, due to low computation complexity and parameter requirements. However, pointwise convolutions are still time-consuming like regular convolutions. As a result of increasing power consumption, low-power embedded processors have been brought into high-performance computin...
The traditional Yinyang K-means algorithm is computationally expensive when dealing with large-scale clustering problems. An efficient parallel acceleration implementation of the Yinyang K-means algorithm was proposed based on the architectural characteristics of typical many-core CPUs. This implementation was based on a new memory data layout, use...
Image–text cross-modal retrieval aims to bridge the semantic gap between different modalities, allowing for the search of images based on textual descriptions or vice versa. Existing efforts in this field concentrate on coarse-grained feature representation and then utilize pairwise ranking loss to pull image–text positive pairs closer, pushing neg...
Scalability is a crucial factor determining the performance of massive heterogeneous parallel CFD applications on the multi-GPUs platforms, particularly after the single-GPU implementations have achieved optimal performance through numerous optimizations. A novel Data-Centric hybrid MPI-CUDA CFD model is proposed in this paper to enable efficient s...
The development of computational fluid dynamics (CFD) highly depends on high‐performance computers. Computer hardware has evolved rapidly, yet scalable CFD parallel software remains scarce. In this article, we design a highly scalable CFD parallel paradigm for both homogeneous and heterogeneous supercomputers. The paradigm achieves the separation o...
Single-cell RNA-sequencing (scRNA-seq) has emerged as a powerful technique for studying gene expression patterns at the single-cell level. Inferring gene regulatory networks (GRNs) from scRNA-seq data provides insight into cellular phenotypes from the genomic level. However, the high sparsity, noise and dropout events inherent in scRNA-seq data pre...
In this paper, we develop a novel structured mesh generation method, MeshNet. The core of the proposed method is the introduction of deep neural networks to learn high-quality meshing rules and generate desired meshes. To accomplish this, MeshNet employs a well-designed physics-informed neural network to approximate the potential transformation (ma...
Dilated convolutions are widely used to accomplish wide receptive fields while keeping the resolution of feature maps in deep learning applications, such as semantic segmentation and object detection. However, the data locality in dilated convolutions deteriorates rapidly with the increase of dilation rate, which brings a great challenge to the hig...
Structured grid-based sparse matrix-vector multiplication and Gauss–Seidel iterations are very important kernel functions in scientific and engineering computations, both of which are memory intensive and bandwidth-limited. GPDSP is a general purpose digital signal processor, which is a very significant embedded processor that has been introduced i...
An acoustic solver is implemented based on a generic framework for solving PDEs. • Large-scale problems can be efficiently simulated through parallel computing. • Our method shows high accuracy and the ability to support complex scenarios. Graphical abstract and Research highlights will be displayed in online search result lists, the online content...
Physics-informed neural networks (PINNs) have emerged as promising surrogate modes for solving partial differential equations (PDEs). Their effectiveness lies in the ability to capture solution-related features through neural networks. However, original PINNs often suffer from bottlenecks, such as low accuracy and non-convergence, limiting their ap...
With the improvement of security awareness, in order to guarantee information security, more advanced and secure encryption algorithms are applied to Microsoft Office. People also set more complex encryption passwords. However, once the initial password is forgotten, the encrypted information needs to be retrieved. The conventional brute force crac...
Computational fluid dynamics simulation accounts for a large number of workloads in the numerical design optimization of aerodynamics problems. In this paper, we develop AFFNet, an advanced neural network and physics solver coupled framework for accelerating flow field simulations. AFFNet combines the benefits of an attention mechanism, affine tran...
Partial differential equations (PDEs) are an essential computational kernel in physics and engineering. With the advance of deep learning, physics-informed neural networks (PINNs), as a mesh-free method, have shown great potential for fast PDE solving in various applications. To address the issue of low accuracy and convergence problems of existing...
Improving the effectiveness and scalability of implicit algorithms has long been a subject that attracted scientific computing researchers. The generalized minimal residual (GMRES) method is one of the efficient algorithms employed by Computational Fluid Dynamics (CFD). However, due to the inherent sequential properties, GMRES encountered difficult...
In this paper, we present a novel surface mesh generation approach that splits B-rep geometry models into isotropic triangular meshes based on neural networks and splitting lines. In the first stage, a recursive method is designed to generate plentiful data to train the neural network model offline. In the second stage, the implemented mesh generat...
Computational fluid dynamics (CFD) plays a critical role in many scientific and engineering applications, with aerodynamic design optimization being a primary area of interest. Recently, there has been much interest in using artificial intelligence approaches to accelerate this process. One promising method is the graph convolutional neural network...
Depthwise convolutions are widely used in lightweight convolutional neural networks (CNNs). The performance of depthwise convolutions is mainly bounded by the memory access rather than the arithmetic operations for classic convolutions so that direct algorithms are often more efficient than indirect ones (matrix multiplication-, Winograd-, and FFT-...
Lattice Boltzmann method (LBM) has become a powerful method in computational fluid dynamics and has drawn more and more attention in high-performance computing due to its particulate nature and local dynamics, especially on recent multi-core or many-core platforms. This paper develops a parallel software framework for 3D LBM simulation on a heterog...
Relation triple extraction is a combination of named entity recognition and relation prediction. Early works ignore the problem of data overlap when extracting triples, resulting in poor extraction performance. Subsequent works improve the capability of the model to extract overlapping triples through generative and extractive methods. These works...
In recent years, with the rapid development of single-cell sequencing technology, this brings new opportunities and challenges to reconstruct gene regulatory networks. On the one hand, scRNA-seq data reveal statistical information of gene expression at single-cell resolution, which is beneficial to construct gene expression regulatory networks. On...
Matrix transpose is one of the common matrix operations, which is widely employed in various fields such as signal processing, scientific computing, and deep learning. With the popularization of Phytium heterogeneous multi-core DSPs(digital signal processors) developed by National University of Defense Technology, there is a strong demand for high-...
K-Means algorithm is one of the most common clustering algorithms widely applied in various data analysis applications. Yinyang K-Means algorithm is a popular enhanced K-Means algorithm that avoids most unnecessary calculations using triangle inequality. However, Yinyang K-Means algorithm is time-consuming when the problem size is large. Due to the...
Vortex detection plays a fundamental role in turbulence research and engineering problems. However, due to the lack of a mathematically rigorous vortex definition, as well as the absence of any vortex-oriented database, both traditional and machine learning detection methods achieve only limited performance. In this paper, we develop a deep learnin...
Yishui Li Runduo Liu Jie Liu- [...]
Zhe Li
Free energy perturbation-relative binding free energy (FEP-RBFE) prediction has shown its reliability and accuracy in the prediction of protein-ligand binding affinities, which plays a fundamental role in structure-based drug design. In FEP-RBFE predictions, the calculation of each mutation path is associated with a statistical error, and cycle clo...
With the development of high-performance computers, it is necessary to develop efficient parallel algorithms in the field of computational fluid dynamics (CFD). In this study, a novel parallel communication strategy based on asynchronous and packaged communication is proposed. The strategy implements an aggregated communication process, which requi...
Sparse Matrix-Vector Multiplication (SpMV) plays a critical role in many areas of science and engineering applications. The storage space of value array in general real sparse matrices accounts for costly. However, the existing compressed formats cannot balance the compressed rate and computational speed. To address this issue, we propose an effici...
Background
Building biological networks with a certain function is a challenge in systems biology. For the functionality of small (less than ten nodes) biological networks, most methods are implemented by exhausting all possible network topological spaces. This exhaustive approach is difficult to scale to large-scale biological networks. And regula...
Mesh generation remains a key technology in many areas where numerical simulations are required. As numerical algorithms become more efficient and computers become more powerful, the percentage of time devoted to mesh generation becomes higher. In this paper, we present an improved structured mesh generation method. The method formulates the meshin...
Evaluating mesh quality prior to performing the computational fluid dynamics (CFD) simulation is an essential step to ensure the acceptable accuracy of cylinder modelling. However, traditional mesh quality indicators are often insufficient since they only check geometric information on individual distorted elements. To yield more accurate results,...
As a theoretically rigorous and accurate method, FEP-ABFE (Free Energy Perturbation-Absolute Binding Free Energy) calculations showed great potential in drug discovery, but its practical application was difficult due to high computational cost. To rapidly discover antiviral drugs targeting SARS-CoV-2 M pro and TMPRSS2, we performed FEP-ABFE–based v...
General Matrix Multiplication (GEMM) has a wide range of applications in scientific simulation and artificial intelligence. Although traditional libraries can achieve high performance on large regular-shaped GEMMs, they often behave not well on irregular-shaped GEMMs, which are often found in new algorithms and applications of high-performance comp...
The quality of the finite element mesh has a considerable effect on the efficiency and accuracy of computational fluid dynamics (CFD) simulations. To ensure the generated mesh is of good quality, many quality metrics have been proposed to assess the generated mesh, such as aspect ratio, skewness, Jacobian ratio, etc. Such metrics, however, are prim...
Depthwise convolutions are widely used in lightweight convolutional neural networks (CNNs). The performance of depthwise convolutions is mainly bounded by the memory access rather than the arithmetic operations for classic convolutions so that direct algorithms are often more efficient than indirect ones (matrix multiplication-, Winograd-, and FFT-...
With the growing computing power of high-performance computers, efficient parallel algorithms are becoming increasingly important in the development of Computational Fluid Dynamics(CFD). This research presents a novel parallel strategy based on asynchronous and package communication. This strategy tries to enhance the performance of large-scale com...
Named entity recognition aims to find the target entity from the input sentence and determine the category it belongs to. Low-resource means that the training data used by the model is scarce. In order to improve the performance of the model with a small quantity of labeled data, previous works propose the concept of the trigger and introduce trigg...