It is challenging to cluster multi-view data in which the clusters have overlapping *** multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them into single cl...
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It is challenging to cluster multi-view data in which the clusters have overlapping *** multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them into single clusters,increasing clustering *** solution,the multi-view dynamic kernelized evidential clustering method(MvDKE),addresses this by assigning these objects to meta-clusters,a union of several related singleton clusters,effectively capturing the local imprecision in overlapping *** offers two main advantages:firstly,it significantly reduces computational complexity through a dynamic framework for evidential clustering,and secondly,it adeptly handles non-spherical data using kernel techniques within its objective *** on various datasets confirm MvDKE's superior ability to accurately characterize the local imprecision in multi-view non-spherical data,achieving better efficiency and outperforming existing methods in overall performance.
A rigorous model in the far-field for the calculation of the broadband reflectance of multilayered structured semiconductor surfaces is introduced and is applied on semiconductor wafers with single or multiple vias or...
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A rigorous model in the far-field for the calculation of the broadband reflectance of multilayered structured semiconductor surfaces is introduced and is applied on semiconductor wafers with single or multiple vias or trenches. The model is based on four assumptions: (1) the wafer can be divided into units containing a single via or trench, (2) each unit can be divided into subregions, (3) the reflectance of each subregion can be simulated by the reflectance of a layer stack, and (4) the reflectance of the unit can be composed from the reflectance of each subregion, weighted with the ratio of the subregion area to the unit area, and multiplied with a phase factor that considers the depth (or height) relative to the wafer surface. As in the model the via and the wafer surface are clearly separated it is possible to study transparent coatings on the wafer surface as well as transparent fillings of the via or trench. The influence of inclined walls and a curved via bottom is investigated. Dependences upon the via depth and the ratio of via and unit area are shown and discussed.
In this article, we port results from creativity research in the field of cognitive psychology to the field of software engineering. After all, programming is a highly creative endeavor. We explore how to leverage cre...
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In this article, we port results from creativity research in the field of cognitive psychology to the field of software engineering. After all, programming is a highly creative endeavor. We explore how to leverage creativity as a practical problem-solving tool to wield for software developers.
Inverse design of thermodynamic systems can find sets of design parameters that meet the desired thermodynamic performance. Traditionally, the determination of design parameters is performed using optimization methods...
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Code recommendation systems have been widely used in helping developers implement unfamiliar programming *** existing code recommenders or code search engines can retrieve relevant code rapidly with high accuracy,howe...
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Code recommendation systems have been widely used in helping developers implement unfamiliar programming *** existing code recommenders or code search engines can retrieve relevant code rapidly with high accuracy,however,they cannot recommend any code outside similar *** propose an approach to predict the functionality of incomplete programming code by using syntactical information,and providing a list of potential functionalities to guess what the developers want,in order to increase the diversity of *** this paper,we propose a deep learning model called ASTSDL,which uses a sequencebased representation of source code to predict *** extract syntactical information from the abstract syntax tree(AST) of the source code,apply a deep learning model to capture the syntactic and sequential information,and predict the functionality of the source code *** experimental results demonstrate that ASTSDL can effectively predict the functionality of incomplete code with an accuracy above 84% in the top-10 list,even if there is only half of the complete code.
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 i...
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ISBN:
(数字)9789819708017
ISBN:
(纸本)9789819708000;9789819708017
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 imbalance among threads and challenges in vectorization. To address these issues, numerous efforts have focused on optimizing SpMV based on the hardware characteristics of computing platforms. In this paper, we present an optimization on CSR-Based SpMV, since the CSR format is the most widely used and supported by various high-performance sparse computing libraries, on a novel MIMD computing platform Pezy-SC3s. Based on the hardware characteristics of Pezy-SC3s, we tackle poor data locality, workload imbalance, and vectorization challenges in CSRBased SpMV by employing matrix chunking, applying Atomic Cache for workload scheduling, and utilizing SIMD instructions during performing SpMV. As the first study to investigate SpMV optimization on Pezy-SC3s, we evaluate the performance of our work by comparing it with the CSR-Based SpMV and SpMV provided by Nvidia's CuSparse. Through experiments conducted on 2092 matrices obtained from SuiteSparse, we demonstrate that our optimization achieves a maximum speedup ratio of x17.63 and an average of x1.56 over CSR-Based SpMV and an average bandwidth utilization of 35.22% for large-scale matrices (nnz >= 10(6)) compared with 36.17% obtained using CuSparse. These results demonstrate that our optimization effectively harnesses the hardware resources of Pezy-SC3s, leading to improved performance of CSR-Based SpMV.
Deep learning has significantly advanced the audio classification, achieving remarkable results. However, these successes often rely on extensive manual annotation of audio, a labor-intensive and costly process. Activ...
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We apply AI-assisted large language model (LLM) capabilities of GPT-3 targeting high-performance computing (HPC) kernels for (i) code generation, and (ii) auto-parallelization of serial code in C ++, Fortran, Pyt...
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Take-over performance plays a significant role in evaluating drivers' state, and serves as a crucial reference for enhancing control transitions in the context of conditionally automated driving. In this study, we...
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Take-over performance plays a significant role in evaluating drivers' state, and serves as a crucial reference for enhancing control transitions in the context of conditionally automated driving. In this study, we aim to predict minimum anticipated collision time (min ACT), an indicator of drivers' take-over performance, in expectation of promoting safer take-overs via deep learning, so that drivers' state detriment of take-over safety could be adjusted accordingly with intelligent human-machine interaction algorithms predictably. By incorporating multi-source information including drivers' state, drivers' demographics, surrounding traffic features as well as driver-vehicle interaction characteristics, network model “ACTNet” was proposed to facilitate continuous estimation. Depthwise separable convolution and non-local self-attention were utilized to prevent overfitting and establish spatial dependency over fixation heatmap, respectively. To overcome data distribution imbalance, class balanced loss was used in conjunction with regression loss to realize more accurate predictions. Driving simulator experiment was conducted with dataset collected for the subsequent verification of the proposed algorithm. Potentialities of deep learning methods were highlighted for take-over studies, contributing to the design of intelligent human-machine interaction systems in conditional automation. Our findings present a valid method of deep learning in predicting drivers' take-over performance and meanwhile have implications for the development of intelligent adaptive take-over time budget regulation and dynamic drivers' state adjustment algorithms. IEEE
No-reference image quality assessment (NR-IQA) aims to evaluate image quality without using the original reference images. Since the early NR-IQA methods based on distortion types were only applicable to specific dist...
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