In recent years,various adversarial defense methods have been proposed to improve the robustness of deep neural *** training is one of the most potent methods to defend against adversarial ***,the difference in the fe...
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In recent years,various adversarial defense methods have been proposed to improve the robustness of deep neural *** training is one of the most potent methods to defend against adversarial ***,the difference in the feature space between natural and adversarial examples hinders the accuracy and robustness of the model in adversarial *** paper proposes a learnable distribution adversarial training method,aiming to construct the same distribution for training data utilizing the Gaussian mixture *** distribution centroid is built to classify samples and constrain the distribution of the sample *** natural and adversarial examples are pushed to the same distribution centroid to improve the accuracy and robustness of the *** proposed method generates adversarial examples to close the distribution gap between the natural and adversarial examples through an attack algorithm explicitly designed for adversarial *** algorithm gradually increases the accuracy and robustness of the model by scaling ***,the proposed method outputs the predicted labels and the distance between the sample and the distribution *** distribution characteristics of the samples can be utilized to detect adversarial cases that can potentially evade the model *** effectiveness of the proposed method is demonstrated through comprehensive experiments.
The cryo-electron microscopy(cryo-EM)is one of the most powerful technologies available today for structural *** RELION(Regularized Likelihood Optimization)implements a Bayesian algorithm for cryo-EM structure determi...
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The cryo-electron microscopy(cryo-EM)is one of the most powerful technologies available today for structural *** RELION(Regularized Likelihood Optimization)implements a Bayesian algorithm for cryo-EM structure determination,which is one of the most widely used software in this *** researchers have devoted effort to improve the performance of RELION to satisfy the analysis for the ever-increasing volume of *** this paper,we focus on performance analysis of the most time-consuming computation steps in RELION and identify their performance bottlenecks for specific *** propose several performance optimization strategies to improve the overall performance of RELION,including optimization of expectation step,parallelization of maximization step,accelerating the computation of symmetries,and memory affinity *** experiment results show that our proposed optimizations achieve significant speedups of RELION across representative *** addition,we perform roofline model analysis to understand the effectiveness of our optimizations.
Recent advances in deep learning generative models(GMs)have created high capabilities in accessing and assessing complex high-dimensional data,allowing superior efficiency in navigating vast material configuration spa...
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Recent advances in deep learning generative models(GMs)have created high capabilities in accessing and assessing complex high-dimensional data,allowing superior efficiency in navigating vast material configuration space in search of viable *** such capabilities with physically significant data to construct trained models for materials discovery is crucial to moving this emerging field ***,we present a universal GM for crystal structure prediction(CSP)via a conditional crystal diffusion variational autoencoder(Cond-CDVAE)approach,which is tailored to allow user-defined material and physical parameters such as composition and *** is trained on an expansive dataset containing over 670,000 local minimum structures,including a rich spectrum of high-pressure structures,along with ambient-pressure structures in Materials Project *** demonstrate that the Cond-CDVAE model can generate physically plausible structures with high fidelity under diverse pressure conditions without necessitating local optimization,accurately predicting 59.3%of the 3547 unseen ambient-pressure experimental structures within 800 structure samplings,with the accuracy rate climbing to 83.2%for structures comprising fewer than 20 atoms per unit *** results meet or exceed those achieved via conventional CSP methods based on global *** present findings showcase substantial potential of GMs in the realm of CSP.
This article designs the PELAN structure based on the lightweight YOLOv7-tiny model for surface defect detection of hot-rolled steel strips. At the same time, the CA (Channel Attention) is embedded in the feature pyra...
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A math word problem (MWP) is a coherent narrative which reflects the underlying logic of math equations. Successful MWP generation can automate the writing of mathematics questions. Previous methods mainly generate MW...
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In this paper, a bionic-based design of a series-parallel hybrid wheel-legged quadruped robot that can realize wheel-foot transformations is presented to achieve footed stable walking, and a method of measuring the st...
In unsupervised meta-learning, the clustering-based pseudo-labeling approach is an attractive framework, since it is model-agnostic, allowing it to synergize with supervised algorithms to learn from unlabeled data. Ho...
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Text-guided image generative diffusion models achieve fast development on the generation and editing of high-quality images. To extend such success to video editing, some efforts combining image generation with video ...
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The adoption of heterogeneous computing systems based on diverse architectures to achieve exascale computing power has worsened the performance portability problem of scientific applications that were designed to run ...
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ISBN:
(纸本)9798350305487
The adoption of heterogeneous computing systems based on diverse architectures to achieve exascale computing power has worsened the performance portability problem of scientific applications that were designed to run on these platforms. To cope with the challenges posed by supercomputing, new performance portability frameworks have been developed alongside advanced methods and metrics to evaluate the performance portability of heterogeneous applications. However, many studies have shown that the new methods and metrics do not produce coherent results which yield clear conclusions that are required for designing the hardware and software architectures of tomorrow's supercomputing systems. We outline a proposal to establish an open repository of performance portability of applications, benchmarks and models which will be standardized, objective, and based on strict operating and reporting guidelines. Such guidelines will ensure a fair, comparable and meaningful measure of the performance portability while the requirement for a detailed disclosure of the obtained results and the configuration settings will ensure the reproducibility of the reported results.
In the product conceptual design, designers utilize multiple design representations to ideate, externalize, and refine concepts iteratively. Mixed representations, defined as the simultaneous presentation of multiple ...
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