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860 results on '"Jia, Zhe"'

1. Frontiers in high entropy alloys and high entropy functional materials

Author

Zhang, Wen-Tao, Wang, Xue-Qian, Zhang, Feng-Qi, Cui, Xiao-Ya, Fan, Bing-Bing, Guo, Jia-Ming, Guo, Zhi-Min, Huang, Rui, Huang, Wen, Li, Xu-Bo, Li, Meng-Ru, Ma, Yan, Shen, Zhi-Hua, Sun, Yong-Gang, Wang, De-Zhuang, Wang, Fei-Yang, Wang, Li-Qiang, Wang, Nan, Wang, Tian-Li, Wang, Wei, Wang, Xiao-Yang, Wang, Yi-Han, Yu, Fu-Jie, Yin, Yu-Zhen, Zhang, Ling-Kun, Zhang, Yi, Zhang, Jian-Yang, Zhao, Qi, Zhao, Yu-Ping, Zhu, Xin-Dong, Sohail, Yasir, Chen, Ya-Nan, Feng, Tao, Gao, Qi-Long, He, Hai-Yan, Huang, Yong-Jiang, Jiao, Zeng-Bao, Ji, Hua, Jiang, Yao, Li, Qiang, Li, Xiao-Ming, Liao, Wei-Bing, Lin, Huai-Jun, Liu, Hui, Liu, Qi, Liu, Qing-Feng, Liu, Wei-Di, Liu, Xiong-Jun, Lu, Yang, Lu, Yi-Ping, Ma, Wen, Miao, Xue-Fei, Pan, Jie, Wang, Qing, Wu, Hong-Hui, Wu, Yuan, Yang, Tao, Yang, Wei-Ming, Yu, Qian, Zhang, Jin-Yu, Chen, Zhi-Gang, Mao, Liang, Ren, Yang, Shen, Bao-Long, Wang, Xun-Li, Jia, Zhe, Zhu, He, Wu, Zhen-Duo, and Lan, Si

Published
2024
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7. Digital-SMLM for precisely localizing emitters within the diffraction limit

Author

Jia Zhe, Zhou Lingxiao, Li Haoyu, Ni Jielei, Chen Danni, Guo Dongfei, Cao Bo, Liu Gang, Liang Guotao, Zhou Qianwen, Yuan Xiaocong, and Ni Yanxiang

Subjects
single molecule localization microscopy, deep learning, diffraction limit, sub-diffraction-limited spots, digital-smlm, Physics, QC1-999
Abstract

Precisely pinpointing the positions of emitters within the diffraction limit is crucial for quantitative analysis or molecular mechanism investigation in biomedical research but has remained challenging unless exploiting single molecule localization microscopy (SMLM). Via integrating experimental spot dataset with deep learning, we develop a new approach, Digital-SMLM, to accurately predict emitter numbers and positions for sub-diffraction-limit spots with an accuracy of up to 98 % and a root mean square error as low as 14 nm. Digital-SMLM can accurately resolve two emitters at a close distance, e.g. 30 nm. Digital-SMLM outperforms Deep-STORM in predicting emitter numbers and positions for sub-diffraction-limited spots and recovering the ground truth distribution of molecules of interest. We have validated the generalization capability of Digital-SMLM using independent experimental data. Furthermore, Digital-SMLM complements SMLM by providing more accurate event number and precise emitter positions, enabling SMLM to closely approximate the natural state of high-density cellular structures.

Published
2024
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8. An Extended Ductile Fracture Prediction Model Considering Strain Rate Effects

Author

Jia, Zhe, Mu, Lei, Liu, Yang, Zang, Yong, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Mocellin, Katia, editor, Bouchard, Pierre-Olivier, editor, Bigot, Régis, editor, and Balan, Tudor, editor

Published
2024
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29. Extracting dispersion curves from ambient noise correlations using deep learning

Author

Zhang, Xiaotian, Jia, Zhe, Ross, Zachary E., and Clayton, Robert W.

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Physics - Geophysics, Statistics - Machine Learning
Abstract

We present a machine-learning approach to classifying the phases of surface wave dispersion curves. Standard FTAN analysis of surfaces observed on an array of receivers is converted to an image, of which, each pixel is classified as fundamental mode, first overtone, or noise. We use a convolutional neural network (U-net) architecture with a supervised learning objective and incorporate transfer learning. The training is initially performed with synthetic data to learn coarse structure, followed by fine-tuning of the network using approximately 10% of the real data based on human classification. The results show that the machine classification is nearly identical to the human picked phases. Expanding the method to process multiple images at once did not improve the performance. The developed technique will faciliate automated processing of large dispersion curve datasets.

Published
2020
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30. Dissecting the Graphcore IPU Architecture via Microbenchmarking

Author

Jia, Zhe, Tillman, Blake, Maggioni, Marco, and Scarpazza, Daniele Paolo

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Hardware Architecture, Computer Science - Performance
Abstract

This report focuses on the architecture and performance of the Intelligence Processing Unit (IPU), a novel, massively parallel platform recently introduced by Graphcore and aimed at Artificial Intelligence/Machine Learning (AI/ML) workloads. We dissect the IPU's performance behavior using microbenchmarks that we crafted for the purpose. We study the IPU's memory organization and performance. We study the latency and bandwidth that the on-chip and off-chip interconnects offer, both in point-to-point transfers and in a spectrum of collective operations, under diverse loads. We evaluate the IPU's compute power over matrix multiplication, convolution, and AI/ML primitives. We discuss actual performance in comparison with its theoretical limits. Our findings reveal how the IPU's architectural design affects its performance. Moreover, they offer simple mental models to predict an application's performance on the IPU, on the basis of the computation and communication steps it involves. This report is the natural extension to a novel architecture of a continuing effort of ours that focuses on the microbenchmark-based discovery of massively parallel architectures., Comment: 91 pages, 21 figures

Published
2019

33. Dissecting the NVidia Turing T4 GPU via Microbenchmarking

Author

Jia, Zhe, Maggioni, Marco, Smith, Jeffrey, and Scarpazza, Daniele Paolo

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

In 2019, the rapid rate at which GPU manufacturers refresh their designs, coupled with their reluctance to disclose microarchitectural details, is still a hurdle for those software designers who want to extract the highest possible performance. Last year, these very reasons motivated us to dissect the Volta GPU architecture using microbenchmarks. The introduction in August 2018 of Turing, NVidia's latest architecture, pressed us to update our study. In this report, we examine Turing and compare it quantitatively against previous NVidia GPU generations. Specifically, we study the T4 GPU: a low-power board aiming at inference applications. We describe its improvements against its inference-oriented predecessor: the P4 GPU based on the Pascal architecture. Both T4 and P4 GPUs achieve significantly higher frequency-per-Watt figures than their full-size counterparts. We study the performance of the T4's TensorCores, finding a much higher throughput on low-precision operands than on the P4 GPU. We reveal that Turing introduces new instructions that express matrix math more succinctly. We map Turing's instruction space, finding the same encoding as Volta, and additional instructions. We reveal that the Turing TU104 chip has the same memory hierarchy depth as the Volta GV100; cache levels sizes on the TU104 are frequently twice as large as those found on the Pascal GP104. We benchmark each constituent of the T4 memory hierarchy and find substantial overall performance improvements over its P4 predecessor. We studied how clock throttling affects compute-intensive workloads that hit power or thermal limits. Many of our findings are novel, published here for the first time. All of them can guide high-performance software developers get closer to the GPU's peak performance., Comment: 65 pages

Published
2019

40. Dissecting the NVIDIA Volta GPU Architecture via Microbenchmarking

Author

Jia, Zhe, Maggioni, Marco, Staiger, Benjamin, and Scarpazza, Daniele P.

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Performance
Abstract

Every year, novel NVIDIA GPU designs are introduced. This rapid architectural and technological progression, coupled with a reluctance by manufacturers to disclose low-level details, makes it difficult for even the most proficient GPU software designers to remain up-to-date with the technological advances at a microarchitectural level. To address this dearth of public, microarchitectural-level information on the novel NVIDIA GPUs, independent researchers have resorted to microbenchmarks-based dissection and discovery. This has led to a prolific line of publications that shed light on instruction encoding, and memory hierarchy's geometry and features at each level. Namely, research that describes the performance and behavior of the Kepler, Maxwell and Pascal architectures. In this technical report, we continue this line of research by presenting the microarchitectural details of the NVIDIA Volta architecture, discovered through microbenchmarks and instruction set disassembly. Additionally, we compare quantitatively our Volta findings against its predecessors, Kepler, Maxwell and Pascal., Comment: Technical report. First Edition. April 18th, 2018. 66 pages

Published
2018

41. Research progress on SII in digestive system diseases

Author

Hang Hang, Jia Zhe, Zhu Jiali, Lu Sucai

Subjects
systemic immune-inflammation index, disease severity, prognosis, clinical utility, Medicine
Abstract

Hu et al. first proposed the systemic immune-inflammation index （SII） based on prognostic scoring of inflammation and immunity in 2014. The calculation formula is platelet count×neutrophil count/lymphocyte count, which is widely applied in clinical practice because it is non-invasive, easy to calculate and economical. Previous studies have shown that SII reflects the balance between host immunity and inflammatory status. High SII level can be utilized to predict poor outcome in malignancies, and it is correlated with the severity of some diseases. In this article, research progress on the predictive value of SII for the incidence and development of digestive system diseases was reviewed.

Published
2022
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