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452 results on '"Zhang Deli"'

1. Critical nonlocal Schrödinger-Poisson system on the Heisenberg group

Author

Liu Zeyi, Tao Lulu, Zhang Deli, Liang Sihua, and Song Yueqiang

Subjects
heisenberg group, schrödinger-poisson type system, mountain pass theorem, krasnoselskii genus theorem, clark critical point theorem, 35j20, 35r03, 46e35, Analysis, QA299.6-433
Abstract

In this paper, we are concerned with the following a new critical nonlocal Schrödinger-Poisson system on the Heisenberg group:

Published
2021
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14. KEA: Tuning an Exabyte-Scale Data Infrastructure

Author

Zhu, Yiwen, Krishnan, Subru, Karanasos, Konstantinos, Tarte, Isha, Power, Conor, Modi, Abhishek, Kumar, Manoj, Zhang, Deli, Muthyala, Kartheek, Jurgens, Nick, Sakalanaga, Sarvesh, Darbha, Sudhir, Iyer, Minu, Agarwal, Ankita, and Curino, Carlo

Subjects
Computer Science - Databases
Abstract

Microsoft's internal big-data infrastructure is one of the largest in the world -- with over 300k machines running billions of tasks from over 0.6M daily jobs. Operating this infrastructure is a costly and complex endeavor, and efficiency is paramount. In fact, for over 15 years, a dedicated engineering team has tuned almost every aspect of this infrastructure, achieving state-of-the-art efficiency (>60% average CPU utilization across all clusters). Despite rich telemetry and strong expertise, faced with evolving hardware/software/workloads this manual tuning approach had reached its limit -- we had plateaued. In this paper, we present KEA, a multi-year effort to automate our tuning processes to be fully data/model-driven. KEA leverages a mix of domain knowledge and principled data science to capture the essence of our cluster dynamic behavior in a set of machine learning (ML) models based on collected system data. These models power automated optimization procedures for parameter tuning, and inform our leadership in critical decisions around engineering and capacity management (such as hardware and data center design, software investments, etc.). We combine "observational" tuning (i.e., using models to predict system behavior without direct experimentation) with judicious use of "flighting" (i.e., conservative testing in production). This allows us to support a broad range of applications that we discuss in this paper. KEA continuously tunes our cluster configurations and is on track to save Microsoft tens of millions of dollars per year. At the best of our knowledge, this paper is the first to discuss research challenges and practical learnings that emerge when tuning an exabyte-scale data infrastructure.

Published
2021
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15. Control Simulation to Underwater Manipulator

Author

Huan, Shang, Chao, Jiangang, Xie, Lan, Zhang, Yu, Han, Liangliang, Wang, Zhiguo, Zhang, Deli, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Long, Shengzhao, editor, and Dhillon, Balbir S., editor

Published
2023
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16. Control Design to Underwater Robotic Arm

Author

She, Jiahong, Huan, Shang, Xie, Shaoli, Zhang, Deli, Han, Liangliang, Yang, Jian, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Long, Shengzhao, editor, and Dhillon, Balbir S., editor

Published
2023
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19. Prediction and characterization of protein-protein interaction networks in swine

Author

Wang Fen, Liu Min, Song Baoxing, Li Dengyun, Pei Huimin, Guo Yang, Huang Jingfei, and Zhang Deli

Subjects
protein-protein interaction network, Interolog, D-MIST, M-MIST topological properties, Pfam domain annotations, GO annotations, Cytology, QH573-671
Abstract

Abstract Background Studying the large-scale protein-protein interaction (PPI) network is important in understanding biological processes. The current research presents the first PPI map of swine, which aims to give new insights into understanding their biological processes. Results We used three methods, Interolog-based prediction of porcine PPI network, domain-motif interactions from structural topology-based prediction of porcine PPI network and motif-motif interactions from structural topology-based prediction of porcine PPI network, to predict porcine protein interactions among 25,767 porcine proteins. We predicted 20,213, 331,484, and 218,705 porcine PPIs respectively, merged the three results into 567,441 PPIs, constructed four PPI networks, and analyzed the topological properties of the porcine PPI networks. Our predictions were validated with Pfam domain annotations and GO annotations. Averages of 70, 10,495, and 863 interactions were related to the Pfam domain-interacting pairs in iPfam database. For comparison, randomized networks were generated, and averages of only 4.24, 66.79, and 44.26 interactions were associated with Pfam domain-interacting pairs in iPfam database. In GO annotations, we found 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms respectively. However, the number of PPI pairs sharing GO terms in the 10,000 randomized networks reached 52.68%, 75.54%, 27.20% is 0. Finally, we determined the accuracy and precision of the methods. The methods yielded accuracies of 0.92, 0.53, and 0.50 at precisions of about 0.93, 0.74, and 0.75, respectively. Conclusion The results reveal that the predicted PPI networks are considerably reliable. The present research is an important pioneering work on protein function research. The porcine PPI data set, the confidence score of each interaction and a list of related data are available at (http://pppid.biositemap.com/).

Published
2012
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27. Safety Design to Underwater Robotic Arm

Author

Huan, Shang, Chao, Jiangang, Zhang, Yan, She, Jiahong, Han, Liangliang, Yang, Jian, Zhang, Deli, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Long, Shengzhao, editor, and Dhillon, Balbir S., editor

Published
2022
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35. An adaptive-correction algorithm for suppressing interface smearing in incompressible multiphase flows with complex interfacial behavior.

Author

Zhang, Tongwei, Kaku, Chuyo, Zhang, Deli, and Dong, Fei

Subjects
MULTIPHASE flow, INCOMPRESSIBLE flow, TRANSPORT equation, REYNOLDS number, ANALYTICAL solutions, RAYLEIGH-Taylor instability
Abstract

In this paper, a numerical framework for modelling multiphase flows with interface correction is proposed. In original Volume-of-Fluid method, the interface smearing may appear since the interface profile deviates from its equilibrium state, especially in the simulations of complex interface deformation. To solve this problem, a novel correction step is introduced into the procedure after solving the volume fraction transport equation, and this step just works on the region of phase interface. The difference between the current method and the previous work is that the correction coefficient can be adjusted adaptively with the gradient of normal velocity at the interface, which is strongly correlated to the intensity of interface smearing and changes with the computational time and interface position. The validation computations are performed for bubble deformation (initial shapes of two-dimensional four-lobed-star and three-dimensional (3D) box with holes), bubble rising in a channel and Rayleigh–Taylor instability problems (Reynolds number of 100 and 1000). The obtained results show that the unphysical phenomenon of interface smearing is suppressed effectively, and the interface sharpness is improved greatly by the present method. In addition, the mass of bubble deformation by the original method will decrease by 13.3%. While these results obtained by the present method are in good agreement with the analytical solutions or published data. [ABSTRACT FROM AUTHOR]

Published
2024
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