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Phase transitions in the frustrated Ising ladder with stoquastic and nonstoquastic catalysts

Physical Review Research 2021年第4期3卷 043013-043013页

作者： Kabuki Takada Shigetoshi Sota Seiji Yunoki Bibek Pokharel Hidetoshi Nishimori Daniel A. Lidar Department of Physics Tokyo Institute of Technology Yokohama Kanagawa 226-8503 Japan Computational Materials Science Research Team RIKEN Center for Computational Science (R-CCS) Kobe Hyogo 650-0047 Japan Computational Condensed Matter Physics Laboratory RIKEN Cluster for Pioneering Research (CPR) Wako Saitama 351-0198 Japan Computational Quantum Matter Research Team RIKEN Center for Emergent Matter Science (CEMS) Wako Saitama 351-0198 Japan Department of Physics University of Southern California Los Angeles California 90089 USA Center for Quantum Information Science & Technology University of Southern California Los Angeles California 90089 USA Institute of Innovative Research Tokyo Institute of Technology Yokohama Kanagawa 226-8503 Japan Graduate School of Information Sciences Tohoku University Sendai Miyagi 980-8579 Japan RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS) Wako Saitama 351-0198 Japan Department of Electrical & Computer Engineering and Department of Chemistry University of Southern California Los Angeles California 90089 USA

The role of nonstoquasticity in the field of quantum annealing and adiabatic quantum computing is an actively debated topic. We study a strongly-frustrated quasi-one-dimensional quantum Ising model on a two-leg ladder to elucidate how a first-order phase transition with a topological origin is affected by interactions of the ±XX-type. Such interactions are sometimes known as stoquastic (negative sign) and nonstoquastic (positive sign) “catalysts”. Carrying out a symmetry-preserving real-space renormalization group analysis and extensive density-matrix renormalization group computations, we show that the phase diagrams obtained by these two methods are in qualitative agreement with each other and reveal that the first-order quantum phase transition of a topological nature remains stable against the introduction of both XX-type catalysts. This is the first study of the effects of nonstoquasticity on a first-order phase transition between topologically distinct phases. Our results indicate that nonstoquastic catalysts are generally insufficient for removing topological obstacles in quantum annealing and adiabatic quantum computing.

关键词： Adiabatic quantum optimization Phase transitions by order Quantum spin models Density matrix renormalization group Renormalization group

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Design and Implementation of Web-based Church information Systems (Case Study: HKBP Kebon Jeruk)

Design and Implementation of Web-based Church Information Sy...

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International Conference on Electrical Engineering, computer science and Informatics (EECSI)

作者： Armando Ondihon Kristoper Purba Supardi Ernawati Dewi Meilieta Anggriani Porrie Muhammad Syafrullah Master Program in Computer Science Universitas Budi Luhur Jakarta Indonesia Faculty of Information Technology Universitas Budi Luhur Jakarta Indonesia

HKBP Kebon Jeruk Church has a lot of data consisting of church data, Pastor data, Church server data, family data, marital data, baptismal data, and also about church agenda such as the schedule of activities Church, schedule of church service. The problem in HKBP Kebon Jeruk is that the Data is provided and managed manually, as well as difficulties in finding the necessary information. Therefore, the system needs to be built by the HKBP Kebon Jeruk Church to request church management data. The method used in the HKBP Kebon Jeruk system is the Extreme programming method, and the analysis used is the PIECES analysis. The result of this research is to build the HKBP Kebon Jeruk system according to the needs of the user.

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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Resilient Algorithm In Predicting Fertilizer Imports by Major Countries

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IOP Conference Series: Materials science and Engineering 2020年第1期769卷

作者： Solikhun Mochamad Wahyudi M Safii Muhammad Zarlis Doctoral Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Sumatera Utara Indonesia. Faculty of Computer Science and Information Technology Universitas Sumatera Utara Indonesia

In the last five years (2013-2017) Indonesia's fertilizer production experienced volatile growth, but overall tended to increase at a rate of 1.7% per year. The research aims to optimize artificial neural networks with a resilient backpropagation algorithm (Rprop), artificial neural networks are one of the artificial representations of the human brain that always tries to simulate the learning process in the human brain. Sample data used for optimization is fertilizer import data according to the main country of origin and uses 4 architectures, the best results are obtained between architectures 6-8-1, 6-12-1, 6-16-1, and 6-32-1 is architecture 6-32-1 with 100% accuracy.

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Diffusive topological transport in spatiotemporal thermal lattices

arXiv

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arXiv 2021年

作者： Xu, Guoqiang Yang, Yihao Zhou, Xue Chen, Hongsheng Alù, Andrea Qiu, Cheng-Wei Department of Electrical and Computer Engineering National University of Singapore Kent Ridge117583 Singapore Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou China School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing400067 China ZJU-Hangzhou Global Science and Technology Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Zhejiang University Hangzhou310027 China International Joint Innovation Center ZJU-UIUC Institute The Electromagnetics Academy at Zhejiang University Zhejiang University Haining314400 China Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center of the City University of New York New YorkNY10016 United States

Topological insulating phases are usually found in periodic lattices stemming from collective resonant effects, and it may thus be expected that similar features may be prohibited in thermal diffusion, given its purely dissipative and largely incoherent nature. We report the diffusion-based topological states supported by spatiotemporally-modulated advections stacked over a fluidic surface, thereby imitating a periodic propagating potential in effective thermal lattices. We observe edge and bulk states within purely nontrivial and trivial lattices, respectively. At interfaces between these two types of lattices, the diffusive system exhibits interface states, manifesting inhomogeneous thermal properties on the fluidic surface. Our findings establish a framework for topological diffusion and thermal edge/bulk states, and it may empower a distinct mechanism for flexible manipulation of robust heat and mass transfer. © 2021, CC BY.

关键词： Interface states

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Backpropagation Network Optimization Using One Step Secant (OSS) Algorithm

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IOP Conference Series: Materials science and Engineering 2020年第1期769卷

作者： Solikhun Mochamad Wahyudi M. Safii Muhammad Zarlis Doctoral Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Sumatera Utara Indonesia. Faculty of Computer Science and Information Technology Universitas Sumatera Utara Indonesia

Education is one of the main indicators in national development efforts. The government in its efforts to realize national goals to educate the nation's life has made a policy for compulsory 9-year schools, namely elementary and junior high schools. To find out how many residents use the education facilities provided by the government can be seen through school enrollment rates (SPR). A high School Participation Rate (SPR) means showing greater opportunities to access education in general. This study aims to optimize artificial neural networks with the One Step Secant (OSS) algorithm. Artificial Neural Network (ANN) is part of the artificial intelligence system (Artificial Intelligence, AI) which is one of the artificial representations of the human brain that always tries to simulate the learning process in the human brain. The sample data used for optimization is SPR Indonesia data by province. Using 4 architectural models with 5 input variables, 1 shadow layer and 1 output. The best results obtained between architectures 5-4-1, 5-8-1, 5-16-1 and 5-32-1 are architectures 5-16-1. Obtained prediction accuracy comparisons using the One Step Secant (OSS) algorithm and standard algorithms namely 96.97% and 100%. The standard algorithm is superior in accuracy, the One Step Secant (OSS) algorithm is superior in terms of iterations.

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Classification of the Structural Integrity by the General Vibration Method

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Procedia computer science 2019年 157卷 382-387页

作者： Fergyanto E. Gunawan Benfano Soewito Nico Surantha Tuga Mauritsius Nobumasa Sekishita Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Information Systems Information Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 Department of Mechanical Engineering Toyohashi University of Technology Toyohashi Aichi 441-8580 Japan

Traditionally, for the purpose of classifying the integrity of an engineering structure, one may use the information of the natural frequencies or mode shapes or some other measures derived from the two. In this research, we propose a classification method on the basis of the general vibration model. The general vibration model is essentially a set of differential equations describing the dynamics of the structure under consideration. For the purpose of damage detection, a deviation of the general vibration model from the dynamic equilibrium point marks the occurrence of the damage. To demonstrate the effectiveness of the method, we study two simplest dynamical systems consisting of one- and two-concentrated masses subjected to a prescribed dynamic load. The structural damages are introduced artificially by reducing the stiffness of spring in the structures. We find that the general vibration model is more sensitive to damages than the traditional methods.

关键词： General Vibration Model Structural Integrity Structural Health Monitoring Power Spectral Density Damage Index

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A Study of the Sensitivity of the Fuel Consumption to Driving Strategy by Micro Simulation

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Procedia computer science 2019年 157卷 375-381页

Global warming due to excessive emission greenhouse gases is a significant issue faced by humanity, and collectively, cars and trucks contribute about 20% to the entire global-warming gases. For a reason, recently, many attempts have been endured to reduce the emission and to increase fuel efficiency, including vehicle platooning, better driving strategy, and increasing engine efficiency. This study intends to provide a more detail assessment regarding the effects of the driving strategy to fuel consumption. For the purpose, the vehicle dynamics are quantified by employing the car-following model based on the optimal velocity model. The fuel consumption is estimated from the regression model of Ahn 18 . The result suggests that the braking distance strongly affects fuel consumption.

关键词： Optimal Velocity Model Micro Simulation Car-Following Model Fuel Efficiency

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High Performance Computing (HPC) Implementation: A Survey 1

High Performance Computing (HPC) Implementation: A Survey

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1st Indonesian Association for Pattern Recognition International Conference, INAPR 2018

作者： Assiroj, Priati Hananto, April Lia Fauzi, Ahmad Hendric Spits Warnars, Harco Leslie Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Faculty of Computing Universiti Teknologi Malaysia Skudai Johor Malaysia Information System Faculty of Technology and Computer Science United States Buana Perjuangan University Faculty of Technology and Computer Science Information Technology Karawang Indonesia

ISBN: (纸本)9781538694220

To maintain excellence in global hyper-competitive economies in the upcoming decades, manufacturers must improve the design, development, and distribution of subsequent product generations, production technologies, equipment, and processes. High-performance computing (HPC) solves the application of significant challenges using computer modeling, simulation, and analytical technology. By using HPC technology, products design will be faster, making and testing prototypes will save time, the production process can be simplified, the cost of innovation will decrease, and high-value innovation will be quickly developed. This study uses the PRISMA method, based on predetermined keywords and analyzes the algorithms, frameworks and programming languages and the processes that are most commonly used to optimize the Graphics Processing Unit (GPU). The results of this study can be a reference for future research on the implementation of High-Performance Computing (HPC) Application by combine modeling, simulation, and analysis. © 2018 IEEE.

关键词： Graphics processing unit

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Alert and Response of Earthquake and Tsunami for Community Based Disaster Risk Reduction

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Journal of Physics: Conference Series 2021年第1期1811卷

作者： Rr M I Retno Susilorini Andre Kurniawan Pamudji Ridwan Sanjaya Amrizarois Ismail Ardhito Hayyu Amasto Department of Infrastructure and Environmental Engineering Faculty of Environmental Science and Technology Soegijapranata Catholic University Semarang Master Program of Environmental and Urban Studies Faculty of Environmental Science and Technology Soegijapranata Catholic University Semarang Department of Information System Faculty of Computer Science Soegijapranata Catholic University Semarang

Indonesia had been suffered by earthquake and tsunami for many centuries. Since many lives have taken by tsunami strikes, alert and response system are very important as important part of Disaster Risk Reduction, especially in area with high population density. This research conducted in Kangkung fishing village in Bandar Lampung coastal area which is an earthquake and tsunami prone area. The aim of the research is to study alert and response of Community Based Disaster Risk Reduction. The research conducted by qualitative method and purposive-descriptive approach. There were 53 respondents which participated in the survey and interview. It is reported that 'Smart Resilient City' model suitable for community needs of alert and response of earthquake and tsunami disaster. This research meets conclusion that 'Smart Resilient City' model may become a great solution in Community Based Disaster Risk Reduction to cope with the hazard of earthquake and tsunami comprehensively and sustainably.

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