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High-Quality Thermal Gibbs Sampling with Quantum Annealing Hardware

Physical Review Applied 2022年第4期17卷 044046-044046页

作者： Jon Nelson Marc Vuffray Andrey Y. Lokhov Tameem Albash Carleton Coffrin Center for Nonlinear Studies Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Advanced Network Science Initiative Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Theoretical Division Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Department of Electrical and Computer Engineering University of New Mexico Albuquerque New Mexico 87131 USA Department of Physics and Astronomy and Center for Quantum Information and Control University of New Mexico Albuquerque New Mexico 87131 USA

Quantum annealing (QA) was originally intended for accelerating the solution of combinatorial optimization tasks that have natural encodings as Ising models. However, recent experiments on QA-hardware platforms have demonstrated that, in the operating regime corresponding to weak interactions, the QA hardware behaves like a noisy Gibbs sampler at a hardware-specific effective temperature. This work builds on those insights and identifies a class of small hardware-native Ising models that are robust to noise effects and proposes a procedure for executing these models on QA hardware to maximize Gibbs-sampling performance. The experimental results indicate that the proposed protocol results in high-quality Gibbs samples from a hardware-specific effective temperature. Furthermore, we show that this effective temperature can be adjusted by modulating the annealing time and energy scale. The procedure proposed in this work provides an approach to using QA hardware for Ising-model sampling presenting potential opportunities for applications in machine learning and physics simulation.

关键词： Adiabatic quantum optimization Machine learning Quantum benchmarking Quantum fluctuations & noise Quantum information architectures & platforms Ising model Simulated annealing

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Obstacle avoidance via hybrid feedback

arXiv

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arXiv 2021年

作者： Berkane, Soulaimane Bisoffi, Andrea Dimarogonas, Dimos V. The Department of Computer Science and Engineering University of Quebec in Outaouais Gatineau Canada ENTEG The J.C. Willems Center for Systems and Control University of Groningen Groningen Netherlands The Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden

In this paper we present a hybrid feedback approach to solve the navigation problem of a point mass in the n−dimensional space containing an arbitrary number of ellipsoidal shape obstacles. The proposed hybrid control algorithm guarantees both global asymptotic stabilization to a reference and avoidance of the obstacles. The intuitive idea of the proposed hybrid feedback is to switch between two modes of control: stabilization and avoidance. The geometric construction of the flow and jump sets of the proposed hybrid controller, exploiting hysteresis regions, guarantees Zeno-free switching between the stabilization and the avoidance modes. Simulation results illustrate the performance of the proposed hybrid control approach for 2-dimensional and 3-dimensional scenarios. © 2021, CC BY.

关键词： Stabilization

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System Identification of User Engagement in mHealth Behavioral Interventions ⁎

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IFAC-PapersOnLine 2024年第15期58卷 508-513页

作者： Mohamed El Mistiri Steven De La Torre Karine Tung Benjamin M. Marlin Misha Pavel Predrag Klasnja Donna Spruijt-Metz Daniel E. Rivera Control Systems Engineering Lab Arizona State University Tempe AZ 85281 USA The Herbert Wertheim School of Public Health and Human Longevity Science University of California San Diego CA 92093 USA Manning College of Information and Computer Sciences University of Massachusetts Amherst Amherst MA 01003 USA Khoury College of Computer Sciences & Bouve College of Health Sciences Northeastern University Boston MA 02115 USA Division of Biomedical and Health Informatics School of Information University of Michigan Ann Arbor MU 48109 USA USC Dornsife Center for Economic and Social Research Department of Psychology and Population and Public Health Sciences University of Southern California Los Angeles CA 90089 USA

Digital behavior change interventions (DBCIs) such as “just-in-time” adaptive interventions (JITAIs) have demonstrated efficacy for increasing physical activity behavior. However, the effectiveness of these interventions is heavily dependent upon user engagement. Despite the inherent dynamic nature of engagement, as it varies over time based on an individual’s changing environment, context, and psychological state, the current understanding of engagement primarily comes from static snapshots of the behavior. The availability of intensive longitudinal data from JITAIs provides a unique opportunity to build and test dynamic models of behavior change from a process systems lens, relying on prediction-error methods from system identification. However, data missingness is a significant practical consideration in this process. Therefore, in this work we address missingness using a Bayesian imputation approach, which we evaluate using data from the HeartSteps II JITAI. Ultimately, the methods presented support the discovery of key factors that impact engagement behavior over time and can play an important role in the development of large-scale personalized interventions.

关键词： eHealth Bayesian methods Computational Social sciences Time series modelling

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Prediction of Myocardial Infarction in Hypertensive Patients Using Machine Learning: A Comparative Study

SSRN

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SSRN 2025年

作者： Wu, Lin Liu, Dinghui Chen, Nanya Hao, Baoshun Liu, Yong Zhou, Bin Ren, Jiangtao Chen, Yanming Qian, Xiaoxian Department of Cardiology The Third Affiliated Hospital of Sun Yat-sen University Guangdong Guangzhou510630 China Department of Endocrinology and Metabolism Medical Center for Comprehensive Weight Control The Third Affiliated Hospital of Sun Yat-sen University Guangdong Guangzhou510630 China Guangdong Provincial Key Laboratory of Diabetology Guangzhou Municipal Key Laboratory of Mechanistic and Translational Obesity Research The Third Affiliated Hospital of Sun Yat-sen University Guangdong Guangzhou510630 China School of Computer Science And Engineering Sun Yat-sen University Guangdong Guangzhou510275 China

Background: Although numerous prediction models have been used to assess the incidence of myocardial infarction (MI) in hypertensive patients, there are rare models using unstructured text from electronic medical record (EMR) based machine learning model. Objectives: The aim of this study was to develop EMR-based models for predicting the occurrence of MI in patients with ***: From Jan 2013 to Dec 2019, a total of 24454 patients with hypertension admitted in the Third Affiliated Hospital of Sun Yat-sen University were screened in this retrospective longitudinal observation cohort. Finally, 14755 patients without previous myocardial infarction at the admission were enrolled and randomized into training and validation test sets in a 3:1 ratio. The model validated in the external data set from three community hospital, inculding 1000 hypertensive patients. The EMR unstructured text including imaging findings, as well as clinical information including demographic information and laboratory test results were used to develop a regression model using a eXtreme Gradient Boosting (XGBoost) to predict the incidence of MI. Furthermore, the performance of XGBoost was compared with that of the C4.5 Decision Tree, COX regression model, and Random forest (RF).Results: Overall, the proportion of MI was 1.2% (175 of 14755). The precision and F1-score in predicting MI reached 83.06% and 82.26%, respectively. The overall area under curve (AUC) for XGBoost model was 80.06% (95% CI: 74.03%-83.0%) in internal data set and 71.5%(95% CI: 63.3%-86.2%) in external data set. The precision of our model is better than that of the C4.5 Decision Tree, COX regression model, and RF. Conclusion: Our results suggest that our machine learning model had acceptable discrimination and well applied at clinical decision thresholds for MI prediction in hypertensive patients. © 2025, The Authors. All rights reserved.

关键词： Prediction models

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Customized Quantum Annealing Schedules

引用

Physical Review Applied 2022年第4期17卷 044005-044005页

作者： Mostafa Khezri Xi Dai Rui Yang Tameem Albash Adrian Lupascu Daniel A. Lidar Department of Electrical Engineering University of Southern California Los Angeles California 90089 USA Center for Quantum Information Science & Technology University of Southern California Los Angeles California 90089 USA Department of Physics and Astronomy University of Waterloo Waterloo Ontario Canada Institute for Quantum Computing University of Waterloo Waterloo Ontario Canada Department of Electrical and Computer Engineering University of New Mexico Albuquerque New Mexico 87131 USA Center for Quantum Information and Control CQuIC University of New Mexico Albuquerque New Mexico 87131 USA Department of Chemistry University of Southern California Los Angeles California 90089 USA Department of Physics University of Southern California Los Angeles California 90089 USA

In a typical quantum annealing protocol, the system starts with a transverse field Hamiltonian that is gradually turned off and replaced by a longitudinal Ising Hamiltonian. The ground state of the Ising Hamiltonian encodes the solution to the computational problem of interest, and the state overlap with this ground state gives the success probability of the annealing protocol. The form of the annealing schedule can have a significant impact on the ground-state overlap at the end of the anneal, so precise control over these annealing schedules can be a powerful tool for increasing success probabilities of annealing protocols. Here we show how superconducting circuits, in particular capacitively shunted flux qubits, can be used to construct quantum annealing systems by providing tools for mapping circuit flux biases to Pauli coefficients. We use this mapping to find customized annealing schedules: appropriate circuit control biases that yield a desired annealing schedule, while accounting for the physical limitations of the circuitry. We then provide examples and proposals that utilize this capability to improve quantum annealing performance.

关键词： Adiabatic quantum optimization Quantum control Quantum information with solid state qubits Quantum protocols Superconducting qubits

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The singular angle of nonlinear systems

arXiv

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arXiv 2021年

作者： Chen, Chao Chen, Wei Zhao, Di Khong, Sei Zhen Qiu, Li The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Hong Kong The Hong Kong University of Science and Technology Shenzhen Research Institute Shenzhen518063 China The Department of Mechanics and Engineering Science Beijing Innovation Center for Engineering Science and Advanced Technology Peking University Beijing100871 China The Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China

In this paper, we introduce an angle notion, called the singular angle, for stable nonlinear systems from an input-output perspective. The proposed system singular angle, based on the angle between L2-signals, describes an upper bound for the "rotating effect" from the system input to output signals. It is, thus, different from the recently appeared nonlinear system phase which adopts the complexification of real-valued signals using the Hilbert transform. It can quantify the passivity and serve as an angular counterpart to the system L2-gain. It also provides an alternative to the nonlinear system phase. A nonlinear small angle theorem, which involves a comparison of the loop system angle with π, is established for feedback stability analysis. When dealing with multi-input multi-output linear time-invariant (LTI) systems, we further come up with the frequency-wise and H∞ singular angle notions based on the matrix singular angle, and develop corresponding LTI small angle theorems. Copyright © 2021, The Authors. All rights reserved.

关键词： Nonlinear systems

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Interacting Effects of Water and N-P-K Fertilizers on the Resource Use Efficiencies and Fruit Yield of Drip-Fertigated Chinese Wolfberry (Lycium Barbarum L.)

SSRN

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SSRN 2023年

作者： Deng, Zhen Eeswaran, Rasu Yin, Juan Abhiram, Gunaratnam Gong, Siyuan College of Information Engineering Ningxia University Yinchuan750021 China College of Civil and Hydraulic Engineering Ningxia University Yinchuan750021 China Department of Plant Soil and Microbial Sciences Michigan State University East LansingMI48824 United States Department of Agronomy Faculty of Agriculture University of Jaffna Ariviyal Nagar Kilinochchi44000 Sri Lanka Ministry of Education Engineering Research Center for Modern Agricultural Water Resources Efficient Utilization in Dry Areas Yinchuan750021 China Ningxia Water-saving Irrigation and Water Resources Control Engineering Technology Research Center Yinchuan750021 China School of Agriculture and Environment Massey University Palmerston North4442 New Zealand Department of Export Agriculture Uva Wellassa University Badulla90000 Sri Lanka School of Computer Science and Software Engineering Nanjing University of Information Science & Technology Nanjing210032 China

Chinese wolfberry (Lycium barbarum L.) is an important cash crop in the Ningxia region of China. However, low water and fertilizer use efficiencies have limited the growth of the Chinese wolfberry industry. Field experiments were conducted in the central Ningxia of Northwest China during 2018-2019 to investigate the interaction effects of different irrigation and fertilizer levels on agronomic performances of Chinese wolfberry, water use efficiency, fertilizer partial productivity and economic benefit. The optimal range of irrigation and fertilizer inputs for a multi-objective optimization problem was determined using multiple regression, entropy weight method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) coupling comprehensive evaluation method. Three drip irrigation levels were designated based on the percentage of reference crop evapotranspiration (ETo) as low (65% ET0: W1), medium (85% ET0: W2) and high (105% ET0: W3) and three N-P2O5-K2O combined fertilization levels (kg ha-1) were designated as low (135-45-90: F1), medium (180-60-120: F2) and high (225-75-150: F3). The results showed that agronomic performances, water use efficiency (WUE), fertilizer partial productivity (PFP) and economic benefits increased initially and then decreased with increasing irrigation levels under the same fertilization level. Water use efficiency (WUE) was low under high irrigation level and high under low irrigation level. The PFP decreased with increasing fertilization levels and the lowest PFP was observed at high level of fertilizer (F3) application. The above parameters reached the maximum value under the medium irrigation level (85% ET0). Moreover, the multi-objective optimization highlighted that 252-262 mm of irrigation and 185-62-123～200-67-133 kg ha-1 of N-P2O5-K2O fertilization level offer more than 90% of yield, WUE, PFP and economic benefits simultaneously. These results provide a scientific reference for resource optimization under drip-

关键词： Multiobjective optimization

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Middle East and North African Health Informatics Association (MENAHIA): Technological initiatives for 'One Health'

引用

Yearbook of Medical Informatics 2023年第1期32卷 320-329页

作者： Abd-Alrazaq, Alaa Al-Shorbaji, Najeeb Lakhdari, Kheira Elbokl, Ahmed Farouk, Hani Wahba, Hoda Elgasser, Naema Mohell, Rania Emara, Tamer Ayatollahi, Haleh Kalhori, Sharareh R. Niakan Rabiei, Reza Tara, Mahmood Akhu-Zaheya, Laila Al-Qutob, Raeda Alrazak, Sadam Alabed Al-Jafar, Eiman Alhuwail, Dari Ghazal, Hassan Dehbi, Zineb El Otmani Al Idrissi, Najib Fihri, Ouafaa Fassi Belyamani, Lahcen Shahzad, Tariq Ahmed, Zakiuddin Ahmad, Arfan Shah, Zubair Hamra, Eman Abu Taweel, Adel Nashwan, Abdulqadir J. Househ, Mowafa Hamdi, Mounir Al-Thani, Dena Alam, Tanvir Alshammari, Abdulwahhab Alnafrani, Sana Alali, Haitham AI Center for Precision Health Weill Cornell Medicine-Qatarc Doha Qatar EHealth Development Association Amman Jordan Abou Bekr Belkaid University of Tlemcen Chetouane Algeria Ain Shams University Virtual Hospital Ain Shams University Cairo Egypt Ain Shams University Cairo Egypt World Health Organization Cairo Egypt Alexandria Regional Blood Transfusion Center Alexandria University Alexandria Egypt Health Management and Economics Research Center Health Management Research Institute Iran University of Medical Sciences Tehran Iran Department of Health Information Management School of Health Management and Information Sciences Iran University of Medical Sciences Tehran Iran Department of Health Information Management School of Allied Medical Sciences Tehran University of Medical Sciences Tehran Iran Peter L. Reichertz Institute for Medical Informatics (PLRI) Technical University of Braunschweig Hannover Medical School Braunschweig Germany Department of Health Information Technology and Management School of Allied Medical Sciences Shahid Beheshti University of Medical Sciencesc Tehran Iran Department of Medical Informatics Faculty of Medicine Mashhad University of Medical Sciencesc Mashhad Iran School of Nursing Jordan University of Science and Technology Jordan Jordan Centre for Disease Control and Prevention Amman Jordan Faculty of Applied Science and Engineering University of Toronto Toronto Canada Kuwait Health Informatics Association Kuwait Information Science Department College of Life Sciences Kuwait University Kuwait Health Informatics Unit Dasman Diabetes Institute Kuwait Mohammed VI University of Health Sciences Casablanca Morocco National Center for Scientific and Technical Research (CNRST) Rabat Morocco Hassan II Agronomic and Veterinary Institute Rabat Morocco COMSATS University Islamabad Sahiwal Campus Islamabad Pakistan Digital Care Karachi Pakistan College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Al Quds Unive

MENAHIA (Middle East and North African Health Informatics Association) is the International Medical Informatics Association chapter dedicated to the Middle East and North Africa region. This region is rapidly growing in terms of the use of health informatics or what has been recently coined digital health. Human health is highly affected by the health of the environment, animal health, food, nutrition, climate change, and many other factors that are beyond the biological or genetic structure of human beings. The impact of animal health and the health of the environment on people's health is an old phenomenon but recent reemerging and appearance of diseases have clearly demonstrated the link between these. The Novel Coronavirus disease (COVID-19) that almost all of us have been suffering from is an example of this. A number of countries in the region have already shown the depth and the work that they do to integrate the concept of 'One Health' in the public health surveillance system as they have described the work that has been done to capture data from databases other than those dealing with human beings. The examples that were provided to monitor the health of animals, agriculture, environmental health, climate change, and man-made and natural disasters are just examples of what countries have been registering in their databases and informing the health authorities of these changes and emerging trends. © The Author(s) 2023.

关键词： 'One Health' 'One Health' informatics Algeria Artificial Intelligence Climate Change Digital Health Egypt Health Informatics Health Information Technologies International Medical Informatics Association (IMIA) Iran Jordan Kuwait MENAHIA Middle East and North Africa Morocco Pakistan Palestine Qatar Saudi Arabia United Arab Emirates Zoonitic Diseases

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Secure and Privacy-preserving Data-sharing Framework based on Blockchain Technology for Al-Najaf/Iraq Oil Refinery

Secure and Privacy-preserving Data-sharing Framework based o...

引用

Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Samir M. Umran SongFeng Lu Zaid Ameen Abduljabbar Zhi Lu Bingyan Feng Lu Zheng Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Ministry of Industry and Minerals Iraqi Cement State Company Baghdad Iraq Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China College of Education for Pure Sciences University of Basrah Technical Computer Engineering Department Al-Kunooze University College Basrah Iraq School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Wuhan Huazhong Numerical Control Co. Ltd Industrial Internet Research Institute Wuhan China College of Computer Science South-Central University for Nationalities Wuhan China

The Industrial Internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network; provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality.

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Industrial Scene Text Detection with Refined Feature-attentive Network

arXiv

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arXiv 2021年

作者： Guan, Tongkun Gu, Chaochen Lu, Changsheng Tu, Jingzheng Feng, Qi Wu, Kaijie Guan, Xinping The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China The College of Engineering and Computer Science The Australian National University CanberraACT2600 Australia

Detecting the marking characters of industrial metal parts remains challenging due to low visual contrast, uneven illumination, corroded surfaces, and cluttered background of metal part images. Affected by these factors, bounding boxes generated by most existing methods could not locate low-contrast text areas very well. In this paper, we propose a refined feature-attentive network (RFN) to solve the inaccurate localization problem. Specifically, we first design a parallel feature integration mechanism to construct an adaptive feature representation from multi-resolution features, which enhances the perception of multi-scale texts at each scale-specific level to generate a high-quality attention map. Then, an attentive proposal refinement module is developed by the attention map to rectify the location deviation of candidate boxes. Besides, a re-scoring mechanism is designed to select text boxes with the best rectified location. To promote the research towards industrial scene text detection, we contribute two industrial scene text datasets, including a total of 102156 images and 1948809 text instances with various character structures and metal parts. Extensive experiments on our dataset and four public datasets demonstrate that our proposed method achieves the state-of-the-art performance. Both code and dataset are available at: https://***/TongkunGuan/RFN. © 2021, CC BY.

关键词： Character recognition

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