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Advances in nuclear detection and readout techniques

Nuclear science and Techniques 2023年第12期34卷 281-358页

作者： Rui He Xiao‑Yang Niu Yi Wang Hong‑Wei Liang Hong‑Bang Liu Ye Tian Hong‑Lin Zhang Chao‑Jie Zou Zhi‑Yi Liu Yun‑Long Zhang Hai‑Bo Yang Ju Huang Hong‑Kai Wang Wei‑Jia Han Bei Cao Gang Chen Cong Dai Li‑Min Duan Rui‑Rui Fan Fang‑Fa Fu Jian‑Hua Guo Dong Han Wei Jiang Xian‑Qin Li Xin Li Zhuo‑Dai Li Yu‑Tie Liang Shun Liao De‑Xu Lin Cheng‑Ming Liu Guo‑Rui Liu Jun‑Tao Liu Ze Long Meng‑Chen Niu Hao Qiu Hu Ran Xiang‑Ming Sun Bo‑Tan Wang Jia Wang Jin‑Xiang Wang Qi‑Lin Wang Yong‑Sheng Wang Xiao‑Chuan Xia Hao‑Qing Xie He‑Run Yang Hong Yin Hong Yuan Chun‑Hui Zhang Rui‑Guang Zhao Ran Zheng Cheng‑Xin Zhao Institute of Modern Physics Chinese Academy of SciencesLanzhou 730000China School of Nuclear Science and Technology University of Chinese Academy of SciencesBeijing 100049China Advanced Energy Science and Technology Guangdong Laboratory Huizhou 516000China Key Laboratory of Particle and Radiation Imaging Department of Engineering PhysicsTsinghua UniversityBeijing 100084China School of Integrated Circuits Dalian University of TechnologyDalian 116020China Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and TechnologyGuangxi UniversityNanning 530004China School of Nuclear Science and Technology Lanzhou UniversityLanzhou 730000China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of ChinaHefei 230026China Department of Radiation Oncology National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100021China Electronic Engineering School Heilongjiang UniversityHarbin 150080China Spallation Neutron Source Science Center Dongguan 523803China Micro‑electronic Department Harbin Institute of TechnologyHarbin 150001China Purple Mountain Observatory Chinese Academy of SciencesNanjing 210034China Liaoning Academy of Materials Shenyang 110167China Istituto Nazionale di Fisica Nucleare Sezione di PerugiaPerugiaItaly School of Mathematics and Physics Chengdu University of TechnologyChengdu 620035China PLAC Key Laboratory of Quark and Lepton Physics(MOE)Central China Normal UniversityWuhan 430079China School of Electronics and Information Northwestern Polytechnical UniversityXi’an 710129China State Key Lab of Superhard Materials College of PhysicsJilin UniversityChangchun 130012China School of Computer Science Northwestern Polytechnical UniversityXi’an 710129China

“A Craftsman Must Sharpen His Tools to Do His Job,”said *** detection and readout techniques are the foundation of particle physics,nuclear physics,and particle astrophysics to reveal the nature of the ***,they are being increasingly used in other disciplines like nuclear power generation,life sciences,environmental sciences,medical sciences,*** article reviews the short history,recent development,and trend of nuclear detection and readout techniques,covering Semiconductor Detector,Gaseous Detector,Scintillation Detector,Cherenkov Detector,Transition Radiation Detector,and Readout *** explaining the principle and using examples,we hope to help the interested reader underst and this research field and bring exciting information to the community.

关键词： Silicon detector Wide bandgap detector Time projection chamber(TPC) Multigap resistive plate chamber detector(MRPC) Scintillation detector Calorimeters Cherenkov detector Transition radiation detector Artificial intelligence Trigger-less readout

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SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation

arXiv

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arXiv 2024年

作者： Zhong, Luying Pi, Yueyang Chen, Zheyi Yu, Zhengxin Miao, Wang Chen, Xing Min, Geyong College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China School of Computing and Communications University of Lancaster United Kingdom School of Engineering Computing and Mathematics University of Plymouth United Kingdom Department of Computer Science University of Exeter United Kingdom

Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most existing FGL studies do not well consider the missing inter-client topology information in real-world scenarios, causing insufficient feature aggregation of multi-hop neighbor clients during model training. Moreover, the classic FGL commonly adopts the FedAvg but neglects the high training costs when the number of clients expands, resulting in the overload of a single edge server. To address these important challenges, we propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. In SpreadFGL, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. Next, a new negative sampling mechanism is developed to make SpreadFGL concentrate on more refined information in downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL follows a distributed training manner that enables fast model convergence. Using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed SpreadFGL. The results show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph algorithms

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Dynamic targets searching assistance based on virtual camera priority

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Virtual Reality & Intelligent Hardware 2021年第6期3卷 484-500页

作者： Zixiang ZHAO Quanwei ZHOU Xiaoguang HAN Lili WANG State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Biomedical Engineering Beihang UniversityBeijing 100191China Department of Mathematics and Theories Peng Cheng LaboratoryShenzhen 518052China The Chinese University of Hong Kong Shenzhen 999077China Shenzhen Research Institute of Big Data Shenzhen 999077China

Background When a user walks freely in an unknown virtual scene and searches for multiple dynamic targets,the lack of a comprehensive understanding of the environment may have a negative impact on the execution of virtual reality *** studies can help users with auxiliary tools,such as top view maps or trails,and exploration guidance,for example,automatically generated paths according to the user location and important static spots in virtual ***,in some virtual reality applications,when the scene has complex occlusions,and the user cannot obtain any real-time position information of the dynamic target,the above assistance cannot help the user complete the task more *** We design a virtual camera priority-based assistance to help the user search dynamic targets *** of forcing users to go to destinations,we provide an optimized instant path to guide them to places where they are more likely to find dynamic targets when they ask for *** assume that a certain number of virtual cameras are fixed in virtual scenes to obtain extra depth maps,which capture the depth information of the scene and the locations of the dynamic *** method automatically analyzes the priority of these virtual cameras,chooses the destination,and generates an instant path to assist the user in finding the dynamic *** method is suitable for various virtual reality applications that do not require manual supervision or *** A user study is designed to evaluate the proposed *** results indicate that compared with three conventional navigation methods,such as the top-view method,our method can help users find dynamic targets more *** advantages include reducing the task completion time,reducing the number of resets,increasing the average distance between resets,and reducing user task *** We presented a method for improving dynamic target searching efficiency in virtual scenes by virtual cam

关键词： Searching assistance Virtual environment Path guidance Redirection

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The boundary of Kirkwood-Dirac quasiprobability

arXiv

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arXiv 2025年

作者： Liu, Lijun Cheng, Shuming Department of Mathematics and Computer Science Shanxi Normal University Taiyuan030006 China College of Electronic and Information Engineering Tongji University Shanghai201804 China National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201203 China

The Kirkwood-Dirac (KD) quasiprobability describes measurement statistics of joint quantum observables, and has generated great interest as prominent indicators of non-classical features in various quantum information processing tasks. It relaxes the Kolmogorov axioms of probability by allowing for negative and even imaginary probabilities, and thus incorporates the classical probability theory as its inner boundary. In this work, we introduce the postquantum quasiprobability under mild assumptions to provide an outer boundary for KD quasiprobability. Specifically, we present qualitative and quantitative evidence to show that the classical, KD, and postquantum quasiprobabilities form a strict hierarchy, in the sense that joint probability distributions are a strict subset of KD quasiprobability distributions that are a strict subset of postquantum ones. Surprisingly, we are able to derive some nontrivial bounds valid for both classical probability and KD quasiprobability, and even valid for the KD quasiprobability generated by an arbitrary number of measurements. Finally, other interesting bounds are obtained, and their implications are noted. Our work solves the fundamental problems of what and how to bound the KD quasiprobability, and hence provides a deeper understanding of utilizing it in quantum information processing. Copyright © 2025, The Authors. All rights reserved.

关键词： Distribution functions

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Differential evolution variants for Searching D- and A-optimal designs

arXiv

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arXiv 2022年

作者： Tong, Lyuyang National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China Department of Computer Science City University of Hong Kong Hong Kong

Optimal experimental design is an essential subfield of statistics that maximizes the chances of experimental success. The D- and A-optimal design is a very challenging problem in the field of optimal design, namely minimizing the determinant and trace of the inverse Fisher information matrix. Due to the flexibility and ease of implementation, traditional evolutionary algorithms (EAs) are applied to deal with a small part of experimental optimization design problems without mathematical derivation and assumption. However, the current EAs remain the issues of determining the support point number, handling the infeasible weight solution, and the insufficient experiment. To address the above issues, this paper investigates differential evolution (DE) variants for finding D- and A-optimal designs on several different statistical models. The repair operation is proposed to automatically determine the support point by combining similar support points with their corresponding weights based on Euclidean distance and deleting the support point with less weight. Furthermore, the repair operation fixes the infeasible weight solution into the feasible weight solution. To enrich our optimal design experiments, we utilize the proposed DE variants to test the D- and A-optimal design problems on 12 statistical models. Compared with other competitor algorithms, simulation experiments show that LSHADE can achieve better performance on the D- and A-optimal design problems. Copyright © 2022, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation

arXiv

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arXiv 2024年

作者： Tao, Wei Zhou, Yucheng Wang, Yanlin Zhang, Hongyu Wang, Haofen Zhang, Wenqiang Shanghai Engineering Research Center of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China School of Software Engineering Sun Yat-sen University Guangdong Zhuhai519082 China School of Big Data and Software Engineering Chongqing University Chongqing China College of Design and Innovation Tongji University Shanghai China Engineering Research Center of AI and Robotics Ministry of Education Academy for Engineering and Technology Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University 220 Handan Road Shanghai200433 China

Commit messages are natural language descriptions of code changes, which are important for software evolution such as code understanding and maintenance. However, previous methods are trained on the entire dataset without considering the fact that a portion of commit messages adhere to good practice (i.e., good-practice commits), while the rest do not. On the basis of our empirical study, we discover that training on good-practice commits significantly contributes to the commit message generation. Motivated by this finding, we propose a novel knowledge-aware denoising learning method called KADEL. Considering that good-practice commits constitute only a small proportion of the dataset, we align the remaining training samples with these good-practice commits. To achieve this, we propose a model that learns the commit knowledge by training on good-practice commits. This knowledge model enables supplementing more information for training samples that do not conform to good practice. However, since the supplementary information may contain noise or prediction errors, we propose a dynamic denoising training method. This method composes a distribution-aware confidence function and a dynamic distribution list, which enhances the effectiveness of the training process. Experimental results on the whole MCMD dataset demonstrate that our method overall achieves state-of-the-art performance compared with previous methods. © 2024, CC BY.

关键词： Sampling

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Uav Digital Twin Open-Source Framework: Case of Study Remote Sensing in the Andean Mountains

SSRN

引用

SSRN 2024年

作者： Valencia, Esteban Toapanta, Francisco Oña, Gabriel Carrillo, Andrey Aláez, Daniel Loyaga, Erick Chamorro, Willian Cruz, Patricio Abad, Jackeline Laboratory of UAS ATA Research Group Mechanical Engineering Department Escuela Politécnica Nacional Quito Ecuador Department of Statistics Computer Science and Mathematics Universidad Pública de Navarra Pamplona31006 Spain Institute of Smart Cities Universidad Pública de Navarra Pamplona31006 Spain

The increasing demand for Unmanned Aerial Vehicles (UAVs) in the aerospace industry emphasizes the need for accurate simulation environments, especially in difficult access regions, where reliability, high trustworthiness, and fail-proof designs are crucial. This study develops an opensource framework for a customized simulation environment for UAVs, using ROS- Gazebo tools combined with Ardupilot. The method comprises a realistic environment reconstruction based on free satellite information, ROS-Gazebo scheme for modeling and testing of the UAV platform and Ardupilot for the mission design and deployment. The methodology seeks to decrease the costs associated with monitoring missions and make more efficient operations in areas with risky and difficult *** order to evaluate the accuracy of the positioning of the UAV the method to simulate real missions, an experimental validation of the digital twin was conducted using real flight data logs from two self-built UAVs equipped with an open-source autopilot implemented on a Pixhawk Cube flight controller. One of the aircraft used to assess the performance of the proposed methodology was of the fixed-wing type. Its flight tests were conducted in the vicinity of the Antisana volcano (4500 masl), where wind gusts reach speeds between 9 and 12 m/s. Through these flight tests, the potential of the developed methodology was successfully validated, revealing maximum errors of 9% in the trajectory along the Z-axis (altitude). The other aircraft used was a quadcopter, and its flight tests took place in Carolina Park in the city of Quito (2800 masl), where wind gusts range between 5 and 7 m/s. These tests under less challenging conditions confirmed the potential of the method developed and allowed to test its versatility. For the quadcopter case an error of 8% in the trajectory along the Z-axis (altitude) was calculated. The results presented highlight the suitability of the proposed method for emulating complex missio

关键词： Fixed wings

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Fuzzy Sandwich Control of Some Nonlinear Systems with a Number of Stochastically Occurring Impulses 13

Fuzzy Sandwich Control of Some Nonlinear Systems with a Numb...

引用

13th International Conference on Information science and Technology, ICIST 2023

作者： Onasanya, Babatunde Oluwaseun Dai, Xiangguang Feng, Yuming Zhang, Wei School of Computer Science and Engineering Chongqing Three Gorges University Chongqing Wanzhou404100 China Department of Mathematics University of Ibadan Ibadan200284 Nigeria Chongqing Engineering Research Center of Internet of Things and Intelligent Control Technology Chongqing Three Gorges University Chongqing Wanzhou404100 China Three Gorges Institute of Artificial Intelligence Chongqing Three Gorges University Chongqing Wanzhou404100 China Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Chongqing Wanzhou404100 China

ISBN: (纸本)9798350313925

In this paper, rather than have a fixed constant matrix in the sandwich control of the nonlinear system, a fuzzy control matrix was used. In addition, simple inequalities that do not require additional computational time and skills in computer programming were used rather than the usual LMI. The result of this method is that the period T required in the control process is shorter and the control time is also reached much faster or reduced compared to the existing sandwich methods that used constant matrices. In other words, the control time of this method is shorter than the control time achieved when using constant matrices. Besides, this method also can be used to control more nonlinear chaotic systems such as Lorenz's, Rössler's and Chen's systems. Numerical simulations were used to demonstrate the effectiveness of this method. © 2023 IEEE.

关键词： Nonlinear systems

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Detection of brain tumor and identification of tumor region using deep neural network on FMRI images 19

Detection of brain tumor and identification of tumor region ...

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19th International Conference on Machine Learning and Cybernetics, ICMLC 2020

作者： Mashiat, Afsara Akhlaque, Reza Rifat Fariha, Fahmeda Hasan Reza, Tanzim Rahman, Md Anisur Parvez, Mohammad Zavid Brac University Software Engineering and HCI Research Group Department of Computer Science and Engineering Bangladesh Charles Sturt University School of Computing and Mathematics Australia

ISBN: (纸本)9780738124261

As brain is the most vital organ of the human body, the affects of brain related diseases can be severe. One of the most harmful diseases is brain tumor, which results in a very short life expectancy of the affected patient. Detection of brain tumor is a challenging task in the early stages. Still, with the help of modern technology and machine learning algorithms, it has become a matter of great interest for research. While detecting the brain tumor of an affected person, we are considering the fMRI data of the patient. Our aim is to identify whether the tumor is present in the patient's brain or not. We use a Convolutional Neural Network(CNN) that is good enough to generate high accuracy. We have used some deeper architecture design VGG16, VGG19, and Inception v3 for better accuracy. Three classification techniques are used namely binary classification, lobe based classification, and position based classification. The main contribution of our proposed work is that we have identified the specific region of the brain where the tumor is located. The region-based classification distinguishes our work from others that are applied on the same dataset. For binary classification, we found approximately 95% accuracy from all the three architectures. Furthermore, we found approximately 78% accuracy for lobe based classification and approximately 97% accuracy for position based classification. The experimental results indicate the superiority of our proposed method in terms of identifying the brain tumor. © 2020 IEEE.

关键词： Brain

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Biomedical data and AI

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science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

关键词： biomedical data mining biomedical data computer vision artificial intelligence artificial intelligence ai integrating biological knowledge models disease screeningto quantitative biology conference

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