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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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A Thin Film Barium Titanate-on-Insulator Optoelectronics Platform with Various Photonic Devices

A Thin Film Barium Titanate-on-Insulator Optoelectronics Pla...

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CLEO: science and Innovations, S and I 2022

作者： Cao, Yu Ng, Jun Da Lin, Hong-Lin Shao, Jianbo Qi, Luo Siew, Shawn Yohanes Dogheche, Elhadj Danner, Aaron J. Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore117583 Singapore Advanced Micro Foundry 11 Science Park Road Singapore117685 Singapore Microsoft Research 21 Station Road CambridgeCB1 2FB United Kingdom Institute of Electronics Microelectronics and Nanotechnology IEMN DOAE Université Polytechnique Hauts-de-France Valenciennes59309 France

We present a thin-film barium titanate-on-insulator platform and its optical and electrooptic properties, some of which are superior to lithium niobate-on-insulator, and discuss our progress in device fabrication, per... 详细信息

ISBN: (纸本)9781557528209

关键词： Barium titanate

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Quantum-Informed Recursive Optimization Algorithms

arXiv

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arXiv 2023年

作者： Finžgar, Jernej Rudi Kerschbaumer, Aron Schuetz, Martin J.A. Mendl, Christian B. Katzgraber, Helmut G. BMW AG Munich Germany Technical University Munich School of CIT Department of Computer Science Garching Germany Amazon Quantum Solutions Lab SeattleWA United States AWS Center for Quantum Computing PasadenaCA United States Technical University of Munich Institute for Advanced Study Garching Germany

We propose and implement a family of quantum-informed recursive optimization (QIRO) algorithms for combinatorial optimization problems. Our approach leverages quantum resources to obtain information that is used in problem-specific classical reduction steps that recursively simplify the problem. These reduction steps address the limitations of the quantum component (e.g., locality) and ensure solution feasibility in constrained optimization problems. Additionally, we use backtracking techniques to further improve the performance of the algorithm without increasing the requirements on the quantum hardware. We showcase the capabilities of our approach by informing QIRO with correlations from classical simulations of shallow circuits of the quantum approximate optimization algorithm (QAOA), solving instances of maximum independent set and maximum satisfiability problems with hundreds of variables. We also demonstrate how QIRO can be deployed on a neutral atom quantum processor to find large independent sets of graphs. In summary, our scheme achieves results comparable to classical heuristics even with relatively weak quantum resources. Furthermore, enhancing the quality of these quantum resources improves the performance of the algorithms. Notably, the modular nature of QIRO offers various avenues for modifications, positioning our work as a template for a broader class of hybrid quantum-classical algorithms for combinatorial optimization. Copyright © 2023, The Authors. All rights reserved.

关键词： Combinatorial optimization

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Pagurus: Eliminating cold startup in serverless computing with inter-action container sharing

arXiv

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arXiv 2021年

作者： Li, Zijun Chen, Quan Guo, Minyi Department of Computer Science and Engineering Shanghai Jiao Tong University China Shanghai Institute for Advanced Communication and Data Science Shanghai Jiao Tong University China

Serverless computing provides fine-grain resource sharing between Cloud tenants through containers. Each function invocation (action) runs in an individual container. When there is not an already started container for a user function, a new container has to be created for it. However, the long cold startup time of a container results in the long response latency of the action. Our investigation shows that the containers for some user actions share most of the software packages. If an action that requires a new container can "borrow" a similar warm container from other actions, the long cold startup can be eliminated. Based on the above finding, we propose Pagurus, a runtime container management system for eliminating the cold startup in serverless computing. Pagurus is comprised of an inter-action container scheduler and an intra-action container scheduler for each action. The inter-action container scheduler schedules shared containers among actions. The intra-action container scheduler deals with the management of the container lifecycle. Our experimental results show that Pagurus effectively eliminates the time-consuming container cold startup. An action may start to run in 10ms with Pagurus, even if there is not warm container for it. Copyright © 2021, The Authors. All rights reserved.

关键词： Containers

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Spontaneous Chirality Flipping in an Orthogonal Spin-Charge Ordered Topological Magnet

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Physical Review X 2024年第1期14卷 011053-011053页

作者： H. Miao J. Bouaziz G. Fabbris W. R. Meier F. Z. Yang H. X. Li C. Nelson E. Vescovo S. Zhang A. D. Christianson H. N. Lee Y. Zhang C. D. Batista S. Blügel Materials Science and Technology Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Peter Grünberg Institut and Institute for Advanced Simulation Forschungszentrum Jülich and JARA D-52425 Jülich Germany Advanced Photon Source Argonne National Laboratory Argonne Illinois 60439 USA Department of Materials Science and Engineering University of Tennessee Knoxville Tennessee USA Advanced Materials Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China National Synchrotron Light Source II Brookhaven National Laboratory Upton New York 11973 USA Max-Planck-Institut fur Physik komplexer Systeme Nothnitzer Straße 38 01187 Dresden Germany Department of Physics and Astronomy The University of Tennessee Knoxville Tennessee 37996 USA Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee Knoxville Tennessee 37996 USA Quantum Condensed Matter Division and Shull-Wollan Center Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA

The asymmetric distribution of chiral objects with opposite chirality is of great fundamental interest ranging from molecular biology to particle physics. In quantum materials, chiral states can build on inversion-symmetry-breaking lattice structures or emerge from spontaneous magnetic ordering induced by competing interactions. Although the handedness of a chiral state can be changed through external fields, a spontaneous chirality flipping has yet to be discovered. We present experimental evidence of chirality flipping via changing temperature in a topological magnet EuAl4, which features orthogonal spin density waves (SDW) and charge density waves (CDW). Using circular dichroism of Bragg peaks in the resonant magnetic x-ray scattering, we find that the chirality of the helical SDW flips through a first-order phase transition with modified SDW wavelength. Intriguingly, we observe that the CDW couples strongly with the SDW and displays a rare commensurate-to-incommensurate transition at the chirality flipping temperature. Combining with first-principles calculations and angle-resolved photoemission spectroscopy, our results support a Fermi surface origin of the helical SDW with intertwined spin, charge, and lattice degrees of freedom in EuAl4. Our results reveal an unprecedented spontaneous chirality flipping and lay the groundwork for a new functional manipulation of chirality through momentum-dependent spin-charge-lattice interactions.

关键词： Charge density waves Chirality Magnetic order Magnetic texture 3-dimensional systems Electron-correlation calculations X-ray diffraction X-ray magnetic circular dichroism

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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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MATE: benchmarking multi-agent reinforcement learning in distributed target coverage control 22

MATE: benchmarking multi-agent reinforcement learning in dis...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Xuehai Pan Mickel Liu Fangwei Zhong Yaodong Yang Song-Chun Zhu Yizhou Wang School of Computer Science Peking University and Center on Frontiers of Computing Studies Peking University and Beijing Institute for General Artificial Intelligence (BIGAI) School of Intelligence Science and Technology Peking University and Beijing Institute for General Artificial Intelligence (BIGAI) School of Intelligence Science and Technology Peking University and Institute for Artificial Intelligence Peking University and Beijing Institute for General Artificial Intelligence (BIGAI) School of Intelligence Science and Technology Peking University and Institute for Artificial Intelligence Peking University and Beijing Institute for General Artificial Intelligence (BIGAI) and Department of Automation Tsinghua University School of Computer Science Peking University and Center on Frontiers of Computing Studies Peking University and Institute for Artificial Intelligence Peking University

ISBN: (纸本)9781713871088

We introduce the Multi-Agent Tracking Environment (MATE), a novel multi-agent environment simulates the target coverage control problems in the real world. MATE hosts an asymmetric cooperative-competitive game consisting of two groups of learning agents—"cameras" and "targets"—with opposing interests. Specifically, "cameras", a group of directional sensors, are mandated to actively control the directional perception area to maximize the coverage rate of targets. On the other side, "targets" are mobile agents that aim to transport cargo between multiple randomly assigned warehouses while minimizing the exposure to the camera sensor networks. To showcase the practicality of MATE, we benchmark the multi-agent reinforcement learning (MARL) algorithms from different aspects, including cooperation, communication, scalability, robustness, and asymmetric self-play. We start by reporting results for cooperative tasks using MARL algorithms (MAPPO, IPPO, QMIX, MADDPG) and the results after augmenting with multi-agent communication protocols (TarMAC, I2C). We then evaluate the effectiveness of the popular self-play techniques (PSRO, fictitious self-play) in an asymmetric zero-sum competitive game. This process of co-evolution between cameras and targets helps to realize a less exploitable camera network. We also observe the emergence of different roles of the target agents while incorporating I2C into target-target communication. MATE is written purely in Python and integrated with OpenAI Gym API to enhance user-friendliness.

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MCDAL: Maximum Classifier Discrepancy for Active Learning

arXiv

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arXiv 2021年

作者： Cho, Jae Won Kim, Dong-Jin Jung, Yunjae Kweon, In So The Department of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of The Electrical Engineering and Computer Science Department University of California BerkeleyCA United States

Recent state-of-the-art active learning methods have mostly leveraged Generative Adversarial Networks (GAN) for sample acquisition;however, GAN is usually known to suffer from instability and sensitivity to hyper-parameters. In contrast to these methods, we propose in this paper a novel active learning framework that we call Maximum Classifier Discrepancy for Active Learning (MCDAL) which takes the prediction discrepancies between multiple classifiers. In particular, we utilize two auxiliary classification layers that learn tighter decision boundaries by maximizing the discrepancies among them. Intuitively, the discrepancies in the auxiliary classification layers' predictions indicate the uncertainty in the prediction. In this regard, we propose a novel method to leverage the classifier discrepancies for the acquisition function for active learning. We also provide an interpretation of our idea in relation to existing GAN based active learning methods and domain adaptation frameworks. Moreover, we empirically demonstrate the utility of our approach where the performance of our approach exceeds the state-of-the-art methods on several image classification and semantic segmentation datasets in active learning setups. © 2021, CC BY.

关键词： Generative adversarial networks

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A machine learning model for disease risk prediction by integrating genetic and non-genetic factors

A machine learning model for disease risk prediction by inte...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Xu, Yu Wang, Chonghao Li, Zeming Cai, Yunpeng Young, Ouzhou Lyu, Aiping Zhang, Lu Hong Kong Baptist University Department of Computer Science Hong Kong Hong Kong Institute for Research and Continuing Education Hong Kong Baptist University Hong Kong Hong Kong Shenzhen Institutes of Advanced Technologies Chinese Academy of Sciences Shenzhen China Yashi Changhua Intelligent Technology Co. Ltd Shenzhen China Hong Kong Baptist University School of Chinese Medicine Hong Kong Hong Kong

ISBN: (纸本)9781665468190

Polygenic risk score (PRS) has been widely used to identify the high-risk individuals from the general population, which would be helpful for disease prevention and early treatment. Many methods have been developed to calculate PRS by weighting and aggregating the phenotype-associated risk alleles from genome-wide association studies. However, only considering genetic effects may not be sufficient for risk prediction because the disease risk is not only related to genetic factors but also non-genetic factors, e.g., diet, physical exercise et al. But it is still a challenge to integrate these genetic and non-genetic factors into a unified machine learning framework for disease risk prediction. In this paper, we proposed PRSIMD (PRS Integrating Multi-source Data), a machine learning model that applies posterior regularization to integrate genetic and non-genetic factors to improve disease risk prediction. Also, we applied Mendelian Randomization analysis to identify the causal non-genetic risk factors for the selected diseases. We applied PRSIMD to predict type 2 diabetes and coronary artery disease from UK Biobank and observed that PRSIMD was significantly better than the existing methods to calculate PRS. In addition, we observed that PRSIMD achieved the better predictive power than the composite risk score. The codes of PRSIMD are available at: https://*** ericcombiolab/PRSIMD © 2022 IEEE.

关键词： Diseases

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Optimal Excitatory and Inhibitory Balance for High Learning Performance in Spiking Neural Networks with Long-Tailed Synaptic Weight Distributions

Optimal Excitatory and Inhibitory Balance for High Learning ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ibuki Matsumoto Sou Nobukawa Tomoki Kurikawa Nobuhiko Wagatsuma Yusuke Sakemi Takashi Kanamaru Nina Sviridova Kazuyuki Aihara Graduate School of Information and Computer Science Chiba Institute of Technology Chiba Japan Department of Complex and Intelligent Systems Future University Hakodate Hokkaido Japan Department of Information Science Toho University Chiba Japan Research Center for Mathematical Engineering Chiba Institute of Technology Chiba Japan School of Advanced Engineering Kogakuin University Tokyo Japan Department of Intelligent Systems Tokyo City University Tokyo Japan International Research Center for Neurointelligence The University of Tokyo Tokyo Japan

Excitatory/inhibitory (E/I) balance is significantly associated with cognitive function. Its imbalance impairs cognitive function, particularly in patients with psychiatric disorders. Recent physiological and modeling findings show that excitatory postsynaptic potentials (EPSPs) have a long-tailed distribution and contribute to the generation of spontaneous activity. Moreover, this spontaneous activity and its response to the external stimulus significantly alternate under the different E/I balance. However, the effects of the E/I balance under long-tailed EPSPs at the functional level remain unknown. Hence, to elucidate this relationship, we constructed a reservoir computing (RC) model generating the long-tailed distribution of EPSPs and investigated the effect of the E/I balance on the learning performance of RC in the memory capacity (MC) task, which measures how correctly delayed input signals can be reproduced. The results revealed that an appropriate E/I balance maximized the MC. This high MC was realized by recurrent spike propagation under long-tailed EPSPs. These findings contribute to the understanding of the effect of the E/I balance in physiologically relevant neural networks.

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