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SSRN 2022年

作者： Han, Dan Chen, Yi Li, Donghui Dong, Hailiang Xu, Bingshe Sang, Shengbo He, Xiuli Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan030024 China Key Laboratory of Interface Science and Engineering in Advanced Materials Taiyuan University of Technology Ministry of Education Taiyuan030024 China State Key Laboratory of Transducer Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Shanxi Research Institute of 6D Artificiat Intelligence Biomedical Science Taiyuan030024 China

Gallium nitride (GaN) has some challenges for gas sensing, such as high detection lower limit (LOD) or controlling the epitaxy of nanostructures. To address these issues, we exclusively designed a device to enhance the gas interaction capabilities of GaN quantum dots (QDs) film fabricated by metal-organic chemical vapor deposition (MOCVD). The GaN QDs film gas sensor exhibited a low LOD (5 ppb) to NO2 at room temperature (RT), which has much superior gas performance than other GaN-based sensors. The results manifest that many ultra-small QDs around 2 nm in diameter are stacked to form QDs of larger size, and confirm the existence of vacancies. The sensor displays a ultra-high electron mobility of 1237.37 cm2 V−1s−1, demonstrate that great shallow donor impurities in GaN QDs film that ionize easily at RT, which is greatly beneficial for gas sensing. The sensing mechanisms were analyzed based on its abundant active sites, ultrahigh carrier mobility, high surface energy and chemical activity, grain boundary barrier and good electrical conductivity. Moreover, the sensor processes high selectivity, repeatability, stability and reusability based on MOCVD technology which can realize precise control of structure, morphology and surface energy level, which is expected to achieve rapid industrialization. © 2022, The Authors. All rights reserved.

关键词： Gallium nitride

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Security enhancement of big data in cloud application using block-chain

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AIP Conference Proceedings 2023年第1期2587卷

作者： Avein Jabar Al-Asadi J. V. S. Arundathi Omdev Dahiya Abdul Wahab Hashmi Ankur Gupta Malik Bader Alazzam Dept. of Information Technology Technical College of Informatics TCI Sulaimani Polytechnic University Sulaymaniyah Iraq Dept. of Computer Science SR & BGNR Government Arts and Science Degree & PG College (Autonomous) Khammam Telangana India School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India Advanced Manufacturing and Mechatronics Lab Mechanical Engineering Department Malaviya National Institute of Technology Jaipur India Dept. of Computer Science and Engineering Vaish College of Engineering Rohtak Haryana India Faculty of Computer Science and Informatics Amman Arab University Amman Jordan

Organizations are inundated on a daily basis with vast, difficult-to-manage volumes of structured and unstructured data, which is known as “big data.” Analysis of large amounts of data may provide useful information that can be used to improve decision-making and boost employee morale. This kind of data storage makes it almost hard to change, hack, or otherwise deceive. Every computer in a block-chain network has a copy of the block-transaction chain’s log, which is called a “block-chain.” In previous researches it has been observed that security of big data is a major issue. Block chain has been found suitable mechanism to deal with such issues. Block-chain has also been considered a service in the context of cloud computing, termed Block-chain as a Service (BaaS). By using virtual storage nodes, block-chain technology may create whole new cloud storage services that are very resistant to data tampering. The proposed research has focused on the enhancement of security of big data from SQL injection attack that is transferred over cloud application. Research is making use of block chain technology in order to resolve the issue of security. The major objective of research is to propose block chain based security model for big data that should be more secure and efficient in case of SQL injection attack. Research work has proposed modified algorithm enhances the security of big data in cloud application against SQL injection, cross script.

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Layer-wise multi-view learning for neural machine translation

arXiv

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arXiv 2020年

作者： Wang, Qiang Li, Changliang Zhang, Yue Xiao, Tong Zhu, Jingbo Machine Intelligence Technology Lab Alibaba DAMO Academy Hangzhou China Kingsoft AI Lab Beijing China Institute of Advanced Technology Westlake Institute for Advanced Study Hangzhou China School of Computer Science and Engineering Northeastern University Shenyang China NiuTrans Research Shenyang China

Traditional neural machine translation is limited to the topmost encoder layer’s context representation and cannot directly perceive the lower encoder layers. Existing solutions usually rely on the adjustment of network architecture, making the calculation more complicated or introducing additional structural restrictions. In this work, we propose layer-wise multi-view learning to solve this problem, circumventing the necessity to change the model structure. We regard each encoder layer’s off-the-shelf output, a by-product in layer-by-layer encoding, as the redundant view for the input sentence. In this way, in addition to the topmost encoder layer (referred to as the primary view), we also incorporate an intermediate encoder layer as the auxiliary view. We feed the two views to a partially shared decoder to maintain independent prediction. Consistency regularization based on KL divergence is used to encourage the two views to learn from each other. Extensive experimental results on five translation tasks show that our approach yields stable improvements over multiple strong baselines. As another bonus, our method is agnostic to network architectures and can maintain the same inference speed as the original model. Copyright © 2020, The Authors. All rights reserved.

关键词： Neural machine translation

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Memory that never forgets:emerging nonvolatile memory and the implication for architecture design

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National Science Review 2018年第4期5卷 577-592页

作者： Guangyu Sun Jishen Zhao Matt Poremba Cong Xu Yuan Xie Center for Energy-Efficient Computing and Applications School of Electronics Engineering and Computer Science Peking University Department of Computer Engineering University of California at Santa Cruz Advanced Micro Devices Inc. Hewlett Packard Lab Department of Electrical and Computer Engineering University of California at Santa Barbara

In order to mitigate the problem of the ‘memory wall', various emerging nonvolatile memory(NVM)technologies have been proposed to replace the traditional ones. These emerging NVMs include spin torque transfer random access memory, PCRAM, resistive random access memory, racetrack memory, *** to traditional memory technologies, they have the advantages of near-zero standby power, high storage density and nonvolatility, which make them competitive for future memory hierarchy ***, it is inefficient to directly apply these NVMs in existing memory architectures. On the one hand,these NVMs have their own limitations, such as long write latency, high write energy, limited write numbers, etc. Thus, proper architecture modification is required to adopt them into the traditional memory hierarchy. On the other hand, the unique features of these NVMs allow new memory architectures in memory subsystems and also introduce new challenges to be solved at the same time. In this paper, we not only review various emerging NVMs but also study typical related work on these topics to investigate their implications for memory architecture design.

关键词： non-volatile memory cache main memory energy efficiency

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Performance Improvement of Hadoop ext4-based Disk I/O

Performance Improvement of Hadoop ext4-based Disk I/O

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International Symposium on Computing and Networking (CANDAR)

作者： Makoto Nakagami Jose A. B. Fortes Saneyasu Yamaguchi Electrical Engineering and Electronics Kogakuin University Graduate School Tokyo Japan Advanced Computer and Information Systems (ACIS) Lab University of Florida Gainesville USA Kogakuin University Tokyo Japan

ISBN: (数字)9781728182216

ISBN: (纸本)9781728182223

Hadoop is one of the most popular big-data analytics platforms, often relying on hard disk drives for storage of big-data amounts that exceed the capacity of solid-state drives. Unlike other data-intensive applications, such as database management systems, big-data processing jobs frequently require extensive sequential I/O requests. Previously proposed methods for improving sequential I/O performance modified the block usage bitmap of the Ext2/3 filesystem in order to actively use the faster disk zones, which are the outer zones in each hard disk drive. However, these methods do not support Ext4, which is the current version of Ext filesystems. In this paper, we discuss a method for improving the sequential I/O performance of the Ext4 filesystem. First, we evaluate the sequential file access throughputs on Ext3, Ext4, and XFS filesystems. We point out that Ext4 does not actively utilize the area freed by deleting existing files, causing declines in file access performance. Second, we propose a method for improving the Ext4 sequential file access performance. The improved Ext4 actively utilizes the faster zones of storage devices by controlling file placement location. Third, we evaluate the proposed filesystem and show that it outperforms existing filesystems. In the case of TeraSort, Hadoop with the proposed Ext4 filesystem performs better than when using the original Ext4 filesystem by as much as 30.1%.

关键词： Performance evaluation Process control Drives Aerospace electronics Hard disks Throughput Database systems

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Advances in nuclear detection and readout techniques

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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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Diffraction-limited imaging with monolayer 2D material-based ultrathin flat lenses

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Light(Science & Applications) 2020年第1期9卷 696-706页

作者： Han Lin Zai-Quan Xu Guiyuan Cao Yupeng Zhang Jiadong Zhou Ziyu Wang Zhichen Wan Zheng Liu Kian Ping Loh Cheng-Wei Qiu Qiaoliang Bao Baohua Jia Centre for Translational Atomaterials Faculty of ScienceEngineering and TechnologySwinburne University of TechnologyP.O.Box 218HawthornVIC 3122Australia Department of Materials Science and Engineering ARC Centre of Excellence in Future Low-Energy Electronics Technologies(FLEET)Monash UniversityWellington RoadClaytonVIC 3800Australia School of Mathematical and Physical Sciences Faculty of ScienceUniversity of Technology Sydney15 BroadwayUltimoNSW 2007Australia Institute of Microscale Optoelectronics Lab of Artificial Microstructure for OptoelectronicsShenzhen University518000 ShenzhenChina School of Materials Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Chemistry National University of SingaporeSingapore 117543Singapore Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore The Australian Research Council(ARC)Industrial Transformation Training Centre in Surface Engineering for Advanced Materials(SEAM) Swinburne University of TechnologyP.O.Box 218HawthornVIC 3122Australia

Ultrathin flat optics allow control of light at the subwavelength scale that is unmatched by traditional refractive *** approach the atomically thin limit,the use of 2D materials is an attractive possibility due to their high refractive ***,achievement of diffraction-limited focusing and imaging is challenged by their thickness-limited spatial resolution and focusing *** we report a universal method to transform 2D monolayers into ultrathin flat *** laser direct writing was applied to generate local scattering media inside a monolayer,which overcomes the longstanding challenge of obtaining sufficient phase or amplitude modulation in atomically thin 2D *** achieved highly efficient 3D focusing with subwavelength resolution and diffractionlimited *** high focusing performance even allows diffraction-limited imaging at different focal positions with varying *** work paves the way for downscaling of optical devices using 2D materials and reports an unprecedented approach for fabricating ultrathin imaging devices.

关键词： ultrathin scattering optics

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A perspective on meta-boundaries

arXiv

引用

arXiv 2022年

作者： Zhang, Xinyan Chen, Jialin Chen, Ruoxi Wang, Chan Cai, Tong Chen, Hongsheng Lin, Xiao Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou310027 China International Joint Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang The Electromagnetics Academy Zhejiang University Zhejiang University Haining314400 China Department of Electrical and Computer Engineering Technion-Israel Institute of Technology Haifa32000 Israel Air and Missile Defense College Air Force Engineering University Xi’an710051 China

The judicious design of electromagnetic boundary provides a crucial route to control light-matter interactions, and it is thus fundamental to basic science and practical applications. General design approaches rely on the manipulation of bulk properties of superstrate or substrate and on the modification of boundary geometries. Due to the recent advent of metasurfaces and low-dimensional materials, the boundary can be flexibly featured with a surface conductivity, which can be rather complex but provide an extra degree of freedom to regulate the propagation of light. In this perspective, we denote the boundary with a non-zero surface conductivity as the meta-boundary. The meta-boundaries are categorized into four types, namely isotropic, anisotropic, biisotropic and bianisotropic meta-boundaries, according to the electromagnetic boundary conditions. Accordingly, the latest development for these four kinds of meta-boundaries are reviewed. Finally, an outlook on the research tendency of meta-boundaries is provided, particularly on the manipulation of light-matter interactions by simultaneously exploiting meta-boundaries and metamaterials. © 2022, CC BY.

关键词： Degrees of freedom (mechanics)

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Adversarial Learning with Cost-Sensitive Classes

arXiv

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arXiv 2021年

作者： Shen, Haojing Chen, Sihong Wang, Ran Wang, Xizhao Big Data Institute College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Guangdong Shenzhen518060 China The College of Mathematics and Statistics Shenzhen University Shenzhen518060 China The Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China

It is necessary to improve the performance of some special classes or to particularly protect them from attacks in adversarial learning. This paper proposes a framework combining cost-sensitive classification and adversarial learning together to train a model that can distinguish between protected and unprotected classes, such that the protected classes are less vulnerable to adversarial examples. We find in this framework an interesting phenomenon during the training of deep neural networks, called Min-Max property, that is, the absolute values of most parameters in the convolutional layer approach zero while the absolute values of a few parameters are significantly larger becoming bigger. Based on this Min-Max property which is formulated and analyzed in a view of random distribution, we further build a new defense model against adversarial examples for adversarial robustness improvement. An advantage of the built model is that it performs better than the standard one and can combine with adversarial training to achieve an improved performance. It is experimentally confirmed that, regarding the average accuracy of all classes, our model is almost as same as the existing models when an attack does not occur and is better than the existing models when an attack occurs. Specifically, regarding the accuracy of protected classes, the proposed model is much better than the existing models when an attack occurs. © 2021, CC BY.

关键词： Deep neural networks

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Automated Detection of Epileptic Spikes and Seizures Incorporating a Novel Spatial Clustering Prior

arXiv

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arXiv 2025年

作者： Dong, Hanyang Sheng, Shurong Wang, Xiongfei Gao, Jia-Hong Sun, Yi Yang, Wanli Xiao, Kuntao Teng, Pengfei Luan, Guoming Lv, Zhao School of Artificial Intelligence Anhui University Hefei230601 China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China Department of Neurosurgery Beijing Key Laboratory of Epilepsy Sanbo Brain Hospital Capital Medical University Beijing China McGovern Institute for Brain Research Peking University Beijing China Center for MRI Research Academy for Advanced Interdisciplinary Studies Peking University Beijing China Beijing City Key Lab for Medical Physics and Engineering Institute of Heavy Ion Physics School of Physics Peking University Beijing China Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

A Magnetoencephalography (MEG) time-series recording consists of multi-channel signals collected by superconducting sensors, with each signal’s intensity reflecting magnetic field changes over time at the sensor location. Automating epileptic MEG spike detection significantly reduces manual assessment time and effort, yielding substantial clinical benefits. Existing research addresses MEG spike detection by encoding neural network inputs with signals from all channel within a time segment, followed by classification. However, these methods overlook simultaneous spiking occurred from nearby sensors. We introduce a simple yet effective paradigm that first clusters MEG channels based on their sensor’s spatial position. Next, a novel convolutional input module is designed to integrate the spatial clustering and temporal changes of the signals. This module is fed into a custom MEEG-ResNet3D developed by the authors, which learns to extract relevant features and classify the input as a spike clip or not. Our method achieves an F1 score of 94.73% on a large real-world MEG dataset Sanbo-CMR collected from two centers, outperforming state-of-the-art approaches by 1.85%. Moreover, it demonstrates efficacy and stability in the Electroencephalographic (EEG) seizure detection task, yielding an improved weighted F1 score of 1.4% compared to current state-of-the-art techniques evaluated on TUSZ, whch is the largest EEG seizure dataset. Copyright © 2025, The Authors. All rights reserved.

关键词： Magnetoencephalography

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