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Roles of Cu doping in YbZn_(2.Sb_(2. for thermoelectric performance enhancement

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Rare Metals 2024年第6期43卷 2869-2875页

作者： Xiong zhang Hai-Hui Li xiao-Liang Cao Bin zhang Hong Wu {3. zhang Guang Han Guo-{3. Wang Hao-Shuang Gu xiao-{3.an zhou College of Physics and Electronic Science Hubei Normal UniversityHuangshi 435002China Analytical and Testing Center Chongqing UniversityChongqing401331China School of Science Chongqing University of Posts and Telecommunications.Chongqing 400065 China College of Materials Science and engineering Chongqing UniversityChongqing 400044China Hubei Collaborative Innovation Center for Advanced Organic Chemical Materials-Hubei Key Laboratory of Ferro&Piezoelectric Materials and Devices Faculty of Physical and Electronic SciencesHubei UniversityWuhan 430062China College of Physics Chongqing UniversityChongqing 401331China

Recent research shows that increasing the carrier density after orbital alignment in AZn_(2.Sb_(2. (A=Ca,Yb,Eu,Sr,Mg)Zintl compounds yields remarkable results with regard to enhanced thermoelectric ***,research on carrier density increase via doping in these compounds is *** this study,we introduced Cu into the Zn sites in YbZn_(2.Sb_(2. and examined the thermoelectric properties.

关键词： properties doping performance

Brain-inspired artificial intelligence research: A review

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Science china(Technological Sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin bao HuaNan LIU Qun zhou TianGang WU Si HUANG TieJun {2. ZhaoFei LU CeWu GONG YiHong {3. ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China department of computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

Scheduling in a Meta Search Engine by Genetic Algorithm

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Wuhan University Journal of Natural Sciences 2001年第Z1期6卷 541-546页

作者： zhang wei {2., {3. bao wen, zhou xiao yu, Huang Hui department of computer Science and engineering, Southeast University, {12. 2.0096,{13. State Key Laboratory of Software engineering, Wuhan University, Wuhan 43.072.{13. department of computer Science and {2. Southeast University Nanjing 210096 China

The meta search engines provide service to the users by dispensing the users' requests to the existing search engines. The existing search engines selected by meta search engine determine the searching quality. Because the performance of the existing search engines and the users' requests are changed dynamically, it is not favorable for the fixed search engines to optimize the holistic performance of the meta search engine. This paper applies the genetic algorithm (GA) to realize the scheduling strategy of agent manager in our meta search engine, GSE(general search engine), which can simulate the evolution process of living things more lively and more efficiently. By using GA, the combination of search engines can be optimized and hence the holistic performance of GSE can be improved dramatically.

关键词： WWW internet search engine meta search engine genetic algorithm agent

Two-dimensional materials for future information technology: status and prospects

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Science china(Information Sciences) 2024年第6期67卷 1-147页

作者： Hao QIU Zhihao yu Tiange ZHAO Qi zhang Mingsheng {2. Pei{3. LI Taotao LI wenzhong bao Yang CHAI Shula CHEN Yiqi CHEN Hui-Ming CHENG Daoxin DAI Zeng{3. DI Zhuo DONG Xidong DUAN yuhan {3. yu FU Jingshu GUO Pengwen GUO yue HAO Jun HE {12. HE Jingyi HU {13.da HU Zehua HU Xinyue HUANG Ziyang HUANG Ali IMRAN Ziqiang KONG Jia LI Qian LI {13.sheng LI Lei LIAO Bilu LIU Can LIU Chunsen LIU Guanyu LIU Kaihui LIU Li{13. LIU Sheng LIU yuan LIU Donglin LU Likuan MA {3. MIAO Zhenhua NI Jing NING Anlian PAN Tian-Ling REN Haowen SHU Litao SUN yue SUN Quanyang TAO Zi-Ao TIAN Dong WANG Hao WANG Haomin WANG Jialong WANG Junyong WANG wenhui WANG Xingjun WANG Yeliang WANG yu{13. WANG Zhenyu WANG Yao WE... School of Electronic Science and engineering Nanjing University School of Integrated Circuits Interdisciplinary Research Center for Future Intelligent Chips (Chip-X)Nanjing University School of Integrated Circuit Science and engineering Nanjing University of Posts and Telecommunications State Key Laboratory of Infrared Physics Shanghai Institute of Technical Physics Chinese Academy of Sciences School of Physics and Key Lab of Quantum Materials and Devices of the Ministry of Education School of Electronic Science & EngineeringSoutheast University College of Integrated Circuits State Key Laboratory of Silicon and Advanced Semiconductor MaterialsZhejiang University Suzhou Laboratory State Key Laboratory of Integrated Chips and Systems School of MicroelectronicsFudan University Shaoxin Laboratory department of Applied Physics The Hong Kong Polytechnic University Key Laboratory for Micro-Nano Physics and Technology of Hunan Province College of Materials Science and EngineeringHunan University College of Integrated Circuits Zhejiang University Shenzhen Geim Graphene Center Shenzhen Key Laboratory of Layered Materials for Value-Added ApplicationsTsinghua-Berkeley Shenzhen Institute & Institute of Materials ResearchTsinghua Shenzhen International Graduate SchoolTsinghua University State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and EngineeringInternational Research Center for Advanced PhotonicsZijingang CampusZhejiang University National Key Laboratory of Materials for Integrated Circuits Shanghai Institute of Microsystem and Information TechnologyChinese Academy of Sciences CAS Key Laboratory of Nanophotonic Materials and Devices & Key Laboratory of Nanodevices and Applications i-LabSuzhou Institute of Nano-Tech and Nano-Bionics (SINANO)Chinese Academy of Sciences College of Chemistry and Chemical engineering Hunan University Key Laboratory of Quantum State Construction and Manipulation (Ministry of Education) Department of PhysicsRenmin University of China School

Over the past 70 years, the semiconductor industry has undergone transformative changes,largely driven by the miniaturization of devices and the integration of innovative structures and materials. Two-dimensional(2.) materials like transition metal dichalcogenides(TMDs) and graphene are pivotal in overcoming the limitations of silicon-based technologies, offering innovative approaches in transistor design and functionality, enabling atomic-thin channel transistors and monolithic 3. integration. We review the important progress in the application of 2. materials in future information technology, focusing in particular on microelectronics and optoelectronics. We comprehensively summarize the key advancements across material production, characterization metrology, electronic devices, optoelectronic devices, and heterogeneous integration on silicon. A strategic roadmap and key challenges for the transition of 2. materials from basic research to industrial development are outlined. To facilitate such a transition, key technologies and tools dedicated to 2. materials must be developed to meet industrial standards, and the employment of AI in material growth, characterizations, and circuit design will be essential. It is time for academia to actively engage with industry to drive the next 10 years of 2. material research.

关键词： two-dimensional (2.) materials information technology production technology standardization of characterization microelectronic devices optoelectronic devices multi-functional integration

BP Network Based Users’Interest Model in Mining WWW Cache

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Wuhan University Journal of Natural Sciences 2006年第1期11卷 243-247页

作者： zhang wei-{2. {3. bao-wen zhang xiao-fang CUI Zi-{2. zhou xiao-yu department of computer Science and Technology Nanjing University o[ Posts and Telecommunications Nanjing 210003 JiangsuChina jiangsu Institute of Software Quality Nanjing 210096 JiangsuChina department of Cornputer Science and Engineering Southeast University Nanjing 210096 Jiangsu China

By analyzing the WWW Cache model, we bring forward a user-interest description method based on the fuzzy theory and user-interest inferential relations based on BP（baek propagation） neural network. By this method, the users＇ interest in the WWW cache can be described and the neural network of users＇ interest can be constructed by positive spread of interest and the negative spread of errors. This neural network can infer the users＇ interest. This model is not the simple extension of the simple interest model, but the round improvement of the model and its related algorithm.

关键词： WWW Internet Interest model neural network data mining

A Feedback-Based Web Search Engine

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A Feedback-Based Web Search Engine

The First Conference on Web Information System and Applications

作者： zhang wei-feng, {3. bao-wen, zhou xiao-yu department of computer Science and engineering. nanjing University of {13. and telecommunications , nanjing 2.0003. jiangsu. china department of computer Science and engineering. Southeast University. nanjing 210096, jiangsu. china jiangsu Institute of Software Quality. nanjing 210096 jiangsu, china The State Key Laboratory of Software engineering. Wuhan University, Wuhan 13.072, Huhei, china

＜正＞ Web search engines are very useful information service tools in the Internet. The current web search engines produce search results relating to the search terms and the actual information collected by them. Since the selections of the search results cannot allcet the future ones, they may not cover most people’s interests, In this paper, feedback information produced by the users’ aceessing lists will be represented by the rough set and can reconstruct the query string and influence the search results. And thus the search engines can provide self-adaptability.

关键词： WWW srarch engine query reconstruction feedback

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通道注意力双线性度量网络

吉林大学学报（工学版） 2024年第2期54卷 524-532页

作者：李晓旭安文娟武继杰李真张珂马占宇兰州理工大学计算机与通信学院兰州730050 华北电力大学电子与通信工程系保定071003 华北电力大学河北省电力物联网技术重点实验室保定071003 北京邮电大学人工智能学院模式识别与智能系统实验室北京100876

针对小样本图像分类任务中模型对不同类的相似图片进行度量时,由于缺少对样本局部重要特征的关注且难以捕捉相似图片间的细微差别,导致出现部分查询样本与正确类原型的分类边界较模糊的问题,提出了一种通道注意力双线性度量网络(CABMN)... 详细信息

针对小样本图像分类任务中模型对不同类的相似图片进行度量时,由于缺少对样本局部重要特征的关注且难以捕捉相似图片间的细微差别,导致出现部分查询样本与正确类原型的分类边界较模糊的问题,提出了一种通道注意力双线性度量网络(CABMN)。该网络首先增加模型对图片局部重要区域的关注度,然后利用双线性哈达玛积操作挖掘该重要区域的深层次二阶特征信息,使模型对图片局部关键区域的定位更精准。对比实验结果表明:本文提出的CABMN在各数据集上的分类性能均有提高,尤其在细粒度数据集CUB-2.0-2.11和Stanford-Cars上达到86.19%和81.51%的分类准确率。

关键词：小样本学习细粒度图像分类度量学习注意力机制哈达玛积

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CEPC Technical Design Report

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Radiation Detection Technology and Methods 2024年第1期8卷 I0003-I0016,1-1091页

作者： Waleed Abdallah Tiago Carlos Adorno de Freitas Konstantin Afanaciev Shakeel Ahmad Ijaz Ahmed xiaocong Ai Abid Aleem Wolfgang Altmannshofer Fabio Alves weiming An Rui An Daniele Paolo Anderle Stefan Antusch Yasuo Arai Andrej Arbuzov Abdesslam Arhrib Mustafa Ashry Sha Bai {2. Bai Yang Bai Vipul Bairathi Csaba Balazs Philip Bambade Yong Ban Tripamo Bandyopadhyay Shou-Shan {3. Desmond P.Barber Ayse Bat Varvara Batozskaya Subash Chandra Behera Alexander Belyaev Michele Bertucci xiao-Jun Bi {2.anjie Bi Tianjian Bian Fabrizio Bianchi Thomas Biekotter Michela Biglietti Shalva Bilanishvili Deng Binglin Denis Bodrov Anton Bogomyagkov Serge Bondarenko Stewart Boogert Maarten Boonekamp Marcello Borri Angelo Bosotti Vincent Boudry Mohammed Boukidi Igor Boyko Ivanka Bozovic Giuseppe Bozzi Jean-Claude Brient Anastasiia Budzinskaya Masroor Bukhari Vladimir Bytev Giacomo Cacciapaglia Hua Cai wenyong Cai Wujun Cai Yijian Cai Yizhou Cai {2.chen Cai Haiying Cai Huacheng Cai Lorenzo Calibbi Junsong Cang Guofu Cao Jianshe Cao Antoine Chance xuejun Chang {2.e Chang Zhe Chang Xin{2.an Chang wei Chao Auttakit Chatrabhuti Yimin Che {2.zhi Che Bin Chen Danping Chen Fuqing Chen Fusan Chen Gang Chen Guoming Chen Hua-Xing Chen Huirun Chen Jinhui Chen Ji-{2.an Chen Kai Chen Mali Chen Mingjun Chen Mingshui Chen Ning Chen Shanhong Chen Shanzhen Chen Shao-Long Chen Shaomin Chen Shiqiang Chen Tianlu Chen wei Chen Xiang Chen xiao{2. Chen Xin Chen xun Chen xurong Chen Ye Chen Ying Chen {2.kai Chen Zelin Chen Zilin Chen Gang Chen Boping Chen Chunhui Chen Hok Chuen Cheng Huajie Cheng Shan Cheng Tongguang Cheng {2.nlong Chi Pietro Chimenti wen Han Chiu Guk Cho Ming-Chung Chu xiaotong Chu Ziliang Chu Guglielmo Coloretti Andreas Crivellin Hanhua Cui xiaohao Cui Zhao{2.an Cui Brunella D'Anzi Ling-{2.n Dai Xinchen Dai xuwen Dai Antonio De Maria Nicola De Filippis Christophe De La Taille Francesca De Mori Chiara De Sio Elisa Del Core Shuangxue Deng wei-Tian Deng Zhi Deng Ziyan Deng Bhupal Dev Tang Dewen Biagio Di Micco Ran Ding Siqin Dingl Yadong Ding Haiyi Dong Jianin AGH University of Science and Technology Faculty of Physics and Applied Computer ScienceKrakow Anhui University HefeiAnhui Applied Physics department Federal Urdu University of ArtsScience and TechnologyIslamabad Banaras Hindu University Institute of ScienceVaranasi Bandirma Onyedi Eylul University Bandirma Beihang University Beijing Beijing Computational Science Research Center Beijing Beijing Conveyi LTD Beijing Beijing Glass Research Institute Co. LtdBeijing Beijing Institute of Technology Beijing Beijing Normal University Beijing Brown University ProvidenceRI Budker Institute of Nuclear Physics Novosibirsk Cadi Ayyad University Laboratory of Fundamental and Applied PhysicsPolydisciplinary FacultyMarrakech Cairo University Giza Center for Theory and Computation National Tsing Hua UniversityHsinchu Central china Normal University WuhanHubei Central South University ChangshaHunan Centro de Fisica Teorica de Particulas Universidade Tecnica de LisboaLisboa Chengdu KaitengSifang Digital Radio&TV Equipment Co. LtdChengduSichuan china academy of engineering physics MianyangSichuan china building materials academy Beijing china Center of Advanced Science and Technology Beijing china Institute of Atomic Energy Beijing china Jiliang University HangzhouZhejiang china Nuclear Instrument CO. LTDBeijing china University of Geosciences Beijing china University of Geosciences WuhanHubei china University of Mining and Technology XuzhouJiangsu china West Normal University NanchongSichuan Chongqing University Department of PhysicsChongqing Chulalongkorn University Bangkok City University of Hong Kong Department of PhysicsHong Kong Cockcroft Institute Daresbury Cornell University IthacaNY CPPM(AMU/IPhU&CNRS/INP) Marseille Dalian Minzu University DalianLiaoning Dalian University of Technology Department of PhysicsDalian Deutsches Elektronen-Synchrotron DESY Hamburg Dzhelepov Laboratory of Nuclear Problems of Joint Institute for Nuclear Research(DLNP JINR) Dubna East china University Of Science And Technol

The Circular Electron Positron Collider(CEPC)is a large scientific project initiated and hosted by china,fostered through extensive collaboration with international *** complex comprises four accelerators:a 3. GeV Linac,a 1.1 GeV Damping Ring,a Booster capable of achieving energies up to 180 GeV,and a Collider operating at varying energy modes(Z,W,H,and tt).The Linac and Damping Ring are situated on the surface,while the subterranean Booster and Collider are housed in a 100 km circumference underground tunnel,strategically accommodating future expansion with provisions for a potential Super Proton Proton Collider(SPPC).The CEPC primarily serves as a Higgs *** its baseline design with synchrotron radiation(SR)power of 3. MW per beam,it can achieve a luminosity of 5×10^(3.)cm^(-2.s^(-1)per interaction point(IP),resulting in an integrated luminosity of 13.ab^(-1)for two IPs over a decade,producing 2.6 million Higgs *** the SR power to 50 MW per beam expands the CEPC's capability to generate 4.3.million Higgs bosons,facilitating precise measurements of Higgs coupling at sub-percent levels,exceeding the precision expected from the HL-LHC by an order of *** Technical Design Report(TDR)follows the Preliminary Conceptual Design Report(Pre-CDR,2.15)and the Conceptual Design Report(CDR,2.18),comprehensively detailing the machine's layout,performance metrics,physical design and analysis,technical systems design,R&D and prototyping efforts,and associated civil engineering ***,it includes a cost estimate and a preliminary construction timeline,establishing a framework for forthcoming engineering design phase and site selection *** is anticipated to begin around 2.2.-2.2.,pending government approval,with an estimated duration of 8 *** commencement of experiments and data collection could potentially be initiated in the mid-{3.s.

关键词： initiated exceeding precise

A multiple-tissue-specific magnetic resonance imaging model for diagnosing Parkinson’s disease: a brain radiomics study

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Neural Regeneration Research 2022年第12期17卷 2743-2749页

作者： xiao-Jun Guan Tao Guo Cheng zhou Ting Gao Jing-Jing Wu Victor Han Steven Cao Hong-Jiang {2. {3.-Yao zhang Min xuan Quan-Quan Gu Pei-{3. Huang Chun-Lei Liu Jia-Li Pu bao-Rong zhang feng Cui xiao-Jun xu Min-Ming zhang department of Radiology the Second Affiliated HospitalZhejiang University School of MedicineHangzhouZhejiang ProvinceChina department of Neurology the Second Affiliated HospitalZhejiang University School of MedicineHangzhouZhejiang ProvinceChina department of Electrical engineering and {2. Sciences University of CaliforniaBerkeleyCAUSA Institute for Medical Imaging Technology School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina School of Information Science and Technology ShanghaiTech UniversityShanghaiChina Helen Wills Neuroscience Institute University of CaliforniaBerkeleyCAUSA department of Radiology Hangzhou Hospital of Traditional Chinese MedicineHangzhouZhejiang ProvinceChina

Brain radiomics can reflect the characteristics of brain ***,the value of T1-weighted images,quantitative susceptibility mapping,and R2.mapping in the diagnosis of Parkinson’s disease(PD)was underestimated in previous *** this prospective study to establish a model for PD diagnosis based on brain imaging information,we collected high-resolution T1-weighted images,R2.mapping,and quantitative susceptibility imaging data from 171 patients with PD and 179 healthy controls recruited from August 2.14 to August *** to the inclusion time,12.2.PD patients and 12. healthy controls were assigned to train the diagnostic model,while the remaining 106 subjects were assigned to the external validation *** extracted 1408 radiomics features,and then used data-driven feature selection to identify informative features that were significant for discriminating patients with PD from normal controls on the training *** informative features so identified were then used to construct a diagnostic model for *** constructed model contained {2. informative radiomics features,mainly representing abnormal subcortical iron distribution(especially in the substantia nigra),structural disorganization(e.g.,in the inferior temporal,paracentral,precuneus,insula,and precentral gyri),and texture misalignment in the subcortical nuclei(e.g.,caudate,globus pallidus,and thalamus).The predictive accuracy of the established model was 81.1±8.0%in the training *** the external validation dataset,the established model showed predictive accuracy of 78.5±2.1%.In the tests of identifying early and drug-naïve PD patients from healthy controls,the accuracies of the model constructed on the same {2. informative features were 80.{2.7.1%and 79.1±6.5%,respectively,while the accuracies were 80.4±6.{2.and 82.9±5.8%for diagnosing middle-to-late PD and those receiving drug management,*** accuracies for predicting tremor-dominant and non-tremor-dominant PD w

关键词： diagnosis imaging biomarker iron magnetic resonance imaging neuroimaging Parkinson’s disease quantitative susceptibility mapping R2.mapping radiomics T1-weighted imaging