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arXiv 2022年

作者： Xia, Lijun Lu, Liangliang Wang, Kun Jiang, Xinhe Zhu, Shining Ma, Xiaosong National Laboratory of Solid-state Microstructures School of Physics Collaborative Innovation Center of Advanced Microstructures State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing210093 China Key Laboratory of Optoelectronic Technology of Jiangsu Province School of Physical Science and Technology Nanjing Normal University Nanjing210023 China Institute for Quantum Computing Baidu Research Beijing100193 China Institute for Quantum Optics and Quantum Information Austrian Academy of Sciences Vienna1090 Austria

High-dimensional entanglement is significant for the fundamental studies of quantum physics and offers unique advantages in various quantum information processing (QIP) tasks. Integrated quantum devices have recently emerged as a promising platform for creating, processing, and detecting complex high-dimensional entangled states. A crucial step towards practical quantum technologies is to verify that these devices work reliably with an optimal strategy. In this work, we experimentally implement an optimal quantum verification strategy on a three-dimensional maximally entangled state using local projective measurements on a silicon photonic chip. A 95% confidence is achieved from 1190 copies to verify the target quantum state. The obtained scaling of infidelity as a function of the number of copies is - 0.5497±0.0002, exceeding the standard quantum limit of -0.5 with 248 standard deviations. Our results indicate that quantum state verification could serve as an efficient tool for complex quantum measurement tasks. © 2022, CC BY-NC-ND.

关键词： Quantum optics

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Collaborative representation-based locality preserving projections for image classification

Collaborative representation-based locality preserving proje...

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3rd Asian Conference on Artificial Intelligence technology, ACAIT 2019

作者： Gou, Jianping Yang, Yuanyuan Liu, Yong Yuan, Yunhao Du, Lan Yang, Hebiao School of Computer Science and Telecommunication Engineering Jiangsu University Zhenjiang Jiangsu212013 China Artificial Intelligence Key Laboratory of Sichuan Province Zigong Sichuan643000 China Department of Computer Science and Technology Yangzhou University Yangzhou Jiangsu225127 China Faculty of Information Technology Monash University Melbourne Australia

Graph embedding has attracted much more research interests in dimensionality reduction. In this study, based on collaborative representation and graph embedding, the authors propose a new linear dimensionality reduction method called collaborative representation-based locality preserving projection (CRLPP). In the CRLPP, they assume that the similar samples should have similar reconstructions by collaborative representation and the similar reconstructions should also have the similar low-dimensional representations in the projected subspace. CRLPP first reconstructs each training sample using the collaborative representation of the other remaining training samples, and then designs the graph construction of all training samples, finally establishes the objective function of graph embedding using the collaborative reconstructions and the constructed graph. The proposed CRLPP can well preserve the intrinsic geometrical and discriminant structures of high-dimensional data in low-dimensional subspace. The effectiveness of the proposed is verified on several image datasets. The experimental results show that the proposed method outperforms the state-of-art dimensionality reduction. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Clustering algorithms

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Nano-Bridge Waveguide Assisted Silicon Polarizing Beam Splitter

SSRN

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SSRN 2022年

作者： Liu, Haipeng Feng, Jijun Chen, Jian Zhou, Wenjie Bi, Qunyu Zeng, Heping School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Key Lab of Advanced Optical Manufacturing Technologies of Jiangsu Province Soochow University Suzhou215006 China Chongqing Key Laboratory of Precision Optics Chongqing Institute of East China Normal University Chongqing401120 China State Key Laboratory of Precision Spectroscopy East China Normal University Shanghai200241 China

A low-loss and wideband silicon polarizing beam splitter is demonstrated with the assistance of a nano-bridge waveguide. Transverse magnetic light can be coupled to the cross port through the nano-bridge waveguide, while the transverse electric light comes out mainly from the through port. The designed device has a coupling length of 19 µm, while an extinction ratio of 24.02 dB can be achieved at a 1565-nm wavelength of L -band. Compared with the conventional directional coupler structure, the proposed device realizes twice coupling for the transverse magnetic light, which can help to effectively improve the fabrication tolerance compatible with commercial CMOS processing. The fabricated device can achieve an extinction ratio of more than 16.64 dB with a low insertion loss, while the extinction ratio of more than 10 dB can be realized in the wavelength range from 1525 to 1610 nm. The device performance can be further improved, which would facilitate its practical applications in commercial integrated optical circuits. © 2022, The Authors. All rights reserved.

关键词： Silicon photonics

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IoT-ID: Robust IoT Device Identification Based on Feature Drift Adaptation

IoT-ID: Robust IoT Device Identification Based on Feature Dr...

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2021 IEEE Global Communications Conference (GLOBECOM)

作者： Qi Chen Yubo Song Brendan Jennings Fan Zhang Bin Xiao Shang Gao Key Laboratory of Computer Network Technology of Jiangsu Province School of Cyber Science and Engineering Southeast University Purple Mountain Laboratories Telecommunications Software & Systems Group Waterford Institute of Technology The Hong Kong Polytechnic University

ISBN: (纸本)9781728181059

Internet of Things (IoT) devices deployed in publicly accessible locations increasingly encounter security threats from device replacement and impersonation attacks. Unfortunately, the limited memory and poor computing capability on such devices make solutions involving complex algorithms or enhanced authentication protocols untenable. To address this issue, device identification technologies based on traffic characteristics finger-printing have been proposed to prevent illegal device intrusion and impersonation. However, because of time-dependent distribution of traffic characteristics, these approaches often become less accurate over time. Meanwhile insufficient attention has been paid to the impact of possible changes on the accuracy of device identification. Therefore, we propose a novel feature selection method based on degree of feature drift and genetic algorithm to keep high accuracy and stability of device identification. The degree of feature drift— relevance of features through time and gain ratio are combined as a composite metric to filter out stable features. Furthermore, in order to perform equally well in device identification, we use the genetic algorithm to select the most discriminate feature subset. Experiments show that the accuracy of device recognition compared with other methods is increased from 86.4% to 94.5%, and the robustness of recognition is also improved.

关键词： Performance evaluation Protocols Fingerprint recognition Feature extraction Robustness Classification algorithms Object recognition

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Modeling and Analysis of Pumping Cell of Nox Sensor - Part Ii: Nox Pumping Cell

SSRN

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SSRN 2022年

作者： Wang, Zhen Deng, Zhong-Hua Wang, Jie Lin, Wei-Xun Fu, Xiao-Wei Li, Xi School of Artificial Intelligence and Automation Key Laboratory of Imaging Processing and Intelligent Control Education Ministry Huazhong University of Science and Technology Hubei Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Guangdong Shenzhen518055 China Lambda Company Jiangsu Changzhou213100 China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan430081 China

To improve the performance of the NOx sensor, a combined model based on the electrochemical model and the diffusion model is developed for the NOx pumping cell of the NOx sensor. Considering that a small amount of oxygen enters the third chamber and part of NO is decomposed in the second chamber, the electrochemical model simultaneously considers the reactions of NO and oxygen at cathode, and the diffusion model additionally considers the diffusion of oxygen and the decomposition of NO. To validate the model at different gas concentrations, the sensor is mounted on the designed test rig. Then, the simulation results are compared with experimental data from the test rig and good agreement is obtained. In addition, three key parameters of the model of the NOx pumping cell are analyzed. The values and changing trends of these three parameters are given in the paper. © 2022, The Authors. All rights reserved.

关键词： Diffusion

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Research Progress of Deep Learning in Stomatology Diagnosis

Research Progress of Deep Learning in Stomatology Diagnosis

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Industrial IoT, Big Data and Supply Chain (IIoTBDSC), International Conference on

作者： Lu Jin Peng Zhuo Wei Chen Rui Zhang Shang Gao School of Computer science Jiangsu University of Science and Technology Zhenjiang China IT Center Stomatology Hospital School of Stomatology Zhejiang University School of Medicine Zhejiang Provincial Clinical Research Center for Oral Diseases Key Laboratory of Oral Biomedical Research of Zhejiang Province Cancer Center of Zhejiang University Hangzhou China

With the development of computer science and technology, artificial intelligence is becoming mature in every field of life, and the medical The combination of deep convolutional neural networks with various fields in the medical industry is also an emerging The combination of deep convolutional neural networks with various fields in the medical industry is also an emerging direction of medical research, which has made impressive achievements in diagnosis and prediction in medical imaging and pathology. The medical industry is stepping into the era of artificial intelligence. deep learning developed from artificial intelligence is changing the traditional Deep learning developed from artificial intelligence is changing the traditional medical diagnosis and treatment model and is bound to be an important development trend of future medicine. This paper reviews the development history of deep learning and the research development of deep learning in assisted diagnosis of stomatology.

关键词： Deep learning Industries Pathology Supply chains Neural networks Market research Convolutional neural networks

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Default mode network static-dynamic functional signatures in first-episode drug-naive major depressive disorder

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Comprehensive Psychoneuroendocrinology 2025年 23卷

作者： Xiaoyun Liu Kaimo Ding Fan Li Xiaotong Zhang Zhenghua Hou Yingying Yin Youyong Kong Yonggui Yuan Department of Psychosomatics and Psychiatry Zhongda Hospital School of Medicine Jiangsu Provincial Key Laboratory of Brain Science and Medicine Southeast University Nanjing China Zhenjiang Mental Health Center Jiangsu Province Zhenjiang China Lab of Image Science and Technology School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing China

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Efficient complete denture metal base design via a dental feature-driven segmentation network

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computers in Biology and Medicine 2024年 175卷 108550-108550页

作者： Li, Cheng Jin, Yaming Du, Yunhan Luo, Kaiyuan Fiorenza, Luca Chen, Hu Tian, Sukun Sun, Yuchun Center of Digital Dentistry Faculty of Prosthodontics Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials No.22 Zhongguancun South Avenue Haidian District Beijing100081 China Nanjing Profeta Intelligent Technology Co. Ltd No. 12 Mozhou East Road Jiangning District Jiangsu Province Nanjing City211111 China Department of Computer Science University of Illinois Urbana-Champaign UrbanaIL61820 United States Biomedicine Discovery Institute Monash University MelbourneVIC3800 Australia

Background and objective: Complete denture is a common restorative treatment in dental patients and the design of the core components (major connector and retentive mesh) of complete denture metal base (CDMB) is the basis of successful restoration. However, the automated design process of CDMB has become a challenging task primarily due to the complexity of manual interaction, low personalization, and low design accuracy. Methods: To solve the existing problems, we develop a computer-aided Segmentation Network-driven CDMB design framework, called CDMB-SegNet, to automatically generate personalized digital design boundaries for complete dentures of edentulous patients. Specifically, CDMB-SegNet consists of a novel upright-orientation adjustment module (UO-AM), a dental feature-driven segmentation network, and a specific boundary-optimization design module (BO-DM). UO-AM automatically identifies key points for locating spatial attitude of the three-dimensional dental model with arbitrary posture, while BO-DM can result in smoother and more personalized designs for complete denture. In addition, to achieve efficient and accurate feature extraction and segmentation of 3D edentulous models with irregular gingival tissues, the light-weight backbone network is also incorporated into CDMB-SegNet. Results: Experimental results on a large clinical dataset showed that CDMB-SegNet can achieve superior performance over the state-of-the-art methods. Quantitative evaluation (major connector/retentive mesh) showed improved Accuracy (98.54 ± 0.58 %/97.73 ± 0.92 %) and IoU (87.42 ± 5.48 %/70.42 ± 7.95 %), and reduced Maximum Symmetric Surface Distance (4.54 ± 2.06 mm/4.62 ± 1.68 mm), Average Symmetric Surface Distance (1.45 ± 0.63mm/1.28 ± 0.54 mm), Roughness Rate (6.17 ± 1.40 %/6.80 ± 1.23 %) and Vertices Number (23.22 ± 1.85/43.15 ± 2.72). Moreover, CDMB-SegNet shortened the overall design time to around 4 min, which is one tenth of the comparison methods. Conclusions: CDMB-SegNet

关键词： computer aided design

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Identifying Alzheimer's disease-associated genes related to N6-methyladenosine modification as potential biomarkers through correlation analysis: A retrospective observational study

NeuroMarkers

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NeuroMarkers 2025年第1期2卷

作者： Lin Yang Pengtao Zheng Cong Huang Chonghao Gao Xinping Pang Hui Liu Yanyu Wei Chaoyang Pang College of Computer Science Sichuan Normal University Chengdu Sichuan Province China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu Sichuan Province China School of Science Xi’an Jiaotong-Liverpool University Suzhou Jiangsu Province China National Key Laboratory of Science and Technology on Vacuum Electronics School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu Sichuan Province China

Alzheimer's disease is a prevalent neurodegenerative disorder characterized by cognitive deficits and abnormal neurological activity. Recent evidence suggests that the pathogenesis of Alzheimer's disease may involve the abnormal expression of specific genes, particularly those associated with N6-methyladenosine modification. N6-methyladenosine is a common mRNA modification that influences mRNA splicing, translation, and stability, thereby regulating physiological processes and potentially contributing to neurodegenerative diseases. Our study aimed to identify and screen genes related to Alzheimer's disease, focusing on the METTL3 gene, a key regulator of m6A modification. We used a novel weighted metrics method that combines the METTL3 gene data with gene chip data from four stages of Alzheimer's disease samples through a Spearman-Cosine correlation weighing *** utilizing a correlation coefficient matrix strategy that integrates Spearman correlation and Pinched cosine correlation, we identified nine significantly differentially expressed genes associated with Alzheimer's disease. These genes, which are highly correlated with METTL3 during the transition from normal to disease states, suggest that m6A modification regulates METTL3 expression and affects the pathogenesis of Alzheimer's disease through multiple molecular pathways. Notably, our findings reveal a profound connection between m6A modification and energy metabolism, with observable effects on mitochondrial function, lipid metabolism, and glycolytic pathways. These genes, potentially serving as biomarkers, could advance the development of therapeutic strategies for Alzheimer's disease. Our results highlight the importance of m6A modification in Alzheimer's disease and suggest that the identified genes could be used as biomarkers for early detection and monitoring of the disease. The clinical significance of our findings lies in the potential to develop targeted therapies and improve patient outcomes

关键词： Alzheimer's disease Biomarkers Correlation coefficient matrix Gene expression M6A modification

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Study on the COVID-19 epidemic in China's Mainland between November 2022 and January 2023,with prediction of its tendency

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Journal of Biosafety and Biosecurity 2023年第1期5卷 39-44页

作者： Yao Bai Zhihang Peng Fengying Wei Zhen Jin Jinjie Wang Ximing Xu Xinyan Zhang Jun Xu Zixiong Ren Bulai Lu Zhaojun Wang Jianguo Xu Senzhong Huang Xi’an Center for Disease Control and Prevention Xi’an 710054ShaanxiChina Department of Biostatistics School of Public HealthNanjing Medical UniversityNanjing 211166JiangsuChina School of Mathematics and Statistics Fuzhou UniversityFuzhou 350116FujianChina Complex Systems Research Center Key Discipline of Computer Science and Technology of“Double-First-Class”Project of Shanxi ProvinceShanxi UniversityTaiyuan 030006ShanxiChina Nankai Institute of Economics Binhai Development InstituteNankai UniversityTianjin 300071China Children’s Hospital of Chongqing Medical University Chongqing 400010China Jinzhou Center for Disease Control and Prevention Jinzhou 121000LiaoningChina School of Statistics and Data Science Nankai UniversityTianjin 300071China Suzhou Center for Disease Control and Prevention Suzhou 215004JiangsuChina State Key Laboratory of Communicable Disease Prevention and Control National Institute for Communicable Disease Control and PreventionChinese Center for Disease Control and PreventionBeijing 102206China Institute of Public Health Nankai UniversityTianjin 300071China School of Statistics and Data Science ZhiYing Research Center for Health DataNankai UniversityTianjin 300071China

The prediction system EpiSIX was used to study the COVID-19 epidemic in China's Mainland between November 2022 and January 2023,based on reported data from December 9,2022,to January 30,2023,released by The Chinese Center for Disease Control and Prevention on February 1,*** kinds of reported data were used for model fitting:the daily numbers of positive nucleic acid tests and deaths,and the daily number of hospital beds taken by COVID-19 *** was estimated that the overall infection rate was 87.54%and the overall case fatality rate was 0.078%–0.116%(median 0.100%).Assuming that a new COVID-19 epidemic outbreak would start in March or April of 2023,induced by a slightly more infectious mutant strain,we predicted a possible large rebound between September and October 2023,with a peak demand of between 800,000 and 900,000 inpatient *** no such new outbreak was induced by other variants,then the current COVID-19 epidemic course in China's Mainland would remain under control until the end of ***,it is suggested that the necessary medical resources be prepared to manage possible COVID-19 epidemic emergencies in the near future,especially for the period between September and October 2023.

关键词： COVID-19 Epidemic Infection rate Case fatality rate Prediction

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