检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Yin, Xuemei Lin, Delang Zhong, Wei Deng, Sunbin Xu, Yuming Chen, Yayi Lu, Lei Li, Guijun Kwok, Hoi Sing Li, Yi Chen, Rongsheng The School of artificial intelligence Zhuhai City Polytechnic College Zhuhai China The School of Microelectronics South China University of Technology Guangzhou China The School of Integrated Circuits Guangdong University of Technology Guangzhou China Purdue University West Lafayette United States The State Key Laboratory of Advanced Displays and Optoelectronics Technologies Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The Hong Kong University of Science and Technology Hong Kong Zhuhai Southern Integrated Circuit Design Service Center Zhuhai China Peking University China

In this paper, in-situ fluorine-doped ZnSnO (ZTO:F) thin films prepared by co-sputtering are proposed. It was found that the F had been successfully introduced into the ZTO thin films and distributed uniformly in the bulk. The corresponding top-contact bottom-gated ZTO:F thin film transistors (TFTs) were fabricated with an annealing temperature of 350 oC. The fabricated TFTs exhibit a field-effect mobility of 14.2 cm2V-1s-1, on-off ratio of over 109, and subthreshold swing (SS) as low as 87 mV/decade due to the incorporation of F. In addition, the bias stability of fabricated TFTs was also examined and the Vth shifts under negative gate bias stress are not more than ±0.1V even without any passivation. It is expected that the In-free ZTO:F amorphous oxide semiconductor and the corresponding TFTs have potential for low-cost and environmentally safe fabrication. © 2023, The Authors. All rights reserved.

关键词： Thin film transistors

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Spatial heterogeneity and organization of tumor mutation burden with immune infiltrates within tumors based on whole slide images correlated with patient survival in bladder cancer

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Journal of Pathology Informatics 2022年 13卷 100105-100105页

作者： Xu, Hongming Clemenceau, Jean René Park, Sunho Choi, Jinhwan Lee, Sung Hak Hwang, Tae Hyun School of Biomedical Engineering Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian 116024 China Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System Dalian University of Technology Dalian 116024 China Department of Artificial Intelligence and Informatics Mayo Clinic Jacksonville 32224 FL United States Department of Hospital Pathology Seoul St.Mary's Hospital College of Medicine The Catholic University of Korea Seoul 06591 South Korea

Background: High tumor mutation burden (TMB-H) could result in an increased number of neoepitopes from somatic mutations expressed by a patient's own tumor cell which can be recognized and targeted by neighboring tumor-infiltrating lymphocytes (TILs). Deeper understanding of spatial heterogeneity and organization of tumor cells and their neighboring immune infiltrates within tumors could provide new insights into tumor progression and treatment response. Methods: Here we first developed computational approaches using whole slide images (WSIs) to predict bladder cancer patients’ TMB status and TILs across tumor regions, and then investigate spatial heterogeneity and organization of regions harboring TMB-H tumor cells and TILs within tumors, as well as their prognostic utility. Results: In experiments using WSIs from The Cancer Genome Atlas (TCGA) bladder cancer (BLCA), our findings show that computational pathology can reliably predict patient-level TMB status and delineate spatial TMB heterogeneity and co-organization with TILs. TMB-H patients with low spatial heterogeneity enriched with high TILs show improved overall survival. Conclusions: Computational approaches using WSIs have the potential to provide rapid and cost-effective TMB testing and TILs detection. Survival analysis illuminates potential clinical utility of spatial heterogeneity and co-organization of TMB and TILs as a prognostic biomarker in BLCA which warrants further validation in future studies. © 2022 The Authors

关键词： Bladder cancer Computational pathology Spatial Heterogeneity Survival prognosis Tumor Immune Microenvironment Tumor mutation burden prediction

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Gvi: Guideable Visual Interpretation on Medical Tomographic Images to Improve the Performance of Deep Network

SSRN

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SSRN 2024年

作者： Liu, Hui Wei, Fan Yan, Lixin Wang, Sushan Jia, Chongfu Zhang, Lina Peng, Jiansheng Xu, Yi School of Control Science and Engineering Dalian University of Technology Dalian China Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System Dalian China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China Department of Cardiovascular Radiology The First Affiliated Hospital of Dalian Medical University Dalian China Department of Radiology The First Affiliated Hospital of Dalian Medical University Dalian China Department of Artificial Intelligence and Manufacturing Hechi University Hechi China

In medical image analysis, the demand for interpretable deep neural networks is rapidly growing. However, a major challenge is that most existing interpretative methods are applied after training, leading to a lack of integration with the model's learning process. As a result, these methods often fail to highlight regions within complex medical images critical for decision-making, such as abnormal tissues or lesions, which are essential for accurate diagnoses and treatment planning. This paper introduces Guided Visual Interpretation (GVI), a framework designed to enhance both the performance and interpretability of deep networks. Building on a deep network model with image-level labels, GVI incorporates a small amount of pixel-level annotations combined with attention mechanisms. These mechanisms facilitate visual interpretation through forward propagation, directing the model's focus to the most relevant regions. By aligning the network's decision-making with human cognitive processes, GVI improves interpretability. In our study, an attention layer was added after the convolutional layers of a pre-trained classification network. GVI is trained using a hybrid supervision approach that integrates pixel-level annotations with a large amount of image-level data. Experimental results on both private and public datasets show that GVI generates visual explanations consistent with human decision-making principles and achieves superior classification accuracy compared to traditional methods. These findings highlight GVI's potential to improve interpretability and diagnostic performance in critical fields like medical imaging. © 2024, The Authors. All rights reserved.

关键词： Medical imaging

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Ensembledeep Clustering Analysis for Time Window Determination of Event-Related Potentials

SSRN

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SSRN 2022年

作者： Mahini, Reza Li, Fan Zarei, Mahdi Nandi, Asoke K. Hämäläinen, Timo Cong, Fengyu Faculty of Information Technology University of Jyvaskyla Jyvaskyla Finland School of Biomedical Engineering Faculty of Electronic and Electrical Engineering Dalian University of Technology China Department of Electronic and Electrical Engineering Brunel University London UxbridgeUB8 3PH United Kingdom School of Artificial Intelligence Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China Key Laboratory of Integrated Circuit and Biomedical Electronic System Dalian University of Technology Liaoning Province Dalian China Department of Bioengineering and Therapeutic Sciences Programs in Biological Sciences and Human Genetics University of California San FranciscoCA94158 United States

Objective: Cluster analysis of spatio-temporal event-related potential (ERP) data is a promising tool for exploring the measurement time window of ERPs. However, even after preprocessing, the remaining noise can result in uncertain cluster maps followed by unreliable time windows while clustering via conventional clustering ***: We designed an ensemble deep clustering pipeline to determine a reliable time window for the ERP of interest from temporal concatenated grand average ERP data. The proposed pipeline includes semi-supervised deep clustering methods initialized by consensus clustering and unsupervised deep clustering methods with end-to-end architectures. Ensemble clustering from those deep clusterings was used by the designed adaptive time window determination to estimate the time ***: After applying simulated and real ERP data, our method successfully obtained the time window for identifying the P3 components (as the interest of both ERP studies) while additional noise (e.g., adding 20dB to -5dB white Gaussian noise) was added to the prepared ***: Compared to the state-of-the-art clustering method, a superior clustering performance was yielded in terms of temporal and spatial properties of time windows from both ERP data. Furthermore, more stable and accurate time windows were elicited as the noise ***: Our study provides a complementary understanding of identifying the cognitive process using deep clustering analysis to the existing studies. Our finding suggests that deep clustering can be used to identify the ERP of interest when the data is imperfect. © 2022, The Authors. All rights reserved.

关键词： Cluster analysis

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Multi-GNSS simulation and its positioning performance for static and dynamic users

Multi-GNSS simulation and its positioning performance for st...

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International Symposium on Autonomous Systems (ISAS)

作者： Kun Li Mengzhi Gao Shuaiyong Zheng Xuanwen Wang Xiaoqin Jin Peng Yang Jianlei Yang Jing Lu Gaole Yao School of Integrated Circuit Science and Engineering Tianjin University of Technology Tianjin China College of Artificial Intelligence Nankai University Tianjin China School of Computer Science and Engineering Tianjin University of Technology Tianjin China State Key Laboratory of Satellite Navigation System and Equipment Technology The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang China Land Satellite Remote Sensing Application Center Ministry of Natural Resources Beijing China Tianjin AtoN Division The Navigation Guarantee Centre of North China Sea Tianjin China

Global navigation satellite system plays an important role in military and civil fields, providing global positioning, navigation and timing services in real time. Unfortunately, the services provided by single constellation or double constellations cannot meet the requirements of high-level navigation, and there are still some problems such as low positioning accuracy and low number of visible satellites at specific circumstances to be solved. In this paper, multi-GNSSs are constructed and their global positioning performance is analyzed with respect to static and dynamic users. Results show that with more constellations, the position dilution of precision is decreased, and the number of visible satellites and the positioning accuracy are improved, which reveals that compared with single constellation or double constellations, multi-GNSSs can obviously improve the positioning performance. The methodology proposed can serve as a technical reference for national integrated PNT systems.

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Diverse Responses in Lattice Thermal Conductivity of n-type/p-type Semiconductors Driven by Asymmetric Electron-Phonon Interactions

arXiv

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arXiv 2024年

作者： Sun, Jianshi Li, Shouhang Tong, Zhen Shao, Cheng Xie, Han An, Meng Zhang, Chuang Zhu, Xiongfei Huang, Chen Xiong, Yucheng Liu, Xiangjun Institute of Micro/Nano Electromechanical System and Integrated Circuit College of Mechanical Engineering Donghua University Shanghai201620 China School of Advanced Energy Sun Yat-Sen University Shenzhen518107 China Thermal Science Research Center Shandong Institute of Advanced Technology Shandong Jinan250103 China Artificial Intelligence Lab SAIC Motor Corporation Shanghai200433 China School of Energy and Materials Shanghai Polytechnic University Shanghai201209 China Department of Mechanical Engineering The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-8656 Japan

Accurately assessing the impact of electron-phonon interaction (EPI) on the lattice thermal conductivity of semiconductors is crucial for the thermal management of electronic devices and a unified physical understanding of this issue is highly desired. In this work, we predict the lattice thermal conductivities of typical direct and indirect bandgap semiconductors accounting for EPI based on mode-level first-principles calculations. It is found that EPI has a larger effect on the lattice thermal conductivity of p-type doping compared to n-type doping in the same semiconductor at high charge carrier concentrations. The stronger EPI in p-type doping is attributed to the relatively higher electron density of states caused by the relatively larger p-orbital component. Furthermore, EPI has a stronger influence on the lattice thermal conductivity of n-type indirect bandgap semiconductors than n-type direct bandgap semiconductors. This is attributed to the relatively lower electron density of states in direct bandgap semiconductors stemming from the s-orbital component. This work reveals that there exist diverse responses in lattice thermal conductivity of n-type/p-type semiconductors, which can be attributed to asymmetric EPIs. Copyright © 2024, The Authors. All rights reserved.

关键词： Electron-phonon interactions

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Seizure detection using dynamic memristor-based reservoir computing and leaky integrate-and-fire neuron for post-processing

APL Machine Learning

引用

APL Machine Learning 2023年第4期1卷

作者： Zhiyu Yang Keqin Liu Rui Yuan Xulei Wu Lei Cai Teng Zhang Yaoyu Tao Yufeng Jin Yuchao Yang School of Electronic and Computer Engineering Peking University Shenzhen 518055 China Beijing Advanced Innovation Center for Integrated Circuit School of Integrated Circuits Peking University Beijing 100871 China Center for Brain Inspired Chips Institute for Artificial Intelligence Frontiers Science Center for Nano-Optoelectronics Peking University Beijing 100871 China Center for Brain Inspired Intelligence Chinese Institute for Brain Research (CIBR) Beijing Beijing 102206 China

Epilepsy is a prevalent neurological disorder, rendering the development of automated seizure detection systems imperative. While complex machine learning models are powerful, their training and hardware deployment remain challenging. The reservoir computing system offers a low-cost solution in terms of both hardware requirements and training. In this paper, we introduce a compact reservoir computing system for seizure detection, based on the α-In2Se3 dynamic memristors. Leaky integrate-and-fire neurons are used for post-processing the output of the system, and experimental results indicate their effectiveness in suppressing erroneous outputs, where both accuracy and specificity are enhanced by over 2.5%. The optimized compact reservoir system achieves 96.40% accuracy, 86.34% sensitivity, and 96.56% specificity in seizure detection tasks. This work demonstrates the feasibility of using reservoir computing for seizure detection and shows its potential for future application in extreme edge devices.

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Nonreciprocal transport in a bilayer of MnBi2Te4 and Pt

arXiv

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arXiv 2022年

作者： Ye, Chen Xie, Xiangnan Lv, Wenxing Huang, Ke Yang, Allen Jian Jiang, Sicong Liu, Xue Zhu, Dapeng Qiu, Xuepeng Tong, Mingyu Zhou, Tong Hsu, Chuang-Han Chang, Guoqing Lin, Hsin Li, Peisen Yang, Kesong Wang, Zhenyu Jiang, Tian Wang, Xiao Renshaw Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link 637371 Singapore State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Physics Laboratory Industrial Training Center Shenzhen Polytechnic Guangdong Shenzhen518055 China Department of NanoEngineering Program of Chemical Engineering University of California La Jolla San DiegoCA92093-0448 United States Program of Materials Science and Engineering University of California La Jolla San DiegoCA92093-0418 United States Institutes of Physical Science and Information Technology Anhui University Hefei230601 China Fert Beijing Institute MIIT Key Laboratory of Spintronics School of Integrated Circuit Science and Engineering Beihang University Beijing100191 China Beihang-Goertek Joint Microelectronics Institute Qingdao Research Institute Beihang University Qingdao266000 China Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology School of Physics Science and Engineering Tongji University Shanghai200092 China College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha410073 China Insitute of Physics Academia Sinica Taipei115229 Taiwan College of Intelligence Science and Technology National University of Defense Technology Changsha410073 China National Innovation Institute of Defense Technology Academy of Military Sciences PLA China Beijing100010 China Beijing Academy of Quantum Information Sciences Beijing100084 China Beijing Institute for Advanced Study National University of Defense Technology Changsha410073 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Ave 639798 Singapore

MnBi2Te4 (MBT) is the first intrinsic magnetic topological insulator with the interaction of spin-momentum locked surface electrons and intrinsic magnetism, and it exhibits novel magnetic and topological phenomena. Recent studies suggested that the interaction of electrons and magnetism can be affected by the Mn-doped Bi2Te3 phase at the surface due to inevitable structural defects. Here we report an observation of nonreciprocal transport, i.e. current-direction-dependent resistance, in a bilayer composed of antiferromagnetic MBT and nonmagnetic Pt. The emergence of the nonreciprocal response below the Néel temperature confirms a correlation between nonreciprocity and intrinsic magnetism in the surface state of MBT. The angular dependence of the nonreciprocal transport indicates that nonreciprocal response originates from the asymmetry scattering of electrons at the surface of MBT mediated by magnon. Our work provides an insight into nonreciprocity arising from the correlation between magnetism and Dirac surface electrons in intrinsic magnetic topological insulators. © 2022, CC BY.

关键词： Magnetism

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One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction

arXiv

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arXiv 2023年

作者： Wang, Zi Yu, Xiaotong Wang, Chengyan Chen, Weibo Wang, Jiazheng Chu, Ying-Hua Sun, Hongwei Li, Rushuai Li, Peiyong Yang, Fan Han, Haiwei Kang, Taishan Lin, Jianzhong Yang, Chen Chang, Shufu Shi, Zhang Hua, Sha Li, Yan Hu, Juan Zhu, Liuhong Zhou, Jianjun Lin, Meijing Guo, Jiefeng Cai, Congbo Chen, Zhong Guo, Di Yang, Guang Qu, Xiaobo Department of Electronic Science Intelligent Medical Imaging R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University China Human Phenome Institute Fudan University China Philips Healthcare China Siemens Healthineers Ltd. China United Imaging Research Institute of Intelligent Imaging China Department of Nuclear Medicine Nanjing First Hospital China Shandong Aoxin Medical Technology Company China Department of Radiology The First Affiliated Hospital of Xiamen University China Department of Radiology Zhongshan Hospital Affiliated to Xiamen University China China Department of Cardiology Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University China Department of Radiology Zhongshan Hospital Fudan University China Department of Cardiovascular Medicine Heart Failure Center Ruijin Hospital Lu Wan Branch Shanghai Jiaotong University School of Medicine China Department of Radiology Ruijin Hospital Shanghai Jiaotong University School of Medicine China Medical Imaging Department The First Affiliated Hospital of Kunming Medical University China Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence China Department of Applied Marine Physics and Engineering Xiamen University China Department of Microelectronics and Integrated Circuit Xiamen University China School of Computer and Information Engineering Xiamen University of Technology China Department of Bioengineering Imperial College London United Kingdom

Magnetic resonance imaging (MRI) is a widely used radiological modality renowned for its radiation-free, comprehensive insights into the human body, facilitating medical diagnoses. However, the drawback of prolonged scan times hinders its accessibility. The k-space undersampling offers a solution, yet the resultant artifacts necessitate meticulous removal during image reconstruction. Although Deep Learning (DL) has proven effective for fast MRI image reconstruction, its broader applicability across various imaging scenarios has been constrained. Challenges include the high cost and privacy restrictions associated with acquiring large-scale, diverse training data, coupled with the inherent difficulty of addressing mismatches between training and target data in existing DL methodologies. Here, we present a novel Physics-Informed Synthetic data learning framework for Fast MRI, called PISF. PISF marks a breakthrough by enabling generalized DL for multi-scenario MRI reconstruction through a single trained model. Our approach separates the reconstruction of a 2D image into many 1D basic problems, commencing with 1D data synthesis to facilitate generalization. We demonstrate that training DL models on synthetic data, coupled with enhanced learning techniques, yields in vivo MRI reconstructions comparable to or surpassing those of models trained on matched realistic datasets, reducing the reliance on real-world MRI data by up to 96%. Additionally, PISF exhibits remarkable generalizability across multiple vendors and imaging centers. Its adaptability to diverse patient populations has been validated through evaluations by ten experienced medical professionals. PISF presents a feasible and cost-effective way to significantly boost the widespread adoption of DL in various fast MRI applications. Importantly, it alleviates the ethical and practical challenges associated with in vivo human data acquisitions, opening new horizons for advancing fast MRI technologies. Copyright © 20

关键词： Magnetic resonance imaging

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Correction: Optimal Number of Clusters by Measuring Similarity Among Topographies for Spatio-Temporal ERP Analysis

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Brain topography 2022年第5-6期35卷 558页

作者： Reza Mahini Peng Xu Guoliang Chen Yansong Li Weiyan Ding Lei Zhang Nauman Khalid Qureshi Timo Hämäläinen Asoke K Nandi Fengyu Cong School of Biomedical Engineering Faculty of Electronic and Electrical Engineering Dalian University of Technology Dalian China. Faculty of Information Technology University of Jyvaskyla Jyvaskyla Finland. Department of Psychiatry Chinese PLA 967Th Hospital LieNing Street Courtyard Dalian China. Reward Competition and Social Neuroscience Lab Department of Psychology School of Social and Behavioral Sciences Nanjing University Nanjing China. Institute for Brain Sciences Nanjing University Nanjing China. Department of Architecture ETH Zurich Zurich Switzerland. Department of Electronic and Electrical Engineering Brunel University London UB8 3PH Uxbridge UK. asoke.nandi@brunel.ac.uk. School of Artificial Intelligence Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China. Key Laboratory of Integrated Circuit and Biomedical Electronic System Dalian University of Technology Dalian Liaoning Province China.

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