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Brain‐X 2023年第4期1卷

作者： Lianjie Zhou Zhongyuan Wu Mubai Sun Jaejin Park Mengdi Han Ming Wang Junsheng Yu Zengfeng Di Yongfeng Mei Wubin Bai Xinge Yu Ki Jun Yu Enming Song Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception Institute of Optoelectronics Fudan University Shanghai China Contribution: Investigation Visualization Writing - original draft Institute of Agro-food Technology Jilin Academy of Agricultural Sciences Changchun Jilin China Contribution: Data curation Investigation Functional Bio-integrated Electronics and Energy Management Lab School of Electrical and Electronic Engineering Yonsei University Seoul Korea Department of Biomedical Engineering College of Future Technology Peking University Beijing China Contribution: Resources Validation State Key Laboratory of Integrated Chips and Systems Frontier Institute of Chip and System Fudan University Shanghai China State Key Laboratory of Electronic Thin Films and Integrated Devices School of Optoelectronic Science and Engineering University of Electronic Science and Technology of China (UESTC) Chengdu Sichuan China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China Department of Material Science Fudan University Shanghai China Department of Applied Physical Sciences University of North Carolina at Chapel Hill Chapel Hill North Carolina USA Department of Biomedical Engineering City University of Hong Kong Hong Kong SAR China Wubin Bai Xinge Yu Ki Jun Yu and Enming Song. Contribution: Conceptualization Methodology Resources Supervision Writing - review & editing

Advanced technologies that can establish intimate, long-lived functional interfaces with neural systems have attracted increasing interest due to their wide-ranging applications in neuroscience, bioelectronic medicine, and the associated treatment of neurodegenerative diseases. A critical challenge of significance remains in the development of electronic platforms that offer conformal contact with soft brain tissue for the sensing or stimulation of brain activities and chronically stable operation in vivo, at scales that range from cellular-level resolution to macroscopic areas. This review summarizes recent advances in this field, with an emphasis on the use of demonstrated concepts, constituent materials, engineered designs, and system integration to address the current challenges. The article begins with an overview of recent bioelectronic platforms with unique form factors, ranging from filamentary probes to conformal sheets and three-dimensional frameworks for alleviating the mechanical mismatch between interface materials and neural tissues. Next, active interfaces which utilize inorganic/organic semiconductor-enabled devices are reviewed, highlighting various working principles of recording mechanisms including capacitively and conductively coupled sensing enabled by high transistor matrices at high spatiotemporal resolution. The subsequent section presents approaches to biological integration which use active materials for multiplexed addressing, local amplification and multimodal operation with high-channel-count and large-scale electronic systems in a safe fashion that provides multi-decade stable performance in both animal models and human subjects. The advances summarized in this review will guide the future direction of this technology and provide a basis for next-generation chronic neural interfaces with long-lived high-performance operation.

关键词： neural interface flexibility passive electrodes active recording barrier materials chronic stability

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Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends

MedComm

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MedComm 2025年第6期6卷 e70247页

作者： Ruoyun Wang Fanxuan Chen Haoman Chen Chenxing Lin Jincen Shuai Yutong Wu Lixiang Ma Xiaoqu Hu Min Wu Jin Wang Qi Zhao Jianwei Shuai Jingye Pan Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine Vision and Brain Health) Wenzhou Institute University of Chinese Academy of Sciences Wenzhou China. Wenzhou Medical University Wenzhou China. UCSC Baskin School of Engineering University of California Santa Cruz California USA. Department of Anatomy Histology & Embryology School of Basic Medical Sciences Fudan University Shanghai China. Department of Medicine Harvard Medical School and Brigham and Women's Hospital Boston Massachusetts USA. Joint Research Centre on Medicine The Affiliated Xiangshan Hospital of Wenzhou Medical University Ningbo China. Stony Brook University Stony Brook New York USA. School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China. Key Laboratory of Intelligent Treatment and Life Support for Critical Diseases of Zhejiang Province Wenzhou China. Department of Big Data in Health Science The First Affiliated Hospital of Wenzhou Medical University Wenzhou China. Zhejiang Engineering Research Center for Hospital Emergency and Process Digitization Wenzhou China.

The high-resolution three-dimensional (3D) images generated with digital breast tomosynthesis (DBT) in the screening of breast cancer offer new possibilities for early disease diagnosis. Early detection is especially important as the incidence of breast cancer increases. However, DBT also presents challenges in terms of poorer results for dense breasts, increased false positive rates, slightly higher radiation doses, and increased reading times. Deep learning (DL) has been shown to effectively increase the processing efficiency and diagnostic accuracy of DBT images. This article reviews the application and outlook of DL in DBT-based breast cancer screening. First, the fundamentals and challenges of DBT technology are introduced. The applications of DL in DBT are then grouped into three categories: diagnostic classification of breast diseases, lesion segmentation and detection, and medical image generation. Additionally, the current public databases for mammography are summarized in detail. Finally, this paper analyzes the main challenges in the application of DL techniques in DBT, such as the lack of public datasets and model training issues, and proposes possible directions for future research, including large language models, multisource domain transfer, and data augmentation, to encourage innovative applications of DL in medical imaging.

关键词： deep learning digital breast tomosynthesis early breast cancer screening medical image analysis public database

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Fluorine-Containing Passivation Layer via Surface Chelation for Inorganic Perovskite Solar Cells

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Angewandte Chemie 2022年第6期135卷

作者： Hao Zhang Prof. Wanchun Xiang Xuejiao Zuo Xiaojing Gu Shiang Zhang Yachao Du Zhiteng Wang Yali Liu Haifeng Wu Peijun Wang Qingyue Cui Hang Su Prof. Qingwen Tian Prof. Shengzhong (Frank) Liu Key Laboratory of Applied Surface and Colloid Chemistry Ministry of Education Shaanxi Key Laboratory for Advanced Energy Devices Shaanxi Engineering Lab for Advanced Energy Technology School of Materials Science and Engineering Shaanxi Normal University No. 620 West Chang'an Avenue Xi'an 710119 P. R. China Contribution: Data curation (lead) Formal analysis (lead) Investigation (lead) Writing - original draft (supporting) Contribution: Conceptualization (lead) Data curation (lead) Writing - original draft (lead) Contribution: Formal analysis (supporting) Investigation (supporting) Contribution: Data curation (supporting) Formal analysis (equal) Investigation (equal) Contribution: Data curation (supporting) Investigation (supporting) Contribution: Data curation (supporting) Formal analysis (supporting) State Key Laboratory of Organic-Inorganic Composites Key Lab of Biomedical Materials of Natural Macromolecules Ministry of Education Beijing Laboratory of Biomedical Materials Beijing University of Chemical Technology No. 15 East Road North Third Ring Chaoyang District Beijing 100029 P. R. China Contribution: Conceptualization (lead) Data curation (lead) Funding acquisition (lead) Project administration (lead) Supervision (lead) Validation (lead) Visualization (lead) Writing - review & editing (lead) Dalian National Laboratory for Clean Energy iChEM Dalian Institute of Chemical Physics Chinese Academy of Sciences No. 457 Zhongshan Road Dalian Liaoning 116023 P. R. China Contribution: Project administration (lead) Supervision (lead) Validation (lead) Writing - review & editing (equal)

Minimizing surface defect is vital to further improve power conversion efficiency (PCE) and stability of inorganic perovskite solar cells (PSCs). Herein, we designed a passivator trifluoroacetamidine (TFA) to suppress CsPbI 3− x Br x film defects. The amidine group of TFA can strongly chelate onto the perovskite surface to suppress the iodide vacancy, strengthened by additional hydrogen bonds. Moreover, three fluorine atoms allow strong intermolecular connection via intermolecular hydrogen bonds, thus constructing a robust shield against moisture. The TFA-treated PSCs exhibit remarkably suppressed recombination, yielding the record PCEs of 21.35 % and 17.21 % for 0.09 cm 2 and 1.0 cm 2 device areas, both of which are the highest for all-inorganic PSCs so far. The device also achieves a PCE of 39.78 % under indoor illumination, the highest for all-inorganic indoor photovoltaic devices. Furthermore, TFA greatly improves device ambient stability by preserving 93 % of the initial PCE after 960 h.

关键词： Chelation Defect Passivation Inorganic Perovskite Solar Cells

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Advanced Variations of Two-Dimensional Principal Component Analysis for Face Recognition

arXiv

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arXiv 2019年

作者： Zhao, Meixiang Jia, Zhigang Cai, Yunfeng Chen, Xiao Gong, Dunwei School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China School of Mathematics and Statistics Jiangsu Key Laboratory of Education Big Data Science and Engineering Jiangsu Normal University Xuzhou221116 China Cognitive Computing Lab Baidu Research Beijing100193 China

The two-dimensional principal component analysis (2DPCA) has become one of the most powerful tools of artificial intelligent algorithms. In this paper, we review 2DPCA and its variations, and propose a general ridge regression model to extract features from both row and column directions. To enhance the generalization ability of extracted features, a novel relaxed 2DPCA (R2DPCA) is proposed with a new ridge regression model. R2DPCA generates a weighting vector with utilizing the label information, and maximizes a relaxed criterion with applying an optimal algorithm to get the essential features. The R2DPCA-based approaches for face recognition and image reconstruction are also proposed and the selected principle components are weighted to enhance the role of main components. Numerical experiments on well-known standard databases indicate that R2DPCA has high generalization ability and can achieve a higher recognition rate than the state-of-the-art methods, including in the deep learning methods such as CNNs, DBNs, and DNNs. Copyright © 2019, The Authors. All rights reserved.

关键词： Face recognition

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Towards Multi-Pose Guided Virtual Try-On Network

Towards Multi-Pose Guided Virtual Try-On Network

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International Conference on Computer Vision (ICCV)

作者： Haoye Dong Xiaodan Liang Xiaohui Shen Bochao Wang Hanjiang Lai Jia Zhu Zhiting Hu Jian Yin School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P.R.China School of Intelligent Systems Engineering Sun Yat-sen University ByteDance AI Lab. School of Computer Science South China Normal University Guangzhou Key Laboratory of Big Data and Intelligent Education Carnegie Mellon University

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Virtual try-on systems under arbitrary human poses have significant application potential, yet also raise extensive challenges, such as self-occlusions, heavy misalignment among different poses, and complex clothes textures. Existing virtual try-on methods can only transfer clothes given a fixed human pose, and still show unsatisfactory performances, often failing to preserve person identity or texture details, and with limited pose diversity. This paper makes the first attempt towards a multi-pose guided virtual try-on system, which enables clothes to transfer onto a person with diverse poses. Given an input person image, a desired clothes image, and a desired pose, the proposed Multi-pose Guided Virtual Try-On Network (MG-VTON) generates a new person image after fitting the desired clothes into the person and manipulating the pose. MG-VTON is constructed with three stages: 1) a conditional human parsing network is proposed that matches both the desired pose and the desired clothes shape; 2) a deep Warping Generative Adversarial Network (Warp-GAN) that warps the desired clothes appearance into the synthesized human parsing map and alleviates the misalignment problem between the input human pose and the desired one; 3) a refinement render network recovers the texture details of clothes and removes artifacts, based on multi-pose composition masks. Extensive experiments on commonly-used datasets and our newly-collected largest virtual try-on benchmark demonstrate that our MG-VTON significantly outperforms all state-of-the-art methods both qualitatively and quantitatively, showing promising virtual try-on performances.

关键词： Shape Three-dimensional displays Generators Hair Task analysis Face Gallium nitride

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Matching Biomedical Ontologies Through Compact Evolutionary Simulated Annealing Algorithm 12th

Matching Biomedical Ontologies Through Compact Evolutionary ...

引用

12th International Conference on Genetic and Evolutionary Computing, ICGEC 2018

作者： Xue, Xingsi Chen, Jie Liu, Jianhua Chen, Dongxu College of Information Science and Engineering Fujian University of Technology Fuzhou Fujian350118 China Intelligent Information Processing Research Center Fujian University of Technology Fuzhou Fujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou Fujian350118 China Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology Fujian350118 China Fujian Medical University Union Hospital Fuzhou Fujian350001 China

ISBN: (纸本)9789811358401

To overcome biomedical ontology’s heterogeneous problem, biomedical ontology matchers are developed to search for bridges of knowledge (or alignment) between heterogeneous biomedical ontologies. Since it is a complex problem, Evolutionary Algorithm (EA) can present a good methodology for matching biomedical ontologies. To improve the efficiency, in this paper, a Compact Evolutionary Simulated Annealing algorithm (CESA) is proposed to match the biomedical ontologies. In particular, CESA utilizes a Probability Vector (PV) to simulate the behavior of population-based EA, and introduces the Simulated Annealing algorithm (SA) as a local search in each generation. The experiment is conducted on the Large Biomed track provided by the Ontology Alignment Evaluation Initiative (OAEI), and the experimental results show the effectiveness of our proposal. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Simulated annealing

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arXiv

引用

arXiv 2019年

作者： Shen, Zhihao Du, Wan Zhao, Xi Zou, Jianhua School of Electronic and Information Engineering Xi'an Jiaotong University Shaanxi Engineering Research Center of Medical and Health Big Data Xi'an710049 China Department of Computer Science and Engineering University of California MercedCA95340 United States School of Management Xi'an Jiaotong University Key Lab of the Ministry of Education for Process Control & Efficiency Egineering Xi'an710049 China

Retrieving similar trajectories from a large trajectory dataset is important for a variety of applications, like transportation planning and mobility analysis. Unlike previous works based on fine-grained GPS trajectories, this paper investigates the feasibility of identifying similar trajectories from cellular data observed by mobile infrastructure, which provide more comprehensive coverage. To handle the large localization errors and low sample rates of cellular data, we develop a holistic system, cellSim, which seamlessly integrates map matching and similar trajectory search. A set of map matching techniques are proposed to transform cell tower sequences into moving trajectories on a road map by considering the unique features of cellular data, like the dynamic density of cell towers and bidirectional roads. To further improve the accuracy of similarity search, map matching outputs M trajectory candidates of different confidence, and a new similarity measure scheme is developed to process the map matching results. Meanwhile, M is dynamically adapted to maintain a low false positive rate of the similarity search, and two pruning schemes are proposed to minimize the computation overhead. Extensive experiments on a dataset of 3,186,000 mobile users and real-world trajectories of 1701 km reveal that cellSim provides high accuracy (precision 62.4% and recall of 89.8%). Copyright © 2019, The Authors. All rights reserved.

关键词： Trajectories

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Simulation and modeling of a solidly mounted resonator 1st

Simulation and modeling of a solidly mounted resonator

引用

1st International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2017

作者： Meng, Sheng-Hui Huang, An-Chi Chen, Ying-Chung Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China Department of Electrical Engineering National Sun Yat-Sen University Kaohsiung Taiwan

ISBN: (纸本)9783319707297

Solidly mounted resonators (SMRs) have a stable structure, high frequency range, and good process yield, and thus are increasingly being used in various applications, including radiofrequency (RF) communication devices. In this study, we simulated SMR structures and thin films of the piezoelectric materials AlN and ZnO using the commonly used RF design program Advanced Design System. © Springer International Publishing AG 2018.

关键词： Zinc oxide

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Effects of annotation granularity in deep learning models for histopathological images

arXiv

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arXiv 2020年

作者： Shi, Jiangbo Gao, Zeyu Zhang, Haichuan Puttapirat, Pargorn Wang, Chunbao Zhang, Xiangrong Li, Chen National Engineering Lab for Big Data Analytics Xi'an Jiao tong University Xi'an Shaanxi710049 China School of Electronic and Information Engineering Xi'an Jiao tong University Xi'an Shaanxi710049 China Shaan xi Province Key Laboratory of Satellite and Terrestrial Network Tech.R&D Xi'an Jiao tong University Xi'an Shaanxi710049 China Department of Pathology First Affiliated Hospital of Xi'an Jiaotong University Xi'an Shaanxi710061 China Institute of Intelligent Information Processing Xidian University Xi'an Shaanxi710071 China

Pathological examination is an important step in cancer diagnosis. Pathologists make diagnosis and pathology report based on observed cell and tissue structure on pathological slides. With the development of statistical machine learning, especially deep learning, automated classifiers are being used to analyze histopathological slides and assist pathologists in diagnosis. Currently, commonly used annotation methods in histopathological slides include image-wise, bounding box, ellipse-wise, pixel-wise. In order to verify the influence of annotation in pathological slide on deep learning model, we design corresponding experiments to test the performance based on annotations with different granularity annotation. In classification, all state-of-the-art deep learning based classifiers perform better when they are trained by the dataset with pixel-wise annotation. On average, precision, recall and F1-score improves by 7.87%, 8.83% and 7.85% respectively. Thus, it is suggested that finer annotations are better utilized by deep learning algorithms in classification tasks. Similarly, semantic segmentation algorithms can achieve 8.33% better segmentation accuracy when trained by pixel-wise annotations. Our study shows that finer-grained annotation can not only improve the performance of deep learning models, but help deep learning model extract more accurate phenotypic information from histopathological slides. An accurate acquisitions of phenotypic information can help pathologists to enquire the model based on which regions and features in the slide were mainly used to calculate the prediction, improve the reliability of the model prediction. The compartmentalized prediction approach similar to this work may contribute to phenotype and genotype association studies. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

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Circularly and Linearly Polarized Luminescence from AIE Luminogens Induced by Super-Aligned Assemblies of Sub-1 nm Nanowires

引用

Angewandte Chemie 2022年第34期134卷

作者： Shujian Rong Prof. Wenxiong Shi Dr. Simin Zhang Prof. Dr. Xun Wang Key Lab of Organic Optoelectronics and Molecular Engineering Department of Chemistry Tsinghua University Beijing 100084 China Contribution: Investigation (lead) Methodology (lead) Writing - original draft (lead) Institute for New Energy Materials and Low Carbon Technologies School of Materials Science and Engineering Tianjin University of Technology Tianjin 300387 China Contribution: Data curation (lead) Formal analysis (lead) Software (lead) Contribution: Conceptualization (equal) Formal analysis (lead) Methodology (lead) Writing - review & editing (equal) Contribution: Conceptualization (lead) Data curation (lead) Formal analysis (lead) Funding acquisition (lead) Investigation (lead) Methodology (lead) Project administration (lead) Resources (lead) Supervision (lead) Validation (lead) Writing - review & editing (lead)

Sub-1 nm nanowires (SNWs) combine the properties of inorganic materials and polymers. They can be highly oriented through assembly, and can also be easily processed. Meanwhile, aggregation-induced emission luminogens (AIEgens) show high potential for optical applications, but they are usually hard to process. The combination of SNWs and AIEgens can enrich both of their applications. In this study, we report that the fluorescence emission intensity of the AIEgens–SNW dispersion is dramatically enhanced due to the flexibility of SNWs. Furthermore, we fabricate two kinds of functional films with circularly polarized luminescence (CPL) and linearly polarized luminescence (LPL) activities. The construction of CPL materials didn't require any chiral chemicals. The construction of LPL materials didn't require an additional stretching process. As a result, we endowed common achiral AIEgens with a high dissymmetry factor of 0.033 and a polarization ratio of 0.44, respectively.

关键词： Aggregation-Induced Emission (AIE) Circularly Polarized Luminescence Linearly Polarized Luminescence Self-Assembly Sub-1 nm Nanowires

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