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arXiv 2022年

作者： Shu, Zheng Cai, Yongqing Center for Data Science Institute of Collaborative Innovation University of Macau Taipa China Joint Key Laboratory The Ministry of Education Institute of Applied Physics and Materials Engineering University of Macau Taipa China

Nitrogen reduction reaction (NRR) which converts nitrogen (N2) to ammonia (NH3) normally requires harsh conditions to break the bound nitrogen bond. Herein, via first-principles calculation we reveal that a superior NRR catalytic activity could be obtained through anchoring atomic catalyst above a phosphorene-like puckering surface of germanium selenide (GeSe). Through examining the single- and double- atoms (B, Fe, W, Mo and Ru) decorated on GeSe, we find that its rippled structure allows an intimate contact between the deposited species and the GeSe which significantly promotes the states hybridization. Amongst the various atomic catalyst, we predict that the Ru dimer decorated GeSe monolayer (Ru2@GeSe) has superior catalytic activity for the N2 fixation and reduction. Through examining the three NRR pathways (distal, alternating and enzymatic), the distal and enzymatic pathway is both the thermodynamically favorable with the maximum Gibbs free energy change (ΔGMAX) of 0.25 and 0.26 eV, respectively. Such a superior activity could be attributed to the filtered states of GeSe by Ru dimer which leads to the effective activation of the adsorbed N2 bond. As an efficient near-infrared absorber of GeSe, the Ru mediated hybridization of GeSe-RuN2 complex enables an in-gap state which further broadens the absorbing window, rendering for a broadband solar absorption and possible photocatalysis. © 2022, CC BY-SA.

关键词： Atoms

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Multi-omics synergy in oncology:Unraveling the complex interplay of radiomic,genoproteomic,and pathological data

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Intelligent Oncology 2025年第1期1卷 17-30页

作者： Yang Luo Yilin Li Mengjie Fang Shuo Wang Lizhi Shao Ruiyang Zou Di Dong Zhenyu Liu Jingwei Wei Jie Tian Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China Beijing Key Laboratory of Molecular Imaging Beijing 100190China School of Life Science and Technology Xidian UniversityXi’an Shaanxi 710126China School of Public Health Anhui Medical UniversityHefei Anhui 230032China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijing 100191China School of Engineering Medicine Beihang UniversityBeijing 100191China Key Laboratory of Big Data-Based Precision Medicine Ministry of Industry and Information TechnologyBeihang UniversityBeijing 100191China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi’an Shaanxi 710126China

The advent of multi-omics approaches has revolutionized the field of oncology by enabling a comprehensive understanding of cancer biology through the integration of diverse biological *** review aims to explore the synergy between three key omics domains:radiomics,genoproteomics,and *** involves extracting high-dimensional data from medical images,providing valuable insights into tumor heterogeneity and treatment ***,encompassing both genomic and proteomic analyses,delves into the molecular mechanisms driving cancer progression and therapeutic *** leverages advanced digital pathology techniques to quantitatively analyze tissue architecture and cellular *** provide an in-depth overview of the methodologies and tools employed in each omics field,highlighting their specific applications in oncology,including cancer diagnosis,biomarker discovery,and prediction of treatment ***,we discuss the integration of multi-omics data,addressing the challenges and innovative solutions for harmonizing these complex *** an examination of recent advancements and case studies,we underscore the critical role of multi-omics in advancing our understanding of cancer and paving the way for more effective and personalized therapeutic strategies.

关键词： Oncology Multi-omics Radiomics Genoproteomics Pathomics

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Dynamic targets searching assistance based on virtual camera priority

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Virtual Reality & Intelligent Hardware 2021年第6期3卷 484-500页

作者： Zixiang ZHAO Quanwei ZHOU Xiaoguang HAN Lili WANG State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Biomedical Engineering Beihang UniversityBeijing 100191China Department of Mathematics and Theories Peng Cheng LaboratoryShenzhen 518052China The Chinese University of Hong Kong Shenzhen 999077China Shenzhen Research Institute of Big Data Shenzhen 999077China

Background When a user walks freely in an unknown virtual scene and searches for multiple dynamic targets,the lack of a comprehensive understanding of the environment may have a negative impact on the execution of virtual reality *** studies can help users with auxiliary tools,such as top view maps or trails,and exploration guidance,for example,automatically generated paths according to the user location and important static spots in virtual ***,in some virtual reality applications,when the scene has complex occlusions,and the user cannot obtain any real-time position information of the dynamic target,the above assistance cannot help the user complete the task more *** We design a virtual camera priority-based assistance to help the user search dynamic targets *** of forcing users to go to destinations,we provide an optimized instant path to guide them to places where they are more likely to find dynamic targets when they ask for *** assume that a certain number of virtual cameras are fixed in virtual scenes to obtain extra depth maps,which capture the depth information of the scene and the locations of the dynamic *** method automatically analyzes the priority of these virtual cameras,chooses the destination,and generates an instant path to assist the user in finding the dynamic *** method is suitable for various virtual reality applications that do not require manual supervision or *** A user study is designed to evaluate the proposed *** results indicate that compared with three conventional navigation methods,such as the top-view method,our method can help users find dynamic targets more *** advantages include reducing the task completion time,reducing the number of resets,increasing the average distance between resets,and reducing user task *** We presented a method for improving dynamic target searching efficiency in virtual scenes by virtual cam

关键词： Searching assistance Virtual environment Path guidance Redirection

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Improving efficiency of the path optimization method for a gauge theory

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Physical Review D 2023年第3期107卷 034509-034509页

作者： Yusuke Namekawa Kouji Kashiwa Hidefumi Matsuda Akira Ohnishi Hayato Takase Education and Research Center for Artificial Intelligence and Data Innovation Hiroshima University Hiroshima 730-0053 Japan Department of Physics Faculty of Science Kyoto University Kyoto 606-8502 Japan Yukawa Institute for Theoretical Physics Kyoto University Kyoto 606-8502 Japan Department of Computer Science and Engineering Faculty of Information Engineering Fukuoka Institute of Technology Fukuoka 811-0295 Japan Department of Physics and Center for Field Theory and Particle Physics Fudan University Shanghai 200433 China

We investigate efficiency of a gauge-covariant neural network and an approximation of the Jacobian in optimizing the complexified integration path toward evading the sign problem in lattice field theories. For the construction of the complexified integration path, we employ the path optimization method. The two-dimensional U(1) gauge theory with the complex gauge-coupling constant is used as a laboratory to evaluate the efficiency. It is found that the gauge-covariant neural network, which is composed of the Stout-like smearing, can enhance the average phase factor, as the gauge-invariant input does. For the approximation of the Jacobian, we test the most drastic case in which we perfectly drop the Jacobian during the learning process. It reduces the numerical cost of the Jacobian calculation from O(N3) to O(1), where N means the number of degrees of freedom of the theory. The path optimization using this Jacobian approximation still enhances the average phase factor at expense of a slight increase of the statistical error.

关键词： Lattice gauge theory Machine learning

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Domain Generalized Solar Cell Defect Segmentation Based on Shape-Aware and Multi-View Meta-Learning

Domain Generalized Solar Cell Defect Segmentation Based on S...

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Chinese Control Conference (CCC)

作者： Kun Liu Ying Yang Jingkun Mao Weipeng Liu School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China Control Engineering Technology Innovation Center of Hebei Province Hebei University of Technology Tianjin P. R. China Department of Automation Tianjin University of Technology Tianjin China

Various defects are inevitably generated in the manufacturing process of solar cells. Deep learning-based methods for defect segmentation under closed situation have achieved remarkable progress. Due to the difference of imaging condition and camera parameter under different production line, there are large differences in brightness distribution of solar cell images. The model trained under closed situation cannot achieve good performance in opened situation such as multi-production lines. In this paper, a new shape-aware and multi-view meta-learning scheme is proposed to improve the model generalization performance for single-domain solar cell defect segmentation. The scheme roots in gradient-based meta-learning. Multi-brightness view information is yielded from the generated augmented images, and explicitly simulating domain shift with virtual meta-train and meta-test during training to mitigate overfitting in single-source domain train and unstable prediction. Importantly, considering tiny and faint solar cell defects are not easily identified under different image brightness conditions, shape edge constraint loss is proposed to encourage shape compactness and shape smoothness of segmentations. Experimental results show that the proposed method has high segmentation accuracy, which outperforms the state-of-the-art methods.

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Multi-Scale Attention Semi-Supervised Network Based on Confidence Iteration and Pseudo-Label Optimization for COVID-19 CT Segmentation

Multi-Scale Attention Semi-Supervised Network Based on Confi...

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Chinese Control Conference (CCC)

作者： Kun Liu Mei Chen Junfei Xie JingKun Mao WeiPeng Liu School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China Control Engineering Technology Innovation Center of Hebei Province Hebei University of Technology Tianjin P. R. China Department of Automation Tianjin University of Technology Tianjin China

Computed tomography (CT) image segmentation for COVID-19 lung infection has made great progress. However, accurate segmentation remains a major challenge due to the varying shape and size of COVID-19 infected areas, the more similar tissue of infected lung images to normal lung images, and the additional problem of a limited number of medical images annotated at the pixel level. In this paper, we propose a semi-supervised segmentation network based on confidence iteration and pseudo-label optimization for COVID-19 CT segmentation. In addition, we develop a multi-scale attention module which can achieve attentional feature fusion at different scales. The experiment evaluated on the COVID-19 CT Segmentation dataset, demonstrate our method achieves higher segmentation accuracy than the state-of-art methods.

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Diffusion-based Graph Generative Methods

arXiv

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arXiv 2024年

作者： Chen, Hongyang Xu, Can Zhen, Lingyu Zhang, Qiang Lin, Xuemin The Research Center for Graph Computing Hangzhou China School of Data Science and Engineering East China Normal University Shanghai China The Research Center for Graph Computing Hangzhou China Hangzhou Institue for Advanced Study UCAS Hangzhou China The College of Computer Science and Technology Zhejiang University Hangzhou China ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou China Antai College of Economics and Management Shanghai Jiao Tong University Shanghai China

Being the most cutting-edge generative methods, diffusion methods have shown great advances in wide generation tasks. Among them, graph generation attracts significant research attention for its broad application in real life. In our survey, we systematically and comprehensively review on diffusion-based graph generative methods. We first make a review on three mainstream paradigms of diffusion methods, which are denoising diffusion probabilistic models, score-based genrative models, and stochastic differential equations. Then we further categorize and introduce the latest applications of diffusion models on graphs. In the end, we point out some limitations of current studies and future directions of future explorations. The summary of existing methods metioned in this survey is in our Github: https://***/zhejiangzhuque/Diffusion-based-Graph-Generative-Methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration

arXiv

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arXiv 2024年

作者： Zhang, Xiaoqing Nie, Qiushi Xiao, Zunjie Zhao, Jilu Wu, Xiao Guo, Pengxin Li, Runzhi Liu, Jin Wei, Yanjie Pan, Yi Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Actuarial Science The University of Hong Kong 999077 Hong Kong Cooperative Innovation Center of Internet Healthcare Zhengzhou University Zhengzhou450001 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830046 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

Spatial pooling (SP) and cross-channel pooling (CCP) operators have been applied to aggregate spatial features and pixel-wise features from feature maps in deep neural networks (DNNs), respectively. Their main goal is to reduce computation and memory overhead without visibly weakening the performance of DNNs. However, SP often faces the problem of losing the subtle feature representations, while CCP has a high possibility of ignoring salient feature representations, which may lead to both miscalibration of confidence issues and suboptimal medical classification results. To address these problems, we propose a novel dual-view framework, the first to systematically investigate the relative roles of SP and CCP by analyzing the difference between spatial features and pixel-wise features. Based on this framework, we propose a new pooling method, termed dual-view pyramid pooling (DVPP), to aggregate multi-scale dual-view features. DVPP aims to boost both medical image classification and confidence calibration performance by fully leveraging the merits of SP and CCP operators from a dual-axis perspective. Additionally, we discuss how to fulfill DVPP with five parameter-free implementations. Extensive experiments on six 2D/3D medical image classification tasks show that our DVPP surpasses state-of-the-art pooling methods in terms of medical image classification results and confidence calibration across different DNNs. © 2024, CC BY.

关键词： Deep neural networks

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Brain Age Estimation from MRI Using a Two-Stage Cascade Network with Ranking Loss 23rd

Brain Age Estimation from MRI Using a Two-Stage Cascade Netw...

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23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Liu, Ziyang Cheng, Jian Zhu, Haogang Zhang, Jicong Liu, Tao Beijing Advanced Innovation Center for Biomedical Engineering The School of Biological Science and Medical Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beijing China School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9783030597276

As age increases, human brains will be aged, and people tend to experience cognitive decline with a higher risk of neuro-degenerative disease and dementia. Recently, it was reported that deep neural networks, e.g., 3D convolutional neural networks (CNN), are able to predict chronological age accurately in healthy people from their T1-weighted magnetic resonance images (MRI). The predicted age, called as "brain age" or "brain predicted age", could be a biomarker of the brain ageing process. In this paper, we propose a novel 3D convolutional network, called as two-stage-age-net (TSAN), for brain age estimation from T1-weighted MRI data. Compared with the state-of-the-art CNN by Coleetal., TSAN has several improvements: 1) TSAN uses a two-stage cascade architecture, where the first network is to estimate a discretized age range, then the second network is to further estimate the brain age more accurately;2) Besides using the traditional mean square error (MSE) loss between chronological and estimated ages, TSAN considers two additional novel ranking losses, based on paired samples and a batch of samples, for regularizing the training process;3) TSAN uses densely connected paths to combine feature maps with different scales;4) TSAN considers gender labels as input features for the network, considering brains of male and female age differently. The proposed TSAN was validated in three public datasets. The experiments showed that TSAN could provide accurate brain age estimation in healthy subjects, yielding a mean absolute error (MAE) of 2.428, and a Pearsons correlation coefficient (PCC) of 0.985, between the estimated and the chronological ages. © 2020, Springer Nature Switzerland AG.

关键词： Magnetic resonance

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Monotone Fuzzy Rule Interpolation for TSK-FIS-Like n-Ary Aggregation Functions

Monotone Fuzzy Rule Interpolation for TSK-FIS-Like n-Ary Agg...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Chian Haur Jong Kai Meng Tay Yi Wen Kerk Chee Peng Lim Faculty of Engineering Data Science Centre UNIMAS Water Centre Universiti Malaysia Sarawak Kota Samarahan Malaysia Center for Artificial Intelligence Technology Universiti Kebangsaan Malaysia Bangi Malaysia Institute for Intelligent Systems Research and Innovation Deakin University Geelong VIC Australia

Fuzzy Rule Interpolation (FRI) is important for fuzzy inference systems modeling pertaining to a sparse fuzzy rule base system. The focus of this paper is on a specific class of FRI, i.e., monotone FRI (MFRI), for modeling monotone Takagi-Sugeno-Kang Fuzzy Inference System (TSK-FIS) in the presence of a monotone sparse fuzzy rule base. On the other hand, a function is denoted as an n-ary aggregation function for a given n-dimensional input space and an output space when both the monotone and boundary properties are satisfied. In this paper, a set of sufficient conditions derived from the principles of Ordered Weighted Averaging (OWA) and the concept of orness for TSK-FIS to obey the monotone property is firstly formulated. We show that it is necessary to have a dense fuzzy rule base, which can be obtained by interpolation of fuzzy rules in a sparse fuzzy rule base, for constructing a monotone TSK-FIS. We then devise a two-stage MFRI for establishing monotone TSK-FIS. The first stage comprises a sufficient condition, inspired from the orness concept, to generate intermediate fuzzy membership functions (FMFs). The second stage deduces the monotone consequent of each intermediate rule from the available sparse fuzzy rules. We further extend our MFRI formulation to form TSK-FIS-like n-ary aggregation functions.

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