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

作者： Xu, Rui Liu, Tiantian Ye, Xinchen Lin, Lin Chen, Yen-Wei DUT-RU International School of Information Science and Engineering Dalian University of Technology Dalian China DUT-RU Co-Research Center of Advanced ICT for Active Life Dalian China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian China College of Software Dalian University of Technology Dalian China College of Information Science and Engineering Ritsumeikan University Kusatsu Japan Research Center of Healthcare Data Science Zhejiang Lab Hangzhou China

ISBN: (纸本)9783030597214

Many deep learning based methods have been proposed for retinal vessel segmentation, however few of them focus on the connectivity of segmented vessels, which is quite important for a practical computer-aided diagnosis system on retinal images. In this paper, we propose an efficient network to address this problem. A U-shape network is enhanced by introducing a semantics-guided module, which integrates the enriched semantics information to shallow layers for guiding the network to explore more powerful features. Besides, a recursive refinement iteratively applies the same network over the previous segmentation results for progressively boosting the performance while increasing no extra network parameters. The carefully designed recursive semantics-guided network has been extensively evaluated on several public datasets. Experimental results have shown the efficiency of the proposed method. © 2020, Springer Nature Switzerland AG.

关键词： Semantics

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Can we constrain warm dark matter masses with individual galaxies?

arXiv

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arXiv 2024年

作者： Lin, Shurui Villaescusa-Navarro, Francisco Rose, Jonah Torrey, Paul Farahi, Arya Kollmann, Kassidy E. Garcia, Alex M. Roy, Sandip Kallivayalil, Nitya Vogelsberger, Mark Cai, Yi-Fu Luo, Wentao Department of Astronomy School of Physical Sciences University of Science and Technology of China Anhui Hefei230026 China CAS Key Laboratory for Researches in Galaxies and Cosmology School of Astronomy and Space Science University of Science and Technology of China Anhui Hefei230026 China Center for Computational Astrophysics 162 5th Avenue New YorkNY10010 United States Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544 United States Department of Astronomy University of Florida GainesvilleFL32611 United States Department of Astronomy University of Virginia 530 McCormick Road CharlottesvilleVA22903 United States Departments of Statistics and Data Science University of Texas at Austin AustinTX78757 United States Department of Physics Princeton University PrincetonNJ08544 United States Department of Astronomy University of Virginia 530 McCormick Road CharlottesvilleVA22904 United States Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions Massachusetts Institute of Technology CambridgeMA02139 United States

We study the impact of warm dark matter mass on the internal properties of individual galaxies using a large suite of 1,024 state-of-the-art cosmological hydrodynamic simulations from the DREAMS project. We take individual galaxies’ properties from the simulations, which have different cosmologies, astrophysics, and warm dark matter masses, and train normalizing flows to learn the posterior of the parameters. We find that our models cannot infer the value of the warm dark matter mass, even when the values of the cosmological and astrophysical parameters are given explicitly. This result holds for galaxies with stellar mass larger than 2 × 108M☉/h at both low and high redshifts. We calculate the mutual information and find no significant dependence between the WDM mass and galaxy properties. On the other hand, our models can infer the value of Ωm with a ∼ 10% accuracy from the properties of individual galaxies while marginalizing astrophysics and warm dark matter masses. © 2024, CC BY.

关键词： Galaxies

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Self-Polarization Triggered Multiple Polar Units Toward Electrochemical Reduction of CO2to Ethanol with High Selectivity

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Angewandte Chemie 2023年第26期135卷

作者： Yangyang Zhang Yanxu Chen Xiaowen Wang Yafei Feng Huaikun Zhang Prof. Genqiang Zhang Hefei National (Research Center) for Physical Sciences at the Microscale CAS Key Laboratory of Materials for Energy Conversion Department of Materials Science and Engineering University of Science and Technology of China Hefei Anhui 230026 China Contribution: Data curation (lead) Formal analysis (lead) Writing - original draft (lead)

Electrochemical conversion of CO 2 to highly valuable ethanol has been considered a intriguring strategy for carbon neutruality. However, the slow kinetics of coupling carbon-carbon (C−C) bonds, especially the low selectivity ethanol than ethylene in neutral conditions, is a significant challenge. Herein, the asymmetrical refinement structure with enhanced charge polarization is built in the vertically oriented bimetallic organic frameworks (NiCu-MOF) nanorod array with encapsulated Cu 2 O (Cu 2 O@MOF/CF), which can induce an intensive internal electric field to increase the C−C coupling for producing ethanol in neutral electrolyte. Particularly, when directly employed Cu 2 O@MOF/CF as the self-supporting electrode, the ethanol faradaic efficiency (FE ethanol ) could reach maximum 44.3 % with an energy efficiency of 27 % at a low working-potential of −0.615 V versus the reversible hydrogen electrode (vs. RHE) using CO 2 -saturated 0.5 M KHCO 3 as the electrolyte. Experimental and theoretical studies suggest that the polarization of atomically localized electric fields derived from the asymmetric electron distribution can tune the moderate adsorption of *CO to assist the C−C coupling and reduce the formation energy of H 2 CCHO*-to-*OCHCH 3 for the generation of ethanol. Our research offers a reference for the design of highly active and selective electrocatalysts for reducing CO 2 to multicarbon chemicals.

关键词： Bimetal CO2 DFT Calculations Elctrocatalysis Ethanol Polarization

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Climatic and Biotic Controls of Evapotranspiration Across Grassland Ecosystems on Tibetan Plateau

SSRN

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SSRN 2023年

作者： Peng, Siyuan Yang, Yue Luo, Dengnan Zeng, Xiang Liang, Minqi Tao, Long Zhang, Guangru Li, Pan Liao, Weijie Guo, Qun Cao, Ruochen Li, Yuzhe Zhang, Weirong Hu, Zhongmin School of Geography South China Normal University Shipai Campus Guangzhou510631 China Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation School of Ecology and Environment Hainan University Haikou570228 China Center for Eco-Environment Restoration Engineering of Hainan Province Hainan University Haikou570228 China Facility of Geographical Science Beijing Normal University Beijing100875 China Zhuhai Branch of State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Zhuhai519087 China School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Guangdong Zhuhai510245 China Key Lab of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing101408 China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu210023 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources School of Geography and Ocean Science Nanjing University Jiangsu210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Accurately quantifying spatiotemporal variations in evapotranspiration (ET) and its components on Tibetan Plateau (TP) is crucial for understanding the regional water cycle and energy balance. However, the determinant of spatial variations in ET and its component processes in grassland ecosystems of TP remain unclear. In this study, we used 14 sites observation data and applied Shuttleworth-Wallace-Hu (SWH) model to investigate the spatial relationships between ET, its components, and influencing factors. Our results highlighted the good performance of SWH model. There were corresponding drops in ET on alpine meadow and on alpine steppe. And the ratio of ET and precipitation (ET/PPT) of most sites was less than 1 in alpine meadow. Furthermore, the ratio of transpiration (T) and ET (T/ET) in alpine meadow generally exceeded that in alpine steppe. PPT emerged as the primary driver of interannual spatial variations in ET, while LAI played a crucial role in regulating T/ET. Our results further demonstrated that in the grassland ecosystem of TP, as the aridity index increased, the ET gradually transitioned from being primarily influenced by water supply to energy demand. This transition occurred at an aridity index of 0.73 approximately. The results of this study imply that in the context of global warming, with aridity index in TP decreasing, the ET of more ecosystems will shift from energy-led to water-led. Such findings are of immense significance in enhancing our understanding of the intricate interactions of the water cycle between land and the atmosphere. © 2023, The Authors. All rights reserved.

关键词： Water supply

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CarfRCNN: A Two-stage Effective Model For Instance Segmentation

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Journal of Physics: Conference Series 2021年第1期1884卷

作者： Yujie Chen Pengbiao Zhao Julin Chen Network and Data Security Key Laboratory of Sichuan Province University of Electr onic Science and Technology of China

With the rapid development of deep learning, many instance segmentation models have achieved good results in accuracy and time. But here are still many problems. In this paper, we proposed a two-stage model CarfRCNN. We proposed CAResNet to change the structure of the backbone, making the feature extraction of the input image more refined. At the same time, we also added the CRF module to add smooth constraints to the pixels, so that the segmentation mask fits the target contour more closely. We train and test on the COCO datasets, and compare with the Mask R-CNN. The experimental results prove that the model we proposed can greatly improve the accuracy.

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Catalytic upgrading of ethanol to butanol over a binary catalytic system of FeNiO_x and LiOH

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Chinese Journal of Catalysis 2020年第4期41卷 672-678页

作者： Jifeng Pang Mingyuan Zheng Zhinuo Wang Shimin Liu Xinsheng Li Xianquan Li Junhu Wang Tao Zhang CAS Key Laboratory of Science and Technology on Applied Catalysis Dalian Institute of Chemical PhysicsChinese Academy of SciencesDalian 116023LiaoningChina School of Materials Science and Engineering Dalian Jiaotong UniversityDalian 116028LiaoningChina University of Chinese Academy of Sciences Beijing 100049China Mössbauer Effect Data Center Dalian Institute of Chemical PhysicsChinese Academy of SciencesDalian 116023LiaoningChina

Catalytic conversion of ethanol to butanol is vital to bridge the gap between huge amounts of ethanol production,the limited blending ratio of ethanol in gasoline,and the outstanding performance of *** this work,a highly active binary catalytic system of FeNiOx and LiOH was developed for upgrading of ethanol to *** 24 h reaction at 493 K,the selectivity to butanol reached 71% with>90% high carbon alcohols at 28% ethanol conversion,which was comparable to the performance of some noble metal homogeneous catalysts.

关键词： Ethanol Catalysis Butanol Hydrogen transfer Condensation

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Field test of twin-field quantum key distribution through sending-or-not-sending over 428 km

arXiv

引用

arXiv 2021年

作者： Liu, Hui Jiang, Cong Zhu, Hao-Tao Zou, Mi Yu, Zong-Wen Hu, Xiao-Long Xu, Hai Ma, Shizhao Han, Zhiyong Chen, Jiu-Peng Dai, Yunqi Tang, Shi-Biao Zhang, Weijun Li, Hao You, Lixing Wang, Zhen Zhou, Fei Zhang, Qiang Wang, Xiang-Bin Chen, Teng-Yun Pan, Jian-Wei Hefei National Laboratory for Physical Sciences at Microscale Department of Modern Physics University of Science and Technology ofChina HefeiAnhui230026 China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China HefeiAnhui230026 China Jinan Institute of Quantum Technology JinanShandong250101 China State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China Data Communication Science and Technology Research Institute Beijing100191 China QuantumCTek Corporation Limited HefeiAnhui230088 China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China

Quantum key distribution endows people with information-theoretical security in communications. Twin-field quantum key distribution (TF-QKD) has attracted considerable attention because of its outstanding key rates over long distances. Recently, several demonstrations of TF-QKD have been realized. Nevertheless, those experiments are implemented in the laboratory, remaining a critical question about whether the TF-QKD is feasible in real-world circumstances. Here, by adopting the sending-or-not-sending twin-field QKD (SNS-TF-QKD) with the method of actively odd parity pairing (AOPP), we demonstrate a field-test QKD over 428 km deployed commercial fiber and two users are physically separated by about 300 km in a straight line. To this end, we explicitly measure the relevant properties of the deployed fiber and develop a carefully designed system with high stability. The secure key rate we achieved breaks the absolute key rate limit of repeater-less QKD. The result provides a new distance record for the field test of both TF-QKD and all types of fiber-based QKD systems. Our work bridges the gap of QKD between laboratory demonstrations and practical applications, and paves the way for intercity QKD network with high-speed and measurement-device-independent security. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum cryptography

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On the connectivity of highly dynamic wireless sensor networks in smart factory

On the connectivity of highly dynamic wireless sensor networ...

引用

2019 International Conference on networking and network Applications, NaNA 2019

作者： Gao, Cong Wang, Zhongmin Chen, Yanping School of Computer Science and Technology Xi'an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an China Xi'An University of Posts and Telecommunications Xi'an China

ISBN: (纸本)9781728126296

With the development of smart manufacturing in Industry 4.0, large amount of heterogeneous data are generated from multiple sources. Various data processing techniques can be applied to these data for the purpose of extracting potential information about the status of the manufacturing network. Thus, data are valuable to the analysis and adjustment of the manufacturing processes. Due to the diversity of products, assembly lines and manufacturing processes are highly dynamic. In view of the flexible deployment of wireless sensor networks, they have been widely deployed to conduct data collection. However, due to the limited radio range of a wireless sensor node, the connectivity of a highly dynamic wireless sensor network is a key issue. In this paper, we analyzed the connectivity in a wireless sensor network and accorded three types of connectivity. Then, we developed two network connectivity models from the perspective of one dimension and two dimensions. The degrees of connectivity for both two nodes and the network are elaborated. Finally, the relation between the number of sink nodes and the average degree of connectivity of the network is analyzed based on experimental results. © 2019 IEEE.

关键词： data handling

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Abnormal Scanning Patterns Based on Eye Movement Entropy in Early Psychosis

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2024年

作者： Zhang, Dan Ma, Chunyan Xu, Lihua Liu, Xu Cui, Huiru Wei, Yanyan Zheng, Wensi Hong, Yawen Xie, Yuou Qian, Zhenying Hu, Yegang Tang, Yingying Li, Chunbo Liu, Zhi Chen, Tao Liu, Haichun Zhang, Tianhong Wang, Jijun Shanghai Key Laboratory of Psychotic Disorders Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine People's Republic of China Shanghai China First Clinical Medical College of Nanjing Medical University People's Republic of China Nanjing China Shanghai Institute for Advanced Communication and Data Science Shanghai University People's Republic of China Shanghai China School of Communication and Information Engineering Shanghai University People's Republic of China Shanghai China Labor and Worklife Program Harvard University Cambridge Massachusetts United States Big Data Research Laboratory University of Waterloo Waterloo ON Canada Niacin (Shanghai) Technology Co. Ltd. People's Republic of China Shanghai China Department of Automation Shanghai Jiao Tong University People's Republic of China Shanghai China Shanghai Mental Health Center Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention Shanghai Key Laboratory of Psychotic Disorders Shanghai Jiaotong University School of Medicine People's Republic of China Shanghai China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences People's Republic of China Shanghai China Institute of Psychology and Behavioral Science Shanghai Jiao Tong University People's Republic of China Shanghai China

Background: Restricted scan path mode is hypothesized to explain abnormal scanning patterns in patients with schizophrenia. Here, we calculated entropy scores (drawing on gaze data to measure the statistical randomness of eye movements) to quantify how strategical and random participants were when processing image stimuli. Methods: Eighty-six patients with first-episode schizophrenia (FES), 124 individuals at clinical high risk (CHR) for psychosis, and 115 healthy control participants (HCs) completed an eye-tracking examination while freely viewing 35 static images (each presented for 10 seconds) and cognitive assessments. We compared group differences in the overall entropy score, as well as entropy scores under various conditions. We also investigated the correlations between entropy scores and symptoms and cognitive function. Results: Increased overall entropy scores were noted in the FES and CHR groups compared with the HC group, and these differences were already apparent within 0 to 2.5 seconds. In addition, the CHR group exhibited higher entropy than the HC group when viewing low-meaning images. Moreover, the entropy within 0 to 2.5 seconds showed significant correlations with negative symptoms in the FES group, attention/vigilance scores in the CHR group, and speed of processing and attention/vigilance scores across all 3 groups. Conclusions: The results indicate that individuals with FES and those at CHR scanned pictures more randomly and less strategically than HCs. These patterns also correlated with clinical symptoms and neurocognition. The current study highlights the potential of the eye movement entropy measure as a neurophysiological marker for early psychosis. © 2024 Society of Biological Psychiatry

关键词： Biomarker Clinical high risk for psychosis Cognitive impairments Early psychosis Eye movement Schizophrenia

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Local and global structure-aware entropy regularized mean teacher model for 3d left atrium segmentation 23rd

Local and global structure-aware entropy regularized mean te...

引用

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

作者： Hang, Wenlong Feng, Wei Liang, Shuang Yu, Lequan Wang, Qiong Choi, Kup-Sze Qin, Jing School of Computer Science and Technology Nanjing Tech University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Department of Radiation Oncology Stanford University StanfordCA94305 United States CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China School of Nursing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783030597092

Emerging self-ensembling methods have achieved promising semi-supervised segmentation performances on medical images through forcing consistent predictions of unannotated data under different perturbations. However, the consistency only penalizes on independent pixel-level predictions, making structure-level information of predictions not exploited in the learning procedure. In view of this, we propose a novel structure-aware entropy regularized mean teacher model to address the above limitation. Specifically, we firstly introduce the entropy minimization principle to the student network, thereby adjusting itself to produce high-confident predictions of unannotated images. Based on this, we design a local structural consistency loss to encourage the consistency of inter-voxel similarities within the same local region of predictions from teacher and student networks. To further capture local structural dependencies, we enforce the global structural consistency by matching the weighted self-information maps between two networks. In this way, our model can minimize the prediction uncertainty of unannotated images, and more importantly that it can capture local and global structural information and their complementarity. We evaluate the proposed method on a publicly available 3D left atrium MR image dataset. Experimental results demonstrate that our method achieves outstanding segmentation performances than the state-of-the-art approaches in scenes with limited annotated images. © Springer Nature Switzerland AG 2020.

关键词： Forecasting

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