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arXiv 2022年

作者： Tian, Chunwei Zheng, Menghua Zuo, Wangmeng Zhang, Bob Zhang, Yanning Zhang, David School of Software Northwestern Polytechnical University Shaanxi Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China Peng Cheng Laboratory Guangdong Shenzhen518055 China Department of Computer and Information Science University of Macau 999078 China School of Computer Science Northwestern Polytechnical University Shaanxi Xi’an710129 China Guangdong Shenzhen518172 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

Deep convolutional neural networks (CNNs) are used for image denoising via automatically mining accurate structure information. However, most of existing CNNs depend on enlarging depth of designed networks to obtain better denoising performance, which may cause training difficulty. In this paper, we propose a multi-stage image denoising CNN with the wavelet transform (MWDCNN) via three stages, i.e., a dynamic convolutional block (DCB), two cascaded wavelet transform and enhancement blocks (WEBs) and a residual block (RB). DCB uses a dynamic convolution to dynamically adjust parameters of several convolutions for making a tradeoff between denoising performance and computational costs. WEB uses a combination of signal processing technique (i.e., wavelet transformation) and discriminative learning to suppress noise for recovering more detailed information in image denoising. To further remove redundant features, RB is used to refine obtained features for improving denoising effects and reconstruct clean images via improved residual dense architectures. Experimental results show that the proposed MWDCNN outperforms some popular denoising methods in terms of quantitative and qualitative analysis. Codes are available at https://***/hellloxiaotian/MWDCNN. © 2022, CC BY-NC-ND.

关键词： Wavelet transforms

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A Solution-Based Tabu Search Algorithm for the Resource-Constrained Project Scheduling Problem with Step Deterioration

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Mathematical Problems in engineering 2023年第1期2023卷

作者： Dai, Huafeng Li, Lei Mao, Run Liu, Xingyue Zhou, Kun School of Mechanical Engineering Chengdu University Chengdu610105 China Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province Chengdu610031 China CNPC National Engineering Research Center for Oil & Gas Drilling Equipment Co. Ltd. Chengdu610052 China School of Information Science and Engineering Chengdu University Chengdu610105 China Key Lab. of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Hangzhou310018 China

This paper integrates the classical resource-constrained project scheduling problem with step deterioration and accordingly presents a solution-based tabu search (SB-TS) algorithm to solve it with challenging computations. First, we choose successor list size as the priority rule to generate the initial solution of the presented methods. Next, the corresponding solution decoding and calculation ways are defined to update the related configuration at each iteration. Compared with the state-of-the-art methods, in terms of the known datasets, the proposed SB-TS finds the optimal solution for each instance in J30 and shows strongly competitive performance in J60, J90, and J120. Then, on newly generated datasets, where the step deterioration is attached to, the comparison experimental data state that the SB-TS with high-quality solution is superior to the move-based tabu search (MB-TS) algorithm. In addition, two key components are investigated to emphasize their attributions to the proposed algorithm. © 2023 Huafeng Dai et al.

关键词： Deterioration

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Predictability of real temporal networks

arXiv

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arXiv 2020年

作者： Tang, Disheng Du, Wenbo Shekhtman, Louis Wang, Yijie Havlin, Shlomo Cao, Xianbin Yan, Gang School of Electronic and Information Engineering Beihang University Beijing100191 China School of Physics Science and Engineering Tongji University Shanghai200092 China National Engineering Laboratory of Big Data Application Technologies of Comprehensive Transportation Beijing100191 China Network Science Institute Northeastern University BostonMA02115 United States Department of Physics Bar Ilan University Ramat Gan5290002 Israel Shanghai Institute of Intelligence Science and Technology Tongji University Shanghai200092 China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

Links in most real networks often change over time. Such temporality of links encodes the ordering and causality of interactions between nodes and has a profound effect on network dynamics and function. Empirical evidence has shown that the temporal nature of links in many real-world networks is not random. Nonetheless, it is challenging to predict temporal link patterns while considering the entanglement between topological and temporal link patterns. Here, we propose an entropy-rate-based framework, based on combined topological–temporal regularities, for quantifying the predictability of any temporal network. We apply our framework on various model networks, demonstrating that it indeed captures the intrinsic topological–temporal regularities whereas previous methods considered only temporal aspects. We also apply our framework on 18 real networks of different types and determine their predictability. Interestingly, we find that, for most real temporal networks, despite the greater complexity of predictability brought by the increase in dimension, the combined topological–temporal predictability is higher than the temporal predictability. Our results demonstrate the necessity for incorporating both temporal and topological aspects of networks in order to improve predictions of dynamical processes. Copyright © 2020, The Authors. All rights reserved.

关键词： Topology

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Neural mechanisms of top-down divided and selective spatial attention in visual and auditory perception

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Brain Science Advances 2023年第2期9卷 95-113页

作者： Zhongtian Guan Meng Lin Qiong Wu Jinglong Wu Kewei Chen Hongbin Han Dehua Chui Xu Zhang Chunlin Li School of Biomedical Engineering Capital Medical UniversityBeijing 100069China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Capital Medical UniversityBeijing 100069China Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application Capital Medical UniversityBeijing 100069China Peking University First Hospital Beijing 100034China Biomedical Engineering Laboratory Graduate School of Natural Science and TechnologyOkayama University3-1-1 Tsushima-nakaOkayamaJapan Key Laboratory of Biomimetic Robots and Systems Ministry of EducationBeijing Institute of TechnologyBeijing 100081China Computational Image Analysis Banner Alzheimer’s Institute and Banner Good Samaritan Medical CentrePET CentrePhoenixAZ 85006USA Radiology Peking University Third HospitalBeijing 100191China Neuroscience Research Institute/Peking University Third Hospital Beijing 100191China

Top-down attention mechanisms require the selection of specificobjects or locations;however,the brain mechanism involved when attention is allocated across different modalities is not well *** aim of this study was to use functional magnetic resonance imaging to define the neural mechanisms underlyingdivided and selective spatial attention.A concurrent audiovisual stimulus was used,and subjects were prompted to focus on a visual,auditory and audiovisual stimulus in a Posner *** results confirmed the better performance of selectiveattention compared to devided *** found differences in the activation level of the frontoparietal network,visual/auditorycortex,the putamen and the salience network under differentattention *** further used Granger causality(GC)toexplore effective connectivity differences between *** in GC connectivity between visual and auditory selective tasksreflected the visual dominance effect under spatial *** addition,our results supported the role of the putamen inredistributing attention and the functional separation of the *** summary,we explored the audiovisual top-down allocation of attention and observed the differences in neuralmechanisms under endogenous attention modes,which revealedthe differences in cross-modal expression in visual and auditory attention under attentional modulation.

关键词： top-down mechanism divided spatial attention selective spatial attention fMRI Granger causality

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Allocation of environmental remediation funds using economic risk-cost-benefit analysis: A case study

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GROUND WATER MONITORING AND REMEDIATION 1996年第4期16卷 95-105页

作者： James, BR Huff, DD Trabalka, JR Ketelle, RH Rightmire, CT Bruce James is principle of BRIJ Risk Research. He has extensive experience in the application of decision/risk-cost-benefit analysis to environmental remediation and data-worth problems particularly those in complex heterogeneous geological environments. He has a Ph.D. in geological engineering from the University of British Columbia. (BRIJ Risk Research 1235 Sunset Dr. Saltspring Island B.C. Canada V8K 1E2). Dale Huff is a senior staff scientist in the Environmental Sciences Division at ORNL with active involvement in the Environmental Restoration Program. He has a Ph.D. in hydrology from Stanford University. (Oak Ridge National Laboratory P.O. Box 2008 Oak Ridge TN 37831-6400.) John Trabalka is a senior staff scientist in the Chemical Technology Division at ORNL. He has a wide range of research interests ranging from biogeochemistry to health physics. Trabalka has a Ph.D. in environmental health sciences from the University of Michigan. (Oak Ridge National Laboratory P.O. Box 2008 Oak Ridge TN 37831-6228.) Richard Ketelle is a hydrogeologist in the Energy Division of ORNL and is presently the groundwater manager at ORNL. He has an M.S. in geology from the University of Tennessee. (Oak Ridge National Laboratory P.O. Box 2008 Oak Ridge TN 37831-6185.) Craig Rightmire is a hydrogeologist in the Environmental Sciences Division at ORNL. He is presently manager of the Energy Systems Groundwater Program Office. Rightmire has an M.A. in geology from the Univeristy of Texas Austin. (Oak Ridge National Laboratory P.O. Box 2008 Oak Ridge TN 37831-6400.)

Efficient allocation of remediation resources is a critical need throughout the nation. Economic risk-cost-benefit analysis is an important tool for meeting this need. This paper provides site engineers, geologists. and managers with a conceptual understanding of economic risk-cost-benefit analysis and shows how it can be applied, even in situations where existing data are sparse or poor in quality. An example analysis is applied to the remediation of radioactive waste at Oak Ridge national laboratory, in which the cost-effectiveness is compared for two remediation alternatives: containment of the waste or monitoring only. A data-worth analysis is also carried out to estimate the maximum justifiable exploration budget and the cost-effectiveness of two proposed data collection programs. Results indicate that the methodology has potential in making robust remediation decisions regarding certain types of questions.

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Neural multi-objective combinatorial optimization with diversity enhancement 23

Neural multi-objective combinatorial optimization with diver...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Jinbiao Chen Zizhen Zhang Zhiguang Cao Yaoxin Wu Yining Ma Te Ye Jiahai Wang School of Computer Science and Engineering Sun Yat-sen University P.R. China School of Computing and Information Systems Singapore Management University Singapore Department of Industrial Engineering & Innovation Sciences Eindhoven University of Technology Netherlands Department of Industrial Systems Engineering & Management National University of Singapore Singapore School of Computer Science and Engineering Sun Yat-sen University P.R. China and Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University P.R. China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P.R. China

Most of existing neural methods for multi-objective combinatorial optimization (MOCO) problems solely rely on decomposition, which often leads to repetitive solutions for the respective subproblems, thus a limited Pareto set. Beyond decomposition, we propose a novel neural heuristic with diversity enhancement (NHDE) to produce more Pareto solutions from two perspectives. On the one hand, to hinder duplicated solutions for different subproblems, we propose an indicator-enhanced deep reinforcement learning method to guide the model, and design a heterogeneous graph attention mechanism to capture the relations between the instance graph and the Pareto front graph. On the other hand, to excavate more solutions in the neighborhood of each subproblem, we present a multiple Pareto optima strategy to sample and preserve desirable solutions. Experimental results on classic MOCO problems show that our NHDE is able to generate a Pareto front with higher diversity, thereby achieving superior overall performance. Moreover, our NHDE is generic and can be applied to different neural methods for MOCO.

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BSEN: A BiSiamese Entity Normalization Method for Biomedicine

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Journal of Physics: Conference Series 2020年第1期1693卷

作者： Zirong Peng Qinyong Yu Hui Yang Yongli Wang Nanjing University of Science and Technology Nanjing Jiangsu 210094 China CETC Big Data Research Institute Co. Ltd Guiyang Guizhou 550022 China Big Data Application on improving Government Governance Capabilities National Engineering Laboratory Guiyang Guizhou 550022 China

Normalization of named entities in the field of biomedicine is an important task in biomedical text data mining. Compared with other tasks in biomedical text mining research, there are relatively few researches on entities normalization. In this article, a BiSiamese entity normalization method for biomedicine (BSEN) is proposed. Firstly, the text similarity algorithm is analyzed, and an improved similarity measurement algorithm for biomedical inverse text frequency and cosine (BIC) is proposed. Secondly, the data set is trained in pairs using BiSiamese network and combined with BIC to calculate text similarity. The entity corresponding to the maximum similarity calculated in the normalization knowledge base is the normalized result obtained by the BSEN method. The verification experiments on the verification data set show that the BSEN has achieved better normalization results than the existing methods.

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Tissue Segmentation of Thick-Slice Fetal Brain MR Scans with Guidance from High-Quality Isotropic Volumes

arXiv

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arXiv 2023年

作者： Huang, Shijie Zhang, Xukun Cui, Zhiming Zhang, He Chen, Geng Shen, Dinggang School of Biomedical Engineering ShanghaiTech University Shanghai China Academy for Eng. & Tech. Fudan University Shanghai China Department of Radiology Obstetrics and Gynecology Hospital of Fudan University Shanghai China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering ShanghaiTech University Shanghai201210 China Shanghai United Imaging Intelligence company Ltd. Shanghai200030 China Shanghai Clinical Research Trial Center Shanghai201210 China

Accurate tissue segmentation of thick-slice fetal brain magnetic resonance (MR) scans is crucial for both reconstruction of isotropic brain MR volumes and the quantification of fetal brain development. However, this task is challenging due to the use of thick-slice scans in clinically-acquired fetal brain data. To address this issue, we propose to leverage high-quality isotropic fetal brain MR volumes (and also their corresponding annotations) as guidance for segmentation of thick-slice scans. Due to existence of significant domain gap between high-quality isotropic volume (i.e., source data) and thick-slice scans (i.e., target data), we employ a domain adaptation technique to achieve the associated knowledge transfer (from high-quality volumes to thick-slice scans). Specifically, we first register the available high-quality isotropic fetal brain MR volumes across different gestational weeks to construct longitudinally-complete source data. To capture domain-invariant information, we then perform Fourier decomposition to extract image content and style codes. Finally, we propose a novel Cycle-Consistent Domain Adaptation Network (C2DA-Net) to efficiently transfer the knowledge learned from high-quality isotropic volumes for accurate tissue segmentation of thick-slice scans. Our C2DA-Net can fully utilize a small set of annotated isotropic volumes to guide tissue segmentation on unannotated thick-slice scans. Extensive experiments on a large-scale dataset of 372 clinically acquired thick-slice MR scans demonstrate that our C2DA-Net achieves much better performance than cutting-edge methods quantitatively and qualitatively. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance

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Green and Efficient Method for Recycling Valuable Metals from Scrapped Lithium Cobalt Oxide Cathode Materials

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IOP Conference Series: Earth and Environmental Science 2020年第5期474卷

作者： Miaomiao Guo Xiaoli Xi Dongdong Zhang Hang Yang Fan Bi Yi Hu Zuoren Nie Kaihua Xu National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of Technology Beijing 100124 PR China Chinese National WEEE Recycling Engineering Research Center Jingmen GEM New Material Co. Ltd Jingmen 448124 PR China

In this study, green and efficient method for recycling valuable metals from scrapped lithium cobalt oxide cathode materials to form lithium carbonate and cobalt powder was proposed. To this end, aluminum and iron impurities were efficiently removed from scrapped lithium cobalt oxide cathode materials through alkali dissolution and magnetic separation with removal rates of 99.9% and 98.3%, respectively. After removal of impurities, lithium cobalt oxides were reduced by hydrogen at high temperature of 650 °C for 3 h to yield Li2O, Co, and very small amounts of CoO and LiOH. Next, the mixture was subjected to water leaching and sodium carbonate precipitation to obtain lithium carbonate with purity of 99.5%. The water-leaching Co and small amounts of cobalt oxides were again reduced by hydrogen to form cobalt powder with purity of 99.4%. In sum, the proposed method looks promising in terms of efficiency, easy for implementation, environmentally-friendliness, and potential industrialization.

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A novel liquid precipitation route to synthesize high-quality cobalt carbonate with low chloride ions concentration

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IOP Conference Series: Earth and Environmental Science 2019年第3期295卷

作者： Miaomiao Guo Xiaoli Xi Sen Li Xiaolin Long Zuoren Nie Kaihua Xu National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of Technology Beijing 100124 PR China Chinese National WEEE Recycling Engineering Research Center Jingmen GEM New Material Co. Ltd Jingmen 448124 PR China

Herein, high-quality cobalt carbonate is synthesized by employing a novel liquid phase precipitation route, where CoCl2 and ammonium bicarbonate solutions are used as the raw material and precipitator, respectively. The morphology of primary and secondary particles and agglomeration due to their nucleation and growth are influenced by the stirring speed, reaction temperature and pH value. Furthermore, the influence of process parameters on physiochemical properties has been systematically studied by measuring apparent density, FSSS particle size, specific surface area and chloride ions concentration. Under optimal processing conditions, i.e., stirring speed of 100 rpm, pH value of 7.0-7.1 and a reaction temperature of 55 °C, the as-synthesized cobalt carbonate rendered an apparent density of 0.74 g/cm3 and chloride ions concentration of 19 ppm. The results reveal that the washing efficiency of chloride ions is affected by the morphology of primary particles and stacking mode of the secondary particle in cobalt carbonate. In general, the higher BET specific surface area of cobalt carbonate results in lower chloride ions concentration.

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