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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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Effect of Functional Groups of Hydration Heat Controlling Materials on Cement Hydration and its Mechanism

SSRN

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SSRN 2022年

作者： Liu, Xiao Xie, Hui Luo, Qifeng Xia, Chunlei Ding, Yungchin Chen, Yichen Gao, Ruijun Wei, Zhongyuan Bai, Xiabing He, Rui Zhou, Wu Zhou, Xiangzheng Wang, Ziming Cui, Suping Faculty of Materials and Manufacturing Key Laboratory of Advanced Functional Materials Ministry of Education Beijing University of Technology Beijing100024 China Beijing Municipal Engineering Research Institute Beijing100037 China Institute of Mineral Resources Engineering National Taipei University of Technology Taipei10608 Taiwan China Building Materials Academy Beijing100024 China Tangshan Longyi Tech. Co. Ltd. Tangshan063004 China Beijing NAURA Microelectronics Equipment Co. Ltd. Beijing100176 China Engineering Research Center of Transportation Materials of Ministry of Education Chang’ an University Xi'An710061 China Guangxi Keda Building Material Chemicals Co. Ltd. Nanning530221 China Faculty of Materials and Manufacturing National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of Technology Beijing100124 China

Severe temperature rise of mass concrete caused by cement hydration can lead to thermal cracks which further reduce the durability of concrete. In this study, three types of hydration heat controlling materials (HHCMs) respectively containing pure hydroxyl (polyvinyl alcohol, PVA), carboxyl (polyacrylic acid, PAA) and ester groups (maleic acid-ethylene glycol ester, P(MA-EG)) were synthesized and mixed with cement-based materials, and the hydration heat and setting time of cement pastes and the adiabatic temperature rise and strength of mortars were tested. The results showed that PAA and P(MA-EG) reduced the peak value of hydration heat release rate of cement paste by 77.45% and 62.28% respectively, and prolonged 54.14h and 15.56h respectively. The cumulative heat release within 72h decreased by 42.42% and 64.50% respectively, and the central temperatures of mortars containing PAA and P(MA-EG) decreased by 11.9 °C and 18.5 °C respectively. The mechanism of HHCMs was further revealed in terms of adsorption capacity, hydrodynamic radius, and conductivity and hydration products of cement pastes. The HHCMs containing carboxyl or hydroxyl groups, adsorbed on cement particles by Ca 2+ complexation, while the HHCMs containing ester groups, i.e. P(MA-EG), continuously hydrolyzed to carboxyl groups in alkaline cement pastes, which hydrolysis rate changed with the hydration temperature of cement-based materials to further regulate the hydration process. This study aims to provide technical feasibility and theoretical basis for exploring new avenues of synthesizing HHCMs with different functional groups. These novel HHCMs with great potentials can be as new admixtures to reduce thermal cracks in concrete. © 2022, The Authors. All rights reserved.

关键词： Ethylene glycol

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ChaLearn looking at people: IsoGD and ConGD large-scale RGB-D gesture recognition

arXiv

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arXiv 2019年

作者： Wan, Jun Lin, Chi Wen, Longyin Li, Yunan Miao, Qiguang Escalera, Sergio Anbarjafari, Gholamreza Guyon, Isabelle Guo, Guodong Li, Stan Z. National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China JD Finance Mountain ViewCA United States University of Southern California Los AngelesCA90089-0911 United States School of Computer Science and Technology Xidian University & Xi'an Key Laboratory of Big Data and Intelligent Vision 2nd South Taibai Road Xi'an710071 China Universitat de Barcelona Computer Vision Center Spain iCV Lab Institute of Technology University of Tartu Estonia Faculty of Engineering Hasan Kalyoncu University Gaziantep Turkey Institute of Digital Technologies Loughborough University London United Kingdom ChaLearn United States University Paris-Saclay France institute of Deep Learning Baidu Research National Engineering Laboratory for Deep Learning Technology and Application China

The ChaLearn large-scale gesture recognition challenge has been run twice in two workshops in conjunction with the International Conference on Pattern Recognition (ICPR) 2016 and International Conference on Computer Vision (ICCV) 2017, attracting more than 200 teams around the world. This challenge has two tracks, focusing on isolated and continuous gesture recognition, respectively. This paper describes the creation of both benchmark datasets and analyzes the advances in large-scale gesture recognition based on these two datasets. We discuss the challenges of collecting large-scale ground-truth annotations of gesture recognition, and provide a detailed analysis of the current state-of-the-art methods for large-scale isolated and continuous gesture recognition based on RGB-D video sequences. In addition to recognition rate and mean jaccard index (MJI) as evaluation metrics used in our previous challenges, we also introduce the corrected segmentation rate (CSR) metric to evaluate the performance of temporal segmentation for continuous gesture recognition. Furthermore, we propose a bidirectional long short-term memory (Bi-LSTM) baseline method, determining the video division points based on the skeleton points extracted by convolutional pose machine (CPM). Experiments demonstrate that the proposed Bi-LSTM outperforms the state-of-the-art methods with an absolute improvement of 8.1% (from 0.8917 to 0.9639) of CSR. Copyright © 2019, The Authors. All rights reserved.

关键词： Long short-term memory

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Long term results in children underwent nuss procedure preschool with pectus excavatum: Real-world study with propensity matching

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Heliyon 2024年第20期10卷 e39477页

作者： Wang, Quan Kong, Xiangpan Ren, Chunian Pan, Zhengxia Wu, Chun Li, Hongbo Li, Yonggang Wang, Gang Dai, Jiangtao Department of Cardiothoracic Surgery Children's Hospital of Chongqing Medical University Ministry of Education Key Laboratory of Child Development and Disorders International Science and Technology Cooperation Base of Child Development and Critical Disorders National Clinical Research Center for Child Health and Disorders Chongqing Key Laboratory of Pediatrics Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering Chongqing Higher Institution Engineering Research Center of Children's Medical Big Data Intelligent Application Chongqing China Department of Urology Children's Hospital of Chongqing Medical University China

Background: It is controversial when surgery should be performed for pectus excavatum (PE). The study retrospectively compared the long-term prognosis in PE children undergoing the Nuss procedure at the age of 4–6 versus those performing surgery at the age of 12–14. Methods: 178 patients were included in the retrospective real-world research. Clinical Characteristics were collected at baseline. The endpoint of the study was set as the last follow-up at the age of 18 in outpatient services. Satisfaction and complication rate were set as the primary outcome and were collected at the endpoint. Hospital stay post-surgery and analgesia dosage were regard as the secondary outcome. To reduce potential bias between two different age groups, propensity score matching (PSM) analysis was analysized. Results: The oral analgesic dosage of children in the 4–6 years old age group is significantly lower than that of the 12–14 years old group (0.70/kg, 0.30–1.50/kg versus 1.50/kg, 0.90–2.30/kg, P < 0.001), and children in the younger age group are discharged earlier. There was no difference in overall satisfaction (89.1 %, versus 88.20 %, P = 0.99) and recurrence rate (7.2 %, versus 6.8 %, P = 0.99) between the two groups of when they reach the age of 18. Conclusions: Performing Nuss procedure at the age of 4–6 does not affect the overall long-term satisfaction of the child or increase the long-term recurrence rate. Surgical intervention in children with pectus excavatum preschool can provide a better postoperative experience in hospital compared with intervention in adolescent. © 2024 The Authors

关键词： Long-term results Nuss procedure Pectus excavatum Preschool

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Fusion of Visible and Infrared Images Based on IHS Transformation and Regional Variance Matching Degree

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IOP Conference Series: Earth and Environmental Science 2019年第1期234卷

作者： Chengquan Zhou Jinling Zhao Zhenggao Pan Qi Hong Linsheng Huang National Engineering Research Center for Analysis and Application of Agro-Ecological Big Data Anhui University Hefei 230601 China Beijing Research Center for Information Technology in Agriculture Beijing 100097 China Information Engineering Institute Suzhou University Suzhou 234000 China

It is usually necessary to identify and extract specific characteristics by deriving an informative fused image from multiple images. An effective fusion algorithm was proposed by combining the intensity-hue-saturation (IHS) transformation and the regional variance matching degree (RVMD) in our study. Visible and thermal infrared images of wheat were used as the original data sources. After finishing the IHS transformation, a fusion rule was designed to produce the new component I. More specifically, the high frequency fusion rule was generated by the RVMD with a threshold of 0.5 and a 3 × 3 moving window, and the weighted average was used as the low frequency fusion rule. Experimental results show that the proposed algorithm can avoid producing color distortion in comparison with the IHS transformation, and additionally, it can also enhance the edge contrast and produce more obvious texture resolution. In addition, three quantitative indicators including entropy, standard deviation and average gradient were used to validate the proposed algorithm. The analysis results show that the values of three indicators are respectively 7.82, 63.93, 10.06, which are better than the results derived from the IHS transformation and regional variance fusion.

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Improving The Biodegradability of Rice Straw by Electrochemical Pretreatment

SSRN

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SSRN 2022年

作者： Sun, Shaohua Zhang, Yuanyuan Yang, Zhaoyang Liu, Chunmei Zuo, Xiaoyu Tang, Yang Wan, Pingyu Liu, Yanping Li, Xiujin Coulon, Frederic Hu, Qing Yang, Xiao Jin State Key Laboratory of Chemical Resource Engineering Beijing University of Chemical Technology 15 Beisanhuan East Road Chaoyang District Beijing100029 China Beijing City Environmental Pollution Control and Resource Reuse Engineering Center Beijing University of Chemical Technology 15 Beisanhuan East Road Chaoyang District Beijing100029 China National Fundamental Research Laboratory of New Hazardous Chemicals Assessment and Accident Analysis Beijing University of Chemical Technology Beijing100029 China School of Water Energy and Environment Cranfield University Cranfield MK43 0AL United Kingdom School of Environmental Science and Engineering South University of Science and Technology Guangdong Shenzhen518055 China Beijing Huanding Environmental Big Data Institute No. 1 Wangzhuang Road Beijing100083 China

In this study, the efficacy of three different pretreatments of rice straw including alkaline hydrogen peroxide (NaOH-H2O2), electrochemically produced NaOH-H2O2 (eNaOH-H2O2) and electrohydrolysis (EH) was investigated. All three pretreatments allowed to reach 80% of total biogas production within 23 and 25 days which was 3 to 5 days faster compared to conventional NaOH pretreatment. The cumulative biogas production was enhanced by 8%, 22% and 14% for NaOH-H2O2, eNaOH-H2O2 and EH, respectively compared to traditional treatment. The eNaOH-H2O2 pretreatment decreased the crystallinity of cellulose and created a porous surface vein, thus promoting microbial degradation of lignocellulose. The findings of this study demonstrate that electrochemical pretreatment provides a promising approach for efficiently utilizing rice straw biomass to produce renewable electricity. © 2022, The Authors. All rights reserved.

关键词： Oxidation

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