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6th International Workshop on Specialty Optical Fibers and Their Applications, WSOF 2019

作者： Chu, Yushi Canning, John Fu, Xinghu Luo, Yanhua Cook, Kevin Zhang, Jianzhong Peng, Gang-Ding Tech Lab School of Electrical and Data Engineering University of Technology Sydney SydneyNSW2007 & 2019 Australia Photonics and Optical Communications School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province School of Information Science and Engineering Yanshan University Qinhuangdao066004 China Key Lab of In-Fiber Integrated Optics of Ministry of Education School of Science Harbin Engineering University Harbin150001 China

3D printed glass optical fiber preforms have been fabricated from silica containing resin. The fabrication process includes resin preparation, preform printing, debinding and sintering. Results demonstrate the silica ... 详细信息

ISBN: (纸本)9781510631618

关键词： Sintering

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Facile synthesis of zinc-based organic framework for aqueous Hg (Ⅱ) removal: Adsorption performance and mechanism

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Nano Materials Science 2021年第4期3卷 429-439页

作者： Biao Zeng Wei Wang Sijin He Guo Lin Wenjia Du Jun Chang Zhao Ding The State Key Laboratory of Refractories and Metallurgy Wuhan University of Science and TechnologyWuhan430081PR China Key Laboratory for Ferrous Mtallurgy and Resources Utilization of Ministry of Education Wuhan University of Science and TechnologyWuhan430081PR China China E-port Data Center Division of Chengdu Chengdu610041PR China Electrochemical Innovation Lab Department of Chemical EngineeringUniversity College London(UCL)LondonWC1E 68TUK College of Material and Chemical Engineering Tongren University Tongren554300GuizhouPR China Department of Mechanical Materials and Aerospace Engineering Illinois Institute of TechnologyChicagoIL60616 USA

Mercury(Hg)ions can lead to a serious impact on the environment;therefore,it was necessary to find an effective method for absorbing these toxic Hg ***,the adsorbent(Zn-AHMT)was synthesized from zinc nitrate and 4-amino 3-hydrazine-5 mercapto-1,2,4-triazole(AHMT)by one-step method and,characterized the microstruc-ture and absorption performance by fourier transform infrared spectroscopy(FTIR),field emission scanning electron microscopy(FESEM),X-ray diffraction(XRD),Brunauer-Emmett Teller(BET),Thermal Gravimetric Analyzer(TGA)and X-ray photoelectron spectroscopy(XPS).Through a plethora of measurements,we found that the maximum adsorption capacity was 802.8 mg/g when the optimal pH of Zn-AHMT was *** isothermal and kinetic experiments confirm that the reaction process of Zn-AHMT was chemisorption,while the adsorption process conforms to the Hill model and pseudo second order kinetic *** experiments showed that the adsorption process was spontaneous and *** experiments were performed in the simulated wastewater containing Mn,Mg,Cr,Al,Co,Ni,Hg *** results showed that the Zn-AHMT has a stronger affinity for Hg *** removal rate of Zn-AHMT remained above 98%,indicating that the Zn-AHMT had a good stability validated by three adsorption-desorption repeatable *** to the XPS results,the adsorption reaction of Zn-AHMT was mainly attributed to the chelation and ion *** was further explained by both density functional theory(DFT)calculation and frontier molecular orbital *** therefore propose the adsorption mechanism of *** adsorption reaction facilitates via the synergistic action of S and N ***,the bonding between the adsorbent and the N atom has been proved to be more *** study demonstrated that Zn-AHMT had a promising application prospect in mercury removal.

关键词： Metal-organic frameworks(MOFs) Hg(Ⅱ) Selectivity Adsorption mechanism

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Joint-Motion Mutual Learning for Pose Estimation in Video 24

Joint-Motion Mutual Learning for Pose Estimation in Video

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wu, Sifan Chen, Haipeng Yin, Yifang Hu, Sihao Feng, Runyang Jiao, Yingying Yang, Ziqi Liu, Zhenguang College of Computer Science and Technology Jilin University Changchun China A STAR Singapore Singapore Georgia Institute of Technology AtlantaGA United States School of Artificial Intelligence Jilin University Changchun China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China Institute of Blockchain and Data Security Hangzhou China

ISBN: (纸本)9798400706868

Human pose estimation in videos has long been a compelling yet challenging task within the realm of computer vision. Nevertheless, this task remains difficult because of the complex video scenes, such as video defocus and self-occlusion. Recent methods strive to integrate multi-frame visual features generated by a backbone network for pose estimation. However, they often ignore the useful joint information encoded in the initial heatmap, which is a by-product of the backbone generation. Comparatively, methods that attempt to refine the initial heatmap fail to consider any spatio-temporal motion features. As a result, the performance of existing methods for pose estimation falls short due to the lack of ability to leverage both local joint (heatmap) information and global motion (feature) dynamics. To address this problem, we propose a novel joint-motion mutual learning framework for pose estimation, which effectively concentrates on both local joint dependency and global pixel-level motion dynamics. Specifically, we introduce a context-aware joint learner that adaptively leverages initial heatmaps and motion flow to retrieve robust local joint feature. Given that local joint feature and global motion flow are complementary, we further propose a progressive joint-motion mutual learning that synergistically exchanges information and interactively learns between joint feature and motion flow to improve the capability of the model. More importantly, to capture more diverse joint and motion cues, we theoretically analyze and propose an information orthogonality objective to avoid learning redundant information from multi-cues. Empirical experiments show our method outperforms prior arts on three challenging benchmarks. © 2024 ACM.

关键词： Motion estimation

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IKGN: Intention-aware knowledge Graph Network for POI Recommendation

IKGN: Intention-aware Knowledge Graph Network for POI Recomm...

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IEEE International Conference on data Mining (ICDM)

作者： Xiaoyu Zhu Chenyang Bu Bingbing Dong Shengwei Ji Yi He Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology China School of Artificial Intelligence and Big Data Hefei University China Department of Computer Science Old Dominion University USA Research Center for Knowledge Engineering Zhejiang Lab China

Point-of-Interest (POI) recommendation, pivotal for guiding users to their next interested locale, grapples with the persistent challenge of data sparsity. Whereas knowledge graphs (KGs) have emerged as a favored tool to mitigate the issue, existing KG-based methods tend to overlook two crucial elements: the intention steering users’ location choices and the high-order topological structure within the KG. In this paper, we craft an Intention-aware knowledge Graph (IKG) that harmonizes users’ visit histories, movement trajectories, and location categories to model user intentions. Building upon IKG, our novel Intention-aware knowledge Graph Network (IKGN) delves deeper into the POI recommendation by weighing and propagating node embeddings through an attention mechanism, capturing the unique locational intent of each user. A sequential model like GRU is then employed to ensure a comprehensive representation of users’ short- and long-term location preferences. An empirical study on two real-world datasets validates the effectiveness of our proposed IKGN, with it markedly outshining seven benchmark rival models in both Recall and NDCG metrics. The code of IKGN is available at https://***/Jungle123456/IKGN.

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Marginal Value-Based Edge Resource Pricing and Allocation for Deadline-Sensitive Tasks

Marginal Value-Based Edge Resource Pricing and Allocation fo...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Puwei Wang Zhouxing Sun Ying Zhan Haoran Li Xiaoyong Du School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China Beijing China Guizhou University of Finance and Economics Guiyang City China

In edge computing (EC), resource allocation is to allocate computing, storage and networking resources on the edge nodes (ENs) efficiently and reasonably to tasks generated by users. Due to the resource-limitation of ENs, the tasks often need to compete for the resources. Pricing mechanisms are widely used to deal with the resource allocation problem, and the valuations of tasks play a critical role in the price mechanisms. However, users naturally are not willing to expose the valuations of their tasks due to conflicts of interests. Current research works usually adopt truthful auctions to motivate the users to report honestly the valuations of their tasks. In this paper, we introduce the marginal value to estimate the valuations of tasks, and propose a marginal value-based pricing mechanism using the incentive theory, which motivates the tasks with higher marginal values to actively request more resources. The EC platform sets the resource prices using the price mechanism, and then the users determine their resource requests relying on the resource prices and the valuations of their tasks. After receiving the deadline-sensitive tasks from the users, the resource allocation can be modeled as a knapsack problem with the deadline constraints. Extensive experimental results demonstrate that our approach is computationally efficient and is promising in enhancing the utility of the EC platform and the tasks.

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Balanced Tree Partitioning with Succinct Logic

Balanced Tree Partitioning with Succinct Logic

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IEEE International Conference on Big knowledge (ICBK)

作者： Xindong Wu Shaojing Sheng Peng Zhou Key Laboratory of Knowledge Engineering with Big Data (Heifei Unversity of Technology) Ministry of Education Heifei China School of Compuster Science and Technology Anhui University Heifei China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

As a widely used data structure, graphs are good at characterizing data with internal associations, such as social and biological data. Tree structured data are special and are widely used in many real-world applications, such as organizational structure analysis and genealogical knowledge graph reasoning. For example, in kinship knowledge graph analysis, when a genealogical tree is particularly large (more than 25 levels and 45,000 nodes), it is a great challenge to partition this large tree into a specified number of subtrees with succinct logic and a balanced number of nodes. Therefore, in this paper, we propose the TPA (tree partitioning algorithm) algorithm to achieve a balanced and succinct logic partition of large-scale tree structured data. TPA first extracts all related nodes from a massive graph database and then constructs the convergent subgraph into a complete tree with a specified root node. Specifically, several virtual nodes are supplemented for generation-skipping connected nodes to achieve correct node numbering and partitioning. Finally, a graph partitioning algorithm is executed on the complete tree to obtain a specified number of subtrees with succinct logic and balanced node scales. Experiments conducted on four real-world datasets verify the effectiveness of our TPA algorithm.

关键词： Partitioning algorithms data visualization Convergence data analysis Distributed databases Big data

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Learning Relation-Specific Representations for Few-shot knowledge Graph Completion

arXiv

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arXiv 2022年

作者： Li, Yuling Yu, Kui Zhang, Yuhong Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei230601 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China

Recent years have witnessed increasing interest in few-shot knowledge graph completion (FKGC), which aims to infer unseen query triples for a few-shot relation using a few reference triples about the relation. The primary focus of existing FKGC methods lies in learning relation representations that can reflect the common information shared by the query and reference triples. To this end, these methods learn entity-pair representations from the direct neighbors of head and tail entities, and then aggregate the representations of reference entity pairs. However, the entity-pair representations learned only from direct neighbors may have low expressiveness when the involved entities have sparse direct neighbors or share a common local neighborhood with other entities. Moreover, merely modeling the semantic information of head and tail entities is insufficient to accurately infer their relational information especially when they have multiple relations. To address these issues, we propose a Relation-Specific Context Learning (RSCL) framework, which exploits graph contexts of triples to learn global and local relation-specific representations for few-shot relations. Specifically, we first extract graph contexts for each triple, which can provide long-term entity-relation dependencies. To encode the extracted graph contexts, we then present a hierarchical attention network to capture contextualized information of triples and highlight valuable local neighborhood information of entities. Finally, we design a hybrid attention aggregator to evaluate the likelihood of the query triples at the global and local levels. Experimental results on two public datasets demonstrate that RSCL outperforms state-of-the-art FKGC methods. © 2022, CC BY.

关键词： knowledge graph

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Nonlinear Cross-Domain Feature Representation Learning Method Based on Dual Constraints

Nonlinear Cross-Domain Feature Representation Learning Metho...

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IEEE International Conference on Big knowledge (ICBK)

作者： Han Ding Yuhong Zhang Shuai Yang Yaojin Lin Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Data Science and Intelligence Application Fujian Province University Zhangzhou China

Feature representation learning is a research focus in domain adaptation. Recently, due to the fast training speed, the marginalized Denoising Autoencoder (mDA) as a standing deep learning model has been widely utilized for feature representation learning. However, the training of mDA suffers from the lack of nonlinear relationship and does not explicitly consider the distribution discrepancy between domains. To address these problems, this paper proposes a novel method for feature representation learning, namely Nonlinear cross-domain Feature learning based Dual Constraints (NFDC), which consists of kernelization and dual constraints. Firstly, we introduce kernelization to effectively extract nonlinear relationship in feature representation learning. Secondly, we design dual constraints including Maximum Mean Discrepancy (MMD) and Manifold Regularization (MR) in order to minimize distribution discrepancy during the training process. Experimental results show that our approach is superior to several state-of-the-art methods in domain adaptation tasks.

关键词： Noise reduction Manifolds Training Adaptation models Kernel Deep learning Feature extraction

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Chinese Lexical Simplification

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IEEE/ACM Transactions on Audio Speech and Language Processing 2021年 29卷 1819-1828页

作者： Qiang, Jipeng Lu, Xinyu Li, Yun Yuan, Yunhao Wu, Xindong Department of Computer Science Yangzhou China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei China

Lexical simplification has attracted much attention in many languages, which is the process of replacing complex words in a given sentence with simpler alternatives of equivalent meaning. Although the richness of vocabulary in Chinese makes the text very difficult to read for children and non-native speakers, there is no research work for the Chinese lexical simplification (CLS) task. To circumvent difficulties in acquiring annotations, we manually create the first benchmark dataset for CLS, which can be used for evaluating the lexical simplification systems automatically. To acquire a more thorough comparison, we present five different types of methods as baselines to generate substitute candidates for the complex word that includes synonym-based approach, word embedding-based approach, BERT-based approach, sememe-based approach, and a hybrid approach. Finally, we design the experimental evaluation of these baselines and discuss their advantages and disadvantages. To our best knowledge, this is the first study for CLS task. © 2014 IEEE.

关键词： BERT Lexical simplification pretrained language model unsupervised

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Visual group identification method of technical competitors using LinLog graph clustering algorithm

Visual group identification method of technical competitors ...

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IEEE International Conference on Computer Science and Automation engineering (CSAE)

作者： Hong-qi Han Xiaomi An Donghua Zhu Xuefeng Wang Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China) Ministry of Education School of Information Resource Management Beijing 100872 China School of Management and Economics Beijing Institute of Technology 100872 China

Visualization technique is a powerful method used by science and technology intelligence analysis experts to identify technical competitor groups. Common visualization methods tend to create graphs meeting the aesthetic criteria instead of finding better clusters, and their analysis results may provide misleading information. A process model of technical group identification method was presented using LinLog graph clustering algorithm to find better competitor groups. In the model, technical similarity value of each pair of competitors is measured based on their R&D output in sub-fields, and two competitors have a link when they have high similarity value; LinLog algorithm, which is aimed at producing better clusters, was employed to layout graph with competitors as nodes, their links as edges and technology similarity values as weights of edges. Experiment results show the efficiency of presented method.

关键词： Clustering algorithms Patents Algorithm design and analysis Visualization Springs Layout Companies

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