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International Conference on data engineering

作者： Yanchao Tan Hang Lv Zihao Zhou Wenzhong Guo Bo Xiong Weiming Liu Chaochao Chen Shiping Wang Carl Yang College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany College of Computer Science Zhejiang University Hangzhou China Department of Computer Science Emory University Atlanta United States

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity- based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable.

关键词： Measurement Accuracy Taxonomy Semantics Predictive models Performance gain Linguistics

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SUBDIFFERENTIALLY POLYNOMIALLY BOUNDED FUNCTIONS AND GAUSSIAN SMOOTHING-BASED ZEROTH-ORDER OPTIMIZATION ∗

arXiv

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arXiv 2024年

作者： Lei, Ming Pong, Ting Kei Sun, Shuqin Yue, Man-Chung College of Applied Mathematics Chengdu University of Information Technology Chengdu China Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong Key Laboratory of Optimization Theory and Applications China West Normal University of Sichuan Province School of Mathematics Education China West Normal University Nanchong China Musketeers Foundation Institute of Data Science Department of Data and Systems Engineering The University of Hong Kong Hong Kong

We study the class of subdifferentially polynomially bounded (SPB) functions, which is a rich class of locally Lipschitz functions that encompasses all Lipschitz functions, all gradient- or Hessian-Lipschitz functions, and even some non-smooth locally Lipschitz functions. We show that SPB functions are compatible with Gaussian smoothing (GS), in the sense that the GS of any SPB function is well-defined and satisfies a descent lemma akin to gradient-Lipschitz functions, with the Lipschitz constant replaced by a polynomial function. Leveraging this descent lemma, we propose GS-based zeroth-order optimization algorithms with an adaptive stepsize strategy for minimizing SPB functions, and analyze their convergence rates with respect to both relative and absolute stationarity measures. Finally, we also establish the iteration complexity for achieving a (δ, ϵ)-approximate stationary point, based on a novel quantification of Goldstein stationarity via the GS gradient that could be of independent interest. Copyright © 2024, The Authors. All rights reserved.

关键词： Gaussian distribution

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Highly Sensitive Acetic Anhydride Sensor Based on Cd-Doped Biphase In2o3 Microspheres

SSRN

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SSRN 2023年

作者： Meng, Fanli Zhang, Hua Guo, Yinghao Zhang, Haoting College of Information Science and Engineering Northeastern University Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang110819 China

New drugs emerge in an endless stream, the global drug supply is increasing, and the number of drug users is steadily rising, so the detection of acetic anhydride-a raw material for drug preparation is of great significance for controlling the spread of drugs. The crystal phase of semiconductor metal oxides has an important impact on oxygen vacancy, microscopic morphology, specific surface area, and high-energy surface exposure, which in turn affects the catalytic and gas-sensing effects of materials. Ions doped can sometimes achieve the purpose of changing the crystal phase. In this paper, Cd-doped biphase (cubic phase and hexagonal phase) In2O3 nanospheres sensitive to acetic anhydride were synthesized via one easy hydrothermal process. The fabricated sensor has high sensitivity (372) to 100 ppm acetic anhydride in the dry environment of 220 °C, excellent selectivity and good stability. X-ray diffraction (XRD), energy dispersive spectroscopy (EDS) analysis, and scanning electron microscopy (SEM) were used to observe the crystal phase, elemental information and nanoscale appearance of the prepared compounds, and the gas-sensitive mechanism was briefly analyzed according to the characterization information. The enhancing acetic anhydride sensing properties are owing to the change of internal electronic channels and the crystal structure of In2O3 material caused by Cd-doped. © 2023, The Authors. All rights reserved.

关键词： Scanning electron microscopy

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Qualitative Spatial Reasoning with Oriented Point Relation in 3D Space

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Chinese Journal of Electronics 2019年第2期28卷 325-330页

作者： WANG Shengsheng DONG Ruyi SONG Wenzhuo WANG Chuangfeng College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computing and Knowledge Engineering of Ministry of Education Jilin Vocational College of Industry and Technology

Among qualitative direction relation models, Oriented point relation algebra（OPRAm） is a remarkable model for robot navigation with uncertain direction information. It has great advantages in providing powerful expressions with very limited information compared with other point-based spatial relation *** original OPRAm is defined in 2D space, and its model and reasoning algorithm are found not applicable in 3D space. We proposed a novel direction relation model named OPRA3Dmto extend the original OPRAm to 3D space,and presented a new reasoning algorithm on Oriented point relation algebra in three dimension（OPRA3Dm）. A further study was carried out for composition reasoning on OPRA3Dm. The proposed reasoning algorithm will deduce new information which cannot be directly detected by hardware. The experiment showed the algorithm had some practical significance, it can be applied to the Unmanned aerial vehicle（UAV） navigation and similar scenarios.

关键词： Qualitative spatial reasoning Oriented point relation algebra in three dimension(OPRA3Dm) Composition reasoning Unmanned aerial vehicle navigation

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Can Near-Real-Time Satellite Precipitation Estimations Accurately Reproduce the Record-Breaking Meiyu Event in 2020

SSRN

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SSRN 2023年

作者： Liu, Wen Lü, Haishen Zhu, Yonghua Wang, Xiaoyi Gao, Pei Su, Jianbin State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering National Cooperative Innovation Center for Water Safety and Hydro-science College of Hydrology and Water Resources Hohai University Nanjing210098 China National Tibetan Plateau Data Center Key Laboratory of Tibetan Environmental Changes and Land Surface Processes Institute of Tibetan Plateau Research Chinese Academy of Sciences Beijing100101 China

Due to the continuous heavy rainfall, the Meiyu season is frequently accompanied by a multitude of flood-related disasters. Hence, an accurate and timely monitoring of Meiyu precipitation is crucial for effectively preventing and mitigating these calamities. Given the high potential utility of the near-real-time (NRT) products from Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), namely IMERG-E and IMERG-L products, in acquiring precipitation data, it is important to quantify their errors and uncertainties during the Meiyu season. In this study, using the gauge precipitation observations as benchmark, the performance of IMERG NRT products is assessed and compared during the record-breaking Meiyu event in 2020. Meanwhile, the Motion Vector Kalman product from Global Satellite Mapping of Precipitation (GSMaP-MVK) is also utilized as parallel control. Results indicate that the three satellite products can typically capture the spatial-temporal evolution of Meiyu precipitation while IMERG-L has the best statistical performance. Although the peak time of IMERG-L is about two hours earlier than the observations, it can generally reproduce the diurnal cycle of observed precipitation amount with slight underestimation of peak value. In contrast, IMERG-E and GSMaP-MVK perform much worse. In terms of precipitation frequency and intensity, the two IMERG products both show poor performance in reproducing their diurnal variation. Besides, the IMERG products both outperform GSMaP-MVK on almost all precipitation intensity groups, except for light rain. Meanwhile, potentially affected by varying weather circulations, the bias of IMERG products in the afternoon is significantly higher than in the morning for all precipitation intensities. More importantly, the performance fluctuation of IMERG in reproducing observed diurnal cycle of rainstorm events is closely related to their varied data sources. These findings can serve as a valuable reference fo

关键词： Uncertainty analysis

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Visualizing nonlinear resonance in nanomechanical systems via single-electron tunneling

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Nano Research 2021年第4期14卷 1156-1161页

作者： Xinhe Wang Lin Cong Dong Zhu Zi Yuan Xiaoyang Lin Weisheng Zhao Zaiqiao Bai Wenjie Liang Ximing Sun Guang-Wei Deng Kaili Jiang Fert Beijing Research Institute School of Microelectronics&Beijing Advanced Innovation Centre for Big Data and Brain Computing(BDBC)Beihang UniversityBeijing 100191China State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics and Tsinghua-Foxconn Nanotechnology Research CenterTsinghua UniversityBeijing 100084China Key Laboratory of Quantum Information CASUniversity of Science and Technology of ChinaHefei 230026China Department of Physics Beijing Normal UniversityBeijing 100875China Institute of Physics Chinese Academy of SciencesBeijing 100080China Institute of Nuclear and New Energy Technology Collaborative Innovation Center of Advanced Nuclear Energy TechnologyKey Laboratory of Advanced Reactor EngineeringTsinghua UniversityBeijing 100084China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 610054China

Numerous reports have elucidated the importance of mechanical resonators comprising quantum-dot-embedded carbon nanotubes(CNTs)for studying the effects of single-electron ***,there is a need to investigate the single-electron transport that drives a large amplitude into a nonlinear ***,a CNT hybrid device has been investigated,which comprises a gate-defined quantum dot that is embedded into a mechanical resonator under strong actuation *** Coulomb peak positions synchronously oscillate with the mechanical vibrations,enabling a single-electron Chopper*1 ***,the vibration amplitude of the CNT versus its frequency can be directly visualized via detecting the time-averaged single-electron tunneling *** understand this phenomenon,a general formula is derived for this time-averaged single-electron tunneling current,which agrees well with the experimental *** using this visualization method,a variety of nonlinear motions of a CNT mechanical oscillator have been directly recorded,such as Duffing nonlinearity,parametric resonance,and double-,fractional-,mixed-frequency *** approach opens up burgeoning opportunities for investigating and understanding the nonlinear motion of a nanomechanical system and its interactions with electron transport in quantum regimes.

关键词： carbon nanotube mechanical resonator quantum dot nonlinear coupling

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Breaking IoT data Silos: Trustworthy data Trading with Consortium Blockchain and Zero-knowledge Proof

Breaking IoT Data Silos: Trustworthy Data Trading with Conso...

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International Conference on Computer Supported Cooperative Work in Design

作者： Wanshan Liu Yubing Han Guijuan Wang Yao Huang Anming Dong Jiguo Yu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Big Data Institute Qilu University of Technology Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

The Internet of Things (IoT) connects numerous de-vices and sensors, generating data with significant informational and economic value. However, data silos hinder effective data utilization and trading, leading to the dispersion of data across various devices and systems. Additionally, traditional third-party trading models face challenges related to data security and trust. To address these issues, this paper proposes a secure data trading framework based on a consortium blockchain and designs a corresponding solution. Specifically, it introduces the integration of zero-knowledge proofs into the smart contract scheme for authenticity and integrity verification of transaction data. From the perspective of IoT device users, this paper aims to enable secure data trading through a decentralized platform, using off-chain storage methods to reduce the blockchain's data burden while ensuring security and privacy. Off-chain storage encrypts and securely stores sensitive data, recording only necessary information on the blockchain, effectively protecting user privacy. To validate the practicality of the proposed solution, experiments were conducted using Hyperledger Fabric, demonstrating its feasibility in facilitating secure storage and trustworthy trading of IoT data. Finally, this study analyzes the experimental results and offers valuable insights for future research.

关键词： data privacy Limiting Scalability Smart contracts Reliability engineering Blockchains Sensors Recording Internet of Things Secure storage

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Non-invasively identifying candidates of active surveillance for prostate cancer using magnetic resonance imaging radiomics

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 209-219页

作者： Yuwei Liu Litao Zhao Jie Bao Jian Hou Zhaozhao Jing Songlu Liu Xuanhao Li Zibing Cao Boyu Yang Junkang Shen Ji Zhang Libiao Ji Zhen Kang Chunhong Hu Liang Wang Jiangang Liu Department of Radiology Beijing Friendship HospitalCapital Medical UniversityBeijing 100050China School of Engineering Medicine Beihang UniversityBeijing 100191China Key Laboratory of Big Data-Based Precision Medicine(Beihang University) Ministry of Industry and Information Technology of the People’s Republic of ChinaBeijing 100191China School of Biological Science and Medical Engineering Beihang UniversityBeijing 100191China Department of Radiology the First Affiliated Hospital of Soochow UniversitySuzhou 215006Jiangsu ProvinceChina Department of CT-MR Center the People’s Hospital of JimoQingdao 266200Shandong ProvinceChina Department of Radiology Sinopharm Tongmei General HospitalDatong 037003Shanxi ProvinceChina Department of Urology Beijing Friendship HospitalCapital Medical UniversityBeijing 100050China Department of Radiology the Second Affiliated Hospital of Soochow UniversitySuzhou 215004Jiangsu ProvinceChina Department of Radiology the People’s Hospital of TaizhouTaizhou 225399Jiangsu ProvinceChina Department of Radiology Changshu No.1 People’s HospitalChangshu 215501Jiangsu ProvinceChina Department of Radiology Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030Hubei ProvinceChina Beijing Engineering Research Center of Cardiovascular Wisdom Diagnosis and Treatment Beijing 100191China

Active surveillance(AS)is the primary strategy for managing patients with low or favorable-intermediate risk prostate cancer(PCa).Identifying patients who may benefit from AS relies on unpleasant prostate biopsies,which entail the risk of bleeding and *** the current study,we aimed to develop a radiomics model based on prostate magnetic resonance images to identify AS candidates non-invasively.A total of 956 PCa patients with complete biopsy reports from six hospitals were included in the current multicenter retrospective *** National Comprehensive Cancer Network(NCCN)guidelines were used as reference standards to determine the AS *** discriminate between AS and non-AS candidates,five radiomics models(i.e.,eXtreme Gradient Boosting(XGBoost)AS classifier(XGB-AS),logistic regression(LR)AS classifier,random forest(RF)AS classifier,adaptive boosting(Ada-Boost)AS classifier,and decision tree(DT)AS classifier)were developed and externally validated using a three-fold cross-center validation based on five classifiers:XGBoost,LR,RF,AdaBoost,and *** under the receiver operating characteristic curve(AUC),accuracy(ACC),sensitivity(SEN),and specificity(SPE)were calculated to evaluate the performance of these ***-AS exhibited an average of AUC of 0.803,ACC of 0.693,SEN of 0.668,and SPE of 0.841,showing a better comprehensive performance than those of the other included radiomic ***,the XGB-AS model also presented a promising performance for identifying AS candidates from the intermediate-risk cases and the ambiguous cases with diagnostic discordance between the NCCN guidelines and the Prostate Imaging-Reporting and data System *** results suggest that the XGB-AS model has the potential to help identify patients who are suitable for AS and allow non-invasive monitoring of patients on AS,thereby reducing the number of annual biopsies and the associated risks of bleeding and infection.

关键词： Active surveillance Prostate cancer Magnetic resonance imaging Radiomics

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Hollow Urchin Co-Fe2o3 With Outstanding Selectivity and Fast Responding For Ppb Level Nh3 Sensing Via Lewis Acid-Base Effect

SSRN

引用

SSRN 2023年

作者： Liu, Yang Ji, Hanyang Yuan, Zhenyu Zhu, Hongmin Kong, Lu Gao, Hongliang Meng, Fanli College of Information Science and Engineering Northeastern University Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education Shenyang110819 China

Layered double hydroxides (LDH) are a class of layered compounds, which are popular among researchers because of the large specific surface area and excellent catalytic properties. In this work, Co-Fe2O3 2 was prepared with urchin Co-Fe LDH as a precursor. The morphology of Co-Fe2O3 2 was not changed, and burrs were evenly distributed on the surface with a diameter of about 3-5 nm. Co-Fe2O3 2 showed outstanding gas sensitivity to NH3, with favourable repeatability, stability and selectivity, as well as an ultra-low application detection limit of 10 ppb (response was 5%). The response of the sensor to 10 ppm NH3 at 275 °C was 298% with a fast response/recovery rate (7.2/5.4 s). BET revealed that the specific surface area of Co-Fe2O3 2 was 114.5 m2/g, which could provide more sites for gas adsorption and reaction. The results of XPS, Raman, and FTIR spectras indicated that some Co2+ replaced the position of Fe3+ to generate abundant oxygen vacancy, which could enhance the activity of the sensitive material. The small size effect and the formation of p-n junction increased the adsorption capacity of the sensitive material. Moreover, the synergistic effect of Lewis acid-base and catalytic of Co3O4 greatly improved the selectivity of the sensor to NH3. The growth process and principle of Co-Fe LDH were discussed and analyzed in detail, which provided a new idea for the controlled synthesis of LDH. Co-Fe2O3 2 is a promising gas-sensitive material for the rapid detection of ammonia. © 2023, The Authors. All rights reserved.

关键词： Oxygen vacancies

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MDEC: Mamba-based Debiased Extended Contrast Learning in Sequential Recommendation

MDEC: Mamba-based Debiased Extended Contrast Learning in Seq...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Yan Huang Zhenyu Yang Baojie Xu Wenyue Hu Zhibo Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Recommender systems are critical for mitigating information overload, assisting users in uncovering their latent interests, and enhancing their overall experience. Sequential recommendation leverages users' historical interaction sequences to predict dynamic interests more effectively than traditional rec-ommendation approaches. However, existing models-including RNN-based and Transformer-based methods-face significant limitations. RNNs struggle with vanishing gradients and long-term dependency capture, while Transformers, though effective for long-range relationships, suffer from computational inefficiency due to their quadratic attention complexity. Recent advancements have employed contrastive learning for sequential recommendation, aiming to enhance the consistency between augmented views and improve self-supervised learning signals. Despite their promise, these methods often lack diversity in data augmentation strategies, which restricts their capacity for bias mitigation, resulting in augmented data that still retains inherent biases. To address these challenges, we propose MDEC, a novel sequential modeling framework that leverages State Space Models (SSM) combined with unbiased contrastive learning. MDEC utilizes Mamba to efficiently model user preferences as an alternative to Transformer-based models. Additionally, it integrates graph-based information, including item transition and co-interaction data, to improve data augmentation comprehensively. Finally, we introduce adaptive anchor-enhanced contrastive learning, which adaptively utilizes augmented samples to improve representation quality and bias mitigation. Extensive experiments on multiple datasets demonstrate that MDEC significantly out-performs existing models, showcasing improved efficiency, better mitigation of biases, and enhanced recommendation quality. Code is available at https://***/Echohuangyan/CSLP.

关键词： Adaptation models Federated learning Prevention and mitigation Computational modeling data visualization Contrastive learning Diversity methods Transformers data augmentation Recommender systems

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