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arXiv 2022年

作者： Bao, Shilong Xu, Qianqian Yang, Zhiyong He, Yuan Cao, Xiaochun Huang, Qingming State Key Laboratory of Information Security Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China Alibaba Group China School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University China Key Laboratory of Big Data Mining and Knowledge Management CAS China Peng Cheng Laboratory China

Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, we argue that the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collaborative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to include a multiple set of representations for each user in the system. Based on this embedding paradigm, user preference toward an item is aggregated from different embeddings by taking the minimum item-user distance among the user embedding set. Furthermore, we observe that the diversity of the embeddings for the same user also plays an essential role in the model. To this end, we propose a diversity control regularization term to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could generalize well to unseen test data by tackling the challenge of the annoying operation that comes from the minimum value. Experiments over a range of benchmark datasets speak to the efficacy of DPCML. © 2022, CC BY-NC-SA.

关键词： Embeddings

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Enabling Efficient Random Access to Hierarchically-Compressed data

Enabling Efficient Random Access to Hierarchically-Compresse...

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International Conference on data engineering

作者： Feng Zhang Jidong Zhai Xipeng Shen Onur Mutlu Xiaoyong Du Key Laboratory of Data Engineering and Knowledge Engineering (MOE) and School of Information Renmin University of China Department of Computer Science and Technology Tsinghua University BNRist Computer Science Department North Carolina State University Department of Computer Science ETH Zürich

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Recent studies have shown the promise of direct data processing on hierarchically-compressed text documents. By removing the need for decompressing data, the direct data processing technique brings large savings in both time and space. However, its benefits have been limited to data traversal operations; for random accesses, direct data processing is several times slower than the state-of-the-art baselines. This paper presents a set of techniques that successfully eliminate the limitation, and for the first time, establishes the feasibility of effectively handling both data traversal operations and random data accesses on hierarchically-compressed data. The work yields a new library, which achieves 3.1× speedup over the state-of-the-art on random data accesses to compressed data, while preserving the capability of supporting traversal operations efficiently and providing large (3.9×) space savings.

关键词： Indexing data processing data structures Technological innovation Dictionaries Law data mining

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Intervention Prediction for Patients with Pressure Injury Using Random Forest

Intervention Prediction for Patients with Pressure Injury Us...

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IEEE International Conference on Big knowledge (ICBK)

作者： Liuqi Jin Yan Pan Jiaoyun Yang Lin Han Lin Lv Miki Raviv Ning An Key Laboratory of Knowledge Engineering with Big Data of the Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China Evidence-Based Nursing Center School of Nursing Lanzhou University Lanzhou China Gansu Provincal Hospital Wound and Ostomy Care Center Lanzhou China Vitalerter LTD Ha-Yarden Airport City Israel

ISBN: (纸本)9781665438599

Pressure injury (PI) is one of the major causes of short-term death. Early intervention for patients at risk plays an essential role in PI. However, many nurses may ignore risks. This paper aims to establish a model to predict interventions according to the patient's physical signs, which can help nurses develop care plans. We used data from 1,483 patients with 25 characteristics and 17 interventions. We use the Random Forest and Particle Swarm Optimization (PSO) to optimize model parameters. Then we compared it with KNN, SVM, and Decision Tree. The 10-fold cross-validation result showed that the Random Forest has better accuracy than other methods, with an f1 score of 0.84. This finding proved the feasibility of using machine learning to help formulate care plans according to the classification of index prediction results. Our model shows that hemoglobin, Braden PI score, and age are the three most influential risk factors.

关键词： Support vector machines Conferences Forestry Predictive models Indexes Decision trees Particle swarm optimization

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Stochastic relaxed inertial forward-backward-forward splitting for monotone inclusions in Hilbert spaces

arXiv

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arXiv 2021年

作者： Cui, Shisheng Shanbhag, Uday V. Staudigl, Mathias Vuong, Phan Tu Department of Industrial and Manufacturing Engineering Pennsylvania State University University ParkPA16802 United States Department of Data Science and Knowledge Engineering Maastricht University P.O. Box 616 MaastrichtNL-6200 MD Netherlands Mathematical Sciences University of Southampton Highfield SouthamptonSO17 1BJ United Kingdom

We consider monotone inclusions defined on a Hilbert space where the operator is given by the sum of a maximal monotone operator T and a single-valued monotone, Lipschitz continuous, and expectation-valued operator V. We draw motivation from the seminal work by Attouch and Cabot [1, 2] on relaxed inertial methods for monotone inclusions and present a stochastic extension of the relaxed inertial forward-backward-forward (RISFBF) method. Facilitated by an online variance reduction strategy via a mini-batch approach, we show that (RISFBF) produces a sequence that weakly converges to the solution set. Moreover, it is possible to estimate the rate at which the discrete velocity of the stochastic process vanishes. Under strong monotonicity, we demonstrate strong convergence, and give a detailed assessment of the iteration and oracle complexity of the scheme. When the mini-batch is raised at a geometric (polynomial) rate, the rate statement can be strengthened to a linear (suitable polynomial) rate while the oracle complexity of computing an ∊-solution improves to O(1/∊). Importantly, the latter claim allows for possibly biased oracles, a key theoretical advancement allowing for far broader applicability. By defining a restricted gap function based on the Fitzpatrick function, we prove that the expected gap of an averaged sequence diminishes at a sublinear rate of O(1/k) while the oracle complexity of computing a suitably defined ∊-solution is O(1/∊1+a) where a > 1. Numerical results on two-stage games and an overlapping group Lasso problem illustrate the advantages of our method compared to stochastic forward-backward-forward (SFBF) and SA *** Codes 37H10, 49M37, 65C40, 65K15. © 2021, CC BY.

关键词： Stochastic systems

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On Aggregation in Ensembles of Multilabel Classifiers

arXiv

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arXiv 2020年

作者： Nguyen, Vu-Linh Hüllermeier, Eyke Rapp, Michael Mencía, Eneldo Loza Fürnkranz, Johannes Heinz Nixdorf Institute Department of Computer Science Paderborn University Germany Knowledge Engineering Group TU Darmstadt Germany Computational Data Analytics Group JKU Linz Austria

While a variety of ensemble methods for multilabel classification have been proposed in the literature, the question of how to aggregate the predictions of the individual members of the ensemble has received little attention so far. In this paper, we introduce a formal framework of ensemble multilabel classification, in which we distinguish two principal approaches: "predict then combine" (PTC), where the ensemble members first make loss minimizing predictions which are subsequently combined, and "combine then predict" (CTP), which first aggregates information such as marginal label probabilities from the individual ensemble members, and then derives a prediction from this aggregation. While both approaches generalize voting techniques commonly used for multilabel ensembles, they allow to explicitly take the target performance measure into account. Therefore, concrete instantiations of CTP and PTC can be tailored to concrete loss functions. Experimentally, we show that standard voting techniques are indeed outperformed by suitable instantiations of CTP and PTC, and provide some evidence that CTP performs well for decomposable loss functions, whereas PTC is the better choice for non-decomposable losses. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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TCMKG: A Deep Learning Based Traditional Chinese Medicine knowledge Graph Platform

TCMKG: A Deep Learning Based Traditional Chinese Medicine Kn...

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IEEE International Conference on Big knowledge (ICBK)

作者： Ziqiang Zheng Yongguo Liu Yun Zhang Chuanbiao Wen Knowledge and Data Engineering Laboratory of Chinese Medicine School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China College of Medical Information Engineering Chengdu University of Traditional Chinese Medicine Chengdu China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

As an effective and novel knowledge management technology, knowledge graph can provide a new way for the inheritance and development of traditional Chinese medicine (TCM). However, the construction of the knowledge graph of TCM is still mainly based on structured data at present. With the accumulation of literatures and electronic medical records, a large amount of knowledge is stored in unstructured texts which urgently needs to be extracted for learning. In this study, we extract TCM core concepts and build ontology layer by analyzing the process of TCM diagnosis and treatment. Then we use deep learning to extract entities and their relations for building TCM knowledge graph from unstructured data. Finally, we build an end-to-end platform TCMKG based on knowledge graph, which can provide functions such as knowledge retrieval, visualization and data management for helping the learning and sharing of TCM knowledge.

关键词： Ontologies data visualization Semantics data mining Medical diagnostic imaging Crawlers

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Tuberculosis screening among children and adolescents in China:insights from a mathematical model

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Intelligent Medicine 2023年第3期3卷 157-163页

作者： Janne Estill Yangqin Xu Shouyuan Wu Lidong Hu Nan Yang Shu Yang Yaolong Chen Guobao Li Evidence-Based Medicine Center School of Basic Medical SciencesLanzhou UniversityLanzhouGansu 730000China Institute of Global Health University of GenevaGenevaSwitzerland School of Public Health Lanzhou UniversityLanzhouGansu 730000China Department of Rheumatology and Immunology The First Medical CenterChinese PLA General HospitalBejing 100853China College of Medical Information Engineering Chengdu University of Traditional Chinese MedicineChengduSichuan 610000China Research Unit of Evidence-Based Evaluation and Guidelines Chinese Academy of Medical Sciences(2021RU017)School of Basic Medical SciencesLanzhou UniversityLanzhouGansu 730000China Lanzhou University Institute of Health Data Science LanzhouGansu 730000China WHO Collaborating Centre for Guideline Implementation and Knowledge Translation LanzhouGansu 730000China Department of Lung Disease Shenzhen Third People's HospitalThe Second Affiliated HospitalSouthern University of Science and TechnologyShenzhen Guangdong 518112China

Background Tuberculosis(TB)continues to be prevalent in China also among children and adolescents in *** built a dynamic mathematical model for TB transmission in China,and applied it to compare the epidemic trends 2021–2030 under a range of screening interventions focusing on children and *** We developed a dynamic mathematical model with a flexible *** model can be applied either stochastically or deterministically,and can encompass arbitrary age structure and resistance *** the present version,we used the deterministic version excluding resistance but including age structure with six groups:0–5,6–11,12–14,15–17,18–64,and 65 years and *** parameterized the model by literature data and fitting it to case and death estimates provided by the World Health *** compared the new TB cases and TB-related deaths in each age group over the period 2021–2030 in 10 scenarios that involved intensified screening of particular age groups of children,adolescents,or young adults,or decreased or increased diagnostic accuracy of the *** Screening the entire age class of 18-year-old persons would prevent 517,000 TB cases and 14,600 TB-related deaths between years 2021 and 2030,corresponding to 6.6%and 5.5%decrease from the standard of care projection,*** screening of children aged 6–11 and,to a lesser extent,0–5 years,also reduced TB incidence and mortality,particularly among children of the respective ages but also in other age *** contrast,intensified screening of adolescents did not have a major *** with a simpler and less accurate method resulted in worsened outcomes,which could not be offset by more intensive *** accurate screening and better sensitivity to detect latent TB could prevent 2.3 million TB cases and 68,500 TB deaths in the coming 10 *** Routine screening in schools can efficiently reduce the burden of TB in *** should be intensifie

关键词： Tuberculosis China Children and adolescents Mathematical model Screening

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Applications of machine learning methods for boiling modeling and prediction: A comprehensive review

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Chemical Thermodynamics and Thermal Analysis 2022年 8卷

作者： Rashidi, M.M. Alhuyi Nazari, M. Harley, C. Momoniat, E. Mahariq, I. Ali, N. Faculty of Science University of Johannesburg PO Box 524 Auckland Park 2006 South Africa Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Sichuan Chengdu 610054 China Faculty of New Sciences and Technologies University of Tehran Tehran Iran Data Science Across Disciplines Research Group Institute for the Future of Knowledge Faculty of Engineering and the Built Environment University of Johannesburg Johannesburg South Africa Department of Mathematics and Applied Mathematics University of Johannesburg P.O. Box 524 Auckland Park 2006 South Africa College of Engineering and Technology American University of the Middle East Kuwait

Boiling refers to the heat transfer mechanism that occurs due to the phase transition from liquid to vapor. In comparison with single phase heat transfer, this mechanism has several advantages such as much higher rate at lower temperature differences. Regarding the complexities of two phase heat transfer simulation, due to the involvement of various parameters in this phenomenon, applying intelligent methods such as artificial neural networks could be useful for modeling this type of heat transfer mechanism. In the present article, studies on the modeling of pool boiling heat transfer utilizing machine learning methods have been reviewed, and their findings are reflected. According to the outcomes of the reviewed works, it can be concluded that using intelligent methods can provide accurate predictions of pool boiling heat transfer with a R² of around 0.99 in some cases. In addition, by applying these methods it would be possible to predict the heat transfer in cases of utilizing nanofluids or porous media. The exactness and applicability range of these models is influenced by several elements such as the considered inputs, applied methods and employed functions. Using an appropriate method with optimal parameter values for the relevant intelligent method would lead to higher precision in modeling. © 2022

关键词： Artificial neural network Machine learning Nanofluid Pool boiling

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ER: Equivariance Regularizer for knowledge Graph Completion

arXiv

引用

arXiv 2022年

作者： Cao, Zongsheng Xu, Qianqian Yang, Zhiyong Huang, Qingming State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China

Tensor factorization and distanced based models play important roles in knowledge graph completion (KGC). However, the relational matrices in KGC methods often induce a high model complexity, bearing a high risk of overfitting. As a remedy, researchers propose a variety of different regularizers such as the tensor nuclear norm regularizer. Our motivation is based on the observation that the previous work only focuses on the "size" of the parametric space, while leaving the implicit semantic information widely untouched. To address this issue, we propose a new regularizer, namely, Equivariance Regularizer (ER), which can suppress overfitting by leveraging the implicit semantic information. Specifically, ER can enhance the generalization ability of the model by employing the semantic equivariance between the head and tail entities. Moreover, it is a generic solution for both distance based models and tensor factorization based models. The experimental results indicate a clear and substantial improvement over the state-of-the-art relation prediction methods. © 2022, CC BY.

关键词： knowledge graph

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Simple and Deep Graph Convolutional Networks

arXiv

引用

arXiv 2020年

作者： Chen, Ming Wei, Zhewei Huang, Zengfeng Ding, Bolin Li, Yaliang School of Information Renmin University of China China Gaoling School of Articial Intelligence Renmin University of China China Beijing Key Lab of Big Data Management and Analysis Methods China MOE Key Lab of Data Engineering and Knowledge Engineering China School of Data Science Fudan University China Alibaba Group

Graph convolutional networks (GCNs) are a powerful deep learning approach for graph-structured data. Recently, GCNs and subsequent variants have shown superior performance in various application areas on real-world datasets. Despite their success, most of the current GCN models are shallow, due to the over-smoothing problem. In this paper, we study the problem of designing and analyzing deep graph convolutional networks. We propose the GCNII, an extension of the vanilla GCN model with two simple yet effective techniques: Initial residual and Identity mapping. We provide theoretical and empirical evidence that the two techniques effectively relieves the problem of over-smoothing. Our experiments show that the deep GCNII model outperforms the state-of-the-art methods on various semi- and full-supervised tasks. Code is available at https://***/chennnM/GCNII. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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