Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series

Forecasting on sparse multivariate time series (MTS) aims to model the predictors of future values of time series given their incomplete past, which is important for many emerging applications. However, most existing methods process MTS's individually, and do not leverage the dynamic distributions underlying the MTS's, leading to sub-optimal results when the sparsity is high. To address this challenge, we propose a novel generative model, which tracks the transition of latent clusters, instead of isolated feature representations, to achieve robust modeling. It is characterized by a newly designed dynamic Gaussian mixture distribution, which captures the dynamics of clustering structures, and is used for emitting timeseries. The generative model is parameterized by neural networks. A structured inference network is also designed for enabling inductive analysis. A gating mechanism is further introduced to dynamically tune the Gaussian mixture distributions. Extensive experimental results on a variety of real-life datasets demonstrate the effectiveness of our method.Comment: This paper is accepted by AAAI 202

Highly stretchable conductor inspired by compliant mechanism

Flexible and stretchable conductors have invaluable applications in multiple domains, such as sensors, displays, and electronic skins. The stable conductance exhibited by conductors when subjected to diverse forms of deformation, such as tensile stress, curvature, or torsion, represents a fundamental characteristic. Attaining high conductivity and stretchability simultaneously in conductive materials is a formidable challenge, owing to inherent constraints in materials found in nature. To overcome this problem, an innovative approach of structurally designing conductors using existing materials to achieve high deformability and stretchability, i.e. stretchable conductors inspired by a compliant mechanism is proposed in this paper. Thus, a novel stretchable conductor inspired by flexible mechanisms is introduced. Unlike stretchable conductors based on Kirigami structures, the stretchable conductor based on flexible mechanisms can achieve large in‐plane deformation within the material's strength limit. The concept and design process of the highly deformable stretchable conductor inspired by flexible mechanisms are presented in this paper. Experimental results show that the resistance change ratio of the conductor remains within 0.05% during the 0–200% strain process. The consistency and durability of the conductor during stretching deformation are also confirmed through 500 repetitions of the test. Additionally, the experiments with the electric motor and light‐emitting diode (LED) light confirm the conductor's ability to maintain a stable current

Machine learning prediction model based on enhanced bat algorithm and support vector machine for slow employment prediction.

The employment of college students is an important issue that affects national development and social stability. In recent years, the increase in the number of graduates, the pressure of employment, and the epidemic have made the phenomenon of 'slow employment' increasingly prominent, becoming an urgent problem to be solved. Data mining and machine learning methods are used to analyze and predict the employment prospects for graduates and provide effective employment guidance and services for universities, governments, and graduates. It is a feasible solution to alleviate the problem of 'slow employment' of graduates. Therefore, this study proposed a feature selection prediction model (bGEBA-SVM) based on an improved bat algorithm and support vector machine by extracting 1694 college graduates from 2022 classes in Zhejiang Province. To improve the search efficiency and accuracy of the optimal feature subset, this paper proposed an enhanced bat algorithm based on the Gaussian distribution-based and elimination strategies for optimizing the feature set. The training data were input to the support vector machine for prediction. The proposed method is experimented by comparing it with peers, well-known machine learning models on the IEEE CEC2017 benchmark functions, public datasets, and graduate employment prediction dataset. The experimental results show that bGEBA-SVM can obtain higher prediction Accuracy, which can reach 93.86%. In addition, further education, student leader experience, family situation, career planning, and employment structure are more relevant characteristics that affect employment outcomes. In summary, bGEBA-SVM can be regarded as an employment prediction model with strong performance and high interpretability

Characteristics and achievements of the Xin'an Medical School

The Xin'an Medical School began in the Jin Dynasty (266–420), developed in the Song Dynasty (960–1279), prospered in the Ming and Qing dynasties (1368–1911), and has been passed down to the modern era. As a school of medicine with distinct regional characteristics, it has contributed to the development of traditional Chinese medicine and exerted far-reaching influence, mainly in literature resources, medical theory, clinical application, and spiritual culture. This paper intends to discuss its academic characteristics and contribution to the development of traditional Chinese medicine, focusing on its formation, academic inheritance and innovation, overseas popularization, and integration of Confucianism, Taoism, and Buddhism in medicine. Keywords: Xin'an Medical School, Regional medical schools, Ancient Huizhou, Achievements and characteristic

Optimization results of GEBA with advanced peers on the IEEE CEC2017.

Optimization results of GEBA with advanced peers on the IEEE CEC2017.</p

Diagenetic Evolution Mechanism of the Jurassic Tuffaceous Sandstone Reservoir in Qikou Sag, Bohai Bay Basin, East China

Exploring for hydrocarbons in a pyroclastic-affected reservoir is an important research topic. Previous studies have mainly focused on laminated pyroclastic. A large number of dispersed pyroclastic is present in sedimentary rocks, and dispersed volcanic ash strongly influences the diagenetic evolution of sandstone reservoirs. However, these aspects remain understudied. We studied the mechanism of the diagenetic evolution of the Jurassic tuffaceous sandstone reservoir in Qikou Sag of the Bohai Bay Basin by performing inclusion temperature measurements, rock slice identification, and scanning electron microscopy, and using electron microprobes and microzone isotopes. We determined the mechanism of water-rock interaction. Based on microscopic observations, we determined that the main diagenesis included two-stage dolomite cementation, two-stage calcite cementation, quartz cementation, and transformation and dissolution of clay minerals. The hydrolysis and chemical transformation of pyroclastic during burial not only provided an alkaline environment in the early stage of diagenesis but also supplied ions for the formation of microcrystalline quartz and early dolomite and the transformation of clay minerals. Leaching and denudation generated early dissolution caused by a tectonic uplift. Following the epigenetic stage, microbial activity stimulated the formation of early calcite during the shallow burial stage. When the burial temperature of the stratum was 80 °C, the acidic fluid discharged from the thermal evolution of organic matter was neutralized by the soluble components in the pyroclastic, which prevented the formation of a large-scale acidic environment. When the burial temperature exceeded 100 °C, the acidic fluid generated by thermal catalytic decarboxylation of organic matter formed a large quantity of dissolution. The dissolution of plagioclase promoted the overgrowth of quartz and the growth of kaolinite

Details of the GEP dataset.

The employment of college students is an important issue that affects national development and social stability. In recent years, the increase in the number of graduates, the pressure of employment, and the epidemic have made the phenomenon of ’slow employment’ increasingly prominent, becoming an urgent problem to be solved. Data mining and machine learning methods are used to analyze and predict the employment prospects for graduates and provide effective employment guidance and services for universities, governments, and graduates. It is a feasible solution to alleviate the problem of ’slow employment’ of graduates. Therefore, this study proposed a feature selection prediction model (bGEBA-SVM) based on an improved bat algorithm and support vector machine by extracting 1694 college graduates from 2022 classes in Zhejiang Province. To improve the search efficiency and accuracy of the optimal feature subset, this paper proposed an enhanced bat algorithm based on the Gaussian distribution-based and elimination strategies for optimizing the feature set. The training data were input to the support vector machine for prediction. The proposed method is experimented by comparing it with peers, well-known machine learning models on the IEEE CEC2017 benchmark functions, public datasets, and graduate employment prediction dataset. The experimental results show that bGEBA-SVM can obtain higher prediction Accuracy, which can reach 93.86%. In addition, further education, student leader experience, family situation, career planning, and employment structure are more relevant characteristics that affect employment outcomes. In summary, bGEBA-SVM can be regarded as an employment prediction model with strong performance and high interpretability.</div

Fig 3 -

The employment of college students is an important issue that affects national development and social stability. In recent years, the increase in the number of graduates, the pressure of employment, and the epidemic have made the phenomenon of ’slow employment’ increasingly prominent, becoming an urgent problem to be solved. Data mining and machine learning methods are used to analyze and predict the employment prospects for graduates and provide effective employment guidance and services for universities, governments, and graduates. It is a feasible solution to alleviate the problem of ’slow employment’ of graduates. Therefore, this study proposed a feature selection prediction model (bGEBA-SVM) based on an improved bat algorithm and support vector machine by extracting 1694 college graduates from 2022 classes in Zhejiang Province. To improve the search efficiency and accuracy of the optimal feature subset, this paper proposed an enhanced bat algorithm based on the Gaussian distribution-based and elimination strategies for optimizing the feature set. The training data were input to the support vector machine for prediction. The proposed method is experimented by comparing it with peers, well-known machine learning models on the IEEE CEC2017 benchmark functions, public datasets, and graduate employment prediction dataset. The experimental results show that bGEBA-SVM can obtain higher prediction Accuracy, which can reach 93.86%. In addition, further education, student leader experience, family situation, career planning, and employment structure are more relevant characteristics that affect employment outcomes. In summary, bGEBA-SVM can be regarded as an employment prediction model with strong performance and high interpretability.</div