Correlation effects in the electronic structure of the Ni-based superconducting KNi2S2

The LDA plus Gutzwiller variational method is used to investigate the groundstate physical properties of the newly discovered superconducting KNi2S2. Five Ni-3d Wannier-orbital basis are constructed by the density-functional theory, to combine with local Coulomb interaction to describe the normal state electronic structure of Ni-based superconductor. The band structure and the mass enhanced are studied based on a multiorbital Hubbard model by using Gutzwiller approximation method. Our results indicate that the correlation effects lead to the mass enhancement of KNi2S2. Different from the band structure calculated from the LDA results, there are three energy bands across the Fermi level along the X-Z line due to the existence of the correlation effects, which induces a very complicated Fermi surface along the X-Z line. We have also investigated the variation of the quasi-particle weight factor with the hole or electron doping and found that the mass enhancement character has been maintained with the doping.Comment: 12 pages, 6 figure

A novel data analytic model for mining user insurance demands from microblogs

This paper proposes a method based on LDA model and Word2Vec for analyzing Microblog users' insurance demands. First of all, we use LDA model to analyze the text data of Microblog user to get their candidate topic. Secondly, we use CBOW model to implement topic word vectorization and use word similarity calculation to expand it. Then we use K-means model to cluster the expanded words and redefine the topic category. Then we use the LDA model to extract the keywords of various insurance information on the “Pingan Insurance” website and analyze the possibility of users with different demands to purchase various types of insurance with the help of word vector similarity. Finally, the validity of the method in this paper is verified against Microblog user information. The experimental results show that the accuracy, recall rate and F1 value of the LDA-CBOW extending method have been proposed compared with that of the traditional LDA model, respectively, which proves the feasibility of this method. The results of this paper will help insurance companies to accurately grasp the preferences of Microblog users, understand the potential insurance needs of users timely, and lay a foundation for personalized recommendation of insurance products

Online Unsupervised Multi-view Feature Selection

In the era of big data, it is becoming common to have data with multiple modalities or coming from multiple sources, known as "multi-view data". Multi-view data are usually unlabeled and come from high-dimensional spaces (such as language vocabularies), unsupervised multi-view feature selection is crucial to many applications. However, it is nontrivial due to the following challenges. First, there are too many instances or the feature dimensionality is too large. Thus, the data may not fit in memory. How to select useful features with limited memory space? Second, how to select features from streaming data and handles the concept drift? Third, how to leverage the consistent and complementary information from different views to improve the feature selection in the situation when the data are too big or come in as streams? To the best of our knowledge, none of the previous works can solve all the challenges simultaneously. In this paper, we propose an Online unsupervised Multi-View Feature Selection, OMVFS, which deals with large-scale/streaming multi-view data in an online fashion. OMVFS embeds unsupervised feature selection into a clustering algorithm via NMF with sparse learning. It further incorporates the graph regularization to preserve the local structure information and help select discriminative features. Instead of storing all the historical data, OMVFS processes the multi-view data chunk by chunk and aggregates all the necessary information into several small matrices. By using the buffering technique, the proposed OMVFS can reduce the computational and storage cost while taking advantage of the structure information. Furthermore, OMVFS can capture the concept drifts in the data streams. Extensive experiments on four real-world datasets show the effectiveness and efficiency of the proposed OMVFS method. More importantly, OMVFS is about 100 times faster than the off-line methods

Heart failure and patient-reported outcomes in adults with congenital heart disease from 15 countries

Background Heart failure (HF) is the leading cause of mortality and associated with significant morbidity in adults with congenital heart disease. We sought to assess the association between HF and patient-report outcomes in adults with congenital heart disease. Methods and Results As part of the APPROACH-IS (Assessment of Patterns of Patient-Reported Outcomes in Adults with Congenital Heart disease-International Study), we collected data on HF status and patient-reported outcomes in 3959 patients from 15 countries across 5 continents. Patient-report outcomes were: perceived health status (12-item Short Form Health Survey), quality of life (Linear Analogue Scale and Satisfaction with Life Scale), sense of coherence-13, psychological distress (Hospital Anxiety and Depression Scale), and illness perception (Brief Illness Perception Questionnaire). In this sample, 137 (3.5%) had HF at the time of investigation, 298 (7.5%) had a history of HF, and 3524 (89.0%) had no current or past episode of HF. Patients with current or past HF were older and had a higher prevalence of complex congenital heart disease, arrhythmias, implantable cardioverter-defibrillators, other clinical comorbidities, and mood disorders than those who never had HF. Patients with HF had worse physical functioning, mental functioning, quality of life, satisfaction with life, sense of coherence, depressive symptoms, and illness perception scores. Magnitudes of differences were large for physical functioning and illness perception and moderate for mental functioning, quality of life, and depressive symptoms. Conclusions HF in adults with congenital heart disease is associated with poorer patient-reported outcomes, with large effect sizes for physical functioning and illness perception. Registration URL: https://clinicaltrials.gov; Unique identifier: NCT02150603