4,788 research outputs found
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
ROLE OF ADAPTIVE IMMUNITY IN NERVOUS NECROSIS VIRUS PERSISTENT INFECTION ON GROUPER BY TRANSCRIPTOME APPROACH
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
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