A Double Joint Bayesian Approach for J-Vector Based Text-dependent Speaker Verification

J-vector has been proved to be very effective in text-dependent speaker verification with short-duration speech. However, the current state-of-the-art back-end classifiers, e.g. joint Bayesian model, cannot make full use of such deep features. In this paper, we generalize the standard joint Bayesian approach to model the multi-faceted information in the j-vector explicitly and jointly. In our generalization, the j-vector was modeled as a result derived by a generative Double Joint Bayesian (DoJoBa) model, which contains several kinds of latent variables. With DoJoBa, we are able to explicitly build a model that can combine multiple heterogeneous information from the j-vectors. In verification step, we calculated the likelihood to describe whether the two j-vectors having consistent labels or not. On the public RSR2015 data corpus, the experimental results showed that our approach can achieve 0.02\% EER and 0.02\% EER for impostor wrong and impostor correct cases respectively

The Use of Electronic Dictionaries in EFL Classroom

Today’s dictionaries have more information and are easier to access and to understand than ever before. And, with the advent of electronic formats, space is no longer the problem it was. Electronic dictionaries have become more and more attractive, accepted and popular to EFL learners at different levels, using electronic dictionaries in EFL classroom has gradually become an alternative to many. As for teachers, helping students tap into electronic dictionaries effectively is one of the best ways to help them become independent, lifelong language learners. In this essay, the functionality of electronic dictionaries and reasons why they are popular in EFL class will be introduced. Also, some of the current issues related to the integration of electronic dictionaries into EFL instruction and learning will be identified and discussed. The author’s views towards this topic will be presented as well, based on the observation and reflection of using electronic dictionaries in EFL classes at a Chinese university

The effects of environmental inspection on air quality: Evidence from China

To address ecological and environmental issues, central environmental inspection (CEI) coordinated by the Chinese Ministry of Ecology and Environment has been implemented since 2016. This paper aims to comprehensively evaluate how and how much CEI affects air quality. The results of the difference-in-differences models show that CEI improved the air quality and reduced the concentrations of PM2.5, PM10, NO2, and SO2 by 8.8%, 8.1%, 7.9%, and 2.4%, respectively. Moreover, environmental effectiveness was strengthened over the course of four rounds of inspection. The mediating model results indicate that effectiveness was achieved through active public participation, administrative punishments from the central inspectors, and positive rectification actions from the local governments. The greatest improvement in air quality occurred during the on-site inspection period, after which the effects gradually weakened. A review inspection was carried out to supervise the rectification tasks. The adoption of review inspection made the effects on air quality improvement reappear, which verifies that CEI in China is not just a temporary campaign-style enforcement but a normalized and effective governance of air pollution

Synthesis and Biological Evaluation of Ezetimibe Analogs as Possible Cholesterol Absorption Inhibitors

In order to investigate the SAR of Ezetimibe analogs for cholesterol absorption inhibitions, amide group and electron-deficient pyridine ring were introduced to the C-(3) carbon chain of Ezetimibe. Eight new derivatives of the 2-azetidinone cholesterol absorption inhibitors have been synthesized, and all of them were enantiomerically pure. All the new compounds were evaluated for their activity to inhibit cholesterol absorption in hamsters, and most of them showed comparable effects in lowering the levels of total cholesterol in the serum

A graph-cut approach to image segmentation using an affinity graph based on l0−sparse representation of features

International audienceWe propose a graph-cut based image segmentation method by constructing an affinity graph using l0 sparse representation. Computing first oversegmented images, we associate with all segments, that we call superpixels, a collection of features. We find the sparse representation of each set of features over the dictionary of all features by solving a l0-minimization problem. Then, the connection information between superpixels is encoded as the non-zero representation coefficients, and the affinity of connected superpixels is derived by the corresponding representation error. This provides a l0 affinity graph that has interesting properties of long range and sparsity, and a suitable graph cut yields a segmentation. Experimental results on the BSD database demonstrate that our method provides perfectly semantic regions even with a constant segmentation number, but also that very competitive quantitative results are achieved

Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models

Hand, foot, and mouth disease (HFMD) is a worldwide infectious disease, prominent in China. China’s HFMD data are sparse with a large number of observed zeros across locations and over time. However, no previous studies have considered such a zero-inflated problem on HFMD’s spatiotemporal risk analysis and mapping, not to mention for the entire Mainland China at county level. Monthly county-level HFMD cases data combined with related climate and socioeconomic variables were collected. We developed four models, including spatiotemporal Poisson, negative binomial, zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) models under the Bayesian hierarchical modeling framework to explore disease spatiotemporal patterns. The results showed that the spatiotemporal ZINB model performed best. Both climate and socioeconomic variables were identified as significant risk factors for increasing HFMD incidence. The relative risk (RR) of HFMD at the local scale showed nonlinear temporal trends and was considerably spatially clustered in Mainland China. The first complete county-level spatiotemporal relative risk maps of HFMD were generated by this study. The new findings provide great potential for national county-level HFMD prevention and control, and the improved spatiotemporal zero-inflated model offers new insights for epidemic data with the zero-inflated problem in environmental epidemiology and public health

Who buys new energy vehicles in china? Assessing social-psychological predictors of purchasing awareness, intention, and policy

This paper investigates the salience of social-psychological factors in explaining why drivers purchase (or fail to purchase) New Energy Vehicles (NEVs)—including hybrid electric vehicles, battery electric vehicles, and fuel cell electric vehicles—in China. A questionnaire measuring six dimensions (including attitudes, subjective norms, perceived behavioral control, personal norms, low-carbon awareness and policy) was distributed in Tianjin, where aggressive policy incentives for NEVs exist yet adoption rates remain low. Correlation analysis and hierarchical multiple regression analyses are applied data collected through 811 valid questionnaires. We present three main findings. First, there is an “awareness-behavior gap” whereby low-carbon awareness has a moderating effect on purchasing behavior via psychological factors. Second, subjective norms has a stronger influence on intention to purchase New Energy Vehicles than other social-psychological factors. Third, acceptability of government policies has positive significant impact on adoption of New Energy Vehicles, which can provide reference potential template for other countries whose market for New Energy Vehicles is also in an early stage

Few-shot remote sensing scene classification based on multi subband deep feature fusion

Recently, convolutional neural networks (CNNs) have performed well in object classification and object recognition. However, due to the particularity of geographic data, the labeled samples are seriously insufficient, which limits the practical application of CNN methods in remote sensing (RS) image processing. To address the problem of small sample RS image classification, a discrete wavelet-based multi-level deep feature fusion method is proposed. First, the deep features are extracted from the RS images using pre-trained deep CNNs and discrete wavelet transform (DWT) methods. Next, a modified discriminant correlation analysis (DCA) approach is proposed to distinguish easily confused categories effectively, which is based on the distance coefficient of between-class. The proposed approach can effectively integrate the deep feature information of various frequency bands. Thereby, the proposed method obtains the low-dimensional features with good discrimination, which is demonstrated through experiments on four benchmark datasets. Compared with several state-of-the-art methods, the proposed method achieves outstanding performance under limited training samples, especially one or two training samples per class