Spatio-Temporal evolutive Data Infrastructure: a Spatial Data Infrastructure for managing data flows of Territorial Statistical Information

The observation of demographical,economical or environmental indicators over time through maps is crucial.It enables analysing territories and helps stakeholders to take decisions.However,the understanding of Territorial Statistical Information(TSI)is compromised unless comprehensive description of both the statistical methodology used and the spatial and temporal references are given.Thus,in this paper,we stress the importance of metadata descriptions and of their quality that helps assessing data reliability.Furthermore,time-series of such TSI are paramount.They enable analysing a territory over a long period of time and likewise judging the effectiveness of reforms.In light of these observations,we present Spatio-Temporal evolutive Data Infrastructure(STeDI)an innovative Spatial Data Infrastructure(SDI)that enriches the description of a Digital Earth,providing a virtual representation of territories and of their evolution through statistics and time.STeDI aims at managing a whole dataflow of multi-dimensional,multi-scale and multi-temporal TSI,from their acquisition to their dissemination to scientists and policy-makers.The content of this SDI evolves autonomously thanks to automated processes and to a Web platform that help improving the quality of datasets uploaded by experts.Then,STeDI allows visualizing up-to-date time-series reflecting the human activities on a given territory.It helps policy-makers in their decision-making process.

STATISTICAL INFORMATION OF THE DEVELOPMENT OF CHINA'S HEALTH WORK IN 1994

The following general status concerning nation-wide health institutions, hospital beds, personnel and hospital work as well as causes of diseases and death is briefly reported according to the Information Centre of Health Statistics of the Ministry of Public Health of China. HEALTH INSTITUTION, HOSPITAL BEDS AND PERSONNEL In 1994 there was a slight decrease in the number of China’s health institutions while the increasing rate of hospital beds was slowed down and the nmnber of health personnel continued to increase. Health institutions. In 1994 the number of various health institutions in China totalled 191.7 thousand. with a decrease of 1844 as against 1993: there

Protein Residue Contact Prediction Based on Deep Learning and Massive Statistical Features from Multi-Sequence Alignment

Sequence-based protein tertiary structure prediction is of fundamental importance because the function of a protein ultimately depends on its 3 D structure.An accurate residue-residue contact map is one of the essential elements for current ab initio prediction protocols of 3 D structure prediction.Recently,with the combination of deep learning and direct coupling techniques,the performance of residue contact prediction has achieved significant progress.However,a considerable number of current Deep-Learning(DL)-based prediction methods are usually time-consuming,mainly because they rely on different categories of data types and third-party programs.In this research,we transformed the complex biological problem into a pure computational problem through statistics and artificial intelligence.We have accordingly proposed a feature extraction method to obtain various categories of statistical information from only the multi-sequence alignment,followed by training a DL model for residue-residue contact prediction based on the massive statistical information.The proposed method is robust in terms of different test sets,showed high reliability on model confidence score,could obtain high computational efficiency and achieve comparable prediction precisions with DL methods that relying on multi-source inputs.

Micro-Expression Recognition Algorithm Based on Information Entropy Feature

The intensity of the micro-expression is weak,although the directional low frequency components in the image are preserved by many algorithms,the extracted micro-expression ft^ature information is not sufficient to accurately represent its sequences.In order to improve the accuracy of micro-expression recognition,first,each frame image is extracted from,its sequences,and the image frame is pre-processed by using gray normalization,size normalization,and two-dimensional principal component analysis(2DPCA);then,the optical flow method is used to extract the motion characteristics of the reduced-dimensional image,the information entropy value of the optical flow characteristic image is calculated by the information entropy principle,and the information entropy value is analyzed to obtain the eigenvalue.Therefore,more micro-expression feature information is extracted,including more important information,which can further improve the accuracy of micro-expression classification and recognition;finally,the feature images are classified by using the support vector machine(SVM).The experimental results show that the micro-expression feature image obtained by the information entropy statistics can effectively improve the accuracy of micro-expression recognition.

ReLoc:Indoor Visual Localization with Hierarchical Sitemap and View Synthesis

Indoor visual localization,i.e.,6 Degree-of-Freedom camera pose estimation for a query image with respect to a known scene,is gaining increased attention driven by rapid progress of applications such as robotics and augmented reality.However,drastic visual discrepancies between an onsite query image and prerecorded indoor images cast a significant challenge for visual localization.In this paper,based on the key observation of the constant existence of planar surfaces such as floors or walls in indoor scenes,we propose a novel system incorporating geometric information to address issues using only pixelated images.Through the system implementation,we contribute a hierarchical structure consisting of pre-scanned images and point cloud,as well as a distilled representation of the planar-element layout extracted from the original dataset.A view synthesis procedure is designed to generate synthetic images as complementary to that of a sparsely sampled dataset.Moreover,a global image descriptor based on the image statistic modality,called block mean,variance,and color(BMVC),was employed to speed up the candidate pose identification incorporated with a traditional convolutional neural network(CNN)descriptor.Experimental results on a popular benchmark demonstrate that the proposed method outperforms the state-of-the-art approaches in terms of visual localization validity and accuracy.