Efficient Fully Convolution Neural Network for Generating Pixel Wise Robotic Grasps With High Resolution Images

This paper presents an efficient neural network model to generate robotic grasps with high resolution images. The proposed model uses fully convolution neural network to generate robotic grasps for each pixel using 400 $\times$ 400 high resolution RGB-D images. It first down-sample the images to get features and then up-sample those features to the original size of the input as well as combines local and global features from different feature maps. Compared to other regression or classification methods for detecting robotic grasps, our method looks more like the segmentation methods which solves the problem through pixel-wise ways. We use Cornell Grasp Dataset to train and evaluate the model and get high accuracy about 94.42% for image-wise and 91.02% for object-wise and fast prediction time about 8ms. We also demonstrate that without training on the multiple objects dataset, our model can directly output robotic grasps candidates for different objects because of the pixel wise implementation.Comment: Submitted to ROBIO 201

The Impact of Budgetary Policy on Economic Growth in Mali (The search of Non-linear Effect)

Several studies have been conducted in recent years on the effectiveness of budgetary policy on economic growth. Many authors argue that a government's taxation and expenditure policies can change or even affect all economy. By cons, others argue that the economy is still in an overall equilibrium situation and subsequently budgetary policy would be ineffective or even detrimental to economic growth. This study aims to analyse empirically the impact of budgetary policy on economic growth in Mali. Particularly the search of nonlinear effect. Based on institutional benchmarks, a possible non-linear relationship appears. We have through the implementation of robust methods, found optimal thresholds of the deficit to 1% of the GDP and its external and internal financings respectively has 2.6% and -1.6% of the GDP. The study tries to assess the impact of budgetary policy on growth taking account of external public indebtedness stocks level. With an external debt threshold below 67% of GDP it appears that budgetary policy is Keynesian. Keywords: Budgetary Policy, Deficit, Financings, Growth, Threshold Effects, Deb

Social Network Developing Process Driven by Conflict in Mass Contingency Events

AbstractTwo evolutionary mechanisms of mass contingency events are discussed, which are the cognition structure and the social network structure of the vulnerable groups and also the important problem in public security engineering of developing country. The paper analyzes the developing process of social network driven by conflict. Because the abundant participants share the expensive protest cost, the opinion leaders or sponsors organize the social network in the vulnerable group to maintain their legitimate rights and interests. The theoretical research shows that the protest strategy is feasible as soon as the social network reaches the minimal numbers. The CHAM strike event in 2010 provides an excellent case to explain the three-phase developing process of mass contingency events and the hiberarchy social network driven by the conflict. Lastly, it makes the simulation analysis about the social network of the CHAM strike under the Netlogo platform, where the simulating result is in accordance with the theoretical analysis

Surface modification and bleaching of pigmented wool

We treat naturally pigmented karakul wool with a surface modification system of chlorination and catalytic bleaching, then examine its structure and properties. SEM photos reveal the surface morphology of karakul wool, and the Allworden reaction shows the extent of damage to the epicuticle. The results show that the surface modification removes the bulk of the fiber scales and bleaching increases fiber whiteness. After bleaching, the felting propensity of karakul wool improves slightly and its dye uptake decreases. For modified and bleached karakul wool, the felting propensity decreases, the dyeing rate increases, and equilibrium exhaustion decreases compared with untreated karakul wool.<br /

A Knowledge Adoption Model Based Framework for Finding Helpful User-Generated Contents in Online Communities

Many online communities allow their members to provide information helpfulness judgments that can be used to guide other users to useful contents quickly. However, it is a serious challenge to solicit enough user participation in providing feedbacks in online communities. Existing studies on assessing the helpfulness of user-generated contents are mainly based on heuristics and lack of a unifying theoretical framework. In this article we propose a text classification framework for finding helpful user-generated contents in online knowledge-sharing communities. The objective of our framework is to help a knowledge seeker find helpful information that can be potentially adopted. The framework is built on the Knowledge Adoption Model that considers both content-based argument quality and information source credibility. We identify 6 argument quality dimensions and 3 source credibility dimensions based on information quality and psychological theories. Using data extracted from a popular online community, our empirical evaluations show that all the dimensions improve the performance over a traditional text classification technique that considers word-based lexical features only