657 research outputs found
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 400 400
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
Corporate Social Responsibility Reporting, Pyramidal Structure And Political Interference: Evidence From China
This paper attempts to investigate the relation between pyramidal structure and corporate social responsibility (CSR) reporting quality and the effect of political interference on the relation. Based on 1388 Chinese A-share listed firms during 2010-2012, this paper demonstrates that the separation between control and ownership rights is significantly and positively related to the CSR reporting quality in the state-owned firms (SOFs), while negatively related to the CSR reporting quality in the non-state-owned firms (NSOFs). Results also indicate that the pyramidal layer between the bottom firms and their top ultimate owners is negatively related to CSR reporting quality, particularly significant for the NSOFs. Our research enriches the corporate governance literature by giving insights into the mechanism of pyramidal structure in corporate reporting, and extends the understanding of political interference in the CSR field. This study has public policy implications for China as well as a number of other countries in the AsiaâPacific region.
Understanding and predicting synthetic lethal genetic interactions in Saccharomyces cerevisiae using domain genetic interactions
Genetic interactions have been widely used to define functional relationships
between proteins and pathways. In this study, we demonstrated that yeast
synthetic lethal genetic interactions can be explained by the genetic
interactions between domains of those proteins. The domain genetic interactions
rarely overlap with the domain physical interactions from iPfam database and
provide a complementary view about domain relationships. Moreover, we found
that domains in multidomain yeast proteins contribute to their genetic
interactions differently. The domain genetic interactions help more precisely
define the function related to the synthetic lethal genetic interactions, and
then help understand how domains contribute to different functionalities of
multidomain proteins. Using the probabilities of domain genetic interactions,
we were able to predict novel yeast synthetic lethal genetic interactions.
Furthermore, we had also identified novel compensatory pathways from the
predicted synthetic lethal genetic interactions. Our study significantly
improved the understanding of yeast mulitdomain proteins, the synthetic lethal
genetic interactions and the functional relationships between proteins and
pathways.Comment: 36 page, 4 figure
A Domain Oriented LDA Model for Mining Product Defects from Online Customer Reviews
Online reviews provide important demand-side knowledge for product manufacturers to improve product quality. However, discovering and quantifying potential productsâ defects from large amounts of online reviews is a nontrivial task. In this paper, we propose a Latent Product Defect Mining model that identifies critical product defects. We define domain-oriented key attributes, such as components and keywords used to describe a defect, and build a novel LDA model to identify and acquire integral information about product defects. We conduct comprehensive evaluations including quantitative and qualitative evaluations to ensure the quality of discovered information. Experimental results show that the proposed model outperforms the standard LDA model, and could find more valuable information. Our research contributes to the extant product quality analytics literature and has significant managerial implications for researchers, policy makers, customers, and practitioners
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