Image Restoration using Total Variation Regularized Deep Image Prior

In the past decade, sparsity-driven regularization has led to significant improvements in image reconstruction. Traditional regularizers, such as total variation (TV), rely on analytical models of sparsity. However, increasingly the field is moving towards trainable models, inspired from deep learning. Deep image prior (DIP) is a recent regularization framework that uses a convolutional neural network (CNN) architecture without data-driven training. This paper extends the DIP framework by combining it with the traditional TV regularization. We show that the inclusion of TV leads to considerable performance gains when tested on several traditional restoration tasks such as image denoising and deblurring

Task Transfer by Preference-Based Cost Learning

The goal of task transfer in reinforcement learning is migrating the action policy of an agent to the target task from the source task. Given their successes on robotic action planning, current methods mostly rely on two requirements: exactly-relevant expert demonstrations or the explicitly-coded cost function on target task, both of which, however, are inconvenient to obtain in practice. In this paper, we relax these two strong conditions by developing a novel task transfer framework where the expert preference is applied as a guidance. In particular, we alternate the following two steps: Firstly, letting experts apply pre-defined preference rules to select related expert demonstrates for the target task. Secondly, based on the selection result, we learn the target cost function and trajectory distribution simultaneously via enhanced Adversarial MaxEnt IRL and generate more trajectories by the learned target distribution for the next preference selection. The theoretical analysis on the distribution learning and convergence of the proposed algorithm are provided. Extensive simulations on several benchmarks have been conducted for further verifying the effectiveness of the proposed method.Comment: Accepted to AAAI 2019. Mingxuan Jing and Xiaojian Ma contributed equally to this wor

The reciprocal relationship among Chinese senior secondary students’ intrinsic and extrinsic motivation and cognitive engagement in learning mathematics : A three-wave longitudinal study

In the present longitudinal study, cross-lagged path models were applied to investigate the potential reciprocal relationships between senior secondary school students’ motivation and their cognitive engagement, using data from 623 Chinese senior secondary school students across 2 years. The 623 students completed self-reported measures of motivation and engagement at three time points within 2 years. The results suggest that the participants held a mixed type of intrinsic and extrinsic motivation to learn mathematics and did not hold a deep level of cognitive engagement in mathematics learning. Compared with their extrinisic motivation, their intrinsic motivation to learn mathematics was more closely related to their cognitive engagement in mathematics learning, which points to a stronger reciprocal effect between their cognitive engagement and intrinsic motivation. The findings suggest that societal and cultural factors, such as the strong examination culture and high external expectations might be influential factors affecting the reciprocal relationships among students’ motivation and cognitive engagement

HOW TO ADVERTISE APPROAPRIATELY ON THE WORLD WIDE WEB? A MULTI-CONGRUITY ANALYSIS APPROACH

As a popular and important advertising style, Internet advertising has drawn substantial amount of scholarly attention. Previous studies focus on the independent effects of various factors, such as product, consumer, website and ad per se, but few studies consider the impacts of the congruities between these factors on consumer’s attitude toward the ads. In this paper, we propose an integrative model, product-consumer-website-ad model, to articulate how the congruity between factors exerts its effect. We propose that ad appeal (emotional vs. informational) should be designed consistent with the nature of the advertised product (hedonic vs. utilitarian), the nature of the website (hedonic vs. utilitarian) and the thinking styles of consumer (intuitive vs. rational). Personalization plays an important role in the process to achieve the congruity. We also propose that the ad on the website with high reputation will generate more favourable attitude toward it. Implications and future research are also discussed in the paper

Understanding Satisfaction of Knowledge Contributors in Transactional Virtual Communities from A Cost-Benefit Tradeoff Perspective

Knowledge sharing behavior in virtual communities has long been an important area of research. Prior related research has primarily focused on relational virtual community (RVC) where knowledge sharing is regarded as a social exchange behavior, heavily depending on the social concerns such as reciprocity, identification and norms. The objective of our study is to investigate knowledge contributors’ satisfaction in a distinct type of virtual communities (transactional virtual communities, TVCs), where knowledge sharing is mainly guided under the principle of economic exchange, and cost-benefit tradeoff is the primary motives for knowledge sharing. Drawing upon the goal attainment theory, we examine the effects of two types of benefits (e.g., extrinsic and intrinsic benefit) and two types of costs (e.g., actual and opportunity cost) on knowledge contributors’ satisfaction, as well as the mediating role of perceived net goal attainment. A field survey with 205 subjects in a specific TVC in China was conducted to test the research model. We find that knowledge contributors’ perceptions of extrinsic and intrinsic benefits and opportunity cost significantly influence their satisfaction through the full mediation of perceived net goal attainment. Implications and future research are discussed