Search for serendipitous TNO occultation in X-rays

To study the population properties of small, remote objects beyond Neptune's orbit in the outer solar system, of kilometer size or smaller, serendipitous occultation search is so far the only way. For hectometer-sized Trans-Neptunian Objects (TNOs), optical shadows actually disappear because of diffraction. Observations at shorter wave lengths are needed. Here we report the effort of TNO occultation search in X-rays using RXTE/PCA data of Sco X-1 taken from June 2007 to October 2011. No definite TNO occultation events were found in the 334 ks data. We investigate the detection efficiency dependence on the TNO size to better define the sensible size range of our approach and suggest upper limits to the TNO size distribution in the size range from 30 m to 300 m. A list of X-ray sources suitable for future larger facilities to observe is proposed.Comment: Accepted to publish in MNRA

Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal

Deep Convolutional Neural Networks (CNNs) are widely employed in modern computer vision algorithms, where the input image is convolved iteratively by many kernels to extract the knowledge behind it. However, with the depth of convolutional layers getting deeper and deeper in recent years, the enormous computational complexity makes it difficult to be deployed on embedded systems with limited hardware resources. In this paper, we propose two computation-performance optimization methods to reduce the redundant convolution kernels of a CNN with performance and architecture constraints, and apply it to a network for super resolution (SR). Using PSNR drop compared to the original network as the performance criterion, our method can get the optimal PSNR under a certain computation budget constraint. On the other hand, our method is also capable of minimizing the computation required under a given PSNR drop.Comment: This paper was accepted by 2018 The International Symposium on Circuits and Systems (ISCAS

Can Social Exchange Theory Explain Individual Knowledge-Sharing Behavior? A Meta-Analysis

Motivating people to contribute knowledge has become an important research topic and a major challenge for organizations. In order to promote knowledge-sharing, managers need to understand the mechanism that drives individuals to contribute their valuable knowledge. Several theories have been applied to study knowledge-sharing behavior. However, the research settings and findings are often inconsistent. In this study, we use the social exchange theory as our base to develop an extended model that includes IT support and organizational type as moderators. A meta-analysis on 29 reported studies was conducted to examine how different factors in the social exchange theory affect knowledge-sharing behavior. The findings confirm that the social exchange theory plays an important role underlying individuals’ knowledge-sharing behavior. The results also demonstrate that social interaction and trust derived from the social exchange theory and moderated by IT contextual factors can predict individual’s knowledge-sharing behavior

The Crowding Effect Of Rewards On Knowledge-Sharing Behavior In Virtual Communities

Knowledge sharing is an important activity in virtual communities (VC). Recently, some researchers have explored various motivators that may influence VC members\u27 contribution. Although providing rewards has been found to significantly motivate employees to share knowledge in organizational research, it also has been found to diminish intrinsic motivation and lead to reduced efforts in some cases psychology literature. The phenomenon that external intervention (e.g. monetary incentives or punishments) may either undermine (crowd-out) or enhance (crowd-in) intrinsic motivation is called the motivation crowding effect. Based on the motivation crowding theory, this study investigated the moderating effect of monetary incentives on the relationships of motivations and members\u27 intention for knowledge sharing. The research framework includes two motivational factors, intrinsic and extrinsic motivation, for knowledge sharing in virtual communities. The model was tested using a field experiment on 204 VC members of two different virtual communities. The results confirmed the existence of the crowding effect. That is, the relationship between intrinsic motivation and knowledge sharing intention was significantly lowered after the treatment of monetary incentives. The findings suggest that VC managers should carefully consider providing monetary rewards in promoting their websites because monetary incentives can potentially affect the knowledge-sharing behavior of VC members

Retraction and Generalized Extension of Computing with Words

Fuzzy automata, whose input alphabet is a set of numbers or symbols, are a formal model of computing with values. Motivated by Zadeh's paradigm of computing with words rather than numbers, Ying proposed a kind of fuzzy automata, whose input alphabet consists of all fuzzy subsets of a set of symbols, as a formal model of computing with all words. In this paper, we introduce a somewhat general formal model of computing with (some special) words. The new features of the model are that the input alphabet only comprises some (not necessarily all) fuzzy subsets of a set of symbols and the fuzzy transition function can be specified arbitrarily. By employing the methodology of fuzzy control, we establish a retraction principle from computing with words to computing with values for handling crisp inputs and a generalized extension principle from computing with words to computing with all words for handling fuzzy inputs. These principles show that computing with values and computing with all words can be respectively implemented by computing with words. Some algebraic properties of retractions and generalized extensions are addressed as well.Comment: 13 double column pages; 3 figures; to be published in the IEEE Transactions on Fuzzy System