Guanxi in China is a very ancient concept embedded in the Confucian concept of life and one that is a ‚hot' topic in that it is currently attracting increasing attention from both Western and Chinese scholars. One aspect of Guanxi which has been the subject of most of the research of late is the influence of Guanxi on firm performance. However, relatively few studies have examined how Guanxi at the individual level is transferred into a firm to influence its financial performance. This study first reclassifies Guanxi into obligatory, reciprocal, and utilitarian types at the individual level as a means to clarifying the confusion brought above from previous studies. It then provides a conceptual framework in which to systematically characterize the link between Guanxi at the individual level and organizational dynamics: that is, how is Guanxi at the individual level shifted to a firm and how does it affect organizational dynamics of that firm at the organizational level. Finally, it provides a deeper understanding of the financial implications of Guanxi to business firms in China. Copyright Springer Science+Business Media, Inc. 2006China, Guanxi, Individual level, Link, Organizational dynamics, Organizational level,

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Traumatic temporomandibular joint (TMJ) ankylosis can be classified into fibrous, fibro-osseous and bony ankylosis. It is still a huge challenge for oral and maxillofacial surgeons due to the technical difficulty and high incidence of recurrence. The poor outcome of disease may be partially attributed to the limited understanding of its pathogenesis. The purpose of this article was to comprehensively review the literature and summarise results from both human and animal studies related to the genesis of TMJ ankylosis.

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Softcover reprint of the hardcover 1 st edition 2004All rights reserved. No part ofthis publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photo-copying, microfilming, recording, or otherwise, without the prior written permission ofthe publisher, with the exception of any material supplied specifically for the purpose ofbeing entered and executed on a computer system, for exclusive use by the purchaser ofthe work.

We propose a notion of double discrete wavelet transform (DDWT) that is designed to sparsify the blurred image and the blur kernel simultaneously. DDWT greatly enhances our ability to analyze, detect, and process blur kernels and blurry images-the proposed framework handles both global and spatially varying blur kernels seamlessly, and unifies the treatment of blur caused by object motion, optical defocus, and camera shake. To illustrate the potential of DDWT in computer vision and image processing, we develop example applications in blur kernel estimation, deblurring, and near-blur-invariant image feature extraction.

Lane detection is one of the most important tasks in selfdriving. Due to various complex scenarios (e.g., severe occlusion, ambiguous lanes, etc.) and the sparse supervisory signals inherent in lane annotations, lane detection task is still challenging. Thus, it is difficult for ordinary convolutional neural network (CNN) trained in general scenes to catch subtle lane feature from raw image. In this paper, we present a novel module named REcurrent Feature-Shift Aggregator (RESA) to enrich lane feature after preliminary feature extraction with an ordinary CNN. RESA takes advantage of strong shape priors of lanes and captures spatial relationships of pixels across rows and columns. It shifts sliced feature map recurrently in vertical and horizontal directions and enables each pixel to gather global information. With the help of slice-by-slice information propagation, RESA can conjecture lanes accurately in challenging scenarios with weak appearance clues. Moreover, we also propose a Bilateral Up-Sampling Decoder which combines coarse grained feature and fine detailed feature in up-sampling stage, and it can recover low-resolution feature map into pixel-wise prediction meticulously. Our method achieves state-of-the-art results on two popular lane detection benchmarks (CULane and Tusimple). The code will be released publicly available.