Image Super-Resolution Based on Generative Adversarial Networks: A Brief Review

Single image super resolution (SISR) is an important research content in the field of computer vision and image processing. With the rapid development of deep neural networks, different image super-resolution models have emerged. Compared to some traditional SISR methods, deep learning-based methods can complete the superresolution tasks through a single image. In addition, compared with the SISR methods using traditional convolutional neural networks, SISR based on generative adversarial networks (GAN) has achieved the most advanced visual performance. In this review, we first explore the challenges faced by SISR and introduce some common datasets and evaluation metrics. Then, we review the improved network structures and loss functions of GAN-based perceptual SISR. Subsequently, the advantages and disadvantages of different networks are analyzed by multiple comparative experiments. Finally, we summarize the paper and look forward to the future development trends of GAN-based perceptual SISR.     

Model Selection for Unsupervised Learning of Visual Context

This study addresses the problem of choosing the most suitable probabilistic model selection criterion for unsupervised learning of visual context of a dynamic scene using mixture models. A rectified Bayesian Information Criterion (BICr) and a Completed Likelihood Akaike’s Information Criterion (CL-AIC) are formulated to estimate the optimal model order (complexity) for a given visual scene. Both criteria are designed to overcome poor model selection by existing popular criteria when the data sample size varies from small to large and the true mixture distribution kernel functions differ from the assumed ones. Extensive experiments on learning visual context for dynamic scene modelling are carried out to demonstrate the effectiveness of BICr and CL-AIC, compared to that of existing popular model selection criteria including BIC, AIC and Integrated Completed Likelihood (ICL). Our study suggests that for learning visual context using a mixture model, BICr is the most appropriate criterion given sparse data, while CL-AIC should be chosen given moderate or large data sample sizes.     

龙口水利枢纽工程规模论证

从目前电力系统的发展来看,不论是华北电网还是晋、蒙电网都存在着调峰能力不足的问题。为更好地利用黄河托龙段的水能资源,改善下游河道的水流条件,为地方经济建设提供清洁可靠的能源,建设龙口水利枢纽工程已经提到日程上来。龙口水利枢纽工程开发任务为发电、对万家寨电站进行反调节、兼有滞洪削峰等综合利用。根据电力系统的需求、泥沙淤积情况、动能经济指标和反调节等要求,综合考虑,选定正常蓄水位898m,装机容量420MW。龙口水利枢纽工程的建设符合国家关于调整能源结构,优先开发水电和加强基础设施建设,拉动经济增长的部署。     

Performance discrepancies on the California Verbal Learning Test-Second Edition (CVLT-II) in the standardization sample.

The standardization data for the California Verbal Learning Test-Second Edition (CVLT-II; D. C. Delis, J. H. Kramer, E. Kaplan, & B. A. Ober, 2000) were used to evaluate the base rate of 6 specific discrepancies between various key variables. The results indicated that CVLT-II performance discrepancies should equal or exceed 1 or 1.5 z score points (depending on the individual comparison) in the hypothesized direction to be considered potentially unusual. However, because about 1 in 3 persons in the standardization sample displayed at least 1 such large discrepancy, it is concluded that these base rates should be viewed only as a starting point for interpretation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

500kW／380V国产大功率变频器在钻机上的应用

针对国外变频器价格昂贵、调试周期和维修周期长等特点，我公司自主研发了可替代进口机的高性能钻机国产专用变频器。该变频器能够从零开始调速。当钻井卡钻时，能及时断开电动机，在低速提升时，能最大限度地发挥电动机的动力特性，提供220％的转矩输出。变频器具有易安装、调试、维护，性能稳定。应用中据实测数据显示，采用变频改造后，打1口井可节省柴油15t。     

质量控制图系统的实现及在SMT中的应用

本文将控制图理论与计算机技术相结合,开发了质量控制图系统.介绍了该系统的设计方案和关键技术问题,并以电子产品制造中监控SMT机的焊膏厚度为例说明了系统的应用.     

黄河龙口水利枢纽坝基岩体钙质充填夹层地质分析与试验研究

龙口水利枢纽坝基岩体主要由奥陶系中统马家沟组(O2m2)灰岩组成,其中发育有多层软弱夹层。作为其中之一的钙质充填夹层,强度虽不很低,但由于处于坝基下的持力层内,间距不大,发育密集,产状平缓,是坝基浅层抗滑稳定的主要控制因素,因此,在大坝抗滑稳定分析时,应进行分析和研究。     

Quantitative pharmacophore models with inductive logic programming

Three-dimensional models, or pharmacophores, describing Euclidean constraints on the location on small molecules of functional groups (like hydrophobic groups, hydrogen acceptors and donors, etc.), are often used in drug design to describe the medicinal activity of potential drugs (or ‘ligands’). This medicinal activity is produced by interaction of the functional groups on the ligand with a binding site on a target protein. In identifying structure-activity relations of this kind there are three principal issues: (1) It is often difficult to “align” the ligands in order to identify common structural properties that may be responsible for activity; (2) Ligands in solution can adopt different shapes (or `conformations’) arising from torsional rotations about bonds. The 3-D molecular substructure is typically sought on one or more low-energy conformers; and (3) Pharmacophore models must, ideally, predict medicinal activity on some quantitative scale. It has been shown that the logical representation adopted by Inductive Logic Programming (ILP) naturally resolves many of the difficulties associated with the alignment and multi-conformation issues. However, the predictions of models constructed by ILP have hitherto only been nominal, predicting medicinal activity to be present or absent. In this paper, we investigate the construction of two kinds of quantitative pharmacophoric models with ILP: (a) Models that predict the probability that a ligand is “active”; and (b) Models that predict the actual medicinal activity of a ligand. Quantitative predictions are obtained by the utilising the following statistical procedures as background knowledge: logistic regression and naive Bayes, for probability prediction; linear and kernel regression, for activity prediction. The multi-conformation issue and, more generally, the relational representation used by ILP results in some special difficulties in the use of any statistical procedure. We present the principal issues and some solutions. Specifically, using data on the inhibition of the protease Thermolysin, we demonstrate that it is possible for an ILP program to construct good quantitative structure-activity models. We also comment on the relationship of this work to other recent developments in statistical relational learning. Editors: Tamás Horváth and Akihiro Yamamoto     

A call admission and rate control scheme for multimedia support over IEEE 802.11 wireless LANs

Quality of service (QoS) support for multimedia services in the IEEE 802.11 wireless LAN is an important issue for such WLANs to become a viable wireless access to the Internet. In this paper, we endeavor to propose a practical scheme to achieve this goal without changing the channel access mechanism. To this end, a novel call admission and rate control (CARC) scheme is proposed. The key idea of this scheme is to regulate the arriving traffic of the WLAN such that the network can work at an optimal point. We first show that the channel busyness ratio is a good indicator of the network status in the sense that it is easy to obtain and can accurately and timely represent channel utilization. Then we propose two algorithms based on the channel busyness ratio. The call admission control algorithm is used to regulate the admission of real-time or streaming traffic and the rate control algorithm to control the transmission rate of best effort traffic. As a result, the real-time or streaming traffic is supported with statistical QoS guarantees and the best effort traffic can fully utilize the residual channel capacity left by the real-time and streaming traffic. In addition, the rate control algorithm itself provides a solution that could be used above the media access mechanism to approach the maximal theoretical channel utilization. A comprehensive simulation study in ns-2 has verified the performance of our proposed CARC scheme, showing that the original 802.11 DCF protocol can statically support strict QoS requirements, such as those required by voice over IP or streaming video, and at the same time, achieve a high channel utilization. Hongqiang Zhai received the B.E. and M.E. degrees in electrical engineering from Tsinghua University, Beijing, China, in July 1999 and January 2002 respectively. He worked as a research intern in Bell Labs Research China from June 2001 to December 2001, and in Microsoft Research Asia from January 2002 to July 2002. Currently he is pursuing the PhD degree in the Department of Electrical and Computer Engineering, University of Florida. He is a student member of IEEE. Xiang Chen received the B.E. and M.E. degrees in electrical engineering from Shanghai Jiao Tong University, Shanghai, China, in 1997 and 2000, respectively, and the Ph.D. degree in electrical and computer engineering from the University of Florida, Gainesville, in 2005. He is currently a Senior Research Engineer at Motorola Labs, Arlington Heights, IL. His research interests include resource management, medium access control, and quality of service (QoS) in wireless networks. He is a Member of Tau Beta Pi and a student member of IEEE. Yuguang Fang received a Ph.D degree in Systems and Control Engineering from Case Western Reserve University in January 1994, and a Ph.D degree in Electrical Engineering from Boston University in May 1997. From June 1997 to July 1998, he was a Visiting Assistant Professor in Department of Electrical Engineering at the University of Texas at Dallas. From July 1998 to May 2000, he was an Assistant Professor in the Department of Electrical and Computer Engineering at New Jersey Institute of Technology. In May 2000, he joined the Department of Electrical and Computer Engineering at University of Florida where he got the early promotion with tenure in August 2003 and has been an Associate Professor since then. He has published over one hundred (100) papers in refereed professional journals and conferences. He received the National Science Foundation Faculty Early Career Award in 2001 and the Office of Naval Research Young Investigator Award in 2002. He is currently serving as an Editor for many journals including IEEE Transactions on Communications, IEEE Transactions on Wireless Communications, IEEE Transactions on Mobile Computing, and ACM Wireless Networks. He is also actively participating in conference organization such as the Program Vice-Chair for IEEE INFOCOM’2005, Program Co-Chair for the Global Internet and Next Generation Networks Symposium in IEEE Globecom’2004 and the Program Vice Chair for 2000 IEEE Wireless Communications and Networking Conference (WCNC’2000).     

Implicit Motor Learning in Parkinson's Disease.

Objective: This study examined whether disruption of performance is moderated in Parkinson's disease (PD) patients who acquire their motor behaviors in an implicit manner. Method: Twenty-seven patients with PD learned a hammering task in errorless (implicit) or errorful (explicit) conditions and were tested for robustness of motor performance under a secondary task load, which required them to continuously count backward as they performed the hammering task. Results: Patients in the errorless (implicit) motor learning condition exhibited robustness to secondary task loading, whereas patients in the errorful (explicit) motor learning condition did not. Conclusions: Implicit motor learning techniques should be considered by PD rehabilitation specialists in cases in which existing disruption to movements is exacerbated by conscious control. (PsycINFO Database Record (c) 2010 APA, all rights reserved)