Predicting protein secondary structure using a mixed-modal SVM method in a compound pyramid model

Accurate protein secondary structure prediction plays an important role in direct tertiary structure modeling, and can also significantly improve sequence analysis and sequence-structure threading for structure and function determination. Hence improving the accuracy of secondary structure prediction is essential for future developments throughout the field of protein research.In this article, we propose a mixed-modal support vector machine (SVM) method for predicting protein secondary structure. Using the evolutionary information contained in the physicochemical properties of each amino acid and a position-specific scoring matrix generated by a PSI-BLAST multiple sequence alignment as input for a mixed-modal SVM, secondary structure can be predicted at significantly increased accuracy. Using a Knowledge Discovery Theory based on the Inner Cognitive Mechanism (KDTICM) method, we have proposed a compound pyramid model, which is composed of three layers of intelligent interface that integrate a mixed-modal SVM (MMS) module, a modified Knowledge Discovery in Databases (KDD1) process, a mixed-modal back propagation neural network (MMBP) module and so on.Testing against data sets of non-redundant protein sequences returned values for the Q₃ accuracy measure that ranged from 84.0% to 85.6%,while values for the SOV99 segment overlap measure ranged from 79.8% to 80.6%. When compared using a blind test dataset from the CASP8 meeting against currently available secondary structure prediction methods, our new approach shows superior accuracy.Availability: http://www.kdd.ustb.edu.cn/protein_Web/.     

Epitomic Location Recognition

This paper presents a novel method for location recognition, which exploits an epitomic representation to achieve both high efficiency and good generalization. A generative model based on epitomic image analysis captures the appearance and geometric structure of an environment while allowing for variations due to motion, occlusions, and non-Lambertian effects. The ability to model translation and scale invariance together with the fusion of diverse visual features yields enhanced generalization with economical training. Experiments on both existing and new labeled image databases result in recognition accuracy superior to state of the art with real-time computational performance.     

Image tag recommendation based on friendships

The tagging systems have been studied by many researchers in the past decade. Tagging methods have been widely used on the web for searching and recommending images. Social tags are the keywords annotated by users to the images, which contains the information for searching and classifying the images. Tag recommendation system allows mitigating the individual preferences to annotate and recommender images. However, irrelevant and noise tags are frequently included in tags. In this paper, we propose image tag recommendation based on the friends’ relationships in social network (TRboFS) to recommender tags for a new image, both the tags assigned to the favorite images and the friendships of the users who upload the image are employed to predict the tags of the images. Empirical analyses on real datasets show that the proposed approach achieves superior performance to existing approaches.     

A Reducing Saturation Method for DMSP/OLS Nighttime Light Image Combining Landsat Data

Defense Meteorological Satellite Program/Operational Linescan System(DMSP/OLS)is one of the data sources,which can effectively reflect human activities of earth surfaces.During the past decade,DMSP/OLS had been extensively applied in urban extraction and extension study.In the recent year,the Vegetation Adjusted NTL Urban Index(VANUI)has been proposed and had proven to be a simple,convenient and high precision desaturation index to extract urban area.In VANUI method,negative values of imagery were directly eliminated to remove water body,which not only removed the bridge over the river and building but also extracted the aquaculture areas along the coast,thus,this method reduced the extraction accuracy.This paper proposed a new index\|RwNTLI,combining DMSP/OLS nighttime light data and the vegetation index (NDVI)and water index (MNDWI)which were constructed by Landsat data.In this study,Guangzhou was taken as experimental area.By comparing the VANUI index with the ability to identify ground objects as well as the ability to alleviate saturation regions,the result showed RwNTLI index could effectively solve the problem of VANUI as well as eliminate saturation effect of nighttime light imagery.Among them,the correlation between RwNTLI index and RCNTL is better than that of VANUI index and RCNTL.Therefore,RwNTLI index is a simple and effective index of luminous desaturation,which has more advantages than VANUI index in describing the characteristics of night lights of urban areas and will have higher application value in urban built\|up areas in the future.     

Analyzing Regional Inequality of Socioeconomic Development in China with Nighttime Light

Satellite\|derived nighttime light (NTL)data have been extensively used as an efficient proxy measure for monitoring urbanization dynamics and socioeconomic activity.This is because remotely sensed NTL signals can be quantitatively connected to demographic and socioeconomic variables.The recently composited cloud\|free NTL imagery derived from the Visible Infrared Imaging Radiometer Suite (VIIRS)provides spatially detailed observations of human settlements.We quantitatively estimated socioeconomic development inequalities across 30 provinces andmunicipalities in mainland China using VIIRS NTL data associated with both regional gross domestic product (GDP)and population census data.We quantitatively investigated relations between NTL,GDP,and population using a linear regression model.Our results suggest that NTL have significant positive correlations with GDP and population at different levels.Several inequality coefficients were derived from VIIRS data and statistical data at multiple spatial scales.NTL\|derived inequality coefficients enabled us to elicit more detailed information on differences in regional development at multiple levels.Our study of provinces and municipalities revealed that county\|level inequality was more significant than city\|level.The results of population\|weighted NTL inequality indicate an obvious regional disparity with NTL distribution being more unequal in China’s undeveloped western regions compared with eastern regions.Our findings suggest that given the timely and spatially explicit advantages of VIIRS,NTL data are capable of providing comprehensive information regarding inequality at multiple levels,which is not possible through the use of traditional statistical sources.     

基于混合蚁群算法的Web用户会话聚类

会话聚类是一种重要的Web使用挖掘技术,旨在发现相似的用户行为,这是目前电子商务中的热点问题之一。该问题的难度在于要对大规模的会话进行聚类,这些会话被表示成高维向量,加大了对算法高效性的要求。提出了一种ACO和PSO相结合的算法进行会话聚类分析。实验结果表明该算法与ACO算法、PSO算法、K-means算法相比,具有更好的性能。     

一种改进的DCT域数字音频水印算法

针对现有数字音频水印算法不能同时满足隐蔽性与鲁棒性的问题,在添加水印之前,先将水印的二值图像与随机密钥进行加密操作,再对载体进行三级小波变化和DCT变换,最后对比加密后的水印信息与载体信号,得到密钥值,并将密钥嵌入到载体音频的DCT中频系数中。实验结果表明,该算法能在隐蔽性和鲁棒性之间达到一个较好的平衡。     

Scheduling on two identical machines with a speed-up resource

We address a variant of scheduling problem on two identical machines, where we are given an additional speed-up resource. If a job uses the resource, its processing time may decrease. However, at any time the resource can only be used by at most one job. The objective is to minimize the makespan. For the offline version, we present an FPTAS. For the online version where jobs arrive over list, we propose an online algorithm with competitive ratio of 1.781, and show a lower bound of 1.686 for any online algorithm.     

Efficient region-aware large graph construction towards scalable multi-label propagation

With fast growing number of images on photo-sharing websites such as Flickr and Picasa, it is in urgent need to develop scalable multi-label propagation algorithms for image indexing, management and retrieval. It has been well acknowledged that analysis in semantic region level may greatly improve image annotation performance compared to that in the holistic image level. However, region level approach increases the data scale to several orders of magnitude and proposes new challenges to most existing algorithms. In this work, we present a novel framework to effectively compute pairwise image similarity by accumulating the information of semantic image regions. Firstly, each image is encoded as Bag-of-Regions based on multiple image segmentations. Secondly, all image regions are separated into buckets with efficient locality-sensitive hashing (LSH) method, which guarantees high collision probabilities for similar regions. The k-nearest neighbors of each image and the corresponding similarities can be efficiently approximated with these indexed patches. Lastly, the sparse and region-aware image similarity matrix is fed into the multi-label extension of the entropic graph regularized semi-supervised learning algorithm [1]. In combination they naturally yield the capability of handling large-scale dataset. Extensive experiments on NUS-WIDE (260k images) and COREL-5k datasets validate the effectiveness and efficiency of our proposed framework for region-aware and scalable multi-label propagation.     

基于Xilinx System Generator的PID算法快速硬件实现

介绍了利用Xilinx System Generator平台构建模型化数字PID控制算法,并通过FPGA将数字PID算法结合传感器与实际硬件控制对象快速建立控制模型,构成完整的闭环控制。通过对控制效果的实验验证,证明了其控制方法的可行性。