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One key in image quality assessment (IQA) is the design of image representations that can capture the changes of image structures caused by distortions. Recent studies show that sparse coding has emerged as a promising approach to analyzing image structures for IQA. However, existing sparse-coding-based IQA approaches use linear coding models, which ignore the nonlinearities of manifolds of image patches and thus cannot analyze complex image structures well. To overcome such a weakness, in this paper, we introduce nonlinear sparse coding to IQA. A kernel dictionary construction scheme is proposed, which combines analytic dictionaries and learnable dictionaries to guarantee both the stability and effectiveness of kernel sparse coding in the context of IQA. Built upon the kernel dictionary construction, an effective full-reference IQA metric is developed. Benefiting from the considerations on nonlinearities during sparse coding, the proposed IQA metric not only characterizes image distortions better, but also achieves improvement on the consistency with subjective perception, when compared to the metrics built upon linear sparse coding. Such benefits are demonstrated with the experimental results on eight benchmark datasets in terms of common criteria.

In this paper, a metasurface composed of 3-bit coding linear polarization conversion elements and its application to RCS reduction of the patch antenna is intensively studied. At first, 3-bit coding metasurface are constructed by a sequence of eight coded unit cells, which have a similar cross-polarized reflected amplitude response and gradient reflected phase responses covering 0–2π, respectively. Equivalent circuit models of these unit cells are created to describe their electrical behavior for the two linear incident polarizations at the same time. Then, a patch antenna is integrated on the 3-bit metasurface, of which the elements are placed with a 2-dimensional linear coding sequence. The metal square ring is set around the patch antenna to protect it from the disturbance of metasurface. Both the simulation and experiment results demonstrate that the designed metasurface can primarily reduce the antenna RCS at a broadband, while the antenna performances are not degraded significantly.

Purpose The self-rated health (SRH) item is frequently used in health surveys but variations of its form (wording, response options) may hinder comparisons between versions over time or across surveys. The objectives were to determine (a) whether three SRH forms are equivalent, (b) the form with the best construct validity and (c) the best coding scheme to maximize equivalence across forms. Methods We used data from 58,023 respondents of the Swiss Health Survey. Three SRH forms were used. Response options varied across forms and we explored four coding schemes (two considering SRH as continuous, two as dichotomous). Construct validity of the SRH was assessed using 34 health predictors to estimate the explained variance. Results Distributions of response options were similar across SRH forms, except for the “good” and “very good” options (“good” in form 1: 58.6%, form 2: 65.0% and form 3: 44.1%). Explained variances differed across SRH forms, with form 3 providing the best overall explained variance, regardless of coding schemes. The linear coding scheme maximised the equivalence across SRH forms. Conclusion The three SRH forms were not equivalent in terms of construct validity. Studies examining the evolution of SRH over time with surveys using different forms should use the linear coding scheme to maximise equivalence between SRH forms.

Image classification is very important in pattern recognition and computer vision, where, for integrating final representation, feature pooling methods of the max-pooling, sum-pooling and average-pooling have been widely used. In this study, the authors propose a new method called K -strongest responses (KSRs) on the dictionary atoms for integrating the coding coefficients to generate the final representation that is compared with the previous pooling methods, produces better performance for the image classification task. On the basis of the KSR method, to improve classification accuracy and generate more compact and discriminative final representation, a new framework consisting of two-part KSR and bag-of-features is proposed. To evaluate the performance of the proposed method and framework, they apply it to locality-constrained linear coding, linear distance coding and sparse coding by using two datasets from benchmarks of scene classification: 19-class satellite scene and UC Merced Land. The results show that the coding coefficients integrated by their method and framework are more discriminative than other methods.

Locality-constrained Linear Coding (LLC) based visual tracking can give a better and faster tracking performance than traditional sparse representation based tracking methods. However, the existing LLC based methods often use the anchor points near the target to build the sparse coding dictionary for local sparse coding. It may cause a problem that it is hard to discriminate the difference between the negative and positive anchor points in sparse coding dictionary when facing severe background clutter, illumination change and occlusion. In this paper, we propose a context aware sparse coding method to achieve robust visual tracking. The proposed method can prevent the negative anchor points from disturbing the classifier accuracy because it uses a global context regularizer to constrain the sparse coding value of those negative anchor points that are similar to the positive anchor points. Experiment results show that our method can achieve a better tracking performance than state-of-the-art tracking methods do.

A linear code of length n, dimension k and minimum distance d over GF (q) is denoted by [n, k, d] q . One of the main problem in linear coding theory is - for given length and dimension to construct codes with best possible minimum distances. In this talk we present 10 new QC codes.

Image aesthetic evaluation refers to the subjective aesthetic evaluation of images. Computational aesthetics has been widely concerned due to the limitations of subjective evaluation. Aiming at the problem that the existing evaluation methods of image aesthetic quality only extract the low-level features of images and they have a low correlation with human subjective perception, this paper proposes an aesthetic evaluation model based on latent semantic features. The aesthetic features of images are extracted by superpixel segmentation that is based on weighted density POI (Point of Interest), which includes semantic features, texture features, and color features. These features are mapped to feature words by LLC (Locality-constrained Linear Coding) and, furthermore, latent semantic features are extracted using the LDA (Latent Dirichlet Allocation). Finally, the SVM classifier is used to establish the classification prediction model of image aesthetics. The experimental results on the AVA dataset show that the feature coding based on latent semantics proposed in this paper improves the adaptability of the image aesthetic prediction model, and the correlation with human subjective perception reaches 83.75%.

This paper presents a fighting detection algorithm based on hybrid features. An effective coding scheme called locality-constrained linear coding (LLC) is used for the features by incorporating histograms of quadruples optical flow gradient (HQOFG) into local spatio-temporal features. Our results confirm that the fighting detector only with a linear support vector machine for sequence classification performs well. Compared to the fighting detector using bag-of-words (BOW) framework and local spatio-temporal features, the detector proposed in the paper improves the recognition accuracy.

In the problem where there is not enough data to use Deep Learning, Bag-of-Visual-Words (BoVW) is still a good alternative for image classification. In BoVW model, many pooling methods are proposed to incorporate the spatial information of local feature into the image representation vector, but none of the methods devote to making each visual word have its own pooling regions. The practice of designing the same pooling regions for all the words restrains the discriminability of image representation, since the spatial distributions of the local features indexed by different visual words are not same. In this paper, we propose to make each visual word have its own pooling regions, and raise a simple yet effective method for learning pooling region. Concretely, a kind of small window named observation window is used to obtain its responses to each word over the whole image region. The pooling regions of each word are organized by a kind of tree structure, in which each node indicates a pooling region. For each word, its pooling regions are learned by constructing a tree with its labelled coordinate data. The labelled coordinate data consist of the coordinates of responses and image class labels. The effectiveness of our method is validated by observing if there is an obvious classification accuracy improvement after applying our method. Our experimental results on four small datasets (i.e., Scene-15, Caltech-101, Caltech-256 and Corel-10) show that, the classification accuracy is improved by about 1% to 2.5%. We experimentally demonstrate that the practice of making each word have its own pooling regions is beneficial to image classification task, which is the significance of our work.

Purpose Vehicles estimation can be used in evaluating traffic conditions and facilitating traffic control, which is an important task in intelligent transportation system. The paper aims to propose a vehicle-counting method based on the analysis of surveillance videos. Design/methodology/approach The paper proposes a novel two-step method using low-rank representation (LRR) detection and locality-constrained linear coding (LLC) classification to count the number of vehicles in traffic video sequences automatically. The proposed method is based on an offline training to understand an LLC-based classifier with extracted features for vehicle and pedestrian classification, followed by an online counting algorithm to count the number of vehicles detected from the image sequence. Findings The proposed method allows delivery estimation (counting the number of vehicles at each frame only) and total number estimation of vehicles shown in the scene. The paper compares the proposed method with other similar methods on three public data sets. The experimental results show that the proposed method is competitive and effective in terms of computational speed and evaluation accuracy. Research limitations/implications The proposed method does not consider illumination. Hence, the results might be unsatisfactory under low-lighting condition. Therefore, researchers are encouraged to add a term that controls the illumination changes into the energy function of vehicle detection in future work. Originality/value The paper bridges the gap between LRR detection and vehicle counting by taking advantage of existing LLC classification algorithm to distinguish different moving objects.

In this letter, we design a low-latency, low-complexity, and low-transmission power analog linear coding scheme to estimate the finite-length data blocks under a mean-square-error (distortion) criterion. The proposed scheme is based on the analog linear coding and the discrete Fourier transform. A remarkable feature of our scheme is that it does not require knowing the correlation matrix of the data blocks, and therefore, it can be used even if the correlation matrix changes slowly with time.

We study the symmetric degrees-of-freedom (DoF) of partially connected interference networks under linear coding strategies at transmitters without channel state information beyond topology. We assume that the receivers are equipped with reconfigurable antennas that can switch among their preset modes. In such a network setting, we characterize the class of network topologies in which half linear symmetric DoF is achievable. Moreover, we derive a general upper bound on the linear symmetric DoF for arbitrary network topologies. We also show that this upper bound is tight if the transmitters have at most two co-interferers., Comment: This work will be presented in part at the 2016 IEEE International Symposium on Information Theory (ISIT)

It is known that the linear coding capacity of non-multicast networks may depend on the characteristic of the finite field. Such a dependence has been demonstrated in several examples presented in the literature. However, in all such examples, the number of distinct values that the linear coding capacity can take over different characteristics of the finite field is two. For example, the Fano network has linear coding capacity equal to one over even characteristic, and has linear coding capacity equal to 4/5 over odd characteristics. It is a natural question that whether a network can have arbitrarily many different characteristics dependent linear coding capacities. In this study, the authors answer the question for sum-networks, which are networks where the terminals demand the sum of the messages generated by the sources. Specifically, they show that given a set of characteristics (prime numbers) { p 1 , p 2 , … , p n } , indeed there exists a sum-network such that for each different value of the characteristic of the finite field, the linear coding capacity of the sum-network is different.

Facial images embed age, gender, and other rich information that is implicitly related to occupation. In this work, we advocate that occupation prediction from a single facial image is a doable computer vision problem. We extract multilevel hand-crafted features associated with locality-constrained linear coding and convolutional neural network features as image occupation descriptors. To avoid the curse of dimensionality and overfitting, a boost strategy called multichannel SVM is used to integrate features from face and body. Intra- and interclass visual variations are jointly considered in the boosting framework to further improve performance. In the evaluation, we verify the effectiveness of predicting occupation from face and demonstrate promising performance obtained by combining face and body information. More importantly, our work further integrates deep features into the multichannel SVM framework and shows significantly better performance over the state of the art.

Nowadays, distributed storage systems have been widely used in the world, e.g, Google Drive, OneDrive, DropBox, Baidu PCS, etc.. Although these cloud storage services significantly facilitate the file storage for users, the performance of them varies widely when using in different countries. For example, Google Drive has high upload/download speed in America while it has very low upload/download speed in China. Therefore, to enhance the reliability and security of the cloud storage, in this paper, we firstly design a multi-cloud based distributed storage platform, named as, Normal Drive, by utilizing random linear coding and APIs provided by multiple cloud disks. We then implement the Normal Drive and conduct extensive evaluation from different aspects in five different locations around the world to test the performance of it. The experimental results show that the proposed Normal Drive can significantly improve the transmission performance and enhance the reliability of file storage when comparing with single-cloud based distributed storage system.

Circular RNAs (circRNAs) are a new kind of endogenous non-coding RNAs, which have been discovered continuously. More and more studies have shown that circRNAs are related to the occurrence and development of human diseases. Identification of circRNAs associated with diseases can contribute to understand the pathogenesis, diagnosis and treatment of diseases. However, experimental methods of circRNA prediction remain expensive and time-consuming. Therefore, it is urgent to propose novel computational methods for the prediction of circRNA-disease associations. In this study, we develop a computational method called LLCDC that integrates the known circRNA-disease associations, circRNA semantic similarity network, disease semantic similarity network, reconstructed circRNA similarity network, and reconstructed disease similarity network to predict circRNAs related to human diseases. Specifically, the reconstructed similarity networks are obtained by using Locality-Constrained Linear Coding (LLC) on the known association matrix, cosine similarities of circRNAs and diseases. Then, the label propagation method is applied to the similarity networks, and four relevant score matrices are respectively obtained. Finally, we use 5-fold cross validation (5-fold CV) to evaluate the performance of LLCDC, and the AUC value of the method is 0.9177, indicating that our method performs better than the other three methods. In addition, case studies on gastric cancer, breast cancer and papillary thyroid carcinoma further verify the reliability of our method in predicting disease-associated circRNAs.

Millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems have important advantages such as large bandwidths and small wavelengths that enable large antenna arrays. However, the cost and power consumption of the hardware elements is extremely high in mmWave. This limitation makes precoding and combining for multiuser MIMO more constrained, making non-viable the use of fully digital precoders. Hybrid analog-digital transceiver architectures allow to reduce radio frequency (RF) hardware complexity but still, a significant number of RF chains is necessary to accommodate the transmissions from multiple users. In this work, we propose to use a zero-delay analog joint source-channel coding method termed scalar quantizer linear coding (SQLC) to reduce the number of RF chains at the receiver of an uplink multiuser mmWave MIMO system. The use of SQLC allows grouping the users in pairs and halves the number of streams to be managed by the multiuser MIMO receiver. In addition, SQLC performs particularly well when users transmit correlated information.

With the development of high-throughput techniques, various biological molecules are discovered, which includes the circular RNAs (circRNAs). Circular RNA is a novel endogenous noncoding RNA that plays significant roles in regulating gene expression, moderating the microRNAs transcription as sponges, diagnosing diseases, and so on. Based on the circRNA particular molecular structures that are closed-loop structures with neither 5′-3′ polarities nor polyadenylated tails, circRNAs are more stable and conservative than the normal linear coding or noncoding RNAs, which makes circRNAs a biomarker of various diseases. Although some conventional experiments are used to identify the associations between circRNAs and diseases, almost the techniques and experiments are time-consuming and expensive. In this study, we propose a collaboration filtering recommendation system–based computational method, which handles the “cold start” problem to predict the potential circRNA–disease associations, which is named ICFCDA. All the known circRNA–disease associations data are downloaded from circR2Disease database (http://bioinfo.snnu.edu.cn/CircR2Disease/). Based on these data, multiple data are extracted from different databases to calculate the circRNA similarity networks and the disease similarity networks. The collaboration filtering recommendation system algorithm is first employed to predict circRNA–disease associations. Then, the leave-one-out cross validation mechanism is adopted to measure the performance of our proposed computational method. ICFCDA achieves the areas under the curve of 0.946, which is better than other existing methods. In order to further illustrate the performance of ICFCDA, case studies of some common diseases are made, and the results are confirmed by other databases. The experimental results show that ICFCDA is competent in predicting the circRNA–disease associations.

Long noncoding RNAs play a significant role in the occurrence of diseases. Thus, studying the relationship prediction between lncRNAs and disease is becoming more popular. Researchers hope to determine effective treatments by revealing the occurrence and development of diseases at the molecular level. However, the traditional biological experimental way to verify the association between lncRNAs and disease is very time-consuming and expensive. Therefore, we developed a method called LLCLPLDA to predict potential lncRNA–disease associations. First, locality-constrained linear coding (LLC) is leveraged to project the features of lncRNAs and diseases to local-constraint features, and then, a label propagation (LP) strategy is used to mix up the initial association matrix and the obtained features of lncRNAs and diseases. To demonstrate the performance of our method, we compared LLCLPLDA with five methods in the leave-one-out cross-validation and fivefold cross-validation scheme, and the experimental results show that the proposed method outperforms the other five methods. Additionally, we conducted case studies on three diseases: cervical cancer, gliomas, and breast cancer. The top five predicted lncRNAs for cervical cancer and gliomas were verified, and four of the five lncRNAs for breast cancer were also confirmed.

In order to improve the data integrity encryption ability of cloud storage users, a cloud storage user data integrity encryption technology based on random linear coding is proposed. Firstly, the cloud storage user data integrity encryption key of cloud storage user data integrity object is constructed, then the encryption and decryption coding design are carried out, and the random linear coding is used to optimize the digital encryption key to improve the anti-deciphering level. Finally, the simulation results show that the cloud storage user data integrity encryption technology has high random distribution of encryption, the deciphering rate of encrypted data is effectively controlled, and the performance of the cloud storage user data encryption technology is better than that of the traditional method. It effectively ensures the integrity of cloud storage user data.

In video action recognition, the Dollar detector has been widely used to extract Spatio-Temporal Interest Points (STIPs) from action video sequence. It generates two kinds of information: STIP position and the respond value. However, in many cases, the detector respond, which measures the strength of local motion changes, is ignored. By utilizing such information, we propose to build a Hierarchical STIP Saliency (HSS) framework to provide different types of motion information. A novel local feature named Mixed Neighborhood Feature (MNF), which integrates the similarity and position relationship between local features, is put forward, and encoded by localityconstrained linear coding. Then, by partitioning video sequence along temporal direction, a group of sub-STVs are produced, and their corresponding descriptors are obtained with a max-pooling-on-absolute-value technique. In classification stage, Locality-constrained Group Sparse Representation (LGSR) is adopted as classifier to utilize the intrinsic group information of these sub-STV features. The experiments on the KTH and UCF Sports datasets show that in contrast to the classical recognition systems published recently, our recognition system based on the HSS and MNF achieves good performance.

MiRNAs are small non-coding regulatory RNAs which are associated with multiple diseases. Increasing evidence has shown that miRNAs play important roles in various biological and physiological processes. Therefore, the identification of potential miRNA-disease associations could provide new clues to understanding the mechanism of pathogenesis. Although many traditional methods have been successfully applied to discover part of the associations, they are in general time-consuming and expensive. Consequently, computational-based methods are urgently needed to predict the potential miRNA-disease associations in a more efficient and resources-saving way. In this paper, we propose a novel method to predict miRNA-disease associations based on Locality-constrained Linear Coding (LLC). Specifically, we first reconstruct similarity networks for both miRNAs and diseases using LLC and then apply label propagation on the similarity networks to get relevant scores. To comprehensively verify the performance of the proposed method, we compare our method with several state-of-the-art methods under different evaluation metrics. Moreover, two types of case studies conducted on two common diseases further demonstrate the validity and utility of our method. Extensive experimental results indicate that our method can effectively predict potential associations between miRNAs and diseases.

This paper studies the Pliable Index CODing problem (PICOD), which models content-type distribution networks. In the PICOD$(t)$ problem there are $m$ messages, $n$ users and each user has a distinct message side information set, as in the classical Index Coding problem (IC). Differently from IC, where each user has a pre-specified set of messages to decode, in the PICOD$(t)$ a user is "pliable" and is satisfied if it can decode any $t$ messages that are not in its side information set. The goal is to find a code with the shortest length that satisfies all the users. This flexibility in determining the desired message sets makes the PICOD$(t)$ behave quite differently compared to the IC, and its analysis challenging. This paper mainly focuses on the \emph{complete--$S$} PICOD$(t)$ with $m$ messages, where the set $S\subset[m]$ contains the sizes of the side information sets, and the number of users is $n=\sum_{s\in S}\binom{m}{s}$, with no two users having the same side information set. Capacity results are shown for: (i) the \emph{consecutive} complete--$S$ PICOD$(t)$, where $S=[s_{\min}:s_{\max}]$ for some $0 \leq s_{\min} \leq s_{\max} \leq m-t$, and (ii) the \emph{complement-consecutive} complete--$S$ PICOD$(t)$, where $S=[0:m-t]\backslash[s_{\min}:s_{\max}]$, for some $0 < s_{\min} \leq s_{\max} < m-t$. The novel converse proof is inspired by combinatorial design techniques and the key insight is to consider all messages that a user can eventually decode successfully, even those in excess of the $t$ required ones. This allows one to circumvent the need to consider all possible desired message set assignments at the users in order to find the one that leads to the shortest code length. In addition, tight converse results are also shown for those PICOD$(1)$ with circular-arc network topology hypergraph., 38 pages, 4 figures, submitted to IEEE Transactions on Information Theory

Bag of Words (BOW) method with spatio-temporal local features has achieved great performance in human action recognition. However, most of the existing BOW approaches based on vector quantization (VQ) neglect the contextual information of each descriptor, and suffer serious quantization error. There are two main reasons for these: in the first, each local feature is only assigned to one label and second, the information about the spatial layout of the features is disregarded. In this paper, we present a novel and effective coding method called context and locality constrained linear coding (CLLC) to overcome these limitations, in which the relationships among local features and their structural information are preserved. After that, a group-wise sparse representation based classification (GSRC) method is implemented to assign the query sample into one category which yields the smallest reconstruction error. Our method is verified on the challenging databases and achieves comparable performance with state-of-the-art methods. We model a novel and effective human action recognition scheme.We propose context and locality constrained linear coding method to fully describe each local feature."Decisive feature" and "indecisive feature" are aided to collect the contextual information.Group-wise sparse representation based classification method is introduced to category the query samples.Our encoding problem has both analytical solution and approximation solution.