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The coronavirus disease 2019 (COVID-19) epidemic has spread worldwide, posing a great threat to human beings. The stay-home quarantine is an effective way to reduce physical contacts and the associated COVID-19 transmission risk, which requires the support of efficient living materials (such as meats, vegetables, grain, and oil) delivery. Notably, the presence of potential infected individuals increases the COVID-19 transmission risk during the delivery. The deliveryman may be the medium through which the virus spreads among urban residents. However, traditional delivery route optimization methods don't take the virus transmission risk into account. Here, we propose a novel living material delivery route approach considering the possible COVID-19 transmission during the delivery. A complex network-based virus transmission model is developed to simulate the possible COVID-19 infection between urban residents and the deliverymen. A bi-objective model considering the COVID-19 transmission risk and the total route length is proposed and solved by the hybrid meta-heuristics integrating the adaptive large neighborhood search and simulated annealing. The experiment was conducted in Wuhan, China to assess the performance of the proposed approach. The results demonstrate that 935 vehicles will totally travel 56,424.55 km to deliver necessary living materials to 3,154 neighborhoods, with total risk [Formula: see text]. The presented approach reduces the risk of COVID-19 transmission by 67.55% compared to traditional distance-based optimization methods. The presented approach can facilitate a well response to the COVID-19 in the transportation sector.

This letter tries to effectively reduce the dimension of hyperspectral images (HSIs) by jointly considering both the spectral redundancy and spatial continuity through a multilinear transformation with graph embedding in core tensor space. The whole process is constructed in the framework of Tucker decomposition (TD). Since the distance between intraclass samples should be relatively smaller than that of the interclass samples, the reduced tensor cores should maintain this property. To achieve this goal, a graph is embedded to the core tensor space during TD. Moreover, considering the unstability of solution of the previous works, we constrain the projected matrices by orthogonality so that the results can be more stable and the extracted features can be more discriminative. We further analyze the effect of different constrains to TD methods for HSI dimensionality reduction. Finally, the experimental results show the superiority of this method to many other tensor methods.

A complex network is a network with non-trivial topological structures. It contains not just topological information but also attribute information available in the rich content of nodes. Concerning the task of cluster analysis in a complex network, model-based algorithms are preferred over distance-based ones, as they avoid designing specific distance measures. However, their models are only applicable to complex networks where the attribute information is composed of attributes in binary form. To overcome this disadvantage, we introduce a three-layer node-attribute-value hierarchical structure to describe the attribute information in a flexible and interpretable manner. Then, a new Bayesian model is proposed to simulate the generative process of a complex network. In this model, the attribute information is generated by following the hierarchical structure while the links between pairwise nodes are generated by a stochastic blockmodel. To solve the corresponding inference problem, we develop a variational Bayesian algorithm called TARA, which allows us to identify functionally meaningful clusters through an iterative procedure. Our extensive experiment results show that TARA can be an effective algorithm for cluster analysis in a complex network. Moreover, the parallelized version of TARA makes it possible to perform efficiently at its tasks when applied to large complex networks.

Generating text to have a consistent style with only a few observed highly-stylized text samples is a difficult task for image processing. The text style involving the typography, i.e., font, stroke, color, decoration, effects, etc., should be considered for transfer. In this paper, we propose a novel approach to stylize target text by decoding weighted deep features from only a few referenced samples. The deep features, including content and style features of each referenced text, are extracted from a Convolutional Neural Network (CNN) that is optimized for character recognition. Then, we calculate the similarity scores of the target text and the referenced samples by measuring the distance along the corresponding channels from the content features of the CNN when considering only the content, and assign them as the weights for aggregating the deep features. To enforce the stylized text to be realistic, a discriminative network with adversarial loss is employed. We demonstrate the effectiveness of our network by conducting experiments on three different datasets which have various styles, fonts, languages, etc. Additionally, the coefficients for character style transfer, including the character content, the effect of similarity matrix, the number of referenced characters, the similarity between characters, and performance evaluation by a new protocol are analyzed for better understanding our proposed framework.

In this paper, we present a novel double graph regularized double dictionary learning (DGRDDL) method for image classification. The proposed method jointly constructs a number of class-specific sub-dictionaries to capture the most discriminative features (class-specific information) of each class, and a class-shared dictionary to model the common patterns (class-shared information) shared by the images from different classes. A novel double graph regularization is proposed to correctly represent and differentiate these two types of information. Specifically, an intra-class similarity graph constraint is imposed on the representation coefficients over the class-specific dictionaries, and an inter-class similarity graph constraint is applied on the representation coefficients over the class-shared dictionary. In this way, the representations learned by the proposed DGRDDL method can correctly model the local similarity relationships of the class-specific and the class-shared information in images, respectively. Moreover, due to the differences between the intra-class and inter-class similarity graphs, the two types of information can be appropriately separated and captured by the learned dictionaries. We evaluate the performance of the proposed method on six public datasets and compared against those of seven benchmark methods. The experimental results demonstrate the effectiveness and superiority of the proposed method in image classification over the benchmark dictionary learning methods.

In cognitive radio ad hoc networks (CRAHNs), the secondary users' links are interrupted by the arrival of primary users, which leads to a significant increase of the number of transmissions per packet. Since network coding and opportunistic routing can reduce the number of transmissions over unreliable wireless links, they are more suitable for CRAHNs. Network coding-based opportunistic routing has been well studied in previous works for traditional ad hoc networks. However, existing approaches have limitations to handle CRAHNs. First, the routing decisions of these methods aim to find one optimal forwarding set for the whole path from the source to the destination. If the channel conditions change dynamically, the pre-selected forwarding set may become unavailable or non-optimal. Second, their coding schemes are not efficient enough to cope with the dynamic spectrum variation in CRAHNs. In this paper, we attempt to overcome these limitations and propose a network-coding-based geographic segmented opportunistic routing scheme for CRAHNs, by fully embracing its characteristics. In our scheme, the whole path from the source to the destination is cut down into several smaller opportunistic route segments, where the packets are transmitted through multiple segments based on a step-by-step forwarding procedure until all the packets are delivered to the destination. Since our scheme utilizes only local spectrum opportunities, topology information, and geometric conditions to compute the forwarding set for each short-term opportunistic route segment, it can better adapt to dynamic spectrum environments and changing network topologies in CRAHNs. Furthermore, we construct a coding graph to show that our coding problem is a reduction from the maximum clique problem, and we propose an efficient network coding strategy to solve it. Simulation results show that our scheme can achieve a considerable performance, compared with the latest opportunistic routing protocols designed for CRAHNs.

Pseudorandom binary sequences with optimal balance and autocorrelation have many applications in stream cipher, communication, coding theory, etc. Constructing a binary sequences with three-level autocorrelation is equivalent to find the corresponding characteristic set of the sequences that should be an almost difference set. In this letter, we construct three new families of almost difference sets and two new families of difference sets from the cyclotomic classes of order twelve.