In this paper, we investigate the problem of optimizing the overall system secrecy spectral efficiency of cell-Free massive multiple-input multiple-output (CF-mMIMO) with network-assisted full-duplexing (NAFD) system, considering resisting interception by superimposing artificial noise (AN) on the transmitted signal. The access points (APs) are selected by binary mode selection vectors as transmitting or receiving APs flexibly to serve both uplink and downlink users simultaneously. Since optimization variables are tightly coupled, a double-loop strategy is developed to solve the non-covex combinatorial optimization problem. The outer loop is constructed according to greedy search for duplex mode selection, while the inner loop based on the proposed successive convex approximation (SCA) algorithm aims to optimize the transceivers and AN. Simulation results show that the proposed solution is superior to the fixed-mode duplex scheme in terms of secure spectral efficiency and able to achieve similar performance to that of the optimal exhaustive search scheme.

Several techniques exist to automatically synthesize a 2D image resembling an input exemplar texture. Most of the approaches optimize a new image so that the color neighborhoods in the output closely match those in the input, across all scales. In this paper we revisit by-example texture synthesis in the context of additive manufacturing. Our goal is to generate not only colors, but also structure along output surfaces: given an exemplar indicating 'solid' and 'empty' pixels, we generate a similar pattern along the output surface. The core challenge is to guarantee that the pattern is not only fully connected, but also structurally sound. To achieve this goal we propose a novel formulation for on-surface by-example texture synthesis that directly works in a voxel shell around the surface. It enables efficient local updates to the pattern, letting our structural optimizer perform changes that improve the overall rigidity of the pattern. We use this technique in an iterative scheme that jointly optimizes for appearance and structural soundness. We consider fabricability constraints and a user-provided description of a force profile that the object has to resist. Our results fully exploit the capabilities of additive manufacturing by letting users design intricate structures along surfaces. The structures are complex, yet they resemble input exemplars, resulting in a modeling tool accessible to casual users.

Insect species recognition is a typical application of image categorization and object recognition. Unlike generic image categorization datasets (such as the Caltech 101 dataset) that have large variations between categories, the difference of appearance between insect species is so small that only some entomologist experts can distinguish them. Therefore, the state-of-the-art image categorization methods do not perform sufficiently on insect images. In this paper, we propose an insect species recognition method based on class specific sparse representation. On obtaining the vector representation of image via sparse coding of patches, an SVM classifier is used to classify the image into species. We propose two class specific sparse representation methods under weakly supervised learning to discriminate insect species which have substantial similarity to each other. Experimental results show that the proposed methods perform well in insect species recognition and outperform the state-of-the-art methods on generic image categorization.

Graphs are prevalently used to model the relationships between objects in various domains. With the increasing usage of graph databases, it has become more and more demanding to efficiently process graph queries. Querying graph databases is costly since it involves subgraph isomorphism testing, which is an NP-complete problem. In recent years, some effective graph indexes have been proposed to first obtain a candidate answer set by filtering part of the false results and then perform verification on each candidate by checking subgraph isomorphism. Query performance is improved since the number of subgraph isomorphism tests is reduced. However, candidate verification is still inevitable, which can be expensive when the size of the candidate answer set is large. In this paper, we propose a novel indexing technique that constructs a nested inverted-index, called FG-index, based on the set of Frequent subGraphs (FGs). Given a graph query that is an FG in the database, FG-index returns the exact set of query answers without performing candidate verification. When the query is an infrequent graph, FG-index produces a candidate answer set which is close to the exact answer set. Since an infrequent graph means the graph occurs in only a small number of graphs in the database, the number of subgraph isomorphism tests is small. To ensure that the index fits into the main memory, we propose a new notion of -Tolerance Closed Frequent Graphs (-TCFGs), which allows us to flexibly tune the size of the index in a parameterized way. Our extensive experiments verify that query processing using FG-index is orders of magnitude more efficient than using the state-of-the-art graph index.