Urban Spatial Structures From Human Flow By Hodge-Kodaira Decomposition

Human flow in cities indicates social activity and can reveal urban spatial structures based on human behaviours for relevant applications. Scalar potential is a mathematical concept, and if successfully introduced, it can provide an intuitive perspective of human flow. However, the definition of such a potential to the origin-destination flow matrix and determination of its plausibility remain unsolved. Here, we apply Hodge-Kodaira decomposition, in which a matrix is uniquely decomposed into a potential-driven (gradient) flow and a curl flow. We depict the potential landscapes in cities due to commuting flow and reveal how the landscapes have been changed or unchanged by years or transport methods. We then determine how well the commuting flow is described by the potential, by evaluating the percentage of the gradient component for metropolitan areas in the USA and show that the gradient component is almost 100% in several areas; in other areas, however, the curl component is dominant, indicating the importance of circular flow along triangles of places. The potential landscape provides an easy-to-use visualisation tool to show the attractive places of human flow and will aid in various applications in commerce, urban design, and epidemic spreading.

Identifying the Hierarchical Influence Structure Behind Smart Sanctions Using Network Analysis

Smart sanctions are an increasingly popular tool in foreign policy. Countries and international institutions worldwide issue such lists to sanction targeted entities through financial asset freezing, embargoes, and travel restrictions. The relationships between the issuer and the targeted entities in such lists reflect what kind of entities the issuer intends to be against. Thus, analyzing the similarities of sets of targeted entities created by several issuers might pave the way toward understanding the foreign political power structure that influences institutions to take similar actions. In the current paper, by analyzing the smart sanctions lists issued by major countries and international institutions worldwide (a total of 73 countries, 12 international organizations, and 1,700 lists), we identify the hierarchical structure of influence among these institutions that encourages them to take such actions. The Helmholtz–Hodge decomposition is a method that decomposes network flow into a hierarchical gradient component and a loop component and is especially suited for this task. Hence, by performing a Helmholtz–Hodge decomposition of the influence network of these institutions, as constructed from the smart sanctions lists they have issued, we show that meaningful insights about the hierarchical influence structure behind smart sanctions can be obtained.

Learning to Detect Cracks on Damaged Concrete Surfaces Using Two-Branched Convolutional Neural Network

Image sensors are widely used for detecting cracks on concrete surfaces to help proactive and timely management of concrete structures. However, it is a challenging task to reliably detect cracks on damaged surfaces in the real world due to noise and undesired artifacts. In this paper, we propose an autonomous crack detection algorithm based on convolutional neural network (CNN) to solve the problem. To this aim, the proposed algorithm uses a two-branched CNN architecture, consisting of sub-networks named a crack-component-aware (CCA) network and a crack-region-aware (CRA) network. The CCA network is to learn gradient component regarding cracks, and the CRA network is to learn a region-of-interest by distinguishing critical cracks and noise such as scratches. Specifically, the two sub-networks are built on convolution-deconvolution CNN architectures, but also they are comprised of different functional components to achieve their own goals efficiently. The two sub-networks are trained in an end-to-end to jointly optimize parameters and produce the final output of localizing important cracks. Various crack image samples and learning methods are used for efficiently training the proposed network. In the experimental results, the proposed algorithm provides better performance in the crack detection than the conventional algorithms.

The surface layer integral method for the modelling of the radial gravity gradient component over sea surface

For the modelling and determination of the Earth’s external gravity potential as well as its second-order radial derivatives in the space near sea surface, the surface layer integral method was discussed in the paper. The reasons for the applicability of the method over sea surface were discussed. From the original integral formula of disturbing potential based on the surface layer method, the expression of the radial component of the gravity gradient tensor was derived. Furthermore, an identity relation was introduced to modify the formula in order to reduce the singularity problem. Numerical experiments carried out over the marine area of China show that, the modi-fied surface layer integral method effectively improves the accuracy and reliability of the calculation of the second-order radial gradient component of the disturbing potential near sea surface.

Optical Flow and Prediction Residual Based Hybrid Forensic System for Inter-Frame Tampering Detection

In the wake of widespread proliferation of inexpensive and easy-to-use digital content editing software, digital videos have lost the idealized reputation they once held as universal, objective and infallible evidence of occurrence of events. The pliability of digital content and its innate vulnerability to unobtrusive alterations causes us to become skeptical of its validity. However, in spite of the fact that digital videos may not always present a truthful picture of reality, their usefulness in today’s world is incontrovertible. Therefore, the need to verify the integrity and authenticity of the contents of a digital video becomes paramount, especially in critical scenarios such as defense planning and legal trials where reliance on untrustworthy evidence could have grievous ramifications. Inter-frame tampering, which involves insertion/removal/replication of sets of frames into/from/within a video sequence, is among the most un-convoluted and elusive video forgeries. In this paper, we propose a potent hybrid forensic system that detects inter-frame forgeries in compressed videos. The system encompasses two forensic techniques. The first is a novel optical flow analysis based frame-insertion and removal detection procedure, where we focus on the brightness gradient component of optical flow and detect irregularities caused therein by post-production frame-tampering. The second component is a prediction residual examination based scheme that expedites detection and localization of replicated frames in video sequences. Subjective and quantitative results of comprehensive tests on an elaborate dataset under diverse experimental set-ups substantiate the effectuality and robustness of the proposed system.

Fabrication of Multiple Oxidation-Resistant Coating on C/SiC Matrix

Oxidation-resistant coatings with gradient structures on C/SiC composites were fabricated. The gradient oxidation-resistant coatings were composed of three layers. The inner layer was reaction-bonded SiC which was fabricated by silicon infiltration. The middle layer was dense CVD SiC with the gradient component which was produced by the pyrolysis of methyltrichlorosilan (MTS) in excess hydrogen. The outer layer was a sealed layer with B4C and MoSi2 powders which was fabricated by slurry process. Optical microscope and SEM were used to observe the cross-section morphology and microstructure of coatings. XRD and EDS were used to characterize the phase composition and component distribution of coatings. EDS analysis showed the component distribution of CVD SiC coating changed significantly with the change of ratio of MTS to H2. The result indicated that C/SiC composite had good oxidation resistance when it was used at high temperature in the environment of high-speed combustion gas.