Xiaogang Jin

Technical Section: Gradient based image completion by solving the Poisson equation

This paper presents a novel gradient-based image completion algorithm for removing significant objects from natural images or photographs. Our method reconstructs the region of removal in two phases. Firstly, the gradient maps in the removed area are completed through a patch-based filling algorithm. After that, the image is reconstructed from the gradient maps by solving a Poisson equation. A new patch-matching criterion is developed in our approach to govern the completion of gradient maps. Both the gradient and the color information are incorporated in this new criterion, so a better image completion result is obtained. Several examples and comparisons are given at the end of the paper to demonstrate the performance of our gradient-based image completion approach.

Ellipsoid-tree construction for solid objects

As ellipsoids have been employed in the collision handling of many applications in physical simulation and robotics systems, we present a novel algorithm for generating a bounding volume hierarchy (BVH) from a given model with ellipsoids as primitives. Our algorithm approximates the given model by a hierarchical set of optimized bounding ellipsoids. The ellipsoid-tree is constructed by a top-down splitting. Starting from the root of hierarchy, the volume occupied by a given model is divided into k sub-volumes where each is approximated by a volume bounding ellipsoid. Recursively, each sub-volume is then subdivided into ellipsoids for the next level in the hierarchy. The k ellipsoids at each hierarchy level for a sub-volume bounding is generated by a bottom-up algorithm - simply, the sub-volume is initially approximated by m spheres (m » k), which will be iteratively merged into k volume bounding ellipsoids and globally optimized to minimize the approximation error. Benefited from the anisotropic shape of primitives, the ellipsoid-tree constructed in our approach gives tighter volume bound and higher shape fidelity than another widely used BVH, sphere-tree.

Optimal structure of complex networks for minimizing traffic congestion

To design complex networks to minimize traffic congestion, it is necessary to understand how traffic flow depends on network structure. We study data packet flow on complex networks, where the packet delivery capacity of each node is not fixed. The optimal configuration of capacities to minimize traffic congestion is derived and the critical packet generating rate is determined, below which the network is at a free flow state but above which congestion occurs. Our analysis reveals a direct relation between network topology and traffic flow. Optimal network structure, free of traffic congestion, should have two features: uniform distribution of load over all nodes and small network diameter. This finding is confirmed by numerical simulations. Our analysis also makes it possible to theoretically compare the congestion conditions for different types of complex networks. In particular, we find that network with low critical generating rate is more susceptible to congestion. The comparison has been made on the following complex-network topologies: random, scale-free, and regular.

Mathematical Marbling

In this paper, the proposed method takes a mathematical approach with closed-form expressions to simulate marbling. This method improves control, ease of implementation, parallelism, and speed, enabling real-time visual feedback and creation of vivid flowing animations. Users can start designs from a blank sheet, raster images, or videos.

Pathway knockout and redundancy in metabolic networks.

The robustness and stability of complex cellular networks is often attributed to the redundancy of components, including genes, enzymes and pathways. Estimation of redundancy is still an open question in systems biology. Current theoretical tools to measure redundancy have various strengths and shortcomings in providing a comprehensive description of metabolic networks. Specially, there is a lack of effective measures to cover different perturbation situations. Here we present a pathway knockout algorithm to improve quantitative measure of redundancy in metabolic networks grounded on the elementary flux mode (EFM) analysis. The proposed redundancy measure is based on the average ratio of remaining EFMs after knockout of one EFM in the unperturbed state. We demonstrated with four example systems that our algorithm overcomes limits of previous measures, and provides additional information about redundancy in the situation of targeted attacks. Additionally, we compare existing enzyme knockout and our pathway knockout algorithm by the mean-field analysis, which provides mathematical expression for the average ratio of remaining EFMs after both types of knockout. Our results prove that multiple-enzymes knockout does not always yield more information than single-enzyme knockout for evaluating redundancy. Indeed, pathway knockout considers additional effects of structural asymmetry. In the metabolic networks of amino acid anabolism in Escherichia coli and human hepatocytes, and the central metabolism in human erythrocytes, we validate our mean-field solutions and prove the capacity of pathway knockout algorithm. Moreover, in the E. coli model the two sub-networks synthesizing amino acids that are essential and those that are non-essential for humans are studied separately. In contrast to previous studies, we find that redundancy of two sub-networks is similar with each other, and even sub-networks synthesizing essential amino acids can be more redundant.

Software, Data and Modelling News: NCNA: Integrated platform for constructing, visualizing, analyzing and sharing human-mediated nitrogen biogeochemical networks

Human alterations to the nitrogen (N) cycle are closely associated with global environmental and climate change. New tools are necessary to model and analyze the highly complex N cycles emerging from human-mediated ecosystems. We developed a new software, NCNA, to provide three functions: a) rigorous reconstruction of quasi-empirical models (QEMs), b) computer-aided interface for data collection and automatic sensitivity analysis, and c) automatic generation, visualization and network environ analysis (NEA) of N cycling networks.

Study on spatial and temporal mobility pattern of urban taxi services

The analysis of human behavior is the basis of understanding many social phenomena. Based on a large database of taxi billing system, this paper provides an analysis of human mobility data in an urban area of using taxi services in Shanghai. By studying the spatial temporal data of taxi services, it shows that the distribution of running time interval is approximated by Power-law distribution. And it also reflects that the taxi services may have the short-time and small-scaled characteristics, which seems to be consistent with human travel patterns.

Technical Section: Ellipsoidal-blob approximation of 3D models and its applications

This paper presents a technique for automatically approximating a given mesh model with an ellipsoidal blobby model. Firstly, an ellipsoid decomposition algorithm is introduced to approximate given models by ellipsoids. After that, a blobby implicit surface employing ellipsoidal blobs is modelled to fit the sampling points on the given mesh. Finally, the reconstructed ellipsoidal blobby model is applied in two applications: the geometry data reduction and the target shape controlled cloud animation.

The study on the sEMG signal characteristics of muscular fatigue based on the hilbert-huang transform

Muscular fatigue refers to temporary decline of maximal power ability or contractive ability for muscle movement system. The signal of surface electromyographic signal (sEMG) can reflect the changes of muscular fatigue at certain extent. In many years, the application of signal of sEMG on evaluation muscular fatigue mainly focus on two aspects of time and frequency respectively. The new method Hilbert-Huang Transform(HHT) has the powerful ability of analyzing nonlinear and non-stationary data in both time and frequency aspect together. The method has self-adaptive basis and is better for feature extraction as we can obtain the local and instantaneous frequency of the signals. In this paper, we chose an experiment of the static biceps data of twelve adult subjects under the maximal voluntary contraction (MVC) of 80%. The experimental results proved that this method as a new thinking has an obvious potential for the biomedical signal analysis.

The role of community mixing styles in shaping epidemic behaviors in weighted networks.

The dynamics of infectious diseases that are spread through direct contact have been proven to depend on the strength of community structure or modularity within the underlying network. It has been recently shown that weighted networks with similar modularity values may exhibit different mixing styles regarding the number of connections among communities and their respective weights. However, the effect of mixing style on epidemic behavior was still unclear. In this paper, we simulate the spread of disease within networks with different mixing styles: a dense-weak style (i.e., many edges among the communities with small weights) and a sparse-strong style (i.e., a few edges among the communities with large weights). Simulation results show that, with the same modularity: 1) the mixing style significantly influences the epidemic size, speed, pattern and immunization strategy; 2) the increase of the number of communities amplifies the effect of the mixing style; 3) when the mixing style changes from sparse-strong to dense-weak, there is a ‘saturation point’, after which the epidemic size and pattern become stable. We also provide a mean-field solution of the epidemic threshold and size on weighted community networks with arbitrary external and internal degree distribution. The solution explains the effect of the second moment of the degree distribution, and a symmetric effect of internal and external connections (incl. degree distribution and weight). Our study has both potential significance for designing more accurate metrics for the community structure and exploring diffusion dynamics on metapopulation networks.