Abstract:
In many clinical trials, it is important to balance treatment allocation over covariates. Although a great many papers have been published on balancing over discrete covariates, the procedures for continuous covariates have been less well studied. Traditionally, a continuous covariate usually needs to be transformed to a discrete one by splitting its range into several categories. Such practice may lead to loss of information and is susceptible to misspecification of covariate distribution. The more recent papers seek to define an imbalance measure that preserves the nature of continuous covariates and set the allocation rule in order to minimize that measure. We propose a new design, which defines the imbalance measure by the maximum assignment difference when all possible divisions of the covariate range are considered. This measure depends only on ranks of the covariate values and is therefore free of covariate distribution. In addition, we developed an efficient algorithm to implement the new procedure. By simulation studies we show that the new procedure is able to keep good balance properties in comparison with other popular designs.

Abstract:
In the field of photoelectric sensing and measurement, laser triangulation is a superior and widely used technology due to its advantages of high accuracy, rapidity, and non-contact. This paper first applied it to the measurement of pressure, and proposed and designed a laser-type pressure sensor. The sensor measured the center deflection of the circular pressure-sensitive diaphragm using the laser triangulation, then determined the pressure on the diaphragm according to the small-deflection theory. In the aspect of optical system, the sensor used the principle of lens imaging of magnification and constant focus combining with high-resolution photodetector, which further improved the system accuracy effectively. First the paper created the mathematical model of the laser triangulation in view of the high accuracy, small size requirements. Afterward determined the parameters of the diaphragm in light of the linear range and measurement accuracy of the pressure, and did the finite element analysis of the diaphragm using ANSYS. The analysis demonstrates that within the pressure range, meeting the small-deflection theory, the relationship between the pressure and the deflection is nearly linear. The minimum pressure of the sensors designed is 50Pa, the pressure range is 1.4614MPa, and the maximum relative nonlinearity error of the diaphragm is 1.273%. This simulation design provides a forceful and important basis for the realization of the sensor.

Abstract:
Balancing treatment allocation for influential covariates is critical in clinical trials. This has become increasingly important as more and more biomarkers are found to be associated with different diseases in translational research (genomics, proteomics and metabolomics). Stratified permuted block randomization and minimization methods [Pocock and Simon Biometrics 31 (1975) 103-115, etc.] are the two most popular approaches in practice. However, stratified permuted block randomization fails to achieve good overall balance when the number of strata is large, whereas traditional minimization methods also suffer from the potential drawback of large within-stratum imbalances. Moreover, the theoretical bases of minimization methods remain largely elusive. In this paper, we propose a new covariate-adaptive design that is able to control various types of imbalances. We show that the joint process of within-stratum imbalances is a positive recurrent Markov chain under certain conditions. Therefore, this new procedure yields more balanced allocation. The advantages of the proposed procedure are also demonstrated by extensive simulation studies. Our work provides a theoretical tool for future research in this area.

Abstract:
Least box number coverage problem for calculating dimension of fractal networks is a NP-hard problem. Meanwhile, the time complexity of random ball coverage for calculating dimension is very low. In this paper we strictly present the upper bound of relative error for random ball coverage algorithm. We also propose twice-random ball coverage algorithm for calculating network dimension. For many real-world fractal networks, when the network diameter is sufficient large, the relative error upper bound of this method will tend to 0. In this point of view, given a proper acceptable error range, the dimension calculation is not a NP-hard problem, but P problem instead.

Abstract:
It has been proved that the spanning tree from a given network has the optimal synchronizability, which means the index $R=\lambda_{N}/\lambda_{2}$ reaches the minimum 1. Although the optimal synchronizability is corresponding to the minimal critical overall coupling strength to reach synchronization, it does not guarantee a shorter converging time from disorder initial configuration to synchronized state. In this letter, we find that it is the depth of the tree that affects the converging time. In addition, we present a simple and universal way to get such an effective oriented tree in a given network to reduce the converging time significantly by minimizing the depth of the tree. The shortest spanning tree has both the maximal synchronizability and efficiency.

Abstract:
The loop structure plays an important role in many aspects of complex networks and attracts much attention. Among the previous works, Bianconi et al find that real networks often have fewer short loops as compared to random models. In this paper, we focus on the uneven location of loops which makes some parts of the network rich while some other parts sparse in loops. We propose a node removing process to analyze the unevenness and find rich loop cores can exist in many real networks such as neural networks and food web networks. Finally, an index is presented to quantify the unevenness of loop location in complex networks.

Abstract:
Stanley Milgram's small world experiment presents "six degrees of separation" of our world. One phenomenon of the experiment still puzzling us is that how individuals operating with the social network information with their characteristics can be very adept at finding the short chains. The previous works on this issue focus whether on the methods of navigation in a given network structure, or on the effects of additional information to the searching process. In this paper, we emphasize that the growth and shape of network architecture is tightly related to the individuals' attributes. We introduce a method to reconstruct nodes' intimacy degree based on local interaction. Then we provide an intimacy based approach for orientation in networks. We find that the basic reason of efficient search in social networks is that the degree of "intimacy" of each pair of nodes decays with the length of their shortest path exponentially. Meanwhile, the model can explain the hubs limitation which was observed in real-world experiment.

Abstract:
Modularity Q is an important function for identifying community structure in complex networks. In this paper, we prove that the modularity maximization problem is equivalent to a nonconvex quadratic programming problem. This result provide us a simple way to improve the efficiency of heuristic algorithms for maximizing modularity Q. Many numerical results demonstrate that it is very effective.

Abstract:
Social network structure is very important for understanding human information diffusing, cooperating and competing patterns. It can bring us with some deep insights about how people affect each other. As a part of complex networks, social networks have been studied extensively. Many important universal properties with which we are quite familiar have been recovered, such as scale free degree distribution, small world, community structure, self-similarity and navigability. According to some empirical investigations, we conclude that our social network also possesses another important universal property. The spatial structure of social network is scale invariable. The distribution of geographic distance between friendship is about $Pr(d)\propto d^{-1}$ which is harmonious with navigability. More importantly, from the perspective of searching information, this kind of property can benefit individuals most.

Abstract:
We numerically study bootstrap percolation on Kleinberg's spatial networks, in which the probability density function of a node to have a long-range link at distance $r$ scales as $P(r)\sim r^{\alpha}$. Setting the ratio of the size of the giant active component to the network size as the order parameter, we find a critical exponent $\alpha_{c}=-1$, above which a hybrid phase transition is observed, with both the first-order and second-order critical points being constant. When $\alpha<\alpha_{c}$, the second-order critical point increases as the decreasing of $\alpha$, and there is either absent of the first-order phase transition or with a decreasing first-order critical point as the decreasing of $\alpha$, depending on other parameters. Our results expand the current understanding on the spreading of information and the adoption of behaviors on spatial social networks.