Kinematic Basis of Emergent Energetics of Complex Dynamics

Stochastic kinematic description of a complex dynamics is shown to dictate an energetic and thermodynamic structure. An energy function $\varphi(x)$ emerges as the limit of the generalized, nonequilibrium free energy of a Markovian dynamics with vanishing fluctuations. In terms of the $\nabla\varphi$ and its orthogonal field $\gamma(x)\perp\nabla\varphi$, a general vector field $b(x)$ can be decomposed into $-D(x)\nabla\varphi+\gamma$, where $\nabla\cdot\big(\omega(x)\gamma(x)\big)=$ $-\nabla\omega D(x)\nabla\varphi$. The matrix $D(x)$ and scalar $\omega(x)$, two additional characteristics to the $b(x)$ alone, represent the local geometry and density of states intrinsic to the statistical motion in the state space at $x$. $\varphi(x)$ and $\omega(x)$ are interpreted as the emergent energy and degeneracy of the motion, with an energy balance equation $d\varphi(x(t))/dt=\gamma D^{-1}\gamma-bD^{-1}b$, reflecting the geometrical $\|D\nabla\varphi\|^2+\|\gamma\|^2=\|b\|^2$. The partition function employed in statistical mechanics and J. W. Gibbs' method of ensemble change naturally arise; a fluctuation-dissipation theorem is established via the two leading-order asymptotics of entropy production as $\epsilon\to 0$. The present theory provides a mathematical basis for P. W. Anderson's emergent behavior in the hierarchical structure of complexity science.Comment: 7 page

Topological quantum phase transition in an S=2 spin chain

We construct a model Hamiltonian for S = 2 spin chain, where a variable parameter $\alpha$ is introduced. The edge spin is S = 1 for $\alpha = 0$, and S = 3/2 for $\alpha = 1$. Due to the topological distinction of the edge states, these two phases must be separated by one or several topological quantum phase transitions. We investigate the nature of the quantum phase transition by DMRG calculation, and propose a phase diagram for this model.Comment: 5 pages, 4 figure

Novel Self-passivation Rule and Structure of CdTe sigma3 (112) Grain Boundary

The theoretical study of grain boundaries (GBs) in polycrystalline semiconductors is currently stalemated by their complicated nature, which is difficult to extract from any direct experimental characterization. Usually, coincidence-site-lattice (CSL) models are constructed simply by aligning two symmetric planes, ignoring various possible reconstructions. Here, we propose a general self-passivation rule to determine the low-energy GB reconstruction, and find new configurations for the CdTe sigma3 (112) GBs. First-principles calculations show that it has lower formation energies than the prototype GBs adopted widely in previous studies. Surprisingly, the reconstructed GBs show self-passivated electronic properties without deep-level states in the band gap. Based on the reconstructed configurations, we revisited the influence of CdCl2 post-treatment on the CdTe GBs, and found that the addition of both Cd and Cl atoms in the GB improves the photovoltaic properties by promoting self-passivation and inducing n-type levels, respectively. The present study provides a new route for further studies of GBs in covalent polycrystalline semiconductors and also highlights that previous studies on the GBs of multinary semiconductors which are based on the unreconstructed prototype GB models, should be revisited.Comment: 11 pages, 4 figure

Approximate Closest Community Search in Networks

Recently, there has been significant interest in the study of the community search problem in social and information networks: given one or more query nodes, find densely connected communities containing the query nodes. However, most existing studies do not address the "free rider" issue, that is, nodes far away from query nodes and irrelevant to them are included in the detected community. Some state-of-the-art models have attempted to address this issue, but not only are their formulated problems NP-hard, they do not admit any approximations without restrictive assumptions, which may not always hold in practice. In this paper, given an undirected graph G and a set of query nodes Q, we study community search using the k-truss based community model. We formulate our problem of finding a closest truss community (CTC), as finding a connected k-truss subgraph with the largest k that contains Q, and has the minimum diameter among such subgraphs. We prove this problem is NP-hard. Furthermore, it is NP-hard to approximate the problem within a factor $(2-\varepsilon)$, for any $\varepsilon >0$. However, we develop a greedy algorithmic framework, which first finds a CTC containing Q, and then iteratively removes the furthest nodes from Q, from the graph. The method achieves 2-approximation to the optimal solution. To further improve the efficiency, we make use of a compact truss index and develop efficient algorithms for k-truss identification and maintenance as nodes get eliminated. In addition, using bulk deletion optimization and local exploration strategies, we propose two more efficient algorithms. One of them trades some approximation quality for efficiency while the other is a very efficient heuristic. Extensive experiments on 6 real-world networks show the effectiveness and efficiency of our community model and search algorithms

Study on evaluation of International Science and Technology Cooperation Project (ISTCP) in China

This paper presents an overview of evaluation of ISTCP in China. We discuss briefly the history of evaluation and the strengths and weaknesses of different assessment systems. On this basis, with Analytical Hierarchy Process (AHP), we establish evaluation indicator system for ISTCP that includes research project establishment evaluation, mid-period evaluation system, effect evaluation system, and confirm the value of each indicator. At the same time, we established expert database, project database, research organization database, researcher database etc. We therefore establish an evaluation platform for international science and technology cooperation project. We use it to realize full process supervision from evaluation expert selection to project management

Noninvasive prediction of Blood Lactate through a machine learning-based approach.

We hypothesized that blood lactate concentration([Lac]blood) is a function of cardiopulmonary variables, exercise intensity and some anthropometric elements during aerobic exercise. This investigation aimed to establish a mathematical model to estimate [Lac]blood noninvasively during constant work rate (CWR) exercise of various intensities. 31 healthy participants were recruited and each underwent 4 cardiopulmonary exercise tests: one incremental and three CWR tests (low: 35% of peak work rate for 15 min, moderate: 60% 10 min and high: 90% 4 min). At the end of each CWR test, venous blood was sampled to determine [Lac]blood. 31 trios of CWR tests were employed to construct the mathematical model, which utilized exponential regression combined with Taylor expansion. Good fitting was achieved when the conditions of low and moderate intensity were put in one model; high-intensity in another. Standard deviation of fitting error in the former condition is 0.52; in the latter is 1.82 mmol/liter. Weighting analysis demonstrated that, besides heart rate, respiratory variables are required in the estimation of [Lac]blood in the model of low/moderate intensity. In conclusion, by measuring noninvasive cardio-respiratory parameters, [Lac]blood during CWR exercise can be determined with good accuracy. This should have application in endurance training and future exercise industry