Club Convergence of House Prices: Evidence from China's Ten Key Cities

The latest global financial tsunami and its follow-up global economic recession has uncovered the crucial impact of housing markets on financial and economic systems. The Chinese stock market experienced a markedly fall during the global financial tsunami and China's economy has also slowed down by about 2\%-3\% when measured in GDP. Nevertheless, the housing markets in diverse Chinese cities seemed to continue the almost nonstop mania for more than ten years. However, the structure and dynamics of the Chinese housing market are less studied. Here we perform an extensive study of the Chinese housing market by analyzing ten representative key cities based on both linear and nonlinear econophysical and econometric methods. We identify a common collective driving force which accounts for 96.5\% of the house price growth, indicating very high systemic risk in the Chinese housing market. The ten key cities can be categorized into clubs and the house prices of the cities in the same club exhibit an evident convergence. These findings from different methods are basically consistent with each other. The identified city clubs are also consistent with the conventional classification of city tiers. The house prices of the first-tier cities grow the fastest, and those of the third- and fourth-tier cities rise the slowest, which illustrates the possible presence of a ripple effect in the diffusion of house prices in different cities.Comment: 16 Latex pages including 6 figures and 4 table

Graphic Method for Arbitrary nn-body Phase Space

In quantum field theory, the phase space integration is an essential part in all theoretical calculations of cross sections and decay widths. It is also needed for computing the imaginary part of a physical amplitude. A key problem is to get the phase space formula expressed in terms of any chosen invariant masses in an $n$-body system. We propose a graphic method to quickly get the phase space formula of any given invariant masses intuitively for an arbitrary $n$-body system in general $D$-dimensional spacetime, with the involved momenta in any reference frame. The method also greatly simplifies the phase space calculation just as what Feynman diagrams do in calculating scattering amplitudes.Comment: More explanations, generalization to the general spacetime dimensions include

Decentralized Approximate Newton Methods for Convex Optimization on Networked Systems

In this paper, a class of Decentralized Approximate Newton (DEAN) methods for addressing convex optimization on a networked system are developed, where nodes in the networked system seek for a consensus that minimizes the sum of their individual objective functions through local interactions only. The proposed DEAN algorithms allow each node to repeatedly perform a local approximate Newton update, which leverages tracking the global Newton direction and dissipating the discrepancies among the nodes. Under less restrictive problem assumptions in comparison with most existing second-order methods, the DEAN algorithms enable the nodes to reach a consensus that can be arbitrarily close to the optimum. Moreover, for a particular DEAN algorithm, the nodes linearly converge to a common suboptimal solution with an explicit error bound. Finally, simulations demonstrate the competitive performance of DEAN in convergence speed, accuracy, and efficiency

A Smooth Double Proximal Primal-Dual Algorithm for a Class of Distributed Nonsmooth Optimization Problem

This technical note studies a class of distributed nonsmooth convex consensus optimization problem. The cost function is a summation of local cost functions which are convex but nonsmooth. Each of the local cost functions consists of a twice differentiable convex function and two lower semi-continuous convex functions. We call this problem as single-smooth plus double-nonsmooth (SSDN) problem. Under mild conditions, we propose a distributed double proximal primal-dual optimization algorithm. The double proximal operator is designed to deal with the difficulty caused by the unproximable property of the summation of those two nonsmooth functions. Besides, it can also guarantee that the proposed algorithm is locally Lipschitz continuous. An auxiliary variable in the double proximal operator is introduced to estimate the subgradient of the second nonsmooth function. Theoretically, we conduct the convergence analysis by employing Lyapunov stability theory. It shows that the proposed algorithm can make the states achieve consensus at the optimal point. In the end, nontrivial simulations are presented and the results demonstrate the effectiveness of the proposed algorithm

Long GRBs as a Tool to Investigate Star Formation in Dark Matter Halos

First stars can only form in structures that are suitably dense, which can be parametrized by the minimum dark matter halo mass $M_{\rm min}$. $M_{\rm min}$ must plays an important role in star formation. The connection of long gamma-ray bursts (LGRBs) with the collapse of massive stars has provided a good opportunity for probing star formation in dark matter halos. We place some constraints on $M_{\rm min}$ using the latest $Swift$ LGRB data. We conservatively consider that LGRB rate is proportional to the cosmic star formation rate (CSFR) and an additional evolution parametrized as $(1+z)^{\alpha}$, where the CSFR model as a function of $M_{\rm min}$. Using the $\chi^{2}$ statistic, the contour constraints on the $M_{\rm min}$--$\alpha$ plane show that at the $1\sigma$ confidence level, we have $M_{\rm min}<10^{10.5}$ $\rm M_{\odot}$ from 118 LGRBs with redshift $z<4$ and luminosity $L_{\rm iso}>1.8\times10^{51}$ erg $\rm s^{-1}$. We also find that adding 12 high-\emph{z} $(4<z<5)$ LGRBs (consisting of 104 LGRBs with $z<5$ and $L_{\rm iso}>3.1\times10^{51}$ erg $\rm s^{-1}$) could result in much tighter constraints on $M_{\rm min}$, for which, $10^{7.7}\rm M_{\odot}<M_{\rm min}<10^{11.6}\rm M_{\odot}$ ($1\sigma$). Through Monte Carlo simulations, we estimate that future five years of Sino-French spacebased multiband astronomical variable objects monitor (\emph{SVOM}) observations would tighten these constraints to $10^{9.7}\rm M_{\odot}<M_{\rm min}<10^{11.3}\rm M_{\odot}$. The strong constraints on $M_{\rm min}$ indicate that LGRBs are a new promising tool for investigating star formation in dark matter halos.Comment: 23 pages, 6 figures, 1 table, accepted by Journal of High Energy Astrophysic

Effects of temperature gradient on the interface microstructure and diffusion of diffusion couples: phase-field simulation

The temporal interface microstructure and diffusion in the diffusion couples with the mutual interactions of temperature gradient, concentration difference and initial aging time of the alloys were studied by phase-field simulation, the diffusion couples are produced by the initial aged spinodal alloys with different compositions. Temporal composition evolution and volume fraction of the separated phase indicates the element diffusion direction through the interface under the temperature gradient. The increased temperature gradient induces a wide single-phase region at two sides of the interface. The uphill diffusion proceeds through the interface, no matter the diffusion directions are up or down to the temperature gradient. For an alloy with short initial aging time, phase transformation accompanying the interdiffusion results in the straight interface with the single-phase regions at both sides. Comparing with the temperature gradient, composition difference of diffusion couple and initial aging time of the alloy show greater effect on the diffusion and interface microstructure.Comment: 18 pages,11 figure

Bounds and Constructions of Locally Repairable Codes: Parity-check Matrix Approach

A $q$-ary $(n,k,r)$ locally repairable code (LRC) is an $[n,k,d]$ linear code over $\mathbb{F}_q$ such that every code symbol can be recovered by accessing at most $r$ other code symbols. The well-known Singleton-like bound says that $d \le n-k-\lceil k/r\rceil +2$ and an LRC is said to be optimal if it attains this bound. In this paper, we study the bounds and constructions of LRCs from the view of parity-check matrices. Firstly, a simple and unified framework based on parity-check matrix to analyze the bounds of LRCs is proposed. Several useful structural properties on $q$-ary optimal LRCs are obtained. We derive an upper bound on the minimum distance of $q$-ary optimal $(n,k,r)$-LRCs in terms of the field size $q$. Then, we focus on constructions of optimal LRCs over binary field. It is proved that there are only 5 classes of possible parameters with which optimal binary $(n,k,r)$-LRCs exist. Moreover, by employing the proposed parity-check matrix approach, we completely enumerate all these 5 classes of possible optimal binary LRCs attaining the Singleton-like bound in the sense of equivalence of linear codes.Comment: 18 page

Systemic risk and spatiotemporal dynamics of the US housing market

Housing markets play a crucial role in economies and the collapse of a real-estate bubble usually destabilizes the financial system and causes economic recessions. We investigate the systemic risk and spatiotemporal dynamics of the US housing market (1975-2011) at the state level based on the Random Matrix Theory (RMT). We identify rich economic information in the largest eigenvalues deviating from RMT predictions and unveil that the component signs of the eigenvectors contain either geographical information or the extent of differences in house price growth rates or both. Our results show that the US housing market experienced six different regimes, which is consistent with the evolution of state clusters identified by the box clustering algorithm and the consensus clustering algorithm on the partial correlation matrices. Our analysis uncovers that dramatic increases in the systemic risk are usually accompanied with regime shifts, which provides a means of early detection of housing bubbles.Comment: 8 RevTex pages including 4 eps figure

Role of intensity fluctuations in third-order correlation double-slit interference of thermal light

A third-order double-slit interference experiment with pseudo-thermal light source in the high-intensity limit has been performed by actually recording the intensities in three optical paths. It is shown that not only can the visibil- ity be dramatically enhanced compared to the second-order case as previously theoretically predicted and shown experimentally, but also that the higher visi- bility is a consequence of the contribution of third-order correlation interaction terms, which is equal to the sum of all contributions from second-order cor- relation. It is interesting that, when the two reference detectors are scanned in opposite directions, negative values for the third-order correlation term of the intensity fluctuations may appear. The phenomenon can be completely explained by the theory of classical statistical optics, and is the first concrete demonstration of the influence of the third-order correlation terms.Comment: 10 pages,4figure

Low-rank Tensor Bandits

In recent years, multi-dimensional online decision making has been playing a crucial role in many practical applications such as online recommendation and digital marketing. To solve it, we introduce stochastic low-rank tensor bandits, a class of bandits whose mean rewards can be represented as a low-rank tensor. We propose two learning algorithms, tensor epoch-greedy and tensor elimination, and develop finite-time regret bounds for them. We observe that tensor elimination has an optimal dependency on the time horizon, while tensor epoch-greedy has a sharper dependency on tensor dimensions. Numerical experiments further back up these theoretical findings and show that our algorithms outperform various state-of-the-art approaches that ignore the tensor low-rank structure