Mass and Environment as Drivers of Galaxy Evolution in SDSS and zCOSMOS and the Origin of the Schechter Function

We explore the simple inter-relationships between mass, star formation rate, and environment in the SDSS, zCOSMOS, and other deep surveys. We take a purely empirical approach in identifying those features of galaxy evolution that are demanded by the data and then explore the analytic consequences of these. We show that the differential effects of mass and environment are completely separable to z ~ 1, leading to the idea of two distinct processes of "mass quenching" and "environment quenching." The effect of environment quenching, at fixed over-density, evidently does not change with epoch to z ~ 1 in zCOSMOS, suggesting that the environment quenching occurs as large-scale structure develops in the universe, probably through the cessation of star formation in 30%-70% of satellite galaxies. In contrast, mass quenching appears to be a more dynamic process, governed by a quenching rate. We show that the observed constancy of the Schechter M* and α_s for star-forming galaxies demands that the quenching of galaxies around and above M* must follow a rate that is statistically proportional to their star formation rates (or closely mimic such a dependence). We then postulate that this simple mass-quenching law in fact holds over a much broader range of stellar mass (2 dex) and cosmic time. We show that the combination of these two quenching processes, plus some additional quenching due to merging naturally produces (1) a quasi-static single Schechter mass function for star-forming galaxies with an exponential cutoff at a value M* that is set uniquely by the constant of proportionality between the star formation and mass quenching rates and (2) a double Schechter function for passive galaxies with two components. The dominant component (at high masses) is produced by mass quenching and has exactly the same M* as the star-forming galaxies but a faint end slope that differs by Δα_s ~ 1. The other component is produced by environment effects and has the same M* and α_s as the star-forming galaxies but an amplitude that is strongly dependent on environment. Subsequent merging of quenched galaxies will modify these predictions somewhat in the denser environments, mildly increasing M* and making α_s slightly more negative. All of these detailed quantitative inter-relationships between the Schechter parameters of the star-forming and passive galaxies, across a broad range of environments, are indeed seen to high accuracy in the SDSS, lending strong support to our simple empirically based model. We find that the amount of post-quenching "dry merging" that could have occurred is quite constrained. Our model gives a prediction for the mass function of the population of transitory objects that are in the process of being quenched. Our simple empirical laws for the cessation of star formation in galaxies also naturally produce the "anti-hierarchical" run of mean age with mass for passive galaxies, as well as the qualitative variation of formation timescale indicated by the relative α-element abundances

Minimal Length Effects on Entanglement Entropy of Spherically Symmetric Black Holes in Brick Wall Model

We compute the black hole horizon entanglement entropy for a massless scalar field in the brick wall model by incorporating the minimal length. Taking the minimal length effects on the occupation number $n(\omega,l)$ and the Hawking temperature into consideration, we obtain the leading UV divergent term and the subleading logarithmic term in the entropy. The leading divergent term scales with the horizon area. The subleading logarithmic term is the same as that in the usual brick wall model without the minimal length.Comment: 15 pages. arXiv admin note: substantial text overlap with arXiv:1501.0602

Action Growth in f(R)f\left(R\right) Gravity

Inspired by the recent "Complexity = Action" conjecture, we use the approach proposed by Lehner et al. to calculate the rate of the action of the WheelerDeWitt patch at late times for static uncharged and charged black holes in $f\left( R\right)$ gravity. Our results have the same expressions in terms of the mass, charge, and electrical potentials at the horizons of black holes as in Einstein's gravity. In the context of $f\left( R\right)$ gravity, the Lloyd bound is saturated for uncharged black holes but violated for charged black holes near extremality. For charged black holes far away from the ground states, the Lloyd bound is violated in four dimensions but satisfied in higher dimensions.Comment: 18 pages, 2 figure

Black Hole Radiation with Modified Dispersion Relation in Tunneling Paradigm: Free-fall Frame

Due to the exponential high gravitational red shift near the event horizon of a black hole, it might appear that the Hawking radiation would be highly sensitive to some unknown high energy physics. To study effects of any unknown physics at the Planck scale on the Hawking radiation, the dispersive field theory models have been proposed, which are variations of Unruh's sonic black hole analogy. In this paper, we use the Hamilton-Jacobi method to investigate the dispersive field theory models. The preferred frame is the free-fall frame of the black hole. The dispersion relation adopted agrees with the relativistic one at low energy but is modified near the Planck mass $m_{p}$. The corrections to the Hawking temperature are calculated for massive and charged particles to $\mathcal{O}\left( m_{p}^{-2}\right)$ and neutral and massless particles with $\lambda=0$ to all orders. The Hawking temperature of radiation agrees with the standard one at the leading order. After the spectrum of radiation near the horizon is obtained, we use the brick wall model to compute the thermal entropy of a massless scalar field near the horizon of a 4D spherically symmetric black hole and a 2D one. Finally, the luminosity of a Schwarzschild black hole is calculated by using the geometric optics approximation.Comment: 28 pages. arXiv admin note: substantial text overlap with arXiv:1505.0304

Estimation of Partially Linear Regression Model under Partial Consistency Property

In this paper, utilizing recent theoretical results in high dimensional statistical modeling, we propose a model-free yet computationally simple approach to estimate the partially linear model $Y=X\beta+g(Z)+\varepsilon$. Motivated by the partial consistency phenomena, we propose to model $g(Z)$ via incidental parameters. Based on partitioning the support of $Z$, a simple local average is used to estimate the response surface. The proposed method seeks to strike a balance between computation burden and efficiency of the estimators while minimizing model bias. Computationally this approach only involves least squares. We show that given the inconsistent estimator of $g(Z)$, a root $n$ consistent estimator of parametric component $\beta$ of the partially linear model can be obtained with little cost in efficiency. Moreover, conditional on the $\beta$ estimates, an optimal estimator of $g(Z)$ can then be obtained using classic nonparametric methods. The statistical inference problem regarding $\beta$ and a two-population nonparametric testing problem regarding $g(Z)$ are considered. Our results show that the behavior of test statistics are satisfactory. To assess the performance of our method in comparison with other methods, three simulation studies are conducted and a real dataset about risk factors of birth weights is analyzed

The SSM Toolbox for Matlab

State Space Models (SSM) is a MATLAB 7.0 software toolbox for doing time series analysis by state space methods. The software features fully interactive construction and combination of models, with support for univariate and multivariate models, complex time-varying (dynamic) models, non-Gaussian models, and various standard models such as ARIMA and structural time-series models. The software includes standard functions for Kalman filtering and smoothing, simulation smoothing, likelihood evaluation, parameter estimation, signal extraction and forecasting, with incorporation of exact initialization for filters and smoothers, and support for missing observations and multiple time series input with common analysis structure. The software also includes implementations of TRAMO model selection and Hillmer-Tiao decomposition for ARIMA models. The software will provide a general toolbox for doing time series analysis on the MATLAB platform, allowing users to take advantage of its readily available graph plotting and general matrix computation capabilities.Comment: Software available from author

Intelligent Traffic Light Control Using Distributed Multi-agent Q Learning

The combination of Artificial Intelligence (AI) and Internet-of-Things (IoT), which is denoted as AI-powered Internet-of-Things (AIoT), is capable of processing huge amount of data generated from a large number of devices and handling complex problems in social infrastructures. As AI and IoT technologies are becoming mature, in this paper, we propose to apply AIoT technologies for traffic light control, which is an essential component for intelligent transportation system, to improve the efficiency of smart city's road system. Specifically, various sensors such as surveillance cameras provide real-time information for intelligent traffic light control system to observe the states of both motorized traffic and non-motorized traffic. In this paper, we propose an intelligent traffic light control solution by using distributed multi-agent Q learning, considering the traffic information at the neighboring intersections as well as local motorized and non-motorized traffic, to improve the overall performance of the entire control system. By using the proposed multi-agent Q learning algorithm, our solution is targeting to optimize both the motorized and non-motorized traffic. In addition, we considered many constraints/rules for traffic light control in the real world, and integrate these constraints in the learning algorithm, which can facilitate the proposed solution to be deployed in real operational scenarios. We conducted numerical simulations for a real-world map with real-world traffic data. The simulation results show that our proposed solution outperforms existing solutions in terms of vehicle and pedestrian queue lengths, waiting time at intersections, and many other key performance metrics

Maximizing spectral radii of uniform hypergraphs with few edges

In this paper we investigate the hypergraphs whose spectral radii attain the maximum among all uniform hypergraphs with given number of edges. In particular we characterize the hypergraph(s) with maximum spectral radius over all unicyclic hypergraphs, linear or power unicyclic hypergraphs with given girth, linear or power bicyclic hypergraphs, respectively

Topological Superconductors in Correlated Chern Insulators

In this paper, we realize a topological superconductor (TSC) in correlated topological insulator - the interacting spinful Haldane model. We consider the electrons on the Haldane model with on-site negative-U interaction and then study its properties by mean field theory and random-phase-approximation (RPA) approach. We found that in the intermediate interaction region, the ground state becomes a TSC with the Chern number 2. We also study its edge states and the zero modes of the pi-flux.Comment: 13 pages, 14 figure

The impacts of carbon emissions on global manufacturing value chain relocation: Theoretical and empirical development of a meso-level model

As a stark contrast to the diminishing media profile of the UN climate change talks, the global manufacturers appear to have become more carbon aware than ever before. Carbon audits have been carried out within many corporations to assess the carbon intensity of production processes. This is partly to address cost issues of the present (i.e. the recent rise in fossil fuel prices) and of the future (e.g. new carbon related taxes and trade tariffs). Moreover, the adoption of low carbon, clean manufacturing processes has become an increasingly prominent part of branding for many products, which could affect market share and business performance in ways that go beyond questions of cost competitiveness. How will this carbon awareness affect the configuration of the value chains of global manufacturing? Will the individual manufacturers’ decisions lead to an effective reduction of total carbon emissions at the global value chain scale? Our paper aims to answer these questions through developing a theoretical model and testing it empirically through case studies of global value chains. The model accounts explicitly costs of energy, carbon, other intermediate inputs and primary inputs in the production and transport of each component, product assembly and delivery to the market. Much work has been done on the value chain location problem – e.g. on the production unbundling among different countries from a macro-economic perspective, or on operations management at the microscopic or individual manufacturer level. It is only until recently that the economic and technology aspects have been combined in the study of global value chains (for example in the paper by Baldwin and Venables in last year’s ERSA Congress). The appropriate spatial scale for our research questions would appear to be at a meso-level: i.e. the model goes beyond the micro-level operational analysis of a single plant to cover the entire value chain for a given product, but does not cover the full interactions at the macro level. This perspective is relatively rare in the literature and provides a tool that connects the micro level and macro level perspectives.