Citizen Participation, Trust, and Literacy on Government Legitimacy: The Case of Environmental Governance

This paper explores the role of direct citizen participation, trust, and environmental literacy and its impact on the legitimacy of government institutions in the context of environmental governance in the U.S. The current knowledge regarding the dynamics of the institutional legitimacy at the policy level is significantly lacking. This paper addresses broadly the effects of direct citizen participation, citizen trust, and environmental literacy on government legitimacy. Findings indicate that citizens prefer government institutions over ordinary people on leading environmental governance. However, the preference was less clear when asked to choose between the government and businesses as to who should lead environmental governance. Implications for government leadership on the issues of environmental policy are discussed at the end of the pape

A study of the Quintet for Piano and Strings by Richard Danielpour

Richard Danielpour is recognized as one of the most successful and acclaimed composers today. His music is often described as neo-romantic: full of grand gestures, highly accessible, brilliantly orchestrated, and rhythmically powerful and exciting. His music is based on the traditions of European classical music; however, it also combines the American vernacular of the 20th century, including jazz, rock, and pop music. His special interests in metaphysics and non-Western culture, especially Zen Buddhism, are also reflected in his compositions. This study examines Danielpour’s Quintet for Piano and Strings, written in 1988. The work consists of three movements with the descriptive titles: “Annunciation,” “Atonement,” and “Apotheosis.” In general, the work shows many of the composer’s compositional characteristics such as references to traditional tonal language procedure, metaphysical ideas (developmental narratives), and rhythmic energy. The purpose of this study is to illustrate characteristics of Danielpour’s compositional style found in the Piano Quintet, and provide some insights from a theoretical and stylistic perspective. The first chapter provides biographical information about the composer and some background about his Piano Quintet. Three central chapters are devoted to detailed formal analysis of the three movements, focusing on issues such as the formal structure, tonal center, and interaction between diatonic and special collections (e.g. octatonic, whole-tone, and pentatonic scales). A final chapter takes up general issues of relationships between and among the movements, focused on Danielpour’s stylistic characteristics found in the Quintet for Piano and Strings

Hawking temperature from scattering off the charged 2D black hole

The charged 2D black hole is visualized as presenting an potential barrier $V^{OUT}(r^*)$ to on-coming tachyon wave. Since this takes the complicated form, an approximate form $V^{APP}(r^*)$ is used for scattering analysis. We calculate the reflection and transmission coefficients for scattering of tachyon off the charged 2D black hole. The Hawking temperature is also derived from the reflection coefficient by Bogoliubov transformation. In the limit of $Q \to 0$, we recover the Hawking temperature of the 2D dilaton black hole.Comment: 12 pages 3 figures, RevTeX, to obtain figures contact author ([email protected]

A Method for Attitude Control Based on a Mathematical Model for an Inverted Pendulum-Type Mobile Robot

A method for attitude control based on a mathematical model for an inverted pendulum-type mobile robot was proposed. The inverted pendulum-type mobile robot was designed and the mathematical modeling was conducted. The parameters of the mobile robot were estimated and the state-space model of mobile robot was obtained by the substitution of the estimated parameters into the mathematical model. The transfer function of the mobile robot is applied to generate the root-locus diagram used for the estimation of the gains of the PID controller. The attitude control method including a PID controller, non-linear elements, and integral saturation prevention was designed and simulated. The experiment was conducted by applying the method to the mobile robot. In the attitude control experiment, the performance of attitude recovery from ±12° tilted initial state with a settling time of 0.98s and a percent overshoot of 40.1% was obtained. Furthermore, the attitude maintaining robustness against disturbance was verified

Gaussian Multiresolution Models: Exploiting Sparse Markov and Covariance Structure

In this paper, we consider the problem of learning Gaussian multiresolution (MR) models in which data are only available at the finest scale, and the coarser, hidden variables serve to capture long-distance dependencies. Tree-structured MR models have limited modeling capabilities, as variables at one scale are forced to be uncorrelated with each other conditioned on other scales. We propose a new class of Gaussian MR models in which variables at each scale have sparse conditional covariance structure conditioned on other scales. Our goal is to learn a tree-structured graphical model connecting variables across scales (which translates into sparsity in inverse covariance), while at the same time learning sparse structure for the conditional covariance (not its inverse) within each scale conditioned on other scales. This model leads to an efficient, new inference algorithm that is similar to multipole methods in computational physics. We demonstrate the modeling and inference advantages of our approach over methods that use MR tree models and single-scale approximation methods that do not use hidden variables

Propagating waves in an extremal black string

We investigate the black string in the context of the string theories. It is shown that the graviton is the only propagating mode in the (2+1)--dimensional extremal black string background. Both the dilation and axion turn out to be non-propagating modes.Comment: Minor corrections, 11 pages in ReVTeX, no figure

Learning Latent Tree Graphical Models

We study the problem of learning a latent tree graphical model where samples are available only from a subset of variables. We propose two consistent and computationally efficient algorithms for learning minimal latent trees, that is, trees without any redundant hidden nodes. Unlike many existing methods, the observed nodes (or variables) are not constrained to be leaf nodes. Our first algorithm, recursive grouping, builds the latent tree recursively by identifying sibling groups using so-called information distances. One of the main contributions of this work is our second algorithm, which we refer to as CLGrouping. CLGrouping starts with a pre-processing procedure in which a tree over the observed variables is constructed. This global step groups the observed nodes that are likely to be close to each other in the true latent tree, thereby guiding subsequent recursive grouping (or equivalent procedures) on much smaller subsets of variables. This results in more accurate and efficient learning of latent trees. We also present regularized versions of our algorithms that learn latent tree approximations of arbitrary distributions. We compare the proposed algorithms to other methods by performing extensive numerical experiments on various latent tree graphical models such as hidden Markov models and star graphs. In addition, we demonstrate the applicability of our methods on real-world datasets by modeling the dependency structure of monthly stock returns in the S&P index and of the words in the 20 newsgroups dataset