Quasi-monotonic segmentation of state variable behavior for reactive control

Real-world agents must react to changing conditions as they execute planned tasks. Conditions are typically monitored through time series representing state variables. While some predicates on these times series only consider one measure at a time, other predicates, sometimes called episodic predicates, consider sets of measures. We consider a special class of episodic predicates based on segmentation of the the measures into quasi-monotonic intervals where each interval is either quasi-increasing, quasi-decreasing, or quasi-flat. While being scale-based, this approach is also computational efficient and results can be computed exactly without need for approximation algorithms. Our approach is compared to linear spline and regression analysis

On the stability of spherically symmetric spacetimes in metric f(R) gravity

We consider stability properties of spherically symmetric spacetimes of stars in metric f(R) gravity. We stress that these not only depend on the particular model, but also on the specific physical configuration. Typically configurations giving the desired $\gamma_{\rm PPN} \approx 1$ are strongly constrained, while those corresponding to $\gamma_{\rm PPN} \approx 1/2$ are less affected. Furthermore, even when the former are found strictly stable in time, the domain of acceptable static spherical solutions typically shrinks to a point in the phase space. Unless a physical reason to prefer such a particular configuration can be found, this poses a naturalness problem for the currently known metric f(R) models for accelerating expansion of the Universe.Comment: Published version, 9 pages, 3 figure

An Efficient Interpolation Technique for Jump Proposals in Reversible-Jump Markov Chain Monte Carlo Calculations

Selection among alternative theoretical models given an observed data set is an important challenge in many areas of physics and astronomy. Reversible-jump Markov chain Monte Carlo (RJMCMC) is an extremely powerful technique for performing Bayesian model selection, but it suffers from a fundamental difficulty: it requires jumps between model parameter spaces, but cannot efficiently explore both parameter spaces at once. Thus, a naive jump between parameter spaces is unlikely to be accepted in the MCMC algorithm and convergence is correspondingly slow. Here we demonstrate an interpolation technique that uses samples from single-model MCMCs to propose inter-model jumps from an approximation to the single-model posterior of the target parameter space. The interpolation technique, based on a kD-tree data structure, is adaptive and efficient in modest dimensionality. We show that our technique leads to improved convergence over naive jumps in an RJMCMC, and compare it to other proposals in the literature to improve the convergence of RJMCMCs. We also demonstrate the use of the same interpolation technique as a way to construct efficient "global" proposal distributions for single-model MCMCs without prior knowledge of the structure of the posterior distribution, and discuss improvements that permit the method to be used in higher-dimensional spaces efficiently.Comment: Minor revision to match published versio

Using spin to understand the formation of LIGO's black holes

With the detection of four candidate binary black hole (BBH) mergers by the Advanced LIGO detectors thus far, it is becoming possible to constrain the properties of the BBH merger population in order to better understand the formation of these systems. Black hole (BH) spin orientations are one of the cleanest discriminators of formation history, with BHs in dynamically formed binaries in dense stellar environments expected to have spins distributed isotropically, in contrast to isolated populations where stellar evolution is expected to induce BH spins preferentially aligned with the orbital angular momentum. In this work we propose a simple, model-agnostic approach to characterizing the spin properties of LIGO's BBH population. Using measurements of the effective spin of the binaries, which is LIGO's best constrained spin parameter, we introduce a simple parameter to quantify the fraction of the population that is isotropically distributed, regardless of the spin magnitude distribution of the population. Once the orientation characteristics of the population have been determined, we show how measurements of effective spin can be used to directly constrain the underlying BH spin magnitude distribution. Although we find that the majority of the current effective spin measurements are too small to be informative, with LIGO's four BBH candidates we find a slight preference for an underlying population with aligned spins over one with isotropic spins (with an odds ratio of 1.1). We argue that it will be possible to distinguish symmetric and anti-symmetric populations at high confidence with tens of additional detections, although mixed populations may take significantly more detections to disentangle. We also derive preliminary spin magnitude distributions for LIGO's black holes, under the assumption of aligned or isotropic populations

Residential Consumption of Gas and Electricity in the U.S.: The Role of Prices and Income

We study residential demand for electricity and gas, working with nationwide household-level data that cover recent years, namely 1997-2007. Our dataset is a mixed panel/multi-year cross-sections of dwellings/households in the 50 largest metropolitan areas in the United States as of 2008. To our knowledge, this is the most comprehensive set of data for examining household residential energy usage at the national level, containing the broadest geographical coverage, and with the longest longitudinal component (up to 6 observations per dwelling). We estimate static and dynamic models of electricity and gas demand. We find strong household response to energy prices, both in the short and long term. From the static models, we get estimates of the own price elasticity of electricity demand in the -0.860 to -0.667 range, while the own price elasticity of gas demand is -0.693 to -0.566. These results are robust to a variety of checks. Contrary to earlier literature (Metcalf and Hassett, 1999; Reiss and White, 2005), we find no evidence of significantly different elasticities across households with electric and gas heat. The price elasticity of electricity demand declines with income, but the magnitude of this effect is small. These results are in sharp contrast to much of the literature on residential energy consumption in the United States, and with the figures used in current government agency practice. Our results suggest that there might be greater potential for policies which affect energy price than may have been previously appreciated.Residential Electricity and Gas Demand, Price Elasticity Of Energy Demand, Static Model, Dynamic Panel Data Model, Partial Adjustment Model

Residential Consumption of Gas and Electricity in the U.S.: The Role of Prices and Income

We study residential demand for electricity and gas, working with nationwide household-level data that cover recent years, namely 1997-2007. Our dataset is a mixed panel/multi-year cross-sections of dwellings/households in the 50 largest metropolitan areas in the United States as of 2008. To our knowledge, this is the most comprehensive set of data for examining household residential energy usage at the national level, containing the broadest geographical coverage, and with the longest longitudinal component (up to 6 observations per dwelling). We estimate static and dynamic models of electricity and gas demand. We find strong household response to energy prices, both in the short and long term. From the static models, we get estimates of the own price elasticity of electricity demand in the -0.860 to -0.667 range, while the own price elasticity of gas demand is -0.693 to -0.566. These results are robust to a variety of checks. Contrary to earlier literature (Metcalf and Hassett, 1999; Reiss and White, 2005), we find no evidence of significantly different elasticities across households with electric and gas heat. The price elasticity of electricity demand declines with income, but the magnitude of this effect is small. These results are in sharp contrast to much of the literature on residential energy consumption in the United States, and with the figures used in current government agency practice. Our results suggest that there might be greater potential for policies which affect energy price than may have been previously appreciated.residential electricity and gas demand, price elasticity of energy demand, static model, dynamic panel data model, partial adjustment model

Flood Insurance Demand along the Gulf and Florida Coast

The objective of this research is to identify factors that influence both the decision (yes or no) and level of flood insurance among coastal homeowners in the southeast U.S. Recently flood damage has dramatically increased (Flood), and Crossett et al. (2004) report that coastal populations are growing. And in spite of rising costs of living in coastal areas, people are willing to pay more for access to ocean views and other natural amenities associated with coastal living (Bin and Kruse, 2006). Although the federal government provides flood insurance programs and encourages at-risk residents to insure their property from flood, rates of uptake remain low (Burby, 2001; Kunreuther, 2006; Landry and Jahan-Parvar, 2009). The National Flood Insurance Program (NFIP) was created to provide often subsidized premiums to cover losses which private insurance markets failed to offer. However, as Kunreuther et al.(1978) argue, many people do not bother to prepare, and have a low willingness to pay for coverage, even if subsidized (Kunreuther 1996). However, of those who have previously experienced flooding, they tend to insure their properties more (McClelland, Schulze, and Coursey 1993). Based on previous literature, we identified key factors to establish testable hypotheses regarding effect on flood insurance demand. These include: income, previous flood experience, the presence of a mortgage, home location (both flood zone status and distance from the shore), participation in CRS, the distance from the coast, the house construction year as well as measures of respondent risk preferences and perceptions. Data on flood coverage level and the above explanatory variables were obtained via revealed-preference online survey method, contracted through Knowledge Networks (KN) during August-September 2010. We chose to contract with KN for several reasons. First, they are, to our knowledge, the only survey firm that can legitimately say they have a true probability based sample for an online survey because they recruit by phone and/or mail (randomly selected using random-digit dialing (RDD) or by using address-based sampling); additionally they provide internet access to households that do not have it. KN was also contracted to overcome the typical of low response rate when surveying the general public. KN uses an online panel (called the “Knowledge Panel”). KN Panel members that were homeowners were sampled from 95 counties in Gulf Coast and Florida Atlantic Coast counties in AL, FL, LA, MS, and TX, with an 47% response rate (720 observations), with 67% from FL, 24% from TX, 5% from LA, and 4% collectively from AL and MS. As expected, insurance purchase is positively affected by the individual’s risk perception, their risk preference, whether or not they have a mortgage, flood zone residence, their income, CRS, previous flood experience, and the year of construction of house. Coefficients of mortgage and risk perception, income, flood zone are significant at 0.05 the level. Additionally, the coefficient of distance from the coast is only significant at the 0.1 level.Flood Insurance, Risk, Insurance Demand, Environmental Economics and Policy, Risk and Uncertainty,

Solar system constraints on Rindler acceleration

We discuss the classical tests of general relativity in the presence of Rindler acceleration. Among these tests the perihelion shifts give the tightest constraints and indicate that the Pioneer anomaly cannot be caused by a universal solar system Rindler acceleration. We address potential caveats for massive test-objects. Our tightest bound on Rindler acceleration that comes with no caveats is derived from radar echo delay and yields |a|<3nm/s^2.Comment: 7 pages, v2: minor changes, added references, v3: corrected typos, extended Table 1, corrected bound on measurement of gravitational redshif

Testing General Relativity with Current Cosmological Data

Deviations from general relativity, such as could be responsible for the cosmic acceleration, would influence the growth of large scale structure and the deflection of light by that structure. We clarify the relations between several different model independent approaches to deviations from general relativity appearing in the literature, devising a translation table. We examine current constraints on such deviations, using weak gravitational lensing data of the CFHTLS and COSMOS surveys, cosmic microwave background radiation data of WMAP5, and supernova distance data of Union2. Markov Chain Monte Carlo likelihood analysis of the parameters over various redshift ranges yields consistency with general relativity at the 95% confidence level.Comment: 11 pages; 7 figures; typographical errors corrected; this is the published versio