Gravitino production in hybrid inflationary models

It has been recently shown that it is possible to excite gravitinos in an expanding background due to a time varying scalar field oscillating at the bottom of the inflationary potential. The two components of the gravitino, namely helicity 1/2 and helicity 3/2, are excited differently due to the presence of different time varying mass scales in the problem. In this paper we analyse the production of both the helicities in a multi-chiral scenario, in particular focusing on a general model of hybrid inflation. Fermion production in hybrid models is very much different from that of the chaotic models discussed so far in the literature. In this paper we give a full account of gravitino production analytically and numerically. It is noticed that the creation of gravitinos does not take place in the first few oscillations of the inflaton field, rather the production is a gradual and delayed process. It takes roughly 30-40 oscillations to build up the production and for the saturation to take place it can even take longer time, depending on the model parameters. We give an estimation of the reheat temperature and a brief discussion upon back-reaction on the fermionic production, which could change the gravitino abundance.Comment: New comments added, appendix improved. Final version to appear in Phys. Rev.

Joint Alignment and Modeling of Correlated Behavior Streams

The Variable Time-Shift Hidden Markov Model (VTS- HMM) is proposed for learning and modeling pairs of cor- related streams. Unlike previous coupled models for time series, the VTS-HMM accounts for varying time shifts be- tween correlated events in pairs of streams having different properties. The VTS-HMM is learned on a set of pairs of unaligned streams and, thus, learning entails simultaneous estimation of the varying time shifts and of the parameters of the model. The formulation is demonstrated in the analysis of videos of dyadic social interactions between children and adults in the Multimodal Dyadic Behavior Dataset (MMDB). In dyadic social interactions, an agent starts an interaction with one or more \u201cinitiating behaviors\u201d that elicit one or more \u201cresponding behaviors\u201d from the partner within a temporal window. The proposed VTS-HMM explicitly accounts for varying time shifts between initiating and responding behaviors in these behavior streams. The experiments confirm that modeling of these varying time shifts in the VTS-HMM can yield improved estimation of the level of engagement of the child and adult and more accurate dis- crimination among complex activities

A graphical perspective of marginal structural models : an application for the estimation of the effect of physical activity on blood pressure

Estimating causal effects requires important prior subject-matter knowledge and, sometimes, sophisticated statistical tools. The latter is especially true when targeting the causal effect of a time-varying exposure in a longitudinal study. Marginal structural models (MSMs) are a relatively new class of causal models which effectively deal with the estimation of the effects of time-varying exposures. MSMs have traditionally been embedded in the counterfactual framework to causal inference. In this paper, we use the causal graph framework to enhance the implementation of MSMs. We illustrate our approach using data from a prospective cohort study, the Honolulu Heart Program. These data consist of 8006 men at baseline. To illustrate our approach, we focused on the estimation of the causal effect of physical activity on blood pressure, which were measured at three time-points. First, a causal graph is built to encompass prior knowledge. This graph is then validated and improved utilizing structural equation models. We estimated the aforementioned causal effect using MSMs for repeated measures and guided the implementation of the models with the causal graph. Employing the causal graph framework, we also show the validity of fitting conditional MSMs for repeated measures in the context implied by our data

Continuous Time Structural Equation Modeling with R Package ctsem

We introduce ctsem, an R package for continuous time structural equation modeling of panel (N > 1) and time series (N = 1) data, using full information maximum likelihood. Most dynamic models (e.g., cross-lagged panel models) in the social and behavioural sciences are discrete time models. An assumption of discrete time models is that time intervals between measurements are equal, and that all subjects were assessed at the same intervals. Violations of this assumption are often ignored due to the difficulty of accounting for varying time intervals, therefore parameter estimates can be biased and the time course of effects becomes ambiguous. By using stochastic differential equations to estimate an underlying continuous process, continuous time models allow for any pattern of measurement occasions. By interfacing to OpenMx, ctsem combines the flexible specification of structural equation models with the enhanced data gathering opportunities and improved estimation of continuous time models. ctsem can estimate relationships over time for multiple latent processes, measured by multiple noisy indicators with varying time intervals between observations. Within and between effects are estimated simultaneously by modeling both observed covariates and unobserved heterogeneity. Exogenous shocks with different shapes, group differences, higher order diffusion effects and oscillating processes can all be simply modeled. We first introduce and define continuous time models, then show how to specify and estimate a range of continuous time models using ctsem

Path design and receding horizon control for collision avoidance system of cars

The paper deals with path design and control realization problems of collision avoidance systems (CAS) of cars (ground vehicles). CAS emergency path design is based on the principle of elastic band with improved reaction forces for road borders and static obstacles allowing quick computation of the force equilibrium. The CAS path (reference signal) is smoothed and realized using receding horizon control (RHC). The car can be modelled by full (non-affine) or simplified (input affine) nonlinear models. The nonlinear predictive control problem is solved by using time varying linearization along appropriately chosen nominal control and state sequences, and analytical solution of the minimization of a quadratic criterion satisfying end-constraint. Differential geometric approach (DGA), known from control literature for the input affine nonlinear model, has been used for control initialization in the first horizon. For state estimation Kalman filters and measurements of two antenna GPS and Inertial Navigation System (INS) are used. A stand-alone software has been been developed using the C Compiler of MATLAB R2006a satisfying real time expectations

Channel estimation and tracking algorithms for vehicle to vehicle communications

The vehicle-to-vehicle (V2V) communications channels are highly time-varying, making reliable communication difficult. This problem is particularly challenging because the standard of the V2V communications (IEEE 802.11p standard) is based on the WLAN IEEE 802.11a standard, which was designed for indoor, relatively stationary channels; so the IEEE 802.11p standard is not customized for outdo or, highly mobile non-stationary channels. In this thesis,We propose Channel estimation and tracking algorithms that are suitable for highly-time varying channels. The proposed algorithms utilize the finite alphabet property of the transmitted symbol, time domain truncation, decision-directed as well as pilot information. The proposed algorithm s improve the overall system performance in terms of bit error rates, enabling the system to achieve higher data rates and larger packet lengths at high relative velocities. Simulation results show that the proposed algorithms achieve improved performance for all the V2V channel models with different velocities, and for different modulation schemes and packet sizes as compared to the conventional least squares and other previously proposed channel estimation techniques for V2V channels

Accurate range estimation for an electric vehicle including changing environmental conditions and traction system efficiency

Range anxiety is an obstacle to the acceptance of electric vehicles (EVs), caused by drivers\u27 uncertainty regarding their vehicle\u27s state of charge (SoC) and the energy required to reach their destination. Most estimation methods for these variables use simplified models with many assumptions that can result in significant error, particularly if dynamic and environmental conditions are not considered. For example, the combined efficiency of the inverter drive and electric motor varies throughout the route and is not constant as assumed in most range estimation methods. This study proposes an improved method for SoC and range estimation by taking into account location-dependent environmental conditions and time-varying drive system losses. To validate the method, an EV was driven along a selected route and the measured EV battery SoC at the destination was compared with that predicted by the algorithm. The results demonstrated excellent accuracy in the SoC and range estimation, which should help alleviate range anxiety