Informed Proposal Monte Carlo

Any search or sampling algorithm for solution of inverse problems needs guidance to be efficient. Many algorithms collect and apply information about the problem on the fly, and much improvement has been made in this way. However, as a consequence of the the No-Free-Lunch Theorem, the only way we can ensure a significantly better performance of search and sampling algorithms is to build in as much information about the problem as possible. In the special case of Markov Chain Monte Carlo sampling (MCMC) we review how this is done through the choice of proposal distribution, and we show how this way of adding more information about the problem can be made particularly efficient when based on an approximate physics model of the problem. A highly nonlinear inverse scattering problem with a high-dimensional model space serves as an illustration of the gain of efficiency through this approach

Time-frequency representation of earthquake accelerograms and inelastic structural response records using the adaptive chirplet decomposition and empirical mode decomposition

In this paper, the adaptive chirplet decomposition combined with the Wigner-Ville transform and the empirical mode decomposition combined with the Hilbert transform are employed to process various non-stationary signals (strong ground motions and structural responses). The efficacy of these two adaptive techniques for capturing the temporal evolution of the frequency content of specific seismic signals is assessed. In this respect, two near-field and two far-field seismic accelerograms are analyzed. Further, a similar analysis is performed for records pertaining to the response of a 20-story steel frame benchmark building excited by one of the four accelerograms scaled by appropriate factors to simulate undamaged and severely damaged conditions for the structure. It is shown that the derived joint time–frequency representations of the response time histories capture quite effectively the influence of non-linearity on the variation of the effective natural frequencies of a structural system during the evolution of a seismic event; in this context, tracing the mean instantaneous frequency of records of critical structural responses is adopted. The study suggests, overall, that the aforementioned techniques are quite viable tools for detecting and monitoring damage to constructed facilities exposed to seismic excitations

Neural coding of naturalistic motion stimuli

We study a wide field motion sensitive neuron in the visual system of the blowfly {\em Calliphora vicina}. By rotating the fly on a stepper motor outside in a wooded area, and along an angular motion trajectory representative of natural flight, we stimulate the fly's visual system with input that approaches the natural situation. The neural response is analyzed in the framework of information theory, using methods that are free from assumptions. We demonstrate that information about the motion trajectory increases as the light level increases over a natural range. This indicates that the fly's brain utilizes the increase in photon flux to extract more information from the photoreceptor array, suggesting that imprecision in neural signals is dominated by photon shot noise in the physical input, rather than by noise generated within the nervous system itself.Comment: 15 pages, 4 figure

TFAW: wavelet-based signal reconstruction to reduce photometric noise in time-domain surveys

There have been many efforts to correct systematic effects in astronomical light curves to improve the detection and characterization of planetary transits and astrophysical variability. Algorithms like the Trend Filtering Algorithm (TFA) use simultaneously-observed stars to remove systematic effects, and binning is used to reduce high-frequency random noise. We present TFAW, a wavelet-based modified version of TFA. TFAW aims to increase the periodic signal detection and to return a detrended and denoised signal without modifying its intrinsic characteristics. We modify TFA's frequency analysis step adding a Stationary Wavelet Transform filter to perform an initial noise and outlier removal and increase the detection of variable signals. A wavelet filter is added to TFA's signal reconstruction to perform an adaptive characterization of the noise- and trend-free signal and the noise contribution at each iteration while preserving astrophysical signals. We carried out tests over simulated sinusoidal and transit-like signals to assess the effectiveness of the method and applied TFAW to real light curves from TFRM. We also studied TFAW's application to simulated multiperiodic signals, improving their characterization. TFAW improves the signal detection rate by increasing the signal detection efficiency (SDE) up to a factor ~2.5x for low SNR light curves. For simulated transits, the transit detection rate improves by a factor ~2-5x in the low-SNR regime compared to TFA. TFAW signal approximation performs up to a factor ~2x better than bin averaging for planetary transits. The standard deviations of simulated and real TFAW light curves are ~40x better than TFA. TFAW yields better MCMC posterior distributions and returns lower uncertainties, less biased transit parameters and narrower (~10x) credibility intervals for simulated transits. We present a newly-discovered variable star from TFRM.Comment: Accepted for publication by A&A. 13 pages, 16 figures and 5 table