The Local Radio-IR Relation in M51

We observed M51 at three frequencies, 1.4 GHz (20 cm), 4.9 GHz (6 cm), and 8.4 GHz (3.6 cm), with the Very Large Array and the Effelsberg 100 m telescope to obtain the highest quality radio continuum images of a nearby spiral galaxy. These radio data were combined with deconvolved Spitzer IRAC 8 μm and MIPS 24 μm images to search for and investigate local changes in the radio-IR correlation. Utilizing wavelet decomposition, we compare the distribution of the radio and IR emission on spatial scales between 200 pc and 30 kpc. We show that the radio-IR correlation is not uniform across the galactic disk. It presents a complex behavior with local extrema corresponding to various galactic structures, such as complexes of H II regions, spiral arms, and interarm filaments, indicating that the contribution of the thermal and non-thermal radio emission is a strong function of environment. In particular, the relation of the 24 μm and 20 cm emission presents a linear relation within the spiral arms and globally over the galaxy, while it deviates from linearity in the interarm and outer regions as well in the inner region, with two different behaviors: it is sublinear in the interarm and outer region and overlinear in the central 3.5 kpc. Our analysis suggests that the changes in the radio/IR correlation reflect variations of interstellar medium properties between spiral arms and interarm region. The good correlation in the spiral arms implies that 24 μm and 20 cm are tracing recent star formation, while a change in the dust opacity, "Cirrus" contribution to the IR emission and/or the relation between the magnetic field strength and the gas density can explain the different relations found in the interarm, outer, and inner regions

A multi-view approach to cDNA micro-array analysis

The official published version can be obtained from the link below.Microarray has emerged as a powerful technology that enables biologists to study thousands of genes simultaneously, therefore, to obtain a better understanding of the gene interaction and regulation mechanisms. This paper is concerned with improving the processes involved in the analysis of microarray image data. The main focus is to clarify an image's feature space in an unsupervised manner. In this paper, the Image Transformation Engine (ITE), combined with different filters, is investigated. The proposed methods are applied to a set of real-world cDNA images. The MatCNN toolbox is used during the segmentation process. Quantitative comparisons between different filters are carried out. It is shown that the CLD filter is the best one to be applied with the ITE.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the National Science Foundation of China under Innovative Grant 70621001, Chinese Academy of Sciences under Innovative Group Overseas Partnership Grant, the BHP Billiton Cooperation of Australia Grant, the International Science and Technology Cooperation Project of China under Grant 2009DFA32050 and the Alexander von Humboldt Foundation of Germany

Doctor of Philosophy

dissertationThree-dimensional (3D) models of industrial plant primitives are used extensively in modern asset design, management, and visualization systems. Such systems allow users to efficiently perform tasks in Computer Aided Design (CAD), life-cycle management, construction progress monitoring, virtual reality training, marketing walk-throughs, or other visualization. Thus, capturing industrial plant models has correspondingly become a rapidly growing industry. The purpose of this research was to demonstrate an efficient way to ascertain physical model parameters of reflectance properties of industrial plant primitives for use in CAD and 3D modeling visualization systems. The first part of this research outlines the sources of error corresponding to 3D models created from Light Detection and Ranging (LiDAR) point clouds. Fourier analysis exposes the error due to a LiDAR system's finite sampling rate. Taylor expansion illustrates the errors associated with linearization due to flat polygonal surfaces. Finally, a statistical analysis of the error associated with LiDar scanner hardware is presented. The second part of this research demonstrates a method for determining Phong specular and Oren-Nayar diffuse reflectance parameters for modeling and rendering pipes, the most ubiquitous form of industrial plant primitives. For specular reflectance, the Phong model is used. Estimates of specular and diffuse parameters of two ideal cylinders and one measured cylinder using brightness data acquired from a LiDAR scanner are presented. The estimated reflectance model of the measured cylinder has a mean relative error of 2.88% and a standard deviation of relative error of 4.0%. The final part of this research describes a method for determining specular, diffuse and color material properties and applies the method to seven pipes from an industrial plant. The colorless specular and diffuse properties were estimated by numerically inverting LiDAR brightness data. The color ambient and diffuse properties are estimated using k-means clustering. The colorless properties yielded estimated brightness values that are within an RMS of 3.4% with a maximum of 7.0% and a minimum of 1.6%. The estimated color properties effected an RMS residual of 13.2% with a maximum of 20.3% and a minimum of 9.1%

Deep CCD Surface Photometry of Galaxy Clusters I: Methods and Initial Studies of Intracluster Starlight

We report the initial results of a deep imaging survey of galaxy clusters. The primary goals of this survey are to quantify the amount of intracluster light as a function of cluster properties, and to quantify the frequency of tidal debris. We outline the techniques needed to perform such a survey, and we report findings for the first two galaxy clusters in the survey: Abell 1413, and MKW 7 . These clusters vary greatly in richness and structure. We show that our surface photometry reliably reaches to a surface brightness of \mu_v = 26.5 mags per arcsec. We find that both clusters show clear excesses over a best-fitting r^{1/4} profile: this was expected for Abell 1413, but not for MKW 7. Both clusters also show evidence of tidal debris in the form of plumes and arc-like structures, but no long tidal arcs were detected. We also find that the central cD galaxy in Abell 1413 is flattened at large radii, with an ellipticity of $\approx 0.8$, the largest measured ellipticity of any cD galaxy to date.Comment: 58 pages, 24 figures, accepted for publication in the Astrophysical Journal. Version has extremely low resolution figures to comply with 650k limit. High resolution version is available at http://burro.astr.cwru.edu/johnf/icl1.ps.gz Obtaining high resolution version is strongly reccomende

Investigation of iterative image reconstruction in three-dimensional optoacoustic tomography

Iterative image reconstruction algorithms for optoacoustic tomography (OAT), also known as photoacoustic tomography, have the ability to improve image quality over analytic algorithms due to their ability to incorporate accurate models of the imaging physics, instrument response, and measurement noise. However, to date, there have been few reported attempts to employ advanced iterative image reconstruction algorithms for improving image quality in three-dimensional (3D) OAT. In this work, we implement and investigate two iterative image reconstruction methods for use with a 3D OAT small animal imager: namely, a penalized least-squares (PLS) method employing a quadratic smoothness penalty and a PLS method employing a total variation norm penalty. The reconstruction algorithms employ accurate models of the ultrasonic transducer impulse responses. Experimental data sets are employed to compare the performances of the iterative reconstruction algorithms to that of a 3D filtered backprojection (FBP) algorithm. By use of quantitative measures of image quality, we demonstrate that the iterative reconstruction algorithms can mitigate image artifacts and preserve spatial resolution more effectively than FBP algorithms. These features suggest that the use of advanced image reconstruction algorithms can improve the effectiveness of 3D OAT while reducing the amount of data required for biomedical applications

Ribosome recycling, diffusion, and mRNA loop formation in translational regulation

We explore and quantify the physical and biochemical mechanisms that may be relevant in the regulation of translation. After elongation and detachment from the 3' termination site of mRNA, parts of the ribosome machinery can diffuse back to the initiation site, especially if it is held nearby, enhancing overall translation rates. The elongation steps of the mRNA-bound ribosomes are modeled using exact and asymptotic results of the totally asymmetric exclusion process (TASEP).Since the ribosome injection rates of the TASEP depend on the local concentrations at the initiation site, a source of ribosomes emanating from the termination end can feed back to the initiation site, leading to a self-consistent set of equations for the steady-state ribosome throughput. Additional mRNA binding factors can also promote loop formation, or cyclization, bringing the initiation and termination sites into close proximity. The probability distribution of the distance between the initiation and termination sites is described using simple noninteracting polymer models. We find that the initiation, or initial ribosome adsorption binding required for maximal throughput can vary dramatically depending on certain values of the bulk ribosome concentration and diffusion constant. If cooperative interactions among the loop-promoting proteins and the initiation/termination sites are considered, the throughput can be further regulated in a nonmonotonic manner. Potential experiments to test the hypothesized physical mechanisms are discussed.Comment: 21 pp, 11 .eps figs, realigned figures and magin

An Integrated Multi-Time-Scale Modeling for Solar Irradiance Forecasting Using Deep Learning

For short-term solar irradiance forecasting, the traditional point forecasting methods are rendered less useful due to the non-stationary characteristic of solar power. The amount of operating reserves required to maintain reliable operation of the electric grid rises due to the variability of solar energy. The higher the uncertainty in the generation, the greater the operating-reserve requirements, which translates to an increased cost of operation. In this research work, we propose a unified architecture for multi-time-scale predictions for intra-day solar irradiance forecasting using recurrent neural networks (RNN) and long-short-term memory networks (LSTMs). This paper also lays out a framework for extending this modeling approach to intra-hour forecasting horizons thus, making it a multi-time-horizon forecasting approach, capable of predicting intra-hour as well as intra-day solar irradiance. We develop an end-to-end pipeline to effectuate the proposed architecture. The performance of the prediction model is tested and validated by the methodical implementation. The robustness of the approach is demonstrated with case studies conducted for geographically scattered sites across the United States. The predictions demonstrate that our proposed unified architecture-based approach is effective for multi-time-scale solar forecasts and achieves a lower root-mean-square prediction error when benchmarked against the best-performing methods documented in the literature that use separate models for each time-scale during the day. Our proposed method results in a 71.5% reduction in the mean RMSE averaged across all the test sites compared to the ML-based best-performing method reported in the literature. Additionally, the proposed method enables multi-time-horizon forecasts with real-time inputs, which have a significant potential for practical industry applications in the evolving grid.Comment: 19 pages, 12 figures, 3 tables, under review for journal submissio