Integrating Temporal and Spectral Features of Astronomical Data Using Wavelet Analysis for Source Classification

Temporal and spectral information extracted from a stream of photons received from astronomical sources is the foundation on which we build understanding of various objects and processes in the Universe. Typically astronomers fit a number of models separately to light curves and spectra to extract relevant features. These features are then used to classify, identify, and understand the nature of the sources. However, these feature extraction methods may not be optimally sensitive to unknown properties of light curves and spectra. One can use the raw light curves and spectra as features to train classifiers, but this typically increases the dimensionality of the problem, often by several orders of magnitude. We overcome this problem by integrating light curves and spectra to create an abstract image and using wavelet analysis to extract important features from the image. Such features incorporate both temporal and spectral properties of the astronomical data. Classification is then performed on those abstract features. In order to demonstrate this technique, we have used gamma-ray burst (GRB) data from the NASA's Swift mission to classify GRBs into high- and low-redshift groups. Reliable selection of high-redshift GRBs is of considerable interest in astrophysics and cosmology.Comment: Accepted and Published in 2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), Imaging: Earth and Beyond (Washington DC, October 13-15, 2015) Conference Proceeding

Vascular Segmentation Algorithms for Generating 3D Atherosclerotic Measurements

Atherosclerosis manifests as plaques within large arteries of the body and remains as a leading cause of mortality and morbidity in the world. Major cardiovascular events may occur in patients without known preexisting symptoms, thus it is important to monitor progression and regression of the plaque burden in the arteries for evaluating patient\u27s response to therapy. In this dissertation, our main focus is quantification of plaque burden from the carotid and femoral arteries, which are major sites for plaque formation, and are straight forward to image noninvasively due to their superficial location. Recently, 3D measurements of plaque burden have shown to be more sensitive to the changes of plaque burden than one-/two-dimensional measurements. However, despite the advancements of 3D noninvasive imaging technology with rapid acquisition capabilities, and the high sensitivity of the 3D plaque measurements of plaque burden, they are still not widely used due to the inordinate amount of time and effort required to delineate artery walls plus plaque boundaries to obtain 3D measurements from the images. Therefore, the objective of this dissertation is developing novel semi-automated segmentation methods to alleviate measurement burden from the observer for segmentation of the outer wall and lumen boundaries from: (1) 3D carotid ultrasound (US) images, (2) 3D carotid black-blood magnetic resonance (MR) images, and (3) 3D femoral black-blood MR images. Segmentation of the carotid lumen and outer wall from 3DUS images is a challenging task due to low image contrast, for which no method has been previously reported. Initially, we developed a 2D slice-wise segmentation algorithm based on the level set method, which was then extended to 3D. The 3D algorithm required fewer user interactions than manual delineation and the 2D method. The algorithm reduced user time by ≈79% (1.72 vs. 8.3 min) compared to manual segmentation for generating 3D-based measurements with high accuracy (Dice similarity coefficient (DSC)\u3e90%). Secondly, we developed a novel 3D multi-region segmentation algorithm, which simultaneously delineates both the carotid lumen and outer wall surfaces from MR images by evolving two coupled surfaces using a convex max-flow-based technique. The algorithm required user interaction only on a single transverse slice of the 3D image for generating 3D surfaces of the lumen and outer wall. The algorithm was parallelized using graphics processing units (GPU) to increase computational speed, thus reducing user time by 93% (0.78 vs. 12 min) compared to manual segmentation. Moreover, the algorithm yielded high accuracy (DSC \u3e 90%) and high precision (intra-observer CV \u3c 5.6% and inter-observer CV \u3c 6.6%). Finally, we developed and validated an algorithm based on convex max-flow formulation to segment the femoral arteries that enforces a tubular shape prior and an inter-surface consistency of the outer wall and lumen to maintain a minimum separation distance between the two surfaces. The algorithm required the observer to choose only about 11 points on its medial axis of the artery to yield the 3D surfaces of the lumen and outer wall, which reduced the operator time by 97% (1.8 vs. 70-80 min) compared to manual segmentation. Furthermore, the proposed algorithm reported DSC greater than 85% and small intra-observer variability (CV ≈ 6.69%). In conclusion, the development of robust semi-automated algorithms for generating 3D measurements of plaque burden may accelerate translation of 3D measurements to clinical trials and subsequently to clinical care

Machine-z: Rapid Machine Learned Redshift Indicator for Swift Gamma-ray Bursts

Studies of high-redshift gamma-ray bursts (GRBs) provide important information about the early Universe such as the rates of stellar collapsars and mergers, the metallicity content, constraints on the re-ionization period, and probes of the Hubble expansion. Rapid selection of high-z candidates from GRB samples reported in real time by dedicated space missions such as Swift is the key to identifying the most distant bursts before the optical afterglow becomes too dim to warrant a good spectrum. Here we introduce "machine-z", a redshift prediction algorithm and a "high-z" classifier for Swift GRBs based on machine learning. Our method relies exclusively on canonical data commonly available within the first few hours after the GRB trigger. Using a sample of 284 bursts with measured redshifts, we trained a randomized ensemble of decision trees (random forest) to perform both regression and classification. Cross-validated performance studies show that the correlation coefficient between machine-z predictions and the true redshift is nearly 0.6. At the same time our high-z classifier can achieve 80% recall of true high-redshift bursts, while incurring a false positive rate of 20%. With 40% false positive rate the classifier can achieve ~100% recall. The most reliable selection of high-redshift GRBs is obtained by combining predictions from both the high-z classifier and the machine-z regressor.Comment: Accepted to the Monthly Notices of the Royal Astronomical Society Journal (10 pages, 10 figures, and 3 Tables

Analytical Approach for the Determination of the Luminosity Distance in a Flat Universe with Dark Energy

Recent cosmological observations indicate that the present universe is flat and dark energy dominated. In such a universe, the calculation of the luminosity distance, d_L, involve repeated numerical calculations. In this paper, it is shown that a quite efficient approximate analytical expression, having very small uncertainties, can be obtained for d_L. The analytical calculation is shown to be exceedingly efficient, as compared to the traditional numerical methods and is potentially useful for Monte-Carlo simulations involving luminosity distances.Comment: 3 pages, 4 figures, Accepted for publication in MNRA

Teacher Leadership: Charismatic Characteristics of Sri Lankan School Teachers

This study identifies a number of charismatic leadership characteristics of school teachers in Sri Lanka. Since charisma is an instrumental asset in any work context, leaders’ charismatic characteristics can make extraordinary effects on follower toward mission accomplishment. In the teaching-learning context, teachers are viewed as great leaders that make magnificent transformations in the students. A teacher becomes the leader in the classroom as well and they are playing a significant role to build a culture of learning in the classroom that, finally everyone benefits. Therefore charismatic leadership characteristics in teachers as leaders can make the teaching-learning process more and more effective. This study followed a qualitative research approach, collecting data from interviewing twenty school teachers. It was reported that most of the school teachers are leaders with charismatic characteristics such as mission formulation for the students, unconditioned commitment towards the students’ accomplishments, trust on the students, taking personal risk in directing students, demonstration of unusual behaviors and emotional arousal through attractive communication. The implication of this study is imperative for teacher training and performance appraisals.Keywords: Teacher leadership, charismatic leadership, teaching-learning process, school teacher

Leaders for the Banking Industry: An investigation on effective leadership.

Leadership is critical in achieving performance and yet not exhausted, keeping scholars to uncover more findings on effective leadership styles. This study expected to identify the effective leadership style for enhanced employee performance in the banking industry in Sri Lanka. The banking industry has a unique work environment that stresses performance targets, long working hours, and error-free transactions while making the customers happy. Thus, leadership is a critical stimulus that this study focused on. The findings illustrate that transformational leadership style is the most present style among the bankers in Sri Lanka, and employee performance is above average with transformational leaders. Overall, scores in the transformational leadership style were found to be strongly correlated with employee performance. The results suggest that supervisors in the banking sector need to use a lot of transformational leadership behaviours or rather embrace a transactional leadership style. The implications of the study are significant in HR practices like recruiting and training managers as leaders in the banking sector.Keywords: leadership style, transformational leadership, MLQ, transactional leadership, employee performance)

Spectral and temporal analysis of the joint Swift/BAT-Fermi/GBM GRB sample

Using the gamma-ray bursts simultaneously detected by Swift/BAT and Fermi/GBM we performed a joint spectral and temporal analysis of the prompt emission data and confirm the rough correlation between the BAT-band photon index Gamma_BAT and the peak spectral energy Epeak. With the redshift known sub-sample, we derived the isotropic gamma-ray energy E_gamma,iso and also confirm the E_gamma,iso - Epeak,rest relation, with a larger scatter than the Amati sample but consistent with GBM team analyses. We also compare the T_90 values derived in the GBM band with those derived in the BAT band and find that for long GRBs the BAT T_90 is usually longer than the GBM T_90, while for short GRBs the trend reverses. This is consistent with the soft/hard nature of long/short GRBs and suggests the importance of an energy-dependent temporal analysis of GRBs.Comment: 18 pages, 9 figures, 3 tables, MNRAS accepted, spectral fits updated, conclusions unchange