Miracle-dz/UniFAR-FTUs huggingface dataset:
The Vertical Stellar Kinematics in Face-On Barred Galaxies: Estimating the Ages of Bars

In order to perform a detailed study of the stellar kinematics in the vertical axis of bars, we obtained high signal-to-noise spectra along the major and minor axes of the bars in a sample of 14 face-on galaxies and used them to determine the line-of-sight stellar velocity distribution, parameterized as a Gauss-Hermite series. With these data, we developed a diagnostic tool that allows one to distinguish between recently formed and evolved bars, as well as to estimate their ages, assuming that bars form in vertically thin disks that are recognizable by low values for the vertical velocity dispersion σz. Through N-body realizations of bar unstable disk galaxies we were also able to check the timescales involved in the processes that give bars an important vertical structure. We show that σz in evolved bars is roughly 100 km s-1, which translates to a height scale of about 1.4 kpc, giving support to scenarios in which bulges form through disk material. Furthermore, the bars in our numerical simulations have values for σz generally smaller than 50 km s-1, even after evolving for 2 Gyr, suggesting that a slow process is responsible for making bars as vertically thick as we observe. We verify theoretically that the Spitzer-Schwarzschild mechanism is quantitatively able to explain these observations if we assume that giant molecular clouds are twice as concentrated along the bar as in the rest of the disk.
“What do they say about us on Twitter?”: Hybrid sentiment retrieval for organisations

We conclude this report with a system design and proof-of-concept to show how an adaptable hybrid sentiment classification system is able to improve sentiment analysis for organisations. GreenOnline, a service company in the field of customer services, wants to be able to quantify sentiment for organisations precisely, to create new services for organisations. To start with, this sentiment analysis will be based on Twitter messages. The main challenge during this research was that Tweets, short WOM (Word-Of-Mouth) messages that contain only little words, are highly abbreviated and sentiment is expressed in subtle ways with irony, sarcasm, slang and other linguistic shades of grey [9]. Therefore, the focus of this thesis project was to design a system that is able to combine different sentiment analysis techniques to find sentiment. Also, not only existing algorithms were combined, but also information from the message (message attributes) are regarded as a way to determine the sentiment or which algorithm will classify the sentiment of that message best. Overall, it was regarded that all these different elements leave room for optimisation, for what algorithms and attributes to use and for what messages to select from Twitter for an organisation. To support a process of optimisation for a campaign or organisation another goal was to embrace the ability of system optimisation by (GreenOnline) customer service experts. The result is a design and proof-of-concept implementation of a hybrid and adaptable sentiment analysis system design, which is using implementations of three sub classifier algorithms and message properties, that are combined by a hybrid sentiment classifier in a sentiment value of negative, positive or neutral. This proof-of-concept implementation showed a performance of 71,2% which is a great improvement with respect to the single sub classifications of which the best performance was only 58,2%. By improvement of customer service experts this performance can even grow further.
Motion planning for robotic manipulators with independent wrist joints

Advanced modern humanoid robots often have complex manipulators with a large number of degrees of freedom. Thus, motion planning for such manipulators is a very computationally challenging problem. However, often robotic manipulators allow the wrist degrees of freedom to be controlled independently from the configuration of the rest of the arm. In this paper we show how to split the high dimensional planning problem into two lower-dimensional sub-problems - planning for the main arm joints and planning for the wrist joints, without losing guarantees on completeness. This approach is an extension of our previously developed framework for planning with adaptive dimensionality. Experimentally, we show that this approach is very effective in speeding up planning for robotic arms on Willow Garage's PR2 platform. We compare our algorithm with several popular alternative approaches for performing motion planning for robotic arms. The results we observe illustrate that our algorithm provides a good balance between planning time, planning success rate, path consistency, and path quality.
Empirical evidence-based policy for Islamic psycho-spiritual intervention: using electroencephalography for post-traumatic stress disorder

Providing empirical evidence in an experimental study with randomized control trials design has been considered as a gold standard among the scientific community. Islamic psycho-spiritual study is currently categorized as pseudo-science; hence deserve a more scientific approach to substantiate its proposition. Despite clinical psychology is considered as more scientific, studies indicate that post-traumatic stress disorder (PTSD) has often been misdiagnosed and thus the treatment may not be effective. Therefore, we proposed a more scientific instrument using Electroencephalography (EEG) to measure the effectiveness of Islamic Psycho -spiritual Intervention (IP-SI) among PTSD subjects who were victims of the massive flood 2014/2015 in Malaysia. Objective: This study aims to suggest an empirical evidence-based policy on the effectiveness of the Islamic Psycho-spiritual intervention. The general objective of this study is to test the effect of the Islamic Psycho-spiritual intervention on PTSD subjects who were victims of the massive flood. Method: The gold standard method of randomized controlled trials design was adopted in this experimental study. Sample (n =73) were selected from two different location of transits houses in Kampung Tualang, Kuala Krai, Kelantan, randomly assigned to experimental and control groups. The experimental group received psycho-spiritual treatment of group psycho-education and listening to surah Yasin while wearing EEG cap. The control group did not listen to surah Yasin while wearing the EEG cap. Data were collected using Mindo wireless 32-channels EEG. PTSD was assessed using instrument parallel to Diagnostic Statistical Manual (DSM-5). The collected data was analysed with t-test to examine the significant difference in EEG signals between pre and post of experimental group and control group. Result: The findings indicated there were significant differences in EEG signals with medium effect sizes between pre and post-treatment times for experimental group, t (35) = 3.43, p =. 002, and between control and experiment groups at post treatment time , t (35) =2.569, p=. 009. Thus, the findings provide an empirical evidence to support for the effectiveness of IP-SI. Accordingly, policies for empirical evidence-based Islamic psycho-spiritual intervention are recommended for the government and the related agency.
Speedup via quantum sampling

The Markov Chain Monte Carlo method is at the heart of efficientapproximation schemes for a wide range of problems in combinatorial enumeration and statistical physics. It is therefore very natural and important to determine whether quantum computers can speed-up classical mixing processes based on Markov chains. To this end, we present a new quantum algorithm, making it possible to prepare a quantum sample, i.e., a coherent version of the stationary distribution of a reversible Markov chain. Our algorithm has a significantly better running time than that of a previous algorithm based on adiabatic state generation. We also show that our methods provide a speed-up over a recently proposed method for obtaining ground states of (classical) Hamiltonians.
Empirical Bayes approaches to mixture problems and wavelet regression

We consider model selection in a hierarchical Bayes formulation of the sparse normal linear model in which individual variables have, independently, an unknown prior probability of being included in the model. The focus is on orthogonal designs, which are of particular importance in nonparametric regression via wavelet shrinkage. Empirical Bayes estimates of hyperparameters are easily obtained via the EM algorithm, and this approach is contrasted with a recent conditional likelihood proposal. Our model selection approach yields a straightforward method for data dependent threshold selection in wavelet regression. Performance on standard test sets and data examples is encouraging, especially if a translation invariant form of the estimator is used. Since the method produces separate threshold estimates on each wavelet resolution level, it also comfortably handles stationary correlated error structures.
Cosmic microwave anisotropies from topological defects in an open universe.

We present a general formalism for computing Cosmic Background Radiation (CBR) and density fluctuations in open models with stiff sources. We find analytic Green's functions for the linearized Einstein equations in the presence of stiff sources and use this formalism to estimate the amplitude and harmonic spectrum of microwave background fluctuations produced by topological defects in an open universe. Unlike inflationary models that predict a flat universe and a spectrum of CBR fluctuations that is enhanced at large angular scales, defect models predict that CBR fluctuations are suppressed on angular scales larger than that subtended by the curvature scale. In an $\Omega = 0.2 - 0.4$ universe, these models, when normalized to the amplitude of CBR fluctuations observed by COBE, require a moderate bias factor, $2-3$, to be compatible with the observed fluctuations in galaxy counts. In these models, accurate predictions can be made which are testable through CBR experiments in the near future. A CBR measurement of $\Omega$ would then be possible, up to the limit imposed by cosmic variance. We discuss some of the philosophical implications of an open model and propose a solution to the flatness problem.
A presentation format of architecture description based on the concept of multilayer networks

Formal methods based on abstract models are becoming more and more important in the domain of complex computer networks. On the other hand, processes of design documentation transformation into the formal models are still bound to the skills and ingenuity of individual engineers. Moreover, the human factor involved in data transformation represents a major bottleneck due to the tendency of computer networks to be more and more complex. To address this problem, this work introduces a possible appropriate presentation format of architecture descriptions as a part of detailed design documentation that could allow automated development of trusted formal models for analysis and verifying of complex computer networks.
Inferences About Age Impairments in Inferential Reasoniag

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Towards a system architecture for ICALL

In this paper, we present an on-going project whose overall aim is to develop open-source system architecture for supporting ICALL systems that will facilitate re-use of existing NLP tools and resources on a plug-and-play basis. We introduce the project, describe the approaches adopted by the two language teams, and present two applications being developed using the proposed architecture.
Listening is Listening is Listening: Employees’ Perception of Listening as a Holistic Phenomenon

Interpersonal listening research is marked by a wealth of conceptual definitions and measurement instruments, with a consensus about neither. Therefore, we sought to discover how laypeople, rather than theoreticians, construe listening, and to construct a scale that reflects these perceptions. In Study 1, laypeople listed the features and characteristics of interpersonal listening in four different contexts (general, romantic, colleague-to-colleague, and manager–subordinate). In Study 2, a second sample of individuals rated the centrality of the features found in Study 1 for the definition of listening. These centrality ratings were similar to the frequency of good-listening features in Study 1. In Study 3, we used the features identified in Study 1 and 2 and asked a sample of employees to rate each one regarding their experience with their supervisor or one of their work colleagues listening to them. These ratings yielded a single factor. Thus, we conclude that, although people can describe the complexities of listening, they seem to perceive it as a holistic and unitary experience. Practically, a small set of good items pertaining to perceptions of listening may yield an acceptable, or even excellent, unidimensional reliability estimate.
Hyper-parameters optimization and validation of brain image regression

Based on the non-homogeneity validation method, the hyper-parameter optimization of general regress model on fMRI brain data is analyzed. To acknowledge the abilities of different regression methods we compare ridge regression, support vector regression and Elman recurrent neural network over several feature rating sequences. The original data is obtained from PBAIC2006. Experiment results show that this method has good stability and generalization; also it can be used in the field which lack of knowledge of the relevant fields. Also, we find the linear method is simple, effective on fMRI regress missions, and higher predictive ability but lower calculating costs than other methods on many feature rating sequences.
Pathogenic missense mutation pattern of forkhead box genes in neurodevelopmental disorders

Forkhead box (FOX) proteins are a family of transcription factors. Mutations of three FOX genes, including FOXP1, FOXP2, and FOXG1, have been reported in neurodevelopmental disorders (NDDs). However, due to the lack of site‐specific statistical significance, the pathogenicity of missense mutations of these genes is difficult to determine.
Collection and Analysis of a Parkinson Speech Dataset With Multiple Types of Sound Recordings

There has been an increased interest in speech pattern analysis applications of Parkinsonism for building predictive telediagnosis and telemonitoring models. For this purpose, we have collected a wide variety of voice samples, including sustained vowels, words, and sentences compiled from a set of speaking exercises for people with Parkinson's disease. There are two main issues in learning from such a dataset that consists of multiple speech recordings per subject: 1) How predictive these various types, e.g., sustained vowels versus words, of voice samples are in Parkinson's disease (PD) diagnosis? 2) How well the central tendency and dispersion metrics serve as representatives of all sample recordings of a subject? In this paper, investigating our Parkinson dataset using well-known machine learning tools, as reported in the literature, sustained vowels are found to carry more PD-discriminative information. We have also found that rather than using each voice recording of each subject as an independent data sample, representing the samples of a subject with central tendency and dispersion metrics improves generalization of the predictive model.
Image retrieval using an improved similarity measure: SRIC Similarity with Region Importance and Consistency

Content based image retrieval has become a major research interest recently. This paper presents an improved image similarity measure for image retrieval system. In the region based image comparison, two images are usually compared in terms of sum of the Euclidean distances among their regions. In this work, the image similarity measure is enhanced through a fuzzyfication of regions' importance and inter-region similarity. First, a “Mutual Consistency” function is defined for regions' comparison to avoid undesired inter-region similarity. Thereafter, an improved “Region Importance” function is developed to weight the regions of an image for overall image comparison. Utilizing fuzzy concepts of size and shape features of the regions, these two functions impose additional constrains on similarity measure that helps to improve the image retrieval results. Comparative results with the well-known IRM method are given to illustrate the effectiveness of the proposed approach.
Infection spread in wireless networks with random and adversarial node mobilities

We study the process of the spread of an infection among mobile nodes moving on a finite, grid based map. A random walk and a novel adversarial model are considered as two extreme cases of node mobility. With <i>N</i> nodes, we present analytical and simulation results for both mobility models for a square grid map with size √<i>G</i> × √<i>G</i>. A key finding is that with random mobility the total time to infect all nodes decreases with <i>N</i> while with an adversarial model we observe a reverse trend. Specifically, the random case results in a total infection time of Θ(<i>G</i>log<i>G</i>log<i>N</i>/(<i>N</i>) as opposed to the adversarial case where the total infection time is found to be Θ(√(<i>G</i>log(<i>N</i>). We also explore the possibility of emulating such an infection process as a mobile interaction game with wireless sensor motes, and the above results are complimented by traces obtained from an empirical study with humans as players in an outdoor field.
Church Sponsored Service Through the Lens of Self Determination Theory: A Case Study

CHURCH-SPONSORED SERVICE THROUGH THE LENS OF SELF-DETERMINATION THEORY: A CASE STUDY George Willard Cochran, Jr., Ph.D. The Southern Baptist Theological Seminary, 2013 Chair: Dr. Michael S. Wilder Given the critical need to nudge congregants toward sustainable service, surprisingly few studies have explored the possibility of internalizing Christian beliefs and practices through intrinsically motivating ministry activities. According to SelfDetermination Theory (SDT), an emerging meta-theory of adult motivation, such activities consistently move individuals from external to internal regulation of behavior regardless of domain. Perceiving undeveloped believers as marginalized members of Christ’s body, this emancipatory study seeks to build awareness of the need to empower all believers to make kingdom contributions through intrinsically motivated service that internalizes their faith. Designed as a qualitative single case study, it explores the rich perceptions and divergent meanings associated with church-sponsored service among a critical array of stakeholders in a thriving evangelical church. In a vivid portrait gleaned from observations, interviews and documents, the researcher uses SDT’s theoretical lens to shape the questions, inform the data, and inspire the cause. In the process, traditional assumptions about servant leadership, Christian formation, and spiritual giftedness are challenged. Chapter 1 introduces the research problem, its theological foundation, and practical significance. It also defines the research questions while plotting the study’s limitations. Chapter 2’s examination of the precedent literature recounts the history of motivational theories in general before focusing on SDT’s evolution as a meta-theory. The author also addresses the need for a reliable model of biblical integration to separate the “wheat” of general revelation contained in SDT principles from the “chaff” of man’s wisdom. Chapter 3 presents the research design, defines the population, establishes the selection criteria, and outlines the instrumentation. Chapter 4 analyzes the findings, while Chapter 5 presents the researcher’s conclusions. Finally, Chapter 6 offers a motivational model of Christian service based on the study’s findings. The study’s ultimate aim is to stimulate meaningful dialogue on the need to reform church structures that foster Christian formation through intrinsically motivated, Spirit-empowered service. VITA George Willard Cochran, Jr. EDUCATIONAL B.A., Hiram College, 1974 J.D. , Case Western Reserve University, 1979 M.A., Ashland Theological Seminary, 1993 MINISTERIAL Inner City Youth Ministry, Warren, Ohio, 1985-1986 Juvenile Jail Ministry, Ravenna, Ohio, 1993-1994 Church Leadership Consultant, Streetsboro, Ohio, 1993-2007 ACADEMIC Research Fellow, Sandberg Leadership Center, Ashland, Ohio, 1991-1993 Adjunct Professor, Ashland University, Ashland, Ohio, 2006-2007 Garrett Fellow, The Southern Baptist Theological Seminary, 2009-2011 ORGANIZATIONAL American Institute of Banking Faculty Association, 1987-1989
Stochastic Kronecker Graph on Vertex-Centric BSP

Recently Stochastic Kronecker Graph (SKG), a network generation model, and vertex-centric BSP, a graph processing framework like Pregel, have attracted much attention in the network analysis community. Unfortunately the two are not very well-suited for each other and thus an implementation of SKG on vertex-centric BSP must either be done serially or in an unnatural manner. In this paper, we present a new network generation model, which we call Poisson Stochastic Kronecker Graph (PSKG), that generate edges according to the Poisson distribution. The advantage of PSKG is that it is easily parallelizable on vertex-centric BSP, requires no communication between computational nodes, and yet retains all the desired properties of SKG.
Investigation of the Development of Pre-Academic Skills for Preschoolers in Head Start

Abstract This study modeled the time-dependent nature of pre-academic skills development for a multi-lingual sample of 382 preschoolers attending Head Start. A two-slope latent variable model accurately described the trajectories, which included five assessment points obtained throughout the school year for both pre-literacy and pre-numeracy variables. Classroom quality and child demographic variables including age, gender, native language (Spanish or English), and disability status served as predictors of growth. Results revealed a reversal effect such that steeper growth from October to March was offset, although not completely, by less growth from March to May. Similar interaction effects were observed for predictor variables and their interaction terms across the two slopes. IEP status predicted less growth, while Spanish-language status predicted increased growth, despite the fact that both were associated with low initial achievement. The complexity of the relationship between classroom quality, native language, disability status, and pre-academic growth is discussed.