Miracle-dz/UniFAR-FTUs huggingface dataset:
X-Rays and Protostars in the Trifid Nebula

The Trifid Nebula is a young H II region, recently rediscovered as a "pre-Orion" star-forming region, containing protostars undergoing violent mass ejections visible in optical jets as seen in images from the Infrared Space Observatory and the Hubble Space Telescope. We report the first X-ray observations of the Trifid Nebula using ROSAT and ASCA. The ROSAT image shows a dozen X-ray sources, with the brightest X-ray source being the O7 star, HD 164492, which provides most of the ionization in the nebula. We also identify 85 T Tauri star and young, massive star candidates from near-infrared colors using the JHKs color-color diagram from the Two-Micron All-Sky Survey (2MASS). Ten X-ray sources have counterpart near-infrared sources. The 2MASS stars and X-ray sources suggest there are potentially numerous protostars in the young H II region of the Trifid. ASCA moderate-resolution spectroscopy of the brightest source shows hard emission up to 10 keV with a clearly detected Fe K line. The best model fit is a two-temperature (T = 1.2 × 106 K and 39 × 106 K) thermal model with additional warm absorbing media. The hotter component has an unusually high temperature for either an O star or an H II region; a typical Galactic H II region could not be the primary source for such hot temperature plasma and the Fe XXV line emission. We suggest that the hotter component originates in either the interaction of the wind with another object (a companion star or a dense region of the nebula) or from flares from deeply embedded young stars.
International Conference on Research and Innovation in Information Systems – 2013 ( ICRIIS ’ 13 ) Review on Cloud Service Evaluation and Selection Methods

To evaluate and select a Public Cloud Service (PCS) based on Cloud Service Requesters (CSRs) needs and demands a complex software engineering process. Selecting the wrong PCS may turn out to be costly and adversely affect business processes. To address these issues, many Cloud Evaluation and Selection Methods (CESMs) have been proposed by different researchers. Nevertheless, a number of CESMs’ studies which have addressed the issue of evaluation and selection of the PCS based on the Quality of Service (QoS) have led to a different conclusion. This paper reviews the current limitations; namely, the strengths and weaknesses of CESMs for the PCS as well as the benefits they bring. The result has shown that current studies of CESMs are limited to certain types of quantifiable criteria, such as cost and performance. Non-quantifiable QoS criteria namely, trust, needs further attention. In addition, it has been identified that the majority of CESMs were not specifically designed for any CS layer; hence, it did not assist the CSP to evaluate their PCS with respect to other providers. KeywordsPublic cloud computing, Evaluate and Select methods, Quality of Service, Systematic Review
Advances in telecare over the past 10 years

: This article reviews advances during the past decade or so in telecare (ie, computer-supported social care at home). The need for telecare is discussed along with how it relates to social and health care. The expected benefits of telecare are also discussed. The evolution of telecare technology is reviewed, covering various system generations. The capabilities of present day telecare are covered, along with its advantages, limitations, and barriers to uptake. Recent evaluations and exemplars of telecare are discussed. The user requirements for telecare are presented, complemented by a discussion of the issues in user and professional acceptance. The article concludes with a summary of past developments in telecare and the prospects for the future
B+Hash Tree: optimizing query execution times for on-disk semantic web data structures

: The increasing growth of the Semantic Web has substantially enlarged the amount of data available in RDF format. One proposed solution is to map RDF data to relational databases (RDBs). The lack of a common schema, however, makes this mapping inefficient. Some RDF-native solutions use B+Trees, which are potentially becoming a bottleneck, as the single key-space approach of the Semantic Web may even make their O(log(n)) worst case performance too costly. Alternatives, such as hash-based approaches, suffer from insufficient update and scan performance. In this paper we propose a novel type of index structure called a B+Hash Tree, which combines the strengths of traditional B-Trees with the speedy constant-time lookup of a hash-based structure. Our main research idea is to enhance the B+Tree with a Hash Map to enable constant retrieval time instead of the common logarithmic one of the B+Tree. The result is a scalable, updatable
G2P conversion of names. What can we do (better)?

In this contribution it is shown that a good approach for the grapheme-to-phoneme conversion of proper names (e.g. person names, toponyms, etc), is to use a cascade of a general purpose grapheme-to-phoneme (G2P) converter and a special purpose phoneme-to-phoneme (P2P) converter. The G2P produces an initial transcription that is then transformed by the P2P. The latter is automatically trained on reference transcriptions of names belonging to the envisaged name category (e.g. toponyms). The P2P learning process is conceived in such a way that it can take account of high order determinants of pronunciation, such as specific syllables, name prefixes and name suffixes. The proposed methodology was successfully tested on person names and toponyms, but we believe that it will also offer substantial reductions of the cost for building pronunciation lexicons of other name categories.
CoS-HDFS: Co-Locating Geo-Distributed Spatial Data in Hadoop Distributed File System

Given the recent advancement in the ubiquitous positioning technologies, it is now common to query terabytes of spatial data. These massive data are usually geo-distributed across multiple data centers to ensure their availability. Yet, at least one replica of the data is stored close to where the data are generated. Spatial queries are complex and computationally intensive, and therefore, distributed computation platforms, such as Hadoop are now used to improve their execution time. However, Hadoop is agnostic to the spatial data characteristics, and it randomly partitions and locates the data stored in its distributed file system which degrades the performance of the execution of spatial queries. In this paper, we propose CoS-HDFS, an extension to the Hadoop Distributed File System (HDFS) that takes into account the spatial characteristics of the data and accordingly co-locates them on the HDFS nodes that span multiple data centers. We integrate CoS-HDFS with SpatialHadoop, a MapReduce framework that natively supports spatial data, to make use of its implementation of spatial indexes, operations, and query interfaces. We experimentally demonstrate significant reduction in the network usage and total execution time in the case of spatial join queries on the TIGER dataset.
A Comprehensive Overview of Clustering Algorithms in Pattern Recognition

Machine learning is a branch of artificial intelligence which recognizes complex patterns for making intelligent decisions based on input data values. In machine learning, pattern recognition assigns input value to given set of data labels. Based on the learning method used to generate the output we have the following classification, supervised and unsupervised learning. Unsupervised learning involves clustering and blind signal separation. Supervised learning is also known as classification. This paper mainly focuses on clustering techniques such as K-means clustering, hierarchical clustering which in turn involves agglomerative and divisive clustering techniques. This paper deals with introduction to machine learning, pattern recognition, clustering techniques. We present steps involved in each of these clustering techniques along with an example and the necessary formula used. The paper discusses the advantages and disadvantages of each of these techniques and in turn we make a comparison of K-means and hierarchical clustering techniques. Based on these comparisons we suggest the best suited clustering technique for the specified application.
A Situational Implementation Method for Business Process Management Systems

For the integrated implementation of Business Process Management and supporting information systems many methods are available. Most of these methods, however, apply a one-size fits all approach and do not take into account the specific situation of the organization in which an information system is to be implemented. These situational factors, however, strongly determine the success of any implementation project. In this paper a method is provided that establishes situational factors of and their influence on implementation methods. The provided method enables a more successful implementation project, because the project team can create a more suitable implementation method for business process management system implementation projects.
Achieving maximum system throughput with cooperative relaying: A case study of IEEE 802.16j multi-hop relay

Various types of cooperative relaying (CR) schemes exhibit different levels of throughput and outage performance because of their inherent trade-off between diversity gain and opportunity cost; in other words, the overhead that is associated with cooperation. This article attempts to answer whether cooperative communication is beneficial or not from the system-level viewpoint and furthermore, if it is, how its average throughput can be maximized while maintaining the target outage rate. In order to improve throughput at the required outage performance, we propose a unified selection criterion to deal with different levels of combining gain and opportunity cost associated with each scheme, which allows for the employment of different CR schemes for various positions of the mobile station. Our system-level simulation results for an IEEE 802.16j multi-hop relay confirm the varying levels of trade-offs among different CR schemes and furthermore, show that CR will be a useful means of maximizing the average throughput for a multi-hop relay system as long as each type of the cooperating scheme is carefully selected, depending on the position of the mobile stations.
The Role of Inspirational Leadership and Technology Support for Contextualization on Psychological Contract in Distributed Teams

This study investigates how inspirational leadership and technology support for contextualization influence individuals’ psychological contract and knowledge sharing in distributed teams. Drawing on the intersection between social identity, psychological contract and leadership theories, the current research highlights the importance of inspirational leaders who foster two kinds of psychological contract obligations, namely commitment and reciprocity. The results show a significant impact of inspirational leaders on psychological contract obligations. The impact of inspirational leadership on obligations of reciprocity is strengthened when there is technology support for contextualization. Our findings suggest that psychological contract obligations can motivate employees to engage in knowledge sharing. This provides interesting implications for theory and practice in distributed teams.
Lip-reading based on a fully automatic statistical model

In this paper, we describe audiovisual automatic speech recognition experiments carried using visual parameters extracted from “natural” images. Unlike many other experiments in the AV ASR ﬁeld, these visual parameters are obtained without any hand-labeling phase and are naturally noisy, due to the extraction process. We evaluate our models with different strategies among which : use of a shape model combined with or after an appearance model. For audiovisual parameters integration, we use a basic DI architecture with a ﬁxed weight. We use a new evaluation criterion to measure the quality of parameters which proves to be efﬁ-cient, and aim to use it in the near future, for an adaptive weighting scheme.
An Evaluation of the Swedish System of Active Labor Market Programs in the 1990s

We investigate the presence of short- and long-term effects from joining a Swedish labor market program vis--vis more intense job search in open unemployment. Overall, the impact of the program system is found to have been mixed. Joining a program has increased employment rates among participants, a result robust to a misclassification problem in the data. On the other hand it has also allowed participants to remain significantly longer on unemployment benefits and more generally in the unemployment system, this being particularly the case for those entitled individuals entering a program around the time of their unemployment benefits' exhaustion.
Characterization of Tumor Region Using SOM and Neuro Fuzzy Techniques in Digital Mammography

Nowadays the most common type of cancer in women is breast cancer. This is the second main cause of cancer deaths in women. Digital mammography is the technique which is used to examine the breast. This is very much useful for the detection of breast diseases in women. The automatic detection of tumor or some type of deformity in the medical imaging is done by many researchers to develop some algorithms and methods. In this paper we are using SOM and Fuzzy c-means clustering techniques for tumor detection in digital mammography images. We then further calculate the statistical features of tumor like location of tumor, area, energy, entropy, idm, mean, contrast, mean and standard deviation which helps the radiologist to study the statistical information regarding breast cancer, so that the doctors can give better treatment to patients. For calculating these statistical properties we use region growing and region merging techniques.
On Discrete Painlevé Equations Associated with the Lattice KdV Systems and the Painlevé VI Equation

A new integrable nonautonomous nonlinear ordinary difference equation is presented that can be considered to be a discrete analogue of the Painlevé V equation. Its derivation is based on the similarity reduction on the two‐dimensional lattice of integrable partial differential equations of Korteweg–de Vries (KdV) type. The new equation, which is referred to as generalized discrete Painlevé equation (GDP), contains various “discrete Painlevé equations” as subcases for special values/limits of the parameters, some of which have already been given in the literature. The general solution of the GDP can be expressed in terms of Painlevé VI (PVI) transcendents. In fact, continuous PVI emerges as the equation obeyed by the solutions of the discrete equation in terms of the lattice parameters rather than the lattice variables that label the lattice sites. We show that the bilinear form of PVI is embedded naturally in the lattice systems leading to the GDP. Further results include the establishment of Bäcklund and Schlesinger transformations for the GDP, the corresponding isomonodromic deformation problem, and the self‐duality of its bilinear scheme.
Comparing functional connectivity via thresholding correlations and singular value decomposition

We compare two common methods for detecting functional connectivity: thresholding correlations and singular value decomposition (SVD). We find that thresholding correlations are better at detecting focal regions of correlated voxels, whereas SVD is better at detecting extensive regions of correlated voxels. We apply these results to resting state networks in an fMRI dataset to look for connectivity in cortical thickness.
Using Matching Methods From Both Fisher's Experimental Design and Rubin's Causal Model to Compare Between Two Medical Facilities with Extremely Skewed Number of Subjects

This Dissertation is brought to you for free and open access by the Theses and Dissertations at Loyola eCommons. It has been accepted for inclusion in Dissertations by an authorized administrator of Loyola eCommons. For more information, please contact ecommons@luc.edu. iii ACKNOWLEDGEMENTS I would like to thank all of the people who made this dissertation possible, starting with my wonderful professors in the Research Methodology Department at Loyola University Chicago. Dr. Teresa Pigott proved an excellent sounding board for me from the beginning of my time here, and steered me toward thinking critically in doing research and analyzing the real life data. Dr. Martha Wynne provided me with much perspective on issues of building survey questions and its methodology. Dr. Meng-Jia Bohanon also blessed me with her solid knowledge in multivariate analysis and feedbacks on this study. Finally, I would like to thank one of committee members, Dr. Kathleen Ruroede, who evoked me with the original data and its problem so as to probe the study questions from the beginning of this study and provided her keen insight to the end. All of their sage advice has put me on track when I veered precipitously away from my early goals, and their friendship and encouragement have made the difference in this long and arduous process. I would also like to thank the Graduate School of Loyola University Chicago for providing the funds when the outside funding source was on the verge of discontinuation, which enabled me to continue and complete my research and writing. My friends have provided me with a much needed cheering section. Some prayed for me so that I may be refreshed and ready to confront it all over again, and others, exchanging ideas for the study. In particular, I would like to thank Dr. Finally, I would like to thank the love of my life and my best friend, Mary Bahn. Without her support and prodding, I would never have made it where I am today. Her unfailing good sense, great humor, and unparalleled companionship make me a very lucky man indeed. Above all, I thank God my Savior, Jesus Christ who gave me physical and spiritual life, supported me with hope and love without ceasing, and to whom I dedicate all my work.
Design Methodology for Synthesizing Resonant Clock Networks in the Presence of Dynamic Voltage/Frequency Scaling

The portion of clock power in system is rapidly increasing with the continuous increasing of clock frequency and clock resources. Last two decades, a great research attention has been paid to minimizing the clock power. Recently, it is shown that the structure of resonant clock networks is very effective in saving power since it can store electric energy to the inserted inductors rather than dissipate. On the other side, it has been known that dynamic voltage-frequency scaling (DVFS) is one of the most effectively and widely used power reduction techniques. However, so far no works have addressed the design methodology problem of synthesizing resonant clock networks that are able to operate under the designs with DVFS capability even though the problem is potentially very important to maximize the synergy effect on saving power. In this context, this paper proposes a comprehensive solution to the problem. Precisely, we propose a two-phase synthesis algorithm: 1) formulating the problem of inductor allocation, placement, and adjustable-sizing to support DVFS into a weighted set cover problem with the objective of minimizing total area of inductors and 2) followed by performing the task of resizing of adjustable driving buffers to support the switch of driving strength according to the clock frequencies by DVFS. Through experiments with benchmark circuits, it is shown that for designs with DVFS using 1 and 3 GHz, our algorithm synthesizes resonant clock networks with 25.9% less power on the execution of the clock frequency, which is not supported by resonant clocking in the previous no-DVFS aware resonant clock synthesis algorithm.
Reentry Programs for Students with Disabilities in the Juvenile Justice System: Four State Approaches. inForum.

Evidence strongly supports the notion that juvenile offenders, both with and without disabilities, are significantly more likely to experience successful reentry into their home schools and communities if appropriate programs and supports are in place. In spite of this, comprehensive state-level reentry programs for youth are few and far between. The first section of this document defines reentry, describes recent federal efforts to support successful reentry for youth, discusses the prevalence of youth with disabilities in the juvenile justice system, explains why reentry programming matters and summarizes recommended strategies for successful reentry. The second section describes four states’ efforts to support the successful reentry of juvenile offenders into their home schools and communities. Project Forum at the National Association of State Directors of Special Education (NASDSE) completed this document as part of its cooperative agreement with the U.S. Department of Education’s Office of Special Education Programs (OSEP).
Extremely anisotropic boundary conditions and their optical applications

We discuss here the design of artificial nanoparticles, surfaces and composite materials that may realize extremely anisotropic boundary conditions, with values not easily attainable in natural optical materials. We apply the closed-form analytical solution available in the case of an individual nanosphere composed of two conjoined hemispheres with opposite permittivity in order to show that it may be possible to realize tunable local impedance surfaces with extreme values. In the case of a single nanoparticle, it is shown that a simple mechanical or polarization rotation may tune the effective local impedance from very high to very low values of impedance. Combinations of such engineered nanoparticles forming a metasurface or a metamataerial may provide novel tools for the realization of extreme boundary conditions and effective parameters in the visible. These findings may have a relevant impact in a variety of applications in optics.
GaN resonant cavity light-emitting diodes for plastic optical fiber applications

The optical designs of resonant GaN light-emitting diodes (LEDs) have been determined for maximum extraction efficiency into typical plastic optical fiber of numerical aperture 0.5. An optimum extraction efficiency of 3.9% can be achieved for a practical resonant cavity LED (RCLED), taking account of current growth and processing considerations. The optimized device is a metal-active layer distributed Bragg reflector construction. Constructive interference effects from the top metal mirror are found to play the dominant role in efficiency enhancement. The extraction efficiency of an optimized resonant single-mirror LED is found to be 3.3%, indicating a small compromise in performance compared with the more complex RCLED structure.