Miracle-dz/UniFAR-FTUs huggingface dataset:
Mild hyperhomocysteinemia is associated with impaired renal function but not with progression of small abdominal aortic aneurysms

To study the possible role of homocysteine in aneurysmatic progression, in 1994, 112 of 141 male patients with abdominal aortic aneurysm (AAA) diagnosed by population screening were interviewed and examined. Creatinine clearance estimated and total plasma homocysteine (P-tHcy) was measured by gas chromatography-mass spectrometry. Of the 112 cases, 99 were followed with annual ultrasonography and blood pressure measurements for 1–5 years (mean 2.5 years). The mean creatinine clearance was 70.8+/-23.1 ml/min, while the mean P-tHcy was 16.0+/-10.6 mmol/l, significantly increased compared with an age- and sex-matched reference population (P<0.01). However, no trend of correlation with AAA size or aneurysmal progression was found concerning creatinine clearance and homocysteine level. There were still no trend after adjustment for age, smoking, diastolic blood pressure, and initial ankle blood pressure index. However, the homocysteine level was positively correlated with decreasing creatinine clearance (r=0.36,P<0.01), and this correlation was still present after the same adjustments. A large proportion of men with AAA have mild hyperhomocysteinemia; this does not influence aneurysmal progression and may be due to coexisting impaired renal function. The finding might explain why prospective and intervention studies cannot prove for certain that hyperhomocysteinemia influences atherosclerotic progression, or that treatment is beneficial.
Creation of Large Scale Face Dataset Using Single Training Image

CREATION OF A LARGE SCALE FACE DATASET USING SINGLE TRAINING IMAGE Qun Wang Old Dominion University, May 2012 Co-Directors: Dr. Mohammad A. Karim Dr. Vijayan K. Asari Face recognition (FR) has become one of the most successful applications of image analysis and understanding in computer vision. The learning-based model in FR is considered as one of the most favorable problem-solving methods to this issue, which leads to the requirement of large training data sets in order to achieve higher recognition accuracy. However, the availability of only a limited number of face images for training a FR system is always a common problem in practical applications. A new framework to create a face database from a single input image for training purposes is proposed in this dissertation research. The proposed method employs the integration of 3D Morphable Model (3DMM) and Differential Evolution (DE) algorithms. Benefitting from DE's successful performance, 3D face models can be created based on a single 2D image with respect to various illumination and pose contexts. An image deformation technique is also introduced to enhance the quality of synthesized images. The experimental results demonstrate that the proposed method is able to automatically create a virtual 3D face dataset from a single 2D image with high performance. Moreover the new dataset is capable of providing large number of face images equipped with abundant variations. The validation process shows that there is only an insignificant difference between the input image and the 2D face image projected by the 3D model. Research work is progressing to consider a nonlinear manifold learning methodology to embed the synthetically created dataset of an individual so that a test image of the person will be attracted to the respective manifold for accurate recognition.
Reliability analysis and improvement of multilevel converters

Power converter based variable-frequency drive is much more energy efficient than traditional fixed-speed motor. It can reduce the operating cost of motor drive system significantly. However, the reliability of power converter is a salient concern for both manufacturers and end users as high failure rate will incur additional repairing cost. In high power drive applications, multilevel converters which utilize mature power semiconductors are superior to conventional two-level converters in efficiency and power quality. But the reliability problem of multilevel converters is more serious due to a large number of vulnerable power semiconductors used. Therefore, reliability analysis and improvement of multilevel converters are indispensable for popularization and application of variable-frequency drive. Traditionally, there are two branches in reliability modeling: failure rate methods and Physics-of-Failure methods. The data-driven statistical failure rate methods are widely used in power system for their simplicity and effectiveness. There is a crucial assumption that the failure rate is constant during equipment useful lifetime. The assumption is reasonable in power system where equipment is operating in a relatively stable condition and under good maintenances. However, due to harsh environment and variable load in drive systems, the equipment stress change severely and rapidly leading to a questionable assumption of constant failure. A load-dependent failure rate method is proposed to model the variable load. In the proposed method, the failure rate can be represented as an equivalent constant value or a time-varying function depends on the application. In traditional failure rate methods, the reliability index, Mean Time to Failure (MTTF), is widely used to give a life expectancy. MTTF is the reciprocal of system failure rate in the condition of constant failure rate. With time-varying failure rate, MTTF is almost impossible to calculate in practice. A new reliability index, MTTF consumption is proposed to describe the state of health of a power converter. Multilevel converters with modular design have inherent redundancy which gives them fault-tolerance capability. The network reliability modeling techniques, combinatorics and stochastic process, viii
Comparative Performance Analysis of RPL for Low Power and Lossy Networks based on Different Objective Functions

The Internet of Things (IoT) is an extensive network between people-people, people-things and things-things. With the overgrown opportunities, then it also comes with a lot of challenges proportional to the number of connected things to the network. The IPv6 allows us to connect a huge number of things. For resource-constrained IoT devices, the routing issues are very thought-provoking and for this purpose an IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is proposed. There are multi-HOP paths connecting nodes to the root node. Destination Oriented Directed Acyclic Graph (DODAG) is created taking into account different parameters such as link costs, nodes attribute and objective functions. RPL is flexible and it can be tuned as per application demands, therefore, the network can be optimized by using different objective functions. This paper presents a novel energy efficient analysis of RPL by performing a set of simulations in COOJA simulator. The performance evaluation of RPL is compared by introducing different Objective functions (OF) with multiple metrics for the network.
Users’ Perception and Satisfaction of Current Situation of Home Health Care Services in Jordan

Background: The healthcare system in Jordan is evolving and has to continuously respond to the changing risk profile of the population. The purpose of this study was to examine perception of users and providers of the quality of home health care services. Methods: A descriptive design was used to collect data from a convenience sample of 82 users of home health care services. Results: Users had low to fair satisfaction (30.5% - 69.5%) about the quality of care provided, had moderate satisfaction (72.0% to 81.7%) about the information received, and had low to fair satisfaction about education related to goal of treatment and medication (46.4% - 53.3%). Users had high level of agreement (>70%) that health agencies provided interpersonal care. Conclusion: The ability of the frail people to choose from a variety of cost-effective long-term care services is limited.
A SYSTEM FOR MONITORING OF UAV CAMERA ORIENTATION: DESIGN AND INITIAL ANALYSIS

Abstract. Many unmanned aerial vehicles (UAV) that are used for aerial mapping are equipped with consumer-grade digital cameras, which use CMOS (Complementary metal–oxide–semiconductor) image sensors. Majority of these sensors capture images using an electronic rolling shutter, which can cause distortions on the image if either the camera or the captured objects are moving. This phenomenon is usually ignored in aerial mapping with UAVs in practice. However, there is a lack of published research papers that would prove the effect can be neglected. In this paper, we present the design of the system for monitoring UAV camera orientation. Furthermore, the calibration process to get correct and reliable readings is described. The initial analysis of the data is focused on assessing the accuracy that can be achieved using the proposed system. The main component of our system is a MEMS (Microelectromechanical system) gyroscope. It was selected for its low weight and size, low price and high sampling rates which are all very beneficial characteristics for a system, mounted on a UAV. In a paper, a working prototype is presented that uses the selected MEMS gyroscope connected to a single-board computer. The presented initial analysis of collected data shows, that the system would be capable to indirectly detect the image distortions, caused by camera orientation changes during exposure, in the range of typical ground sample distance (GSD).
A Note on Prediction Markets

In a prediction market, individuals can sequentially place bets on the outcome of a future event. This leaves a trail of personal probabilities for the event, each being conditional on the current individual's private background knowledge and on the previously announced probabilities of other individuals, which give partial information about their private knowledge. By means of theory and examples, we revisit some results in this area. In particular, we consider the case of two individuals, who start with the same overall probability distribution but di erent private information, and then take turns in updating their probabilities. We note convergence of the announced probabilities to a limiting value, which may or may not be the same as that based on pooling their private information.
Connectable components for protein design

Protein design requires reusable, trustworthy, and connectable parts in order to scale to complex challenges. The recent explosion of protein structures stored within the Protein Data Bank provides a wealth of small motifs we can harvest, but we still lack tools to combine them into larger proteins. Here I explore two approaches for connecting reusable protein components on two different length scales. On the atomic scale, I build an interactive search engine for connecting chemical fragments together. Protein fragments built using this search engine recapitulate native-like protein assemblies that can be integrated into existing protein scaffolds using backbone search engines such as MaDCaT. On the protein domain scale, I quantitatively dissect structural variations in two-component systems in order to extract general principles for engineering interfacial flexibility between modular four-helix bundles. These bundles exhibit large scissoring motions where helices move towards or away from the bundle axis and these motions propagate across domain boundaries. Together, these two approaches form the beginnings of a multiscale methodology for connecting reusable protein fragments where there is a constant interplay and feedback between design of atomic structure, secondary structure, and tertiary structure. Rapid iteration, visualization, and search glue these diverse length scales together into a cohesive whole. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Biochemistry & Molecular Biophysics First Advisor William F. DeGrado
A process for developing a student led programme in an acute mental health setting

This paper will describe the process used to develop a student led programme, within a mental health setting. The goal was to provide an effective occupational therapy student placement while increasing the capacity of the service to take students. The student led role enhanced the clinical team's capacity to supervise students. Access to evidence based interventions was increased for service users while student interest in the field of mental health was increased. Student placements in mental health should be structured to include a specific clinical role that enables students to assume responsibility while contributing to ongoing service development.
Advances in high-dimensional covariance matrix estimation

Many applications require precise estimates of high-dimensional covariance matrices. The standard estimator is the sample covariance matrix, which is conceptually simple, fast to compute and has favorable properties in the limit of infinitely many observations. The picture changes when the dimensionality is of the same order as the number of observations. In such cases, the eigenvalues of the sample covariance matrix are highly biased, the condition number becomes large and the inversion of the matrix gets numerically unstable. A number of alternative estimators are superior in the high-dimensional setting, which include as subcategories structured estimators, regularized estimators and spectrum correction methods. In this thesis I contribute to all three areas. In the area of structured estimation, I focus on models with low intrinsic dimensionality. I analyze the bias in Factor Analysis, the state-of-the-art factor model and propose Directional Variance Adjustment (DVA) Factor Analysis, which reduces bias and yields improved estimates of the covariance matrix. Analytical shrinkage of Ledoit and Wolf (LW-Shrinkage) is the most popular regularized estimator. I contribute in three aspects: first, I provide a theoretical analysis of the behavior of LW-Shrinkage in the presence of pronounced eigendirections, a case of great practical relevance. I show that LW-Shrinkage does not perform well in this setting and propose aoc-Shrinkage which yields significant improvements. Second, I discuss the effect of autocorrelation on LW-Shrinkage and review the Sancetta-Estimator, an extension of LW-Shrinkage to autocorrelated data. I show that the Sancetta-Estimator is biased and propose a theoretically and empirically superior estimator with reduced bias. Third, I propose an extension of shrinkage to multiple shrinkage targets. Multi-Target Shrinkage is not restricted to covariance estimation and allows for many interesting applications which go beyond regularization, including transfer learning. I provide a detailed theoretical and empirical analysis. Spectrum correction approaches the problem of covariance estimation by improving the estimates of the eigenvalues of the sample covariance matrix. I discuss the state-of-the-art approach, Nonlinear Shrinkage, and propose a cross-validation based covariance (CVC) estimator which yields competitive performance at increased numerical stability and greatly reduced complexity and computational cost. On all data sets considered, CVC is on par or superior in comparison to the regularized and structured estimators. In the last chapter, I conclude with a discussion of the advantages and disadvantages of all covariance estimators presented in this thesis and give situation-specific recommendations. In addition, the appendix contains a systematic analysis of Linear Discriminant Analysis as a model application, which sheds light on the interdependency between the generative model of the data and various covariance estimators.
REAL WORLD USAGE OF AN ADAPTIVE TESTING ALGORITHM TO UNCOVER LATENT KNOWLEDGE

Abstract At the beginning of every course, it can be expected that several students already have some knowledge of the curriculum. For learning efficiency it is important to quickly identify this latent knowledge. This enables students to begin new learning immediately which in turn prevents the frustration or boredom that might be caused otherwise. A component of the Realize it learning environment that uncovers this latent knowledge is an algorithm called Determine Knowledge . This is offered as an adaptive pretest at the beginning of each course. It allows new students to be automatically placed at the appropriate position within the course. In this paper, we detail the algorithm and review its relative strengths and weaknesses. In particular we discuss the real-world usage of Determine Knowledge run in a higher educational institute in the United States over a two year period. In total this encompasses over 455,000 Determine Knowledge operations performed by over 48,500 unique students. We finish the paper by outlining some of the enhancements made to the algorithm based on the analysis of this data. Keywords: Latent Knowledge, Technology Enhanced Learning, Intelligent Learning Systems.
On the Robustness of Self-Attentive Models

This work examines the robustness of self-attentive neural networks against adversarial input perturbations. Specifically, we investigate the attention and feature extraction mechanisms of state-of-the-art recurrent neural networks and self-attentive architectures for sentiment analysis, entailment and machine translation under adversarial attacks. We also propose a novel attack algorithm for generating more natural adversarial examples that could mislead neural models but not humans. Experimental results show that, compared to recurrent neural models, self-attentive models are more robust against adversarial perturbation. In addition, we provide theoretical explanations for their superior robustness to support our claims.
Genetic Algorithm and Support Vector Regression for Software Effort Estimation

For software developers, accurately forecasting software effort is very important. In the field of software engineering, it is also a very challenging topic. Miscalculated software effort in the early phase might cause a serious consequence. It not only effects the schedule, but also increases the cost price. It might cause a huge deficit. Because all of the different software development team has it is own way to calculate the software effort, the factors affecting project development are also varies. In order to solve these problems, this paper proposes a model which combines genetic algorithm (GA) with support vector machines (SVM). We can find the best parameter of SVM regression by the proposed model, and make more accurate prediction. During the research, we test and verify our model by using the historical data in COCOMO. We will show the results by prediction level (PRED) and mean magnitude of relative error (MMRE).
Filled-gap effects in sentence processing: different accounts compared

It is widely accepted that the human sentence parsing mechanism is subject to real-time constraints that demand some decisions to be made on-line. One of the areas of research in sentence processing has been to look at how long-distance dependencies where there is a relation between a fronted phrase (‘filler’) and its canonical position (‘gap’) are constructed on-line. These dependencies where there is an element that has been displaced are interesting to examine because they provide relevant cues for how sentence interpretation proceeds when the information that can be used to interpret the sentence is not immediately available. Research on the processing of long-distance dependencies has focused on different questions. One of the questions was directed to examine the specific point in time and the position in the sentence at which the parser posits gaps while processing long-distance dependencies. The other was to examine how the fronted phrase is interpreted and if the verb is necessary to interpret the dislocated phrase. This paper focuses on the different approaches that have been suggested to explain how long-distance dependencies are processed and how the fronted phrase gets interpreted. The objective is to look at whether the verb is completely necessary when trying to interpret information on-line. Bearing this in mind, I examine how the different processing theories account for the results of an experiment on clitic pronouns in Spanish. I compare parsing theories that presuppose the existence of gaps, parsing theories that presuppose a direct semantic association and HPSG theories against the Spanish data and conclude that there is pre-verbal information such as clitic pronouns that can be used to interpret displaced elements in sentence processing.
Declarative Metadata Processing with XML and Java

Metadata processing is an essential part of the Astronomy Digital Image Library (ADIL) and the BIMA Image Pipeline because of their complex and evolving data models. In order to choose the best technical solutions for these systems, a number of techniques have been evaluated; XML, Java, XSLT, DOM and Quick are described here. An increased use of declarative (as distinguished from procedural) knowledge is found to improve the stability and flexibility of such systems.
Developing a research culture in the undergraduate curriculum

A great deal of value is placed on student research within universities, exemplified by the prominent role of the dissertation or extended written work at the end of many programmes, and the more general benefits of embedding research-based learning into a curricu- lum in order to develop higher-order learning. This article reports on a collaborative problem-based learning (PBL) activity undertaken by staff and students to run an undergraduate conference for first year students on how to develop a research culture. The aim is to better understand how students undertake research and how a research culture might be inculcated much earlier in undergraduate programmes.
Analytical Assessment of DC Components Generated by Renewable Energy Resources with Inverter-Based Interconnection System due to Even Harmonics

This paper deals with the assessment of DC components generated by renewable energy resources with inverter-based interconnection system to the electric grid. DC injection is a critical issue related to power quality of distribution network systems with high penetration of inverter-based interconnection systems. This type of interface systems may improve the performance of the electric generation unit and affect positively or negatively the power quality of the distribution network depending on the proper or improper designation. The investigation of the various causes of DC components and the analytical assessment of their maximum levels are crucial for the proper operation of inverter-based interface systems and the limitation of DC injection. A method based on analytical calculations using a computer software has been implemented for the assessment of DC components contained on an inverter's output voltage when even harmonics are present on the network voltage. Moreover, a simulation package was used to demonstrate the existence of DC components under various conditions. It was proved by the current analysis that the amounts of DC components generated when even harmonics are present on the network voltage can be high under abnormalities on the power grid but they are not considerable under normal operating conditions.