Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-reported Diabetes Prevalence and Risk factors in South Africa: Results from the World Health Survey

Using the data from the South African World Health Survey (WHS), the current study aims to assess the self-reported prevalence of diabetes and associated risk factors by socio-economic characteristics in South Africa. A population-based survey of 2314 participants (1116 men and 1236 women) was conducted in South Africa in 2003. Results indicate an overall self-reported diabetes prevalence of 9.1%, 7.1% for men and 10.8% for women. In terms of diabetes risk factors, high rates of overweight or obesity (58.4%), physical inactivity (55.2%), less than 5 servings of fruits and vegetables in a day (69.3%), and medium or high stress levels (63.3%) were found; 18.4% were smoking daily and 7.3% were risky drinkers. Univariate regression analyses found that from the six different risk factors none apart from having high stress were associated with the occurrence of diabetes. Greater age was associated with obesity and inversely associated with insufficient consumption of fruits and vegetables. Daily smoking and being physically inactive were inversely associated with risky drinking. Low socioeconomic background was associated with unhealthy diet, physical inactivity and stress.
Psychological and cognitive functioning in children and adolescents with congenital heart disease: a meta-analysis.

OBJECTIVE Findings in the literature are inconsistent on the impact of congenital heart disease (CHD) on the psychological and cognitive functioning of children and adolescents. The aim of the present study was to systematically review this empirical body of literature. METHODS We conducted a meta-analysis to review studies on behavior problems and cognitive functioning in CHD. RESULTS Only older children and adolescents with CHD displayed an increased risk of overall, internalizing, and to a lesser extent externalizing behavior problems. In addition, patients with severe CHD exhibited lower cognitive functioning than patients with less severe CHD, specifically with respect to performance intelligence. Moreover, decreased cognitive functioning remained relatively stable across different age groups. CONCLUSIONS Children with severe heart disease may benefit from interventions specifically targeting perceptual organizational abilities, such as visual-spatial abilities. Moreover, older children and adolescents with CHD may benefit from psychological interventions reducing anxiety symptoms and depression.
Mosaiced wide-field VLBI observations of the Lockman Hole/XMM

Active galactic nuclei (AGN) play a decisive role in galaxy evolution, particularly so when they launch powerful jets, which reshape their surroundings. However, identifying them is difficult, since radio observations typically have a resolution between 1 arcsec and 10 arcsec, which is equally sensitive to radio emission from star-forming activity and from AGN. Very Long Baseline Interferometry (VLBI) observations allow only the most compact non-thermal emission to be filtered from radio survey data. The observational and computational demands to do this in large surveys have, until recently, been too high to make this practical. Only the recent advent of wide-field observing techniques have enabled such observations, and we here present the results of a survey of 217 radio sources in the Lockman Hole/XMM field. We describe in detail some new aspects of the calibration, including primary beam correction, multi-source self-calibration, and mosaicing.
Conceptual discrete wavelet transformation speech hashing for content authentication

Conceptual hash functions provide a tool for fast and reliable identification of content authentication. Robust hashing for multimedia authentication is an emergent research area. A different key-dependent robust speech hashing based upon speech construction model is proposed in this article. The proposed hash function is based on the essential frequency series. Robust hash is calculated based on linear spectrum frequencies which model the verbal territory. The correlation between LSFs is decoupled by discrete wavelet transformation (DWT). A randomization structure controlled by a secret key is used in hash generation for random feature selection. The hash function is key-dependent and collision resistant. Temporarily, it is extremely robust to content protective operations besides having high accuracy of tampering localization. They are found, the first, to perform very adequately in identification and verification tests, and the second, to be very robust to a large range of attacks. Furthermore, it can be addressed the issue of security of hashes and proposed a keying technique, and thereby a key-dependent hash function.
Functional Aspects of Cardiovascular Nuclear Magnetic Resonance Imaging Techniques and Application

91. Sechtem U, Pflugfelder PW, Cassidy MM, White RD, Cheitlin MD, Schiller NB, Higgins CB. Mitral or aortic regurgitation: quantification of regurgitant volumes with cine MR imaging. Radiology. 1988;167:425-430. 92. Globits S, Mayr H, Frank H, Neuhold A, Glogar D. Quantification of regurgitant lesions by MRI. Int J Cardiac Imaging. 1991;6: 109-116. 93. Wagner S, Auffermann W, Buser P, Lim TH, Kircher B, Pflugfelder P, Higgins CB. Diagnostic accuracy and estimation of the severity of valvular regurgitation from the signal void on cine magnetic resonance imaging. Am Heart J. 1989;118:760-767. 94. Helmcke F, Nanda NC, Hsiung MC, Soto B, Adey CK, Goyal RG, Gatewood RP. Color Doppler assessment of mitral regurgitation with orthogonal planes. Circulation. 1987;75:175-183. 95. Underwood SR, Klipstein RH, Firmin DN, Fox KM, PooleWilson PA, Rees RSO, Longmore DB. Magnetic resonance assessment of aortic and mitral regurgitation. Br Heart J. 1986;56:
Driver workload classification through neural network modeling using physiological indicators

Advanced Driver Assistance Systems may have a positive effect on traffic flow efficiency, the environment, safety and comfort. However these systems may have a negative impact on driving behavior following a change in driver workload. It is therefore crucial to develop a so-called driver workload manager. In order to manage driver workload an adequate classification of driver workload is indispensible. In this contribution we propose to classify and predict driver workload through physiological indicators of driver workload, driver characteristics and characteristics of the driving condition using a neural network modeling approach. We show that the proposed network yields a very good classification of driver workload. The contribution finishes with a discussion section and recommendations for future research.
Representative & Informative Query Selection for Learning to Rank using Submodular Functions

The performance of Learning to Rank algorithms strongly depend on the number of labelled queries in the training set, while the cost incurred in annotating a large number of queries with relevance judgements is prohibitively high. As a result, constructing such a training dataset involves selecting a set of candidate queries for labelling. In this work, we investigate query selection strategies for learning to rank aimed at actively selecting unlabelled queries to be labelled so as to minimize the data annotation cost. %total number of labelled queries -- without degrading the ranking performance. In particular, we characterize query selection based on two aspects of \emph{informativeness} and \emph{representativeness} and propose two novel query selection strategies (i) Permutation Probability based query selection and (ii) Topic Model based query selection which capture the two aspects, respectively. We further argue that an ideal query selection strategy should take into account both these aspects and as our final contribution, we present a submodular objective that couples both these aspects while selecting query subsets. We evaluate the quality of the proposed strategies on three real world learning to rank datasets and show that the proposed query selection methods results in significant performance gains compared to the existing state-of-the-art approaches.
Exploiting prosodic breaks in language modeling with random forests

We propose a novel method of exploiting prosodic breaks in language modeling for automatic speech recognition (ASR) based on the random forest language model (RFLM), which is a collection of randomized decision tree language models and can potentially ask any questions about the history in order to predict the future. We demonstrate how questions about prosodic breaks can be easily incorporated into the RFLM and present two language models which treat prosodic breaks as observable and hidden variables, respectively. Meanwhile, we show empirically that a finer grained prosodic break is needed for language modeling. Experimental results showed that given prosodic breaks, we were able to reduce the LM perplexity by a significant margin, suggesting a prosodic N -best rescoring approach for ASR.
Scheduling Problems with Communication Costs 3 . 1 Latency Augmented Dags

A number of authors have used the diamond dag as a model of a computation for the analysis of parallel performance. We use a model of multiprocessor systems in which the cost of communication is handled in twodifferent ways. As in previouswork, there is a delay between the result of a computationbeing sent fromoneprocessor and its arival at another. In addition, however, communications between a pair of processors incur overheads which are computations and which must be scheduled at both source and destination processors. We show how the parameters of such a model may be derived from a Distributed Memory MIMD architecture, and use the values soderived to predict the performance of a particular partition of the diamond dag, this we validate against experimental data from the performance of some numerical software whose computation can be modelled as a diamond dag. We show the relationship between the perfomance of this software and architectural parameters of our model and indicate how various improvements in themulticomputer would improve the performance of the software. Finally we discuss in detail the relationship between our model and the architecture we are using, and indicate properties of thenumerical softwarewhichmaymean that ourmodel is accurate, whereas in other computations it would be less so. 1Supported by SERC Contract B18534: Novel Architecture Computing Research. 2Current Address: Institute of High Temperatures, Academy of Sciences, Moscow, Russia. Work funded in part by a Royal Society Visiting Fellowship 3Performedwhile the authorwas visiting theDepartment of Computer Scienceat EdinburghUniversity under the EC Erasmus Programme
DDDAS-based multi-fidelity simulation for online preventive maintenance scheduling in semiconductor supply chain

This research intends to augment the validity of simulation models in the most economic way using the DDDAS (Dynamic Data Driven Application Systems) paradigm. Implementation of DDDAS requires automated switching of model fidelity and incorporating selective, dynamic data into the executing simulation model. Comprehensive system architecture and methodologies are proposed, where the components include 1. ) RT (Real Time) DDDAS simulation, 2. ) grid computing modules, 3. ) Web Service communication server, 4. ) database, 5. ) various sensors, and 6. ) real system. Four algorithms are developed to facilitate integration of the various components in the DDDAS system. They are 1. ) data filtering algorithm using control charts, 2. ) preliminary fidelity selection algorithm using Bayesian belief network, 3. ) fidelity assignment algorithm using integer programming and 4. ) simulation model reconstruction algorithm using multiple linear regression. A prototype DDDAS simulation was successfully implemented for preventive maintenance scheduling in a semiconductor supply chain. The initial results look quite promising.
Approximate estimation of the Cramer-Rao Lower Bound for Sinusoidal Parameters

-In this paper we present new approximation expressions for the Cramer-Rao Lower Bound on unbiased estimates of frequency, phase, amplitude and DC offset for uniformly sampled signal embedded in white-Gaussian noise. This derivation is based on well-known assumptions and a novel set of approximations for finite series of trigonometric functions. The estimated Cramer-Rao Lower Bounds are given in close forms but their expressions are quite complex. It is also shown that when a large number of signal data is available, the asymptotic CRLBs are simplified to the expressions derived by the individual sinusoidal parameter estimation CRLB. Key-Words: Statistical estimation, sinusoidal signals, white Gaussian noise, Cramer-Rao Lower Bound.
Compound-specific carbon isotopes from Earth’s largest flood basalt eruptions directly linked to the end-Triassic mass extinction

A leading hypothesis explaining Phanerozoic mass extinctions and associated carbon isotopic anomalies is the emission of greenhouse, other gases, and aerosols caused by eruptions of continental flood basalt provinces. However, the necessary serial relationship between these eruptions, isotopic excursions, and extinctions has never been tested in geological sections preserving all three records. The end-Triassic extinction (ETE) at 201.4 Ma is among the largest of these extinctions and is tied to a large negative carbon isotope excursion, reflecting perturbations of the carbon cycle including a transient increase in CO2. The cause of the ETE has been inferred to be the eruption of the giant Central Atlantic magmatic province (CAMP). Here, we show that carbon isotopes of leaf wax derived lipids (n-alkanes), wood, and total organic carbon from two orbitally paced lacustrine sections interbedded with the CAMP in eastern North America show similar excursions to those seen in the mostly marine St. Audrie’s Bay section in England. Based on these results, the ETE began synchronously in marine and terrestrial environments slightly before the oldest basalts in eastern North America but simultaneous with the eruption of the oldest flows in Morocco, a CO2 super greenhouse, and marine biocalcification crisis. Because the temporal relationship between CAMP eruptions, mass extinction, and the carbon isotopic excursions are shown in the same place, this is the strongest case for a volcanic cause of a mass extinction to date.
Security awareness and adoption of security controls by smartphone users

Growth in smartphone functionality and adoption makes such devices tempting targets for criminals. The device, but to a greater extent the information it contains, presents a valuable target for attack. It is therefore critical for smartphone users to take precautionary measures against threats, which may come from potential criminals or negligence by the user. Measures include awareness of vulnerabilities and threats as well as adoption of security controls. This study adds to the body of knowledge in this area by empirically exploring these measures. Using a survey approach an analysis of 510 respondents examines security awareness and adoption of security controls. Previous studies claim that smartphone users do not possess good security awareness, which was mostly not the case in our sample. We report on perceived efficacy and adoption of authentication and anti-theft controls, finding several correlations with smartphone OS, language, and gender. We propose that user education using a simple, non-technical design is key to encourage security awareness and adoption of security controls, especially in emerging markets.
Design challenges for high performance and power efficient graphics and mobile memory interfaces

Future generation graphics applications require more than 1TB/s memory bandwidth with a constant power budget as in today's systems. In contrast, future mobile applications require power optimized memory interfaces that can provide sufficient memory bandwidth on the order of 25GB/s. The difference in the optimization criteria results in different design challenges and consequently, different architectural and circuit-level design tradeoffs. In this paper, we compare two different memory interface design examples, one from each area operating at 16Gbps and 3.2Gbps per pin respectively, and highlight their major differences in terms of driver and receiver design, as well as clock generation and distribution. We will also discuss some of the problems facing future generations of memory interfaces that push the limits of performance and power efficiency.
“Empathize with me, Doctor!” Medical Undergraduates Training Project: Development, Application, Six-months Follow-up

The aim of the study was to assess the effectiveness of specially designed, empathy training for medical undergraduates, based on the principles of Person-Centered Approach. Within the context of the humanistic person-centered patient care, the experiential, 60-hour “Empathize with me, Doctor! ” training program contains theory, personal development and skills development. Role plays, experiential exercises, self-awareness exercises, active listening practice and conduction of a person-centered interview constituted the training. Forty-two medical undergraduates (66% females; 29% fourth year of study, 40% fifth, 31% sixth) from the University of Ioannina in Greece applied and all of them completed the empathy training. Forty-five medical students comprised a similar according to age and year of studies control group. The Jefferson Scale of Physician Empathy (JSPE) was used to assess the empathic performance, and Cohen’s d to assess the practical importance of any statistical difference. The JSPE mean score (and standard deviation) before, after and six months follow-up was 109.3(12.7), 121.1(9.0), 121.1(9.5), respectively. The before–after and before–follow-up difference was highly significant (CI95%, p<0.001 in both cases), and of great practical importance (d=1.072, d=1.052, respectively), while no decrease was observed six-months later (CI 95%, p=0.999, d<0.001). Control group reached a JSPE 108.7(10.5), similar to intervention group before training (CI95%, p=0.832), and highly different and important compared to after (CI 95%p<0.001; d=1.268) and follow-up (CI 95%, p<0.001; d=1.238) intervention scores. The “Empathize with me, Doctor! ” improved significantly and importantly medical undergraduates’ empathic performance, which was maintained intact for at least six months.
Becoming a Bridge: Collaborative Autoethnography of Four Female Counseling Psychology Student Leaders

Women with multiply-marginalized identities remain underrepresented in the American Psychological Association and Society of Counseling Psychology leadership. As early entrants into the leadership pipeline, female student leaders can potentially shift that trend; however, we know little about their leadership emergence processes. In this study, we employed collaborative autoethnography to analyze the positional standpoints of four diverse female counseling psychology leaders. We identified themes in their leadership narratives, which began when they were students. The results focused on factors associated with participants’ leadership emergence processes, the role of marginalized identities in participants’ leadership emergence, and the interplay between counseling psychology values and leadership through the theoretical framework of bridge leadership. We found themes of (a) Leadership Attributes, including future orientation, determination, and connection; as well as (b) Opportunities and Mentorship. Other themes included counseling psychology values of Advocacy, Social Justice, Inclusion, Multiculturalism, and Enhancing Training. Recommendations for students and trainers are highlighted.
An Energy-minimizing Interpolation for Robust Multigrid Methods

We propose a robust interpolation for multigrid based on the concepts of energy minimization and approximation. It can handle PDE coefficients of various types on structured or unstructured grids under one framework. The formulation is general; it can be applied to any dimension. We demonstrate numerically the effectiveness of the multigrid method in two dimensions by applying it to a discontinuous coefficient problem, an oscillatory coefficient problem, and an anisotropic problem. Empirically, the convergence rate is independent of the coefficients of the underlying PDE, in addition to being independent of the mesh size. The proposed method is primarily designed for second-order elliptic PDEs, with possible extensions to other classes of problems such as integral equations.
Inter-player Delay Optimization in Multiplayer Cloud Gaming

Novel cloud computing technology makes multiplayer cloud gaming a reality, where players play games that do not run on local devices, but on servers in the cloud. Nevertheless, the necessary communication between player's local device and cloud server increases the response delay of the gaming session. Besides the degrade of responsiveness, the inter-player delay, which is the difference of response delays perceived by players who are interacting with each other, can significantly affect the fairness of the multiplayer game. In this paper, we first introduce the Inter-player Delay Optimization (IDO) problem that aims at minimizing this inter-player delay, while preserving good-enough absolute response delay experienced by players. We further propose an efficient heuristic algorithm to solve the IDO problem. The evaluation result of a comprehensive simulation using a large-scale real-world data trace shows that IDO can reduce up to about 30% of maximum inter-player delay among interacting players comparing with the other existing solution.
Mesures d'hétérogénéité sémantique des systèmes P2P non-structurés

L'autonomie des participants dans les systemes P2P pour le partage de donnees peut conduire a une situation d'heterogeneite semantique dans le cas ou les participants utilisent leurs propres ontologies pour representer leurs donnees. Dans cet article nous commencons par deﬁnir des mesures de disparite entre participants en considerant leurs contextes semantiques. En considerant la topologie du systeme et les disparites entre participants, nous proposons des mesures d'heterogeneite semantique d'un systeme P2P non-structure.
Learning bilinear model for matching queries and documents

The task of matching data from two heterogeneous domains naturally arises in various areas such as web search, collaborative filtering, and drug design. In web search, existing work has designed relevance models to match queries and documents by exploiting either user clicks or content of queries and documents. To the best of our knowledge, however, there has been little work on principled approaches to leveraging both clicks and content to learn a matching model for search. In this paper, we propose a framework for learning to match heterogeneous objects. The framework learns two linear mappings for two objects respectively, and matches them via the dot product of their images after mapping. Moreover, when different regularizations are enforced, the framework renders a rich family of matching models. With orthonormal constraints on mapping functions, the framework subsumes Partial Least Squares (PLS) as a special case. Alternatively, with a l1+l2 regularization, we obtain a new model called Regularized Mapping to Latent Structures (RMLS). RMLS enjoys many advantages over PLS, including lower time complexity and easy parallelization. To further understand the matching framework, we conduct generalization analysis and apply the result to both PLS and RMLS. We apply the framework to web search and implement both PLS and RMLS using a click-through bipartite with metadata representing features of queries and documents. We test the efficacy and scalability of RMLS and PLS on large scale web search problems. The results show that both PLS and RMLS can significantly outperform baseline methods, while RMLS substantially speeds up the learning process.