Miracle-dz/UniFAR-FTUs huggingface dataset:
The Association between Alcohol Exposure and Self-Reported Health Status: The Effect of Separating Former and Current Drinkers

Aims To investigate the direction and degree of potential bias introducedto analyses of drinking and health status which exclude former drinkers from exposure groups. Design: Pooled analysis of 14 waves (1997–2010) of the U.S. National Health Interview Survey (NHIS). Setting General population-based study. Participants 404,462 participants, from 14 waves of the NHIS, who had knownself-reported health status and alcohol consumption status. Measurements Self-reported health status was used as the indicatorof health. Two approaches were used to classify alcohol consumption: (i)separation of former drinkers and current drinkers, and (ii) combined former and current drinkers. The prevalence of fair/ poor health by alcohol use, gender and age with 95% confidence intervals was estimated. The difference in prevalence of fair/ poor health status for lifetime abstainers, former drinkers, current drinkers and drinkers (former drinkers and current drinkers combined) were compared using Poisson regression with robust estimations of variance. Findings Excluding former drinkers from drinker groups exaggerates the difference in health status between abstainers and drinkers, especially for males. Conclusions In cohort study analyses, former drinkers should be assigned to a drinking category based on their previous alcohol consumption patterns and not treated as a discrete exposure group.
New Technology and Techniques Diffusion Tensor Magnetic Resonance Imaging and Fiber Tractography of the Sacral Plexus in Children with Spina Bifida

Department of Radiology (WH, MF, RAJN) and Image Sciences Institute (AL), University Medical Center Utrecht, Departments of Pediatric Urology (PD, TPVMdJ) and Pediatric Neurology (IC), University Children’s Hospitals University Medical Center Utrecht, Utrecht and Neuro Imaging, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente (BtH), Enschede, The Netherlands, and Department of Forensic Medicine and Comparative Medicine Laboratory, Aarhus University (WH), Aarhus, Denmark
Meaning Matters: Senses of Words are More Informative than Words for Cross-domain Sentiment Analysis

Getting labeled data in each domain is always an expensive and a time consuming task. Hence, cross-domain sentiment analysis has emerged as a demanding research area where a labeled source domain facilitates classiﬁer in an unlabeled target domain. However, cross-domain sentiment analysis is still a challenging task because of the differences across domains. A word which is used with positive polarity in the source domain may bear negative polarity in the target domain or vice versa. In addition, a word which is used very scarcely in the source domain may have high impact in the target domain for sentiment classi-ﬁcation. Due to these differences across domains, cross-domain sentiment analysis suffers from negative transfer. In this paper, we propose that senses of words in place of words help to overcome the differences across domains. Results show that senses of words provide a better sentiment classiﬁer in the unlabeled target domain in comparison to words for 12 pairs of source and target domains.
Multiple fundamental frequency estimation using Gaussian smoothness

The goal of a polyphonic music transcription system is to extract a score from an audio signal. A multiple fundamental frequency estimator is the main piece of these systems, whereas tempo detection and key estimation complement them to correctly extract the score. In this work, in order to detect the fundamental frequencies that are present in a signal, a set of candidates are selected from the spectrum, and all their possible combinations are generated. The best combination is chosen in a frame by frame analysis by applying a set of rules, taking into account the harmonic amplitudes and the spectral smoothness measure described in this work. The system was evaluated and compared to other works, yielding competitive results and performance.
A new face recognition algorithm using LBP and NFS

Real-time identity verification and recognition of celebrity's web facial image used in special occasion such as access control and computer security, etc, is a tough issue in face recognition. And it has attracted more and more people's attention recently. This paper proposes a novel effective and real-time face recognition method for solving this problem. Owing to enhance the accuracy ratio, coarse-to-fine structure technique used for gradually improving the recognition ability by improved iteration method which further analyzes the intermediate result of candidates of the coarse stage, is adopted in this paper. The simple and effective Local Binary Patterns (LBP) technique and Nearest Feature Space (NFS) classifier that has the good generalization ability are combined to construct a new way for satisfying the real-time and robustness decision performance. The face image database derived from web is used for our experiment. The experimental results show that the proposed method performs better than traditional approaches in terms of real-time efficiency (0.7s per image) and accuracy (72.5% Average Recognition Rate).
Coordinating the cloud computing service supply chain under asymmetric demand information with quantity discount contract

The article mainly examines coordinated strategies in a cloud computing service supply chain that faces random demand of a single product, and consists of one application infrastructure provider (AIP), one application platform provider (APP) and one application service provider (ASP). We consider a setting that the ASP has accurate demand information while the APP and AIP don't because they are relatively far away from the consumer market. In this paper, we examine the supply chain's performance mainly through discussing three situations: basic model (decentralized control) under asymmetric information, reference model (centralized control) and experimental model (quantity discount contract) under sharing demand information. We provide numerical results to compare the supply chain profits and conduct sensitivity analysis under three models. We find that using quantity discount contract can attain Pareto improvement. We also find that the ASP doesn't have motivation to transfer false demand information. Moreover, the numerical analysis and sensitivity analysis show that reducing the delay cost can improve the profits of the cloud computing service supply chain and its members. What is more, on the same level of delay cost, the impact of quantity discount on the expected profits improvement of upstream members is more than the downstream members.
The Applicability, Concurrent Validity and Internal Consistency Reliability of the Persian Version of the National Institutes of Health Stroke Scale (NIHSS): Evidences for Gender Differences

Article type: Original Article Background: The National Institutes of Health Stroke Scale (NIHSS) can objectively quantify the severity of stroke. However no information is available about psychometric properties and it’s applicability in the Iranian population. Objectives: The present study purposed by utilization of this instrument for neurological deficits measurement due to stroke, to determine the internal consistency reliability and concurrent validity of NIHSS to separate two groups of men and women patients. Materials and Methods: In a cross-sectional study, 206 ischemic stroke patients were selected and the internal consistency reliability and concurrent validity of NIHSS were assessed. For this purpose, Barthel index that measures the functional disability was used. Data also were analyzed by Independent ttest, Chi-square, Pearson correlation, Fisher's z tests and bivariate regression analysis. Results: Internal consistency for men (α=0.881), women (α=0.913) and total patients (α=0.893) was excellent. Negative correlation was found between NIHSS and Barthel index in both men (r=-0.43, p<0.0001) and women (r=0.63, p<0.0001) and this relationship estimated to be more significant among women rather than men (p<0.05). In addition, NIHSS scores could significantly predict the Barthel score in both groups (p<0.0001) but the results revealed the more ability of NIHSS in predicting functional disability for women (R 2 =0.40) rather than men (R 2 =0.18). Conclusion: Persian version of NIHSS was reliable and valid instrument that can be applicable in both men and women with ischemic stroke; however, it was found that the degree of concurrent validity is better among women than men.
An Enhanced Protocol for Solving Broadcast Strom Problem in MANET

: Mobile Ad-Hoc Network (MANET) is wireless networks consisting of a collection of mobile nodes with no fixed infrastructure, where some intermediate nodes should participate in forwarding data packets. Energy conservation is a critical issue in ad hoc wireless networks for node and network life. Enhanced Medium Access control (eMAC) protocol prevents link/routing failures, hidden/exposed terminal problems and broadcast storm problems using an adaptive unreachability reporting mechanism with more energy consumption. Furthermore, An adaptive table broadcasting technique is proposed to distribute topology information in mobile ad hoc networks (MANETs). In this paper, a cross layer design for enhancing the distance based broadcasting protocol is proposed in terms of energy consumption. Instead of using the distance, the reception signal strength is considered. The necessary transmission power to reach an intended device is obtained using the beacons.If the furthest node can be reached using less power than the default value, the transmission power is reduced and it saves energy. Different proposals for enhancing the algorithm are proposed, and they not only save energy but also highly reduce the number of collisions.
Infrastructure awareness

Ubiquitous Computing designs infrastructures that weave into the fabric of everyday life, and become invisible by fading in the background. However, this invisibility keeps users from understanding and adopting them. To address this problem we introduce the notion of Infrastructure Awareness (IA). IA is the user's awareness about properties of an infrastructure. Our hypothesis is that IA facilitates the users' understanding of infrastructures, and thereby supports their adoption. This dissertation investigates three dimensions of IA: conceptual, methodological, and technological. The conceptual dimension defines IA in terms of an awareness model and a design space. The methodological dimension reflects on the usage of user-centred design when designing for invisibility, and proposes a new user-centred design activity for IA systems. The technological dimension creates two proof-of-concept applications, GridOrbit and GridNotify, to illustrate the notion of IA systems.
Prediction of Advertiser Churn for Google AdWords

Google AdWords has thousands of advertisers participating in auctions to show their advertisements. Google’s business model has two goals: first, provide relevant information to users and second, provide advertising opportunities to advertisers to achieve their business needs. To better serve these two parties, it is important to find relevant information for users and at the same time assist advertisers in advertising more efficiently and effectively. In this paper, we try to tackle this problem of better connecting users and advertisers from a customer relationship management point of view. More specifically, we try to retain more advertisers in AdWords by identifying and helping advertisers that are not successful in using Google AdWords. In this work, we first propose a new definition of advertiser churn for AdWords advertisers; second we present a method to carefully select a homogeneous group of advertisers to use in understanding and predicting advertiser churn; and third we build a model to predict advertiser churn using machine learning algorithms.
Long- and short-term treatment with imatinib attenuates the development of chronic kidney disease in experimental anti-glomerular basement membrane nephritis.

BACKGROUND Imatinib is a selective tyrosine kinase inhibitor that can block platelet-derived growth factor (PDGF) receptor activity. Imatinib is also known as an anti-inflammatory agent. We examined the therapeutic effects of long- or short-term imatinib treatment in Wistar-Kyoto (WKY) rats with established anti-glomerular basement membrane (GBM) nephritis. METHODS Nephrotoxic serum (NTS) nephritis was induced in WKY rats on day 0. Groups of animals were given either imatinib or vehicle daily by intraperitoneal injection, from day 7 to day 49 in the long-term treatment study, and from day 7 to 13 in the short-term treatment study; all rats were sacrificed at day 50. RESULTS In long-term treatment, imatinib showed marked renoprotective effects; imatinib suppressed proteinuria, improved renal function, attenuated the development of glomerulosclerosis and tubulointerstitial injury and reduced the expression levels of collagen type I and transforming growth factor-beta (TGF-β) in renal cortex. The key finding of the present study was that short-term treatment with imatinib also significantly attenuated the development of renal injury until day 50, although the degree of renoprotection was slightly inferior to that of long-term treatment. CONCLUSIONS These results suggest that administration of imatinib is a promising strategy for limiting the progression of glomerulonephritis (GN) to end-stage renal failure. In particular, a short period of treatment at an early stage of GN is more beneficial in terms of cost-effectiveness and reduction of adverse effects in comparison to a continuous and long period of treatment.
Text Analysis in R

ABSTRACT Computational text analysis has become an exciting research field with many applications in communication research. It can be a difficult method to apply, however, because it requires knowledge of various techniques, and the software required to perform most of these techniques is not readily available in common statistical software packages. In this teacher’s corner, we address these barriers by providing an overview of general steps and operations in a computational text analysis project, and demonstrate how each step can be performed using the R statistical software. As a popular open-source platform, R has an extensive user community that develops and maintains a wide range of text analysis packages. We show that these packages make it easy to perform advanced text analytics.
Student and Police Partnerships: Cultivating Student Success and Community Development

Scholarship on service-learning and experiential learning increasingly demonstrates benefits for student access and success, but evidence is still needed of the impact of these strategies on sustainable development of communities and their infrastructure. This case study examines a university Police and Society course that partners with four police departments to use both experiential learning and service-learning in the form of ride-alongs, simulation trainings, and written and physical examinations, as well as hosting a police–community relations event. Focus groups of students and officers reveal how service-learning and experiential learning build capacity for police–community relations. Orienting students toward officer procedures, requirements, and culture creates a learning opportunity for both students and departments, while police–community relations activities humanize officers and build public understanding and trust.
Predicting Ball Ownership in Basketball from a Monocular View Using Only Player Trajectories

Tracking objects like a basketball from a monocular view is challenging due to its small size, potential to move at high velocities as well as the high frequency of occlusion. However, humans with a deep knowledge of a game like basketball can predict with high accuracy the location of the ball even without seeing it due to the location and motion of nearby objects, as well as information of where it was last seen. Learning from tracking data is problematic however, due to the high variance in player locations. In this paper, we show that by simply "permuting" the multi-agent data we obtain a compact role-ordered feature which accurately predict the ball owner. We also show that our formulation can incorporate other information sources such as a vision-based ball detector to improve prediction accuracy.
Cross-layer FPS: A SCTP-based cross-layer data scheduling approach

In heterogeneous wireless networks, multi-interfaced mobile terminals may be connected to links with rapidly varying MAC and physical characteristics. On the other hand, current deployment of communication protocols rely on the information available at associated layer only. In order to improve transport layer performance for multi-homed mobile terminals, we propose a cross-layer approach to schedule data over multiple available paths. Our data-striping approach strengthens the cooperation between higher and lower layers and enables transport layer mechanism to exploit information provided by MAC layer. We compare our approach with existing techniques and prove that our cross-layer system improves multipath performance over single-layer multipath systems.
Evaluation of Molecular Perturbation of a Deuterated Protein by Temperature Factor Refinement in X-Ray Structural Analysis of High- Resolution Diffraction Data

Structural analysis of deuterated proteins, which are essential for neutron protein crystallography, involves refinement of X-ray crystallographic data using atomic or molecular thermal stability factors. Analysis of high resolution (~0.9 A) X-ray data can localize some of the hydrogen atoms in a protein molecule. Thermal stabilities and temperature factors are affected by some reasons; one of them is the masses of hydrogen and deuterium atoms. We propose a method to refine X-ray data, taking into account these effects, to show existence probability for deuterium in the protein. Thermal factors were calculated using several physical parameters, and the resultant values were fitted to the experimental thermal factors with high accuracy. This computational method can be applied to analyze and predict the hydrogen/deuterium exchanged states of protein crystals, even small crystals that are unsuitable for neutron crystallography.
An Efficient Denoising Technique for CT Images using Window- based Multi-Wavelet Transformation and Thresholding

Image denoising is one of the most significant tasks in image processing, analysis and image processing applications. Medical Imaging is one among the emerging application areas where the image denoising plays a vital role. In medical imaging, the acquisition techniques and systems introduce noises and artifacts in the medical image that leads to poor quality image. In this occasion, image denoising is an essential pre-requisite, especially in Computed Tomography, which is an important and most common modality in medical imaging. The significance of the denoising is mainly due to that the effectiveness of clinical diagnosis using CT image depends upon the quality of the image. In this paper, we propose an efficient noise reduction technique for CT images using window-based Multi-wavelet transformation and thresholding. The technique removes Additive white Gaussian noise from the CT images as well as it enhances the quality of the images. The proposed technique consists of three different stages of processing, namely, window-based multi-wavelet transformation and thresholding, reconstruction and quality enhancement. In the first two processes, the AWGN is effectively removed from CT images and the images are reconstructed. In the third process, the quality of the images is enhanced by means of filtering techniques. Hence, denoised and quality enhanced CT images can be obtained using the proposed multi-wavelet based denoising technique.
Depressed low frequency power of heart rate variability as an independent predictor of sudden death in chronic heart failure.

AIMS Identification of patients with chronic heart failure at risk for sudden death remains difficult. We sought to assess the prognostic value for all-cause and sudden death of time and frequency domain measures of heart rate variability in chronic heart failure. METHODS AND RESULTS We prospectively enrolled 190 patients with chronic heart failure in sinus rhythm, mean age 61+/-12 years, 109 (57.4%) in NYHA class II and 81 (42.6%) in classes III or IV, mean cardiothoracic ratio 57.6+/-6.4% and mean left ventricular ejection fraction 28.2+/-8.8%, 85 (45%) with ischaemic and 105 (55%) with idiopathic dilated cardiomyopathy. Time and frequency domain measures of heart rate variability were obtained from 24 h Holter ECG recordings, spectral measures were averaged for calculation of daytime (1000h-1900h) and night-time (2300h-0600h) values. During follow-up (22+/-18 months), 55 patients died, 21 of them suddenly and two presented with a syncopal spontaneous sustained ventricular tachycardia. In multivariate analysis, independent predictors for all-cause mortality were: ischaemic heart disease, cardiothoracic ratio > or =60% and standard deviation of all normal RR intervals <67 ms (RR = 2.5, 95% CI 1.5-4.2). Independent predictors of sudden death were: ischaemic heart disease and daytime low frequency power <3.3 ln (ms(2)) (RR = 2.8, 95% CI 1.2-8.6). CONCLUSION Depressed heart rate variability has independent prognostic value in patients with chronic heart failure; spectral analysis identifies an increased risk for sudden death in these patients.
An assessment of implementation of Community-Oriented Primary Care in Kenyan family medicine postgraduate medical education programmes

Background and objectives Family medicine postgraduate programmes in Kenya are examining the benefits of Community-Oriented Primary Care (COPC) curriculum, as a method to train residents in population-based approaches to health care delivery. Whilst COPC is an established part of family medicine training in the United States, little is known about its application in Kenya. We sought to conduct a qualitative study to explore the development and implementation of COPC curriculum in the first two family medicine postgraduate programmes in Kenya. Method Semi-structured interviews of COPC educators, practitioners, and academic stakeholders and focus groups of postgraduate students were conducted with COPC educators, practitioners and academic stakeholders in two family medicine postgraduate programmes in Kenya. Discussions were transcribed, inductively coded and thematically analysed. Results Two focus groups with eight family medicine postgraduate students and interviews with five faculty members at two universities were conducted. Two broad themes emerged from the analysis: expected learning outcomes and important community-based enablers. Three learning outcomes were (1) making a community diagnosis, (2) understanding social determinants of health and (3) training in participatory research. Three community-based enablers for sustainability of COPC were (1) partnerships with community health workers, (2) community empowerment and engagement and (3) institutional financial support. Conclusions Our findings illustrate the expected learning outcomes and important community-based enablers associated with the successful implementation of COPC projects in Kenya and will help to inform future curriculum development in Kenya.
Algorithms for an Optimal A* Search and Linearizing the Search in the Stack Decoder*

Two algorithms are presented for accelerating the operation of a stack decoder. The first is a method for computing the true least upper bound so that an optimal admissible A* search can be performed. The second is a set of methods for linearizing the computation required by a stack decoder. The A* search has been implemented in a continuous speech recognizer simulator and has demonstrated a significant speedup. The linearizing algorithm has been partially implemented in the simulator and has also shown significant computational savings. <<ETX>>