Miracle-dz/UniFAR-FTUs huggingface dataset:
The Trade-Off between Costs and Outcomes: The Case of Acute Myocardial Infarction

Objective To investigate and to quantify the relationship between hospital costs and health outcomes for patients with acute myocardial infarction (AMI) in Veterans Health Administration (VHA) hospitals using individual-level data for costs and outcomes. Data Sources VHA administrative files for the fiscal years 2000–2006. Study Design Costs were defined as costs incurred during the index hospitalization for treatment of AMI. Mortality and readmission, assessed 1 year after the index hospitalization, were used as measures of clinical outcome. We examined health outcomes as a function of costs and other patient-level and hospital-level characteristics using a two-stage Cox proportional hazard model that accounted for competing risks within a multilevel framework. To control for patient comorbidities, we compiled a comprehensive list of comorbidities that have been found in other studies to affect mortality and readmissions. Principal Findings We found that costs were negatively associated with mortality and readmissions. Every U.S.$100 less spent is associated with a 0.63 percent increase in the hazard of dying and a 1.24 percent increase in the hazard to be readmitted conditional on not dying. This main finding remained unchanged after a number of sensitivity checks. Conclusions Our results suggest that there is a trade-off between costs and outcomes. The negative association between costs and mortality suggests that outcomes should be monitored closely when introducing cost-containment programs. Additional studies are needed to examine the cost–outcome relationship for conditions other than AMI to see whether our results are consistent.
Adaptive Filters Based on Tapped Cascaded Allpass Sections

A novel structure called tapped cascaded adaptive allpass (cid:12)lter is proposed. It is basically an adaptive (cid:12)nite impulse response (AFIR) (cid:12)lter where the delays are replaced by allpass (cid:12)lters. The system induces a better stability monitoring mechanism than in a generic adaptive in(cid:12)-nite impulse (AIIR) (cid:12)lter. Two adaptation algorithms are derived. The (cid:12)rst one is for adapting the tap-weights when the allpass (cid:12)lter coef-(cid:12)cients are (cid:12)xed. The second technique adapts the allpass (cid:12)lter coe(cid:14)cients in the least mean square sense. By tuning to appropriate values for poles (or zeros) in the allpass (cid:12)lters, we reduce the number of taps substantially compared to AFIR (cid:12)lter. Fast convergence and low complexity compared to AFIR (cid:12)lters indicate that the new structure is a potential candidate for many practical adaptive (cid:12)ltering applications.
Pacific Asia Conference on Information Systems ( PACIS ) 2014 AN IMPROVED PRIVACY-PRESERVING COLLABORATIVE FILTERING RECOMMENDATION ALGORITHM

Privacy-preserving collaborative filtering is an emerging web-adaptation tool to cope with information overload problem without jeopardizing individuals’ privacy. However, Collaborative filtering with privacy schemes commonly suffers from scalability and sparseness. Moreover, applying privacy measures causes a distortion in collected data, which in turn defects accuracy of such systems. In this work, the concept of privacy-preserving intensity weight, its measurement and an improved method of similarity calculation are introduced to solve the accuracy decreasing problem of the Randomized Perturbation Techniques (RPT) based recommendation algorithm. A new formula of similarity is proposed which considers both users’ rating similarity and the level of perturbation. Experimental results show that the improved algorithm outperforms the initial one in accuracy without affecting the effectiveness of privacy protection.
Phylogenetic groups, extended-spectrum β-lactamases and metallo-β-lactamase in Escherichia coli isolated from fecal samples of patients with diarrhea in Iran

Aim: The aims of this study were to investigate the phenotypic and genotypic of extended-spectrum β-lactamase (ESBL) and metallo-β-lactamases (MBLs) and determine phylogenetic background E. coli isolates from fecal samples of patients with diarrhea in Kerman, southeast of Iran Background: The emergence of ESBLs and MBLs-producing E. coli caused problems in antibiotic treatments. E. coli strains can be assigned to four main phylog-groups, including: A, B1, B2 and D. Patients and methods: E. coli isolates (n=216) were obtained from fecal samples of patients with diarrhea between June and December 2013. ESBLs and MBLs were confirmed by disk-diffusion and broth micro-dilution methods. Using PCR, the ESBL-positive isolates were screened to determine the phylo-groups and the presence of blaCTX-M-15, blaOXA-1, blaPER-1, blaVIM and blaIMP genes. Results: ESBL-positive isolates (n= 56) were detected. Among ESBL-positive isolates, 51 isolates were positive for blaCTX-M15 and one isolate was positive for both blaCTX-M-15 and blaOXA-1 genes. None of the isolates were positive for blaPER-1, blaVIM and blaIMP genes. PCR assay for phylotyping of isolates indicated that the isolates were belonged to groups A (54.16%), B1 (11.11%), B2 (12.96%) and D (21.75%). The isolates possessed blaCTX-M-15 gene were belonged to A (35 isolates), B1 (5), B2 (3) and D (8) phylo-groups. Conclusion: Our results indicate that blaCTX-M-15 gene is widespread among diarrheagenic E. coli isolates. ESBL-producing E. coli isolates were disseminated among a diversity of phylo-groups. Further studies are necessary to identify the ESBL genes in relation to phylogenetic groups.
PRESSURE-FLOW STUDIES IN HYPERTROPHIC SUBAORTIC STENOSIS.

In 1942 Robb and Robb (1) suggested that the outflow tract of the left ventricle might be obstructed by an asynchronous contraction of the deep bulbo spiral muscle bundle. Brock described a clinical entity resulting from muscular obstruction to the left ventricular outflow tract in 1957 (2). The pathological anatomy in eight such cases of muscular obstruction was presented by Teare in 1958 (3). Since these reports were published, there have been numerous studies dealing with the symptomatology, physical findings, and laboratory data in well-documented cases of hypertrophic subaortic stenosis (4-7). Although the exact nature of left ventricular ejection in these patients is unknown, Brachfeld and Gorlin postulated that the initial phase of cardiac ejection is relatively unencumbered and that the obstruction to outflow produced by the hypertrophied septal muscle occurs late in systole (4). This concept was based primarily on pressure data, since the phasic aortic blood flow had not been measured. The pressure gradient technique provides a method whereby the instantaneous aortic blood flow can be estimated in the intact patient (8). The purpose of this report is to present the instantaneous blood flow and pressure obtained by this technique in the aorta of five patients with hypertrophic subaortic stenosis. Methods
Deformation prediction of foundation pit using Gaussian Process machine learning

Prediction of deformation of foundation pit by means of conventional method such as mechanics analysis or numerical method often has a large error because the deformation process of foundation pit is a highly complicated nonlinear evolution process. A novel method based on Gaussian Process (GP) machine learning is proposed for solving the problem of deformation prediction of foundation pit. GP is a power tool for solving high dimensional, nonlinear, small sample problems. A GP model for deformation prediction of foundation pit is proposed firstly. The nonlinear reflective relationship between deformations in deep foundation pit and their effective factors is built easily by learning the historical knowledge using the GP model. Furthermore, the other GP model for displacement time series prediction of foundation pit is also proposed. The results of cases studies show two models are feasible. The models both have advantages in low calculation cost and higher precision comparing to the traditional methods. The results of studies also show that GP are more suitable for solving small samples prediction problems comparing to artificial neural networks and Grey method.
Comparison of Automatic Sleep / Wake Detection Algorithms for Cardiovascular Risk

We present the results of a study to compare the performance of a novel algorithm for sleep/wake identification based on actigraphy against the classical sleep/wake detection algorithms proposed by Cole, Sadeh, and Oakley. The comparison was performed using a database containing dominant and non-dominant hand actigrapy signals for 104 subjects (i.e. 208 actigraphy recordings). The new algorithm exhibits better minute-by-minute accuracy, as well as a significant improvement in the avoidance of invalid sleep-wake transitions with respect to the classical ones. These results demonstrate the advantage of the new algorithm over the classical ones for sleep/wake detection in ABPM applications.
Dielectric spectra properties of Low-density polyethylene(LDPE) / zeolite nanoscale composite

Use type 4A nanoscale zeolite powder composite with Low-density polyethylene(LDPE), obtained the different of doping concentration of composite materials which the zeolite weight percentage are from 0.5wt% to 3wt%. By using broadband dielectric spectrometer measure and analyse the dielectric performance in a relatively wide frequency range to LDPE/zeolite composite and pure polyethylene at different temperature (293K, 323K and 353K). The test results show that, with the increase of the concentration of the doping, the LDPE/zeolite composite materials' dielectric constant gradually declined, until below 2.0 when the zeolite weight percentage was 3wt% at 293K. Because zeolite material has a framework of silicon, oxygen and aluminum with porous structure, the empty space in the framework make the dielectric constant of LDPE/zeolite composite reduced. When the temperature rise to 323K and 353K the dielectric constant of LDPE/zeolite composite and pure polyethylene are on a declining curve. The dielectric constant goes to 1.85 when the zeolite weight percentage was 3wt% at 353K and the frequency is 106Hz. At the same time, dielectric losses of the composite material also have obvious changes, in the low frequency area dielectric loss of LDPE/zeolite composite decrease in 10-2~1Hz area and are lower the pure polyethylene. In the 101 ~ 104Hz area, the dielectric losses of LDPE/zeolite composite have a peak in all the samples. When the frequency is more than 105Hz, the dielectric losses are on the rise. This kind of phenomenon can be also concerned with the framework of zeolite within the LDPE/zeolite composite. This new type of composite material keeps the same performance of polyethylene, reduce the dielectric constant and change the dielectric loss features will contribute to improve the breakdown strength of materials.
“It’s Catchy, but It Gets You F*cking Nowhere”: What Viewers of Current Affairs Experience as Captivating Political Information

This paper explores how political information can be told in such a way that news users experience it as captivating. More specifically, it seeks to move beyond Irene Costera Meijer’s “double viewing paradox” and bridge the gap between what attracts and satisfies viewers by developing bottom–up, user-defined, quality criteria for current affairs TV. Items from two Dutch current affairs shows were watched and discussed with fifty-four viewers. A key finding is that participants greatly appreciated feeling enabled to better understand how politics work and impact society. This suggests that what viewers want from political journalism might differ from what journalists produce yet is perfectly compatible with their democratic remit.
Highway Development Decision-Making under Uncertainty: A Real Options Approach

A highway system development involves huge irreversible investments, and requires rigorous modeling and analysis before the implementation decision is made. This decision-making process is embedded with multiple uncertainties due to changes in political, social, and environmental contexts. In this paper, we present a multistage stochastic model for decision making in highway development, operation, expansion, and rehabilitation. This model accounts for the evolution of three uncertainties, namely, traffic demand, land price, and highway deterioration, as well as their interdependence. Real options in both development and operation phases of a highway are also incorporated in the model. A solution algorithm based on the Monte Carlo simulation and least-squares regression is developed. Numerical results show that the proposed model and solution algorithm are promising. This model makes a radical and conceptual step towards optimal decision making in highway engineering, which achieves decision-making optimality that is generally not well defined in traditional policy-based approaches for highway planning.
Privacy Tools Overview

Technological tools to protect online privacy perform many different functions. A variety of tools are available for encrypting files and email, establishing secure channels to web sites, and establishing encrypted ìtunnelsî between two computers on the Internet. These tools prevent eavesdropping and protect data from unauthorized access. In addition, anonymity tools are available that prevent online communications from being linked back to a specific individual and prevent eavesdroppers from learning with whom an individual is communicating. Some tools allow users to build anonymous, yet persistent, relationships with web sites, thus allowing sites to track user behavior or provide customized services without building identifiable user profiles.
GRACCE : A GENETIC ENVIRONMENT FOR DATA MINING

Data mining is the automated search for interesting and useful relationships between attributes in databases. In this regard, the rules used by classifiers are inherently interesting bec ause they distinguish between similar looking data of differing class. In this paper, we introduce the Genetic Rule and Classifier Co nstruction Environment (GRaCCE) as a means for extracting cl assification rules from data. GRaCCE uses a multi-stage, Genet ic Algorithm(GA) based approach to first reduce the feature set and then locate class homogeneous regions within the data. Clas sification rules are subsequently generated from these regions . The utility of GRaCCE is demonstrated by comparing it to a decisi on tree induction technique using several benchmark data sets . We show that GRaCCE achieves competitive classification accur y while producing a more compact decision rule set.
Type-2 micro virtual machine monitor for ARM-based mobile systems

This paper presents the type-2 micro virtual machine monitor (ViMo-T2) for ARM-based mobile systems. It is implemented as a kernel module of the host operating system (OS) and enables to run multiple OSs on a single mobile device simultaneously. Since ViMo-T2 is layered upon the host OS that was already installed by a device manufacture, it is applicable to the existing mobile system at user's own risk, so that time and cost developing a virtualized mobile system can be reduced.
Electric, Hybrid, and Fuel-Cell Vehicles: Architectures and Modeling

With the advent of more stringent regulations related to emissions, fuel economy, and global warming, as well as energy resource constraints, electric, hybrid, and fuel-cell vehicles have attracted increasing attention from vehicle constructors, governments, and consumers. Research and development efforts have focused on developing advanced powertrains and efficient energy systems. This paper reviews the state of the art for electric, hybrid, and fuel-cell vehicles, with a focus on architectures and modeling for energy management. Although classic modeling approaches have often been used, new systemic approaches that allow better understanding of the interaction between the numerous subsystems have recently been introduced.
An efficient k-means algorithm integrated with Jaccard distance measure for document clustering

Document Clustering is a widely studied problem in Text Categorization. It is the process of partitioning or grouping a given set of documents into disjoint clusters where documents in the same cluster are similar. K-means, one of the simplest unsupervised learning algorithms, solves the well known clustering problem following a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed a priori. The main idea is to define k centroids, one for each cluster. This clustering algorithm uses an iterative procedure which converges to one of numerous local minima. We have found that these iterative techniques are especially sensitive to initial starting conditions of the centroid of each cluster and the more the distance among the cluster centroid the better the clustering performance. In simple K-means algorithm the way to initialize the centroid is not specified and one popular way to start is to randomly choose k points of the samples as k centroids but this process does not guarantee to choose the maximum dissimilar documents as the centroid point for k-cluster. In this paper we proposed a modified k-means algorithm which uses Jaccard distance measure for computing the most dissimilar k documents as centroids for k clusters. Our experimental results demonstrate that our proposed K-means algorithm with Jaccard distance measure for computing the centroid improves the clustering performance of the simple K-means algorithm.
From High Dimensional Chaos to Stable Periodic Orbits: The Structure of Parameter Space

Regions in the parameter space of chaotic systems that correspond to stable behavior are often referred to as windows. In this Letter, we elucidate the occurrence of such regions in higher dimensional chaotic systems. We describe the fundamental structure of these windows, and also indicate under what circumstances one can expect to find them. These results are applicable to systems that exhibit several positive Lyapunov exponents, and are of importance to both the theoretical and the experimental understanding of dynamical systems. [S0031-9007(97)03367-X]
Chapter 17 3 D Continuous K-NN Query for a Landmark-based Wayfinding Location-based Service

Wayfinding in unfamiliar indoor and outdoor environments are particularly intricate problems of all the people’s activities. The development of assistive technologies to aid wayfinding is hampered by the lack of reliable and efficient methods providing location information in the environment. Locationbased services (LBS) are systems which support wayfinding task as process. They do not support landmark-based wayfinding although researchers agreed on its efficient role. In addition, one of the challenges of LBS is the continuous query which guarantees the performance of the system. In this paper, we propose an efficient method for 3D continuous k-NN query. Our method is based on three ideas, (1) The dynamic mobile objects have 3D coordinate space (x,y,t) and static landmark with 2D spaces (x,y) (2) selecting landmarks which are the nearest neighbor of one or more continuous queries, (3) indexing the queries rather than the landmarks. Through some experimental evaluation we demonstrate that our method is applicable on increasing runtime and decreasing power consumption of mobile devices.
Inclusive or Inflexible: a Critical Analysis of the School Context in Supporting Children's Genuine Participation

Relatively little attention has been paid to discussing what it means when children's participation in technology design is genuine, even though the importance of their participation is emphasized in the literature. School as a context presents multiple challenges but also numerous benefits for working with children. We examine five projects carried out with children in the school context, and critically consider in which respects this context supports genuine participation of children, in which respects it poses challenges for it, and what this implies for technology design with children. We maintain that researchers need to critically examine the goals set for children's participation: whether they are learning- or material outcome-oriented. This significantly affects the choice of the context for projects.
Specifying Prior Distributions of Functionals

Many Bayesian analyses rely heavily on Markov Chain Monte Carlo (MCMC) techniques. MCMC techniques work fastest when the prior distribution of the parameters is chosen conveniently , usually a conjugate prior. However, this is sometimes at odds with the prior desired by the investigator. We describe two motivating examples where nonconjugate priors are preferred. One is a Dirichlet Process where it is diicult to implement alternative, nonconjugate priors. We develop a method that allows computation to be done with a convenient prior but adjusts the output to have the posterior distribution from the desired prior. In addition to allowing more freedom in choosing prior distributions, the method enables the investigator to perform quick sensitivity analyses, even in nonparametric settings.
Fusing hard and soft computing for fault management in telecommunications systems

Global telecommunication systems are at the heart of the Internet revolution. To support Internet traffic they have built-in redundancy to ensure robustness and quality of service. This requires complex fault management. The traditional hard approach is to reduce the number of alarm events (symptoms) presented to the operating engineer through monitoring, filtering and masking. The goal of the soft approach is to automate the analysis fully so that the underlying fault is determined from the evidence available and presented to the engineer. This paper describes progress toward automated fault identification through a fusion between these soft and hard computing approaches.
A Computer Assisted Language Learning System based on Error Trends Grouping

Mastering several languages almost becomes a necessity in today's globalization environment. Computer Assisted language learning (CALL) systems are as a popular second language learning tool because of its inherent advantages. Through analyzing traditional language education and existing CALL tools, we have developed an experimental CALL system which can give relevance feedbacks through anatomy animation. It is based on deliberate data trained recognition to give error trend relevant feedbacks to language learners. The feedbacks are provided through different visualized speech features as well as articu-latory anatomy animation. The visual features are easier to interpret and compare to the correct pronunciations so that the learning process can be made easier. Comparative evaluations to a simple playback system without feedbacks show that the proposed system is more effective.
A G ] 4 F eb 2 00 2 Homological algebra of quiver bundles

The category of representations of a finite quiver in the category of sheaves of modules on a ringed space is abelian. We show that this category has enough injectives by constructing an explicit injective resolution. From this resolution we deduce a long exact sequence relating the Ext groups in these two categories. We also show that under some hypotheses, the Ext groups are isomorphic to certain hypercohomology groups.