Miracle-dz/UniFAR-FTUs huggingface dataset:
A High Availability Solution for GRID Services

In this work we describe a prototype to obtain the High Availability of GRID services (CE, SE, WN), using the XEN paravirtualization paradigm, coupled with NAGIOS and Cfengine packages. The architecture of the prototype is outlined together with some of the first results; noticeably a dead time of the order of one minute to restart a server either in case of software or hardware failures has been achieved. No failure of running jobs due to CE downtime in this configuration has been observed.
Semi-Parametric Inference for the Partial Area Under the ROC Curve

Diagnostic tests are central in the field of modern medicine. One of the main factors for interpreting a diagnostic test is the discriminatory accuracy. For a continuous-scale diagnostic test, the area under the receiver operating characteristic (ROC) curve, AUC, is a useful onenumber summary index for the diagnostic accuracy of the test. When only a particular region of the ROC curve would be of interest, the partial AUC (pAUC) is a more appropriate index for the diagnostic accuracy. In this thesis, we develop seven confidence intervals for the pAUC under the semi-parametric models for the diseased and non-diseased populations by using the normal approximation, bootstrap and empirical likelihood methods. In addition, we conduct simulation studies to compare the finite sample performance of the proposed confidence intervals for the pAUC. A real example is also used to illustrate the application of the recommended intervals. INDEX WORDS: ROC, AUC, The partial AUC, Diagnostic test, Confidence interval SEMI-PARAMETRIC INFERENCE FOR THE PARTIAL AREA
The structure of a machine-built forecasting system

This paper describes the structure of a rule-based econometric forecasting system designed to produce multi-equation econometric models. The paper describes the functioning of a working system which builds the econometric forecasting equation for each series submitted and produces forecasts of the series. The system employs information criteria and cross validation in the equation building process, and it uses Bayesian model averaging to combine forecasts of individual series. The system outperforms standard benchmarks for a variety of national economic datasets.
A Triple-Random Ensemble Classification Method for Mining Multi-label Data

This paper presents a triple-random ensemble learning method for handling multi-label classification problems. The proposed method integrates and develops the concepts of random subspace, bagging and random k-label sets ensemble learning methods to form an approach to classify multi-label data. It applies the random subspace method to feature space, label space as well as instance space. The devised subsets selection procedure is executed iteratively. Each multi-label classifier is trained using the randomly selected subsets. At the end of the iteration, optimal parameters are selected and the ensemble MLC classifiers are constructed. The proposed method is implemented and its performance compared against that of popular multi-label classification methods. The experimental results reveal that the proposed method outperforms the examined counterparts in most occasions when tested on six small to larger multi-label datasets from different domains. This demonstrates that the developed method possesses general applicability for various multi-label classification problems.
The Recognition and Enhancement of Traffic Sign for the Computer-Generated Image

Image recognition in image understanding is very challenge research topic. However, the study of recognition and enhancement of the specific target for the computer generated image is very less. Compared with the natural image, this cause may be the characteristics (simple in overall) and the scene of applications (the 3D roaming, the game and so on) for the computer-generated image. For recognition and enhancement of the specific target (such as traffic sign) in the computer-generated image, the difficulty is how to accurately recognize and enhance the specific target, and maintaining other information of object do not been changed. In this paper, adopting stepwise refinement method attempts to solve the problem. The proposed method is based on three steps including image preprocessing, image recognition and enhancement. The experimental results show the method can be very good to deal with such issues. The research result shows that the image recognition and image enhancement is still not simple process. Therefore, we imagine that image recognition for complex real scene image also isn't simple issues. But the research idea of stepwise refinement which is proposed in the paper provides a methodological reference for complex image recognition.
Instruction Trace Compression for Rapid Instruction Cache Simulation

Modern application specific instruction set processors (ASIPs) have customizable caches, where the size, associativity and line size can all be customized to suit a particular application. To find the best cache size suited for a particular embedded system, the applications) is/are executed, traces obtained, and caches simulated. Typically, program trace files can range from a few megabytes to several gigabytes. Simulation of cache performance using large program trace files is a time consuming process. In this paper, a novel instruction cache simulation methodology that can operate directly on a compressed program trace file without the need for decompression is presented. This feature allowed our simulation methodology to have an average speed up of 9.67 times compared to the existing state of the art tool (Dinero IV cache simulator), for a range of applications from the Mediabench suite
Appraising Surface Water Quantity and Quality in the Upper Arkansas River Basin in Chaffee County, Colorado

The Arkansas River, one of four major rivers that begin in Colorado, has the largest drainage basin in the state and serves as a vital source of water. At peak flows, the Arkansas River swells by 70% through Chaffee County, in the heart of the Rockies, and only increases by 20% before exiting the mountains onto the high plains near Pueblo. The production of this discharge depends on a complex natural system of snowmelt and groundwater-stream interactions, as well as on numerous human interventions. The Arkansas valley in Chaffee County, from Granite to Salida, is characterized as an intermountain, high altitude, agricultural, small-municipal, and recreational region. Increasing demands on the stream-aquifer system have necessitated more up- to-date, integrated, and technology-driven data to assist water users, managers, and decision makers in moving toward optimal overall water use. Monthly synoptic measurements and triannual water sampling over July 2009 - January 2011 at 19 river and tributary sites provide data on discharge; dissolved oxygen; pH; specific conductance; oxidation reduction potential; temperature; and major dissolved ions, uranium, selenium, and other select minerals. Data analysis for the range of hydrologic conditions during the study period reveals flow conditions similar to long-term averages. Relationships of specific conductance and total dissolved solids derived from field samples yields equations useful for estimation of mass loading in the river system. Spatial and temporal analysis of tributary to river discharge and dissolved solids loading helps quantify and qualify water sources as well as potential aquifer interactions. Overall water quality is good, with some constituents worthy of continued active monitoring.
A Search Phillips Curve A generalised search model to understand the way economic policies shape the transmission of shocks

A generalised search model with matching on product, labour and financial markets is presented in order to analyse the impact of ma rket frictions on the pass-through of shocks. The model includes an endogen ous demand for money that arises from matching frictions on the financia l m rket. Moreover, price posting on retail markets allows to establish a f orward-looking Search Phillips Curve. The model is estimated using Bayesia n techniques on US data. The policy analysis illustrates the importance of fi nancial market deregulation for a rapid pass-through of monetary policy sh ocks. Moreover, labour market policies in particular activation policies are shown to insulate employment from wage and technology shocks. Finally , increased product market competition has an ambiguous effect on the sa crifice ratio depending on whether entry rates are increased or exit proba bilities.
The Influence of Self-Efficacy on the Development of Entrepreneurial Intentions and Actions

The contextual factors of entrepreneurship consist of social, political, and economic variables such as displacement, changes in markets, and government deregulation (Bird, 1988). Entrepreneurial intentions are further structured by both rational/analytic thinking (goal-directed behavior) and intuitive/holistic thinking (vision). These thought processes underlie the creation of formal business plans, opportunity analysis, and other goal-directed behavior. This paper further develops Bird's model of entrepreneurial intentionality by suggesting that individual self-efficacy, which has been defined as a person's belief in his or her capability to perform a task, influences the development of both entrepreneurial intentions and actions or behaviors.
The influence of perinatal loss on anxiety in multigravidas.

OBJECTIVE To compare multigravid women with and without a history of perinatal loss on state anxiety, pregnancy anxiety, and optimism. DESIGN Comparative descriptive; cross-sectional. SETTING Private obstetric offices in a small northeastern city in the United States. PARTICIPANTS The sample included 160 women who were between 17 and 28 weeks gestation: 96 multigravidas with no history of loss and 74 women with a history of one or two losses. MAIN OUTCOME MEASURES State anxiety, pregnancy anxiety, optimism, and perinatal loss history. RESULTS No group differences were found on demographic variables, state anxiety, or optimism. However, pregnancy anxiety was higher in women with a history of perinatal loss. Pregnancy anxiety was also correlated with desire to see care provider more often and number of phone calls between visits, and was not correlated with the number of living children. CONCLUSION Women experiencing pregnancy subsequent to perinatal loss have greater pregnancy anxiety: That is, they are more concerned about their pregnancies and their babies than women without a history of perinatal loss. State anxiety and optimism do not differentiate these two groups. This heightened anxiety should be acknowledged and more frequent contact with the care provider should be offered.
An Abundant Tissue Macrophage Population in the Adult Murine Heart with a Distinct Alternatively-Activated Macrophage Profile

Cardiac tissue macrophages (cTMs) are a previously uncharacterised cell type that we have identified and characterise here as an abundant GFP+ population within the adult Cx3cr1GFP/+ knock-in mouse heart. They comprise the predominant myeloid cell population in the myocardium, and are found throughout myocardial interstitial spaces interacting directly with capillary endothelial cells and cardiomyocytes. Flow cytometry-based immunophenotyping shows that cTMs exhibit canonical macrophage markers. Gene expression analysis shows that cTMs (CD45+CD11b+GFP+) are distinct from mononuclear CD45+CD11b+GFP+ cells sorted from the spleen and brain of adult Cx3cr1GFP/+ mice. Gene expression profiling reveals that cTMs closely resemble alternatively-activated anti-inflammatory M2 macrophages, expressing a number of M2 markers, including Mrc1, CD163, and Lyve-1. While cTMs perform normal tissue macrophage homeostatic functions, they also exhibit a distinct phenotype, involving secretion of salutary factors (including IGF-1) and immune modulation. In summary, the characterisation of cTMs at the cellular and molecular level defines a potentially important role for these cells in cardiac homeostasis.
Correlation between the peak spectral energy of gamma-ray bursts and the peak luminosity of the underlying supernovae: implication for the nature of the gamma-ray burst–supernova connection

In this paper, we present a correlation between the peak spectral energy of gamma-ray bursts (GRBs) and the peak bolometric luminosity of the underlying supernovae (SNe), based on a sample of four pairs of GRBs‐SNe with a spectroscopically confirmed connection. Combining it with the well-known relation between the peak spectral energy and the isotropic equivalent energy of GRBs, we obtain an upper limit on the isotropic energy of GRBs, which is ≈ 10 52 erg(L SN,peak/10 43 erg s −1 ) 10 , where LSN,peak is the peak bolometric luminosity of the SNe. Our results suggest that the critical parameter determining the GRB‐SN connection is the peak luminosity of SNe, rather than the feature of the SN spectra and/or the SN explosion energy as commonly hypothesized. Because it is generally believed that the peak luminosity of SNe powered by radioactive decays is related to the amount of 56 Ni produced in the SN explosion, the mass of 56 Ni may be a key physical factor for understanding the nature of GRBs and their connection with SNe. Application of our relation to Type Ibc SNe with normal peak luminosities indicates that, if those normal SNe have GRBs accompanying them, the GRBs would be extremely soft and subenergetic in gamma-rays and, hence, easier to detect with X-ray or UV detectors than with gamma-ray detectors.
A multi-sensor management method based on particle swarm algorithm

In order to use limited sensor resources, the problem of multi-sensor multi-target optimization allocation is analyzed, based on target information gain as efficiency, presents a sensor management algorithm combined with the idea of particle swarm optimization. For meeting the demand for multiple targets and scanning space, using particle swarm algorithm search for the optimal value through iterative, and manage multi-sensor resources scientifically and reasonably according to this optimal criterion. This algorithm is simulated, and compared with the random allocation method. Simulation results show that the sensor management algorithm based on information gain and particle swarm algorithm is effective, and has higher stability and faster convergence speed, stronger practicability.
The monte carlo database system: Stochastic analysis close to the data

The application of stochastic models and analysis techniques to large datasets is now commonplace. Unfortunately, in practice this usually means extracting data from a database system into an external tool (such as SAS, R, Arena, or Matlab), and then running the analysis there. This extract-and-model paradigm is typically error-prone, slow, does not support fine-grained modeling, and discourages what-if and sensitivity analyses. In this article we describe MCDB, a database system that permits a wide spectrum of stochastic models to be used in conjunction with the data stored in a large database, without ever extracting the data. MCDB facilitates in-database execution of tasks such as risk assessment, prediction, and imputation of missing data, as well as management of errors due to data integration, information extraction, and privacy-preserving data anonymization. MCDB allows a user to define “random” relations whose contents are determined by stochastic models. The models can then be queried using standard SQL. Monte Carlo techniques are used to analyze the probability distribution of the result of an SQL query over random relations. Novel “tuple-bundle” processing techniques can effectively control the Monte Carlo overhead, as shown in our experiments.
Multi-domain-based Automatic Image Representation Using Semantic Tolerance Relation Models

The nature of the concepts regarding images in many domains are imprecise, and the interpretation of finding similar images is also ambiguous and subjective on the level of human perception. To solve these problems, in this paper, images' semantic categories and the tolerance degree between them are defined systematically, and the machine learning-based approach of modeling tolerance relations between the semantic classes is proposed. Furthermore, the method of the semantic tolerance relation model-based image representation and the corresponding image semantic categorization algorithm is also presented. We apply the proposed approach to the representations of images regarding the nature vs. man-made domain, human vs. non-human domain, and temporal domain, and compare the categorization results of them with the results not using semantic tolerance relation model. The results show the effectiveness of proposed method.
An Improved Unknown Word Recognition Model based on Multi-Knowledge Source Method

Unknown word recognition (UWR) is a difficult and foundational task in lexical processing and content-based understanding, and it can improve many text-based processing applications, such as information extraction, question answer system, electronic meeting system. However the unified dealing approach is difficult to exploit more domain knowledge features, so the performance cannot be further improved easily, since UWR has been proved to be NP-hard problem. This paper presents a novel method for UWR task, which divides the UWR into several hard sub-tasks that usually encountering different difficulties, accordingly, several language models are adopted to solve the special sub-tasks, so as to exert the ability of each model in addressing special problems. Firstly, a class-based trigram is used in basic word segmentation, aided with absolute smoothing algorithm to overcome data sparseness, and maximum entropy model (ME) is used to recognize named entity. New word detection adopts variance and conditional random fields algorithm. Secondly, multi-knowledge features are effectively extracted and utilized in whole processing. Our system participated in the Second International Chinese Word Segmentation Bakeoff (SIGHAN2005), and got the overall performance 97.2% F-measure in MSRA open test
Corpus Support for Machine Translation at LDC

This paper describes LDC's efforts in collecting, creating and processing different types of linguistic data, including lexicons, parallel text, multiple translation corpora, and human assessment of translation quality, to support the research and development in Machine Translation. Through a combination of different procedures and core technologies, the LDC was able to create very large, high quality, and cost-efficient corpora, which have contributed significantly to recent advances in Machine Translation. Multiple translation corpora and human assessment together facilitate, validate and improve automatic evaluation metrics, which are vital to the development of MT systems. The Bilingual Internet Text Search (BITS) and Champollion sentence aligner enable the finding and processing of large quantities of parallel text. All specifications and tools used by LDC and described in the paper are or will be available to the general public.
Education and the Political Economy of Environmental Protection

We develop a political economy model that might explain the different environmental performance of countries, through educational choices. Individuals decide whether to invest in additional education according to their expectations regarding future environmental quality. They also vote on a tax that will be exclusively used to finance environmental protection. We show that the model may generate multiple equilibria and agents' expectations may be self-fulfilling when the public policy is endogenous. Then, we analyse the long-term implications of a public policy that would favour education and make it possible to select the higher equilibrium.
Three-dimensional optical frequency domain imaging in conventional percutaneous coronary intervention: the potential for clinical application.

Two-dimensional (2D) frequency domain optical coherence tomography (FD-OCT) has enhanced our understanding of coronary atherosclerotic disease and is increasingly being used in conventional percutaneous coronary intervention (PCI) to elucidate mechanisms of disease and improve our understanding of complex coronary anatomy. Since the first report of three-dimensional (3D) OCT applied in human coronary vessels,1 the technology has rapidly progressed.2–10 Currently, the main limitation of this technology is the need for off-line creation of 3D reconstructions—prototypes of current generation ‘real time’ (i.e. available peri-procedurally at the ‘push-of-a-button’) remain experimental, work in progress, and are limited by relatively poor image quality/resolution.4 As of now, the potential clinical application of 3D FD-OCT remains undefined. Recently, the application of this emerging technology to the coronary bifurcation has allowed visualization and assessment of jailed side branches (SideBs) at a level of detail not previously reported.2–7 The assessment of a jailed SideB, after implantation of a bioresorbable scaffold in the main branch (MainB) of a bifurcation, lead to the proposal of a new classification system based on the assessment of the number of compartments the SideB ostium was divided into, with examples of how this may potentially effect the neointimal response and subsequent coverage of the struts.2 More recently, the application of this technology to the coronary bifurcation in patients implanted with conventional metallic stents, utilizing the Terumo optical frequency domain imaging (OFDI) system, was described for the first time.3 Hypotheses related to types of coronary bifurcation (‘parallel’ and ‘perpendicular’ bifurcations) based on the bifurcation angle, and how this leads to certain specific characteristics of the carina, which potentially made the SideB more vulnerable to the effects of carina shift and potential SideB closure, were described. Furthermore, the potential practical application of 3D FD-OCT in guiding the …
Alternative Oxidase Expression in the Mouse Enables Bypassing Cytochrome c Oxidase Blockade and Limits Mitochondrial ROS Overproduction

Cyanide-resistant non-phosphorylating respiration is known in mitochondria from plants, fungi, and microorganisms but is absent in mammals. It results from the activity of an alternative oxidase (AOX) that conveys electrons directly from the respiratory chain (RC) ubiquinol pool to oxygen. AOX thus provides a bypath that releases constraints on the cytochrome pathway and prevents the over-reduction of the ubiquinone pool, a major source of superoxide. RC dysfunctions and deleterious superoxide overproduction are recurrent themes in human pathologies, ranging from neurodegenerative diseases to cancer, and may be instrumental in ageing. Thus, preventing RC blockade and excess superoxide production by means of AOX should be of considerable interest. However, because of its energy-dissipating properties, AOX might produce deleterious effects of its own in mammals. Here we show that AOX can be safely expressed in the mouse (MitAOX), with major physiological parameters being unaffected. It neither disrupted the activity of other RC components nor decreased oxidative phosphorylation in isolated mitochondria. It conferred cyanide-resistance to mitochondrial substrate oxidation and decreased reactive oxygen species (ROS) production upon RC blockade. Accordingly, AOX expression was able to support cyanide-resistant respiration by intact organs and to afford prolonged protection against a lethal concentration of gaseous cyanide in whole animals. Taken together, these results indicate that AOX expression in the mouse is innocuous and permits to overcome a RC blockade, while reducing associated oxidative insult. Therefore, the MitAOX mice represent a valuable tool in order to investigate the ability of AOX to counteract the panoply of mitochondrial-inherited diseases originating from oxidative phosphorylation defects.