Miracle-dz/UniFAR-FTUs huggingface dataset:
Migration of a library catalogue into RDA linked open data

The catalogue of the Biblioteca Virtual Miguel de Cervantes contains about 200,000 records which were originally created in compliance with the MARC21 standard. The entries in the catalogue have been recently migrated to a new relational database whose data model adheres to the conceptual models promoted by the International Federation of Library Associations and Institutions (IFLA), in particular, to the FRBR and FRAD specifications. The database content has been later mapped, by means of an automated procedure, to RDF triples which employ basically the RDA vocabulary (Resource Description and Access) to describe the entities, as well as their properties and relationships. This RDF-based semantic description of the catalogue is now accessible online through an interface which supports browsing and searching the information. Due to their open nature, these public data can be easily linked and used for new applications created by external developers and institutions. The methods applied for the automation of the conversion, which build upon open-source software components, are described here.
High-dimensional Gaussian graphical model selection: walk summability and local separation criterion

We consider the problem of high-dimensional Gaussian graphical model selection. We identify a set of graphs for which an efficient estimation algorithm exists, and this algorithm is based on thresholding of empirical conditional covariances. Under a set of transparent conditions, we establish structural consistency (or sparsistency) for the proposed algorithm, when the number of samples n = Ω(Jmin-2 log p), where p is the number of variables and Jmin is the minimum (absolute) edge potential of the graphical model. The sufficient conditions for sparsistency are based on the notion of walk-summability of the model and the presence of sparse local vertex separators in the underlying graph. We also derive novel non-asymptotic necessary conditions on the number of samples required for sparsistency.
Impact of correlation on predictive ability of biomarkers

In this paper, we investigate how the correlation structure of independent variables affects the discrimination of risk prediction model. Using multivariate normal data and binary outcome, we prove that zero correlation among predictors is often detrimental for discrimination in a risk prediction model and negatively correlated predictors with positive effect sizes are beneficial. A very high multiple R‐squared from regressing the new predictor on the old ones can also be beneficial. As a practical guide to new variable selection, we recommend to select predictors that have negative correlation with the risk score based on the existing variables. This step is easy to implement even when the number of new predictors is large. We illustrate our results by using real‐life Framingham data suggesting that the conclusions hold outside of normality. The findings presented in this paper might be useful for preliminary selection of potentially important predictors, especially is situations where the number of predictors is large. Copyright © 2013 John Wiley & Sons, Ltd.
A Case of Resistance: Herbicide-tolerant Soybeans in Bolivia

Introduction The adoption of herbicide-tolerant (HT) soybeans1 in Bolivia deepens our understanding of the dimensions of biotech crops in extremely poor countries. In 2009, Article 255 (#8) of the new Bolivian National Constitution (Gaceta Oficial del Estado Plurinacional de Bolivia, 2009) prohibited the importation, production, and commercialization of genetically modified organisms (GMOs) Article 408 added that the production, importation, and commercialization of transgenics shall be regulated by law. In fact, a law enacted in 2005 (Gaceta Oficial, 2005) authorized the commercialization of one event of glyphosate resistance. HT soybean is the only transgenic crop grown by farmers in Bolivia; the crop was introduced initially by farmers who brought the seed from Argentina and Brazil. The anti-GMO perspective of the national government and public groups has also been reinforced by direct incentives to grow conventional soybeans. As part of the People’s Trade Agreement (TCP) and the Bolivarian Alliance for the People of Our America (ALBA), the government of Venezuela furnished credit to small-scale farmers who produce and export non-HT soybeans (Bolpress, 2007). At the time of this research, very little was publicly known—even by the national oilseeds producers organization (Asociación de Productores de Oleaginosas y Trigo [ANAPO])—concerning the use of HT soybeans. The Bolivian constitution of 2009 requires periodic revision of the norms governing the use of this technology, taking social and economic considerations into account. In response to this requirement, the results of a pilot field study conducted in 2007-2008 are presented here.2 Soybean is a cash crop that is not native to Bolivia, but it is important to the Bolivian national economy as an industrial export crop. The mechanized organization of soybean production means that most growers are not smallholders by the global standards of hand-hoe agriculture but are of very small scale from the perspective of the soybean sector in the United States or any other Latin American producer of the crop. The region of Santa Cruz, where soybean production is concentrated, is one of the more economically developed, commer-
The WebShop E-Commerce Framework

This paper presents an e-commerce framework called WebShop, which was developed by the authors for the purpose of demonstrating the use of UML and the UML-F in the domain of Web applications. Thus, the WebShop is not regarded as a full-fledged system out of which real Web stores can be derived. For example, the framework in the presented version does not encounter security features. However, it presents the most important variation points related to online catalogs. The UML-F Web site http://www.UML-F.net provides the Java source files and some sample adaptations of WebShop.
Neural Clinical Paraphrase Generation with Attention

Paraphrase generation is important in various applications such as search, summarization, and question answering due to its ability to generate textual alternatives while keeping the overall meaning intact. Clinical paraphrase generation is especially vital in building patient-centric clinical decision support (CDS) applications where users are able to understand complex clinical jargons via easily comprehensible alternative paraphrases. This paper presents Neural Clinical Paraphrase Generation (NCPG), a novel approach that casts the task as a monolingual neural machine translation (NMT) problem. We propose an end-to-end neural network built on an attention-based bidirectional Recurrent Neural Network (RNN) architecture with an encoder-decoder framework to perform the task. Conventional bilingual NMT models mostly rely on word-level modeling and are often limited by out-of-vocabulary (OOV) issues. In contrast, we represent the source and target paraphrase pairs as character sequences to address this limitation. To the best of our knowledge, this is the first work that uses attention-based RNNs for clinical paraphrase generation and also proposes an end-to-end character-level modeling for this task. Extensive experiments on a large curated clinical paraphrase corpus show that the attention-based NCPG models achieve improvements of up to 5.2 BLEU points and 0.5 METEOR points over a non-attention based strong baseline for word-level modeling, whereas further gains of up to 6.1 BLEU points and 1.3 METEOR points are obtained by the character-level NCPG models over their word-level counterparts. Overall, our models demonstrate comparable performance relative to the state-of-the-art phrase-based non-neural models.
Determinants of Customer Loyalty in China C2C E-Commerce: From a Social Network Perspective

China’s C2C electronic market has been growing very rapidly. The largest Chinese online auction company, Eachnet, was purchased by eBay but faced tough competition from another Chinese company, Taobao. Taobao’s market share is now twice as large as eBay’s. Why has eBay quickly lost its market share to newly-launched Taobao? This paper developed and tested a theoretical model that explains the effects of social relation and customer value on online customer loyalty and shopping behavior. This model was tested using empirical data (N=224) and PLS-Graph software. Results show that customers’ trust networks and customer value significantly influenced their loyalty to vendors, while customers’ affect networks and loyalty significantly influenced their shopping behaviors. The results suggest that the current C2C electronic market in China is a social environment where customers’ loyalty to a vendor can be built more easily by the vendor’s social relation management services than by transaction services. These findings advance theory by understanding online consumer loyalty from a social network perspective. The present study contributes by explaining the determinants of consumer’s shopping behavior and loyalty in China C2C Electronic Commerce.
Gemini: An Adaptive Performance-Fairness Scheduler for Data-Intensive Cluster Computing

In data-intensive cluster computing platforms such as Hadoop YARN, performance and fairness are two important factors for system design and optimizations. Many previous studies are either for performance or for fairness solely, without considering the tradeoff between performance and fairness. Recent studies observe that there is a tradeoff between performance and fairness because of resource contention between users/jobs. However, their scheduling algorithms for bi-criteria optimization between performance and fairness are static, without considering the impact of different workload characteristics on the tradeoff between performance and fairness. In this paper, we propose an adaptive scheduler called Gemini for Hadoop YARN. We first develop a model with the regression approach to estimate the performance improvement and the fairness loss under the sharing computation compared to the exclusive non-sharing scenario. Next, we leverage the model to guide the resource allocation for pending tasks to optimize the performance of the cluster given the user-defined fairness level. Instead of using a static scheduling policy, Gemini adaptively decides the proper scheduling policy according to the current running workload. We implement Gemini in Hadoop YARN. Experimental results show that Gemini outperforms the state-of-the-art approach in two aspects. 1) For the same fairness loss, Gemini improves the performance by up to 225% and 200% in real deployment and the large-scale simulation, respectively, 2) For the same performance improvement, Gemini reduces the fairness loss up to 70% and 62.5% in real deployment and the large-scale simulation, respectively.
Parallel Hierarchical Solvers and Preconditioners for Boundary Element Methods

The method of moments is an important tool for solving boundary integral equations arising in a variety of applications. It transforms the physical problem into a dense linear system. Due to the large number of variables and the associated computational requirements, these systems are solved iteratively using methods such as GMRES, CG and its variants. The core operation of these iterative solvers is the application of the system matrix to a vector. This requires θ(n^2) operations and memory using accurate dense methods. The computational complexity can be reduced to O(nlog n) and the memory requirement to θ(n) using hierarchical approximation techniques. The algorithmic speedup from approximation can be combined with parallelism to yield very fast dense solvers. In this paper, we present efficient parallel formulations of dense iterative solvers based on hierarchical approximations for solving the integral form of Laplace equation. We study the impact of various parameters on the accuracy and performance of the parallel solver. We present two preconditioning techniques for accelerating the convergence of the iterative solver. These techniques are based on an inner-outer scheme and a block diagonal scheme based on a truncated Green's function. We present detailed experimental results on up to 256 processors of a Cray T3D.
Stochastic Constraint Programming

To model combinatorial decision problems involving uncertainty and probability, we introduce stochastic constraint programming. Stochastic constraint programs contain both decision variables (which we can set) and stochastic variables (which follow a probability distribution). They combine together the best features of traditional constraint satisfaction, stochastic integer programming, and stochastic satisfiability. We give a semantics for stochastic constraint programs, and propose a number of complete algorithms and approximation procedures. Finally, we discuss a number of extensions of stochastic constraint programming to relax various assumptions like the independence between stochastic variables, and compare with other approaches for decision making under uncertainty.
Lupus Nephritis: An Overview of Recent Findings

Lupus nephritis (LN) is one of the most serious complications of systemic lupus erythematosus (SLE) since it is the major predictor of poor prognosis. In susceptible individuals suffering of SLE, in situ formation and deposit of immune complexes (ICs) from apoptotic bodies occur in the kidneys as a result of an amplified epitope immunological response. IC glomerular deposits generate release of proinflammatory cytokines and cell adhesion molecules causing inflammation. This leads to monocytes and polymorphonuclear cells chemotaxis. Subsequent release of proteases generates endothelial injury and mesangial proliferation. Presence of ICs promotes adaptive immune response and causes dendritic cells to release type I interferon. This induces maturation and activation of infiltrating T cells, and amplification of Th2, Th1 and Th17 lymphocytes. Each of them, amplify B cells and activates macrophages to release more proinflammatory molecules, generating effector cells that cannot be modulated promoting kidney epithelial proliferation and fibrosis. Herein immunopathological findings of LN are reviewed.
Feature Preserving Point Set Surfaces based on Non‐Linear Kernel Regression

Moving least squares (MLS) is a very attractive tool to design effective meshless surface representations. However, as long as approximations are performed in a least square sense, the resulting definitions remain sensitive to outliers, and smooth‐out small or sharp features. In this paper, we address these major issues, and present a novel point based surface definition combining the simplicity of implicit MLS surfaces [ SOS04 , Kol05 ] with the strength of robust statistics. To reach this new definition, we review MLS surfaces in terms of local kernel regression, opening the doors to a vast and well established literature from which we utilize robust kernel regression. Our novel representation can handle sparse sampling, generates a continuous surface better preserving fine details, and can naturally handle any kind of sharp features with controllable sharpness. Finally, it combines ease of implementation with performance competing with other non‐robust approaches.
Transport layer performance in 5G mmWave cellular

The millimeter wave (mmWave) bands are likely to play a significant role in next generation cellular systems due to the possibility of very high throughput thanks to the availability of massive bandwidth and high-dimensional antennas. Especially in Non-Line-of-Sight conditions, significant variations in the received RF power can occur as a result of the scattering from nearby building and terrain surfaces. Scattering objects come and go as the user moves through the local environment. At the higher end of the mmWave band, rough surface scatter generates cluster-based small-scale fading, where signal levels can vary by more than 20 dB over just a few wavelengths. This high level of channel variability may present significant challenges for congestion control. Using our recently developed end-to-end mmWave ns3-based framework, this paper presents the first performance evaluation of TCP congestion control in next-generation mmWave networks. Importantly, the framework can incorporate detailed models of the mmWave channel, beamforming and tracking algorithms, and builds on statistical channel models derived from real measurements in New York City, as well as detailed ray traces.
Outcomes of Living and Deceased Donor Liver Transplant Recipients With Hepatocellular Carcinoma: Results of the A2ALL Cohort †

Hepatocellular carcinoma (HCC) represents an increasing fraction of liver transplant indications; the role of living donor liver transplant (LDLT) remains unclear. In the Adult‐to‐Adult Living Donor Liver Transplantation Cohort Study, patients with HCC and an LDLT or deceased donor liver transplant (DDLT) for which at least one potential living donor had been evaluated were compared for recurrence and posttransplant mortality rates. Mortality from date of evaluation of each recipient's first potential living donor was also analyzed. Unadjusted 5‐year HCC recurrence was significantly higher after LDLT (38%) than DDLT (11%), (p = 0.0004). After adjustment for tumor characteristics, HCC recurrence remained significantly different between LDLT and DDLT recipients (hazard ratio (HR) = 2.35; p = 0.04) for the overall cohort but not for recipients transplanted following the introduction of MELD prioritization. Five‐year posttransplant survival was similar in LDLT and DDLT recipients from time of transplant (HR = 1.32; p = 0.27) and from date of LDLT evaluation (HR = 0.73; p = 0.36). We conclude that the higher recurrence observed after LDLT is likely due to differences in tumor characteristics, pretransplant HCC management and waiting time.
RDFind: Scalable Conditional Inclusion Dependency Discovery in RDF Datasets

Inclusion dependencies (INDs) form an important integrity constraint on relational databases, supporting data management tasks, such as join path discovery and query optimization. Conditional inclusion dependencies (CINDs), which define including and included data in terms of conditions, allow to transfer these capabilities to RDF data. However, CIND discovery is computationally much more complex than IND discovery and the number of CINDs even on small RDF datasets is intractable. To cope with both problems, we first introduce the notion of pertinent CINDs with an adjustable relevance criterion to filter and rank CINDs based on their extent and implications among each other. Second, we present RDFind, a distributed system to efficiently discover all pertinent CINDs in RDF data. RDFind employs a lazy pruning strategy to drastically reduce the CIND search space. Also, its exhaustive parallelization strategy and robust data structures make it highly scalable. In our experimental evaluation, we show that RDFind is up to 419 times faster than the state-of-the-art, while considering a more general class of CINDs. Furthermore, it is capable of processing a very large dataset of billions of triples, which was entirely infeasible before.
Realisations and alternations in German /r/-realisation

Rhotics, generally believed to be phonetically heterogeneous – are usually classified into one group of sounds due to their similar phonological behaviour and their diachronic and synchronic alternation [13, 12, 31, and 1]. Previous research has shown considerable segmental variation, especially in the realisation of /r/ in German which lead some authors to conclude that a positive description of the German phoneme /r/ does not make sense [11].
A Survey of Unstructured Mesh Generation Technology

AbstractA brief survey of some of the fundamental algorithms in unstructured mesh generation is presented.Included is a discussion and categorization of triangle, tetrahedral, quadrilateral and hexahedral meshgeneration methods currently in use in academia and industry. Also included is a brief discussion ofsmoothing, cleanup and refinement algorithms. An informal survey of currently available mesh generationsoftware is also provided comparing some of their main features.1. IntroductionAutomatic unstructured mesh generation is a relatively new field. Within its short life span we have seentremendous advances in many diverse fields. Once in a while, it is useful to step back from our ownexpertise and look at the entire picture of what is going on in the field. The purpose of this survey is togive some perspective to what the current trends are in mesh generation and outline some of the majortechnology areas, who is working in these fields and what software is available.Probably the simplest approach is to first break down the technology based on the shape of elementgenerated. We will consider triangle and quad generation methods in 2D and tetrahedral and hexahedralmethods in 3D. Straddled between 2D and 3D, we have surface meshing, which has it’s own set of issues.In addition we have another set of issues dealing with post processing of the mesh including smoothing,cleanup and refinement. Within each of these issues, have emerged a few clear categories of algorithms,which tend to dominate much of the literature and software. Not included in this survey are a wide varietyof equally important related topics such as adaptive, anisotropic and parallel mesh generation as well asdata structure and geometry management issues. Because of the immense scope of the field of unstructuredmesh generation, I have limited this survey to include what I consider the more fundamental aspects of thefield. Since I do not purport to be an expert in all fields of mesh generation, this will be at best, a cursorylook at the main issues in each category.1. 1 Software SurveyAs part of this paper, I conducted an informal survey of software vendors, research labs and educationalinstitutions that develop mesh and grid generation software. The purpose was to get a broad picture of whowas currently involved in developing software and what common algorithms were employed. The resultsof the survey are included as an appendix to this paper. They are also posted on the World Wide Web