Miracle-dz/UniFAR-FTUs huggingface dataset:
Sickle cell disease: a neglected chronic disease of increasing global health importance

Sickle cell disease (SCD) is a single gene disorder causing a debilitating systemic syndrome characterised by chronic anaemia, acute painful episodes, organ infarction and chronic organ damage and by a significant reduction in life expectancy. The origin of SCD lies in the malarial regions of the tropics where carriers are protected against death from malaria and hence enjoy an evolutionary advantage. More recently, population migration has meant that SCD now has a worldwide distribution and that a substantial number of children are born with the condition in higher-income areas, including large parts of Europe and North and South America. Newborn screening, systematic clinical follow-up and prevention of sepsis and organ damage have led to an increased life expectancy among people with SCD in many such countries; however, in resource-limited settings where the majority continue to be born, most affected children continue to die in early childhood, usually undiagnosed, due to the lack of effective programmes for its early detection and treatment. As new therapies emerge, potentially leading to disease amelioration or cure, it is of paramount importance that the significant burden of SCD in resource-poor countries is properly recognised.
Cohort study of sibling effect, infectious diseases, and risk of atopic dermatitis during first 18 months of life

Abstract Objectives To determine whether early infectious diseases could explain the association between number of siblings and other markers of microbial exposure and the development of atopic dermatitis before the age of 18 months. Design Cohort study. Information on atopic dermatitis, infectious diseases occurring before 6 months of age, number of siblings, early day care, pet keeping, farm residence, and background factors was collected in telephone interviews. Setting Danish national birth cohort. Participants 24 341 mother-child pairs. Main outcome measures Incidence rate ratios of atopic dermatitis. Results 13 070 children (54%) had at least one clinically apparent infectious disease before 6 months of age. At age 18 months, 2638 (10.8%) of the children had had atopic dermatitis. The risk of atopic dermatitis increased with each infectious disease before 6 months of age (incidence rate ratio 1.08, 95% confidence interval 1.04 to 1.13). The risk of atopic dermatitis decreased with each additional exposure to three or more siblings, day care, pet ownership, and farm residence (0.86, 0.81 to 0.93). Conclusions Early infections do not seem to protect against allergic diseases. The protective effect of number of siblings, day care, pet ownership, and farm residence remained after adjustment for clinically apparent infectious diseases, suggesting that the effect is established independently early in life.
Toward Informed Resource Management in the Cloud

More and more companies are externalizing their computing infrastructures to the cloud to reduce the increasing maintenance cost of computing environments. Minimizing the amount of hardware resource and power consumption in use is one of the main services that such a cloud infrastructure must ensure. This objective can be done either by the customer at the application level (by dynamically sizing the application based on the workload), or by the provider at the virtualization level (by consolidating virtual machines based on the infrastructure's utilization rate). Many research works investigate resource management policies separately. In this paper, we show that the different strategies for cloud resource management, including server consolidation only, dynamic application sizing only, both policies at the same time, do not fully bring benefits to the cloud actors when being implemented without cooperation. Finally, we propose and evaluate a cooperative model to combine the efficiency from these strategies in reducing power consumption and keeping application's Quality of Service.
Skyline localization for mountain images

In this paper, we propose a novel method for automatically locating the skyline that represents the shape of mountains. The appearances of mountain and sky are variable because of the weather, season or region. In order to extract the skyline of mountains under complicated and variable circumstances, support vector machine (SVM) is applied for the prediction of a part of the skyline between sky region and mountain region by using the color, statistics features and location information of edge. Then, the linking of incomplete fragments of skyline is formulated as a shortest path problem and solved by dynamic programming strategy. Our experimental results demonstrate that the proposed method is accurate and robust.
Complementary clinical effects of red complex bacteria on generalized periodontitis in a caucasian population.

OBJECTIVES To relate five periodontopathogenic bacteria, including the red complex, to the severity, extent, and inflammation of the periodontal lesion in Caucasian patients with generalized aggressive and chronic periodontitis and to explore whether tobacco use is associated with a specific bacterial profile. MATERIALS AND METHODS A cross-sectional and analytic study was conducted in patients with aggressive and chronic periodontitis. Data were gathered on socio-demographic and periodontal variables, and RH-PCR was used to determine subgingival bacterial profile. Linear and logistic regression analyses were performed. RESULTS The study included 60 patients with aggressive and 123 with chronic periodontitis. Total red complex bacteria count was higher in aggressive periodontitis, mainly due to T. denticola (P = 0.015). In both periodontitis types, models showed an association between T. forsythia count and probing depth (B = 0.157, P = 0.030) and between T. denticola count and higher bleeding scores (B = 2.371, P = 0.027). Smoking did not affect the red complex bacteria count in either disease. CONCLUSIONS The prevalence of red complex bacteria was similar between aggressive and chronic periodontitis, but their count was higher in the former. In both diseases, T. forsythia was associated with greater severity and T. denticola with more severe bleeding. Tobacco smoking was not associated with the presence of red complex bacteria in either disease.
Las grandes empresas y la conciliación de la vida laboral y personal en España

La promoción de un contexto social más favorable a la conciliación de la vida laboral y personal (en adelante nos referiremos a ello sólo como «conciliación») juega un papel destacado en la política de igualdad de oportunidades de hombres y mujeres y de no discriminación por razón de género. En este sentido, este objetivo ha ido adquiriendo cada vez mayor centralidad en los planes de igualdad de oportunidades tanto a nivel de la Unión Europea como en la de los Estados miembros. Para la Unión Europea, la promoción de la conciliación de la vida privada, y especialmente de la vida familiar, y la vida laboral de los ciudadanos de la Unión se ha convertido en un objetivo prioritario por el profundo alcance que tienen las dificultades de conciliar ambos espacios. Los efectos negativos de estas dificultades se hacen sentir no sólo en la calidad de vida y en la salud de los trabajadores, sino también a lo largo de toda la estructura social y en la economía, y no sólo a corto, sino también a largo plazo. La caída de la fecundidad, el envejecimiento de la población, la viabilidad de los sistemas de protección social, la disponibilidad de una mano de obra cualificada y productiva, la retención del talento en las empresas, entre otros aspectos, se asocian cada vez más a las dificultades de conciliación.
Segment boundary estimation using recurrent neural networks

This paper describes a segment (e.g. phoneme) bound-aryestimation method based on recurrentneural networks (RNNs). The proposed method only requires acoustic observations to accurately estimate segment boundaries. Experimental results show that the proposed method can estimate segment boundaries signi(cid:12)cantly better than an HMM based method. Furthermore, we incorporate the RNN based segment boundary estimator into the HMM based and segment based recognition systems. As a result, the segment boundary estimates give useful information for reducing computational complexity and improving recognition performance.
A capacitor-free CMOS low-dropout voltage regulator

This paper presents novel frequency compensation techniques for low-dropout (LDO) voltage regulator. An enhanced active feedback frequency compensation is employed to improve its frequency response. This LDO can provide high stability for loading current range up to 100 mA without loading capacitors. Moreover, the total compensation capacitors only require 7 pF for this technique. This allows us to integrate the compensation capacitors within the LDO chip easily. The proposed LDO regulator was designed using TSMC 0.35-µm CMOS technology. With an active area of 0.14 mm2, the quiescent current is only 40 µA. The input voltage is ranged from 1.73 V to 5 V for loading current of 100 mA and the output voltage of 1.5 V. The main advantage of this approach is that the LDO circuit can be stable when we connect external load capacitors with ultra low ESR, or even when we eliminate the load capacitors.
A novel comprehensive adaptive control of voltage & reactive power in substation

Manual operation in power substation's VQC is out of date, and the nine-area control method has been widely adopted, but there are problems and deficiency for this method. This paper improves the nine-area control method, and presents a novel comprehensive VQC control technology. With the help of high-performance MCU, it implements adaptive prediction of electrical parameters, and then fuzzy logic is applied to optimize the control strategy. The simulating experiments show that, this novel method can decrease the operations of the capacitors and the tap of loaded transformers, and the voltage quality can be enhanced too.
Relational Similarity Measurement between Word-pairs Using Multi-Task Lasso

Relational similarity measurement as a popular research area in the field of natural language processing, is widely used in information retrieval, word sense disambiguation, machine translation and so on. The existing approaches are mostly based on extracting semantic features as feature matrixes from the large-scale corpus and using the corresponding method to process these feature matrixes to compute the relational similarity between word-pairs. However, the extracted semantic features are loosely distributed, which make the sparseness of feature matrixes. This paper proposes a Multi-Task Lasso based Relational similarity measure method (MTLRel), which makes snippets retrieved from a web search engine as the semantic information sources of a word-pair, then builds the feature matrix by extracting predefined patterns from snippets, compress and denoise the feature matrix into a feature vector using a multi-task lasso method, finally measures the relational similarity between two word-pairs by computing the cosine of the angle between two feature vectors. The MTLRel approach achieves an accuracy rate of 50.3% by testing 374 SAT analogy questions with lower time consumption.
Nonlinear Earthquake Analysis of Reinforced Concrete Frames with Fiber and Bernoulli-Euler Beam-Column Element

A beam-column element based on the Euler-Bernoulli beam theory is researched for nonlinear dynamic analysis of reinforced concrete (RC) structural element. Stiffness matrix of this element is obtained by using rigidity method. A solution technique that included nonlinear dynamic substructure procedure is developed for dynamic analyses of RC frames. A predicted-corrected form of the Bossak-α method is applied for dynamic integration scheme. A comparison of experimental data of a RC column element with numerical results, obtained from proposed solution technique, is studied for verification the numerical solutions. Furthermore, nonlinear cyclic analysis results of a portal reinforced concrete frame are achieved for comparing the proposed solution technique with Fibre element, based on flexibility method. However, seismic damage analyses of an 8-story RC frame structure with soft-story are investigated for cases of lumped/distributed mass and load. Damage region, propagation, and intensities according to both approaches are researched.
Designing Effective Decision Support Using Decisional Guidance

This study explores the decision outcomes achieved by 122 subjects using a DSS for an insolvency decision making task. Decision quality improvements occur as a result of collaboration between the decision maker and the DSS at two crucial points in the decision making process. Firstly when the decision maker initially interacts with the DSS to generate a recommendation, and secondly when the decision maker decides whether to incorporate that recommendation into their decision. Good technology design will assist a decision maker to generate a high quality recommendation; however the ensuing acceptance or otherwise of the recommendation is what ultimately affects the decision outcome. The study encompasses exploration of DSS use, and how decision outcomes are subsequently impacted. The results show that providing decisional guidance, in particular suggestive guidance, helped novice decision makers produce higher quality recommendations; and that adoption of those recommendations improved decision quality. The results are consistent with the theoretical premise that a key design issue is providing appropriate guidance to fit the task and individual, rather than simply guidance per se. The results show decision performance is a function of both how the technology is designed and used; and whether the user incorporates the recommendation received into their final decision. DSS use is a necessary, but not sufficient, precursor to improved decision outcomes.
Big data gathering and mining pipelines for CRM using open-source

Customer Relationship Management (CRM) is currently the fastest growing sector of enterprise software, estimated to increase to $36.5B worldwide by 2017. CRM technologies increasingly use data mining primitives across multiple applications. At the same time, the growth of big data has led to the evolution of an open source big data software stack (primarily powered by Apache software) that rivals traditional enterprise database (RDBMS) stacks. New technologies such as Kafka, Storm, HBase have significantly enriched this open source stack, alongside more established technologies such as Hadoop MapReduce and Mahout. Today, enterprises have a choice to make regarding which stack they will choose to power their big data applications. However, there are no published studies in literature on enterprise big data pipelines built using open source components supporting CRM. Specific questions that enterprises have include: how is the data processed and analyzed in such pipelines? What are the building blocks of such pipelines? How long does each step of this processing take? In this work, we answer these questions for a large scale (serving over a 100M customers) industrial CRM pipeline that incorporates data mining, and serves several applications. Our pipeline has, broadly, two parts. The first is a data gathering part that uses Kafka, Storm, and HBase. The second is a data mining part that uses Mahout and Hadoop MapReduce. We also provide timings for common tasks in the second part such as data preprocessing for machine learning, clustering, reservoir sampling, and frequent itemset extraction.
MODELLING AND ANALYSIS TOOLS

The paper focuses on the differential interferometric SAR (Synthetic Aperture Radar) technique for the monitoring of terrain surface deformations. The paper begins with a concise description of the properties of the differential interferometric phase, which represents the main observation for the estimation of the deformations. Then the paper discusses of the main features of a new interferometric SAR procedure. In particular, the interferometric SAR processing, the least squares adjustment procedure to estimate the terrain deformations, and the geometric model that is needed to geocode the SAR products are described. The second part of the paper illustrates two applications of the proposed procedure. The first one is a screening analysis, whose main goal is the detection of unknown subsidence phenomena over a large area, based on a limited set of SAR images. The second one is a quantitative analysis of a urban subsidence of small spatial extent, which was based on two independent ascending and descending SAR datasets.
Comparison of ethyleneglycoltetraacetic acid and its magnesium salt as reagents for studying alternative complement pathway function

The divalent cation chelators, ethyleneglycoltetraacetic acid (EGTA) and its magnesium salt, MgEGTA, were compared in studies of alternative complement pathway function. EGTA (0.01 M) inhibited both the rate and the amount of complement activation by zymosan whether compared to nonchelated serum or to serum chelated with MgEGTA (0.01 M). The rate of alternative pathway activation by zymosan was slightly slower in MgEGTA-chelated serum than in nonchelated serum, but the overall amount of complement consumed by a given amount of zymosan was not decreased. MgEGTA chelation spontaneously activated the alternative pathway, as reflected by lysis of erythrocytes from a patient with paroxysmal nocturnal hemoglobinuria. No evidence could be found that MgEGTA either spontaneously activated C2 or facilitated zymosan activation of C2. Suggested guidelines for the use of these chelators are advanced.
A quality-based framework for physical data warehouse design

Data warehousing is a software infrastructure which supports OLAP applications by providing a collection of tools which allow data extraction and cleaning, data integration and aggregation, and data organization into multidimensional structures which are suitable for decision making. At the design level, a data warehouse is defined as a hierarchy of view expressions. One key problem of the design of a data warehouse is the selection of a set of views to materialize in order to meet the performance requirements, that is, to minimize both the evaluation cost of the users queries and the maintenance cost of the materialized expressions. Existing approaches either propose algorithms that explore the search space by enumerating and evaluating all the possible solutions, or introduce heuristics that restrict the search space. In this paper, we propose a framework to support the physical design of a data warehouse; it is based on a logical model, called the view synthesis graph. A level of overlapping is defined to characterize the graph. This level allows to order the search space. The framework integrates some quality factors that can be used either to drive the exploration of the search space, or to reduce the search space, or both.
Surface smoothing based on a sphere shape model

The smoothing of polyhedral surfaces is important for the analysis and visualization of three-dimensional medical images. Simple Gaussian surface smoothing has a wellknown drawback of causing surface shrinkage. This has led to various signal processing algorithms for surface mesh filtering. In this paper, a new approach to surface mesh smoothing without shrinkage is presented. The approach is based on a representation of sphere shaped simplex meshes. The surface smoothing is formulated as a quadratic optimization problem, which is shown to be uniquely solvable. The cost function to be optimized penalizes deviations from the sphere shape while maintaining the overall shape of the original mesh. A linear time gradient descent algorithm for solving the optimization problem is derived. The surface smoothing achieved this way is fast, simple, reversible, and does not cause surface shrinkage. We demonstrate good properties of the algorithm by filtering synthetic surfaces as well as surfaces extracted from three-dimensional medical images. In addition, quantitative comparisons to competing algorithms are made. These further illustrate the advantages of the proposed sphere filtering algorithm.
FAST: Quick Application Launch on Solid-State Drives

Application launch performance is of great importance to system platform developers and vendors as it greatly affects the degree of users' satisfaction. The single most effective way to improve application launch performance is to replace a hard disk drive (HDD) with a solid state drive (SSD), which has recently become affordable and popular. A natural question is then whether or not to replace the traditional HDD-aware application launchers with a new SSD-aware optimizer. We address this question by analyzing the inefficiency of the HDD-aware application launchers on SSDs and then proposing a new SSD-aware application prefetching scheme, called the Fast Application STarter (FAST). The key idea of FAST is to overlap the computation (CPU) time with the SSD access (I/O) time during an application launch. FAST is composed of a set of user-level components and system debugging tools provided by the Linux OS (operating system). In addition, FAST uses a system-call wrapper to automatically detect application launches. Hence, FAST can be easily deployed in any recent Linux versions without kernel recompilation. We implemented FAST on a desktop PC with a SSD running Linux 2.6. 32 OS and evaluated it by launching a set of widely-used applications, demonstrating an average of 28% reduction of application launch time as compared to PC without a prefetcher.
Flexible Answer Typing with Discriminative Preference Ranking

An important part of question answering is ensuring a candidate answer is plausible as a response. We present a flexible approach based on discriminative preference ranking to determine which of a set of candidate answers are appropriate. Discriminative methods provide superior performance while at the same time allow the flexibility of adding new and diverse features. Experimental results on a set of focused What . and Which . questions show that our learned preference ranking methods perform better than alternative solutions to the task of answer typing. A gain of almost 0.2 in MRR for both the first appropriate and first correct answers is observed along with an increase in precision over the entire range of recall.