Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimated risk of transmission of the human immunodeficiency virus by screened blood in the United States.

BACKGROUND In the United States, transmission of the human immunodeficiency virus (HIV) by blood transfusion occurs almost exclusively when a recently infected blood donor is infectious but before antibodies to HIV become detectable (during the "window period"). We estimated the risk of HIV transmission caused by transfusion on the basis of the window period associated with the use of current, sensitive enzyme immunosorbent assays and recent data on HIV incidence among blood donors. METHODS We analyzed demographic and laboratory data on more than 4.1 million blood donations obtained in 1992 and 1993 in 19 regions served by the American National Red Cross, as well as the results of HIV-antibody tests of 4.9 million donations obtained in an additional 23 regions. RESULTS We estimated that, in the 19 study regions, 1 donation in every 360,000 (95 percent confidence interval, 210,000 to 1,140,000) was made during the window period. In addition, it is estimated that 1 in 2,600,000 donations was HIV-seropositive but was not identified as such because of an error in the laboratory. We estimated that 15 to 42 percent of window-period donations were discarded because they were seropositive on laboratory tests other than the HIV-antibody test. When these results were extrapolated to include the additional 23 Red Cross service regions, there was a risk of one case of HIV transmission for every 450,000 to 660,000 donations of screened blood. If the Red Cross centers are assumed to be representative of all U.S. blood centers, among the 12 million donations collected nationally each year an estimated 18 to 27 infectious donations are available for transfusion. CONCLUSIONS The estimated risk of transmitting HIV by the transfusion of screened blood is very small and nearly half that estimated previously, primarily because the sensitivity of enzyme immunosorbent assays has been improved.
A Knowledge Acquisition and Management System for Morphological Dictionaries

A system for the acquisition and management of reusable morphological dictionaries is clearly a useful tool for NLP. As such, most currently popular finite-state morphology systems have a number of drawbacks. In the development of Word Manager, these problems have been taken into account. As a result, its knowledge acquisition component is well-developed, and its knowledge representation enables more flexible use than typical finite-state systems.
Visualization of Src and FAK Activity during the Differentiation Process from HMSCs to Osteoblasts

Non-receptor protein kinases FAK and Src play crucial roles in regulating cellular adhesions, growth, migration and differentiation. However, it remains unclear how the activity of FAK and Src is regulated during the differentiation process from mesenchymal stem cells (MSCs) to bone cells. In this study, we used genetically encoded FAK and Src biosensors based on fluorescence resonance energy transfer (FRET) to monitor the FAK and Src activity in live cells during the differentiation process. The results revealed that the FAK activity increased after the induction of differentiation, which peaked around 20–27 days after induction. Meanwhile, the Src activity decreased continuously for 27 days after induction. Therefore, the results showed significant and differential changes of FAK and Src activity upon induction. This opposite trend between FAK and Src activation suggests novel and un-coupled Src/FAK functions during the osteoblastic differentiation process. These results should provide important information for the biochemical signals during the differentiation process of stem cells toward bone cells, which will advance our understanding of bone repair and tissue engineering.
Internet Engineering Task Force (ietf) an Eap Authentication Method Based on the Encrypted Key Exchange (eke) Protocol

The Extensible Authentication Protocol (EAP) describes a framework that allows the use of multiple authentication mechanisms. This document defines an authentication mechanism for EAP called EAP-EKE, based on the Encrypted Key Exchange (EKE) protocol. This method provides mutual authentication through the use of a short, easy to remember password. Compared with other common authentication methods, EAP-EKE is not susceptible to dictionary attacks. Neither does it require the availability of public-key certificates. (IETF). It represents the consensus of the IETF community. It has received public review and has been approved for publication by the Internet Engineering Steering Group (IESG). Not all documents approved by the IESG are a candidate for any level of Internet Standard; see Section 2 of RFC 5741. Information about the current status of this document, any errata, and how to provide feedback on it may be obtained at in effect on the date of publication of this document. Please review these documents carefully, as they describe your rights and restrictions with respect to this document. Code Components extracted from this document must include Simplified BSD License text as described in Section 4.e of the Trust Legal Provisions and are provided without warranty as described in the Simplified BSD License. This document may contain material from IETF Documents or IETF Contributions published or made publicly available before November 10, 2008. The person(s) controlling the copyright in some of this material may not have granted the IETF Trust the right to allow modifications of such material outside the IETF Standards Process. Without obtaining an adequate license from the person(s) controlling the copyright in such materials, this document may not be modified outside the IETF Standards Process, and derivative works of it may not be created outside the IETF Standards Process, except to format it for publication as an RFC or to translate it into languages other than English.
The Dark Side of Democracy: Explaining Ethnic Cleansing

1. The argument 2. Ethnic cleansing in former times 3. Two versions of 'we, the people' 4. Genocidal democracies in the New World 5. Armenia, I: into the danger zone 6. Armenia, II: genocide 7. Nazis, I: radicalization 8. Nazis, II: fifteen hundred perpetrators 9. Nazis, III: genocidal careers 10. Germany's allies and auxiliaries 11. Communist cleansing: Stalin, Mao, Pol Pot 12. Yugoslavia, I: into the danger zone 13. Yugoslavia, II: murderous cleansing 14. Rwanda, I: into the danger zone 15. Rwanda, II: genocide 16. Counterfactual cases: India and Indonesia 17. Combating ethnic cleansing in the world today.
Prediction and Ranking of Highly Popular Web Content

This thesis addresses prediction and ranking tasks using web content data. The main objective is to improve the ability to accurately predict and rank recent and highly popular content, thus enabling a faster and more precise recommendation of such items. The main motivation relates to the profusion of online content, and the increasing demand of users concerning a fast and easy access to relevant content. To fulfill these tasks an extensive review of previous work is carried out in order to define the state-of-the-art and to identify important research opportunities. As a result, three problems are identified and addressed in this thesis: (1) the lack of an interpretable and robust evaluation framework to correctly assess web content popularity prediction models focusing on highly popular web content; (2) issues concerning proposals of popularity prediction models and their ability to predict the rare cases of highly popular content; and (3) the need for recommendation frameworks concerning such items using multi-source data. For each of these problems novel solutions are proposed and extensively evaluated in comparison to existing work. The first problem (1) concerns the evaluation methods commonly used in web content popularity prediction tasks. According to previous work, the popularity of web content is best described by a heavy-tail distribution. As such, at any given moment, most of the content under analysis has a low level of popularity, and a small set of cases has high levels of popularity. Standard evaluation metrics focus on the average behaviour of the data, assuming that each case is equally relevant. Given the predictive focus on highly popular content, it is argued that such assumption may lead to an over-estimation of the models’ predictive accuracy. Therefore, an evaluation framework is proposed, allowing for a robust interpretation of the prediction models’ ability to accurately forecast highly popular web content. The second problem (2) is related to the fact that proposals concerning web content popularity prediction models are based on standard learning approaches. These are commonly biased towards capturing the dynamics of the majority of cases. Given the skewness of web content popularity data, this may lead to poor accuracy towards under-represented cases of highly
Tableau extensions for reasoning with link keys

Link keys allow for generating links across data sets expressed in different ontologies. But they can also be thought of as axioms in a description logic. As such, they can contribute to infer ABox axioms, such as links, or terminological axioms and other link keys. Yet, no reasoning support exists for link keys. Here we extend the tableau method designed for ALC to take link keys into account. We show how this extension enables combining link keys with terminological reasoning with and without ABox and TBox and generate non trivial link keys.
Exploratory Analysis of Graph Data by Leveraging Domain Knowledge

Given the soaring amount of data being generated daily, graph mining tasks are becoming increasingly challenging, leading to tremendous demand for summarization techniques. Feature selection is a representative approach that simplifies a dataset by choosing features that are relevant to a specific task, such as classification, prediction, and anomaly detection. Although it can be viewed as a way to summarize a graph in terms of a few features, it is not well-defined for exploratory analysis, and it operates on a set of observations jointly rather than conditionally (i.e., feature selection from many graphs vs. selection for an input graph conditioned on other graphs). In this work, we introduce EAGLE (Exploratory Analysis of Graphs with domain knowLEdge), a novel method that creates interpretable, feature-based, and domain-specific graph summaries in a fully automatic way. That is, the same graph in different domains–e.g., social science and neuroscience–will be described via different EAGLE summaries, which automatically leverage the domain knowledge and expectations. We propose an optimization formulation that seeks to find an interpretable summary with the most representative features for the input graph so that it is: diverse, concise, domain-specific, and efficient. Extensive experiments on synthetic and real-world datasets with up to ~1M edges and ~400 features demonstrate the effectiveness and efficiency of EAGLE and its benefits over existing methods. We also show how our method can be applied to various graph mining tasks, such as classification and exploratory analysis.
Saving Power in Video Playback on OLED Displays by Acceptable Changes to Perceived Brightness

Displays based on organic light-emitting diodes (OLEDs) are now widely used in mobile devices, in which they are major power consumers. The power drawn by an OLED display increases nonlinearly with sub-pixel intensities-thus reducing brightness saves appreciable power, but can displease users. This paper examines this tradeoff, and proposes a color blending scheme in which each frame is darkened in a way that reduces power consumption significantly while limiting the visual impact. The target lightness of frames is determined, in the LAB color space, from average intensities of the original frames; and these average values are in turn obtained by adaptive sampling, so as to reduce the computational overhead. An Android smartphone uses 12%-36% less power when running this scheme, compared to standard video playback, while a user evaluation suggested that reducing brightness to a certain extent can be largely unnoticed, or readily tolerated.
Twitter Sentiment Mining: A Multi Domain Analysis

Microblogging such as Twitter provides a rich source of information about products, personalities, and trends, etc. We proposed a simple methodology for analyzing sentiment of users in Twitter. First, we automatically collected Twitter corpus in positive and negative tweets. Second, we built a simple sentiment classifier by utilizing the Naive Bayes model to determine the positive and negative sentiment of a tweet. Third, we tested the classifier against a collection of users' opinions from five interesting domains of Twitter, i.e., news, finance, job, movies, and sport. The experimental results show that it is feasible to use Twitter corpus alone to classify new tweet for a certain domain applications.
Distributed service orchestration: Interweaving switch and service functions across domains

Orchestration across multiple domains is important for providing end-to-end services in software defined infrastructures. In this paper, we address the service function chain (SFC) mapping problem in a multi-domain network with nodes having different functions, i.e., service functions and/or switch functions. We present two different abstractions of the topology graph at the distributed orchestrators associated with each domain and propose vertex-centric distributed computing algorithms to compute SFC mappings. We implement the proposed algorithms in an open source vertex-centric distributed computing software, Giraph, and evaluate their performance.
Jawi Stemmer: Evaluation of Stemmers Based on Strength and Accuracy

Stemmers are important for Information Retrieval and essential for Natural Language Processing. Many researchers have focused on the effect of stemming on document retrieval and measured their stemmers with recall and precision. Applications in text mining (i.e., document indexing and clustering) require great accuracy in index terms. The aim of this paper is to evaluate a Jawi stemmer based on its strength and accuracy. To achieve the result, a control experiment was carried out. Using the results obtained from the experiments conducted in this study, the Jawi stemmer was deemed to be the best stemmer (accuracy = 85.08%), followed by the Abdullah stemmer (accuracy = 81.17%), and the Ahmad stemmer (accuracy = 78.67%).
Measurement Systems in the Hapex-Mobilhy Regional Evapotranspiration Experiment

The HAPEX-MOBILHY (Hydrologic-Atmospheric Pilot Experiment - Modelisation du Bilan Hydrique) regional evapotranspiration experiment was conducted in the south-west of France during the spring and summer of 1986. The measurement systems included: a) neutron probe monitoring of soil moisture content; b) energy balance stations; c) masts to measure profiles of humidity, temperature and wind; d) Bowen ration station; e) eddy correlation station above a forest canopy; f) weighing lysimeter; g) atmospheric soundings with balloons for temperature, humidity and pressure profiles; h) stream gaging and water table monitoring; i) aircraft equipment with remote sensing instrumentation; j) aircraft equipped with water vapor flux measuring instrumentation; and k) LANDSAT and SPOT satellites. This paper focuses on the ground-based measurement systems and the differences in time and spatial scales involved. A description of measurement techniques and comparisons of results for particular systems are given.
Supervised and unsupervised Web-based language model domain adaptation

Domain language model adaptation consists in re-estimating probabilities of a baseline LM in order to better match the specifics of a given broad topic of interest. To do so, a common strategy is to retrieve adaptation texts from the Web based on a given domain-representative seed text. In this paper, we study how the selection of this seed text influences the adaptation process and the performances of resulting adapted language models in automatic speech recognition. More precisely, the goal of this original study is to analyze the differences of our Web-based adaptation approach between the supervised case, in which the seed text is manually generated, and the unsupervised case, where the seed text is given by an automatic transcript. Experiments were carried out on data sourced from a real-world use case, more specifically, videos produced for a university YouTube channel. Results show that our approach is quite robust since the unsupervised adaptation provides similar performance to the supervised case in terms of the overall perplexity and word error rate.
Hyperthermia treatment of tumors by mesenchymal stem cell-delivered superparamagnetic iron oxide nanoparticles

Magnetic hyperthermia – a potential cancer treatment in which superparamagnetic iron oxide nanoparticles (SPIONs) are made to resonantly respond to an alternating magnetic field (AMF) and thereby produce heat – is of significant current interest. We have previously shown that mesenchymal stem cells (MSCs) can be labeled with SPIONs with no effect on cell proliferation or survival and that within an hour of systemic administration, they migrate to and integrate into tumors in vivo. Here, we report on some longer term (up to 3 weeks) post-integration characteristics of magnetically labeled human MSCs in an immunocompromized mouse model. We initially assessed how the size and coating of SPIONs dictated the loading capacity and cellular heating of MSCs. Ferucarbotran® was the best of those tested, having the best like-for-like heating capability and being the only one to retain that capability after cell internalization. A mouse model was created by subcutaneous flank injection of a combination of 0.5 million Ferucarbotran-loaded MSCs and 1.0 million OVCAR-3 ovarian tumor cells. After 2 weeks, the tumors reached ~100 µL in volume and then entered a rapid growth phase over the third week to reach ~300 µL. In the control mice that received no AMF treatment, magnetic resonance imaging (MRI) data showed that the labeled MSCs were both incorporated into and retained within the tumors over the entire 3-week period. In the AMF-treated mice, heat increases of ~4°C were observed during the first application, after which MRI indicated a loss of negative contrast, suggesting that the MSCs had died and been cleared from the tumor. This post-AMF removal of cells was confirmed by histological examination and also by a reduced level of subsequent magnetic heating effect. Despite this evidence for an AMF-elicited response in the SPION-loaded MSCs, and in contrast to previous reports on tumor remission in immunocompetent mouse models, in this case, no significant differences were measured regarding the overall tumor size or growth characteristics. We discuss the implications of these results on the clinical delivery of hyperthermia therapy to tumors and on the possibility that a preferred therapeutic route may involve AMF as an adjuvant to an autologous immune response.
Effect of Site of Shunt on Left Heart‐Volume Characteristics in Children with Ventricular Septal Defect and Patent Ductus Arteriosus

Quantitative cineangiocardiographic technics have been utilized to determine left ventricular (LV) and left atrial (LA) volumes and LV muscle mass in 58 patients with isolated ventricular septal defect, 14 of whom were infants less than 2 years old, and in 25 patients with isolated patent ductus arteriosus, 13 of whom were infants. Patients were divided according to the degree of left-to-right shunt into small (<35%), moderate (35 to 50%), and large (>50%) shunt groups. Data obtained in both the VSD and the PDA groups were compared with normal values. Left ventricular end-diastolic volume and mass and LA maximal volumes in the patients of both groups who had shunts of 35% or more were greater than normal and showed a linear increase with increasing left-to-right shunt. The LV ejection fraction was decreased from normal in infants with either VSD or PDA and a shunt of more than 50%. This variable was normal in older children with either VSD or PDA.Patients with an aortic or a ventricular defect and equivalent shunts did not differ significantly in terms of LV end-diastolic volume, LV mass, LV ejected fraction, or LA maximal volume, normalized for body surface area. Patients with a patent ductus demonstrated the following differences when compared with patients with ventricular septal defect: (1) elevated LV end-diastolic pressure, (2) elevated LV end-diastolic stress, and (3) elevated value for LV mass (/m2 BSA) per degree of shunt in children over 2 years of age with a patent ductus (P<0.05). These results indicate that left ventricular distensibility is decreased in patients with aortic left-to-right shunts compared to that in patients with ventricular left-to-right shunts of equivalent magnitude.
Enhancing the Utility of Generalization for Privacy Preserving Re-publication of Dynamic Datasets

Anonymized publication on static micro data can be achieved with heavy information loss by Generalization. An enhanced utility of Generalization known as Angelization produces the same level of anonymization but with minimal information loss. In reality, there may be a need to publish another version of micro data, after insertions and deletions. Anonymization is applicable to any generalization principles like k-Anonymity, l-diversity and t-closeness. Incremental m-invariance with Angelization preserves privacy in re-publication of dynamic micro data after insertions and deletions. Mondrian algorithm is used for the partitioning in Angelization. m-invariance also supports publication of marginals from the generalized micro data. KL-divergence is employed for quantifying the discrepancy of two distributions. The reconstruction error will be measured as the KL-divergence between the reconstructed distribution and the original distribution. Data reconstruction error is minimal in m-invariance with enhanced utility of Generalization. General Terms Retrieval Model, Information Search and Retrieval.
Modelling the Impacts of Land Use Change on Stream Flow in the Kimwarer Catchment Using SWAT

The Kimwarer River basin covers 138.2 km2. It has experienced ecosystem degradation due to extensive farming that has impacted on water yield. This study was undertaken to assess the impacts of land use changes on river flow using SWAT, a mathematical model that has the potential to predict the impact of land management practices on water at catchment scale. Current and historic flow data were collected for model calibration and validation. The model was then used to simulate stream flow for different land use and land cover scenarios by varying the extend of forest cover and agriculture. The model was successfully calibrated and validated for stream flow, and proved capable of predicting flow with R2 and NSE values of 0.79 and 0.31 respectively. During validation, the model predicted flows with R2 and NSE values of 0.70 and 0.50 respectively. For scenario analysis to determine the effect of land use change on stream flow, it was observed that runoff decreased with increase in forest cover, while base-flow increased. Introduction of terraces as a management operation on agricultural land reduced runoff by 46%. It is evident from the study that the current trend of land use change affects stream flow.