978 resultados para Multi-class steganalysis
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Highly available software systems occasionally need to be updated while avoiding downtime. Dynamic software updates reduce down-time, but still require the system to reach a quiescent state in which a global update can be performed. This can be difficult for multi-threaded systems. We present a novel approach to dynamic updates using first-class contexts, called Theseus. First-class contexts make global updates unnecessary: existing threads run to termination in an old context, while new threads start in a new, updated context; consistency between contexts is ensured with the help of bidirectional transformations. We show that for multi-threaded systems with coherent memory, first-class contexts offer a practical and flexible approach to dynamic updates, with acceptable overhead.
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Decadal-to-century scale trends for a range of marine environmental variables in the upper mesopelagic layer (UML, 100–600 m) are investigated using results from seven Earth System Models forced by a high greenhouse gas emission scenario. The models as a class represent the observation-based distribution of oxygen (O2) and carbon dioxide (CO2), albeit major mismatches between observation-based and simulated values remain for individual models. By year 2100 all models project an increase in SST between 2 °C and 3 °C, and a decrease in the pH and in the saturation state of water with respect to calcium carbonate minerals in the UML. A decrease in the total ocean inventory of dissolved oxygen by 2% to 4% is projected by the range of models. Projected O2 changes in the UML show a complex pattern with both increasing and decreasing trends reflecting the subtle balance of different competing factors such as circulation, production, remineralization, and temperature changes. Projected changes in the total volume of hypoxic and suboxic waters remain relatively small in all models. A widespread increase of CO2 in the UML is projected. The median of the CO2 distribution between 100 and 600m shifts from 0.1–0.2 mol m−3 in year 1990 to 0.2–0.4 mol m−3 in year 2100, primarily as a result of the invasion of anthropogenic carbon from the atmosphere. The co-occurrence of changes in a range of environmental variables indicates the need to further investigate their synergistic impacts on marine ecosystems and Earth System feedbacks.
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PURPOSE Survivin is a member of the inhibitor-of-apoptosis family. Essential for tumor cell survival and overexpressed in most cancers, survivin is a promising target for anti-cancer immunotherapy. Immunogenicity has been demonstrated in multiple cancers. Nonetheless, few clinical trials have demonstrated survivin-vaccine-induced immune responses. EXPERIMENTAL DESIGN This phase I trial was conducted to test whether vaccine EMD640744, a cocktail of five HLA class I-binding survivin peptides in Montanide(®) ISA 51 VG, promotes anti-survivin T-cell responses in patients with solid cancers. The primary objective was to compare immunologic efficacy of EMD640744 at doses of 30, 100, and 300 μg. Secondary objectives included safety, tolerability, and clinical efficacy. RESULTS In total, 49 patients who received ≥2 EMD640744 injections with available baseline- and ≥1 post-vaccination samples [immunologic-diagnostic (ID)-intention-to-treat] were analyzed by ELISpot- and peptide/MHC-multimer staining, revealing vaccine-activated peptide-specific T-cell responses in 31 patients (63 %). This cohort included the per study protocol relevant ID population for the primary objective, i.e., T-cell responses by ELISpot in 17 weeks following first vaccination, as well as subjects who discontinued the study before week 17 but showed responses to the treatment. No dose-dependent effects were observed. In the majority of patients (61 %), anti-survivin responses were detected only after vaccination, providing evidence for de novo induction. Best overall tumor response was stable disease (28 %). EMD640744 was well tolerated; local injection-site reactions constituted the most frequent adverse event. CONCLUSIONS Vaccination with EMD640744 elicited T-cell responses against survivin peptides in the majority of patients, demonstrating the immunologic efficacy of EMD640744.
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Hypothyroidism is a complex clinical condition found in both humans and dogs, thought to be caused by a combination of genetic and environmental factors. In this study we present a multi-breed analysis of predisposing genetic risk factors for hypothyroidism in dogs using three high-risk breeds-the Gordon Setter, Hovawart and the Rhodesian Ridgeback. Using a genome-wide association approach and meta-analysis, we identified a major hypothyroidism risk locus shared by these breeds on chromosome 12 (p = 2.1x10-11). Further characterisation of the candidate region revealed a shared ~167 kb risk haplotype (4,915,018-5,081,823 bp), tagged by two SNPs in almost complete linkage disequilibrium. This breed-shared risk haplotype includes three genes (LHFPL5, SRPK1 and SLC26A8) and does not extend to the dog leukocyte antigen (DLA) class II gene cluster located in the vicinity. These three genes have not been identified as candidate genes for hypothyroid disease previously, but have functions that could potentially contribute to the development of the disease. Our results implicate the potential involvement of novel genes and pathways for the development of canine hypothyroidism, raising new possibilities for screening, breeding programmes and treatments in dogs. This study may also contribute to our understanding of the genetic etiology of human hypothyroid disease, which is one of the most common endocrine disorders in humans.
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AIMS Transcatheter mitral valve replacement (TMVR) is an emerging technology with the potential to treat patients with severe mitral regurgitation at excessive risk for surgical mitral valve surgery. Multimodal imaging of the mitral valvular complex and surrounding structures will be an important component for patient selection for TMVR. Our aim was to describe and evaluate a systematic multi-slice computed tomography (MSCT) image analysis methodology that provides measurements relevant for transcatheter mitral valve replacement. METHODS AND RESULTS A systematic step-by-step measurement methodology is described for structures of the mitral valvular complex including: the mitral valve annulus, left ventricle, left atrium, papillary muscles and left ventricular outflow tract. To evaluate reproducibility, two observers applied this methodology to a retrospective series of 49 cardiac MSCT scans in patients with heart failure and significant mitral regurgitation. For each of 25 geometrical metrics, we evaluated inter-observer difference and intra-class correlation. The inter-observer difference was below 10% and the intra-class correlation was above 0.81 for measurements of critical importance in the sizing of TMVR devices: the mitral valve annulus diameters, area, perimeter, the inter-trigone distance, and the aorto-mitral angle. CONCLUSIONS MSCT can provide measurements that are important for patient selection and sizing of TMVR devices. These measurements have excellent inter-observer reproducibility in patients with functional mitral regurgitation.
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Cable-stayed bridges represent nowadays key points in transport networks and their seismic behavior needs to be fully understood, even beyond the elastic range of materials. Both nonlinear dynamic (NL-RHA) and static (pushover) procedures are currently available to face this challenge, each with intrinsic advantages and disadvantages, and their applicability in the study of the nonlinear seismic behavior of cable-stayed bridges is discussed here. The seismic response of a large number of finite element models with different span lengths, tower shapes and class of foundation soil is obtained with different procedures and compared. Several features of the original Modal Pushover Analysis (MPA) are modified in light of cable-stayed bridge characteristics, furthermore, an extension of MPA and a new coupled pushover analysis (CNSP) are suggested to estimate the complex inelastic response of such outstanding structures subjected to multi-axial strong ground motions.
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El audio multicanal ha avanzado a pasos agigantados en los últimos años, y no solo en las técnicas de reproducción, sino que en las de capitación también. Por eso en este proyecto se encuentran ambas cosas: un array microfónico, EigenMike32 de MH Acoustics, y un sistema de reproducción con tecnología Wave Field Synthesis, instalado Iosono en la Jade Höchscule Oldenburg. Para enlazar estos dos puntos de la cadena de audio se proponen dos tipos distintos de codificación: la reproducción de la toma horizontal del EigenMike32; y el 3er orden de Ambisonics (High Order Ambisonics, HOA), una técnica de codificación basada en Armónicos Esféricos mediante la cual se simula el campo acústico en vez de simular las distintas fuentes. Ambas se desarrollaron en el entorno Matlab y apoyadas por la colección de scripts de Isophonics llamada Spatial Audio Matlab Toolbox. Para probar éstas se llevaron a cabo una serie de test en los que se las comparó con las grabaciones realizadas a la vez con un Dummy Head, a la que se supone el método más aproximado a nuestro modo de escucha. Estas pruebas incluían otras grabaciones hechas con un Doble MS de Schoeps que se explican en el proyecto “Sally”. La forma de realizar éstas fue, una batería de 4 audios repetida 4 veces para cada una de las situaciones garbadas (una conversación, una clase, una calle y un comedor universitario). Los resultados fueron inesperados, ya que la codificación del tercer orden de HOA quedo por debajo de la valoración Buena, posiblemente debido a la introducción de material hecho para un array tridimensional dentro de uno de 2 dimensiones. Por el otro lado, la codificación que consistía en extraer los micrófonos del plano horizontal se mantuvo en el nivel de Buena en todas las situaciones. Se concluye que HOA debe seguir siendo probado con mayores conocimientos sobre Armónicos Esféricos; mientras que el otro codificador, mucho más sencillo, puede ser usado para situaciones sin mucha complejidad en cuanto a espacialidad. In the last years the multichannel audio has increased in leaps and bounds and not only in the playback techniques, but also in the recording ones. That is the reason of both things being in this project: a microphone array, EigenMike32 from MH Acoustics; and a playback system with Wave Field Synthesis technology, installed by Iosono in Jade Höchscule Oldenburg. To link these two points of the audio chain, 2 different kinds of codification are proposed: the reproduction of the EigenMike32´s horizontal take, and the Ambisonics´ third order (High Order Ambisonics, HOA), a codification technique based in Spherical Harmonics through which the acoustic field is simulated instead of the different sound sources. Both have been developed inside Matlab´s environment and supported by the Isophonics´ scripts collection called Spatial Audio Matlab Toolbox. To test these, a serial of tests were made in which they were compared with recordings made at the time by a Dummy Head, which is supposed to be the closest method to our hearing way. These tests included other recording and codifications made by a Double MS (DMS) from Schoeps which are explained in the project named “3D audio rendering through Ambisonics techniques: from multi-microphone recordings (DMS Schoeps) to a WFS system, through Matlab”. The way to perform the tests was, a collection made of 4 audios repeated 4 times for each recorded situation (a chat, a class, a street and college canteen or Mensa). The results were unexpected, because the HOA´s third order stood under the Well valuation, possibly caused by introducing material made for a tridimensional array inside one made only by 2 dimensions. On the other hand, the codification that consisted of extracting the horizontal plane microphones kept the Well valuation in all the situations. It is concluded that HOA should keep being tested with larger knowledge about Spherical Harmonics; while the other coder, quite simpler, can be used for situations without a lot of complexity with regards to spatiality.
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Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models recently proposed to deal with multi-dimensional classification problems, where each instance in the data set has to be assigned to more than one class variable. In this paper, we propose a Markov blanket-based approach for learning MBCs from data. Basically, it consists of determining the Markov blanket around each class variable using the HITON algorithm, then specifying the directionality over the MBC subgraphs. Our approach is applied to the prediction problem of the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson’s Disease Questionnaire (PDQ-39) in order to estimate the health-related quality of life of Parkinson’s patients. Fivefold cross-validation experiments were carried out on randomly generated synthetic data sets, Yeast data set, as well as on a real-world Parkinson’s disease data set containing 488 patients. The experimental study, including comparison with additional Bayesian network-based approaches, back propagation for multi-label learning, multi-label k-nearest neighbor, multinomial logistic regression, ordinary least squares, and censored least absolute deviations, shows encouraging results in terms of predictive accuracy as well as the identification of dependence relationships among class and feature variables.
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In this paper, we propose a system for authenticating local bee pollen against fraudulent samples using image processing and classification techniques. Our system is based on the colour properties of bee pollen loads and the use of one-class classifiers to reject unknown pollen samples. The latter classification techniques allow us to tackle the major difficulty of the problem, the existence of many possible fraudulent pollen types. Also presented is a multi-classifier model with an ambiguity discovery process to fuse the output of the one-class classifiers. The method is validated by authenticating Spanish bee pollen types, the overall accuracy of the final system of being 94%. Therefore, the system is able to rapidly reject the non-local pollen samples with inexpensive hardware and without the need to send the product to the laboratory.
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The educational platform Virtual Science Hub (ViSH) has been developed as part of the GLOBAL excursion European project. ViSH (http://vishub.org/) is a portal where teachers and scientist interact to create virtual excursions to science infrastructures. The main motivation behind the project was to connect teachers - and in consequence their students - to scientific institutions and their wide amount of infrastructures and resources they are working with. Thus the idea of a hub was born that would allow the two worlds of scientists and teachers to connect and to innovate science teaching. The core of the ViSH?s concept design is based on virtual excursions, which allow for a number of pedagogical models to be applied. According to our internal definition a virtual excursion is a tour through some digital context by teachers and pupils on a given topic that is attractive and has an educational purpose. Inquiry-based learning, project-based and problem-based learning are the most prominent approaches that a virtual excursion may serve. The domain specific resources and scientific infrastructures currently available on the ViSH are focusing on life sciences, nano-technology, biotechnology, grid and volunteer computing. The virtual excursion approach allows an easy combination of these resources into interdisciplinary teaching scenarios. In addition, social networking features support the users in collaborating and communicating in relation to these excursions and thus create a community of interest for innovative science teaching. The design and development phases were performed following a participatory design approach. An important aspect in this process was to create design partnerships amongst all actors involved, researchers, developers, infrastructure providers, teachers, social scientists, and pedagogical experts early in the project. A joint sense of ownership was created and important changes during the conceptual phase were implemented in the ViSH due to early user feedback. Technology-wise the ViSH is based on the latest web technologies in order to make it cross-platform compatible so that it works on several operative systems such as Windows, Mac or Linux and multi-device accessible, such as desktop, tablet and mobile devices. The platform has been developed in HTML5, the latest standard for web development, assuring that it can run on any modern browser. In addition to social networking features a core element on the ViSH is the virtual excursions editor. It is a web tool that allows teachers and scientists to create rich mash-ups of learning resources provided by the e-Infrastructures (i.e. remote laboratories and live webcams). These rich mash-ups can be presented in either slides or flashcards format. Taking advantage of the web architecture supported, additional powerful components have been integrated like a recommendation engine to provide personalized suggestions about educational content or interesting users and a videoconference tool to enhance real-time collaboration like MashMeTV (http://www.mashme.tv/).
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Hoy en día, con la evolución continua y rápida de las tecnologías de la información y los dispositivos de computación, se recogen y almacenan continuamente grandes volúmenes de datos en distintos dominios y a través de diversas aplicaciones del mundo real. La extracción de conocimiento útil de una cantidad tan enorme de datos no se puede realizar habitualmente de forma manual, y requiere el uso de técnicas adecuadas de aprendizaje automático y de minería de datos. La clasificación es una de las técnicas más importantes que ha sido aplicada con éxito a varias áreas. En general, la clasificación se compone de dos pasos principales: en primer lugar, aprender un modelo de clasificación o clasificador a partir de un conjunto de datos de entrenamiento, y en segundo lugar, clasificar las nuevas instancias de datos utilizando el clasificador aprendido. La clasificación es supervisada cuando todas las etiquetas están presentes en los datos de entrenamiento (es decir, datos completamente etiquetados), semi-supervisada cuando sólo algunas etiquetas son conocidas (es decir, datos parcialmente etiquetados), y no supervisada cuando todas las etiquetas están ausentes en los datos de entrenamiento (es decir, datos no etiquetados). Además, aparte de esta taxonomía, el problema de clasificación se puede categorizar en unidimensional o multidimensional en función del número de variables clase, una o más, respectivamente; o también puede ser categorizado en estacionario o cambiante con el tiempo en función de las características de los datos y de la tasa de cambio subyacente. A lo largo de esta tesis, tratamos el problema de clasificación desde tres perspectivas diferentes, a saber, clasificación supervisada multidimensional estacionaria, clasificación semisupervisada unidimensional cambiante con el tiempo, y clasificación supervisada multidimensional cambiante con el tiempo. Para llevar a cabo esta tarea, hemos usado básicamente los clasificadores Bayesianos como modelos. La primera contribución, dirigiéndose al problema de clasificación supervisada multidimensional estacionaria, se compone de dos nuevos métodos de aprendizaje de clasificadores Bayesianos multidimensionales a partir de datos estacionarios. Los métodos se proponen desde dos puntos de vista diferentes. El primer método, denominado CB-MBC, se basa en una estrategia de envoltura de selección de variables que es voraz y hacia delante, mientras que el segundo, denominado MB-MBC, es una estrategia de filtrado de variables con una aproximación basada en restricciones y en el manto de Markov. Ambos métodos han sido aplicados a dos problemas reales importantes, a saber, la predicción de los inhibidores de la transcriptasa inversa y de la proteasa para el problema de infección por el virus de la inmunodeficiencia humana tipo 1 (HIV-1), y la predicción del European Quality of Life-5 Dimensions (EQ-5D) a partir de los cuestionarios de la enfermedad de Parkinson con 39 ítems (PDQ-39). El estudio experimental incluye comparaciones de CB-MBC y MB-MBC con los métodos del estado del arte de la clasificación multidimensional, así como con métodos comúnmente utilizados para resolver el problema de predicción de la enfermedad de Parkinson, a saber, la regresión logística multinomial, mínimos cuadrados ordinarios, y mínimas desviaciones absolutas censuradas. En ambas aplicaciones, los resultados han sido prometedores con respecto a la precisión de la clasificación, así como en relación al análisis de las estructuras gráficas que identifican interacciones conocidas y novedosas entre las variables. La segunda contribución, referida al problema de clasificación semi-supervisada unidimensional cambiante con el tiempo, consiste en un método nuevo (CPL-DS) para clasificar flujos de datos parcialmente etiquetados. Los flujos de datos difieren de los conjuntos de datos estacionarios en su proceso de generación muy rápido y en su aspecto de cambio de concepto. Es decir, los conceptos aprendidos y/o la distribución subyacente están probablemente cambiando y evolucionando en el tiempo, lo que hace que el modelo de clasificación actual sea obsoleto y deba ser actualizado. CPL-DS utiliza la divergencia de Kullback-Leibler y el método de bootstrapping para cuantificar y detectar tres tipos posibles de cambio: en las predictoras, en la a posteriori de la clase o en ambas. Después, si se detecta cualquier cambio, un nuevo modelo de clasificación se aprende usando el algoritmo EM; si no, el modelo de clasificación actual se mantiene sin modificaciones. CPL-DS es general, ya que puede ser aplicado a varios modelos de clasificación. Usando dos modelos diferentes, el clasificador naive Bayes y la regresión logística, CPL-DS se ha probado con flujos de datos sintéticos y también se ha aplicado al problema real de la detección de código malware, en el cual los nuevos ficheros recibidos deben ser continuamente clasificados en malware o goodware. Los resultados experimentales muestran que nuestro método es efectivo para la detección de diferentes tipos de cambio a partir de los flujos de datos parcialmente etiquetados y también tiene una buena precisión de la clasificación. Finalmente, la tercera contribución, sobre el problema de clasificación supervisada multidimensional cambiante con el tiempo, consiste en dos métodos adaptativos, a saber, Locally Adpative-MB-MBC (LA-MB-MBC) y Globally Adpative-MB-MBC (GA-MB-MBC). Ambos métodos monitorizan el cambio de concepto a lo largo del tiempo utilizando la log-verosimilitud media como métrica y el test de Page-Hinkley. Luego, si se detecta un cambio de concepto, LA-MB-MBC adapta el actual clasificador Bayesiano multidimensional localmente alrededor de cada nodo cambiado, mientras que GA-MB-MBC aprende un nuevo clasificador Bayesiano multidimensional. El estudio experimental realizado usando flujos de datos sintéticos multidimensionales indica los méritos de los métodos adaptativos propuestos. ABSTRACT Nowadays, with the ongoing and rapid evolution of information technology and computing devices, large volumes of data are continuously collected and stored in different domains and through various real-world applications. Extracting useful knowledge from such a huge amount of data usually cannot be performed manually, and requires the use of adequate machine learning and data mining techniques. Classification is one of the most important techniques that has been successfully applied to several areas. Roughly speaking, classification consists of two main steps: first, learn a classification model or classifier from an available training data, and secondly, classify the new incoming unseen data instances using the learned classifier. Classification is supervised when the whole class values are present in the training data (i.e., fully labeled data), semi-supervised when only some class values are known (i.e., partially labeled data), and unsupervised when the whole class values are missing in the training data (i.e., unlabeled data). In addition, besides this taxonomy, the classification problem can be categorized into uni-dimensional or multi-dimensional depending on the number of class variables, one or more, respectively; or can be also categorized into stationary or streaming depending on the characteristics of the data and the rate of change underlying it. Through this thesis, we deal with the classification problem under three different settings, namely, supervised multi-dimensional stationary classification, semi-supervised unidimensional streaming classification, and supervised multi-dimensional streaming classification. To accomplish this task, we basically used Bayesian network classifiers as models. The first contribution, addressing the supervised multi-dimensional stationary classification problem, consists of two new methods for learning multi-dimensional Bayesian network classifiers from stationary data. They are proposed from two different points of view. The first method, named CB-MBC, is based on a wrapper greedy forward selection approach, while the second one, named MB-MBC, is a filter constraint-based approach based on Markov blankets. Both methods are applied to two important real-world problems, namely, the prediction of the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors, and the prediction of the European Quality of Life-5 Dimensions (EQ-5D) from 39-item Parkinson’s Disease Questionnaire (PDQ-39). The experimental study includes comparisons of CB-MBC and MB-MBC against state-of-the-art multi-dimensional classification methods, as well as against commonly used methods for solving the Parkinson’s disease prediction problem, namely, multinomial logistic regression, ordinary least squares, and censored least absolute deviations. For both considered case studies, results are promising in terms of classification accuracy as well as regarding the analysis of the learned MBC graphical structures identifying known and novel interactions among variables. The second contribution, addressing the semi-supervised uni-dimensional streaming classification problem, consists of a novel method (CPL-DS) for classifying partially labeled data streams. Data streams differ from the stationary data sets by their highly rapid generation process and their concept-drifting aspect. That is, the learned concepts and/or the underlying distribution are likely changing and evolving over time, which makes the current classification model out-of-date requiring to be updated. CPL-DS uses the Kullback-Leibler divergence and bootstrapping method to quantify and detect three possible kinds of drift: feature, conditional or dual. Then, if any occurs, a new classification model is learned using the expectation-maximization algorithm; otherwise, the current classification model is kept unchanged. CPL-DS is general as it can be applied to several classification models. Using two different models, namely, naive Bayes classifier and logistic regression, CPL-DS is tested with synthetic data streams and applied to the real-world problem of malware detection, where the new received files should be continuously classified into malware or goodware. Experimental results show that our approach is effective for detecting different kinds of drift from partially labeled data streams, as well as having a good classification performance. Finally, the third contribution, addressing the supervised multi-dimensional streaming classification problem, consists of two adaptive methods, namely, Locally Adaptive-MB-MBC (LA-MB-MBC) and Globally Adaptive-MB-MBC (GA-MB-MBC). Both methods monitor the concept drift over time using the average log-likelihood score and the Page-Hinkley test. Then, if a drift is detected, LA-MB-MBC adapts the current multi-dimensional Bayesian network classifier locally around each changed node, whereas GA-MB-MBC learns a new multi-dimensional Bayesian network classifier from scratch. Experimental study carried out using synthetic multi-dimensional data streams shows the merits of both proposed adaptive methods.
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An effective K-12 science education is essential to succeed in future phases of the curriculum and the e-Infrastructures for education provide new opportunities to enhance it. This paper presents ViSH Viewer, an innovative web tool to consume educational content which aims to facilitate e-Science infrastructures access through a next generation learning object called "Virtual Excursion". Virtual Excursions provide a new way to explore science in class by taking advantage of e-Infrastructure resources and their integration with other educational contents, resulting in the creation of a reusable, interoperable and granular learning object. In order to better understand how this tool can allow teachers and students a joyful exploration of e-Science, we also present three Virtual Excursion examples. Details about the design, development and the tool itself are explained in this paper as well as the concept, structure and metadata of the new learning object.
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The multi-dimensional classification problem is a generalisation of the recently-popularised task of multi-label classification, where each data instance is associated with multiple class variables. There has been relatively little research carried out specific to multi-dimensional classification and, although one of the core goals is similar (modelling dependencies among classes), there are important differences; namely a higher number of possible classifications. In this paper we present method for multi-dimensional classification, drawing from the most relevant multi-label research, and combining it with important novel developments. Using a fast method to model the conditional dependence between class variables, we form super-class partitions and use them to build multi-dimensional learners, learning each super-class as an ordinary class, and thus explicitly modelling class dependencies. Additionally, we present a mechanism to deal with the many class values inherent to super-classes, and thus make learning efficient. To investigate the effectiveness of this approach we carry out an empirical evaluation on a range of multi-dimensional datasets, under different evaluation metrics, and in comparison with high-performing existing multi-dimensional approaches from the literature. Analysis of results shows that our approach offers important performance gains over competing methods, while also exhibiting tractable running time.
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Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these classes are often strongly correlated, modeling the dependencies between them allows MDC methods to improve their performance – at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies, one of the most popular and highest-performing methods for multi-label classification (MLC), a particular case of MDC which involves only binary classes (i.e., labels). The original CC algorithm makes a greedy approximation, and is fast but tends to propagate errors along the chain. Here we present novel Monte Carlo schemes, both for finding a good chain sequence and performing efficient inference. Our algorithms remain tractable for high-dimensional data sets and obtain the best predictive performance across several real data sets.
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Self-incompatibility in Brassica is controlled by a single multi-allelic locus (S locus), which contains at least two highly polymorphic genes expressed in the stigma: an S glycoprotein gene (SLG) and an S receptor kinase gene (SRK). The putative ligand-binding domain of SRK exhibits high homology to the secretory protein SLG, and it is believed that SLG and SRK form an active receptor kinase complex with a self-pollen ligand, which leads to the rejection of self-pollen. Here, we report 31 novel SLG sequences of Brassica oleracea and Brassica campestris. Sequence comparisons of a large number of SLG alleles and SLG-related genes revealed the following points. (i) The striking sequence similarity observed in an inter-specific comparison (95.6% identity between SLG14 of B. oleracea and SLG25 of B. campestris in deduced amino acid sequence) suggests that SLG diversification predates speciation. (ii) A perfect match of the sequences in hypervariable regions, which are thought to determine S specificity in an intra-specific comparison (SLG8 and SLG46 of B. campestris) and the observation that the hypervariable regions of SLG and SRK of the same S haplotype were not necessarily highly similar suggests that SLG and SRK bind different sites of the pollen ligand and that they together determine S specificity. (iii) Comparison of the hypervariable regions of SLG alleles suggests that intragenic recombination, together with point mutations, has contributed to the generation of the high level of sequence variation in SLG alleles. Models for the evolution of SLG/SRK are presented.