Diffusion Tensor Imaging in Alzheimer's disease

Attentional control and Information processing speed are central concepts in cognitive psychology and neuropsychology. Functional neuroimaging and neuropsychological assessment have depicted theoretical models considering attention as a complex and non-unitary process. One of its component processes, Attentional set-shifting ability, is commonly assessed using the Trail Making Test (TMT). Performance in the TMT decreases with increasing age in adults, Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD). Besides, speed of information processing (SIP) seems to modulate attentional performance. While neural correlates of attentional control have been widely studied, there are few evidences about the neural substrates of SIP in these groups of patients. Different authors have suggested that it could be a property of cerebral white matter, thus, deterioration of the white matter tracts that connect brain regions related to set-shifting may underlie the age-related, MCI and AD decrease in performance. The aim of this study was to study the anatomical dissociation of attentional and speed mechanisms. Diffusion tensor imaging (DTI) provides a unique insight into the cellular integrity of the brain, offering an in vivo view into the microarchitecture of cerebral white matter. At the same time, the study of ageing, characterized by white matter decline, provides the opportunity to study the anatomical substrates speeded or slowed information processing. We hypothesized that FA values would be inversely correlated with time to completion on Parts A and B of the TMT, but not the derived scores B/A and B-A.

In vitro reconstitution of light-harvesting POR-protochlorophyllide complex with protochlorophyllides a and b

NADPH:protochlorophyllide oxidoreductase (POR; EC1.1.33.1) is a key enzyme for the light-induced greening of angiosperms. In barley, two POR proteins exist, termed PORA and PORB. These have previously been proposed to form higher molecular weight light-harvesting complexes in the prolamellar body of etioplasts (Reinbothe, C., Lebedev, N., and Reinbothe, S. (1999)Nature 397, 80–84). Here we report the in vitro reconstitution of such complexes from chemically synthesized protochlorophyllides (Pchlides) a andb and galacto- and sulfolipids. Low temperature (77 K) fluorescence measurements revealed that the reconstituted, lipid-containing complex displayed the same characteristics of photoactive Pchlide 650/657 as the presumed native complex in the prolamellar body. Moreover, Pchlide F650/657 was converted to chlorophyllide (Chlide) 684/690 upon illumination of the reconstituted complex with a 1-ms flash of white light. Identification and quantification of acetone-extractable pigments revealed that only the PORB-bound Pchlide a had been photoactive and was converted to Chlide a, whereas Pchlide b bound to the PORA remained photoinactive. Nondenaturing PAGE of the reconstituted Pchlide a/b-containing complex further demonstrated a size similar to that of the presumed native complexin vivo, suggesting that both complexes may be identical.

Flexible low-cost system to test batteries and ultracapacitors for electric and hybrid vehicles in real working conditions

Batteries and ultracapacitors for hybrid and electric vehicles must satisfy very demanding working conditions that are not usual in other applications. In this sense, specific tests must be performed in order to draw accurate conclusions about their behaviour. To do so, new advanced test benches are needed. These platforms must allow the study of a wide variety of energy storage systems under conditions similar to the real ones. In this paper, a flexible, low-cost and highly customizable system is presented. This system allows batteries and ultracapacitors to be tested in many and varied ways, effectively emulating the working conditions that they face in an electric vehicle. The platform was specifically designed to study energy storage systems for electric and hybrid vehicles, meaning that it is suitable to test different systems in many different working conditions, including real driving cycles. This flexibility is achieved keeping the cost of the platform low, which makes the proposed test bench a feasible alternative for the industry. As an example of the functionality of the platform, a test consisting of a 17-minute ARTEMIS urban cycle with a NiMH battery pack is presented.

Optical absorption and applications of the ABO4 (A=Ca, Pb and B= Mo, W) semiconductors

Ternary molybdates and tungstates ABO4 (A=Ca, Pb and B= Mo, W) are a group of materials that could be used for a variety of optoelectronic applications. We present a study of the optoelectronic properties based on first-principles using several orbitaldependent one-electron potentials applied to several orbital subspaces. The optical properties are split into chemical-species contributions in order to quantify the microscopic contributions. Furthermore, the effect of using several one-electron potentials and orbital subspaces is analyzed. From the results, the larger contribution to the optical absorption comes from the B-O transitions. The possible use as multi-gap solar cell absorbents is analyzed.

OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web

OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web 1. INTRODUCTION. LINGUISTIC TOOLS AND ANNOTATIONS: THEIR LIGHTS AND SHADOWS Computational Linguistics is already a consolidated research area. It builds upon the results of other two major ones, namely Linguistics and Computer Science and Engineering, and it aims at developing computational models of human language (or natural language, as it is termed in this area). Possibly, its most well-known applications are the different tools developed so far for processing human language, such as machine translation systems and speech recognizers or dictation programs. These tools for processing human language are commonly referred to as linguistic tools. Apart from the examples mentioned above, there are also other types of linguistic tools that perhaps are not so well-known, but on which most of the other applications of Computational Linguistics are built. These other types of linguistic tools comprise POS taggers, natural language parsers and semantic taggers, amongst others. All of them can be termed linguistic annotation tools. Linguistic annotation tools are important assets. In fact, POS and semantic taggers (and, to a lesser extent, also natural language parsers) have become critical resources for the computer applications that process natural language. Hence, any computer application that has to analyse a text automatically and ‘intelligently’ will include at least a module for POS tagging. The more an application needs to ‘understand’ the meaning of the text it processes, the more linguistic tools and/or modules it will incorporate and integrate. However, linguistic annotation tools have still some limitations, which can be summarised as follows: 1. Normally, they perform annotations only at a certain linguistic level (that is, Morphology, Syntax, Semantics, etc.). 2. They usually introduce a certain rate of errors and ambiguities when tagging. This error rate ranges from 10 percent up to 50 percent of the units annotated for unrestricted, general texts. 3. Their annotations are most frequently formulated in terms of an annotation schema designed and implemented ad hoc. A priori, it seems that the interoperation and the integration of several linguistic tools into an appropriate software architecture could most likely solve the limitations stated in (1). Besides, integrating several linguistic annotation tools and making them interoperate could also minimise the limitation stated in (2). Nevertheless, in the latter case, all these tools should produce annotations for a common level, which would have to be combined in order to correct their corresponding errors and inaccuracies. Yet, the limitation stated in (3) prevents both types of integration and interoperation from being easily achieved. In addition, most high-level annotation tools rely on other lower-level annotation tools and their outputs to generate their own ones. For example, sense-tagging tools (operating at the semantic level) often use POS taggers (operating at a lower level, i.e., the morphosyntactic) to identify the grammatical category of the word or lexical unit they are annotating. Accordingly, if a faulty or inaccurate low-level annotation tool is to be used by other higher-level one in its process, the errors and inaccuracies of the former should be minimised in advance. Otherwise, these errors and inaccuracies would be transferred to (and even magnified in) the annotations of the high-level annotation tool. Therefore, it would be quite useful to find a way to (i) correct or, at least, reduce the errors and the inaccuracies of lower-level linguistic tools; (ii) unify the annotation schemas of different linguistic annotation tools or, more generally speaking, make these tools (as well as their annotations) interoperate. Clearly, solving (i) and (ii) should ease the automatic annotation of web pages by means of linguistic tools, and their transformation into Semantic Web pages (Berners-Lee, Hendler and Lassila, 2001). Yet, as stated above, (ii) is a type of interoperability problem. There again, ontologies (Gruber, 1993; Borst, 1997) have been successfully applied thus far to solve several interoperability problems. Hence, ontologies should help solve also the problems and limitations of linguistic annotation tools aforementioned. Thus, to summarise, the main aim of the present work was to combine somehow these separated approaches, mechanisms and tools for annotation from Linguistics and Ontological Engineering (and the Semantic Web) in a sort of hybrid (linguistic and ontological) annotation model, suitable for both areas. This hybrid (semantic) annotation model should (a) benefit from the advances, models, techniques, mechanisms and tools of these two areas; (b) minimise (and even solve, when possible) some of the problems found in each of them; and (c) be suitable for the Semantic Web. The concrete goals that helped attain this aim are presented in the following section. 2. GOALS OF THE PRESENT WORK As mentioned above, the main goal of this work was to specify a hybrid (that is, linguistically-motivated and ontology-based) model of annotation suitable for the Semantic Web (i.e. it had to produce a semantic annotation of web page contents). This entailed that the tags included in the annotations of the model had to (1) represent linguistic concepts (or linguistic categories, as they are termed in ISO/DCR (2008)), in order for this model to be linguistically-motivated; (2) be ontological terms (i.e., use an ontological vocabulary), in order for the model to be ontology-based; and (3) be structured (linked) as a collection of ontology-based bject, Predicate, Object> triples, as in the usual Semantic Web languages (namely RDF(S) and OWL), in order for the model to be considered suitable for the Semantic Web. Besides, to be useful for the Semantic Web, this model should provide a way to automate the annotation of web pages. As for the present work, this requirement involved reusing the linguistic annotation tools purchased by the OEG research group (http://www.oeg-upm.net), but solving beforehand (or, at least, minimising) some of their limitations. Therefore, this model had to minimise these limitations by means of the integration of several linguistic annotation tools into a common architecture. Since this integration required the interoperation of tools and their annotations, ontologies were proposed as the main technological component to make them effectively interoperate. From the very beginning, it seemed that the formalisation of the elements and the knowledge underlying linguistic annotations within an appropriate set of ontologies would be a great step forward towards the formulation of such a model (henceforth referred to as OntoTag). Obviously, first, to combine the results of the linguistic annotation tools that operated at the same level, their annotation schemas had to be unified (or, preferably, standardised) in advance. This entailed the unification (id. standardisation) of their tags (both their representation and their meaning), and their format or syntax. Second, to merge the results of the linguistic annotation tools operating at different levels, their respective annotation schemas had to be (a) made interoperable and (b) integrated. And third, in order for the resulting annotations to suit the Semantic Web, they had to be specified by means of an ontology-based vocabulary, and structured by means of ontology-based bject, Predicate, Object> triples, as hinted above. Therefore, a new annotation scheme had to be devised, based both on ontologies and on this type of triples, which allowed for the combination and the integration of the annotations of any set of linguistic annotation tools. This annotation scheme was considered a fundamental part of the model proposed here, and its development was, accordingly, another major objective of the present work. All these goals, aims and objectives could be re-stated more clearly as follows: Goal 1: Development of a set of ontologies for the formalisation of the linguistic knowledge relating linguistic annotation. Sub-goal 1.1: Ontological formalisation of the EAGLES (1996a; 1996b) de facto standards for morphosyntactic and syntactic annotation, in a way that helps respect the Attribute, Value> triple structure recommended for annotations in these works (which is isomorphic to the bject, Predicate, Object> triple structures used in the context of the Semantic Web). Sub-goal 1.2: Incorporation into this preliminary ontological formalisation of other existing standards and standard proposals relating the levels mentioned above, such as those currently under development within ISO/TC 37 (the ISO Technical Committee dealing with Terminology, which deals also with linguistic resources and annotations). Sub-goal 1.3: Generalisation and extension of the recommendations in EAGLES (1996a; 1996b) and ISO/TC 37 to the semantic level, for which no ISO/TC 37 standards have been developed yet. Sub-goal 1.4: Ontological formalisation of the generalisations and/or extensions obtained in the previous sub-goal as generalisations and/or extensions of the corresponding ontology (or ontologies). Sub-goal 1.5: Ontological formalisation of the knowledge required to link, combine and unite the knowledge represented in the previously developed ontology (or ontologies). Goal 2: Development of OntoTag’s annotation scheme, a standard-based abstract scheme for the hybrid (linguistically-motivated and ontological-based) annotation of texts. Sub-goal 2.1: Development of the standard-based morphosyntactic annotation level of OntoTag’s scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996a) and also the recommendations included in the ISO/MAF (2008) standard draft. Sub-goal 2.2: Development of the standard-based syntactic annotation level of the hybrid abstract scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996b) and the ISO/SynAF (2010) standard draft. Sub-goal 2.3: Development of the standard-based semantic annotation level of OntoTag’s (abstract) scheme. Sub-goal 2.4: Development of the mechanisms for a convenient integration of the three annotation levels already mentioned. These mechanisms should take into account the recommendations included in the ISO/LAF (2009) standard draft. Goal 3: Design of OntoTag’s (abstract) annotation architecture, an abstract architecture for the hybrid (semantic) annotation of texts (i) that facilitates the integration and interoperation of different linguistic annotation tools, and (ii) whose results comply with OntoTag’s annotation scheme. Sub-goal 3.1: Specification of the decanting processes that allow for the classification and separation, according to their corresponding levels, of the results of the linguistic tools annotating at several different levels. Sub-goal 3.2: Specification of the standardisation processes that allow (a) complying with the standardisation requirements of OntoTag’s annotation scheme, as well as (b) combining the results of those linguistic tools that share some level of annotation. Sub-goal 3.3: Specification of the merging processes that allow for the combination of the output annotations and the interoperation of those linguistic tools that share some level of annotation. Sub-goal 3.4: Specification of the merge processes that allow for the integration of the results and the interoperation of those tools performing their annotations at different levels. Goal 4: Generation of OntoTagger’s schema, a concrete instance of OntoTag’s abstract scheme for a concrete set of linguistic annotations. These linguistic annotations result from the tools and the resources available in the research group, namely • Bitext’s DataLexica (http://www.bitext.com/EN/datalexica.asp), • LACELL’s (POS) tagger (http://www.um.es/grupos/grupo-lacell/quees.php), • Connexor’s FDG (http://www.connexor.eu/technology/machinese/glossary/fdg/), and • EuroWordNet (Vossen et al., 1998). This schema should help evaluate OntoTag’s underlying hypotheses, stated below. Consequently, it should implement, at least, those levels of the abstract scheme dealing with the annotations of the set of tools considered in this implementation. This includes the morphosyntactic, the syntactic and the semantic levels. Goal 5: Implementation of OntoTagger’s configuration, a concrete instance of OntoTag’s abstract architecture for this set of linguistic tools and annotations. This configuration (1) had to use the schema generated in the previous goal; and (2) should help support or refute the hypotheses of this work as well (see the next section). Sub-goal 5.1: Implementation of the decanting processes that facilitate the classification and separation of the results of those linguistic resources that provide annotations at several different levels (on the one hand, LACELL’s tagger operates at the morphosyntactic level and, minimally, also at the semantic level; on the other hand, FDG operates at the morphosyntactic and the syntactic levels and, minimally, at the semantic level as well). Sub-goal 5.2: Implementation of the standardisation processes that allow (i) specifying the results of those linguistic tools that share some level of annotation according to the requirements of OntoTagger’s schema, as well as (ii) combining these shared level results. In particular, all the tools selected perform morphosyntactic annotations and they had to be conveniently combined by means of these processes. Sub-goal 5.3: Implementation of the merging processes that allow for the combination (and possibly the improvement) of the annotations and the interoperation of the tools that share some level of annotation (in particular, those relating the morphosyntactic level, as in the previous sub-goal). Sub-goal 5.4: Implementation of the merging processes that allow for the integration of the different standardised and combined annotations aforementioned, relating all the levels considered. Sub-goal 5.5: Improvement of the semantic level of this configuration by adding a named entity recognition, (sub-)classification and annotation subsystem, which also uses the named entities annotated to populate a domain ontology, in order to provide a concrete application of the present work in the two areas involved (the Semantic Web and Corpus Linguistics). 3. MAIN RESULTS: ASSESSMENT OF ONTOTAG’S UNDERLYING HYPOTHESES The model developed in the present thesis tries to shed some light on (i) whether linguistic annotation tools can effectively interoperate; (ii) whether their results can be combined and integrated; and, if they can, (iii) how they can, respectively, interoperate and be combined and integrated. Accordingly, several hypotheses had to be supported (or rejected) by the development of the OntoTag model and OntoTagger (its implementation). The hypotheses underlying OntoTag are surveyed below. Only one of the hypotheses (H.6) was rejected; the other five could be confirmed. H.1 The annotations of different levels (or layers) can be integrated into a sort of overall, comprehensive, multilayer and multilevel annotation, so that their elements can complement and refer to each other. • CONFIRMED by the development of: o OntoTag’s annotation scheme, o OntoTag’s annotation architecture, o OntoTagger’s (XML, RDF, OWL) annotation schemas, o OntoTagger’s configuration. H.2 Tool-dependent annotations can be mapped onto a sort of tool-independent annotations and, thus, can be standardised. • CONFIRMED by means of the standardisation phase incorporated into OntoTag and OntoTagger for the annotations yielded by the tools. H.3 Standardisation should ease: H.3.1: The interoperation of linguistic tools. H.3.2: The comparison, combination (at the same level and layer) and integration (at different levels or layers) of annotations. • H.3 was CONFIRMED by means of the development of OntoTagger’s ontology-based configuration: o Interoperation, comparison, combination and integration of the annotations of three different linguistic tools (Connexor’s FDG, Bitext’s DataLexica and LACELL’s tagger); o Integration of EuroWordNet-based, domain-ontology-based and named entity annotations at the semantic level. o Integration of morphosyntactic, syntactic and semantic annotations. H.4 Ontologies and Semantic Web technologies (can) play a crucial role in the standardisation of linguistic annotations, by providing consensual vocabularies and standardised formats for annotation (e.g., RDF triples). • CONFIRMED by means of the development of OntoTagger’s RDF-triple-based annotation schemas. H.5 The rate of errors introduced by a linguistic tool at a given level, when annotating, can be reduced automatically by contrasting and combining its results with the ones coming from other tools, operating at the same level. However, these other tools might be built following a different technological (stochastic vs. rule-based, for example) or theoretical (dependency vs. HPS-grammar-based, for instance) approach. • CONFIRMED by the results yielded by the evaluation of OntoTagger. H.6 Each linguistic level can be managed and annotated independently. • REJECTED: OntoTagger’s experiments and the dependencies observed among the morphosyntactic annotations, and between them and the syntactic annotations. In fact, Hypothesis H.6 was already rejected when OntoTag’s ontologies were developed. We observed then that several linguistic units stand on an interface between levels, belonging thereby to both of them (such as morphosyntactic units, which belong to both the morphological level and the syntactic level). Therefore, the annotations of these levels overlap and cannot be handled independently when merged into a unique multileveled annotation. 4. OTHER MAIN RESULTS AND CONTRIBUTIONS First, interoperability is a hot topic for both the linguistic annotation community and the whole Computer Science field. The specification (and implementation) of OntoTag’s architecture for the combination and integration of linguistic (annotation) tools and annotations by means of ontologies shows a way to make these different linguistic annotation tools and annotations interoperate in practice. Second, as mentioned above, the elements involved in linguistic annotation were formalised in a set (or network) of ontologies (OntoTag’s linguistic ontologies). • On the one hand, OntoTag’s network of ontologies consists of − The Linguistic Unit Ontology (LUO), which includes a mostly hierarchical formalisation of the different types of linguistic elements (i.e., units) identifiable in a written text; − The Linguistic Attribute Ontology (LAO), which includes also a mostly hierarchical formalisation of the different types of features that characterise the linguistic units included in the LUO; − The Linguistic Value Ontology (LVO), which includes the corresponding formalisation of the different values that the attributes in the LAO can take; − The OIO (OntoTag’s Integration Ontology), which  Includes the knowledge required to link, combine and unite the knowledge represented in the LUO, the LAO and the LVO;  Can be viewed as a knowledge representation ontology that describes the most elementary vocabulary used in the area of annotation. • On the other hand, OntoTag’s ontologies incorporate the knowledge included in the different standards and recommendations for linguistic annotation released so far, such as those developed within the EAGLES and the SIMPLE European projects or by the ISO/TC 37 committee: − As far as morphosyntactic annotations are concerned, OntoTag’s ontologies formalise the terms in the EAGLES (1996a) recommendations and their corresponding terms within the ISO Morphosyntactic Annotation Framework (ISO/MAF, 2008) standard; − As for syntactic annotations, OntoTag’s ontologies incorporate the terms in the EAGLES (1996b) recommendations and their corresponding terms within the ISO Syntactic Annotation Framework (ISO/SynAF, 2010) standard draft; − Regarding semantic annotations, OntoTag’s ontologies generalise and extend the recommendations in EAGLES (1996a; 1996b) and, since no stable standards or standard drafts have been released for semantic annotation by ISO/TC 37 yet, they incorporate the terms in SIMPLE (2000) instead; − The terms coming from all these recommendations and standards were supplemented by those within the ISO Data Category Registry (ISO/DCR, 2008) and also of the ISO Linguistic Annotation Framework (ISO/LAF, 2009) standard draft when developing OntoTag’s ontologies. Third, we showed that the combination of the results of tools annotating at the same level can yield better results (both in precision and in recall) than each tool separately. In particular, 1. OntoTagger clearly outperformed two of the tools integrated into its configuration, namely DataLexica and FDG in all the combination sub-phases in which they overlapped (i.e. POS tagging, lemma annotation and morphological feature annotation). As far as the remaining tool is concerned, i.e. LACELL’s tagger, it was also outperformed by OntoTagger in POS tagging and lemma annotation, and it did not behave better than OntoTagger in the morphological feature annotation layer. 2. As an immediate result, this implies that a) This type of combination architecture configurations can be applied in order to improve significantly the accuracy of linguistic annotations; and b) Concerning the morphosyntactic level, this could be regarded as a way of constructing more robust and more accurate POS tagging systems. Fourth, Semantic Web annotations are usually performed by humans or else by machine learning systems. Both of them leave much to be desired: the former, with respect to their annotation rate; the latter, with respect to their (average) precision and recall. In this work, we showed how linguistic tools can be wrapped in order to annotate automatically Semantic Web pages using ontologies. This entails their fast, robust and accurate semantic annotation. As a way of example, as mentioned in Sub-goal 5.5, we developed a particular OntoTagger module for the recognition, classification and labelling of named entities, according to the MUC and ACE tagsets (Chinchor, 1997; Doddington et al., 2004). These tagsets were further specified by means of a domain ontology, namely the Cinema Named Entities Ontology (CNEO). This module was applied to the automatic annotation of ten different web pages containing cinema reviews (that is, around 5000 words). In addition, the named entities annotated with this module were also labelled as instances (or individuals) of the classes included in the CNEO and, then, were used to populate this domain ontology. • The statistical results obtained from the evaluation of this particular module of OntoTagger can be summarised as follows. On the one hand, as far as recall (R) is concerned, (R.1) the lowest value was 76,40% (for file 7); (R.2) the highest value was 97, 50% (for file 3); and (R.3) the average value was 88,73%. On the other hand, as far as the precision rate (P) is concerned, (P.1) its minimum was 93,75% (for file 4); (R.2) its maximum was 100% (for files 1, 5, 7, 8, 9, and 10); and (R.3) its average value was 98,99%. • These results, which apply to the tasks of named entity annotation and ontology population, are extraordinary good for both of them. They can be explained on the basis of the high accuracy of the annotations provided by OntoTagger at the lower levels (mainly at the morphosyntactic level). However, they should be conveniently qualified, since they might be too domain- and/or language-dependent. It should be further experimented how our approach works in a different domain or a different language, such as French, English, or German. • In any case, the results of this application of Human Language Technologies to Ontology Population (and, accordingly, to Ontological Engineering) seem very promising and encouraging in order for these two areas to collaborate and complement each other in the area of semantic annotation. Fifth, as shown in the State of the Art of this work, there are different approaches and models for the semantic annotation of texts, but all of them focus on a particular view of the semantic level. Clearly, all these approaches and models should be integrated in order to bear a coherent and joint semantic annotation level. OntoTag shows how (i) these semantic annotation layers could be integrated together; and (ii) they could be integrated with the annotations associated to other annotation levels. Sixth, we identified some recommendations, best practices and lessons learned for annotation standardisation, interoperation and merge. They show how standardisation (via ontologies, in this case) enables the combination, integration and interoperation of different linguistic tools and their annotations into a multilayered (or multileveled) linguistic annotation, which is one of the hot topics in the area of Linguistic Annotation. And last but not least, OntoTag’s annotation scheme and OntoTagger’s annotation schemas show a way to formalise and annotate coherently and uniformly the different units and features associated to the different levels and layers of linguistic annotation. This is a great scientific step ahead towards the global standardisation of this area, which is the aim of ISO/TC 37 (in particular, Subcommittee 4, dealing with the standardisation of linguistic annotations and resources).

Estimation of a probability in inverse binomial sampling under normalized linear-linear and inverse-linear loss

Sequential estimation of the success probability p in inverse binomial sampling is considered in this paper. For any estimator pˆ , its quality is measured by the risk associated with normalized loss functions of linear-linear or inverse-linear form. These functions are possibly asymmetric, with arbitrary slope parameters a and b for pˆ

and pˆ>p , respectively. Interest in these functions is motivated by their significance and potential uses, which are briefly discussed. Estimators are given for which the risk has an asymptotic value as p→0, and which guarantee that, for any p∈(0,1), the risk is lower than its asymptotic value. This allows selecting the required number of successes, r, to meet a prescribed quality irrespective of the unknown p. In addition, the proposed estimators are shown to be approximately minimax when a/b does not deviate too much from 1, and asymptotically minimax as r→∞ when a=b.

Simulation of academic performance rates, estimation of indicators of subjects and of a complete degree

The purpose of this paper is to present a program written in Matlab-Octave for the simulation of the time evolution of student curricula, i.e, how students pass their subjects along time until graduation. The program computes, from the simulations, the academic performance rates for the subjects of the study plan for each semester as well as the overall rates, which are a) the efficiency rate defined as the ratio of the number of students passing the exam to the number of students who registered for it and b) the success rate, defined as the ratio of the number of students passing the exam to the number of students who not only registered for it but also actually took it. Additionally, we compute the rates for the bachelor academic degree which are established for Spain by the National Quality Evaluation and Accreditation Agency (ANECA) and which are the graduation rate (measured as the percentage of students who finish as scheduled in the plan or taking an extra year) and the efficiency rate (measured as the percentage of credits which a student who graduated has really taken). The simulation is done in terms of the probabilities of passing all the subjects in their study plan. The application of the simulator to Polytech students in Madrid, where requirements for passing are specially stiff in first and second year subjects, is particularly relevant to analyze student cohorts and the probabilities of students finishing in the minimum of four years, or taking and extra year or two extra years, and so forth. It is a very useful tool when designing new study plans. The calculation of the probability distribution of the random variable "number of semesters a student has taken to complete the curricula and graduate" is difficult or even unfeasible to obtain analytically, and this is even truer when we incorporate uncertainty in parameter estimation. This is why we apply Monte Carlo simulation which not only provides illustration of the stochastic process but also a method for computation. The stochastic simulator is proving to be a useful tool for identification of the subjects most critical in the distribution of the number of semesters for curriculum vitae (CV) completion and subsequently for a decision making process in terms of CV planning and passing standards in the University. Simulations are performed through a graphical interface where also the results are presented in appropriate figures. The Project has been funded by the Call for Innovation in Education Projects of Universidad Politécnica de Madrid (UPM) through a Project of its school Escuela Técnica Superior de Ingenieros Industriales ETSII during the period September 2010-September 2011.

Estimation of a probability in inverse binomial sampling under normalized linear-linear and inverse-linear loss

Sequential estimation of the success probability $p$ in inverse binomial sampling is considered in this paper. For any estimator $\hatvap$, its quality is measured by the risk associated with normalized loss functions of linear-linear or inverse-linear form. These functions are possibly asymmetric, with arbitrary slope parameters $a$ and $b$ for $\hatvap < p$ and $\hatvap > p$ respectively. Interest in these functions is motivated by their significance and potential uses, which are briefly discussed. Estimators are given for which the risk has an asymptotic value as $p \rightarrow 0$, and which guarantee that, for any $p \in (0,1)$, the risk is lower than its asymptotic value. This allows selecting the required number of successes, $\nnum$, to meet a prescribed quality irrespective of the unknown $p$. In addition, the proposed estimators are shown to be approximately minimax when $a/b$ does not deviate too much from $1$, and asymptotically minimax as $\nnum \rightarrow \infty$ when $a=b$.

Identification and correction of besouro's faults: impact on test‐driven development experiments

Context. This thesis is framed in experimental software engineering. More concretely, it addresses the problems arisen when assessing process conformance in test-driven development experiments conducted by UPM's Experimental Software Engineering group. Process conformance was studied using the Eclipse's plug-in tool Besouro. It has been observed that Besouro does not work correctly in some circumstances. It creates doubts about the correction of the existing experimental data which render it useless. Aim. The main objective of this work is the identification and correction of Besouro's faults. A secondary goal is fixing the datasets already obtained in past experiments to the maximum possible extent. This way, existing experimental results could be used with confidence. Method. (1) Testing Besouro using different sequences of events (creation methods, assertions etc..) to identify the underlying faults. (2) Fix the code and (3) fix the datasets using code specially created for this purpose. Results. (1) We confirmed the existence of several fault in Besouro's code that affected to Test-First and Test-Last episode identification. These faults caused the incorrect identification of 20% of episodes. (2) We were able to fix Besouro's code. (3) The correction of existing datasets was possible, subjected to some restrictions (such us the impossibility of tracing code size increase to programming time. Conclusion. The results of past experiments dependent upon Besouro's data could no be trustable. We have the suspicion that more faults remain in Besouro's code, whose identification requires further analysis.