941 research outputs found

    An Analysis of Aspect Composition Problems

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    The composition of multiple software units does not always yield the desired results. In particular, aspect-oriented composition mechanisms introduce new kinds of composition problems. These are caused by different characteristics as compared to object-oriented composition, such as inverse dependencies. The aim of this paper is to contribute to the understanding of aspect-oriented composition problems, and eventually composition problems in a more general context. To this extent we propose and illustrate a systematic approach to analyze composition problems in a precise and concrete manner. In this approach we represent aspect-based composition mechanisms as transformation rules on program graphs. We explicitly model and show where composition problems occur, in a way that can easily be fully automated. In this paper we focus on structural superimposition (cf. intertype declarations) to illustrate our approach; this results in the identification of three categories of causes of composition problems. \u

    Catalogue of unexpected interactions between aspects

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    Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 200

    A Connectionist Theory of Phenomenal Experience

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    When cognitive scientists apply computational theory to the problem of phenomenal consciousness, as many of them have been doing recently, there are two fundamentally distinct approaches available. Either consciousness is to be explained in terms of the nature of the representational vehicles the brain deploys; or it is to be explained in terms of the computational processes defined over these vehicles. We call versions of these two approaches vehicle and process theories of consciousness, respectively. However, while there may be space for vehicle theories of consciousness in cognitive science, they are relatively rare. This is because of the influence exerted, on the one hand, by a large body of research which purports to show that the explicit representation of information in the brain and conscious experience are dissociable, and on the other, by the classical computational theory of mind – the theory that takes human cognition to be a species of symbol manipulation. But two recent developments in cognitive science combine to suggest that a reappraisal of this situation is in order. First, a number of theorists have recently been highly critical of the experimental methodologies employed in the dissociation studies – so critical, in fact, it’s no longer reasonable to assume that the dissociability of conscious experience and explicit representation has been adequately demonstrated. Second, classicism, as a theory of human cognition, is no longer as dominant in cognitive science as it once was. It now has a lively competitor in the form of connectionism; and connectionism, unlike classicism, does have the computational resources to support a robust vehicle theory of consciousness. In this paper we develop and defend this connectionist vehicle theory of consciousness. It takes the form of the following simple empirical hypothesis: phenomenal experience consists in the explicit representation of information in neurally realized PDP networks. This hypothesis leads us to re-assess some common wisdom about consciousness, but, we will argue, in fruitful and ultimately plausible ways

    Decoding dynamic brain patterns from evoked responses: A tutorial on multivariate pattern analysis applied to time-series neuroimaging data

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    Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analysing fMRI data. Although decoding methods have been extensively applied in Brain Computing Interfaces (BCI), these methods have only recently been applied to time-series neuroimaging data such as MEG and EEG to address experimental questions in Cognitive Neuroscience. In a tutorial-style review, we describe a broad set of options to inform future time-series decoding studies from a Cognitive Neuroscience perspective. Using example MEG data, we illustrate the effects that different options in the decoding analysis pipeline can have on experimental results where the aim is to 'decode' different perceptual stimuli or cognitive states over time from dynamic brain activation patterns. We show that decisions made at both preprocessing (e.g., dimensionality reduction, subsampling, trial averaging) and decoding (e.g., classifier selection, cross-validation design) stages of the analysis can significantly affect the results. In addition to standard decoding, we describe extensions to MVPA for time-varying neuroimaging data including representational similarity analysis, temporal generalisation, and the interpretation of classifier weight maps. Finally, we outline important caveats in the design and interpretation of time-series decoding experiments.Comment: 64 pages, 15 figure

    Localization Precise in Urban Area

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    Nowadays, stand-alone Global Navigation Satellite System (GNSS) positioning accuracy is not sufficient for a growing number of land users. Sub-meter or even centimeter accuracy is becoming more and more crucial in many applications. Especially for navigating rovers in the urban environment, final positioning accuracy can be worse as the dramatically lack and contaminations of GNSS measurements. To achieve a more accurate positioning, the GNSS carrier phase measurements appear mandatory. These measurements have a tracking error more precise by a factor of a hundred than the usual code pseudorange measurements. However, they are also less robust and include a so-called integer ambiguity that prevents them to be used directly for positioning. While carrier phase measurements are widely used in applications located in open environments, this thesis focuses on trying to use them in a much more challenging urban environment. To do so, Real Time-Kinematic (RTK) methodology is used, which is taking advantage on the spatially correlated property of most code and carrier phase measurements errors. Besides, the thesis also tries to take advantage of a dual GNSS constellation, GPS and GLONASS, to strengthen the position solution and the reliable use of carrier phase measurements. Finally, to make up the disadvantages of GNSS in urban areas, a low-cost MEMS is also integrated to the final solution. Regarding the use of carrier phase measurements, a modified version of Partial Integer Ambiguity Resolution (Partial-IAR) is proposed to convert as reliably as possible carrier phase measurements into absolute pseudoranges. Moreover, carrier phase Cycle Slip (CS) being quite frequent in urban areas, thus creating discontinuities of the measured carrier phases, a new detection and repair mechanism of CSs is proposed to continuously benefit from the high precision of carrier phases. Finally, tests based on real data collected around Toulouse are used to test the performance of the whole methodology

    The Morphosyntactic Parser: Developing and testing a sentence processor that uses underspecified morphosyntactic features

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    This dissertation presents a fundamentally new approach to describe not only the architecture of the language system but also the processes behind its capability to predict, analyze and integrate linguistic input into its representation in a parsimonious way. By the example of morphosyntax, underspecified case, the use of decomposed, binary case, number and gender features to account for syncretism, will offer insights into both: Carrying over this idea to language processing raises the question whether the language system—limited in its storage capacity—makes use of similar means of representational parsimony during the processing of linguistic input. This thesis will propose a processing system that is tightly related to the aforementioned architectural assumptions of morphosyntactically underspecified lexical entries as a parsimonious way of representation. In that sense, prediction is viewed as the language system’s drive to avoid feature deviance from one incrementally available linguistic element to another subsequentially incoming one. In this way, the parser’s goal is to maintain minimal feature deviance or at best feature identity to keep processing load as low as possible. This approach allows for position-dependent hypothesis with regard to the expected processing load. To test the processor’s claims, the electrophysiological data of a series of event-related brain potential (ERP) experiments will be presented. The results suggest that with the input’s increased feature deviance the amplitude of an ERP component sensitive for prediction error increases. In comparison to that, elements that rather maintain feature identity and that do not lack or introduce additional features to the analysis do not increase processing difficulty. These results indicate that the language processing system uses the available features of morphosyntactically underspecified mental entries to build up larger constituents. The experiments showed, that this buildup process is determined by the language system’s drive to avoid feature deviance

    Computational Methods for Assessment and Prediction of Viral Evolutionary and Epidemiological Dynamics

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    The ability to comprehend the dynamics of viruses’ transmission and their evolution, even to a limited extent, can significantly enhance our capacity to predict and control the spread of infectious diseases. An example of such significance is COVID-19 caused by the severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). In this dissertation, I am proposing computational models that present more precise and comprehensive approaches in viral outbreak investigations and epidemiology, providing invaluable insights into the transmission dynamics, and potential inter- ventions of infectious diseases by facilitating the timely detection of viral variants. The first model is a mathematical framework based on population dynamics for the calculation of a numerical measure of the fitness of SARS-CoV-2 subtypes. The second model I propose here is a transmissibility estimation method based on a Bayesian approach to calculate the most likely fitness landscape for SARS-CoV-2 using a generalized logistic sub-epidemic model. Using the proposed model I estimate the epistatic interaction networks of spike protein in SARS-CoV-2. Based on the community structure of these epistatic networks, I propose a computational framework that predicts emerging haplotypes of SARS-CoV-2 with altered transmissibility. The last method proposed in this dissertation is a maximum likelihood framework that integrates phylogenetic and random graph models to accurately infer transmission networks without requiring case-specific data

    MIXING IT UP: THE IMPACT OF EPISODIC INTROGRESSION ON THE EVOLUTION OF HIGH-LATITUDE MESOCARNIVORES

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    At high latitudes, climatic oscillations have triggered repeated episodes of organismal divergence by geographically isolating populations. For terrestrial species, extended isolation in glacial refugia – ice-free regions that enable terrestrial species persistence through glacial maxima – is hypothesized to stimulate allopatric divergence. Alternatively, upon glacial recession, divergent populations expanded from independent glacial refugia and often contacted other diverging populations. In the absence of reproductive isolating mechanisms, this biogeographic process may trigger hybridization and ultimately, gene flow between divergent taxa. My dissertation research aims to understand how these episodic periods of isolation and contact have impacted the evolution of high latitude species. To understand the role of episodic isolation and gene flow on the evolution and diversification of high-latitude species, my dissertation integrates genetic, genomic, and morphometric characters across multiple high-latitude mesocarnivore mammals within the hyper-diverse Mustelidae family. Overall, I identified substantial cryptic diversity in the Arctic and highlight the complementary roles of glacial and interglacial cycles in the evolution and structuring of high latitude biota

    Sensor Array Processing with Manifold Uncertainty

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    <p>The spatial spectrum, also known as a field directionality map, is a description of the spatial distribution of energy in a wavefield. By sampling the wavefield at discrete locations in space, an estimate of the spatial spectrum can be derived using basic wave propagation models. The observable data space corresponding to physically realizable source locations for a given array configuration is referred to as the array manifold. In this thesis, array manifold ambiguities for linear arrays of omni-directional sensors in non-dispersive fields are considered. </p><p>First, the problem of underwater a hydrophone array towed behind a maneuvering platform is considered. The array consists of many hydrophones mounted to a flexible cable that is pulled behind a ship. The towed cable will bend or distort as the ship performs maneuvers. The motion of the cable through the turn can be used to resolve ambiguities that are inherent to nominally linear arrays. The first significant contribution is a method to estimate the spatial spectrum using a time-varying array shape in a dynamic field and broadband temporal data. Knowledge of the temporal spectral shape is shown to enhance detection performance. The field is approximated as a sum of uncorrelated planewaves located at uniform locations in angle, forming a gridded map on which a maximum likelihood estimate for broadband source power is derived. Uniform linear arrays also suffer from spatial aliasing when the inter-element spacing exceeds a half-wavelength. Broadband temporal knowledge is shown to significantly reduce aliasing and thus, in simulation, enhance target detection in interference dominated environments. </p><p>As an extension, the problem of towed array shape estimation is considered when the number and location of sources are unknown. A maximum likelihood estimate of the array shape using the field directionality map is derived. An acoustic-based array shape estimate that exploits the full 360^\circ field via field directionality mapping is the second significant contribution. Towed hydrophone arrays have heading sensors in order to estimate array shape, but these sensors can malfunction during sharp turns. An array shape model is described that allows the heading sensor data to be statistically fused with heading sensor. The third significant contribution is method to exploit dynamical motion models for sharp turns for a robust array shape estimate that combines acoustic and heading data. The proposed array shape model works well for both acoustic and heading data and is valid for arbitrary continuous array shapes.</p><p>Finally, the problem of array manifold ambiguities for static under-sampled linear arrays is considered. Under-sampled arrays are non-uniformly sampled with average spacing greater than a half-wavelength. While spatial aliasing only occurs in uniformly sampled arrays with spacing greater than a half-wavelength, under-sampled arrays have increased spatial resolution at the cost of high sidelobes compared to half-wavelength sampled arrays with the same number of sensors. Additionally, non-uniformly sampled arrays suffer from rank deficient array manifolds that cause traditional subspace based techniques to fail. A class of fully agumentable arrays, minimally redundant linear arrays, is considered where the received data statistics of a uniformly spaced array of the same length can be reconstructed in wide sense stationary fields at the cost of increased variance. The forth significant contribution is a reduced rank processing method for fully augmentable arrays to reduce the variance from augmentation with limited snapshots. Array gain for reduced rank adaptive processing with diagonal loading for snapshot deficient scenarios is analytically derived using asymptotic results from random matrix theory for a set ratio of sensors to snapshots. Additionally, the problem of near-field sources is considered and a method to reduce the variance from augmentation is proposed. In simulation, these methods result in significant average and median array gains with limited snapshots.</p>Dissertatio
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