115,548 research outputs found

    Rainfall Prediction: A Comparative Analysis of Modern Machine Learning Algorithms for Time-Series Forecasting

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    Rainfall forecasting has gained utmost research relevance in recent times due to its complexities and persistent applications such as flood forecasting and monitoring of pollutant concentration levels, among others. Existing models use complex statistical models that are often too costly, both computationally and budgetary, or are not applied to downstream applications. Therefore, approaches that use Machine Learning algorithms in conjunction with time-series data are being explored as an alternative to overcome these drawbacks. To this end, this study presents a comparative analysis using simplified rainfall estimation models based on conventional Machine Learning algorithms and Deep Learning architectures that are efficient for these downstream applications. Models based on LSTM, Stacked-LSTM, Bidirectional-LSTM Networks, XGBoost, and an ensemble of Gradient Boosting Regressor, Linear Support Vector Regression, and an Extra-trees Regressor were compared in the task of forecasting hourly rainfall volumes using time-series data. Climate data from 2000 to 2020 from five major cities in the United Kingdom were used. The evaluation metrics of Loss, Root Mean Squared Error, Mean Absolute Error, and Root Mean Squared Logarithmic Error were used to evaluate the models' performance. Results show that a Bidirectional-LSTM Network can be used as a rainfall forecast model with comparable performance to Stacked-LSTM Networks. Among all the models tested, the Stacked-LSTM Network with two hidden layers and the Bidirectional-LSTM Network performed best. This suggests that models based on LSTM-Networks with fewer hidden layers perform better for this approach; denoting its ability to be applied as an approach for budget-wise rainfall forecast applications

    Differences in external match load metrics between professional and semi-professional football players

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    This study aimed to investigate the differences in external match load between professional and semi-professional footballers, and also aimed to investigate whether periods of fixture congestion throughout the season had an effect on the external match load of players at either the professional or semi-professional level. This study consisted of data from 51 football players, 21 professional and 30 semi-professional footballers, playing in the 2019/2020 football season. The data collected was obtained via MEMS (microelectromechanical systems) devices, which measured the players’ total distance, high-speed distance, accelerations, decelerations and player load. Once the external match load data was quantified, a comparison between playing levels took place using a univariate ANOVA. A two-way repeated measures ANOVA was used to examine if significant differences existed in external match load variables across player performance level (2 levels) and time of the season (3 levels) during periods of time when teams experienced fixture congestion. This study found that professional players travelled significantly greater distances in a 90 minute match (10.93 ± 2.46 vs 9.02 ± 1.56 km respectively; P<0.001). No differences in high-speed distance were observed between playing level (P=0.70), whereas semiprofessional players recorded significantly greater player load value than the professional players (88.6 ± 12.2 vs 68.8 ± 18.9% respectively; P<0.001). Periods of fixture congestion were not found to significantly affect any of the match load variables at either playing level despite the time of the season. In conclusion, neither playing level was found to exhibit a superior level of external match load. The other major finding of this thesis was that fixture congestion did not affect match load. Further research is required to quantify and compare the external match load at the non-elite professional and semi-professional level of football, as these levels of football are largely ignored in this field of literature

    Predicted impact of climate change on the distribution of the Critically Endangered golden mantella (Mantella aurantiaca) in Madagascar

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    The impact of climate change on Malagasy amphibians remains poorly understood. Equally, deforestation, fragmentation, and lack of connectivity between forest patches may leave vulnerable species isolated in habitat that no longer suits their environmental or biological requirements. We assess the predicted impact of climate change by 2085 on the potential distribution of a Critically Endangered frog species, the golden mantella (Mantella aurantiaca), that is confined to a small area of the central rainforest of Madagascar. We identify potential population distributions and climatically stable areas. Results suggest a potential south-eastwardly shift away from the current range and a decrease in suitable habitat from 2110 km2 under current climate to between 112 km2 – 138 km2 by the year 2085 – less than 7% of currently available suitable habitat. Results also indicate that the amount of golden mantella habitat falling within protected areas decreases by 86% over the same period. We recommend research to ascertain future viability and the feasibility of expanding protection to newly identified potential sites. This information can then be used in future conservation actions such as habitat restoration, translocations, re-introductions or the siting of further wildlife corridors or protected areas

    Gendered spaces in contemporary Irish poetry

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    The thrust of this thesis is summarized by the following questions: How does contemporary Irish poetry migrate from traditional conceptions of identity drawn on by the cultural nationalism of the Irish Literary Revival, and what effects does this have on understanding gendered and national identity formation? Chapters are on the following: Seamus Heaney, Tom Paulin, Paul Muldoon, MedbhMcGuckian, Eavan Boland and Sara Berkeley. These poets are chosen for discussion since their work most effectively engages with the relationship between woman and nation, the representation of gendered national identity, and the importance of feminist and post-colonial theorization. Focusing on poetry worth and South of the border from the last fifteen years, the thesis asks how a younger generation of poets provide a response to nationality which is significantly different from their predecessors. The thesis is composed of three parts: the first understand how the male poets depart from conventional conceptions of the nation with reference to post-colonial theorization; the second explores how feminist theorization informs readings of how the female poets respond to the nation; the final part investigates migration in the poetry and problematizes this in terms of post-nationalism. Discussing the issue of deterritorialization in Irish poetry, the thesis notice how as the poets attempt to take flight from the mythologies of nationhood, they undermine the monoliths of gendered and national identity inscribed within Irish political discourse, which is typified at a representative level by the figure of Mother Ireland or Cathleen Ni Houlihan. Investigating the ways in which gender and nation, and the body and space are reinscribed by the poets, the thesis argues that their poetry challenges authentic conceptions of Irish identity and the nation-state, so as to loosen the legacy of a colonial and nationalist inheritance

    Anytime algorithms for ROBDD symmetry detection and approximation

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    Reduced Ordered Binary Decision Diagrams (ROBDDs) provide a dense and memory efficient representation of Boolean functions. When ROBDDs are applied in logic synthesis, the problem arises of detecting both classical and generalised symmetries. State-of-the-art in symmetry detection is represented by Mishchenko's algorithm. Mishchenko showed how to detect symmetries in ROBDDs without the need for checking equivalence of all co-factor pairs. This work resulted in a practical algorithm for detecting all classical symmetries in an ROBDD in O(|G|³) set operations where |G| is the number of nodes in the ROBDD. Mishchenko and his colleagues subsequently extended the algorithm to find generalised symmetries. The extended algorithm retains the same asymptotic complexity for each type of generalised symmetry. Both the classical and generalised symmetry detection algorithms are monolithic in the sense that they only return a meaningful answer when they are left to run to completion. In this thesis we present efficient anytime algorithms for detecting both classical and generalised symmetries, that output pairs of symmetric variables until a prescribed time bound is exceeded. These anytime algorithms are complete in that given sufficient time they are guaranteed to find all symmetric pairs. Theoretically these algorithms reside in O(n³+n|G|+|G|³) and O(n³+n²|G|+|G|³) respectively, where n is the number of variables, so that in practice the advantage of anytime generality is not gained at the expense of efficiency. In fact, the anytime approach requires only very modest data structure support and offers unique opportunities for optimisation so the resulting algorithms are very efficient. The thesis continues by considering another class of anytime algorithms for ROBDDs that is motivated by the dearth of work on approximating ROBDDs. The need for approximation arises because many ROBDD operations result in an ROBDD whose size is quadratic in the size of the inputs. Furthermore, if ROBDDs are used in abstract interpretation, the running time of the analysis is related not only to the complexity of the individual ROBDD operations but also the number of operations applied. The number of operations is, in turn, constrained by the number of times a Boolean function can be weakened before stability is achieved. This thesis proposes a widening that can be used to both constrain the size of an ROBDD and also ensure that the number of times that it is weakened is bounded by some given constant. The widening can be used to either systematically approximate an ROBDD from above (i.e. derive a weaker function) or below (i.e. infer a stronger function). The thesis also considers how randomised techniques may be deployed to improve the speed of computing an approximation by avoiding potentially expensive ROBDD manipulation

    Predicting potential wildfire severity across Southern Europe with global data sources

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    .The large environmental and socioeconomic impacts of wildfires in Southern Europe require the development of efficient generalizable tools for fire danger analysis and proactive environmental management. With this premise, we aimed to study the influence of different environmental variables on burn severity, as well as to develop accurate and generalizable models to predict burn severity. To address these objectives, we selected 23 wildfires (131,490 ha) across Southern Europe. Using satellite imagery and geospatial data available at the planetary scale, we spatialized burn severity as well as 20 pre-burn environmental variables, which were grouped into climatic, topographic, fuel load-type, fuel load-moisture and fuel continuity predictors. We sampled all variables and divided the data into three independent datasets: a training dataset, used to perform univariant regression models, random forest (RF) models by groups of variables, and RF models including all predictors (full and parsimonious models); a second dataset to analyze interpolation capacity within the training wildfires; and a third dataset to study extrapolation capacity to independent wildfires. Results showed that all environmental variables determined burn severity, which increased towards the mildest climatic conditions, sloping terrain, high fuel loads, and coniferous vegetation. In general, the highest predictive and generalization capacities were found for fuel load proxies obtained though multispectral imagery, both in the individual analysis and by groups of variables. The full and parsimonious models outperformed all, the individual models, models by groups, and formerly developed predictive models of burn severity, as they were able to explain up to 95%, 59% and 25% of variance when applied to the training, interpolation and extrapolation datasets respectively. Our study is a benchmark for progress in the prediction of fire danger, provides operational tools for the identification of areas at risk, and sets the basis for the design of pre-burn management actions.S

    Facial expression recognition and intensity estimation.

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    Doctoral Degree. University of KwaZulu-Natal, Durban.Facial Expression is one of the profound non-verbal channels through which human emotion state is inferred from the deformation or movement of face components when facial muscles are activated. Facial Expression Recognition (FER) is one of the relevant research fields in Computer Vision (CV) and Human-Computer Interraction (HCI). Its application is not limited to: robotics, game, medical, education, security and marketing. FER consists of a wealth of information. Categorising the information into primary emotion states only limit its performance. This thesis considers investigating an approach that simultaneously predicts the emotional state of facial expression images and the corresponding degree of intensity. The task also extends to resolving FER ambiguous nature and annotation inconsistencies with a label distribution learning method that considers correlation among data. We first proposed a multi-label approach for FER and its intensity estimation using advanced machine learning techniques. According to our findings, this approach has not been considered for emotion and intensity estimation in the field before. The approach used problem transformation to present FER as a multilabel task, such that every facial expression image has unique emotion information alongside the corresponding degree of intensity at which the emotion is displayed. A Convolutional Neural Network (CNN) with a sigmoid function at the final layer is the classifier for the model. The model termed ML-CNN (Multilabel Convolutional Neural Network) successfully achieve concurrent prediction of emotion and intensity estimation. ML-CNN prediction is challenged with overfitting and intraclass and interclass variations. We employ Visual Geometric Graphics-16 (VGG-16) pretrained network to resolve the overfitting challenge and the aggregation of island loss and binary cross-entropy loss to minimise the effect of intraclass and interclass variations. The enhanced ML-CNN model shows promising results and outstanding performance than other standard multilabel algorithms. Finally, we approach data annotation inconsistency and ambiguity in FER data using isomap manifold learning with Graph Convolutional Networks (GCN). The GCN uses the distance along the isomap manifold as the edge weight, which appropriately models the similarity between adjacent nodes for emotion predictions. The proposed method produces a promising result in comparison with the state-of-the-art methods.Author's List of Publication is on page xi of this thesis

    Lecture Notes on Quantum Field Theory I

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    El documento ha sido corregido por el Servicio de Política Linguística de la Universidad de Valencia.Material docente preparado para la asignatura Teoría Cuántica de Campos en el Master de Física Avanzada.These lectures notes are based on the material covered in the course on Quantum Field Theory I in the Master in Advanced Physics at the University of Valencia, delivered in the years 2017-2021
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