520 research outputs found

    Development of criteria suitable for machine learning based on morphological hierarchical trees

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    The goal of this work is to study image criteria to be assigned to morphological trees such as Max/Mintree, Binary Partition Trees or similar representations to be able to classify the tree node and to identify the presence of object of interest in the scene.Nowadays the technology is changing the way of performing and it is adapting towards Artificial Intelligence. However this technique is still being introduced and is not common in the domain of image processing based on morphological trees. This thesis focuses on the creation of a criterion based on machine learning to be assigned into morphological tree. The developed criterion is based on a Convolutional Neural Network, called Overfeat, which runs in to the nodes of a Binary Partition Tree, in order to be able to detect traffic signs. It has turned out to be a suitable criterion to identify traffic sings in images but it has room of improvement due to its performance is lower than 70% of success.Hoy en día la tecnología está cambiando su forma de actuar y se está adaptando hacia la Inteligencia Artificial. Aunque esta técnica se está introduciendo, no es muy común en el dominio del procesamiento de imagen basado en arboles morfológicos. Esta tesis se centra en la creación de un criterio basado en Machine learning que se asigna a un árbol morfológico. El criterio desarrollado en este proyecto se basa en una Red Neuronal Colvolucional, llamada Overfeat, que trabaja sobre los nodos de un árbol de partición binaria, para ser capaz de identificar señales de tráfico. El criterio ha resultado ser adecuado para identificar señales de tráfico pero aún tiene margen de mejora ya que los resultados obtenidos no son superiores al 70% de acierto.Avui en dia la tecnologia esta canviant la seva forma d'actuar i s'està adaptant cap a la Intel·ligència Artificial. Tot i que aquesta tècnica s'està introduint no és gaire comú en el domini del processament d'imatge basat en arbres morfològics. Aquesta tesis es centra en la creació d'un criteri basat en machine learning que s'assigna a un arbre morfològic. El criteri desenvolupat en aquest projecte es basa en una Xarxa Neuronal Convolucional, anomenada Overfeat, que treballa sobre els nodes d'un arbre de partició binaria, per ser capaç d'identificar senyals de transit. El criteri ha resultat ser adequat per identificar senyals de transit però encara te marge de millora ja que els resultats obtinguts no son superiors al 70% d'encert

    Major inconsistencies of inferred population genetic structure estimated in a large set of domestic horse breeds using microsatellites

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    STRUCTURE remains the most applied software aimed at recovering the true, but unknown, population structure from microsatellite or other genetic markers. About 30% of structure-based studies could not be reproduced (Molecular Ecology, 21, 2012, 4925). Here we use a large set of data from 2,323 horses from 93 domestic breeds plus the Przewalski horse, typed at 15 microsatellites, to evaluate how program settings impact the estimation of the optimal number of population clusters Kopt that best describe the observed data. Domestic horses are suited as a test case as there is extensive background knowledge on the history of many breeds and extensive phylogenetic analyses. Different methods based on different genetic assumptions and statistical procedures (dapc, flock, PCoA, and structure with different run scenarios) all revealed general, broad-scale breed relationships that largely reflect known breed histories but diverged how they characterized small-scale patterns. structure failed to consistently identify Kopt using the most widespread approach, the ΔK method, despite very large numbers of MCMC iterations (3,000,000) and replicates (100). The interpretation of breed structure over increasing numbers of K, without assuming a Kopt, was consistent with known breed histories. The over-reliance on Kopt should be replaced by a qualitative description of clustering over increasing K, which is scientifically more honest and has the advantage of being much faster and less computer intensive as lower numbers of MCMC iterations and repetitions suffice for stable results. Very large data sets are highly challenging for cluster analyses, especially when populations with complex genetic histories are investigated.info:eu-repo/semantics/publishedVersio

    An Empirical Test of the Dutch Disease Hypothesis using a Gravity Model of Trade

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    Although the core model of the Dutch Disease makes unambiguous predictions regarding the negative effect of a resource boom on a country’s manufacturing exports, the empirical literature that has followed has not clearly identified this effect. I attribute this to the failure of the existing literature to combine enough data to produce a sufficiently powerful and exogenous test. I will use the World Trade Database to systematically test this hypothesis in a gravity model of trade. World energy prices are used to bypass issues of endogeneity regarding primary exports. A one percent increase in world energy price is estimated to decrease a net energy exporter’s real manufacturing exports by almost half a percent. Similarly, after instrumentation, a one percent increase in an energy exporting country’s net energy exports is estimated decrease the country’s real manufacturing exports by 8 percent. The corresponding confidence intervals are tight and these results are shown to be quite robust.Dutch disease, resource booms, gravity model, manufacturing exports, energy, trade, industry.

    The Scale and Extent of Political Economies of the Middle Bronze Age Jazīrah and the Bilād al-Šām (c. 1800-1600 BCE)

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    The present thesis investigates the material scale of six political economies distributed across the dry-farming plains and piedmonts of the Middle Bronze Age Jazīrah and the Bilād al-Šām. This is done using a comparative and interdisciplinary approach combining the large-scale analysis of administrative cuneiform texts with the compilation of relevant archaeological survey datasets. Drawing on theories and methods developed in landscape archaeology and historical sociology, the thesis builds a regional analysis of economic scale through a focus on three analytical units; the institutional household, the parent site, and the associated micro-region. Based on a dataset extracted from c. 1500 administrative cuneiform texts from the six study sites, the analytical chapters present a comprehensive discussion of the socio-economic and technological context of chief agricultural and animal resources and the material scale of their production, manipulation, circulation, and consumption. These investigations are undertaken focusing on three spheres of social action, namely the urban neighbourhood, agricultural regimes, and livestock management. The analysis concludes by drawing together quantitative data on various aspects of the institutional household economy to assess its material scale relative to the subsistence needs of its parent site and associated micro-region. The thesis demonstrates the limited material capabilities of a group of early political organisations relative to their social setting, both at the level of the parent settlement and, more forcefully, at the surrounding hinterland. It underscores the role of nascent political organisations as local and very resilient economic infrastructures across a politically volatile period of Bronze Age history. In line with recent and comparable investigations on Bronze Age economies, these findings offer critical revisions of traditional notions of the power of the early state. In methodological terms, the thesis formulates a novel means of combining large-scale analyses of text and material culture at a regional level, which can be applied in future studies

    Spatial and Temporal Variation in Atlantic herring (Clupea harengus) Otolith Shape During the Summer Feeding Migration in the North Sea

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    Masteroppgave i biologiBIO399MAMN-BIOMAMN-HAVS

    Intelligent human action recognition using an ensemble model of evolving deep networks with swarm-based optimization.

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    Automatic interpretation of human actions from realistic videos attracts increasing research attention owing to its growing demand in real-world deployments such as biometrics, intelligent robotics, and surveillance. In this research, we propose an ensemble model of evolving deep networks comprising Convolutional Neural Networks (CNNs) and bidirectional Long Short-Term Memory (BLSTM) networks for human action recognition. A swarm intelligence (SI)-based algorithm is also proposed for identifying the optimal hyper-parameters of the deep networks. The SI algorithm plays a crucial role for determining the BLSTM network and learning configurations such as the learning and dropout rates and the number of hidden neurons, in order to establish effective deep features that accurately represent the temporal dynamics of human actions. The proposed SI algorithm incorporates hybrid crossover operators implemented by sine, cosine, and tanh functions for multiple elite offspring signal generation, as well as geometric search coefficients extracted from a three-dimensional super-ellipse surface. Moreover, it employs a versatile search process led by the yielded promising offspring solutions to overcome stagnation. Diverse CNN–BLSTM networks with distinctive hyper-parameter settings are devised. An ensemble model is subsequently constructed by aggregating a set of three optimized CNN–BLSTM​ networks based on the average prediction probabilities. Evaluated using several publicly available human action data sets, our evolving ensemble deep networks illustrate statistically significant superiority over those with default and optimal settings identified by other search methods. The proposed SI algorithm also shows great superiority over several other methods for solving diverse high-dimensional unimodal and multimodal optimization functions with artificial landscapes

    Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models

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    We introduce Jais and Jais-chat, new state-of-the-art Arabic-centric foundation and instruction-tuned open generative large language models (LLMs). The models are based on the GPT-3 decoder-only architecture and are pretrained on a mixture of Arabic and English texts, including source code in various programming languages. With 13 billion parameters, they demonstrate better knowledge and reasoning capabilities in Arabic than any existing open Arabic and multilingual models by a sizable margin, based on extensive evaluation. Moreover, the models are competitive in English compared to English-centric open models of similar size, despite being trained on much less English data. We provide a detailed description of the training, the tuning, the safety alignment, and the evaluation of the models. We release two open versions of the model -- the foundation Jais model, and an instruction-tuned Jais-chat variant -- with the aim of promoting research on Arabic LLMs. Available at https://huggingface.co/inception-mbzuai/jais-13b-chatComment: Arabic-centric, foundation model, large-language model, LLM, generative model, instruction-tuned, Jais, Jais-cha

    Quantifying and reducing biases in paleogenomic research

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    The emergence of high-throughput DNA sequencing technologies has enabled the sequencing of genomes at unprecedented rates and low costs. In parallel, paleogenomics research, which extends the study of ancient DNA molecules to whole genomes, has led to a number of transformative discoveries in evolutionary biology, environmental sciences, and even medicine. However, ancient DNA has a number of properties that make it challenging to investigate, including short fragment length, contamination, and damage, which are often exacerbated at genomic scales. It is now clear that numerous biases are pervasive in paleogenomics investigations, and their influence on downstream inferences is undeniable. In this thesis, I present results from a series of interrelated empirical studies that investigate issues related to reference bias and reproducibility in paleogenomics, providing the relevant historical and technical background in the introduction. In Chapter 1 of this thesis, I benchmark a range of short read alignment methods and algorithms available to paleogenomicists and quantify the impact of reference bias on downstream inferences. I show that the current standard alignment method in the paleogenomics field, i.e., using the BWA-aln software with specific settings developed during the early stages of paleogenomics, is still one of the best available tools for minimising the impact of reference bias. However, reference bias can be decreased even further when using NovoAlign software and an augmented version of the linear reference that incorporates known variants using IUPAC characters. In Chapter 2, I extend this investigation to include the recently developed variation graph methods to paleogenomic datasets, and assess its impact on a series of contentious population genetics inferences when compared to the two best performing 4 traditional (linear) alignment methods identified in chapter 1. Consistent with the results from chapter 1, the added variation captured by variation graphs make them less susceptible to reference bias than linear alignments (including IUPAC augmented methods). I also show that changes in bioinformatic parameters and sample choice can lead to subtle but significant differences in statistical inferences that could impact interpretations. Therefore, in the third chapter, I emphasise the importance of reproducibility in paleogenomic research, and make a series of recommendations regarding the minimum reported information required across all key steps of data processing and analyses to ensure reproducibility of paleogenomic results. Finally, in the discussion chapter, I summarise my findings and discuss their implications for the field of paleogenomics as well as potential directions for future research. Ultimately, this knowledge should help improve the reliability and robustness of paleogenomic inferences, leading to an improved understanding of population history and evolutionary phenomena.Thesis (Ph.D.) -- University of Adelaide, School of Biological Sciences, 202

    Metabonomic characterisation of the thoroughbred racehorse

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    Mammalian metabolism is known to be influenced by a number of physiological and environmental factors and the metabolic phenotype of an individual includes contributions from diet and the intestinal microbiota. Intestinal wellbeing is paramount for mammalian health and it is increasingly evident that intestinal bacteria have the ability to influence the development of an array of diseases. The horse is a hindgut fermenter- a sophisticated fermentation vat, housing a plethora of gut microbes that liberate energy from high cellulose diets. Investigating the horse will further enhance our knowledge of the symbiotic relationship between the mammalian host and its consortium of gut microbes. Plasma, urine and faecal biological matrices were explored using nuclear magnetic resonance spectroscopy to identify the dominant metabolites present in a healthy racehorse population. Multivariate statistics allowed differences in metabolic profiles to be analysed between horses and within individual horses. 106 metabolites were catalogued, providing a reference tool for ‘normal’ horse NMR data. Urine samples provided the highest percentage of gut microbial derived metabolites. 32 racehorses were subsequently longitudinally sampled to investigate sources of metabolic variation such as yard origin, exercise intensity and behavioural phenotype. Gut microbial co-metabolites; such as hippurate, quinate and p-cresol glucuronide were found to be significantly associated with a number of sources of variation. Equine oral stereotypical behaviour (EOS), abrupt dietary change and high-starch diets are risk factors for colic. Gut microbes can indirectly influence behaviour and it has been postulated that stereotypical abnormalities, such as autism and EOS could be related to changes in gut microbial composition and metabolism. Urinary quinate- a dietary and gut microbial co-metabolite was found to be significantly increased in horses that displayed crib-biting behaviour compared to matched controls. Metabolic profiles from biofluids of horses on a diet trial exploring 3 diets; a traditional high-starch racing diet; a high-fat alternative and a grass only diet highlighted significant differences in gut microbial metabolism. A grass only diet had the highest level of gut microbial co-metabolites such as hippurate in comparison to the other diets and the high-fat alternative was most similar to this ‘natural’ grass metabolome. Conversely, a high-starch diet was associated with higher faecal lactic acid levels, suggesting a shift in pH and therefore microbial environment.Open Acces
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