8,380 research outputs found

    Dynamic Feature Engineering and model selection methods for temporal tabular datasets with regime changes

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    The application of deep learning algorithms to temporal panel datasets is difficult due to heavy non-stationarities which can lead to over-fitted models that under-perform under regime changes. In this work we propose a new machine learning pipeline for ranking predictions on temporal panel datasets which is robust under regime changes of data. Different machine-learning models, including Gradient Boosting Decision Trees (GBDTs) and Neural Networks with and without simple feature engineering are evaluated in the pipeline with different settings. We find that GBDT models with dropout display high performance, robustness and generalisability with relatively low complexity and reduced computational cost. We then show that online learning techniques can be used in post-prediction processing to enhance the results. In particular, dynamic feature neutralisation, an efficient procedure that requires no retraining of models and can be applied post-prediction to any machine learning model, improves robustness by reducing drawdown in regime changes. Furthermore, we demonstrate that the creation of model ensembles through dynamic model selection based on recent model performance leads to improved performance over baseline by improving the Sharpe and Calmar ratios of out-of-sample prediction performances. We also evaluate the robustness of our pipeline across different data splits and random seeds with good reproducibility of results

    Anuário científico da Escola Superior de Tecnologia da Saúde de Lisboa - 2021

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    É com grande prazer que apresentamos a mais recente edição (a 11.ª) do Anuário Científico da Escola Superior de Tecnologia da Saúde de Lisboa. Como instituição de ensino superior, temos o compromisso de promover e incentivar a pesquisa científica em todas as áreas do conhecimento que contemplam a nossa missão. Esta publicação tem como objetivo divulgar toda a produção científica desenvolvida pelos Professores, Investigadores, Estudantes e Pessoal não Docente da ESTeSL durante 2021. Este Anuário é, assim, o reflexo do trabalho árduo e dedicado da nossa comunidade, que se empenhou na produção de conteúdo científico de elevada qualidade e partilhada com a Sociedade na forma de livros, capítulos de livros, artigos publicados em revistas nacionais e internacionais, resumos de comunicações orais e pósteres, bem como resultado dos trabalhos de 1º e 2º ciclo. Com isto, o conteúdo desta publicação abrange uma ampla variedade de tópicos, desde temas mais fundamentais até estudos de aplicação prática em contextos específicos de Saúde, refletindo desta forma a pluralidade e diversidade de áreas que definem, e tornam única, a ESTeSL. Acreditamos que a investigação e pesquisa científica é um eixo fundamental para o desenvolvimento da sociedade e é por isso que incentivamos os nossos estudantes a envolverem-se em atividades de pesquisa e prática baseada na evidência desde o início dos seus estudos na ESTeSL. Esta publicação é um exemplo do sucesso desses esforços, sendo a maior de sempre, o que faz com que estejamos muito orgulhosos em partilhar os resultados e descobertas dos nossos investigadores com a comunidade científica e o público em geral. Esperamos que este Anuário inspire e motive outros estudantes, profissionais de saúde, professores e outros colaboradores a continuarem a explorar novas ideias e contribuir para o avanço da ciência e da tecnologia no corpo de conhecimento próprio das áreas que compõe a ESTeSL. Agradecemos a todos os envolvidos na produção deste anuário e desejamos uma leitura inspiradora e agradável.info:eu-repo/semantics/publishedVersio

    Image classification over unknown and anomalous domains

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    A longstanding goal in computer vision research is to develop methods that are simultaneously applicable to a broad range of prediction problems. In contrast to this, models often perform best when they are specialized to some task or data type. This thesis investigates the challenges of learning models that generalize well over multiple unknown or anomalous modes and domains in data, and presents new solutions for learning robustly in this setting. Initial investigations focus on normalization for distributions that contain multiple sources (e.g. images in different styles like cartoons or photos). Experiments demonstrate the extent to which existing modules, batch normalization in particular, struggle with such heterogeneous data, and a new solution is proposed that can better handle data from multiple visual modes, using differing sample statistics for each. While ideas to counter the overspecialization of models have been formulated in sub-disciplines of transfer learning, e.g. multi-domain and multi-task learning, these usually rely on the existence of meta information, such as task or domain labels. Relaxing this assumption gives rise to a new transfer learning setting, called latent domain learning in this thesis, in which training and inference are carried out over data from multiple visual domains, without domain-level annotations. Customized solutions are required for this, as the performance of standard models degrades: a new data augmentation technique that interpolates between latent domains in an unsupervised way is presented, alongside a dedicated module that sparsely accounts for hidden domains in data, without requiring domain labels to do so. In addition, the thesis studies the problem of classifying previously unseen or anomalous modes in data, a fundamental problem in one-class learning, and anomaly detection in particular. While recent ideas have been focused on developing self-supervised solutions for the one-class setting, in this thesis new methods based on transfer learning are formulated. Extensive experimental evidence demonstrates that a transfer-based perspective benefits new problems that have recently been proposed in anomaly detection literature, in particular challenging semantic detection tasks

    Socio-endocrinology revisited: New tools to tackle old questions

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    Animals’ social environments impact their health and survival, but the proximate links between sociality and fitness are still not fully understood. In this thesis, I develop and apply new approaches to address an outstanding question within this sociality-fitness link: does grooming (a widely studied, positive social interaction) directly affect glucocorticoid concentrations (GCs; a group of steroid hormones indicating physiological stress) in a wild primate? To date, negative, long-term correlations between grooming and GCs have been found, but the logistical difficulties of studying proximate mechanisms in the wild leave knowledge gaps regarding the short-term, causal mechanisms that underpin this relationship. New technologies, such as collar-mounted tri-axial accelerometers, can provide the continuous behavioural data required to match grooming to non-invasive GC measures (Chapter 1). Using Chacma baboons (Papio ursinus) living on the Cape Peninsula, South Africa as a model system, I identify giving and receiving grooming using tri-axial accelerometers and supervised machine learning methods, with high overall accuracy (~80%) (Chapter 2). I then test what socio-ecological variables predict variation in faecal and urinary GCs (fGCs and uGCs) (Chapter 3). Shorter and rainy days are associated with higher fGCs and uGCs, respectively, suggesting that environmental conditions may impose stressors in the form of temporal bottlenecks. Indeed, I find that short days and days with more rain-hours are associated with reduced giving grooming (Chapter 4), and that this reduction is characterised by fewer and shorter grooming bouts. Finally, I test whether grooming predicts GCs, and find that while there is a long-term negative correlation between grooming and GCs, grooming in the short-term, in particular giving grooming, is associated with higher fGCs and uGCs (Chapter 5). I end with a discussion on how the new tools I applied have enabled me to advance our understanding of sociality and stress in primate social systems (Chapter 6)

    SYSTEMS METHODS FOR ANALYSIS OF HETEROGENEOUS GLIOBLASTOMA DATASETS TOWARDS ELUCIDATION OF INTER-TUMOURAL RESISTANCE PATHWAYS AND NEW THERAPEUTIC TARGETS

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    In this PhD thesis is described an endeavour to compile litterature about Glioblastoma key molecular mechanisms into a directed network followin Disease Maps standards, analyse its topology and compare results with quantitative analysis of multi-omics datasets in order to investigate Glioblastoma resistance mechanisms. The work also integrated implementation of Data Management good practices and procedures

    Wintertime Infiltration and the Thermal Dynamics of Black Spruce Peatlands within the Boreal Plains

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    Preliminary investigations (2005-2008) of subsurface temperatures at various ecosites at a Central Alberta research station within the Boreal Plains revealed a tendency for numerical models to predict lower than observed soil temperatures during the wintertime. In addition, black spruce peatlands were observed to have above freezing temperatures throughout most of the winter in the shallow subsurface, while other ecosites would be frozen at similar depths. The focus of this thesis was to determine why black spruce peatlands were warmer during the winter through field observations and numerical simulations, and any impact that may have on the hydrological cycle. In subsequent investigations the depth of monitoring was increased from 50 cm to 300 cm allowing for a comprehensive thermal profile over a three-year period (2009 to 2011) to be observed at several ecosites. There was a total of eighteen ecological sites, three of each of six types, which included coniferous, deciduous, harvested, burnt, shallow peatlands, and deep peatlands. Observed wintertime temperatures during the three-year period confirmed the preliminary results that the subsurface of deep peatlands would be frozen for significantly less time than both shallow peatlands and upland sites. It was unclear why the shallow subsurface of the deep peatlands barely ever experienced sub-zero temperatures, especially considering that the thermal conductivities of the wet peat should have resulted in a frost depth deeper than observed, even when considering latent heat losses. From December 2014 to May 2016 an additional field study was conducted at the deep peatlands, with a focus on the winter months and the possible physical and biological effects of black spruce trees and sphagnum moss on winter infiltration and subsurface temperatures. The objective of the additional study was to develop a conceptual model for the thermal dynamics of a black spruce peatland which could explain the warmer subsurface temperatures and the shallower than expected depth of frost. In addition, an algorithm was developed to allow for more accurate simulation of observed subsurface temperatures. The biological effects were quantified by monitoring adjacent living and dead black spruce trunk temperatures, along with the sphagnum moss layer temperatures overlying the peat. During the 2015 deployment of additional temperature probes at the black spruce peatland sites large amounts of snowmelt from the tree crown were observed to pool around the base of the spruce trees. Snow captured by the black spruce crown completely melted away after a few sunny days. The snowmelt from the crown then dripped and drained towards the base of the black spruce trees, where often a deep “cavity” would exist to the depth of the water table. The installation of moisture content probes within the cavity, loosely filled with peat, revealed increased moisture content following sunny days. Throughout the winter there were also minor increases in observed groundwater elevation following the sunny days as a result of the infiltration of crown drip through the cavity. There was also a very large increase in observed groundwater elevation at the end of March, likely due to the infiltration of upland snowmelt through the unfrozen black spruce cavity. The existence of wintertime infiltration within black spruce peatlands was confirmed through field observations and groundwater measurements. Data from the additional temperature probes facilitated the development of a conceptual model that could explain why the subsurface did not freeze, and accounting for wintertime infiltration through the tree cavity. Sphagnum moss was observed to be a strong insulator, with temperatures never decreasing below freezing in the underlying peat, even under the tree crown where there was a minimal snowpack. Black spruce trees were also observed to warm the subsurface beginning in April resulting in colder than expected tree trunk temperatures. The conduction of longwave radiation to the root zone from the crown through sap circulation is also a plausible explanation for the circular patterns of snowmelt around the black spruce tree root mats amid a prolonged snowpack observed until at least early May beyond the extent of the black spruce tree’s roots. Physically based numerical models utilizing the Simultaneous Heat and Water (SHAW) model were then constructed for six of the eighteen ecological sites from the 2009 to 2011 field study. Models were constructed for: two of the deep peatland sites, with black spruce; two of the shallow peatland sites, with lodgepole pine and black spruce; and two of the burnt sites, with lodgepole pine. The burnt sites were subsequently referred to as upland. The numerical models were constructed and calibrated in increasing complexity, beginning with the upland sites, followed by the shallow peatland sites and then the deep peatland sites. The numerical model was able to accurately simulate subsurface temperatures and the observed depth of frost at the pine upland sites without any alteration to the code, and to a lesser degree the shallow, drier, peatland sites. However, the numerical model, as with the previous attempts from the initial investigation, failed to identify a set of parameters that resulted in the successful simulation of the observed subsurface thermal profile at the deep peatland sites. The numerical model also erroneously predicted frost to the groundwater table. The SHAW numerical model was then updated to calculate the thermal conductivity of the sphagnum moss layer in series instead of parallel. By representing sphagnum moss in series, along with estimating new snowpack thermal conductivity coefficients, the Root Mean Square Error (RMSE) of the wintertime simulated temperatures were substantially reduced for deep peatland vadose zones and were better than those achieved for the upland sites. Using this approach, the wintertime temperatures of the deep peatlands were accurately simulated for the first time at the site, supporting the conceptual model that the insulation provided by sphagnum moss prevents the vadose zone from freezing. The unfrozen vadose zone, along with draining of the crown snowmelt to black spruce tree cavities allowed for wintertime infiltration to occur. The model simulations demonstrate the importance of having a healthy sphagnum moss layer overlying the peat to prevent the subsurface from freezing and inhibiting the infiltration of snowmelt from the black spruce tree cavity. It further highlights the need to better understand and incorporate biologically mediated effects, such as the insulating capacity of live versus dead moss and peat, into physically based models

    IMPROVED IMAGE QUALITY IN CONE-BEAM COMPUTED TOMOGRAPHY FOR IMAGE-GUIDED INTERVENTIONS

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    In the past few decades, cone-beam computed tomography (CBCT) emerged as a rapidly developing imaging modality that provides single rotation 3D volumetric reconstruction with sub-millimeter spatial resolution. Compared to the conventional multi-detector CT (MDCT), CBCT exhibited a number of characteristics that are well suited to applications in image-guided interventions, including improved mechanical simplicity, higher portability, and lower cost. Although the current generation of CBCT has shown strong promise for high-resolution and high-contrast imaging (e.g., visualization of bone structures and surgical instrumentation), it is often believed that CBCT yields inferior contrast resolution compared to MDCT and is not suitable for soft-tissue imaging. Aiming at expanding the utility of CBCT in image-guided interventions, this dissertation concerns the development of advanced imaging systems and algorithms to tackle the challenges of soft-tissue contrast resolution. The presented material includes work encompassing: (i) a comprehensive simulation platform to generate realistic CBCT projections (e.g., as training data for deep learning approaches); (ii) a new projection domain statistical noise model to improve the noise-resolution tradeoff in model-based iterative reconstruction (MBIR); (iii) a novel method to avoid CBCT metal artifacts by optimization of the source-detector orbit; (iv) an integrated software pipeline to correct various forms of CBCT artifacts (i.e., lag, glare, scatter, beam hardening, patient motion, and truncation); (v) a new 3D reconstruction method that only reconstructs the difference image from the image prior for use in CBCT neuro-angiography; and (vi) a novel method for 3D image reconstruction (DL-Recon) that combines deep learning (DL)-based image synthesis network with physics-based models based on Bayesian estimation of the statical uncertainty of the neural network. Specific clinical challenges were investigated in monitoring patients in the neurological critical care unit (NCCU) and advancing intraoperative soft-tissue imaging capability in image-guided spinal and intracranial neurosurgery. The results show that the methods proposed in this work substantially improved soft-tissue contrast in CBCT. The thesis demonstrates that advanced imaging approaches based on accurate system models, novel artifact reduction methods, and emerging 3D image reconstruction algorithms can effectively tackle current challenges in soft-tissue contrast resolution and expand the application of CBCT in image-guided interventions

    Hopping, Landing, and Balancing with Springs

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    This work investigates the interaction of a planar double pendulum robot and springs, where the lower body (the leg) has been modified to include a spring-loaded passive prismatic joint. The thesis explores the mechanical advantage of adding a spring to the robot in hopping, landing, and balancing activities by formulating the motion problem as a boundary value problem; and also provides a control strategy for such scenarios. It also analyses the robustness of the developed controller to uncertain spring parameters, and an observer solution is provided to estimate these parameters while the robot is performing a tracking task. Finally, it shows a study of how well IMUs perform in bouncing conditions, which is critical for the proper operation of a hopping robot or a running-legged one

    Aflatoxins

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    Aflatoxins are a group of highly toxic and carcinogenic substances that occur naturally and can be found in food substances. Aflatoxins are secondary metabolites of certain strains of fungi Aspergillus flavus and Aspergillus parasiticus as well as the less common Aspergillus nomius. Aflatoxins B1, B2, G1, and G2 are the most important members, which can be categorized into two groups according to chemical structure. As a result of the adverse health effects of mycotoxins, their levels have been strictly regulated, especially in food and feed samples. Therefore, their accurate identification and determination remain a herculean task due to their presence in the complex food matrix. The great public concern and the strict legislation incited the development of sensitive analytical methods that are discussed in this book

    Remote Sensing Monitoring of Land Surface Temperature (LST)

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    This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research
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