10,346 research outputs found

    Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent

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    Gaussian processes are a powerful framework for quantifying uncertainty and for sequential decision-making but are limited by the requirement of solving linear systems. In general, this has a cubic cost in dataset size and is sensitive to conditioning. We explore stochastic gradient algorithms as a computationally efficient method of approximately solving these linear systems: we develop low-variance optimization objectives for sampling from the posterior and extend these to inducing points. Counterintuitively, stochastic gradient descent often produces accurate predictions, even in cases where it does not converge quickly to the optimum. We explain this through a spectral characterization of the implicit bias from non-convergence. We show that stochastic gradient descent produces predictive distributions close to the true posterior both in regions with sufficient data coverage, and in regions sufficiently far away from the data. Experimentally, stochastic gradient descent achieves state-of-the-art performance on sufficiently large-scale or ill-conditioned regression tasks. Its uncertainty estimates match the performance of significantly more expensive baselines on a large-scale Bayesian optimization task

    Space object identification and classification from hyperspectral material analysis

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    This paper presents a data processing pipeline designed to extract information from the hyperspectral signature of unknown space objects. The methodology proposed in this paper determines the material composition of space objects from single pixel images. Two techniques are used for material identification and classification: one based on machine learning and the other based on a least square match with a library of known spectra. From this information, a supervised machine learning algorithm is used to classify the object into one of several categories based on the detection of materials on the object. The behaviour of the material classification methods is investigated under non-ideal circumstances, to determine the effect of weathered materials, and the behaviour when the training library is missing a material that is present in the object being observed. Finally the paper will present some preliminary results on the identification and classification of space objects

    Deep ensemble model-based moving object detection and classification using SAR images

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    In recent decades, image processing and computer vision models have played a vital role in moving object detection on the synthetic aperture radar (SAR) images. Capturing of moving objects in the SAR images is a difficult task. In this study, a new automated model for detecting moving objects is proposed using SAR images. The proposed model has four main steps, namely, preprocessing, segmentation, feature extraction, and classification. Initially, the input SAR image is pre-processed using a histogram equalization technique. Then, the weighted Otsu-based segmentation algorithm is applied for segmenting the object regions from the pre-processed images. When using the weighted Otsu, the segmented grayscale images are not only clear but also retain the detailed features of grayscale images. Next, feature extraction is carried out by gray-level co-occurrence matrix (GLCM), median binary patterns (MBPs), and additive harmonic mean estimated local Gabor binary pattern (AHME-LGBP). The final step is classification using deep ensemble models, where the objects are classified by employing the ensemble deep learning technique, combining the models like the bidirectional long short-term memory (Bi-LSTM), recurrent neural network (RNN), and improved deep belief network (IDBN), which is trained with the features extracted previously. The combined models increase the accuracy of the results significantly. Furthermore, ensemble modeling reduces the variance and modeling method bias, which decreases the chances of overfitting. Compared to a single contributing model, ensemble models perform better and make better predictions. Additionally, an ensemble lessens the spread or dispersion of the model performance and prediction accuracy. Finally, the performance of the proposed model is related to the conventional models with respect to different measures. In the mean-case scenario, the proposed ensemble model has a minimum error value of 0.032, which is better related to other models. In both median- and best-case scenario studies, the ensemble model has a lower error value of 0.029 and 0.015

    Statistical analysis of grouped text documents

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    L'argomento di questa tesi sono i modelli statistici per l'analisi dei dati testuali, con particolare attenzione ai contesti in cui i campioni di testo sono raggruppati. Quando si ha a che fare con dati testuali, il primo problema è quello di elaborarli, per renderli compatibili dal punto di vista computazionale e metodologico con i metodi matematici e statistici prodotti e continuamente sviluppati dalla comunità scientifica. Per questo motivo, la tesi passa in rassegna i metodi esistenti per la rappresentazione analitica e l'elaborazione di campioni di dati testuali, compresi i "Vector Space Models", le "rappresentazioni distribuite" di parole e documenti e i "contextualized embeddings". Questa rassegna comporta la standardizzazione di una notazione che, anche all'interno dello stesso approccio di rappresentazione, appare molto eterogenea in letteratura. Vengono poi esplorati due domini di applicazione: i social media e il turismo culturale. Per quanto riguarda il primo, viene proposto uno studio sull'autodescrizione di gruppi diversi di individui sulla piattaforma StockTwits, dove i mercati finanziari sono gli argomenti dominanti. La metodologia proposta ha integrato diversi tipi di dati, sia testuali che variabili categoriche. Questo studio ha agevolato la comprensione sul modo in cui le persone si presentano online e ha trovato stutture di comportamento ricorrenti all'interno di gruppi di utenti. Per quanto riguarda il turismo culturale, la tesi approfondisce uno studio condotto nell'ambito del progetto "Data Science for Brescia - Arts and Cultural Places", in cui è stato addestrato un modello linguistico per classificare le recensioni online scritte in italiano in quattro aree semantiche distinte relative alle attrazioni culturali della città di Brescia. Il modello proposto permette di identificare le attrazioni nei documenti di testo, anche quando non sono esplicitamente menzionate nei metadati del documento, aprendo così la possibilità di espandere il database relativo a queste attrazioni culturali con nuove fonti, come piattaforme di social media, forum e altri spazi online. Infine, la tesi presenta uno studio metodologico che esamina la specificità di gruppo delle parole, analizzando diversi stimatori di specificità di gruppo proposti in letteratura. Lo studio ha preso in considerazione documenti testuali raggruppati con variabile di "outcome" e variabile di gruppo. Il suo contributo consiste nella proposta di modellare il corpus di documenti come una distribuzione multivariata, consentendo la simulazione di corpora di documenti di testo con caratteristiche predefinite. La simulazione ha fornito preziose indicazioni sulla relazione tra gruppi di documenti e parole. Inoltre, tutti i risultati possono essere liberamente esplorati attraverso un'applicazione web, i cui componenti sono altresì descritti in questo manoscritto. In conclusione, questa tesi è stata concepita come una raccolta di studi, ognuno dei quali suggerisce percorsi di ricerca futuri per affrontare le sfide dell'analisi dei dati testuali raggruppati.The topic of this thesis is statistical models for the analysis of textual data, emphasizing contexts in which text samples are grouped. When dealing with text data, the first issue is to process it, making it computationally and methodologically compatible with the existing mathematical and statistical methods produced and continually developed by the scientific community. Therefore, the thesis firstly reviews existing methods for analytically representing and processing textual datasets, including Vector Space Models, distributed representations of words and documents, and contextualized embeddings. It realizes this review by standardizing a notation that, even within the same representation approach, appears highly heterogeneous in the literature. Then, two domains of application are explored: social media and cultural tourism. About the former, a study is proposed about self-presentation among diverse groups of individuals on the StockTwits platform, where finance and stock markets are the dominant topics. The methodology proposed integrated various types of data, including textual and categorical data. This study revealed insights into how people present themselves online and found recurring patterns within groups of users. About the latter, the thesis delves into a study conducted as part of the "Data Science for Brescia - Arts and Cultural Places" Project, where a language model was trained to classify Italian-written online reviews into four distinct semantic areas related to cultural attractions in the Italian city of Brescia. The model proposed allows for the identification of attractions in text documents, even when not explicitly mentioned in document metadata, thus opening possibilities for expanding the database related to these cultural attractions with new sources, such as social media platforms, forums, and other online spaces. Lastly, the thesis presents a methodological study examining the group-specificity of words, analyzing various group-specificity estimators proposed in the literature. The study considered grouped text documents with both outcome and group variables. Its contribution consists of the proposal of modeling the corpus of documents as a multivariate distribution, enabling the simulation of corpora of text documents with predefined characteristics. The simulation provided valuable insights into the relationship between groups of documents and words. Furthermore, all its results can be freely explored through a web application, whose components are also described in this manuscript. In conclusion, this thesis has been conceived as a collection of papers. It aimed to contribute to the field with both applications and methodological proposals, and each study presented here suggests paths for future research to address the challenges in the analysis of grouped textual data

    Classical and quantum algorithms for scaling problems

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    This thesis is concerned with scaling problems, which have a plethora of connections to different areas of mathematics, physics and computer science. Although many structural aspects of these problems are understood by now, we only know how to solve them efficiently in special cases.We give new algorithms for non-commutative scaling problems with complexity guarantees that match the prior state of the art. To this end, we extend the well-known (self-concordance based) interior-point method (IPM) framework to Riemannian manifolds, motivated by its success in the commutative setting. Moreover, the IPM framework does not obviously suffer from the same obstructions to efficiency as previous methods. It also yields the first high-precision algorithms for other natural geometric problems in non-positive curvature.For the (commutative) problems of matrix scaling and balancing, we show that quantum algorithms can outperform the (already very efficient) state-of-the-art classical algorithms. Their time complexity can be sublinear in the input size; in certain parameter regimes they are also optimal, whereas in others we show no quantum speedup over the classical methods is possible. Along the way, we provide improvements over the long-standing state of the art for searching for all marked elements in a list, and computing the sum of a list of numbers.We identify a new application in the context of tensor networks for quantum many-body physics. We define a computable canonical form for uniform projected entangled pair states (as the solution to a scaling problem), circumventing previously known undecidability results. We also show, by characterizing the invariant polynomials, that the canonical form is determined by evaluating the tensor network contractions on networks of bounded size

    Koopman Kernel Regression

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    Many machine learning approaches for decision making, such as reinforcement learning, rely on simulators or predictive models to forecast the time-evolution of quantities of interest, e.g., the state of an agent or the reward of a policy. Forecasts of such complex phenomena are commonly described by highly nonlinear dynamical systems, making their use in optimization-based decision-making challenging. Koopman operator theory offers a beneficial paradigm for addressing this problem by characterizing forecasts via linear time-invariant (LTI) ODEs, turning multi-step forecasts into sparse matrix multiplication. Though there exists a variety of learning approaches, they usually lack crucial learning-theoretic guarantees, making the behavior of the obtained models with increasing data and dimensionality unclear. We address the aforementioned by deriving a universal Koopman-invariant reproducing kernel Hilbert space (RKHS) that solely spans transformations into LTI dynamical systems. The resulting Koopman Kernel Regression (KKR) framework enables the use of statistical learning tools from function approximation for novel convergence results and generalization error bounds under weaker assumptions than existing work. Our experiments demonstrate superior forecasting performance compared to Koopman operator and sequential data predictors in RKHS.Comment: Accepted to the thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023

    Advancing aviation safety through machine learning and psychophysiological data: a systematic review

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    In the aviation industry, safety remains vital, often compromised by pilot errors attributed to factors such as workload, fatigue, stress, and emotional disturbances. To address these challenges, recent research has increasingly leveraged psychophysiological data and machine learning techniques, offering the potential to enhance safety by understanding pilot behavior. This systematic literature review rigorously follows a widely accepted methodology, scrutinizing 80 peer-reviewed studies out of 3352 studies from five key electronic databases. The paper focuses on behavioral aspects, data types, preprocessing techniques, machine learning models, and performance metrics used in existing studies. It reveals that the majority of research disproportionately concentrates on workload and fatigue, leaving behavioral aspects like emotional responses and attention dynamics less explored. Machine learning models such as tree-based and support vector machines are most commonly employed, but the utilization of advanced techniques like deep learning remains limited. Traditional preprocessing techniques dominate the landscape, urging the need for advanced methods. Data imbalance and its impact on model performance is identified as a critical, under-researched area. The review uncovers significant methodological gaps, including the unexplored influence of preprocessing on model efficacy, lack of diversification in data collection environments, and limited focus on model explainability. The paper concludes by advocating for targeted future research to address these gaps, thereby promoting both methodological innovation and a more comprehensive understanding of pilot behavior

    Quantifying Equity Risk Premia: Financial Economic Theory and High-Dimensional Statistical Methods

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    The overarching question of this dissertation is how to quantify the unobservable risk premium of a stock when its return distribution varies over time. The first chapter, titled “Theory-based versus machine learning-implied stock risk premia”, starts with a comparison of two competing strands of the literature. The approach advocated by Martin and Wagner (2019) relies on financial economic theory to derive a closed-form approximation of conditional risk premia using information embedded in the prices of European options. The other approach, exemplified by the study of Gu et al. (2020), draws on the flexibility of machine learning methods and vast amounts of historical data to determine the unknown functional form. The goal of this study is to determine which of the two approaches produces more accurate measurements of stock risk premia. In addition, we present a novel hybrid approach that employs machine learning to overcome the approximation errors induced by the theory-based approach. We find that our hybrid approach is competitive especially at longer investment horizons. The second chapter, titled “The uncertainty principle in asset pricing”, introduces a representation of the conditional capital asset pricing model (CAPM) in which the betas and the equity premium are jointly characterized by the information embedded in option prices. A unique feature of our model is that its implied components represent valid measurements of their physical counterparts without the need for any further risk adjustment. Moreover, because the model’s time-varying parameters are directly observable, the model can be tested without any of the complications that typically arise from statistical estimation. One of the main empirical findings is that the well-known flat relationship between average predicted and realized excess returns of beta-sorted portfolios can be explained by the uncertainty governing market excess returns. In the third chapter, titled “Multi-task learning in cross-sectional regressions”, we challenge the way in which cross-sectional regressions are used to test factor models with time-varying loadings. More specifically, we extend the procedure by Fama and MacBeth (1973) by systematically selecting stock characteristics using a combination of l1- and l2-regularization, known as the multi-task Lasso, and addressing the bias that is induced by selection via repeated sample splitting. In the empirical part of this chapter, we apply our testing procedure to the option-implied CAPM from chapter two, and find that, while variants of the momentum effect lead to a rejection of the model, the implied beta is by far the most important predictor of cross-sectional return variation

    Explainable Early Prediction of Gestational Diabetes Biomarkers by Combining Medical Background and Wearable Devices: A Pilot Study with a Cohort Group in South Africa

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    This study aims to explore the potential of Internet of Things (IoT) devices and explainable Artificial Intelligence (AI) techniques in predicting biomarker values associated with GDM when measured 13 - 16 weeks prior to diagnosis. We developed a system that forecasts biomarkers such as LDL, HDL, triglycerides, cholesterol, HbA1c, and results from the Oral Glucose Tolerance Test (OGTT) including fasting glucose, 1-hour, and 2-hour post- load glucose values. These biomarker values are predicted based on sensory measurements collected around week 12 of pregnancy, including continuous glucose levels, short physical movement recordings, and medical background information. To the best of our knowledge, this is the first study to forecast GDM-associated biomarker values 13 to 16 weeks prior to the GDM screening test, using continuous glucose monitoring devices, a wristband for activity detection, and medical background data. We applied machine learning models, specifically Decision Tree and Random Forest Regressors, along with Coupled-Matrix Tensor Factorisation (CMTF) and Elastic Net techniques, examining all possible combinations of these methods across different data modalities. The results demonstrated good performance for most biomarkers. On average, the models achieved Mean Squared Error (MSE) between 0.29 and 0.42 and Mean Absolute Error (MAE) between 0.23 and 0.45 for biomarkers like HDL, LDL, cholesterol, and HbA1c. For the OGTT glucose values, the average MSE ranged from 0.95 to 2.44, and the average MAE ranged from 0.72 to 0.91. Additionally, the utilisation of CMTF with Alternating Least Squares technique yielded slightly better results (0.16 MSE and 0.07 MAE on average) compared to the well-known Elastic Net feature se- lection technique. While our study was conducted with a limited cohort in South Africa, our findings offer promising indications regarding the potential for predicting biomarker values in pregnant women through the integration of wearable devices and medical background data in the analysis. Nevertheless, further validation on a larger, more diverse cohort is imperative to substantiate these encouraging results

    Integrated Spatio-Temporal Deep Clustering (ISTDC) for cognitive workload assessment

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    Traditional high-dimensional electroencephalography (EEG) features (spectral or temporal) may not always attain satisfactory results in cognitive workload estimation. In contrast, deep representation learning (DRL) transforms high-dimensional data into cluster-friendly low-dimensional feature space. Therefore, this paper proposes an Integrated Spatio-Temporal Deep Clustering (ISTDC) model that uses DRL followed by a clustering method to achieve better clustering performance. The proposed model is illustrated using four Algorithms and Variational Bayesian Gaussian Mixture Model (VBGMM) clustering method. Temporal and spatial Variational Auto Encoder (VAE) models (mentioned in Algorithm 2 and Algorithm 3) learn temporal and spatial latent features from sequence-wise EEG signals and scalp topographical maps using the Long short-term memory and Convolutional Neural Network models. The concatenated spatio-temporal latent feature (mentioned in Algorithm 4) is passed to the VBGMM clustering method to efficiently estimate workload levels of -back task. For the 0-back vs. 2-back task, the proposed model achieves the maximum mean clustering accuracy of 98.0%, and it improves by 11.0% over the state-of-the-art method. The results also indicate that the proposed multimodal approach outperforms temporal and spatial latent feature-based unimodal models in workload assessment
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