External beam radiation therapy is currently one of the most commonly used modalities for treating cancer. With the rise of new technologies and increasing computational power, machine learning, deep learning and artificial intelligence applications used for classification and regression problems have begun to find their way into the field of radiation oncology. One such application is the automated generation of radiotherapy treatment plans, which must be optimized for every single patient. The department of radiation physics in Lund, Sweden, has developed an autoplanning software, which in combination with a commercially available treatment planning system (TPS), can be used for automatic creation of clinical treatment plans. The parameters of a multivariable cost function are changed iteratively, making it possible to generate a great amount of different treatment plans for a single patient. The output leads to optimal, near-optimal, clinically acceptable or even non-acceptable treatment plans. In this thesis, the possibility of using machine and deep learning to minimize the amount of treatment plans generated by the autoplanning software as well as the possibility of finding cost function parameters that lead to clinically acceptable optimal or near-optimal plans is evaluated. Data augmentation is used to create matrices of optimal treatment plan parameters, which are stored in a training database.  Patient specific training features are extracted from the TPS, as well as from the bottleneck layer of a trained deep neural network autoencoder. The training features are then matched against the same features extracted for test patients, using a k-nearest neighbor algorithm. Finally, treatment plans for a new patient are generated using the output plan parameter matrices of its nearest neighbors. This allows for a reduction in computation time as well as for finding suitable cost function parameters for a new patient.

Despite decades of research, offline handwriting recognition (HWR) of historical documents remains a challenging problem, which if solved could greatly improve the searchability of online cultural heritage archives. Historical documents are plagued with noise, degradation, ink bleed-through, overlapping strokes, variation in slope and slant of the writing, and inconsistent layouts. Often the documents in a collection have been written by thousands of authors, all of whom have significantly different writing styles. In order to better capture the variations in writing styles we introduce a novel data augmentation technique. This methods achieves state-of-the-art results on modern datasets written in English and French and a historical dataset written in German.HWR models are often limited by the accuracy of the preceding steps of text detection and segmentation.Motivated by this, we present a deep learning model that jointly learns text detection, segmentation, and recognition using mostly images without detection or segmentation annotations.Our Start, Follow, Read (SFR) model is composed of a Region Proposal Network to find the start position of handwriting lines, a novel line follower network that incrementally follows and preprocesses lines of (perhaps curved) handwriting into dewarped images, and a CNN-LSTM network to read the characters. SFR exceeds the performance of the winner of the ICDAR2017 handwriting recognition competition, even when not using the provided competition region annotations.

We discuss efficient Bayesian estimation of dynamic covariance matrices in multivariate time series through a factor stochastic volatility model. In particular, we propose two interweaving strategies (Yu and Meng, Journal of Computational and Graphical Statistics, 20(3), 531-570, 2011) to substantially accelerate convergence and mixing of standard MCMC approaches. Similar to marginal data augmentation techniques, the proposed acceleration procedures exploit non-identifiability issues which frequently arise in factor models. Our new interweaving strategies are easy to implement and come at almost no extra computational cost; nevertheless, they can boost estimation efficiency by several orders of magnitude as is shown in extensive simulation studies. To conclude, the application of our algorithm to a 26-dimensional exchange rate data set illustrates the superior performance of the new approach for real-world data. / Series: Research Report Series / Department of Statistics and Mathematics

A capacidade de o marketing mensurar e comunicar o valor de suas atividades e investimentos tem sido uma das prioridades de pesquisa na área nos últimos anos. Para atingir esse objetivo, a capacidade de mensurar adequadamente os ativos de marketing, como o Customer Lifetime Value e, de forma agregada, o Customer Equity, torna-se essencial, pois esses ativos são considerados os elementos capazes de traduzir em valores monetários o resultado dos diversos investimentos realizados pela área de marketing. Diante da mensuração desses valores, é possível o planejamento e a realização de ações mais precisas por parte dos profissionais de marketing. Sendo assim, no presente estudo objetivou-se construir e aplicar um modelo de estimação de Customer Lifetime Value no modo bottom-up (individual por cliente) em uma amostra de clientes de uma empresa do setor de serviços financeiros. O modelo bayesiano hierárquico aplicado, com três regressões estruturadas conforme o modelo Seemingly Unrelated Regressions (SUR) (ZELNER, 1971), foi construído a partir dos trabalhos de Kumar et al. (2008), Kumar e Shah (2009) e Cowles, Carlin e Connet (1996). Os resultados evidenciaram (1) que o modelo foi capaz de estimar com consistência o valor futuro de 84% dos clientes analisados; (2) que esse valor estimado traduz o potencial de rentabilidade que pode ser esperado futuramente para cada cliente; (3) que a base de clientes pode ser segmentada a partir do Customer Lifetime Value. Diante do conhecimento do valor futuro de cada cliente, se vislumbrou possibilidades de ações que tragam melhorias para gestão de clientes tradicionalmente utilizada, principalmente no que diz respeito à alocação dos recursos de marketing. / The marketing capacity to measure and to communicate the value resultant of its activities and investments has been one of the area top research priorities in the last few years. In order to achieve this objective, the capacity to appropriately measure the marketing assets, as the Customer Lifetime Value and, in aggregate form, the Customer Equity, has been pointed out as essential, because this assets are considered elements capable of translating the result of marketing investments into monetary values. Given the measurement of those values, marketers become able to plan and take more precise actions. Thus, the objective of present study is to build and test a bottom-up Customer Lifetime Value estimation model to a sample of customers from a company of finance services. The bayesian hierarchical model, composed of three regressions structured according to the Seemingly Unrelated Regressions (SUR) model (ZELNER, 1971), was built from the works of Kumar et al. (2008), Kumar and Shah (2009) and Cowles, Carlin and Connet (1996). The results show that (1) the model was capable to estimate with consistency the future value of 84% of the analyzed customers; (2) this estimated future values indicate the potential profitability of each customer; (3) the customer base can be segmented from the Customer Lifetime Value. Given the knowledge obtained about the future value of each customer and the segments established, several actions that can bring improvements to the traditional way of managing customers were suggested, in special those concerning marketing resource allocation.

A capacidade de o marketing mensurar e comunicar o valor de suas atividades e investimentos tem sido uma das prioridades de pesquisa na área nos últimos anos. Para atingir esse objetivo, a capacidade de mensurar adequadamente os ativos de marketing, como o Customer Lifetime Value e, de forma agregada, o Customer Equity, torna-se essencial, pois esses ativos são considerados os elementos capazes de traduzir em valores monetários o resultado dos diversos investimentos realizados pela área de marketing. Diante da mensuração desses valores, é possível o planejamento e a realização de ações mais precisas por parte dos profissionais de marketing. Sendo assim, no presente estudo objetivou-se construir e aplicar um modelo de estimação de Customer Lifetime Value no modo bottom-up (individual por cliente) em uma amostra de clientes de uma empresa do setor de serviços financeiros. O modelo bayesiano hierárquico aplicado, com três regressões estruturadas conforme o modelo Seemingly Unrelated Regressions (SUR) (ZELNER, 1971), foi construído a partir dos trabalhos de Kumar et al. (2008), Kumar e Shah (2009) e Cowles, Carlin e Connet (1996). Os resultados evidenciaram (1) que o modelo foi capaz de estimar com consistência o valor futuro de 84% dos clientes analisados; (2) que esse valor estimado traduz o potencial de rentabilidade que pode ser esperado futuramente para cada cliente; (3) que a base de clientes pode ser segmentada a partir do Customer Lifetime Value. Diante do conhecimento do valor futuro de cada cliente, se vislumbrou possibilidades de ações que tragam melhorias para gestão de clientes tradicionalmente utilizada, principalmente no que diz respeito à alocação dos recursos de marketing. / The marketing capacity to measure and to communicate the value resultant of its activities and investments has been one of the area top research priorities in the last few years. In order to achieve this objective, the capacity to appropriately measure the marketing assets, as the Customer Lifetime Value and, in aggregate form, the Customer Equity, has been pointed out as essential, because this assets are considered elements capable of translating the result of marketing investments into monetary values. Given the measurement of those values, marketers become able to plan and take more precise actions. Thus, the objective of present study is to build and test a bottom-up Customer Lifetime Value estimation model to a sample of customers from a company of finance services. The bayesian hierarchical model, composed of three regressions structured according to the Seemingly Unrelated Regressions (SUR) model (ZELNER, 1971), was built from the works of Kumar et al. (2008), Kumar and Shah (2009) and Cowles, Carlin and Connet (1996). The results show that (1) the model was capable to estimate with consistency the future value of 84% of the analyzed customers; (2) this estimated future values indicate the potential profitability of each customer; (3) the customer base can be segmented from the Customer Lifetime Value. Given the knowledge obtained about the future value of each customer and the segments established, several actions that can bring improvements to the traditional way of managing customers were suggested, in special those concerning marketing resource allocation.

A capacidade de o marketing mensurar e comunicar o valor de suas atividades e investimentos tem sido uma das prioridades de pesquisa na área nos últimos anos. Para atingir esse objetivo, a capacidade de mensurar adequadamente os ativos de marketing, como o Customer Lifetime Value e, de forma agregada, o Customer Equity, torna-se essencial, pois esses ativos são considerados os elementos capazes de traduzir em valores monetários o resultado dos diversos investimentos realizados pela área de marketing. Diante da mensuração desses valores, é possível o planejamento e a realização de ações mais precisas por parte dos profissionais de marketing. Sendo assim, no presente estudo objetivou-se construir e aplicar um modelo de estimação de Customer Lifetime Value no modo bottom-up (individual por cliente) em uma amostra de clientes de uma empresa do setor de serviços financeiros. O modelo bayesiano hierárquico aplicado, com três regressões estruturadas conforme o modelo Seemingly Unrelated Regressions (SUR) (ZELNER, 1971), foi construído a partir dos trabalhos de Kumar et al. (2008), Kumar e Shah (2009) e Cowles, Carlin e Connet (1996). Os resultados evidenciaram (1) que o modelo foi capaz de estimar com consistência o valor futuro de 84% dos clientes analisados; (2) que esse valor estimado traduz o potencial de rentabilidade que pode ser esperado futuramente para cada cliente; (3) que a base de clientes pode ser segmentada a partir do Customer Lifetime Value. Diante do conhecimento do valor futuro de cada cliente, se vislumbrou possibilidades de ações que tragam melhorias para gestão de clientes tradicionalmente utilizada, principalmente no que diz respeito à alocação dos recursos de marketing. / The marketing capacity to measure and to communicate the value resultant of its activities and investments has been one of the area top research priorities in the last few years. In order to achieve this objective, the capacity to appropriately measure the marketing assets, as the Customer Lifetime Value and, in aggregate form, the Customer Equity, has been pointed out as essential, because this assets are considered elements capable of translating the result of marketing investments into monetary values. Given the measurement of those values, marketers become able to plan and take more precise actions. Thus, the objective of present study is to build and test a bottom-up Customer Lifetime Value estimation model to a sample of customers from a company of finance services. The bayesian hierarchical model, composed of three regressions structured according to the Seemingly Unrelated Regressions (SUR) model (ZELNER, 1971), was built from the works of Kumar et al. (2008), Kumar and Shah (2009) and Cowles, Carlin and Connet (1996). The results show that (1) the model was capable to estimate with consistency the future value of 84% of the analyzed customers; (2) this estimated future values indicate the potential profitability of each customer; (3) the customer base can be segmented from the Customer Lifetime Value. Given the knowledge obtained about the future value of each customer and the segments established, several actions that can bring improvements to the traditional way of managing customers were suggested, in special those concerning marketing resource allocation.

This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates from a high dimensional set of psychological measurements. (authors' abstract)

No description available.

The early discovery of gastrointestinal (GI) disorders can significantly decrease the fatality rate of severe afflictions. Video capsule endoscopy (VCE) is a technique that produces an eight hour long recording of the GI tract that needs to be manually reviewed. This has led to the demand for AI-based solutions, but unfortunately, the lack of labeled data has been a major obstacle. In 2020 the Kvasir-Capsule dataset was produced which is the largest labeled dataset of GI abnormalities to date, but challenges still exist.The dataset suffers from unbalanced and very similar data created from labeled video frames. To avoid specialization to the specific data the creators of the set constructed an official split which is encouraged to use for testing. This study evaluates the use of transfer learning, Data augmentation and binary classification to detect GI abnormalities. The performance of machine learning (ML) classification is explored, with and without official split-based testing. For the performance evaluation, a specific focus will be on achieving a low rate of false negatives. The proposition behind this is that the most important aspect of an automated detection system for GI abnormalities is a low miss rate of possible lethal abnormalities. The results from the controlled experiments conducted in this study clearly show the importance of using official split-based testing. The difference in performance between a model trained and tested on the same set and a model that uses official split-based testing is significant. This enforces that without the use of official split-based testing the model will not produce reliable and generalizable results. When using official split-based testing the performance is improved compared to the initial baseline that is presented with the Kvasir-Capsule set. Some experiments in the study produced results with as low as a 1.56% rate of false negatives but with the cost of lowered performance for the normal class.

Joint models for the association of a longitudinal binary and a longitudinal continuous process are proposed for situations in which their association is of direct interest. The models are parameterized such that the dependence between the two processes is characterized by unconstrained regression coefficients. Bayesian variable selection techniques are used to parsimoniously model these coefficients. A Markov chain Monte Carlo (MCMC) sampling algorithm is developed for sampling from the posterior distribution, using data augmentation steps to handle missing data. Several technical issues are addressed to implement the MCMC algorithm efficiently. The models are motivated by, and are used for, the analysis of a smoking cessation clinical trial in which an important question of interest was the effect of the (exercise) treatment on the relationship between smoking cessation and weight gain.