10,585 research outputs found

    latent Dirichlet allocation method-based nowcasting approach for prediction of silver price

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    Silver is a metal that offers significant value to both investors and companies. The purpose of this study is to make an estimation of the price of silver. While making this estimation, it is planned to include the frequency of searches on Google Trends for the words that affect the silver price. Thus, it is aimed to obtain a more accurate estimate. First, using the Latent Dirichlet Allocation method, the keywords to be analyzed in Google Trends were collected from various articles on the Internet. Mining data from Google Trends combined with the information obtained by LDA is the new approach this study took, to predict the price of silver. No study has been found in the literature that has adopted this approach to estimate the price of silver. The estimation was carried out with Random Forest Regression, Gaussian Process Regression, Support Vector Machine, Regression Trees and Artificial Neural Networks methods. In addition, ARIMA, which is one of the traditional methods that is widely used in time series analysis, was also used to benchmark the accuracy of the methodology. The best MSE ratio was obtained as 0,000227131 ± 0.0000235205 by the Regression Trees method. This score indicates that it would be a valid technique to estimate the price of "Silver" by using Google Trends data using the LDA method

    Decoding spatial location of attended audio-visual stimulus with EEG and fNIRS

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    When analyzing complex scenes, humans often focus their attention on an object at a particular spatial location in the presence of background noises and irrelevant visual objects. The ability to decode the attended spatial location would facilitate brain computer interfaces (BCI) for complex scene analysis. Here, we tested two different neuroimaging technologies and investigated their capability to decode audio-visual spatial attention in the presence of competing stimuli from multiple locations. For functional near-infrared spectroscopy (fNIRS), we targeted dorsal frontoparietal network including frontal eye field (FEF) and intra-parietal sulcus (IPS) as well as superior temporal gyrus/planum temporal (STG/PT). They all were shown in previous functional magnetic resonance imaging (fMRI) studies to be activated by auditory, visual, or audio-visual spatial tasks. We found that fNIRS provides robust decoding of attended spatial locations for most participants and correlates with behavioral performance. Moreover, we found that FEF makes a large contribution to decoding performance. Surprisingly, the performance was significantly above chance level 1s after cue onset, which is well before the peak of the fNIRS response. For electroencephalography (EEG), while there are several successful EEG-based algorithms, to date, all of them focused exclusively on auditory modality where eye-related artifacts are minimized or controlled. Successful integration into a more ecological typical usage requires careful consideration for eye-related artifacts which are inevitable. We showed that fast and reliable decoding can be done with or without ocular-removal algorithm. Our results show that EEG and fNIRS are promising platforms for compact, wearable technologies that could be applied to decode attended spatial location and reveal contributions of specific brain regions during complex scene analysis

    Assessing performance of artificial neural networks and re-sampling techniques for healthcare datasets.

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    Re-sampling methods to solve class imbalance problems have shown to improve classification accuracy by mitigating the bias introduced by differences in class size. However, it is possible that a model which uses a specific re-sampling technique prior to Artificial neural networks (ANN) training may not be suitable for aid in classifying varied datasets from the healthcare industry. Five healthcare-related datasets were used across three re-sampling conditions: under-sampling, over-sampling and combi-sampling. Within each condition, different algorithmic approaches were applied to the dataset and the results were statistically analysed for a significant difference in ANN performance. The combi-sampling condition showed that four out of the five datasets did not show significant consistency for the optimal re-sampling technique between the f1-score and Area Under the Receiver Operating Characteristic Curve performance evaluation methods. Contrarily, the over-sampling and under-sampling condition showed all five datasets put forward the same optimal algorithmic approach across performance evaluation methods. Furthermore, the optimal combi-sampling technique (under-, over-sampling and convergence point), were found to be consistent across evaluation measures in only two of the five datasets. This study exemplifies how discrete ANN performances on datasets from the same industry can occur in two ways: how the same re-sampling technique can generate varying ANN performance on different datasets, and how different re-sampling techniques can generate varying ANN performance on the same dataset

    Um modelo para suporte automatizado ao reconhecimento, extração, personalização e reconstrução de gráficos estáticos

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    Data charts are widely used in our daily lives, being present in regular media, such as newspapers, magazines, web pages, books, and many others. A well constructed data chart leads to an intuitive understanding of its underlying data and in the same way, when data charts have wrong design choices, a redesign of these representations might be needed. However, in most cases, these charts are shown as a static image, which means that the original data are not usually available. Therefore, automatic methods could be applied to extract the underlying data from the chart images to allow these changes. The task of recognizing charts and extracting data from them is complex, largely due to the variety of chart types and their visual characteristics. Computer Vision techniques for image classification and object detection are widely used for the problem of recognizing charts, but only in images without any disturbance. Other features in real-world images that can make this task difficult are not present in most literature works, like photo distortions, noise, alignment, etc. Two computer vision techniques that can assist this task and have been little explored in this context are perspective detection and correction. These methods transform a distorted and noisy chart in a clear chart, with its type ready for data extraction or other uses. The task of reconstructing data is straightforward, as long the data is available the visualization can be reconstructed, but the scenario of reconstructing it on the same context is complex. Using a Visualization Grammar for this scenario is a key component, as these grammars usually have extensions for interaction, chart layers, and multiple views without requiring extra development effort. This work presents a model for automated support for custom recognition, and reconstruction of charts in images. The model automatically performs the process steps, such as reverse engineering, turning a static chart back into its data table for later reconstruction, while allowing the user to make modifications in case of uncertainties. This work also features a model-based architecture along with prototypes for various use cases. Validation is performed step by step, with methods inspired by the literature. This work features three use cases providing proof of concept and validation of the model. The first use case features usage of chart recognition methods focused on documents in the real-world, the second use case focus on vocalization of charts, using a visualization grammar to reconstruct a chart in audio format, and the third use case presents an Augmented Reality application that recognizes and reconstructs charts in the same context (a piece of paper) overlaying the new chart and interaction widgets. The results showed that with slight changes, chart recognition and reconstruction methods are now ready for real-world charts, when taking time, accuracy and precision into consideration.Os gráficos de dados são amplamente utilizados na nossa vida diária, estando presentes nos meios de comunicação regulares, tais como jornais, revistas, páginas web, livros, e muitos outros. Um gráfico bem construído leva a uma compreensão intuitiva dos seus dados inerentes e da mesma forma, quando os gráficos de dados têm escolhas de conceção erradas, poderá ser necessário um redesenho destas representações. Contudo, na maioria dos casos, estes gráficos são mostrados como uma imagem estática, o que significa que os dados originais não estão normalmente disponíveis. Portanto, poderiam ser aplicados métodos automáticos para extrair os dados inerentes das imagens dos gráficos, a fim de permitir estas alterações. A tarefa de reconhecer os gráficos e extrair dados dos mesmos é complexa, em grande parte devido à variedade de tipos de gráficos e às suas características visuais. As técnicas de Visão Computacional para classificação de imagens e deteção de objetos são amplamente utilizadas para o problema de reconhecimento de gráficos, mas apenas em imagens sem qualquer ruído. Outras características das imagens do mundo real que podem dificultar esta tarefa não estão presentes na maioria das obras literárias, como distorções fotográficas, ruído, alinhamento, etc. Duas técnicas de visão computacional que podem ajudar nesta tarefa e que têm sido pouco exploradas neste contexto são a deteção e correção da perspetiva. Estes métodos transformam um gráfico distorcido e ruidoso em um gráfico limpo, com o seu tipo pronto para extração de dados ou outras utilizações. A tarefa de reconstrução de dados é simples, desde que os dados estejam disponíveis a visualização pode ser reconstruída, mas o cenário de reconstrução no mesmo contexto é complexo. A utilização de uma Gramática de Visualização para este cenário é um componente chave, uma vez que estas gramáticas têm normalmente extensões para interação, camadas de gráficos, e visões múltiplas sem exigir um esforço extra de desenvolvimento. Este trabalho apresenta um modelo de suporte automatizado para o reconhecimento personalizado, e reconstrução de gráficos em imagens estáticas. O modelo executa automaticamente as etapas do processo, tais como engenharia inversa, transformando um gráfico estático novamente na sua tabela de dados para posterior reconstrução, ao mesmo tempo que permite ao utilizador fazer modificações em caso de incertezas. Este trabalho também apresenta uma arquitetura baseada em modelos, juntamente com protótipos para vários casos de utilização. A validação é efetuada passo a passo, com métodos inspirados na literatura. Este trabalho apresenta três casos de uso, fornecendo prova de conceito e validação do modelo. O primeiro caso de uso apresenta a utilização de métodos de reconhecimento de gráficos focando em documentos no mundo real, o segundo caso de uso centra-se na vocalização de gráficos, utilizando uma gramática de visualização para reconstruir um gráfico em formato áudio, e o terceiro caso de uso apresenta uma aplicação de Realidade Aumentada que reconhece e reconstrói gráficos no mesmo contexto (um pedaço de papel) sobrepondo os novos gráficos e widgets de interação. Os resultados mostraram que com pequenas alterações, os métodos de reconhecimento e reconstrução dos gráficos estão agora prontos para os gráficos do mundo real, tendo em consideração o tempo, a acurácia e a precisão.Programa Doutoral em Engenharia Informátic

    From wallet to mobile: exploring how mobile payments create customer value in the service experience

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    This study explores how mobile proximity payments (MPP) (e.g., Apple Pay) create customer value in the service experience compared to traditional payment methods (e.g. cash and card). The main objectives were firstly to understand how customer value manifests as an outcome in the MPP service experience, and secondly to understand how the customer activities in the process of using MPP create customer value. To achieve these objectives a conceptual framework is built upon the Grönroos-Voima Value Model (Grönroos and Voima, 2013), and uses the Theory of Consumption Value (Sheth et al., 1991) to determine the customer value constructs for MPP, which is complimented with Script theory (Abelson, 1981) to determine the value creating activities the consumer does in the process of paying with MPP. The study uses a sequential exploratory mixed methods design, wherein the first qualitative stage uses two methods, self-observations (n=200) and semi-structured interviews (n=18). The subsequent second quantitative stage uses an online survey (n=441) and Structural Equation Modelling analysis to further examine the relationships and effect between the value creating activities and customer value constructs identified in stage one. The academic contributions include the development of a model of mobile payment services value creation in the service experience, introducing the concept of in-use barriers which occur after adoption and constrains the consumers existing use of MPP, and revealing the importance of the mobile in-hand momentary condition as an antecedent state. Additionally, the customer value perspective of this thesis demonstrates an alternative to the dominant Information Technology approaches to researching mobile payments and broadens the view of technology from purely an object a user interacts with to an object that is immersed in consumers’ daily life

    Statistical Learning for Gene Expression Biomarker Detection in Neurodegenerative Diseases

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    In this work, statistical learning approaches are used to detect biomarkers for neurodegenerative diseases (NDs). NDs are becoming increasingly prevalent as populations age, making understanding of disease and identification of biomarkers progressively important for facilitating early diagnosis and the screening of individuals for clinical trials. Advancements in gene expression profiling has enabled the exploration of disease biomarkers at an unprecedented scale. The work presented here demonstrates the value of gene expression data in understanding the underlying processes and detection of biomarkers of NDs. The value of novel approaches to previously collected -omics data is shown and it is demonstrated that new therapeutic targets can be identified. Additionally, the importance of meta-analysis to improve power of multiple small studies is demonstrated. The value of blood transcriptomics data is shown in applications to researching NDs to understand underlying processes using network analysis and a novel hub detection method. Finally, after demonstrating the value of blood gene expression data for investigating NDs, a combination of feature selection and classification algorithms were used to identify novel accurate biomarker signatures for the diagnosis and prognosis of Parkinson’s disease (PD) and Alzheimer’s disease (AD). Additionally, the use of feature pools based on previous knowledge of disease and the viability of neural networks in dimensionality reduction and biomarker detection is demonstrated and discussed. In summary, gene expression data is shown to be valuable for the investigation of ND and novel gene biomarker signatures for the diagnosis and prognosis of PD and AD

    Fiabilité de l’underfill et estimation de la durée de vie d’assemblages microélectroniques

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    Abstract : In order to protect the interconnections in flip-chip packages, an underfill material layer is used to fill the volumes and provide mechanical support between the silicon chip and the substrate. Due to the chip corner geometry and the mismatch of coefficient of thermal expansion (CTE), the underfill suffers from a stress concentration at the chip corners when the temperature is lower than the curing temperature. This stress concentration leads to subsequent mechanical failures in flip-chip packages, such as chip-underfill interfacial delamination and underfill cracking. Local stresses and strains are the most important parameters for understanding the mechanism of underfill failures. As a result, the industry currently relies on the finite element method (FEM) to calculate the stress components, but the FEM may not be accurate enough compared to the actual stresses in underfill. FEM simulations require a careful consideration of important geometrical details and material properties. This thesis proposes a modeling approach that can accurately estimate the underfill delamination areas and crack trajectories, with the following three objectives. The first objective was to develop an experimental technique capable of measuring underfill deformations around the chip corner region. This technique combined confocal microscopy and the digital image correlation (DIC) method to enable tri-dimensional strain measurements at different temperatures, and was named the confocal-DIC technique. This techique was first validated by a theoretical analysis on thermal strains. In a test component similar to a flip-chip package, the strain distribution obtained by the FEM model was in good agreement with the results measured by the confocal-DIC technique, with relative errors less than 20% at chip corners. Then, the second objective was to measure the strain near a crack in underfills. Artificial cracks with lengths of 160 μm and 640 μm were fabricated from the chip corner along the 45° diagonal direction. The confocal-DIC-measured maximum hoop strains and first principal strains were located at the crack front area for both the 160 μm and 640 μm cracks. A crack model was developed using the extended finite element method (XFEM), and the strain distribution in the simulation had the same trend as the experimental results. The distribution of hoop strains were in good agreement with the measured values, when the model element size was smaller than 22 μm to capture the strong strain gradient near the crack tip. The third objective was to propose a modeling approach for underfill delamination and cracking with the effects of manufacturing variables. A deep thermal cycling test was performed on 13 test cells to obtain the reference chip-underfill delamination areas and crack profiles. An artificial neural network (ANN) was trained to relate the effects of manufacturing variables and the number of cycles to first delamination of each cell. The predicted numbers of cycles for all 6 cells in the test dataset were located in the intervals of experimental observations. The growth of delamination was carried out on FEM by evaluating the strain energy amplitude at the interface elements between the chip and underfill. For 5 out of 6 cells in validation, the delamination growth model was consistent with the experimental observations. The cracks in bulk underfill were modelled by XFEM without predefined paths. The directions of edge cracks were in good agreement with the experimental observations, with an error of less than 2.5°. This approach met the goal of the thesis of estimating the underfill initial delamination, areas of delamination and crack paths in actual industrial flip-chip assemblies.Afin de protéger les interconnexions dans les assemblages, une couche de matériau d’underfill est utilisée pour remplir le volume et fournir un support mécanique entre la puce de silicium et le substrat. En raison de la géométrie du coin de puce et de l’écart du coefficient de dilatation thermique (CTE), l’underfill souffre d’une concentration de contraintes dans les coins lorsque la température est inférieure à la température de cuisson. Cette concentration de contraintes conduit à des défaillances mécaniques dans les encapsulations de flip-chip, telles que la délamination interfaciale puce-underfill et la fissuration d’underfill. Les contraintes et déformations locales sont les paramètres les plus importants pour comprendre le mécanisme des ruptures de l’underfill. En conséquent, l’industrie utilise actuellement la méthode des éléments finis (EF) pour calculer les composantes de la contrainte, qui ne sont pas assez précises par rapport aux contraintes actuelles dans l’underfill. Ces simulations nécessitent un examen minutieux de détails géométriques importants et des propriétés des matériaux. Cette thèse vise à proposer une approche de modélisation permettant d’estimer avec précision les zones de délamination et les trajectoires des fissures dans l’underfill, avec les trois objectifs suivants. Le premier objectif est de mettre au point une technique expérimentale capable de mesurer la déformation de l’underfill dans la région du coin de puce. Cette technique, combine la microscopie confocale et la méthode de corrélation des images numériques (DIC) pour permettre des mesures tridimensionnelles des déformations à différentes températures, et a été nommée le technique confocale-DIC. Cette technique a d’abord été validée par une analyse théorique en déformation thermique. Dans un échantillon similaire à un flip-chip, la distribution de la déformation obtenues par le modèle EF était en bon accord avec les résultats de la technique confocal-DIC, avec des erreurs relatives inférieures à 20% au coin de puce. Ensuite, le second objectif est de mesurer la déformation autour d’une fissure dans l’underfill. Des fissures artificielles d’une longueuer de 160 μm et 640 μm ont été fabriquées dans l’underfill vers la direction diagonale de 45°. Les déformations circonférentielles maximales et principale maximale étaient situées aux pointes des fissures correspondantes. Un modèle de fissure a été développé en utilisant la méthode des éléments finis étendue (XFEM), et la distribution des contraintes dans la simuation a montré la même tendance que les résultats expérimentaux. La distribution des déformations circonférentielles maximales était en bon accord avec les valeurs mesurées lorsque la taille des éléments était plus petite que 22 μm, assez petit pour capturer le grand gradient de déformation près de la pointe de fissure. Le troisième objectif était d’apporter une approche de modélisation de la délamination et de la fissuration de l’underfill avec les effets des variables de fabrication. Un test de cyclage thermique a d’abord été effectué sur 13 cellules pour obtenir les zones délaminées entre la puce et l’underfill, et les profils de fissures dans l’underfill, comme référence. Un réseau neuronal artificiel (ANN) a été formé pour établir une liaison entre les effets des variables de fabrication et le nombre de cycles à la délamination pour chaque cellule. Les nombres de cycles prédits pour les 6 cellules de l’ensemble de test étaient situés dans les intervalles d’observations expérimentaux. La croissance de la délamination a été réalisée par l’EF en évaluant l’énergie de la déformation au niveau des éléments interfaciaux entre la puce et l’underfill. Pour 5 des 6 cellules de la validation, le modèle de croissance du délaminage était conforme aux observations expérimentales. Les fissures dans l’underfill ont été modélisées par XFEM sans chemins prédéfinis. Les directions des fissures de bord étaient en bon accord avec les observations expérimentales, avec une erreur inférieure à 2,5°. Cette approche a répondu à la problématique qui consiste à estimer l’initiation des délamination, les zones de délamination et les trajectoires de fissures dans l’underfill pour des flip-chips industriels

    Optimizing transcriptomics to study the evolutionary effect of FOXP2

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    The field of genomics was established with the sequencing of the human genome, a pivotal achievement that has allowed us to address various questions in biology from a unique perspective. One question in particular, that of the evolution of human speech, has gripped philosophers, evolutionary biologists, and now genomicists. However, little is known of the genetic basis that allowed humans to evolve the ability to speak. Of the few genes implicated in human speech, one of the most studied is FOXP2, which encodes for the transcription factor Forkhead box protein P2 (FOXP2). FOXP2 is essential for proper speech development and two mutations in the human lineage are believed to have contributed to the evolution of human speech. To address the effect of FOXP2 and investigate its evolutionary contribution to human speech, one can utilize the power of genomics, more specifically gene expression analysis via ribonucleic acid sequencing (RNA-seq). To this end, I first contributed in developing mcSCRB-seq, a highly sensitive, powerful, and efficient single cell RNA-seq (scRNA-seq) protocol. Previously having emerged as a central method for studying cellular heterogeneity and identifying cellular processes, scRNA-seq was a powerful genomic tool but lacked the sensitivity and cost-efficiency of more established protocols. By systematically evaluating each step of the process, I helped find that the addition of polyethylene glycol increased sensitivity by enhancing the cDNA synthesis reaction. This, along with other optimizations resulted in developing a sensitive and flexible protocol that is cost-efficient and ideal in many research settings. A primary motivation driving the extensive optimizations surrounding single cell transcriptomics has been the generation of cellular atlases, which aim to identify and characterize all of the cells in an organism. As such efforts are carried out in a variety of research groups using a number of different RNA-seq protocols, I contributed in an effort to benchmark and standardize scRNA-seq methods. This not only identified methods which may be ideal for the purpose of cell atlas creation, but also highlighted optimizations that could be integrated into existing protocols. Using mcSCRB-seq as a foundation as well as the findings from the scRNA-seq benchmarking, I helped develop prime-seq, a sensitive, robust, and most importantly, affordable bulk RNA-seq protocol. Bulk RNA-seq was frequently overlooked during the efforts to optimize and establish single-cell techniques, even though the method is still extensively used in analyzing gene expression. Introducing early barcoding and reducing library generation costs kept prime-seq cost-efficient, but basing it off of single-cell methods ensured that it would be a sensitive and powerful technique. I helped verify this by benchmarking it against TruSeq generated data and then helped test the robustness by generating prime-seq libraries from over seventeen species. These optimizations resulted in a final protocol that is well suited for investigating gene expression in comprehensive and high-throughput studies. Finally, I utilized prime-seq in order to develop a comprehensive gene expression atlas to study the function of FOXP2 and its role in speech evolution. I used previously generated mouse models: a knockout model containing one non-functional Foxp2 allele and a humanized model, which has a variant Foxp2 allele with two human-specific mutations. To study the effect globally across the mouse, I helped harvest eighteen tissues which were previously identified to express FOXP2. By then comparing the mouse models to wild-type mice, I helped highlight the importance of FOXP2 within lung development and the importance of the human variant allele in the brain. Both mcSCRB-seq and prime-seq have already been used and published in numerous studies to address a variety of biological and biomedical questions. Additionally, my work on FOXP2 not only provides a thorough expression atlas, but also provides a detailed and cost-efficient plan for undertaking a similar study on other genes of interest. Lastly, the studies on FOXP2 done within this work, lay the foundation for future studies investigating the role of FOXP2 in modulating learning behavior, and thereby affecting human speech
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