Classification of diffraction patterns in single particle imaging experiments performed at X-ray free-electron lasers using a convolutional neural network

Single particle imaging (SPI) is a promising method for native structure determination which has undergone a fast progress with the development of X-ray Free-Electron Lasers. Large amounts of data are collected during SPI experiments, driving the need for automated data analysis. The necessary data analysis pipeline has a number of steps including binary object classification (single versus multiple hits). Classification and object detection are areas where deep neural networks currently outperform other approaches. In this work, we use the fast object detector networks YOLOv2 and YOLOv3. By exploiting transfer learning, a moderate amount of data is sufficient for training of the neural network. We demonstrate here that a convolutional neural network (CNN) can be successfully used to classify data from SPI experiments. We compare the results of classification for the two different networks, with different depth and architecture, by applying them to the same SPI data with different data representation. The best results are obtained for YOLOv2 color images linear scale classification, which shows an accuracy of about 97% with the precision and recall of about 52% and 61%, respectively, which is in comparison to manual data classification.Comment: 23 pages, 6 figures, 3 table

Análise do HEVC escalável : desempenho e controlo de débito

Mestrado em Engenharia Eletrónica e TelecomunicaçõesEsta dissertação apresenta um estudo da norma de codificação de vídeo de alta eficiência (HEVC) e a sua extensão para vídeo escalável, SHVC. A norma de vídeo SHVC proporciona um melhor desempenho quando codifica várias camadas em simultâneo do que quando se usa o codificador HEVC numa configuração simulcast. Ambos os codificadores de referência, tanto para a camada base como para a camada superior usam o mesmo modelo de controlo de débito, modelo R-λ, que foi otimizado para o HEVC. Nenhuma otimização de alocação de débito entre camadas foi até ao momento proposto para o modelo de testes (SHM 8) para a escalabilidade do HEVC (SHVC). Derivamos um novo modelo R-λ apropriado para a camada superior e para o caso de escalabilidade espacial, que conduziu a um ganho de BD-débito de 1,81% e de BD-PSNR de 0,025 em relação ao modelo de débito-distorção existente no SHM do SHVC. Todavia, mostrou-se também nesta dissertação que o proposto modelo de R-λ não deve ser usado na camada inferior (camada base) no SHVC e por conseguinte no HEVC.This dissertation provides a study of the High Efficiency Video Coding standard (HEVC) and its scalable extension, SHVC. The SHVC provides a better performance when encoding several layers simultaneously than using an HEVC encoder in a simulcast configuration. Both reference encoders, in the base layer and in the enhancement layer use the same rate control model, R-λ model, which was optimized for HEVC. No optimal bitrate partitioning amongst layers is proposed in scalable HEVC (SHVC) test model (SHM 8). We derived a new R-λ model for the enhancement layer and for the spatial case which led to a DB-rate gain of 1.81% and DB-PSNR gain of 0.025 in relation to the rate-distortion model of SHM-SHVC. Nevertheless, we also show in this dissertation that the proposed model of R-λ should not be used neither in the base layer nor in HEVC

Online Exploration: Browsing Behavior and Website Feature Preferences

This exploratory study examines the novel variable of cross-category online browse range (the variety of product categories browsed online by a consumer) and its relationship to general website feature preferences. Utilizing data collected through an online survey, the results are based on a final sample of 313 respondents from the United States, 287 of whom were University students, and 26 of whom were contacts of the research team. The general nature of cross-category online browse range was examined using simple correlation, MANOVA, and ANOVA. Results indicate that the variable is normally distributed throughout the sample population and positively associated with time spent online purchasing, time spent online browsing, online shopping intention (purchasing, browsing, and searching), and Domain Specific Innovativeness. Though cross-category online browse range is weakly related to the amount of hours spent online in general, it was not found to be significantly related to any of the demographic variables tested, or to Internet experience. A discriminant analysis revealed that consumers in the discrete cross-category online browse range groups (low, medium, high) differed in their preference for a variety of hedonically-oriented website features, the majority of which composed a function representing online exploration. Results from this study provide support for the idea that the individual difference of cross-category online browse range may reflect manifestations of several interrelated concepts, including exploratory shopping behavior, hedonic shopping motivation, and consumer innovativeness. In addition, this study illustrates the importance of accounting for individual differences in consumers\u27 online navigation habits and highlights the potential that exists in collecting meaningful cross-category clickstream data. For practitioners in particular, the results provide insights into how one can structure a shopping website to appeal to those consumers most likely to seek out new retail websi

Online Exploration: Browsing Behavior and Website Feature Preferences

This exploratory study examines the novel variable of cross-category online browse range (the variety of product categories browsed online by a consumer) and its relationship to general website feature preferences. Utilizing data collected through an online survey, the results are based on a final sample of 313 respondents from the United States, 287 of whom were University students, and 26 of whom were contacts of the research team. The general nature of cross-category online browse range was examined using simple correlation, MANOVA, and ANOVA. Results indicate that the variable is normally distributed throughout the sample population and positively associated with time spent online purchasing, time spent online browsing, online shopping intention (purchasing, browsing, and searching), and Domain Specific Innovativeness. Though cross-category online browse range is weakly related to the amount of hours spent online in general, it was not found to be significantly related to any of the demographic variables tested, or to Internet experience. A discriminant analysis revealed that consumers in the discrete cross-category online browse range groups (low, medium, high) differed in their preference for a variety of hedonically-oriented website features, the majority of which composed a function representing online exploration. Results from this study provide support for the idea that the individual difference of cross-category online browse range may reflect manifestations of several interrelated concepts, including exploratory shopping behavior, hedonic shopping motivation, and consumer innovativeness. In addition, this study illustrates the importance of accounting for individual differences in consumers\u27 online navigation habits and highlights the potential that exists in collecting meaningful cross-category clickstream data. For practitioners in particular, the results provide insights into how one can structure a shopping website to appeal to those consumers most likely to seek out new retail websi