Variational auto-encoders with Student's t-prior

We propose a new structure for the variational auto-encoders (VAEs) prior, with the weakly informative multivariate Student's t-distribution. In the proposed model all distribution parameters are trained, thereby allowing for a more robust approximation of the underlying data distribution. We used Fashion-MNIST data in two experiments to compare the proposed VAEs with the standard Gaussian priors. Both experiments showed a better reconstruction of the images with VAEs using Student's t-prior distribution

Optimizing the AI Development Process by Providing the Best Support Environment

The purpose of this study is to investigate the development process for Artificial inelegance (AI) and machine learning (ML) applications in order to provide the best support environment. The main stages of ML are problem understanding, data management, model building, model deployment and maintenance. This project focuses on investigating the data management stage of ML development and its obstacles as it is the most important stage of machine learning development because the accuracy of the end model is relying on the kind of data fed into the model. The biggest obstacle found on this stage was the lack of sufficient data for model learning, especially in the fields where data is confidential. This project aimed to build and develop a framework for researchers and developers that can help solve the lack of sufficient data during data management stage. The framework utilizes several data augmentation techniques that can be used to generate new data from the original dataset which can improve the overall performance of the ML applications by increasing the quantity and quality of available data to feed the model with the best possible data. The framework was built using python language to perform data augmentation using deep learning advancements

Graduate Catalog, 1996-1999, New Jersey Institute of Technology

https://digitalcommons.njit.edu/coursecatalogs/1003/thumbnail.jp

High precision angle calibration for spherical measurement systems

The European Synchrotron Radiation Facility (ESRF) located in Grenoble, France is a joint facility supported and shared by 19 European countries. It operates the most powerful synchrotron radiation source in Europe. Synchrotron radiation sources address many important questions in modern science and technology. They can be compared to “super microscopes”, revealing invaluable information in numerous fields of diverse research such as physics, medicine, biology, geophysics and archaeology. For the ESRF accelerators and beam lines to work correctly, alignment is of critical importance. Alignment tolerances are typically much less than one millimetre and often in the order of several micrometers over the 844 m ESRF storage ring circumference. To help maintain these tolerances, the ESRF has, and continues to develop calibration techniques for high precision spherical measurement system (SMS) instruments. SMSs are a family of instruments comprising automated total stations (theodolites equipped with distance meters), referred to here as robotic total stations (RTSs); and laser trackers (LTs). The ESRF has a modern distance meter calibration bench (DCB) used for the calibration of SMS electronic distance meters. At the limit of distance meter precision, the only way to improve positional uncertainty in the ESRF alignment is to improve the angle measuring capacity of these instruments. To this end, the horizontal circle comparator (HCC) and the vertical circle comparator (VCC) have been developed. Specifically, the HCC and VCC are used to calibrate the horizontal and vertical circle readings of SMS instruments under their natural working conditions. Combined with the DCB, the HCC and VCC provide a full calibration suite for SMS instruments. This thesis presents their development, functionality and in depth uncertainty evaluation. Several unique challenges are addressed in this work. The first is the development and characterization of the linked encoders configuration (LEC). This system, based on two continuously rotating angle encoders, is designed improve performance by eliminating residual encoder errors. The LEC can measure angle displacements with an estimated uncertainty of at least 0.044 arc seconds. Its uncertainty is presently limited by the instrumentation used to evaluate it. Secondly, in depth investigation has lead to the discovery of previously undocumented error-motion effects in ultra-precision angle calibration. Finally, methods for rigorous characterisation and extraction of rotary table error motions and their uncertainty evaluation using techniques not previously discussed in the literature have been developed

General Undergraduate Catalog, 1991-1992

Marshall University General Undergraduate Catalog for the 1991-1992 academic year.https://mds.marshall.edu/catalog_1990-1999/1000/thumbnail.jp

Engineering handbook

2001 handbook for the faculty of Engineerin

1987 April, Memphis State University bulletin

Vol. 76, No. 1 of the Memphis State University bulletin containing the undergraduate catalog for 1987-88, 1987 April.https://digitalcommons.memphis.edu/speccoll-ua-pub-bulletins/1164/thumbnail.jp