Spacecraft attitude detection system by stellar reference Patent

Attitude detection system using stellar references for three-axis control and spin stabilized spacecraf

Inconsistencies in the MIT bag model of hadrons

It is shown that what is commonly referred to as the MIT `bag' model of hadrons is thermodynamically wrong: The adiabatic conditions between pressure and temperature, and between pressure and volume imply the third, an adiabatic relation between temperature and volume. Consequently, the bag model is destitute of any predictive power since it reduces to a single adiabatic state. The virial theorems proposed by the MIT group are shown to be the result of the normal power density of states of a non-degenerate gas and not the exponential density of states of the Hagedorn mass spectrum. A number of other elementary misconceptions and inaccuracies are also pointed out.Comment: 9 page

The determination of measures of software reliability

Measurement of software reliability was carried out during the development of data base software for a multi-sensor tracking system. The failure ratio and failure rate were found to be consistent measures. Trend lines could be established from these measurements that provide good visualization of the progress on the job as a whole as well as on individual modules. Over one-half of the observed failures were due to factors associated with the individual run submission rather than with the code proper. Possible application of these findings for line management, project managers, functional management, and regulatory agencies is discussed. Steps for simplifying the measurement process and for use of these data in predicting operational software reliability are outlined

Velocity lag of solid particles in oscillating gases and in gases passing through normal shock waves

The velocity lag of micrometer size spherical particles is theoretically determined for gas particle mixtures passing through a stationary normal shock wave and also for particles embedded in an oscillating gas flow. The particle sizes and densities chosen are those considered important for laser Doppler velocimeter applications. The governing equations for each flow system are formulated. The deviation from Stokes flow caused by inertial, compressibility, and rarefaction effects is accounted for in both flow systems by use of an empirical drag coefficient. Graphical results are presented which characterize particle tracking as a function of system parameters

The Variational Homoencoder: Learning to learn high capacity generative models from few examples

Hierarchical Bayesian methods can unify many related tasks (e.g. k-shot classification, conditional and unconditional generation) as inference within a single generative model. However, when this generative model is expressed as a powerful neural network such as a PixelCNN, we show that existing learning techniques typically fail to effectively use latent variables. To address this, we develop a modification of the Variational Autoencoder in which encoded observations are decoded to new elements from the same class. This technique, which we call a Variational Homoencoder (VHE), produces a hierarchical latent variable model which better utilises latent variables. We use the VHE framework to learn a hierarchical PixelCNN on the Omniglot dataset, which outperforms all existing models on test set likelihood and achieves strong performance on one-shot generation and classification tasks. We additionally validate the VHE on natural images from the YouTube Faces database. Finally, we develop extensions of the model that apply to richer dataset structures such as factorial and hierarchical categories.Comment: UAI 2018 oral presentatio