A comparison of time domain boundary conditions for acoustic waves in wave guides

Researchers consider several types of boundary conditions in the context of time domain models for acoustic waves. Experiments with four different duct terminations (hard wall, free radiation, foam, and wedge) were carried out in a wave duct from which reflection coefficients over a wide frequency range were measured. These reflection coefficients were used to estimate parameters in the time domain boundary conditions. A comparison of the relative merits of the models in describing the data is presented. Boundary conditions which yield a good fit of the model to the experimental data were found for all duct terminations except the wedge

Formal software measurements for object-oriented business models.

This paper presents a set of metrics and pseudo-metrics for the measurement of conceptual distances in M.E.R.O.D.E. business models. The measures are developed and validated using measure and measurement theory. It is argued that this metrics set constitutes a strong formal basis for the further assessment and prediction of relevant internal and external attributes of object-oriented specifications.Keywords: object type, business model, conceptual distance, measure theory, measurement theory, metric, pseudo-metric, scale type, measure validation.Measurement; Model; Models; Software;

Optimal State Discrimination in General Probabilistic Theories

We investigate a state discrimination problem in operationally the most general framework to use a probability, including both classical, quantum theories, and more. In this wide framework, introducing closely related family of ensembles (which we call a {\it Helstrom family of ensembles}) with the problem, we provide a geometrical method to find an optimal measurement for state discrimination by means of Bayesian strategy. We illustrate our method in 2-level quantum systems and in a probabilistic model with square-state space to reproduce e.g., the optimal success probabilities for binary state discrimination and $N$ numbers of symmetric quantum states. The existences of families of ensembles in binary cases are shown both in classical and quantum theories in any generic cases.Comment: 9 pages, 6 figure

A clustering algorithm for multivariate data streams with correlated components

Common clustering algorithms require multiple scans of all the data to achieve convergence, and this is prohibitive when large databases, with data arriving in streams, must be processed. Some algorithms to extend the popular K-means method to the analysis of streaming data are present in literature since 1998 (Bradley et al. in Scaling clustering algorithms to large databases. In: KDD. p. 9-15, 1998; O'Callaghan et al. in Streaming-data algorithms for high-quality clustering. In: Proceedings of IEEE international conference on data engineering. p. 685, 2001), based on the memorization and recursive update of a small number of summary statistics, but they either don't take into account the specific variability of the clusters, or assume that the random vectors which are processed and grouped have uncorrelated components. Unfortunately this is not the case in many practical situations. We here propose a new algorithm to process data streams, with data having correlated components and coming from clusters with different covariance matrices. Such covariance matrices are estimated via an optimal double shrinkage method, which provides positive definite estimates even in presence of a few data points, or of data having components with small variance. This is needed to invert the matrices and compute the Mahalanobis distances that we use for the data assignment to the clusters. We also estimate the total number of clusters from the data.Comment: title changed, rewritte

Some Varieties of Superparadox. The implications of dynamic contradiction, the characteristic form of breakdown of breakdown of sense to which self-reference is prone

The Problem of the Paradoxes came to the fore in philosophy and mathematics with the discovery of Russell's Paradox in 1901. It is the "forgotten" intellectual-scientific problem of the Twentieth Century, because for more than sixty years a pretence was maintained, by a consensus of logicians, that the problem had been "solved"

Optimizing the Use of the United States Army OH-58D Helicopter Simulator and Aircraft for Full-Authority Digital Electronic Control Manual Throttle Training

Over the past decade the United States Army has used a Full-Authority Digital Electronic Control (FADEC) system to control fuel flow to the engine of the OH-58D helicopter. Currently, part of the training is primarily conducted for the scenario of a FADEC system failure in the aircraft. Because of the complexity of this task, a number of accidents have occurred resulting in minor to severe damage to the aircraft. The United States Army has recently fielded two OH-58D Operational Flight Training Simulators in an effort to increase training efficiency and effectiveness. It is anticipated that the simulators will provide a safer environment and an effected transfer of training to the aircraft. Currently the OH-58D training unit has implemented the simulator into the manual throttle stage of training. This implementation has occurred through verification and validation of the Program of Instruction (POI) currently in use. An investigation into the transfer of training from the simulator to the aircraft was conducted to further optimize the distributions of training time in the simulator versus the aircraft. The primary source of data was collected from aircraft and simulator trials and flight hours to evaluate the transfer effectiveness ratio. The secondary source of data was collected through the use of pilot surveys and questionnaires. The pilots reported a mean workload rating of 2.52 using the Bedford Workload Rating Scale in the aircraft after the simulator, which indicates a low workload. The Pilots reported mild to moderate simulator sickness symptoms after flying in the simulator. A total severity score of 20.06 was computed through the use of the Simulator Sickness Questionnaire. When compared to other helicopter simulators this score is fairly high. Overall there were low Pilot-Vehicle Interface problems in the simulator and aircraft. There was no decline in Situational Awareness from the simulator to the aircraft. The overall Transfer Effectiveness Ratios indicated a positive Transfer of Training. The current Program of Instruction and simulator hours are validated. The focus in the simulator should be placed on Method of Instructions step two “failure at a hover” and step four “running landing or approach to a hover.

Variational inference for sparse network reconstruction from count data

In multivariate statistics, the question of finding direct interactions can be formulated as a problem of network inference - or network reconstruction - for which the Gaussian graphical model (GGM) provides a canonical framework. Unfortunately, the Gaussian assumption does not apply to count data which are encountered in domains such as genomics, social sciences or ecology. To circumvent this limitation, state-of-the-art approaches use two-step strategies that first transform counts to pseudo Gaussian observations and then apply a (partial) correlation-based approach from the abundant literature of GGM inference. We adopt a different stance by relying on a latent model where we directly model counts by means of Poisson distributions that are conditional to latent (hidden) Gaussian correlated variables. In this multivariate Poisson lognormal-model, the dependency structure is completely captured by the latent layer. This parametric model enables to account for the effects of covariates on the counts. To perform network inference, we add a sparsity inducing constraint on the inverse covariance matrix of the latent Gaussian vector. Unlike the usual Gaussian setting, the penalized likelihood is generally not tractable, and we resort instead to a variational approach for approximate likelihood maximization. The corresponding optimization problem is solved by alternating a gradient ascent on the variational parameters and a graphical-Lasso step on the covariance matrix. We show that our approach is highly competitive with the existing methods on simulation inspired from microbiological data. We then illustrate on three various data sets how accounting for sampling efforts via offsets and integrating external covariates (which is mostly never done in the existing literature) drastically changes the topology of the inferred network