NISS WebSwap: A Web Service for Data Swapping

Data swapping is a statistical disclosure limitation practice that alters records in the data to be released by switching values of attributes across pairs of records in a fraction of the original data. Web Services are an exciting new form of distributed computing that allow users to invoke remote applications nearly transparently. National Institute of Statistical Sciences (NISS) has recently started hosting NISS Web Services as a service and example to the statistical sciences community. In this paper we describe and provide usage information for NISS WebSwap the initial NISS Web Service, which swaps one or more attributes (fields) between user-specified records in a microdata file, uploading the original data file from the user's computer and downloading the file containing the swapped records.

Vapour pressures of some inorganic sulphates at high temperatures

The vapour pressures of some inorganic sulphates at high temperatures were determined by the combined use of the Knudsen effusion, transpiration, and matrix isolation methods. After a detailed comparison with the results of other investigators, it is concluded that the principal vapour species in the case of K₂SO₄, Rb₂SO₄, and Cs₂SO₄ are the undecomposed sulphate molecules themselves, and in the case of Li₂SO₄ the decomposition products, Li, SO₂, and O₂. The sodium salt also decomposes to some extent into Na, SO₂, and O₂, but it is deduced that the vapour concentration of the species Na₂O₄ is probably greater than was formerly supposed. The decomposition of alkaline earth sulphates is also discussed, especially in the light of the dependence of the Knudsen effusion results upon the size of the orifice used. On the basis of the vapour constitutions deduced, thermodynamic functions for the important species present are tabulated up to 1400 K or above. Reference is also made to the function of sodium sulphate in the glass-making industry and to the possible mechanism of its corrosive action on furnace walls

TIME-FREQUENCY APPROXIMATION AND FEATURE EXTRACTION FOR RANGE-DEPENDENT UNDERWATER SOUND PROPAGATION

Sonar systems are used in localization, detection and classification of various objects in marine environments. Unlike the propagation of sound in air, which is largely unaffected by dispersion, the underwater channel can be highly dispersive, especially in shallow water environments. Such channels also introduce other significant propagation effects, including multipath and frequency-dependent energy attenuation due to interactions of the sound with the ocean surface and bottom. Compensating for these propagation effects is important with regard to classification of underwater objects based on their sonar backscatter, as the target “signature” will be different at different locations. Previous work in our lab has developed feature extraction methods for dispersion-invariant classification, and approximation methods to solve for dispersive propagation, in range-independent environments. Such environments, wherein the channel characteristics do not change with propagation distance, represent an idealistic assumption that generally does not hold for long-range propagation in underwater channels. In this work we concentrate on range-dependent guided wave propagation. We begin with an examination of the classification performance of the previously developed range-independent features in a range-dependent model, namely an ideal wedge waveguide. Motivated by the degradation in classification performance of these features, we derive new features that mitigate the range-dependent dispersion effects and show that the derived features outperform range-independent features in a wedge waveguide. We also derive the approximate Wigner distribution for a pulse propagating in this range-dependent environment, and highlight similarities and differences of this new result with a previously developed range-independent approximation. This approximation can be a useful tool for estimating the evolution of pulse propagating in a range-dependent channel. Finally, we explore a second range-dependent model, namely the Parabolic Equation, which can be adapted to a wide array of propagation environments and media. We derive features that are invariant to dispersion and attenuation from this model

Channel Characterization and Object Classification in Non-Stationary and Uncertain Environments

Classification of SONAR targets in underwater environments has long been a challenging problem. These are mainly due to the presence of undesirable effects like dispersion, attenuation and self-noise. Furthermore, we also have to contend with range dependent environments, like the continental shelf/littoral regions, where most of the human and aquatic life's activities occur. Our work consists of analyzing the propagation in these environments from a pulse-evolution perspective. We look at cases where characterizing wave propagation using conventional Fourier-spectral analysis is infeasible for practical applications and instead resort to a phase-space approximation for it. We derive the phase-space approximations for a variety of propagating waves and limiting boundary conditions. We continue our past work on invariant features to enhance classification performance; we simulate the derived features for waves with cylindrical spreading. Another area of our work includes looking at the equation governing the wave propagation from a phase space perspective. It has been shown before that reformulating the classical wave equation in the phase-space provides interesting insights to the solution of the equation. It has been posited that this would be especially useful for non-stationary functions, like the ones governing SONAR propagation underwater. We perform classification of real world SONAR data measured by the JRP ( DRDC-Atlantic, NURC, ARL-PSU, NRL) program. We use a 'classic' MPE classifier on the given non-stationary and contrast its performance with an MPE classifier augmented by a Linear Time Varying (LTV) filter, to assess the impact of adding a time-varying pre-filter to a classifier (MPE) deemed optimal for stationary additive white Gaussian noise. We show that the addition of the time-varying pre-filter to augment the standard MPE classifier does increase the performance of the classifier. Finally, we look at the self-noise problem that is commonly present in the littoral regions of the ocean, which also happens to be the region where most of shallow water sound propagation occurs. We look at phase-space approach to the stochastic models that simulate the effect of signal dependent noise reverberations and attempt to design time-varying estimators that would mitigate the problem at hand. We perform simulations that corroborate our premise. Further directions in the aforementioned areas are also presented

A renormalisation approach to excitable reaction-diffusion waves in fractal media

Of fundamental importance to wave propagation in a wide range of physical phenomena is the structural geometry of the supporting medium. Recently, there have been several investigations on wave propagation in fractal media. We present here a renormalization approach to the study of reaction-diffusion (RD) wave propagation on finitely ramified fractal structures. In particular we will study a Rinzel-Keller (RK) type model, supporting travelling waves on a Sierpinski gasket (SG), lattice

Patient and Sample Identification. out of the Maze?

Background: Patient and sample misidentification may cause significant harm or discomfort to the patients, especially when incorrect data is used for performing specific healthcare activities. It is hence obvious that efficient and quality care can only start from accurate patient identification. There are many opportunities for misidentification in healthcare and laboratory medicine, including homonymy, incorrect patient registration, reliance on wrong patient data, mistakes in order entry, collection of biological specimens from wrong patients, inappropriate sample labeling and inaccurate entry or erroneous transmission of test results through the laboratory information system. Many ongoing efforts are made to prevent this important healthcare problem, entailing streamlined strategies for identifying patients throughout the healthcare industry by means of traditional and innovative identifiers, as well as using technologic tools that may enhance both the quality and efficiency of blood tubes labeling. The aim of this article is to provide an overview about the liability of identification errors in healthcare, thus providing a pragmatic approach for diverging the so-called patient identification crisis

God may not play dice, but human observers surely do

We investigate indeterminism in physical observations. For this, we introduce a distinction between genuinely indeterministic (creation-1 and discovery-1) observational processes, and fully deterministic (creation-2 and discovery-2) observational processes, which we analyze by drawing a parallel between the localization properties of microscopic entities, like electrons, and the lateralization properties of macroscopic entities, like simple elastic bands. We show that by removing the randomness incorporated in certain of our observational processes, acquiring over them a better control, we also alter these processes in such a radical way that in the end they do not correspond anymore to the observation of the same property. We thus conclude that a certain amount of indeterminism must be accepted and welcomed in our physical observations, as we cannot get rid of it without also diminishing our discriminative power. We also provide in our analysis some elements of clarification regarding the non-spatial nature of microscopic entities, which we illustrate by using an analogy with the process of objectification of human concepts. Finally, the important notion of relational properties is properly defined, and the role played by indeterminism in their characterization clarified