Shear induced breaking of large internal solitary waves

The stability properties of 24 experimentally generated internal solitary waves (ISWs) of extremely large amplitude, all with minimum Richardson number less than 1/4, are investigated. The study is supplemented by fully nonlinear calculations in a three-layer fluid. The waves move along a linearly stratified pycnocline (depth h2) sandwiched between a thin upper layer (depth h1) and a deep lower layer (depth h3), both homogeneous. In particular, the wave-induced velocity profile through the pycnocline is measured by particle image velocimetry (PIV) and obtained in computation. Breaking ISWs were found to have amplitudes (a1) in the range a1>2.24 √h1h2(1+h2/h1), while stable waves were on or below this limit. Breaking ISWs were investigated for 0.27 0.86 and stable waves for Lx/λ < 0.86. The results show a sort of threshold-like behaviour in terms of Lx/λ. The results demonstrate that the breaking threshold of Lx/λ = 0.86 was sharper than one based on a minimum Richardson number and reveal that the Richardson number was found to become almost antisymmetric across relatively thick pycnoclines, with the minimum occurring towards the top part of the pycnoclinePostprintPeer reviewe

Isolated Star Formation: A Compact HII Region in the Virgo Cluster

We report on the discovery of an isolated, compact HII region in the Virgo cluster. The object is located in the diffuse outer halo of NGC 4388, or could possibly be in intracluster space. Star formation can thus take place far outside the main star forming regions of galaxies. This object is powered by a small starburst with an estimated mass of \sim 400\msun and age of \sim 3\myr. From a total sample of 17 HII region candidates, the present rate of isolated star formation estimated in our Virgo field is small, \sim 10^{-6} Msun arcmin}^{-2} yr^{-1}. However, this mode of star formation might have been more important at higher redshifts and be responsible for a fraction of the observed intracluster stars and total cluster metal production. This object is relevant also for distance determinations with the planetary nebula luminosity function from emission line surveys, for high-velocity clouds and the in situ origin of B stars in the Galactic halo, and for local enrichment of the intracluster gas by Type II supernovae.Comment: 5 pages, LaTeX, 1 figure. ApJ Letters, in press (scheduled Dec 1, 2002

Using affective avatars and rich multimedia content for education of children with autism

Autism is a communication disorder that mandates early and continuous educational interventions on various levels like the everyday social, communication and reasoning skills. Computer-aided education has recently been considered as a likely intervention method for such cases, and therefore different systems have been proposed and developed worldwide. In more recent years, affective computing applications for the aforementioned interventions have also been proposed to shed light on this problem. In this paper, we examine the technological and educational needs of affective interventions for autistic persons. Enabling affective technologies are visited and a number of possible exploitation scenarios are illustrated. Emphasis is placed in covering the continuous and long term needs of autistic persons by unobtrusive and ubiquitous technologies with the engagement of an affective speaking avatar. A personalised prototype system facilitating these scenarios is described. In addition the feedback from educators for autistic persons is provided for the system in terms of its usefulness, efficiency and the envisaged reaction of the autistic persons, collected by means of an anonymous questionnaire. Results illustrate the clear potential of this effort in facilitating a very promising autism intervention

Ares I Scale Model Acoustic Tests Instrumentation for Acoustic and Pressure Measurements

The Ares I Scale Model Acoustic Test (ASMAT) was a development test performed at the Marshall Space Flight Center (MSFC) East Test Area (ETA) Test Stand 116. The test article included a 5% scale Ares I vehicle model and tower mounted on the Mobile Launcher. Acoustic and pressure data were measured by approximately 200 instruments located throughout the test article. There were four primary ASMAT instrument suites: ignition overpressure (IOP), lift-off acoustics (LOA), ground acoustics (GA), and spatial correlation (SC). Each instrumentation suite incorporated different sensor models which were selected based upon measurement requirements. These requirements included the type of measurement, exposure to the environment, instrumentation check-outs and data acquisition. The sensors were attached to the test article using different mounts and brackets dependent upon the location of the sensor. This presentation addresses the observed effect of the sensors and mounts on the acoustic and pressure measurements

Low-rank Characteristic Tensor Density Estimation Part II: Compression and Latent Density Estimation

Learning generative probabilistic models is a core problem in machine learning, which presents significant challenges due to the curse of dimensionality. This paper proposes a joint dimensionality reduction and non-parametric density estimation framework, using a novel estimator that can explicitly capture the underlying distribution of appropriate reduced-dimension representations of the input data. The idea is to jointly design a nonlinear dimensionality reducing auto-encoder to model the training data in terms of a parsimonious set of latent random variables, and learn a canonical low-rank tensor model of the joint distribution of the latent variables in the Fourier domain. The proposed latent density model is non-parametric and universal, as opposed to the predefined prior that is assumed in variational auto-encoders. Joint optimization of the auto-encoder and the latent density estimator is pursued via a formulation which learns both by minimizing a combination of the negative log-likelihood in the latent domain and the auto-encoder reconstruction loss. We demonstrate that the proposed model achieves very promising results on toy, tabular, and image datasets on regression tasks, sampling, and anomaly detection

Structural and dielectric studies of the phase behaviour of the topological ferroelectric La1-xNdxTaO4

We thank the University of St Andrews and EPSRC (via DTG studentships to CALD and JG) for funding,The layered perovskite LaTaO4 has been prepared in its polar orthorhombic polymorphic form at ambient temperature. Although no structural phase transition is observed in the temperature interval 25° C < T < 500 °C, a very large axial thermal contraction effect is seen, which can be ascribed to an anomalous buckling of the perovskite octahedral layer. The non-polar monoclinic polymorph can be stabilised at ambient temperature by Nd-doping. A composition La0.90Nd0.10TaO4 shows a first-order monoclinic-orthorhombic (non-polar to polar) transition in the region 250° C < T < 350 °C. Dielectric responses are observed at both the above structural events but, despite the ‘topological ferroelectric’ nature of orthorhombic LaTaO4, we have not succeeded in obtaining ferroelectric P–E hysteresis behaviour. Structural relationships in the wider family of AnBnX3n+2 layered perovskites are discussed.Publisher PDFPeer reviewe

Information-theoretic Feature Selection via Tensor Decomposition and Submodularity

Feature selection by maximizing high-order mutual information between the selected feature vector and a target variable is the gold standard in terms of selecting the best subset of relevant features that maximizes the performance of prediction models. However, such an approach typically requires knowledge of the multivariate probability distribution of all features and the target, and involves a challenging combinatorial optimization problem. Recent work has shown that any joint Probability Mass Function (PMF) can be represented as a naive Bayes model, via Canonical Polyadic (tensor rank) Decomposition. In this paper, we introduce a low-rank tensor model of the joint PMF of all variables and indirect targeting as a way of mitigating complexity and maximizing the classification performance for a given number of features. Through low-rank modeling of the joint PMF, it is possible to circumvent the curse of dimensionality by learning principal components of the joint distribution. By indirectly aiming to predict the latent variable of the naive Bayes model instead of the original target variable, it is possible to formulate the feature selection problem as maximization of a monotone submodular function subject to a cardinality constraint - which can be tackled using a greedy algorithm that comes with performance guarantees. Numerical experiments with several standard datasets suggest that the proposed approach compares favorably to the state-of-art for this important problem