Anomaly-Sensitive Dictionary Learning for Unsupervised Diagnostics of Solid Media

This paper proposes a strategy for the detection and triangulation of structural anomalies in solid media. The method revolves around the construction of sparse representations of the medium's dynamic response, obtained by learning instructive dictionaries which form a suitable basis for the response data. The resulting sparse coding problem is recast as a modified dictionary learning task with additional spatial sparsity constraints enforced on the atoms of the learned dictionaries, which provides them with a prescribed spatial topology that is designed to unveil anomalous regions in the physical domain. The proposed methodology is model agnostic, i.e., it forsakes the need for a physical model and requires virtually no a priori knowledge of the structure's material properties, as all the inferences are exclusively informed by the data through the layers of information that are available in the intrinsic salient structure of the material's dynamic response. This characteristic makes the approach powerful for anomaly identification in systems with unknown or heterogeneous property distribution, for which a model is unsuitable or unreliable. The method is validated using both syntheticallyComment: Submitted to the Proceedings of the Royal Society

On the evaluation of quasi-periodic Green functions and wave-scattering at and around Rayleigh-Wood anomalies

This article presents full-spectrum, well-conditioned, Green-function methodologies for evaluation of scattering by general periodic structures, which remains applicable on a set of challenging singular configurations, usually called Rayleigh-Wood (RW) anomalies (at which the quasi-periodic Green function ceases to exist), where most existing quasi-periodic solvers break down. After reviewing a variety of existing fast-converging numerical procedures commonly used to compute the classical quasi-periodic Green-function, the present work explores the difficulties they present around RW-anomalies and introduces the concept of hybrid “spatial/spectral” representations. Such expressions allow both the modification of existing methods to obtain convergence at RW-anomalies as well as the application of a slight generalization of the Woodbury-Sherman-Morrison formulae together with a limiting procedure to bypass the singularities. (Although, for definiteness, the overall approach is applied to the scalar (acoustic) wave-scattering problem in the frequency domain, the approach can be extended in a straightforward manner to the harmonic Maxwell's and elasticity equations.) Ultimately, this thorough study of RW-anomalies yields fast and highly-accurate solvers, which are demonstrated with a variety of simulations of wave-scattering phenomena by arrays of particles, crossed impenetrable and penetrable diffraction gratings and other related structures. In particular, the methods developed in this article can be used to “upgrade” classical approaches, resulting in algorithms that are applicable throughout the spectrum, and it provides new methods for cases where previous approaches are either costly or fail altogether. In particular, it is suggested that the proposed shifted Green function approach may provide the only viable alternative for treatment of three-dimensional high-frequency configurations with either one or two directions of periodicity. A variety of computational examples are presented which demonstrate the flexibility of the overall approach

Effective mass anomalies in strained Si thin films and crystals

Effective mass anomalies due to the geometrical effects are investigated in silicon nanostructures using first-principles calculations for the first time. In \{111\} and \{110\} biaxially strained Si, it is found that longitudinal effective mass is extraordinarily enhanced for both thin films and crystals. This mass enhancement is caused by the change of the band structure with double minima into that with a single minimum due to strain and confinement. At the transition point, it is analytically shown that the effective mass diverges. The dependences of the confinement thickness on the anomalies are qualitatively explained by an extension of the effective mass approximation.Comment: 4 pages, 5 figures, submitted to Phys. Rev. Let

Shuttle TPS thermal performance and analysis methodology

Thermal performance of the thermal protection system was approximately as predicted. The only extensive anomalies were filler bar scorching and over-predictions in the high Delta p gap heating regions of the orbiter. A technique to predict filler bar scorching has been developed that can aid in defining a solution. Improvement in high Delta p gap heating methodology is still under study. Minor anomalies were also examined for improvements in modeling techniques and prediction capabilities. These include improved definition of low Delta p gap heating, an analytical model for inner mode line convection heat transfer, better modeling of structure, and inclusion of sneak heating. The limited number of problems related to penetration items that presented themselves during orbital flight tests were resolved expeditiously, and designs were changed and proved successful within the time frame of that program

Influence of Arctic sea-ice variability on Pacific trade winds.

A conceptual model connecting seasonal loss of Arctic sea ice to midlatitude extreme weather events is applied to the 21st-century intensification of Central Pacific trade winds, emergence of Central Pacific El Nino events, and weakening of the North Pacific Aleutian Low Circulation. According to the model, Arctic Ocean warming following the summer sea-ice melt drives vertical convection that perturbs the upper troposphere. Static stability calculations show that upward convection occurs in annual 40- to 45-d episodes over the seasonally ice-free areas of the Beaufort-to-Kara Sea arc. The episodes generate planetary waves and higher-frequency wave trains that transport momentum and heat southward in the upper troposphere. Regression of upper tropospheric circulation data on September sea-ice area indicates that convection episodes produce wave-mediated teleconnections between the maximum ice-loss region north of the Siberian Arctic coast and the Intertropical Convergence Zone (ITCZ). These teleconnections generate oppositely directed trade-wind anomalies in the Central and Eastern Pacific during boreal winter. The interaction of upper troposphere waves with the ITCZ air-sea column may also trigger Central Pacific El Nino events. Finally, waves reflected northward from the ITCZ air column and/or generated by triggered El Nino events may be responsible for the late winter weakening of the Aleutian Low Circulation in recent years

Analysis of the planetary boundary layer with a database of large-eddy simulation experiments

Observational studies of a planetary boundary layer (PBL) are difficult. Ground-born measurements usually characterize only a small portion of the PBL immediately above the surface. Air-born measurements cannot be obtained close to the surface and therefore cannot capture any significant portion of the PBL interior. Moreover, observations are limited in choice of instrumentation, time, duration, location of measurements and occasional weather conditions. Although turbulence-resolving simulations with a large-eddy simulation (LES) code do not supplant observational studies, they provide valuable complementary information on different aspect of the PBL dynamics, which otherwise difficult to acquire. These circumstances motivated development of a medium-resolution database (DATABASE64) of turbulence-resolving simulations, which is available on ftp://ftp.nersc.no/igor/. DATABASE64 covers a range of physical parameters typical for the barotropic SBL over a homogeneous rough surface. LES runs in DATABASE64 simulate 16 hours' evolution of the PBL turbulence. They are utilized to study both transition and equilibrium SBL cases as well as to calibrate turbulence parameterizations of meteorological models. The data can be also used to falsify theoretical constructions with regards to the PBL

Deep Generative Model using Unregularized Score for Anomaly Detection with Heterogeneous Complexity

Accurate and automated detection of anomalous samples in a natural image dataset can be accomplished with a probabilistic model for end-to-end modeling of images. Such images have heterogeneous complexity, however, and a probabilistic model overlooks simply shaped objects with small anomalies. This is because the probabilistic model assigns undesirably lower likelihoods to complexly shaped objects that are nevertheless consistent with set standards. To overcome this difficulty, we propose an unregularized score for deep generative models (DGMs), which are generative models leveraging deep neural networks. We found that the regularization terms of the DGMs considerably influence the anomaly score depending on the complexity of the samples. By removing these terms, we obtain an unregularized score, which we evaluated on a toy dataset and real-world manufacturing datasets. Empirical results demonstrate that the unregularized score is robust to the inherent complexity of samples and can be used to better detect anomalies.Comment: An extended version of a manuscript in Proc. of The 2018 International Joint Conference on Neural Networks (IJCNN2018

Magnetic transitions and magnetodielectric effect in the antiferromagnet SrNdFeO4_4

We investigated the magnetic phase diagram of single crystals of SrNdFeO$_{4}$ by measuring the magnetic properties, the specific heat and the dielectric permittivity. The system has two magnetically active ions, Fe$^{3+}$ and Nd$^{3+}$. The Fe$^{3+}$ spins are antiferromagnetically ordered below 360 K with the moments lying in the ab-plane, and undergo a reorientation transition at about 35-37 K to an antiferromagnetic order with the moments along the c-axis. A short-range, antiferromagnetic ordering of Nd$^{3+}$ along the c-axis was attributed to the reorientation of Fe$^{3+}$ followed by a long-range ordering at lower temperature [S. Oyama {\it et al.} J. Phys.: Condens. Matter. {\bf 16}, 1823 (2004)]. At low temperatures and magnetic fields above 8 T, the Nd$^{3+}$ moments are completely spin-polarized. The dielectric permittivity also shows anomalies associated with spin configuration changes, indicating that this compound has considerable coupling between spin and lattice. A possible magnetic structure is proposed to explain the results.Comment: 8 pages, 10 figures, submitted to PR

Deep Learning Inversion of Electrical Resistivity Data

The inverse problem of electrical resistivity surveys (ERSs) is difficult because of its nonlinear and ill-posed nature. For this task, traditional linear inversion methods still face challenges such as suboptimal approximation and initial model selection. Inspired by the remarkable nonlinear mapping ability of deep learning approaches, in this article, we propose to build the mapping from apparent resistivity data (input) to resistivity model (output) directly by convolutional neural networks (CNNs). However, the vertically varying characteristic of patterns in the apparent resistivity data may cause ambiguity when using CNNs with the weight sharing and effective receptive field properties. To address the potential issue, we supply an additional tier feature map to CNNs to help those aware of the relationship between input and output. Based on the prevalent U-Net architecture, we design our network (ERSInvNet) that can be trained end-to-end and can reach a very fast inference speed during testing. We further introduce a depth weighting function and a smooth constraint into loss function to improve inversion accuracy for the deep region and suppress false anomalies. Six groups of experiments are considered to demonstrate the feasibility and efficiency of the proposed methods. According to the comprehensive qualitative analysis and quantitative comparison, ERSInvNet with tier feature map, smooth constraints, and depth weighting function together achieve the best performance.Comment: IEEE Transactions on Geoscience and Remote Sensing, 202

Cluster and reentrant anomalies of nearly Gaussian core particles

We study through integral equation theory and numerical simulations the structure and dynamics of fluids composed of ultrasoft, nearly Gaussian particles. Namely, we explore the fluid phase diagram of a model in which particles interact via the generalized exponential potential u(r)=\epsilon exp[-(r/\sigma)^n], with a softness exponent n slightly larger than 2. In addition to the well-known anomaly associated to reentrant melting, the structure and dynamics of the fluid display two additional anomalies, which are visible in the isothermal variation of the structure factor and diffusivity. These features are correlated to the appearance of dimers in the fluid phase and to the subsequent modification of the cluster structure upon compression. We corroborate these results through an analysis of the local minima of the potential energy surface, in which clusters appear as much tighter conglomerates of particles. We find that reentrant melting and clustering coexist for softness exponents ranging from 2^+ up to values relevant for the description of amphiphilic dendrimers, i.e., n=3.Comment: 10 pages, 8 figure