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Shock-driven transition to turbulence: Emergence of power-law scaling
Here, we consider two cases of interaction between a planar shock and a cylindrical density interface. In the first case (planar normal shock), the axis of the gas cylinder is parallel to the shock front and baroclinic vorticity deposited by the shock is predominantly two dimensional (directed along the axis of the cylinder). In the second case, the cylinder is tilted, resulting in an oblique shock interaction and a fully-three-dimensional shock-induced vorticity field. Furthermore, the statistical properties of the flow for both cases are analyzed based on images from two orthogonal visualization planes, using structure functions of the intensity maps of fluorescent tracer premixed with heavy gas. And at later times, these structure functions exhibit power-law-like behavior over a considerable range of scales. Manifestation of this behavior is remarkably consistent in terms of dimensionless time τ defined based on Richtmyer's linear theory within the range of Mach numbers from 1.1 to 2.0 and the range of gas cylinder tilt angles with respect to the plane of the shock front (0–30°).
Capacity of noncoherent MFSK channels
Performance limits theoretically achievable over noncoherent channels perturbed by additive Gaussian noise in hard decision, optimal, and soft decision receivers are computed as functions of the number of orthogonal signals and the predetection signal-to-noise ratio. Equations are derived for orthogonal signal capacity, the ultimate MFSK capacity, and the convolutional coding and decoding limit. It is shown that performance improves as the signal-to-noise ratio increases, provided the bandwidth can be increased, that the optimum number of signals is not infinite (except for the optimal receiver), and that the optimum number decreases as the signal-to-noise ratio decreases, but is never less than 7 for even the hard decision receiver.
Enhancements to the GRIDGEN structured grid generation system for internal and external flow applications
GRIDGEN is a government domain software package for interactive generation of multiple block grids around general configurations. Though it has been freely available since 1989, it has not been widely embraced by the internal flow community due to a misconception that it was designed for external flow use only. In reality GRIDGEN has always worked for internal flow applications, and GRIDGEN ongoing enhancements are increasing the quality of and efficiency with which grids for external and internal flow problems may be constructed. The software consists of four codes used to perform the four steps of the grid generation process. GRIDBLOCK is first used to decompose the flow domain into a collection of component blocks and then to establish interblock connections and flow solver boundary conditions. GRIDGEN2D is then used to generate surface grids on the outer shell of each component block. GRIDGEN3D generates grid points on the interior of each block, and finally GRIDVUE3D is used to inspect the resulting multiple block grid. Three of these codes (GRIDBLOCK, GRIDGEN2D, and GRIDVUE3D) are highly interactive and graphical in nature, and currently run on Silicon Graphics, Inc., and IBM RS/6000 workstations. The lone batch code (GRIDGEN3D) may be run on any of several Unix based platforms. Surface grid generation in GRIDGEN2D is being improved with the addition of higher order surface definitions (NURBS and parametric surfaces input in IGES format and bicubic surfaces input in PATRAN Neutral File format) and double precision mathematics. In addition, two types of automation have been added to GRIDGEN2D that reduce the learning curve slope for new users and eliminate work for experienced users. Volume grid generation using GRIDGEN3D has been improved via the addition of an advanced hybrid control function formulation that provides both orthogonality and clustering control at the block faces and clustering control on the block interior.
Adaptive wavelet collocation methods for initial value boundary problems of nonlinear PDE's
We have designed a cubic spline wavelet decomposition for the Sobolev space H(sup 2)(sub 0)(I) where I is a bounded interval. Based on a special 'point-wise orthogonality' of the wavelet basis functions, a fast Discrete Wavelet Transform (DWT) is constructed. This DWT transform will map discrete samples of a function to its wavelet expansion coefficients in O(N log N) operations. Using this transform, we propose a collocation method for the initial value boundary problem of nonlinear PDE's. Then, we test the efficiency of the DWT transform and apply the collocation method to solve linear and nonlinear PDE's.
Characterization of oriented cracks with differential strain analysis
Linear strain of a rock sample as a function of hydrostatic pressure can be measured with a precision of 2 millionths. Such high-precision data for three orthogonal directions allow calculation of the distribution function for the porosity due to cracks' closing completely at a given pressure. Such data for at least six independent directions yield the zero-pressure strain tensor due to cracks' closing completely at a given pressure. The principal values and axes of this tensor distribution function provide information about the orientation of cracks as a function of closure pressure. The mathematical basis for the technique is developed, and the technique is illustrated with differential strain data for two samples, the Westerly (Rhode Island) granite and the Twin Sisters (Washington) dunite. Strain-tensor calculations reveal that each of these samples has a different type of anisotropic crack distribution.
Semiclassical wave-packets emerging from interaction with an environment
We study the quantum evolution in dimension three of a system composed by a test particle interacting with an environment made of N harmonic oscillators. At time zero the test particle is described by a spherical wave, i.e., a highly correlated continuous superposition of states with well localized position and momentum, and the oscillators are in the ground state. Furthermore, we assume that the positions of the oscillators are not collinear with the center of the spherical wave. Under suitable assumptions on the physical parameters characterizing the model, we give an asymptotic expression of the solution of the Schrödinger equation of the system with an explicit control of the error. The result shows that the approximate expression of the wave function is the sum of two terms, orthogonal in L{sup 2}(R{sup 3(N+1)}) and describing rather different situations. In the first one, all the oscillators remain in their ground state and the test particle is described by the free evolution of a slightly deformed spherical wave. The second one consists of a sum of N terms where in each term there is only one excited oscillator and the test particle is correspondingly described by the free evolution of a wave packet, well concentrated in position and momentum. Moreover, the wave packet emerges from the excited oscillator with an average momentum parallel to the line joining the oscillator with the center of the initial spherical wave. Such wave packet represents a semiclassical state for the test particle, propagating along the corresponding classical trajectory. The main result of our analysis is to show how such a semiclassical state can be produced, starting from the original spherical wave, as a result of the interaction with the environment.
Density functional theory based simulations of silicon nanowire field effect transistors
First-principles density functional theory (DFT) based, atomistic, self-consistent device simulations are performed for realistically sized Si nanowire field effect transistors (NW FETs) having tens of thousands of atoms. Through mode space transformation, DFT Hamiltonian and overlap matrices are reduced in size from a few thousands to around one hundred. Ultra-efficient quantum-mechanical transport calculations in the non-equilibrium Green's function formalism in a non-orthogonal basis are therefore made possible. The n-type and p-type Si NW FETs are simulated and found to exhibit similar device performance in the nanoscale regime.
Giant gravitons, Harish-Chandra integrals, and BPS states in symplectic and orthogonal $\mathcal{N}$ = 4 SYM
We find generating functions for half BPS correlators in $\mathcal{N}$ = 4 SYM theories with gauge groups Sp(2N), SO(2N + 1), and SO(2N) by computing the norms of a class of BPS coherent states. These coherent states are built from operators involving Harish-Chandra integrals. Such operators have an interpretation as localized giant gravitons in the bulk of anti-de-Sitter space. This extends the analysis of [1] to Sp(2N), SO(2N + 1), and SO(2N) gauge theories. We show that we may use ordinary Schur functions as a basis for the sector of states with no cross-caps in these theories. This is consistent with the construction of these theories as orientifold projections of an SU(2N) theory. We make note of some relations between the symmetric functions that appear in the expansion of these coherent states and symplectic Schur functions. We also comment on some connections to Schubert calculus and Gromov-Witten invariants, which suggest that the Harish-Chandra integral may be extended to such problems.
On solvability and integrability of the Rabi model
The quasi-exactly solvable Rabi model is investigated within the framework of the Bargmann Hilbert space of analytic functions B. On applying the theory of orthogonal polynomials, the eigenvalue equation and eigenfunctions are shown to be determined in terms of three systems of monic orthogonal polynomials. The formal Schweber quantization criterion for an energy variable x, originally expressed in terms of infinite continued fractions, can be recast in terms of a meromorphic function F(z)=a{sub 0}+∑{sub k=1}{sup ∞}M{sub k}/(z−ξ{sub k}) in the complex plane C with real simple poles ξ{sub k} and positive residues M{sub k}. The zeros of F(x) on the real axis determine the spectrum of the Rabi model. One obtains at once that, on the real axis, (i) F(x) monotonically decreases from +∞ to −∞ between any two of its subsequent poles ξ{sub k} and ξ{sub k+1}, (ii) there is exactly one zero of F(x) for x∈(ξ{sub k},ξ{sub k+1}), and (iii) the spectrum corresponding to the zeros of F(x) does not have any accumulation point. Additionally, one can provide a much simpler proof that the spectrum in each parity eigenspace B{sub ±} is necessarily nondegenerate. Thereby the calculation of spectra is greatly facilitated. Our results allow us to critically examine recent claims regarding solvability and integrability of the Rabi model. -- Highlights: •Schweber’s criterion shown equivalent to a meromorphic function F with real simple poles and positive residues. •Calculation of spectra determined as zeros of F greatly facilitated: one has exactly one zero between subsequent poles of F. •Spectrum in a given parity eigenspace is necessarily nondegenerate. •Recent claims regarding solvability and integrability of the Rabi model found to be largely unsubstantiated.
Understanding software faults and their role in software reliability modeling
This study is a direct result of an on-going project to model the reliability of a large real-time control avionics system. In previous modeling efforts with this system, hardware reliability models were applied in modeling the reliability behavior of this system. In an attempt to enhance the performance of the adapted reliability models, certain software attributes were introduced in these models to control for differences between programs and also sequential executions of the same program. As the basic nature of the software attributes that affect software reliability become better understood in the modeling process, this information begins to have important implications on the software development process. A significant problem arises when raw attribute measures are to be used in statistical models as predictors, for example, of measures of software quality. This is because many of the metrics are highly correlated. Consider the two attributes: lines of code, LOC, and number of program statements, Stmts. In this case, it is quite obvious that a program with a high value of LOC probably will also have a relatively high value of Stmts. In the case of low level languages, such as assembly language programs, there might be a one-to-one relationship between the statement count and the lines of code. When there is a complete absence of linear relationship among the metrics, they are said to be orthogonal or uncorrelated. Usually the lack of orthogonality is not serious enough to affect a statistical analysis. However, for the purposes of some statistical analysis such as multiple regression, the software metrics are so strongly interrelated that the regression results may be ambiguous and possibly even misleading. Typically, it is difficult to estimate the unique effects of individual software metrics in the regression equation. The estimated values of the coefficients are very sensitive to slight changes in the data and to the addition or deletion of variables in the regression equation. Since most of the existing metrics have common elements and are linear combinations of these common elements, it seems reasonable to investigate the structure of the underlying common factors or components that make up the raw metrics. The technique we have chosen to use to explore this structure is a procedure called principal components analysis. Principal components analysis is a decomposition technique that may be used to detect and analyze collinearity in software metrics. When confronted with a large number of metrics measuring a single construct, it may be desirable to represent the set by some smaller number of variables that convey all, or most, of the information in the original set. Principal components are linear transformations of a set of random variables that summarize the information contained in the variables. The transformations are chosen so that the first component accounts for the maximal amount of variation of the measures of any possible linear transform; the second component accounts for the maximal amount of residual variation; and so on. The principal components are constructed so that they represent transformed scores on dimensions that are orthogonal. Through the use of principal components analysis, it is possible to have a set of highly related software attributes mapped into a small number of uncorrelated attribute domains. This definitively solves the problem of multi-collinearity in subsequent regression analysis. There are many software metrics in the literature, but principal component analysis reveals that there are few distinct sources of variation, i.e. dimensions, in this set of metrics. It would appear perfectly reasonable to characterize the measurable attributes of a program with a simple function of a small number of orthogonal metrics each of which represents a distinct software attribute domain.
Time-dependent density-functional theory of high-order harmonic generation from noble-gas atoms driven by orthogonally polarized two-color laser fields
An orthogonally polarized two-color (OTC) field of frequency ratio 1 : 2 is known to generate both odd and even harmonic spectra in the mutually orthogonal directions of the fundamental and the second-harmonic fields, respectively. Recollision angles of an ionized electron driven back to its parent ion by an OTC field are inferred from the intensity ratio of odd and even harmonics, where the relative phase delays between the two-color fields can be used in time-resolved measurements on a subcycle time scale. Here, we present the ab initio, many-electron calculation of time-resolved recollision angles of noble-gas atoms driven by an OTC field, based on the time-dependent density-functional theory. Our results agree well with experimental data by Shafir et al. in Nat. Phys. 5, 412 (2009) of helium atom and New J. Phys. 12, 073032 (2010) of neon atom.
Entropy and complexity analysis of hydrogenic Rydberg atoms
The internal disorder of hydrogenic Rydberg atoms as contained in their position and momentum probability densities is examined by means of the following information-theoretic spreading quantities: the radial and logarithmic expectation values, the Shannon entropy, and the Fisher information. As well, the complexity measures of Cramer-Rao, Fisher-Shannon, and Lopez Ruiz-Mancini-Calvet types are investigated in both reciprocal spaces. The leading term of these quantities is rigorously calculated by use of the asymptotic properties of the concomitant entropic functionals of the Laguerre and Gegenbauer orthogonal polynomials which control the wavefunctions of the Rydberg states in both position and momentum spaces. The associated generalized Heisenberg-like, logarithmic and entropic uncertainty relations are also given. Finally, application to linear (l= 0), circular (l=n- 1), and quasicircular (l=n- 2) states is explicitly done.
Year 2 Final Report - Protein Function Prediction Platform (v.0.5)
Upon completion of our second year of development in a 3-year development cycle, we have completed a prototype protein structure-function annotation and function prediction system: Protein Function Prediction (PFP) platform (v.0.5). We have met our milestones for Years 1 and 2 and are positioned to continue development in completion of our original statement of work, or a reasonable modification thereof, in service to DTRA Programs involved in diagnostics and medical countermeasures research and development. The PFP platform is a multi-scale computational modeling system for protein structure-function annotation and function prediction. As of this writing, PFP is the only existing fully automated, high-throughput, multi-scale modeling, whole-proteome annotation platform, and represents a significant advance in the field of genome annotation (Fig. 1). PFP modules perform protein functional annotations at the sequence, systems biology, protein structure, and atomistic levels of biological complexity (Fig. 2). Because these approaches provide orthogonal means of characterizing proteins and suggesting protein function, PFP processing maximizes the protein functional information that can currently be gained by computational means. Comprehensive annotation of pathogen genomes is essential for bio-defense applications in pathogen characterization, threat assessment, and medical countermeasure design and development in that it can short-cut the time and effort required to select and characterize protein biomarkers.
Efficient numerical method for evaluating normal and anomalous time-domain equilibrium Green's functions in inhomogeneous systems
In this work we develop EPOCH (equilibrium propagator by orthogonal polynomial chain), a computationally efficient method to calculate the time-dependent equilibrium Green's functions, including the anomalous Green's functions of superconductors, to capture the time evolution in large inhomogeneous systems. The EPOCH method generalizes the Chebyshev wave-packet propagation method from quantum chemistry and efficiently incorporates the Fermi-Dirac statistics that is needed for equilibrium quantum condensed matter systems. The computational cost of EPOCH scales only linearly in the system degrees of freedom, generating an extremely efficient algorithm also for very large systems. We demonstrate the power of the EPOCH method by calculating the time evolution of an excitation near a superconductor–normal metal interface in two and three dimensions, capturing transmission as well as normal and Andreev reflections.
Accurate Calculation of Oscillator Strengths for CI II Lines Using Non-orthogonal Wavefunctions
Non-orthogonal orbitals technique in the multiconfiguration Hartree-Fock approach is used to calculate oscillator strengths and transition probabilities for allowed and intercombination lines in Cl II. The relativistic corrections are included through the Breit-Pauli Hamiltonian. The Cl II wave functions show strong term dependence. The non-orthogonal orbitals are used to describe the term dependence of radial functions. Large sets of spectroscopic and correlation functions are chosen to describe adequately strong interactions in the 3s(sup 2)3p(sup 3)nl (sup 3)Po, (sup 1)Po and (sup 3)Do Rydberg series and to properly account for the important correlation and relaxation effects. The length and velocity forms of oscillator strength show good agreement for most transitions. The calculated radiative lifetime for the 3s3p(sup 5) (sup 3)Po state is in good agreement with experiment.
Bayesian inference of structured latent spaces from neural population activity with the orthogonal stochastic linear mixing model
The brain produces diverse functions, from perceiving sounds to producing arm reaches, through the collective activity of populations of many neurons. Determining if and how the features of these exogenous variables (e.g., sound frequency, reach angle) are reflected in population neural activity is important for understanding how the brain operates. Often, high-dimensional neural population activity is confined to low-dimensional latent spaces. However, many current methods fail to extract latent spaces that are clearly structured by exogenous variables. This has contributed to a debate about whether or not brains should be thought of as dynamical systems or representational systems. Here, we developed a new latent process Bayesian regression framework, the orthogonal stochastic linear mixing model (OSLMM) which introduces an orthogonality constraint amongst time-varying mixture coefficients, and provide Markov chain Monte Carlo inference procedures. We demonstrate superior performance of OSLMM on latent trajectory recovery in synthetic experiments and show superior computational efficiency and prediction performance on several real-world benchmark data sets. We primarily focus on demonstrating the utility of OSLMM in two neural data sets: μ ECoG recordings from rat auditory cortex during presentation of pure tones and multi-single unit recordings form monkey motor cortex during complex arm reaching. We show that OSLMM achieves superior or comparable predictive accuracy of neural data and decoding of external variables (e.g., reach velocity). Most importantly, in both experimental contexts, we demonstrate that OSLMM latent trajectories directly reflect features of the sounds and reaches, demonstrating that neural dynamics are structured by neural representations. Together, these results demonstrate that OSLMM will be useful for the analysis of diverse, large-scale biological time-series datasets.
SuFEx‐Based Polysulfonate Formation from Ethenesulfonyl Fluoride–Amine Adducts
Abstract The SuFEx‐based polycondensation between bisalkylsulfonyl fluorides (AA monomers) and bisphenol bis( t ‐butyldimethylsilyl) ethers (BB monomers) using [Ph 3 P=N−PPh 3 ] + [HF 2 ] − as the catalyst is described. The AA monomers were prepared via the highly reliable Michael addition of ethenesulfonyl fluoride and amines/anilines while the BB monomers were obtained from silylation of bisphenols by t ‐butyldimethylsilyl chloride. With these reactions, a remarkable diversity of monomeric building blocks was achieved by exploiting readily available amines, anilines, and bisphenols as starting materials. The SuFEx‐based polysulfonate formation reaction exhibited excellent efficiency and functional group tolerance, producing polysulfonates with a variety of side chain functionalities in >99 % conversion within 10 min to 1 h. When bearing an orthogonal group on the side chain, the polysulfonates can be further functionalized via click‐chemistry‐based post‐polymerization modification.