Parameter estimation in pair hidden Markov models

This paper deals with parameter estimation in pair hidden Markov models (pair-HMMs). We first provide a rigorous formalism for these models and discuss possible definitions of likelihoods. The model being biologically motivated, some restrictions with respect to the full parameter space naturally occur. Existence of two different Information divergence rates is established and divergence property (namely positivity at values different from the true one) is shown under additional assumptions. This yields consistency for the parameter in parametrization schemes for which the divergence property holds. Simulations illustrate different cases which are not covered by our results.Comment: corrected typo

X-ray photoemission spectroscopy determination of the InN/yttria stabilized cubic-zirconia valence band offset

The valence band offset of wurtzite InN(0001)/yttria stabilized cubic-zirconia (YSZ)(111) heterojunctions is determined by x-ray photoemission spectroscopy to be 1.19±0.17 eV giving a conduction band offset of 3.06±0.20 eV. Consequently, a type-I heterojunction forms between InN and YSZ in the straddling arrangement. The low lattice mismatch and high band offsets suggest potential for use of YSZ as a gate dielectric in high-frequency InN-based electronic devices

Addition-Deletion Networks

We study structural properties of growing networks where both addition and deletion of nodes are possible. Our model network evolves via two independent processes. With rate r, a node is added to the system and this node links to a randomly selected existing node. With rate 1, a randomly selected node is deleted, and its parent node inherits the links of its immediate descendants. We show that the in-component size distribution decays algebraically, c_k ~ k^{-beta}, as k-->infty. The exponent beta=2+1/(r-1) varies continuously with the addition rate r. Structural properties of the network including the height distribution, the diameter of the network, the average distance between two nodes, and the fraction of dangling nodes are also obtained analytically. Interestingly, the deletion process leads to a giant hub, a single node with a macroscopic degree whereas all other nodes have a microscopic degree.Comment: 8 pages, 5 figure

Reorientation of Spin Density Waves in Cr(001) Films induced by Fe(001) Cap Layers

Proximity effects of 20 \AA thin Fe layers on the spin density waves (SDWs) in epitaxial Cr(001) films are revealed by neutron scattering. Unlike in bulk Cr we observe a SDW with its wave vector Q pointing along only one {100} direction which depends dramatically on the film thickness t_{Cr}. For t_{Cr} < 250 \AA the SDW propagates out-of-plane with the spins in the film plane. For t_{Cr} > 1000 \AA the SDW propagates in the film plane with the spins out-of-plane perpendicular to the in-plane Fe moments. This reorientation transition is explained by frustration effects in the antiferromagnetic interaction between Fe and Cr across the Fe/Cr interface due to steps at the interface.Comment: 4 pages (RevTeX), 3 figures (EPS

Modeling long-range memory with stationary Markovian processes

In this paper we give explicit examples of power-law correlated stationary Markovian processes y(t) where the stationary pdf shows tails which are gaussian or exponential. These processes are obtained by simply performing a coordinate transformation of a specific power-law correlated additive process x(t), already known in the literature, whose pdf shows power-law tails 1/x^a. We give analytical and numerical evidence that although the new processes (i) are Markovian and (ii) have gaussian or exponential tails their autocorrelation function still shows a power-law decay =1/T^b where b grows with a with a law which is compatible with b=a/2-c, where c is a numerical constant. When a<2(1+c) the process y(t), although Markovian, is long-range correlated. Our results help in clarifying that even in the context of Markovian processes long-range dependencies are not necessarily associated to the occurrence of extreme events. Moreover, our results can be relevant in the modeling of complex systems with long memory. In fact, we provide simple processes associated to Langevin equations thus showing that long-memory effects can be modeled in the context of continuous time stationary Markovian processes.Comment: 5 figure

Constructing seasonally adjusted data with time-varying confidence intervals

Seasonal adjustment methods transform observed time series data into estimated data, where these estimated data are constructed such that they show no or almost no seasonal variation. An advantage of model-based methods is that these can provide confidence intervals around the seasonally adjusted data. One particularly useful time series model for seasonal adjustment is the basic structural time series [BSM] model. The usual premise of the BSM is that the variance of each of the components is constant. In this paper we address the possibility that the variance of the trend component in a macro-economic time series in some way depends on the business cycle. One reason for doing so is that one can expect that there is more uncertainty in recession periods. We extend the BSM by allowing for a business-cycle dependent variance in the level equation. Next we show how this affects the confidence intervals of seasonally adjusted data. We apply our extended BSM to monthly US unemployment and we show that the estimated confidence intervals for seasonally adjusted unemployment change with past changes in the oil price

Demonstration of dual-band infrared thermal imaging at Grass Valley Creek bridges

We demonstrated dual-band infrared (DBIR) thermal imaging at the Grass Valley Creek Bridges near Redding CA. DBIR thermal imaging is an enabling technology for rapid, reliable, bridge deck inspections while minimizing lane closures. bridge-deck inspections were conducted from a mobile DBIR bridge inspection laboratory during November 2-3, 1995. We drove this self-contained unit at limited highway speeds over 0.4 lane miles of bridge deck. Using two thermal IR bands, we distinguished delaminations from clutter. Clutter, or unwanted thermal detail, occurs from foreign materials or uneven shade on the bridge deck surface. By mapping the DBIR spectral- response differences at 3-5 {mu}m and 8-12 {mu}m, we removed foreign material clutter. By mapping the deck diurnal thermal inertia variations, we removed clutter from uneven shade. Thermal inertia is a bulk deck property, the square root of thermal conductivity x density x heat capacity. Delaminated decks have below-average thermal inertias, or above-average day-night temperature excursions. Compared to normal decks areas, delaminated deck areas were typically 2 or 3 {degrees}C warmer at noon, and 0.5{degrees}C cooler at night. The mobile DBIR bridge inspection laboratory is currently undergoing extensive testing to examine bridges by the Federal Highway Administration

Chiral and herringbone symmetry breaking in water-surface monolayers

We report the observation from monolayers of eicosanoic acid in the L′2 phase of three distinct out-of-plane first-order diffraction peaks, indicating molecular tilt in a nonsymmetry direction and hence the absence of mirror symmetry. At lower pressures the molecules tilt in the direction of their nearest neighbors. In this region we find a structural transition, which we tentatively identify as the rotator-herringbone transition L2d−L2h

Coverage, Continuity and Visual Cortical Architecture

The primary visual cortex of many mammals contains a continuous representation of visual space, with a roughly repetitive aperiodic map of orientation preferences superimposed. It was recently found that orientation preference maps (OPMs) obey statistical laws which are apparently invariant among species widely separated in eutherian evolution. Here, we examine whether one of the most prominent models for the optimization of cortical maps, the elastic net (EN) model, can reproduce this common design. The EN model generates representations which optimally trade of stimulus space coverage and map continuity. While this model has been used in numerous studies, no analytical results about the precise layout of the predicted OPMs have been obtained so far. We present a mathematical approach to analytically calculate the cortical representations predicted by the EN model for the joint mapping of stimulus position and orientation. We find that in all previously studied regimes, predicted OPM layouts are perfectly periodic. An unbiased search through the EN parameter space identifies a novel regime of aperiodic OPMs with pinwheel densities lower than found in experiments. In an extreme limit, aperiodic OPMs quantitatively resembling experimental observations emerge. Stabilization of these layouts results from strong nonlocal interactions rather than from a coverage-continuity-compromise. Our results demonstrate that optimization models for stimulus representations dominated by nonlocal suppressive interactions are in principle capable of correctly predicting the common OPM design. They question that visual cortical feature representations can be explained by a coverage-continuity-compromise.Comment: 100 pages, including an Appendix, 21 + 7 figure