Forecasting US output growth with non-linear models in the presence of data uncertainty

We consider the impact of data revisions on the forecast performance of a SETAR regime-switching model of U.S. output growth. The impact of data uncertainty in real-time forecasting will affect a model's forecast performance via the effect on the model parameter estimates as well as via the forecast being conditioned on data measured with error. We find that benchmark revisions do affect the performance of the non-linear model of the growth rate, and that the performance relative to a linear comparator deteriorates in real-time compared to a pseudo out-of-sample forecasting exercise.

On the spectra and pseudospectra of a class of non-self-adjoint random matrices and operators

In this paper we develop and apply methods for the spectral analysis of non-selfadjoint tridiagonal infinite and finite random matrices, and for the spectral analysis of analogous deterministic matrices which are pseudo-ergodic in the sense of E. B. Davies (Commun. Math. Phys. 216 (2001), 687–704). As a major application to illustrate our methods we focus on the “hopping sign model” introduced by J. Feinberg and A. Zee (Phys. Rev. E 59 (1999), 6433–6443), in which the main objects of study are random tridiagonal matrices which have zeros on the main diagonal and random ±1’s as the other entries. We explore the relationship between spectral sets in the finite and infinite matrix cases, and between the semi-infinite and bi-infinite matrix cases, for example showing that the numerical range and p-norm ε - pseudospectra (ε > 0, p ∈ [1,∞] ) of the random finite matrices converge almost surely to their infinite matrix counterparts, and that the finite matrix spectra are contained in the infinite matrix spectrum Σ. We also propose a sequence of inclusion sets for Σ which we show is convergent to Σ, with the nth element of the sequence computable by calculating smallest singular values of (large numbers of) n×n matrices. We propose similar convergent approximations for the 2-norm ε -pseudospectra of the infinite random matrices, these approximations sandwiching the infinite matrix pseudospectra from above and below.

Single-carrier frequency domain equalisation for hammerstein communication systems using complex-valued neural networks

Single-carrier (SC) block transmission with frequency-domain equalisation (FDE) offers a viable transmission technology for combating the adverse effects of long dispersive channels encountered in high-rate broadband wireless communication systems. However, for high bandwidthefficiency and high power-efficiency systems, the channel can generally be modelled by the Hammerstein system that includes the nonlinear distortion effects of the high power amplifier (HPA) at transmitter. For such nonlinear Hammerstein channels, the standard SC-FDE scheme no longer works. This paper advocates a complex-valued (CV) B-spline neural network based nonlinear SC-FDE scheme for Hammerstein channels. Specifically, We model the nonlinear HPA, which represents the CV static nonlinearity of the Hammerstein channel, by a CV B-spline neural network, and we develop two efficient alternating least squares schemes for estimating the parameters of the Hammerstein channel, including both the channel impulse response coefficients and the parameters of the CV B-spline model. We also use another CV B-spline neural network to model the inversion of the nonlinear HPA, and the parameters of this inverting B-spline model can easily be estimated using the standard least squares algorithm based on the pseudo training data obtained as a natural byproduct of the Hammerstein channel identification. Equalisation of the SC Hammerstein channel can then be accomplished by the usual one-tap linear equalisation in frequency domain as well as the inverse B-spline neural network model obtained in time domain. Extensive simulation results are included to demonstrate the effectiveness of our nonlinear SC-FDE scheme for Hammerstein channels.

Real-time factor model forecasting and the effects of instability

Factor forecasting models are shown to deliver real-time gains over autoregressive models for US real activity variables during the recent period, but are less successful for nominal variables. The gains are largely due to the Financial Crisis period, and are primarily at the shortest (one quarter ahead) horizon. Excluding the pre-Great Moderation years from the factor forecasting model estimation period (but not from the data used to extract factors) results in a marked fillip in factor model forecast accuracy, but does the same for the AR model forecasts. The relative performance of the factor models compared to the AR models is largely unaffected by whether the exercise is in real time or is pseudo out-of-sample.

A novel transport assimilation method for the Atlantic meridional overturning circulation at 26°N

One of the prerequisites for achieving skill in decadal climate prediction is to initialize and predict the circulation in the Atlantic Ocean successfully. The RAPID array measures the Atlantic Meridional Overturning Circulation (MOC) at 26°N. Here we develop a method to include these observations in the Met Office Decadal Prediction System (DePreSys). The proposed method uses covariances of overturning transport anomalies at 26°N with ocean temperature and salinity anomalies throughout the ocean to create the density structure necessary to reproduce the observed transport anomaly. Assimilating transport alone in this way effectively reproduces the observed transport anomalies at 26°N and is better than using basin-wide temperature and salinity observations alone. However, when the transport observations are combined with in situ temperature and salinity observations in the analysis, the transport is not currently reproduced so well. The reasons for this are investigated using pseudo-observations in a twin experiment framework. Sensitivity experiments show that the MOC on monthly time-scales, at least in the HadCM3 model, is modulated by a mechanism where non-local density anomalies appear to be more important for transport variability at 26°N than local density gradients.

Nonlinear equalization of Hammerstein OFDM systems

A practical orthogonal frequency-division multiplexing (OFDM) system can generally be modelled by the Hammerstein system that includes the nonlinear distortion effects of the high power amplifier (HPA) at transmitter. In this contribution, we advocate a novel nonlinear equalization scheme for OFDM Hammerstein systems. We model the nonlinear HPA, which represents the static nonlinearity of the OFDM Hammerstein channel, by a B-spline neural network, and we develop a highly effective alternating least squares algorithm for estimating the parameters of the OFDM Hammerstein channel, including channel impulse response coefficients and the parameters of the B-spline model. Moreover, we also use another B-spline neural network to model the inversion of the HPA’s nonlinearity, and the parameters of this inverting B-spline model can easily be estimated using the standard least squares algorithm based on the pseudo training data obtained as a byproduct of the Hammerstein channel identification. Equalization of the OFDM Hammerstein channel can then be accomplished by the usual one-tap linear equalization as well as the inverse B-spline neural network model obtained. The effectiveness of our nonlinear equalization scheme for OFDM Hammerstein channels is demonstrated by simulation results.

B-spline neural network based single-carrier frequency domain equalisation for Hammerstein channels

A practical single-carrier (SC) block transmission with frequency domain equalisation (FDE) system can generally be modelled by the Hammerstein system that includes the nonlinear distortion effects of the high power amplifier (HPA) at transmitter. For such Hammerstein channels, the standard SC-FDE scheme no longer works. We propose a novel Bspline neural network based nonlinear SC-FDE scheme for Hammerstein channels. In particular, we model the nonlinear HPA, which represents the complex-valued static nonlinearity of the Hammerstein channel, by two real-valued B-spline neural networks, one for modelling the nonlinear amplitude response of the HPA and the other for the nonlinear phase response of the HPA. We then develop an efficient alternating least squares algorithm for estimating the parameters of the Hammerstein channel, including the channel impulse response coefficients and the parameters of the two B-spline models. Moreover, we also use another real-valued B-spline neural network to model the inversion of the HPA’s nonlinear amplitude response, and the parameters of this inverting B-spline model can be estimated using the standard least squares algorithm based on the pseudo training data obtained as a byproduct of the Hammerstein channel identification. Equalisation of the SC Hammerstein channel can then be accomplished by the usual one-tap linear equalisation in frequency domain as well as the inverse Bspline neural network model obtained in time domain. The effectiveness of our nonlinear SC-FDE scheme for Hammerstein channels is demonstrated in a simulation study.

Adaptive B-spline neural network based nonlinear equalization for high-order QAM systems with nonlinear transmit high power amplifier

High bandwidth-efficiency quadrature amplitude modulation (QAM) signaling widely adopted in high-rate communication systems suffers from a drawback of high peak-toaverage power ratio, which may cause the nonlinear saturation of the high power amplifier (HPA) at transmitter. Thus, practical high-throughput QAM communication systems exhibit nonlinear and dispersive channel characteristics that must be modeled as a Hammerstein channel. Standard linear equalization becomes inadequate for such Hammerstein communication systems. In this paper, we advocate an adaptive B-Spline neural network based nonlinear equalizer. Specifically, during the training phase, an efficient alternating least squares (LS) scheme is employed to estimate the parameters of the Hammerstein channel, including both the channel impulse response (CIR) coefficients and the parameters of the B-spline neural network that models the HPA’s nonlinearity. In addition, another B-spline neural network is used to model the inversion of the nonlinear HPA, and the parameters of this inverting B-spline model can easily be estimated using the standard LS algorithm based on the pseudo training data obtained as a natural byproduct of the Hammerstein channel identification. Nonlinear equalisation of the Hammerstein channel is then accomplished by the linear equalization based on the estimated CIR as well as the inverse B-spline neural network model. Furthermore, during the data communication phase, the decision-directed LS channel estimation is adopted to track the time-varying CIR. Extensive simulation results demonstrate the effectiveness of our proposed B-Spline neural network based nonlinear equalization scheme.

Importance of allelopathy as peudo-mixotrophy for the dynamics and diversity of phytoplankton

Understanding the dynamics and diversity of marine phytoplankton is essential for predicting oceanic primary production, oxygen generation and carbon sequestration. Several top-down and bottom-up factors lead to complex phytoplankton dynamics. Complexities further arise from inter-species interactions within phytoplankton communities. Consequently, some of the basic questions on phytoplankton diversity, identified long ago, still puzzle the ecologists: for example, what regulates the diversity in simple systems where species compete for limiting resources? In this context, allelopathic interaction among phytoplankton species has been identified as a potential driver of their dynamics and regulator of their diversity. This chapter deals with the importance of allelopathy in regulating the outcome of nutrient competition among phytoplankton species, through analysis of a resource-competition model. It demonstrates that, through the mechanism of pseudo-mixotrophy - proposed earlier by the author - allelopathy provides essential growth advantage to weaker competitors, and stabilizes resource competition, which ensures the coexistence of two phytoplankton on a single nutrient. In simple nutrient-phytoplankton interactions where higher-trophic influences are negligible, this mechanism theoretically promotes phytoplankton diversity, and can potentially support high diversity in natural phytoplankton communities.

Thermoelectric properties of TiS2 mechanically alloyed compounds

Bulk polycrystalline samples in the series Ti1−xNbxS2 (0 ≤ x ≤ 0.075) were prepared using mechanical alloying synthesis and spark plasma sintering. X-ray diffraction analysis coupled with high resolution transmission electron microscopy indicates the formation of trigonal TiS2 by high energy ball-milling. The as-synthesized particles consist of pseudo-ordered TiS2 domains of around 20–50 nm, joined by bent atomic planes. This bottom-up approach leads, after spark plasma sintering, to homogeneous solid solutions, with a niobium solubility limit of x = 0.075. Microstructural observations evidence the formation of small crystallites in the bulk compounds with a high density of stacking faults. The large grain boundary concentration coupled with the presence of planar defects, leads to a substantial decrease in the thermal conductivity to 1.8 W/mK at 700 K. This enables the figure of merit to reach ZT = 0.3 at 700 K for x = 0.05, despite the lower electron mobility in mechanically alloyed samples due to small crystallite/grain size and structural defects.

Perception of eating practices and stages of change among Brazilian adolescents

Objective. To evaluate the perception of eating practices and the stages of change among adolescents. Methods. Cross-sectional study involving a representative sample of 390 adolescents from 11 public schools in the city of Piracicaba, Brazil, in 2004. Food consumption was identified by a food frequency questionnaire and the perception of eating practices evaluation was conducted by comparing food consumption and individual classification of healthy aspects of the diet. The participants were classified within stages of change by means of a specific algorithm. A reclassification within new stages of change was proposed to identify adolescents with similar characteristics regarding food consumption and perception. Results. Low consumption of fruit and vegetables and high consumption of sweets and fats were identified. More than 44% of the adolescents had a mistaken perception of their diet. A significant relationship between the stages of change and food consumption was observed. The reclassification among stages of change, through including the pseudo-maintenance and non-reflective action stages was necessary, considering the high proportion of adolescents who erroneously classified their diets as healthy. Conclusion. Classification of the adolescents into stages of change, together with consumption and perception data, enabled identification of groups at risk, in accordance with their inadequate dietary habits and non-recognition of such habits. (C) 2009 Elsevier Inc. All rights reserved.

The RAINY3D Code: The Treatment of Condensation in Nova Remnants during Nebular Phase

Classical nova remnants are important scenarios for improving the photoionization modeling. This work describes the pseudo-three-dimensional code RAINY3D, which drives the photoionization code Cloudy as a subroutine. Photoionization simulations of old nova remnants are also presented and discussed. In these simulations we analyze the effect of condensation in the remnant spectra. The condensed mass fraction affects the Balmer lines by a factor of greater than 4 when compared with homogeneous models, and this directly impacts the shell mass determination. The He II 4686/H beta ratio decreases by a factor of 10 in clumpy shells. These lines are also affected by the clump size and density distributions. The behavior of the strongest nebular line observed in nova remnants is also analyzed for heterogeneous shells. The gas diagnoses in novae ejecta are thought to be more accurate during the nebular phase, but we have determined that at this phase the matter distribution can strongly affect the derived shell physical properties and chemical abundances.

BIRD FORAGING IN ANACARDIUM PATCHES IN CENTRAL BRAZILIAN FIRE BREAKS: RELATIONSHIP BETWEEN FLOCK SIZE AND PATCH SIZE

Fire management ran increase the biomass of some plant species at fire breaks in reserves of the Cerrado. For example, numerous and large patches of monkey-nuts (Anacardium humile, Anacardiaceae) provide abundant food resources for wildlife in the lower strata of savanna woodlands managed by fire. The objective of this study was to examine the exploitation of A. humile patches by birds in managed savanna woodlands (fire breaks) at Emas National Park, southwest Brazil. The relationship between flock size and the size of Anacardium patches were also investigated. Fire breaks were sampled in September and October 2006, when fruits and flowers were abundant. Ara ararauna was often recorded exploiting resources of Anacardium patches. This species and other psittacids (Amazona aestiva, Alipiopsittaca xanthops, and Diopsittaca nobilis) consumed seeds usually on the ground around fruiting patches. Members of Aratinga aurea flocks and Ramphastos toco consumed pseudo-fruits. Larger flocks detected were those of A. aurea and A. ararauna. Groups of A. ararauna that exploited larger patches tended to be larger than flocks that exploited smaller patches. This study suggests that intra- and interspecific interactions and characteristics of Anacardium patches and of the surrounding vegetation are involved in the feeding ecology of birds in the lower stratum of managed woodlands. Fruiting Anacardium patches attract numerous frugivorous birds to fire breaks at Emas National Park. Further research is needed to a better understanding of the influence of fire management on birds in the Cerrado. Accepted 31 July 2009.

Evolutionary fuzzy clustering of relational data

This paper is concerned with the computational efficiency of fuzzy clustering algorithms when the data set to be clustered is described by a proximity matrix only (relational data) and the number of clusters must be automatically estimated from such data. A fuzzy variant of an evolutionary algorithm for relational clustering is derived and compared against two systematic (pseudo-exhaustive) approaches that can also be used to automatically estimate the number of fuzzy clusters in relational data. An extensive collection of experiments involving 18 artificial and two real data sets is reported and analyzed. (C) 2011 Elsevier B.V. All rights reserved.

Isostructural M(RL)(2)(hfac)(2) complexes with RL=5-(4-[N-tert-butyl-N-aminoxyl]phenyl)pyrimidine

5-(4-(N-tert-Butyl-N-aminoxylphenyl)) pyrimidine (RL, 4PPN) forms crystallographically isostructural and isomorphic pseudo-octahedral M(RL)(2)(hfac)(2) complexes with M(hfac)(2), M = Zn, Cu, Ni, Co, and Mn. Multiple close contacts occur between sites of significant spin density of the organic radical units. Magnetic behavior of the Zn, Cu, Ni, Co complexes appears to involve multiple exchange pathways, with multiple close crystallographic contacts between sites that EPR (of 4PPN) indicates to have observable spin density. Powder EPR spectra at room temperature and low temperature are reported for each complex. Near room temperature, the magnetic moments of the complexes are roughly equal to those expected by a sum of non-interacting moments (two radicals plus ion). As temperature decreases, AFM exchange interactions become evident in all of the complexes. The closest fits to the magnetic data were found for a 1-D Heisenberg AFM chain model in the Zn(II) complex (J/k = (-)7 K), and for three-spin RL-M-RL exchange in the other complexes (J/k = (-)26 K, (-)3 K, (-) 6 K, for Cu(II), Ni(II), and Co(II) complexes, respectively). (C) 2008 Elsevier B.V. All rights reserved.