Cost function adaptation: a stochastic gradient algorithm for data echo cancellation

A family of stochastic gradient algorithms and their behaviour in the data echo cancellation work platform are presented. The cost function adaptation algorithms use an error exponent update strategy based on an absolute error mapping, which is updated at every iteration. The quadratic and nonquadratic cost functions are special cases of the new family. Several possible realisations are introduced using these approaches. The noisy error problem is discussed and the digital recursive filter estimator is proposed. The simulation outcomes confirm the effectiveness of the proposed family of algorithms.

Irish Credit Unions: Investigating performance determinants and the opportunity cost of regulatory compliance

The study investigates how producer-specific environmental factors influence the performance of Irish credit unions. The empirical analysis uses a two-stage approach. The first stage measures efficiency by a data envelopment analysis (DEA) estimator, which explicitly incorporates the production of undesirable outputs such as bad loans in the modelling, and the second stage uses truncated regression to infer how various factors influence the (bias-corrected) estimated efficiency. A key finding of the analysis is that 68% of Irish credit unions do not incur an extra opportunity cost in meeting regulatory guidance on bad debt.

Turbo channel estimation and equalisation for a superposition-based cooperative system

This study investigates the superposition-based cooperative transmission system. In this system, a key point is for the relay node to detect data transmitted from the source node. This issued was less considered in the existing literature as the channel is usually assumed to be flat fading and a priori known. In practice, however, the channel is not only a priori unknown but subject to frequency selective fading. Channel estimation is thus necessary. Of particular interest is the channel estimation at the relay node which imposes extra requirement for the system resources. The authors propose a novel turbo least-square channel estimator by exploring the superposition structure of the transmission data. The proposed channel estimator not only requires no pilot symbols but also has significantly better performance than the classic approach. The soft-in-soft-out minimum mean square error (MMSE) equaliser is also re-derived to match the superimposed data structure. Finally computer simulation results are shown to verify the proposed algorithm.

Immune activation in Retinal Aging: A Gene Expression Study.

PURPOSE
To investigate changes in gene expression during aging of the retina in the mouse.

METHODS
Total RNA was extracted from the neuroretina of young (3-month-old) and old (20-month-old) mice and processed for microarray analysis. Age-related, differentially expressed genes were assessed by the empiric Bayes shrinkagemoderated t-statistics method. Statistical significance was based on dual criteria of a ratio of change in gene expression >2 and a P < 0.01. Differential expression in 11 selected genes was further verified by real-time PCR. Functional pathways involved in retinal ageing were analyzed by an online software package (DAVID-2008) in differentially expressed gene lists. Age-related changes in differential expression in the identified retinal molecular pathways were further confirmed by immunohistochemical staining of retinal flat mounts and retinal cryosections.

RESULTS
With ageing of the retina, 298 genes were upregulated and 137 genes were downregulated. Functional annotation showed that genes linked to immune responses (Ir genes) and to tissue stress/injury responses (TS/I genes) were most likely to be modified by ageing. The Ir genes affected included those regulating leukocyte activation, chemotaxis, endocytosis, complement activation, phagocytosis, and myeloid cell differentiation, most of which were upregulated, with only a few downregulated. Increased microglial and complement activation in the aging retina was further confirmed by confocal microscopy of retinal tissues. The most strongly upregulated gene was the calcitonin receptor (Calcr; >40-fold in old versus young mice).

CONCLUSIONS
The results suggest that retinal ageing is accompanied by activation of gene sets, which are involved in local inflammatory responses. A modified form of low-grade chronic inflammation (para-inflammation) characterizes these aging changes and involves mainly the innate immune system. The marked upregulation of Calcr in ageing mice most likely reflects this chronic inflammatory/stress response, since calcitonin is a known systemic biomarker of inflammation/sepsis. © Association for Research in Vision and Ophthalmology.

Maximum likelihood estimation of higher-order integer-valued autoregressive processes

In this article, we extend the earlier work of Freeland and McCabe [Journal of time Series Analysis (2004) Vol. 25, pp. 701–722] and develop a general framework for maximum likelihood (ML) analysis of higher-order integer-valued autoregressive processes. Our exposition includes the case where the innovation sequence has a Poisson distribution and the thinning is binomial. A recursive representation of the transition probability of the model is proposed. Based on this transition probability, we derive expressions for the score function and the Fisher information matrix, which form the basis for ML estimation and inference. Similar to the results in Freeland and McCabe (2004), we show that the score function and the Fisher information matrix can be neatly represented as conditional expectations. Using the INAR(2) speci?cation with binomial thinning and Poisson innovations, we examine both the asymptotic e?ciency and ?nite sample properties of the ML estimator in relation to the widely used conditional least
squares (CLS) and Yule–Walker (YW) estimators. We conclude that, if the Poisson assumption can be justi?ed, there are substantial gains to be had from using ML especially when the thinning parameters are large.

Reducing Uncertainty in Aeroelastic Flutter Boundaries Using Experimental Data

Flutter prediction as currently practiced is usually deterministic, with a single structural model used to represent an aircraft. By using interval analysis to take into account structural variability, recent work has demonstrated that small changes in the structure can lead to very large changes in the altitude at which
utter occurs (Marques, Badcock, et al., J. Aircraft, 2010). In this follow-up work we examine the same phenomenon using probabilistic collocation (PC), an uncertainty quantification technique which can eficiently propagate multivariate stochastic input through a simulation code,
in this case an eigenvalue-based fluid-structure stability code. The resulting analysis predicts the consequences of an uncertain structure on incidence of
utter in probabilistic terms { information that could be useful in planning
flight-tests and assessing the risk of structural failure. The uncertainty in
utter altitude is confirmed to be substantial. Assuming that the structural uncertainty represents a epistemic uncertainty regarding the
structure, it may be reduced with the availability of additional information { for example aeroelastic response data from a flight-test. Such data is used to update the structural uncertainty using Bayes' theorem. The consequent
utter uncertainty is significantly reduced across the entire Mach number range.

The Geography of Conflict and Death in Belfast, Northern Ireland

The conflict known as the oTroubleso in Northern Ireland began during the late 1960s and is defined by political and ethno-sectarian violence between state, pro-state, and anti-state forces. Reasons for the conflict are contested and complicated by social, religious, political, and cultural disputes, with much of the debate concerning the victims of violence hardened by competing propaganda-conditioning perspectives. This article introduces a database holding information on the location of individual fatalities connected with the contemporary Irish conflict. For each victim, it includes a demographic profile, home address, manner of death, and the organization responsible. Employing geographic information system (GIS) techniques, the database is used to measure, map, and analyze the spatial distribution of conflict-related deaths between 1966 and 2007 across Belfast, the capital city of Northern Ireland, with respect to levels of segregation, social and economic deprivation, and interfacing. The GIS analysis includes a kernel density estimator designed to generate smooth intensity surfaces of the conflict-related deaths by both incident and home locations. Neighborhoods with high-intensity surfaces of deaths were those with the highest levels of segregation ( 90 percent Catholic or Protestant) and deprivation, and they were located near physical barriers, the so-called peacelines, between predominantly Catholic and predominantly Protestant communities. Finally, despite the onset of peace and the formation of a power-sharing and devolved administration (the Northern Ireland Assembly), disagreements remain over the responsibility and ocommemorationo of victims, sentiments that still uphold division and atavistic attitudes between spatially divided Catholic and Protestant populations.

Discrete Conditional Phase-type model (DC_Ph) for patient waiting time with a logistic regression component to predict patient admission to hospital

Discrete Conditional Phase-type (DC-Ph) models are a family of models which represent skewed survival data conditioned on specific inter-related discrete variables. The survival data is modeled using a Coxian phase-type distribution which is associated with the inter-related variables using a range of possible data mining approaches such as Bayesian networks (BNs), the Naïve Bayes Classification method and classification regression trees. This paper utilizes the Discrete Conditional Phase-type model (DC-Ph) to explore the modeling of patient waiting times in an Accident and Emergency Department of a UK hospital. The resulting DC-Ph model takes on the form of the Coxian phase-type distribution conditioned on the outcome of a logistic regression model.

Exploring statistical and population aspects of network complexity

The characterization and the definition of the complexity of objects is an important but very difficult problem that attracted much interest in many different fields. In this paper we introduce a new measure, called network diversity score (NDS), which allows us to quantify structural properties of networks. We demonstrate numerically that our diversity score is capable of distinguishing ordered, random and complex networks from each other and, hence, allowing us to categorize networks with respect to their structural complexity. We study 16 additional network complexity measures and find that none of these measures has similar good categorization capabilities. In contrast to many other measures suggested so far aiming for a characterization of the structural complexity of networks, our score is different for a variety of reasons. First, our score is multiplicatively composed of four individual scores, each assessing different structural properties of a network. That means our composite score reflects the structural diversity of a network. Second, our score is defined for a population of networks instead of individual networks. We will show that this removes an unwanted ambiguity, inherently present in measures that are based on single networks. In order to apply our measure practically, we provide a statistical estimator for the diversity score, which is based on a finite number of samples.

Complex pattern of genetic structuring in the Atlantic salmon (Salmo salar L.) of the River Foyle system in northwest Ireland: disentangling the evolutionary signal from population stochasticity

Little is known about the microevolutionary processes shaping within river population genetic structure of aquatic organisms characterized by high levels of homing and spawning site fidelity. Using a microsatellite panel, we observed complex and highly significant levels of intrariver population genetic substructure and Isolation-by-Distance, in the Atlantic salmon stock of a large river system. Two evolutionary models have been considered explaining mechanisms promoting genetic substructuring in Atlantic salmon, the member-vagrant and metapopulation models. We show that both models can be simultaneously used to explain patterns and levels of population structuring within the Foyle system. We show that anthropogenic factors have had a large influence on contemporary population structure observed. In an analytical development, we found that the frequently used estimator of genetic differentiation, F-ST, routinely underestimated genetic differentiation by a factor three to four compared to the equivalent statistic Jost's D-est (Jost 2008). These statistics also showed a near-perfect correlation. Despite ongoing discussions regarding the usefulness of "adjusted" F-ST statistics, we argue that these could be useful to identify and quantify qualitative differences between populations, which are important from management and conservation perspectives as an indicator of existence of biologically significant variation among tributary populations or a warning of critical environmental damage.

Nonparametric Estimation of Data Dimensionality Prior to Data Compression: the case of the Human Development Index

In many applications in applied statistics researchers reduce the complexity of a data set by combining a group of variables into a single measure using factor analysis or an index number. We argue that such compression loses information if the data actually has high dimensionality. We advocate the use of a non-parametric estimator, commonly used in physics (the Takens estimator), to estimate the correlation dimension of the data prior to compression. The advantage of this approach over traditional linear data compression approaches is that the data does not have to be linearized. Applying our ideas to the United Nations Human Development Index we find that the four variables that are used in its construction have dimension three and the index loses information.

Modified Information Criteria and Selection of Long Memory Time Series Models

The problem of model selection of a univariate long memory time series is investigated once a semi parametric estimator for the long memory parameter has been used. Standard information criteria are not consistent in this case. A Modified Information Criterion (MIC) that overcomes these difficulties is introduced and proofs that show its asymptotic validity are provided. The results are general and cover a wide range of short memory processes. Simulation evidence compares the new and existing methodologies and empirical applications in monthly inflation and daily realized volatility are presented.

Sliding window two-thermocouple sensor characterization for variable flow environments

A two-thermocouple sensor characterization method for use in variable flow applications is proposed. Previous offline methods for constant velocity flow are extended using sliding data windows and polynomials to accommodate variable velocity. Analysis of Monte-Carlo simulation studies confirms that the unbiased and consistent parameter estimator outperforms alternatives in the literature and has the added advantage of not requiring a priori knowledge of the time constant ratio of thermocouples. Experimental results from a test rig are also presented. © 2008 The Institute of Measurement and Control.

State Estimation for Anaerobic Digesters using the ADM1

The optimization of full-scale biogas plant operation is of great importance to make biomass a competitive source of renewable energy. The implementation of innovative control and optimization algorithms, such as Nonlinear Model Predictive Control, requires an online estimation of operating states of biogas plants. This state estimation allows for optimal control and operating decisions according to the actual state of a plant. In this paper such a state estimator is developed using a calibrated simulation model of a full-scale biogas plant, which is based on the Anaerobic Digestion Model No.1. The use of advanced pattern recognition methods shows that model states can be predicted from basic online measurements such as biogas production, CH4 and CO2 content in the biogas, pH value and substrate feed volume of known substrates. The machine learning methods used are trained and evaluated using synthetic data created with the biogas plant model simulating over a wide range of possible plant operating regions. Results show that the operating state vector of the modelled anaerobic digestion process can be predicted with an overall accuracy of about 90%. This facilitates the application of state-based optimization and control algorithms on full-scale biogas plants and therefore fosters the production of eco-friendly energy from biomass.

Efficient Marginal Likelihood Computation for Gaussian Process Regression

In a Bayesian learning setting, the posterior distribution of a predictive model arises from a trade-off between its prior distribution and the conditional likelihood of observed data. Such distribution functions usually rely on additional hyperparameters which need to be tuned in order to achieve optimum predictive performance; this operation can be efficiently performed in an Empirical Bayes fashion by maximizing the posterior marginal likelihood of the observed data. Since the score function of this optimization problem is in general characterized by the presence of local optima, it is necessary to resort to global optimization strategies, which require a large number of function evaluations. Given that the evaluation is usually computationally intensive and badly scaled with respect to the dataset size, the maximum number of observations that can be treated simultaneously is quite limited. In this paper, we consider the case of hyperparameter tuning in Gaussian process regression. A straightforward implementation of the posterior log-likelihood for this model requires O(N^3) operations for every iteration of the optimization procedure, where N is the number of examples in the input dataset. We derive a novel set of identities that allow, after an initial overhead of O(N^3), the evaluation of the score function, as well as the Jacobian and Hessian matrices, in O(N) operations. We prove how the proposed identities, that follow from the eigendecomposition of the kernel matrix, yield a reduction of several orders of magnitude in the computation time for the hyperparameter optimization problem. Notably, the proposed solution provides computational advantages even with respect to state of the art approximations that rely on sparse kernel matrices.