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The present paper reports on the ability of neural networks (NN) and linear stochastic estimation (LSE) tools to predict the evolution of skin friction in a minimal turbulent channel (R e τ = 165) after applying an actuation near the wall that is localized in space and time. Two different NN architectures are compared, namely multilayer perceptrons (MLP) and convolutional neural networks (CNN). The paper describes the effect that the predictive horizon and the type/size/number of wall-based sensors have on the performance of each estimator. The performance of MLPs and LSEs is very similar, and becomes independent of the sensor's size when they are smaller than 60 wall units. For sufficiently small sensors, the CNN outperforms MLPs and LSEs, suggesting that CNNs are able incorporate some of the non-linearities of the near-wall cycle in their prediction of the skin friction evolution after the actuation. Indeed, the CNN is the only architecture able to achieve reasonable predictive capabilities using pressure sensors only. The predictive horizon has a strong effect on the predictive capacity of both NN and LSE, with a Pearson correlation coefficient that varies from 0.95 for short times (i.e., of the order of the actuation time) to less than 0.4 for times of the order of an eddy turn-over time. The analysis of the weights and filters in the LSE and NNs show that all estimators are targeting wall-signatures consistent with streaks, which is interpreted as the streak being the most causal feature in the near-wall cycle for the present forcing. • LSE and NNs can predict skin friction changes in wall-bounded turbulent flows. • Convolutional NN outperform LSE and dense NN when sensors are small. • LSE and NN target near-wall streaks aligned with actuator to predict drag change. • Convolutional NN model non-linear near-wall dynamics better than dense NN and LSE. • Overall, shear sensors provide more information than pressure sensors. [ABSTRACT FROM AUTHOR]

Understanding the relationship between time series is essential for predictive modeling and decision-making in several domains. In this article, we introduce and demonstrate the properties of a novel measure derived from wavelet and α -divergence, namely wavelet energy α -divergence measure. Unlike other traditional wavelet entropy energy-based measures, which primarily serve to assess the inherent predictability of one time series, this new measure takes into account pairs of time series and it reveals how the knowledge of one series can reduce uncertainty in another series across the time as well as the frequency domain. Specifically, our measure can indicate to which extent we can extract information content from one series that is related to the second series. This analysis is performed for both low and high-frequency events by varying the order parameter α of the divergence, and for both short and long-run by analyzing the scale value of the wavelet. Moreover, we apply the method to a real-world dataset highlighting the impact of this measure. • Wavelet analysis for signal reconstruction combined with α - divergence for predictability analysis. • Short-frequency and high-frequency analysis, and short-run and long-run examination. • Application to commodity time series. [ABSTRACT FROM AUTHOR]

We propose an information-theoretic Markov aggregation framework that is motivated by two objectives: 1) The Markov chain observed through the aggregation mapping should be Markov. 2) The aggregated chain should retain the temporal dependence structure of the original chain. We analyze our parameterized cost function and show that it contains previous cost functions as special cases, which we critically assess. Our simple optimization heuristic for deterministic aggregations characterizes the optimization landscape for different parameter values. [ABSTRACT FROM AUTHOR]

An innovative seamless probabilistic forecasting system has been developed within the EU project PROFORCE (Bridging of PRObabilistic FORecasts and Civil protEction). The system merges four different ensemble prediction systems and provides weather forecasts and the corresponding forecast uncertainties in a seamless way from several days ahead to the nowcasting range with increasing time and space resolution. The probabilistic information from the seamless system is tailored to the needs of the end users in civil protection and is illustrated on a user-friendly web portal. The seamless weather forecasting and warning system is impact based and was designed by meteorologists and civil protection users together. This article describes the seamless system and discusses its perception in civil protection based on several severe weather situations occurring during the two years of the project. It has been demonstrated that the seamless forecasts allow a longer preparation time due to earlier warnings. The probability information can help in planning and decision making in civil protection. The weaknesses of the forecasting system, in particular the limits in predicting small scale convective systems, are also discussed in the paper. [ABSTRACT FROM AUTHOR]

The general theory of stochastic differential equations is presented in this chapter, including the theoretical background on howmeasured statistics from time series can be used to develop a stochastic parameterization. The general rules of stochastic calculus, including the important and often overlooked differences between Ito and Stratonovich calculus, are mentioned, and references are provided in which more detail may be found. We discuss how Stratonovich calculus is usually appropriate for fluid systems, whereas Ito calculus is often appropriate for data assimilation. We also discuss some common numerical pitfalls awaiting the unwary modeler, and warn against unsophisticated random number generators. Finally, we offer a selection of examples showing the importance of the variability of unresolved scales in an ocean model and, by citation, a variety of methods that have been employed. [ABSTRACT FROM AUTHOR]

State-of-the-art wind power forecasts beyond a few hours ahead rely on global numerical weather prediction models to forecast the future large-scale atmospheric state. Often they provide initial and boundary conditions for nested high resolution simulations. In this paper, both upper and lower bounds on forecast range are identified within which global ensemble forecasts provide skilful information for system-wide wind power applications. An upper bound on forecast range is associated with the limit of predictability, beyond which forecasts have no more skill than predictions based on climatological statistics. A lower bound is defined at the lead time beyond which the resolved uncertainty associated with estimating the future large-scale atmospheric state is larger than the unresolved uncertainty associated with estimating the system-wide wind power response to a given large-scale state. The bounds of skilful ensemble forecast range are quantified for three leading global forecast systems. The power system of Great Britain (GB) is used as an example because independent verifying data is available from National Grid. The upper bound defined by forecasts of GB-total wind power generation at a specific point in time is found to be 6-8 days. The lower bound is found to be 1.4-2.4 days. Both bounds depend on the global forecast system and vary seasonally. In addition, forecasts of the probability of an extreme power ramp event were found to possess a shorter limit of predictability (4.5-5.5 days). The upper bound on this forecast range can only be extended by improving the global forecast system (outside the control of most users) or by changing the metric used in the probability forecast. Improved downscaling and microscale modelling of the wind farm response may act to decrease the lower bound. The potential gain from such improvements have diminishing returns beyond the short-range (out to around 2 days). [ABSTRACT FROM AUTHOR]

We consider predicting and predictability for discrete time processes in a pathwise setting where only a sole one-sided semi-infinite sequence of past values is available, and where statistical properties of the ensemble of the paths are unknown. We obtain some sufficient conditions of predictability in the terms of degeneracy of frequency characteristics representing one-sided modifications of the Z-transform. These characteristics are defined for one-sided semi-infinite sequences representing of the past values. Some predictors are discussed. [ABSTRACT FROM AUTHOR]

In this article, I argue that trust, as usually deployed in organisations, is what Mead (1923) calls a cult value. This is an idealisation-rarely achieved in reality--which can be paradoxically inspirational/aspirational and destructive at the same time. When creating a culture of trust becomes a management project, it becomes an instrument of power, with the negative effects of stifling reflexivity, compelling conformity of behaviour, etc. As professional organisations (e.g. Mintzberg 1989), in which communities of practice are prevalent, hospitals are especially resistant to top-down trust building programmes. In contrast to the business literature on how to build an organisation-wide culture of trust, I suggest that genuine trusting relationships develop spontaneously in smaller groupings, both formal and ad hoc, which are characterised by interdependency, common interest and stability over time. This has implications for how we understand and foster trust in organisational life. [ABSTRACT FROM AUTHOR]

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In the Polynomial Global Approach to Time Series Analysis, the most costly (computationally speaking) step is the finding of the fitting polynomial. Here we present two routines that improve the forecasting. In the first, an algorithm that greatly improves this situation is introduced and implemented. The heart of this procedure is implemented on the specific routine which performs a mapping with great efficiency. In comparison with the similar procedure of the TimeS package developed by Carli et al. (2014), an enormous gain in efficiency and an increasing in accuracy are obtained. Another development in this work is the establishment of a level of confidence in global prediction with a statistical test for evaluating if the minimization performed is suitable or not. The other program presented in this article applies the Shapiro–Wilk test for checking the normality of the distribution of errors and calculates the expected deviation. The development is employed in observed and simulated time series to illustrate the performance obtained. Program summary Program title: LinMapTS Catalogue identifier: AFAJ_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AFAJ_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 9850 No. of bytes in distributed program, including test data, etc.: 223921 Distribution format: tar.gz Programming language: Maple 16. Computer: Any capable of running Maple. Operating system: Any capable of running Maple. Tested on Windows ME, Windows XP, Windows 7. RAM: 128 MB bytes Classification: 4.3, 4.9, 5. Nature of problem: Time series analysis and improving forecast capability. Solution method: The basis of the solution method is the result published in [1]. Restrictions: Global variables X [i] are used in the generated map; If more than 2000 vectors are employed in the global mapping the normality test is not applicable. Unusual features: When many polynomial coefficients are calculated (e.g., 55) their values can be different in distinct computers. These discrepancies do not affect significantly the accuracy in forecasting. Running time: A few seconds are required for the usual applications. References: [1] H. Carli, L. Duarte, L. da Mota, A maple package for improved global mapping forecast, Comp. Phts. Comm. 185(2014)1115 [ABSTRACT FROM AUTHOR]

A new class of deterministic dynamical systems, termed semipredictable dynamical systems, is presented. The spatiotemporal evolution of these systems have both predictable and unpredictable traits, as found in natural complex systems. We prove a general result: The dynamics of any deterministic nonlinear cellular automaton (CA) with p possible dynamical states can be decomposed at each instant of time in a superposition of N layers involving p 0 , p 1 , …, p N − 1 dynamical states each, where the p k ∈ N , k ∈ [ 0 , N − 1 ] are divisors of p . If the divisors coincide with the prime factors of p this decomposition is unique . Conversely, we also prove that N CA working on symbols p 0 , p 1 , …, p N − 1 can be composed to create a graded CA rule with N different layers. We then show that, even when the full spatiotemporal evolution can be unpredictable, certain traits (layers) can exactly be predicted. We present explicit examples of such systems involving compositions of Wolfram’s 256 elementary CA and a more complex CA rule acting on a neighborhood of two sites and 12 symbols and whose rule table corresponds to the smallest Moufang loop M 12 ( S 3 , 2). [ABSTRACT FROM AUTHOR]

k -Anonymous microaggregation emerges as an essential building block in statistical disclosure control a field concerning the postprocessing of the demographic portion of surveys containing sensitive information in order to safeguard the anonymity of the respondents. Traditionally this form of microaggregation has been formulated to characterize both the privacy attained and the inherent information loss due to the aggregation of quasi-identifiers which may otherwise be exploited to reidentify the individuals to which a record in a published database refers. Because the ulterior purposes of such databases involve the analysis of the statistical dependence between demographic attributes and sensitive data we must articulate mechanisms to enable the preservation of the statistical dependence between quasi-identifiers and confidential attributes beyond the mere degradation of the quasi-identifiers alone. This work addresses the problem of k -anonymous microaggregation with preservation of statistical dependence in a formal systematic manner modeling statistical dependence as predictability of the confidential attributes from the perturbed quasi-identifiers. We proceed by introducing a second mean squared error term in a combined Lagrangian cost that enables us to regulate the trade-off between quasi-identifier distortion and the confidential-attribute predictability. A Lagrangian multiplier enables us to gracefully weigh the importance of each of the two competing objectives. [ABSTRACT FROM AUTHOR]

By measuring the nonconscious consumer response to products, concepts and before/after results, it is possible to make decisions for product development and marketing, as well as develop product claims. These possibilities are discussed herein, as are two case studies. [ABSTRACT FROM AUTHOR]

The article presents research which examined the behavioral treatments for separation anxiety in canine. Topics covered include the changes in the environment around dogs and other proposed pharmacological treatments for separation anxiety. Also mentioned are the possible effects of anxiety disorders on animals such as contextual fear.

Abstract: The present work elaborates on predictability and information aspects of dynamical systems, in connection with the connectivity features of their network representation. The basic idea underlying this work is to map the set of coarse-grained states of a dynamical system onto a set of network nodes and transitions between them onto a set of network links. Based on the vertex centrality of these nodes, we define (a) a local indicator of predictability, (b) a measure of the information that is available about the state of the system after one transition occurring within an arbitrary long time window and (c) an upper bound for the time horizon of predictability. We address the cases of the tent and the cusp maps, as representative examples of Markov and non-Markov processes. An analytical exact result for the horizon of predictability is obtained for the tent map, as well as for its higher iterates, and its connection with the corresponding network diameters is discussed. Similarly, analytical expressions are derived for the bounds of the predictability horizon in the case of the cusp map. [Copyright &y& Elsevier]

Abstract: In a typical strategic asset allocation problem, the out-of-sample certainty equivalent returns for a long-horizon investor with constant relative risk aversion computed from a range of vector autoregressions (VARs) are compared with those from nonlinear models that account for bull and bear regimes. In a horse race in which models are not considered in their individuality but instead as an overall class, it is found that a power utility investor with a relative risk aversion of 5 and a 5 year horizon is ready to pay as much as 8.1% in real terms to be allowed to select models from the Markov switching (MS) class, while analogous calculation for the whole class of expanding window VARs leads to a disappointing 0.3% per annum. Most (if not all) VARs cannot produce portfolio rules, hedging demands, or out-of-sample performances that approximate those obtained from equally simple nonlinear frameworks. [Copyright &y& Elsevier]

Abstract: The performance of an ARCH model selection algorithm based on the standardized prediction error criterion (SPEC) is evaluated. The evaluation of the algorithm is performed by comparing different volatility forecasts in option pricing through the simulation of an options market. Traders employing the SPEC model selection algorithm use the model with the lowest sum of squared standardized one-step-ahead prediction errors for obtaining their volatility forecast. The cumulative profits of the participants in pricing 1-day index straddle options always using variance forecasts obtained by GARCH, EGARCH and TARCH models are compared to those made by the participants using variance forecasts obtained by models suggested by the SPEC algorithm. The straddles are priced on the Standard and Poor 500 (S & P 500) index. It is concluded that traders, who base their selection of an ARCH model on the SPEC algorithm, achieve higher profits than those, who use only a single ARCH model. Moreover, the SPEC algorithm is compared with other criteria of model selection that measure the ability of the ARCH models to forecast the realized intra-day volatility. In this case too, the SPEC algorithm users achieve the highest returns. Thus, the SPEC model selection method appears to be a useful tool in selecting the appropriate model for estimating future volatility in pricing derivatives. [Copyright &y& Elsevier]

Landslide distributions in two major areas of northern Japan, Tohoku and Hokkaido, are analysed for multifractal properties. For the latter data set, also the multifractal spectrum for the spatial landslide size distribution is determined and compared to the probability distribution. It is concluded that the fields possess definite multifractal character. This finding is supported by the known multifractality of the main triggering processes, rain and earthquakes. Further support comes from a configuration entropy analysis which is found to be a useful complimentary tool to multifractal analysis. Models leading to multifractality are briefly reviewed. Careful attention is paid to the algorithms used and to the verification of the numerical results. Some general suggestions concerning numerical methods are made. [ABSTRACT FROM AUTHOR]

Upholds the equivalence between the sociological notion of reflexivity and the idea of self-reference, which epistemologically and structurally distinguish those complex systems where humans participate as elements. Once there is a conceptual bridge between sociology and cybernetics, the reflexivity processes are analysed, and the expression feed-before is proposed for the corresponding circuits, as they operate after the input, but before the output characteristic of the other complex systems is produced. After distinguishing between micro- and macro-reflexivity, studies mutual connections and submits graphic designs of both types of circuit. Also briefly analyses the tempo of social reflexivity, while deducing the consequences regarding the predictability of social dynamics. [ABSTRACT FROM AUTHOR]

Forward Networks' software platform enables network and applications teams to verify intent and predict network behavior, Forward's solution brings the benefits of intent-based networking to large enterprises: agility, predictability, and scalability. [ABSTRACT FROM AUTHOR]