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Previous models of visual search have hypothesized either a random search or a repeated systematic search strategy. Although both models reproduce well the cumulative search time distribution, F(t), neither fully accords with eye movement data. A new model is proposed in which search is intended to be systematic but suffers from imperfect memory. Systematic search is then a special case in which the memory is perfect, and random search a special case in which the memory is totally lacking. The model was derived for single and multiple occurrences of a single fault (or target) type. Where the model could be proved to be insoluble, a simulation model was used. Simulation results were compared with the previous calculations of Morawski, Drury, and Karwan (1980) and were shown to give identical results for pure random and pure systematic search. As the parameters of the memory model were varied, a family of curves between these extremes was produced.

Not Available ; Agricultural time-series data concerning production, prices, export and import of several agricultural commodities is published by Indian government along with other private agricultural sectors every year. The analysis of these factors is necessary to formulate and apply several policies regarding food acquisition and its distribution, quality and quantity of import and export products, pricing structure, MSP of agricultural commodities etc. Box – Jenkins's Autoregressive integrated moving average (ARIMA) model is broadly utilized in the field of time-series. In the field of time-series analysis, it is assumed by most of the researchers that the data points of different time lags do not depend on each other, i.e. absence of long memory process. But in agriculture, market price data exhibits that the observation are dependent on distant past. This is the possible indication of long memory process or long range dependency in the mean model. Autoregressive fractionally integrated autoregressive moving average (ARFIMA) model is generally used to portray the characteristic features of the long memory time series models as well as for the forecasting purposes. In this study wavelet decomposition is used for increasing the forecasting accuracy of the ARFIMA model. Daily wholesale data of wheat of Rewari market of Haryana for the period of January, 2010 to November, 2017 is used for the demonstration of our approach. ; Not Available

Political scientists often wish to classify documents based on their content to measure variables, such as the ideology of political speeches or whether documents describe a Militarized Interstate Dispute. Simple classifiers often serve well in these tasks. However, if words occurring early in a document alter the meaning of words occurring later in the document, using a more complicated model that can incorporate these time-dependent relationships can increase classification accuracy. Long short-term memory (LSTM) models are a type of neural network model designed to work with data that contains time dependencies. We investigate the conditions under which these models are useful for political science text classification tasks with applications to Chinese social media posts as well as US newspaper articles. We also provide guidance for the use of LSTM models.

International audience ; This paper extends the results of Byers, Davidson and Peel (1997) on long memory in support for the Conservative and Labour Parties in the UK using longer samples and additional poll series. It finds continuing support for the ARFIMA(0,d,0) model though with somewhat smaller values of the long memory parameter. We find that the move to telephone polling in the mid-1990s has no apparent effect on the estimated value of d for either party. Finally, we find that we cannot reject the hypotheses that the parties share a common long memory parameter which we estimate at around 0.65.

This paper extends the results of Byers, Davidson and Peel (1997) on long memory in support for the Conservative and Labour Parties in the UK using longer samples and additional poll series. It finds continuing support for the ARFIMA(0,d,0) model though with somewhat smaller values of the long memory parameter. We find that the move to telephone polling in the mid-1990s has no apparent effect on the estimated value of d for either party. Finally, we find that we cannot reject the hypotheses that the parties share a common long memory parameter which we estimate at around 0.65.

Brand alliances between the brands from different categories are increasingly becoming popular (Smarandescu, Rose and Wedell, 2013). This is particular relevant to the emerging countries context where multinational brands due to strong impact of cultural and country-specific political and economic governance policies are establishing cross-category brand alliances with local brands to gain brand success and customer loyalty. Existing studies investigated cross-category brand alliances according to the aspects of brand order, consumer ethnocentrism, the country of origin and brand familiarity. However, these studies primarily incorporate end-user impact factors in measuring and understanding the cross-category brand alliances performance. Brands as business perspective is required to understand the impacts of brand resources and attributes on the cross-category brand alliance. This study focuses on the cross-category brand alliances and attempts to develop a framework to measure the performance of cross-category brand alliances. The framework constructs are derived from Associative Network Memory (ANM) Model and Resource Based View (RBV) theories. The framework is developed by evaluating the interviews with the marketing managers of brands in the brand alliance case studies from an emerging country, Turkey.