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Regarding the Soviet past, idea of the dichotomous nature of Ukrainian historical memory is widespread both in society and in academia. According to it, two models of memory are widespread in Ukrainian society — the Ukrainian national memory and the Soviet one as the opposed to it. The article shows that this view is simplified. The Ukrainian national and Soviet models of memory are not a dichotomy but a continuum. Based on the analysis of sociological surveys, a number of versions have been identified within each of the memory models. In particular, within the Ukrainian national model there are such versions as: "resistance", "realitistic", "post-Soviet". The difference between them lies in the attitude towards the Soviet past. In the "resistance" version the Soviet past is completely rejected, in the "realitistic" version it is perceived as an integral part of the Ukrainian past, and in the "post-Soviet" version there is a mechanistic combination of Soviet and Ukrainian national narratives, with an emphasis on the Ukrainian component. Within the Soviet model there are such versions as: "Ukrainian", "fraternal peoples", and "reunification". The criterion for distinguishing them is the attitude to the Ukrainian historical narrative. In the case of the "Ukrainian" version, there is a combination of Soviet and Ukrainian narratives, but with an emphasis on the Soviet component. In the "fraternal peoples'" version the Ukrainian identity is seen as equal to the Russian one and as the "reunification". And in the "reunification" version Ukrainian identity is seen as a part of supranational identity. Also four types of historical memory are distinguished by the criterion of combining models within individual consciousness. Two of them are "pure" — Ukrainian national and Soviet-imperial, and two are "mixed" — ambivalent, where two models are combined, and indefinite, where none of the memory models is important to individuals. The article also analyzes the regional prevalence of both memory models and types.

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.