The paper presents a multidimensional (MD) approach to recommender systems that can provide recommendations based on additional contextual information besides the typical information on users and items used in most of the current recommender systems. This approach supports multiple dimensions, extensive profiling, and hierarchical aggregation of recommendations. The paper also presents a multidimensional rating estimation method capable of selecting two-dimensional segments of ratings pertinent to the recommendation context and applying standard collaborative filtering or other traditional two-dimensional rating estimation techniques to these segments. A comparison of the multidimensional and two-dimensional rating estimation approaches is made, and the tradeoffs between the two are studied. Moreover, the paper introduces a combined rating estimation method that identifies the situations where the MD approach outperforms the standard two-dimensional approach and uses the MD approach in those situations and the standard two-dimensional approach elsewhere. Finally, the paper presents a pilot empirical study of the combined approach, using a multidimensional movie recommender system that was developed for implementing this approach and testing its performance. 1 1.

We test hypotheses concerning human cue combination in a slant estimation task. Observers

In the present study, we introduce a simple iterative procedure that allows to correct the outputs of a classifier with respect to the new a priori probabilities of a new data set to be scored, even when these new a priori probabilities are unknown in advance. We also show that a significant increase in classification accuracy can be observed when using this procedure properly. More specifically, by applying the correcting procedure to the outputs of a simple logistic regression model, we observe an increase of 5.8% of classification rate on a di#cult real-world multi-class problem -- the automatic labeling of geographical maps based on remote sensing information.

In two experiments, we examine how observers ierversMEzE themiMCVj parts of sampled, planar contoursi 3-D space. We variz (1) contour type(liMkC orparabolizk (2)oriEj#fiMxz of the planecontaiVzz the contour and (3) the number ofpoiqfi on a sampled contour. InterpolatiE performance was very accurate, comparable to results fromVerniC tasks. SettiM variMCfifiFfi washiMz#j along the lie ofsijj and for theparaboli contour.Settir varir.MEV# decreasewir ireaseMCz number ofpoizj oneizfiF contour, suggestiF that observers do not use all avaiFqFMx relevantievantMEjq in this task.

Lossy image compression techniques allow arbitrarily high compression rates but at the price of poor image quality. We applied maximum likelihood difference scaling to evaluate image quality of nine images, each compressed via vector quantization to ten different levels, within two different color spaces, RGB and CIE 1976 L * a * b *.InL * a * b * space, images could be compressed on average by 32 % more than in RGB space, with little additional loss in quality. Further compression led to marked perceptual changes. Our approach permits a rapid, direct measurement of the consequences of image compression for human observers. © 2007 Optical

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The image of a material’s surface varies not only with viewing and illumination conditions, but also with the material’s surface properties, including its 3-D texture and specularity. Previous studies on the visual perception of surface material have typically focused on single material properties, ignoring possible interactions. In this study, we used a conjoint-measurement design to determine how observers represent perceived 3-D texture (‘‘bumpiness’’) and specularity (‘‘glossiness’’) and modeled how each of these two surface-material properties affects perception of the other. Observers made judgments of bumpiness and glossiness of surfaces that varied in both surface texture and specularity. We quantified how changes in each surface-material property affected judgments of the other and found that a simple additive model captured visual perception of texture and specularity and their interaction. Conjoint measurement is potentially a powerful tool for analyzing perception of surface material in realistic environments.

Abstract: The propensity functions of the Stochastic Simulation Algorithm (SSA) have been derived under the point-molecule assumption, i.e., that the total volume of the molecules is negligible compared to the volume of the container. In this paper we investigate the impact of relaxing the point-molecule assumption. We report results of computational experiments on systems of N hard discs of diameter l moving ballistically inside finite two-dimensional containers. Since the SSA requires that interreaction times be exponentially distributed, we study the distribution of times t between when the system is initialized in a well-stirred state and when the first inter-molecular collision occurs. We find thet-distribution to be well approximated by an exponential distribution, with deviations from exponentiality becoming apparent in systems with small numbers of molecules in the limits of very low and very high area density (defined as the ratio of the area of the molecule disks to the total area of the system). As expected, when l ® 0 the impact of the area excluded by the molecules on the t-distribution is negligible. For high area density it appears that the molecules effectively exclude an area which starts out greater than the close-packed area of their disks, for systems with few2 molecules, but approaches the close-packed area as the number of molecules increases. This suggests that the effect of reactant-excluded volume on reaction kinetics in dense, finite, two and three dimensional systems may be underestimated, and underlines the importance of studying it further.

by Kenneth Kelley III The average rate of change is a key concept in longitudinal analyses that examine change over time. However, this concept has been misunderstood both implicitly and explicitly in the literature. The present work attempts to clarify the concept and show unequivocally the mathematical definition and meaning of the average rate of change. Oftentimes the slope from the straight-line growth model is interpreted as though it were the average rate of change. It is shown, however, that this is generally not the case and holds true in only a limited number of situations. General equations are presented for the bias and discrepancy factor when the slope from the straight-line growth model is used to estimate the average rate of change. The importance of fitting an appropriate individual growth model is discussed, as are the benefits provided by nonlinear models for longitudinal data. CONTENTS

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