806 resultados para Representation Targets


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In this paper, we investigate how a multilinear model can be used to represent human motion data. Based on technical modes (referring to degrees of freedom and number of frames) and natural modes that typically appear in the context of a motion capture session (referring to actor, style, and repetition), the motion data is encoded in form of a high-order tensor. This tensor is then reduced by using N-mode singular value decomposition. Our experiments show that the reduced model approximates the original motion better then previously introduced PCA-based approaches. Furthermore, we discuss how the tensor representation may be used as a valuable tool for the synthesis of new motions.

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Target difficulty is often argued to increase performance. While this association is well established in experimental research, empirical evidence in field research is rather mixed. We attempt to explain this inconsistency by analyzing the importance of intra-year target revisions, which are especially prevalent in real-world field settings. Using survey and archival data from 97 firms, we find that firms with more challenging business unit targets revise targets more often, in line with asymmetric, downward target revisions. Results further show that the degree to which targets are revised during a period results in negative effects on firm performance, as the anticipation of revision negatively affects the business unit management’s performance incentives. Additionally, we find that using targets predominantly for either decision-making or control influences the overall performance effects of target revisions. Our findings may partially explain the mixed field study evidence regarding the effects of target difficulty.

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The African great lakes are of utmost importance for the local economy (fishing), as well as being essential to the survival of the local people. During the past decades, these lakes experienced fast changes in ecosystem structure and functioning, and their future evolution is a major concern. In this study, for the first time a set of one-dimensional lake models are evaluated for Lake Kivu (2.28°S; 28.98°E), East Africa. The unique limnology of this meromictic lake, with the importance of salinity and subsurface springs in a tropical high-altitude climate, presents a worthy challenge to the seven models involved in the Lake Model Intercomparison Project (LakeMIP). Meteorological observations from two automatic weather stations are used to drive the models, whereas a unique dataset, containing over 150 temperature profiles recorded since 2002, is used to assess the model’s performance. Simulations are performed over the freshwater layer only (60 m) and over the average lake depth (240 m), since salinity increases with depth below 60 m in Lake Kivu and some lake models do not account for the influence of salinity upon lake stratification. All models are able to reproduce the mixing seasonality in Lake Kivu, as well as the magnitude and seasonal cycle of the lake enthalpy change. Differences between the models can be ascribed to variations in the treatment of the radiative forcing and the computation of the turbulent heat fluxes. Fluctuations in wind velocity and solar radiation explain inter-annual variability of observed water column temperatures. The good agreement between the deep simulations and the observed meromictic stratification also shows that a subset of models is able to account for the salinity- and geothermal-induced effects upon deep-water stratification. Finally, based on the strengths and weaknesses discerned in this study, an informed choice of a one-dimensional lake model for a given research purpose becomes possible.

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This paper presents problems arising from the lack of standardized methods for recording skeletal remains. Using practical examples it is shown how preservation and representation of bones can distort observations and how this can be reduced by systematic data acquisition.