We present new deterministic methods that given two eigenspace models, each representing a set of n-dimensional observations will: (1) merge the models to yield a representation of the union of the sets; (2) split one model from another to represent the difference between the sets; as this is done, we accurately keep track of the mean. These methods are more efficient than computing new eigenspace models directly from the observations when the eigenmodels are dimensionally small compared to the total number of observations. Such methods are important because they provide a basis for novel techniques in machine learning, using a dynamic split-andmerge paradigm to optimally cluster observations. Here we present a theoretical derivation of the methods, empirical results relating to the efficiency and accuracy of the techniques, and three general applications, including the on-line construction of Gaussian mixture models.

Eigenspace models are a convenient way to represent sets of observations with widespread applications, including classification. In this paper we describe a new constructive method for incrementally adding observations to an eigenspace model. Our contribution is to explicitly account for a change in origin as well as a change in the number of eigenvectors needed in the basis set. No other method we have seen considers change of origin, yet both are needed if an eigenspace model is to be used for classification purposes. We empirically compare our incremental method with two alternatives from the literature and show our method is the more useful for classification because it computes the smaller eigenspace model representing the observations.

hteractionist analyses of social organization stimulate examination of how social situations and collective activity are shaped. Meta-power, the creation and control of distal situations, and organization as a structuration of metapower are used as tools for exploring the shaping of situations. Five metapower processes are presented: strategic agency, rules and conventions, structuring situations, culture construction, and empowering delegates. These processes illustrate how situations are created or altered. This paper offers a view of social organization that emphasizes relations among situations, linkages between consequences and conditions, and networks of collective activity across space and time. The conclusion calls for additional research to make more explicit the nature of social organization and its social conditions.

The sociological investigation of public policy continues to be plagued by scholarly adherence to a conventional framework that reifies the policy process as a set of segmented and sequential stages. To overcome this problem, policy is presented as the processual, ongoing practical accomplishment of the transformation of intentions. Within this framework, the realization of intentions is shown as both constrained and enabled by (1) organizational context and conventions, (2) linkages between multiple sites and phases of the policy process, (3) the mobilization of resources, and (4) a dynamic and multifaceted conceptualization of power.Public policy has been of significant interest to sociologists since the discipline emerged with a pervasive social reform orientation in the late nineteenth century. Over the last thirty years such sociological and social scientific scholarship has influenced the formulation of numerous policies and programs by analyzing their contexts, processes, and consequences. While such efforts have been constructive and the resulting literature instructive, the contributions of this work have been limited. The single greatest limitation has been the acceptance of a view of policy that structures and distorts the reality of policy production. The continued acceptance of this conventional framework belies the messy, complex, and dynamic nature of the policy process. The conventional framework for the study of policy utilizes what Paul Sabatier and HankJenkins-Smith (1993) characterize as the "textbook approach." Using a stages heuristic, the policy process is depicted as a set of segmented, separated, functionally sequenced stages. Typical stages are agenda setting, formulation, enactment, implementation, evaluation, and feedback. The conventional model has a commonsense institutional logic to it that highlights the emergence of problems, debate about alternatives, legislative action, bureaucratic implementation, impact analysis, and calculations about continuance/alteration, in that irreversible order. It has the appearance of a planning rationality that reinforces democratic theory and legitimates technocratic authority.

The contribution of this paper is a novel framework for synthesizing nonphotorealistic animations from real video sequences. We demonstrate that, through automated mid-level analysis of the video sequence as a spatiotemporal volume--a block of frames with time as the third dimension--we are able to generate animations in a wide variety of artistic styles, exhibiting a uniquely high degree of temporal coherence. In addition to rotoscoping, matting, and novel temporal effects unique to our method, we demonstrate the extension of static nonphotorealistic rendering (NPR) styles to video, including painterly, sketchy, and cartoon shading. We demonstrate how this novel coherent shading framework may be combined with our earlier motion emphasis work to produce a comprehensive "Video Paintbox" capable of rendering complete cartoon-styled animations from video clips.