have been proposed and a variety of extensions have been made to the original formulation. We present an overview of image alignment, describing most of the algorithms in a consistent framework. We concentrate on the inverse compositional algorithm, an efficient algorithm that we recently proposed. We

present a general and unified framework for finite element assembly. Based on this framework, we propose a specific software interface (UFC) between problem-specific and general-purpose components of finite element programs. The interface is general in the sense that it applies to a wide range of finite

Search-theoretic models of monetary exchange are based on explicit descriptions of the frictions that make money essential. However, tractable versions of these models typically make strong assumptions that render them ill suited for monetary policy analysis. We propose a new framework, based

. In this paper, we propose a unified framework for testing effort dependent software reliability growth models incorporating imperfect debugging and error generation. The proposed framework represents the realistic case of time delays between the different stages of fault removal process i.e Failure Observation

constraint satisfaction), and branch and bound (for combinatorial optimization). The paper presents the bucket-elimination framework as a unifying theme across probabilistic and deterministic reasoning tasks and show how conditioning search can be augmented to systematically trade space for time.

Probabilistic inference algorithms for belief updating, finding the most probable explanation, the maximum a posteriori hypothesis, and the maximum expected utility are reformulated within the bucket elimination framework. This emphasizes the principles common to many of the algorithms appearing

Rare objects are often of great interest and great value. Until recently, however, rarity has not received much attention in the context of data mining. Now, as increasingly complex real-world problems are addressed, rarity, and the related problem of imbalanced data, are taking center stage. This article discusses the role that rare classes and rare cases play in data mining. The problems that can result from these two forms of rarity are described in detail, as are methods for addressing these problems. These descriptions utilize examples from existing research, so that this article provides a good survey of the literature on rarity in data mining. This article also demonstrates that rare classes and rare cases are very similar phenomena---both forms of rarity are shown to cause similar problems during data mining and benefit from the same remediation methods.

This paper develops a framework for estimating household preferences for school and neighborhood attributes in the presence of sorting. It embeds a boundary discontinuity design in a heterogeneous residential choice model, addressing the endogeneity of school and neighborhood characteristics

-coded checks and issue logical as well as continuous-variable control commands. The interaction of these different types of dynamics and information leads to a challenging set of "hybrid" control problems. We propose a very general framework that systematizes the notion of a hybrid system, combining

Channel assignment problems in the time, frequency and code domains have thus far been studied separately. Exploiting the similarity of constraints that characterize assignments within and across these domains, we introduce the first unified framework for the study of assignment problems. Our