Abstract Drawing on the correspondence between the graph Laplacian, the Laplace-Beltrami op erator on a manifold , and the connections to the heat equation , we propose a geometrically motivated algorithm for constructing a representation for data sampled from a low dimensional manifold embedded

with ambiguities regarding the processes by which individuals adapt to changing levels in stimulation. Individuals' levels of activation decrease markedly as a function of familiarity with a given stimulus situation. However, after a period of rest, re-presentation of the same stimulus situation will once

intact. Furthermore, this region is demonstrably activated during number processing. I postulate that higher-level cultural developments in arithmetic emerge through the establishment of linkages between this core analogical representation (the " number line ") and other verbal and visual

The technology underlying text search engines has advanced dramatically in the past decade. The development of a family of new index representations has led to a wide range of innovations in index storage, index construction, and query evaluation. While some of these developments have been

to be settled. This precis focuses on my core theory and disregards most of the background material reviewed at length in the book itself. My central hypothesis is that there were three major cognitive transformations by which the modern human mind emerged over several million years, starting with a complex

Abstract—This paper presents an extension of our previous work which proposes a new speaker representation for speaker verification. In this modeling, a new low-dimensional speaker- and channel-dependent space is defined using a simple factor analysis. This space is named the total variability

A fundamental problem in knowledge representation is how to revise knowledge when new, contradictory information is obtained. This paper formulates some desirable principles of knowledge revision, and investigates a new theory of knowledge revision that realizes these principles. This theory

Knowledge Representation is the area of Artificial Intelligence (AI) concerned with how knowledge can be represented symbolically and manipulated in an automated way by reasoning programs. It is at the very core of a radical idea about how to understand intelligence: instead of trying to understand

This paper describes Livingstone, an implemented kernel for a model-based reactive self-configuring autonomous system. It presents a formal characterization of Livingstone's representation formalism, and reports on our experience with the implementation in a variety of domains. Livingstone

We propose a new probabilistic algorithm for online mapping of unknown environments with teams of robots. At the core of the algorithm is a technique that combines fast maximum likelihood map growing with a Monte Carlo localizer that uses particle representations. The combination of both yields