function approximation. We argue that objects provide an abstract representation of data that is well suited for the input and output of neural networks, as well as other statistical learning techniques. By examining the results of a simple sorting example, we illustrate the efficacy of these techniques. I.

locations. We propose approximate techniques to determine the relative locations and orientations of camera sensors without any use of landmarks or positioning technologies. Our techniques determine the degree and range of overlap for each camera and show this information can be exploited for duty cycling

Connectivity augmentation and orientation are two fundamental classes of problems related to graph connectivity. The former includes minimum cost spanning trees, k-edge-connected subgraph and more generally, survivable network design problems [9]. In the orientation problems the goal is to find

sensor networks where; 1) nodes collaborate in an ad hoc fashion by using SD techniques to discover (and announce) resources locally and over the public Internet, 2) nodes preserve power through aggressive utilization of low power (sleep) modes, while yet being reachable for clients according to defined

between network entities in a connection oriented network. Users are treated as utility maximizing entities who allocate the available band-width among themselves by playing a distributed, noncooperative rate game. The ensuing Nash equilibrium is analyzed for the single link Erlang network and the multi

A novice search engine user may find searching the web for information difficult and frustrating because she may naturally express search goals rather than the topic keywords search engines need. In this paper, we present GOOSE (goal-oriented search engine), an adaptive search engine interface

Abstract We propose a stochastic orienteering problem on a network of queues with time windows at customers. While this problem is of wide applicability, we study it in the context of routing and scheduling a textbook salesperson who visits professors on campus to gain textbook adoptions

the original graph) directed path. This problem is NP-complete and hard to approximate to within sub-polynomial factors. Here we provide a first polynomial-size integer linear program formulation for this problem, which allows its exact solution in seconds on current networks. We apply our algorithm to orient

. This is motivated by a multicasting problem in undirected networks as well as a generalization of some classical problems in graph theory. The main results of this paper are the following approximate min-max relations: • Given an undirected hypergraph H, if S is 2k-hyperedge-connected in H, then H has a Steiner

and show that if a local algorithm finds a constant-factor approximation of a simple PO-checkable graph problem with the help of unique identifiers, then the same approximation ratio can be achieved on anonymous networks. As a corollary of our result, we derive a tight lower bound on the local