35,818 research outputs found
The Hunting of the Bump: On Maximizing Statistical Discrepancy
Anomaly detection has important applications in biosurveilance and
environmental monitoring. When comparing measured data to data drawn from a
baseline distribution, merely, finding clusters in the measured data may not
actually represent true anomalies. These clusters may likely be the clusters of
the baseline distribution. Hence, a discrepancy function is often used to
examine how different measured data is to baseline data within a region. An
anomalous region is thus defined to be one with high discrepancy.
In this paper, we present algorithms for maximizing statistical discrepancy
functions over the space of axis-parallel rectangles. We give provable
approximation guarantees, both additive and relative, and our methods apply to
any convex discrepancy function. Our algorithms work by connecting statistical
discrepancy to combinatorial discrepancy; roughly speaking, we show that in
order to maximize a convex discrepancy function over a class of shapes, one
needs only maximize a linear discrepancy function over the same set of shapes.
We derive general discrepancy functions for data generated from a one-
parameter exponential family. This generalizes the widely-used Kulldorff scan
statistic for data from a Poisson distribution. We present an algorithm running
in that computes the maximum
discrepancy rectangle to within additive error , for the Kulldorff
scan statistic. Similar results hold for relative error and for discrepancy
functions for data coming from Gaussian, Bernoulli, and gamma distributions.
Prior to our work, the best known algorithms were exact and ran in time
.Comment: 11 pages. A short version of this paper will appear in SODA06. This
full version contains an additional short appendi
A survey of new technology for cockpit application to 1990's transport aircraft simulators
Two problems were investigated: inter-equipment data transfer, both on board the aircraft and between air and ground; and crew equipment communication via the cockpit displays and controls. Inter-equipment data transfer is discussed in terms of data bus and data link requirements. Crew equipment communication is discussed regarding the availability of CRT display systems for use in research simulators to represent flat panel displays of the future, and of software controllable touch panels
Highly parallel sparse Cholesky factorization
Several fine grained parallel algorithms were developed and compared to compute the Cholesky factorization of a sparse matrix. The experimental implementations are on the Connection Machine, a distributed memory SIMD machine whose programming model conceptually supplies one processor per data element. In contrast to special purpose algorithms in which the matrix structure conforms to the connection structure of the machine, the focus is on matrices with arbitrary sparsity structure. The most promising algorithm is one whose inner loop performs several dense factorizations simultaneously on a 2-D grid of processors. Virtually any massively parallel dense factorization algorithm can be used as the key subroutine. The sparse code attains execution rates comparable to those of the dense subroutine. Although at present architectural limitations prevent the dense factorization from realizing its potential efficiency, it is concluded that a regular data parallel architecture can be used efficiently to solve arbitrarily structured sparse problems. A performance model is also presented and it is used to analyze the algorithms
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