1,904 research outputs found
Construction of a model of the Venus surface and its use in processing radar observations
An algorithm is described for constructing the model of the Venus surface as an expansion in spherical functions. The relief expansion coefficients were obtained up to the coefficient S sub 99. The surface picture representation is given according to this expansion. The surface model constructed was used for processing radar observations. The use of the surface model allows improved agreement between the design and measured values of radar ranges
Generalized Green Functions and current correlations in the TASEP
We study correlation functions of the totally asymmetric simple exclusion
process (TASEP) in discrete time with backward sequential update. We prove a
determinantal formula for the generalized Green function which describes
transitions between positions of particles at different individual time
moments. In particular, the generalized Green function defines a probability
measure at staircase lines on the space-time plane. The marginals of this
measure are the TASEP correlation functions in the space-time region not
covered by the standard Green function approach. As an example, we calculate
the current correlation function that is the joint probability distribution of
times taken by selected particles to travel given distance. An asymptotic
analysis shows that current fluctuations converge to the process.Comment: 46 pages, 3 figure
On a conjecture of Widom
We prove a conjecture of H.Widom stated in [W] (math/0108008) about the
reality of eigenvalues of certain infinite matrices arising in asymptotic
analysis of large Toeplitz determinants. As a byproduct we obtain a new proof
of A.Okounkov's formula for the (determinantal) correlation functions of the
Schur measures on partitions.Comment: 9 page
Can We Learn to Beat the Best Stock
A novel algorithm for actively trading stocks is presented. While traditional
expert advice and "universal" algorithms (as well as standard technical trading
heuristics) attempt to predict winners or trends, our approach relies on
predictable statistical relations between all pairs of stocks in the market.
Our empirical results on historical markets provide strong evidence that this
type of technical trading can "beat the market" and moreover, can beat the best
stock in the market. In doing so we utilize a new idea for smoothing critical
parameters in the context of expert learning
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