, implies a more general, enduring pattern of error, bias, or both that assumes a particular direction or shape. As the evidence will show, the illusions to be considered (unrealistically positive self-evaluations, exaggerated perceptions of control, and unrealistic optimism) do indeed seem to be pervasive

Domestic investors hold a substantially larger proportion of their wealth portfolios in domestic assets than standard portfolio theory would suggest, a phenomenon called "equity home bias. " In the absence of this bias, investors would optimally diversify domestic output risk using

Level-of-detail (LOD) representations are an important tool for realtime rendering of complex geometric environments. The previously introduced progressive mesh representation defines for an arbitrary triangle mesh a sequence of approximating meshes optimized for view-independent LOD. In this paper

for discontinuities along C 2 curves and is essentially optimal. In comparison, the squared error of n-term wavelet approximations only converges as n −1 as n → ∞, which is considerably worst than the optimal behavior.

A supervised learning algorithm (Scaled Conjugate Gradient, SCG) with superlinear convergence rate is introduced. The algorithm is based upon a class of optimization techniques well known in numerical analysis as the Conjugate Gradient Methods. SCG uses second order information from the neural

TCP is a reliable transport protocol tuned to perform well in traditional networks made up of links with low bit-error rates. Networks with higher bit-error rates, such as those with wireless links and mobile hosts, violate many of the assumptions made by TCP, causing degraded end

allocation schemes. We have also quantified the improvement in terms of the overall required transmit power, the bit-error rate (BER), or the area of coverage for a given outage probability.

at the base station and mobile host, and preserve the end-to-end semantics of TCP. One part of the modifications, called the snoop module, caches packets at the base station and performs local retransmissions across the wireless link to alleviate the problems caused by high bit-error rates. The second part

of the transmitted signals, perturbed by additive noise. Under the assumptions of symbol-synchronous transmissions and white Gaussian noise, we analyze the detection mechanism at the receiver, comparing different detectors by their bit error rate in the low background noise region, and by their worst-case behavior

approach with Eigenface and Fisherface methods on three different face data sets. Experimental results suggest that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition. Index Terms—Face recognition, principal component analysis, linear