45,248 research outputs found
Dark energy: a quantum fossil from the inflationary Universe?
The discovery of dark energy (DE) as the physical cause for the accelerated
expansion of the Universe is the most remarkable experimental finding of modern
cosmology. However, it leads to insurmountable theoretical difficulties from
the point of view of fundamental physics. Inflation, on the other hand,
constitutes another crucial ingredient, which seems necessary to solve other
cosmological conundrums and provides the primeval quantum seeds for structure
formation. One may wonder if there is any deep relationship between these two
paradigms. In this work, we suggest that the existence of the DE in the present
Universe could be linked to the quantum field theoretical mechanism that may
have triggered primordial inflation in the early Universe. This mechanism,
based on quantum conformal symmetry, induces a logarithmic,
asymptotically-free, running of the gravitational coupling. If this evolution
persists in the present Universe, and if matter is conserved, the general
covariance of Einstein's equations demands the existence of dynamical DE in the
form of a running cosmological term whose variation follows a power law of the
redshift.Comment: LaTeX, 14 pages, extended discussion. References added. Accepted in
J. Phys. A: Mathematical and Theoretica
Selling to Consumers with Endogenous Types
For many goods (such as experience goods or addictive goods), consumers' preferences may change over time.In this paper, we examine a monopolist's optimal pricing schedule when current consumption can affect a consumer's valuation in the future and valuations are unobservable.We assume that consumers are anonymous, i.e. the monopolist can't observe a consumer's past consumption history.For myopic consumers, the optimal consumption schedule is distorted upwards, involving substantial discounts for low valuation types.This pushes low types into higher valuations, from which rents can be extracted.For forward looking consumers, there may be a further upward distortion of consumption due to a reversal of the adverse selection effect; low valuation consumers now have a strong interest in consumption in order to increase their valuations.Firms will find it profitable to educate consumers and encourage forward looking behaviorendogenous types;experience goods;addictive goods;price discrimation
Design study of a fission-electric cell reactor
Fission electric cell reactor to generation of power in spac
Caustic formation in expanding condensates of cold atoms
We study the evolution of density in an expanding Bose-Einstein condensate
that initially has a spatially varying phase, concentrating on behaviour when
these phase variations are large. In this regime large density fluctuations
develop during expansion. Maxima have a characteristic density that diverges
with the amplitude of phase variations and their formation is analogous to that
of caustics in geometrical optics. We analyse in detail caustic formation in a
quasi-one dimensional condensate, which before expansion is subject to a
periodic or random optical potential, and we discuss the equivalent problem for
a quasi-two dimensional system. We also examine the influence of many-body
correlations in the initial state on caustic formation for a Bose gas expanding
from a strictly one-dimensional trap. In addition, we study a similar
arrangement for non-interacting fermions, showing that Fermi surface
discontinuities in the momentum distribution give rise in that case to sharp
peaks in the spatial derivative of the density. We discuss recent experiments
and argue that fringes reported in time of flight images by Chen and co-workers
[Phys. Rev. A 77, 033632 (2008)] are an example of caustic formation.Comment: 10 pages, 5 figures. Published versio
An associative memory for the on-line recognition and prediction of temporal sequences
This paper presents the design of an associative memory with feedback that is
capable of on-line temporal sequence learning. A framework for on-line sequence
learning has been proposed, and different sequence learning models have been
analysed according to this framework. The network model is an associative
memory with a separate store for the sequence context of a symbol. A sparse
distributed memory is used to gain scalability. The context store combines the
functionality of a neural layer with a shift register. The sensitivity of the
machine to the sequence context is controllable, resulting in different
characteristic behaviours. The model can store and predict on-line sequences of
various types and length. Numerical simulations on the model have been carried
out to determine its properties.Comment: Published in IJCNN 2005, Montreal, Canad
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