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The Equity Premium
Recent research on the equity risk premium has questioned the ability of historical
estimates of the risk premium to provide reliable estimates of the expected risk
premium. We calculate the equity risk premium for a number of countries over longer
horizons than has been attempted to date. We show that the realised US equity
premium is consistent with the premia obtained elsewhere. Furthermore, using well
over a century of data, we find that current estimates of the equity premia are close to
those observed during the pre-1914 era. This is of particular relevance given the
argument that the financial environment during that period bears a closer resemblance
to today than the 1914-1945 period, and possibly also the 1945-1971 period. This
points to a current equity risk premium that is considerably lower than consensus
forecasts (Welch 2001)
Universal Charge-Radius Relation for Subatomic and Astrophysical Compact Objects
Electron-positron pair creation in supercritical electric fields limits the
net charge of any static, spherical object, such as superheavy nuclei,
strangelets, and Q-balls, or compact stars like neutron stars, quark stars, and
black holes. For radii between fm and fm the upper bound
on the net charge is given by the universal relation , and for
larger radii (measured in fm or km) . For objects with nuclear density the relation corresponds to
() and (), where is the baryon number. For some systems this
universal upper bound improves existing charge limits in the literature
Deep Convolutional Neural Networks for Interpretable Analysis of EEG Sleep Stage Scoring
Sleep studies are important for diagnosing sleep disorders such as insomnia,
narcolepsy or sleep apnea. They rely on manual scoring of sleep stages from raw
polisomnography signals, which is a tedious visual task requiring the workload
of highly trained professionals. Consequently, research efforts to purse for an
automatic stage scoring based on machine learning techniques have been carried
out over the last years. In this work, we resort to multitaper spectral
analysis to create visually interpretable images of sleep patterns from EEG
signals as inputs to a deep convolutional network trained to solve visual
recognition tasks. As a working example of transfer learning, a system able to
accurately classify sleep stages in new unseen patients is presented.
Evaluations in a widely-used publicly available dataset favourably compare to
state-of-the-art results, while providing a framework for visual interpretation
of outcomes.Comment: 8 pages, 1 figure, 2 tables, IEEE 2017 International Workshop on
Machine Learning for Signal Processin
Extended Superconformal Algebras from Classical and Quantum Hamiltonian Reduction
We consider the extended superconformal algebras of the Knizhnik-Bershadsky
type with -algebra like composite operators occurring in the commutation
relations, but with generators of conformal dimension 1, and 2,
only. These have recently been neatly classified by several groups, and we
emphasize the classification based on hamiltonian reduction of affine Lie
superalgebras with even subalgebras . We reveiw the situation
and improve on previous formulations by presenting generic and very compact
expressions valid for all algebras, classical and quantum. Similarly generic
and compact free field realizations are presented as are corresponding
screening charges. Based on these a discussion of singular vectors is
presented. (Based on talk by J.L. Petersen at the Int. Workshop on "String
Theory, Quantum Gravity and the Unification of the Fundamental Interactions",
Rome Sep. 21-26, 1992)Comment: 30 pages, NBI-HE-92-8
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