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 $4\times10^2$ fm and $10^4$ fm the upper bound on the net charge is given by the universal relation $Z=0.71R_{fm}$, and for larger radii (measured in fm or km) $Z = 7 \times 10^{-5} R_{fm}^2 = 7 \times 10^{31} R_{km}^2$. For objects with nuclear density the relation corresponds to $Z \approx 0.7 A^{1/3}$ ($10^{8} < A < 10^{12}$) and $Z \approx 7\times10^{-5} A^{2/3}$ ($A > 10^{12}$), where $A$ is the baryon number. For some systems this universal upper bound improves existing charge limits in the literature

Arcus: exploring the formation and evolution of clusters, galaxies, and stars

Arcus, a Medium Explorer (MIDEX) mission, was selected by NASA for a Phase A study in August 2017. The observatory provides high-resolution soft X-ray spectroscopy in the 12-50Å bandpass with unprecedented sensitivity: effective areas of >450 cm^2 and spectral resolution >2500. The Arcus key science goals are (1) to measure the effects of structure formation imprinted upon the hot baryons that are predicted to lie in extended halos around galaxies, groups, and clusters, (2) to trace the propagation of outflowing mass, energy, and momentum from the vicinity of the black hole to extragalactic scales as a measure of their feedback and (3) to explore how stars, circumstellar disks and exoplanet atmospheres form and evolve. Arcus relies upon the same 12m focal length grazing-incidence silicon pore X-ray optics (SPO) that ESA has developed for the Athena mission; the focal length is achieved on orbit via an extendable optical bench. The focused X-rays from these optics are diffracted by high-efficiency Critical-Angle Transmission (CAT) gratings, and the results are imaged with flight-proven CCD detectors and electronics. The power and telemetry requirements on the spacecraft are modest. Mission operations are straightforward, as most observations will be long (~100 ksec), uninterrupted, and pre-planned, although there will be capabilities to observe sources such as tidal disruption events or supernovae with a ~3 day turnaround. Following the 2nd year of operation, Arcus will transition to a proposal-driven guest observatory facility

Arcus: the x-ray grating spectrometer explorer

Arcus will be proposed to the NASA Explorer program as a free-flying satellite mission that will enable high-resolution soft X-ray spectroscopy (8-50) with unprecedented sensitivity – effective areas of >500 sq cm and spectral resolution >2500. The Arcus key science goals are (1) to determine how baryons cycle in and out of galaxies by measuring the effects of structure formation imprinted upon the hot gas that is predicted to lie in extended halos around galaxies, groups, and clusters, (2) to determine how black holes influence their surroundings by tracing the propagation of out-flowing mass, energy and momentum from the vicinity of the black hole out to large scales and (3) to understand how accretion forms and evolves stars and circumstellar disks by observing hot infalling and outflowing gas in these systems. Arcus relies upon grazing-incidence silicon pore X-ray optics with the same 12m focal length (achieved using an extendable optical bench) that will be used for the ESA Athena mission. The focused X-rays from these optics will then be diffracted by high-efficiency off-plane reflection gratings that have already been demonstrated on sub-orbital rocket flights, imaging the results with flight-proven CCD detectors and electronics. The power and telemetry requirements on the spacecraft are modest. The majority of mission operations will not be complex, as most observations will be long (~100 ksec), uninterrupted, and pre-planned, although there will be limited capabilities to observe targets of opportunity, such as tidal disruption events or supernovae with a 3-5 day turnaround. After the end of prime science, we plan to allow guest observations to maximize the science return of Arcus to the community

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