Self-tuning of threshold for a two-state system

A two-state system (TSS) under time-periodic perturbations (to be regarded as input signals) is studied in connection with self-tuning (ST) of threshold and stochastic resonance (SR). By ST, we observe the improvement of signal-to-noise ratio (SNR) in a weak noise region. Analytic approach to a tuning equation reveals that SNR improvement is possible also for a large noise region and this is demonstrated by Monte Carlo simulations of hopping processes in a TSS. ST and SR are discussed from a little more physical point of energy transfer (dissipation) rate, which behaves in a similar way as SNR. Finally ST is considered briefly for a double-well potential system (DWPS), which is closely related to the TSS

Metallic characteristics in superlattices composed of insulators, NdMnO3/SrMnO3/LaMnO3

We report on the electronic properties of superlattices composed of three different antiferromagnetic insulators, NdMnO3/SrMnO3/LaMnO3 grown on SrTiO3 substrates. Photoemission spectra obtained by tuning the x-ray energy at the Mn 2p -> 3d edge show a Fermi cut-off, indicating metallic behavior mainly originating from Mn e_g electrons. Furthermore, the density of states near the Fermi energy and the magnetization obey a similar temperature dependence, suggesting a correlation between the spin and charge degrees of freedom at the interfaces of these oxides

Effects of Dopamine Medication on Sequence Learning with Stochastic Feedback in Parkinson's Disease

A growing body of evidence suggests that the midbrain dopamine system plays a key role in reinforcement learning and disruption of the midbrain dopamine system in Parkinson's disease (PD) may lead to deficits on tasks that require learning from feedback. We examined how changes in dopamine levels (“ON” and “OFF” their dopamine medication) affect sequence learning from stochastic positive and negative feedback using Bayesian reinforcement learning models. We found deficits in sequence learning in patients with PD when they were “ON” and “OFF” medication relative to healthy controls, but smaller differences between patients “OFF” and “ON”. The deficits were mainly due to decreased learning from positive feedback, although across all participant groups learning was more strongly associated with positive than negative feedback in our task. The learning in our task is likely mediated by the relatively depleted dorsal striatum and not the relatively intact ventral striatum. Therefore, the changes we see in our task may be due to a strong loss of phasic dopamine signals in the dorsal striatum in PD

Star Formation Histories of Dwarf Spheroidal and Dwarf Elliptical Galaxies in the Local Universe

We present the star formation histories (SFHs) of early-type dwarf galaxies, dSphs and dEs, in the local universe within z=0.01. The SFHs of early-type dwarf galaxies are characterized by pre-enriched, metal-poor old stellar populations, absence of moderately old stars that have ages of a few Gyr. There are some differences in the SFHs of dSphs and dEs. In particular, dSphs formed old ($\gtrsim10$ Gyr old) metal-poor stars $\sim2$ times more than dEs. The effects of reionization and feedback from supernova explosions are thought to be strong enough to remove the gas left, which prevent moderately old stellar populations in dSphs. In contrast, the ejected gas are not completely removed from dEs and fall back to ignite burst of star formation at a few Gyr after the first period of violent bursts of star formation, showing a suppression of star formation at lookback time $\approx 9.6$ Gyr. The second peak of star formation at lookback time $\approx 4.5$ Gyr in dEs produce moderately old stellar populations. Distinction between dSphs and dEs is useful to examine the SFHS of the early-type dwarfs since the cumulative SFHs are most closely related to their morphology. The stellar mass plays an important role in the SFHs of the early-type dwarfs as a driver of star formation, especially in galaxies with primordial origin.Comment: 16 pages, 16 figure