Past electron-positron g-2 experiments yielded sharpest bound on CPT violation for point particles

In our past experiments on a single electron and positron we measured the cyclotron and spin-cyclotron difference frequencies omega_c and omega_a and the ratios a = omega_a/ omega_c at omega_c = 141 Ghz for e^- and e^+ and later, only for e^-, also at 164 Ghz. Here, we do extract from these data, as had not done before, a new and very different figure of merit for violation of CPT symmetry, one similar to the widely recognized impressive limit |m_Kaon - m_Antikaon|/m_Kaon < 10^-18 for the K-mesons composed of two quarks. That expression may be seen as comparing experimental relativistic masses of particle states before and after the C, P, T operations had transformed particle into antiparticle. Such a similar figure of merit for a non-composite and quite different lepton, found by us from our Delta a = a^- - a^+ data, was even smaller, h_bar |omega_a^- - omega_a^+|/2m_0 c^2 = |Delta a| h_bar omega_c/2m_0 c^2) < 3(12) 10^-22.Comment: Improved content, Editorially approved for publication in PRL, LATEX file, 5 pages, no figures, 16

Self-Excitation and Feedback Cooling of an Isolated Proton

The first one-proton self-excited oscillator (SEO) and one-proton feedback cooling are demonstrated. In a Penning trap with a large magnetic gradient, the SEO frequency is resolved to the high precision needed to detect a one-proton spin flip. This is after undamped magnetron motion is sideband-cooled to a 14 mK theoretical limit, and despite random frequency shifts (larger than those from a spin flip) that take place every time sideband cooling is applied in the gradient. The observations open a possible path towards a million-fold improved comparison of the antiproton and proton magnetic moments

Modeling biological face recognition with deep convolutional neural networks

Deep convolutional neural networks (DCNNs) have become the state-of-the-art computational models of biological object recognition. Their remarkable success has helped vision science break new ground and recent efforts have started to transfer this achievement to research on biological face recognition. In this regard, face detection can be investigated by comparing face-selective biological neurons and brain areas to artificial neurons and model layers. Similarly, face identification can be examined by comparing in vivo and in silico multidimensional "face spaces". In this review, we summarize the first studies that use DCNNs to model biological face recognition. On the basis of a broad spectrum of behavioral and computational evidence, we conclude that DCNNs are useful models that closely resemble the general hierarchical organization of face recognition in the ventral visual pathway and the core face network. In two exemplary spotlights, we emphasize the unique scientific contributions of these models. First, studies on face detection in DCNNs indicate that elementary face selectivity emerges automatically through feedforward processing even in the absence of visual experience. Second, studies on face identification in DCNNs suggest that identity-specific experience and generative mechanisms facilitate this particular challenge. Taken together, as this novel modeling approach enables close control of predisposition (i.e., architecture) and experience (i.e., training data), it may be suited to inform long-standing debates on the substrates of biological face recognition.Comment: 41 pages, 2 figures, 1 tabl

Theoretical energies of low-lying states of light helium-like ions

Rigorous quantum electrodynamical calculation is presented for energy levels of the 1^1S, 2^1S, 2^3S, 2^1P_1, and 2^3P_{0,1,2} states of helium-like ions with the nuclear charge Z=3...12. The calculational approach accounts for all relativistic, quantum electrodynamical, and recoil effects up to orders m\alpha^6 and m^2/M\alpha^5, thus advancing the previously reported theory of light helium-like ions by one order in \alpha.Comment: 18 pages, 9 tables, 1 figure, with several misprints correcte

The co-morbidity of anxiety and depression in the perspective of genetic epidemiology. A review of twin and family studies

BACKGROUND: Co-morbidity within anxiety disorders, and between anxiety disorders and depression, is common. According to the theory of Gray and McNaughton, this co-morbidity is caused by recursive interconnections linking the brain regions involved in fear, anxiety and panic and by heritable personality traits such as neuroticism. In other words, co-morbidity can be explained by one disorder being an epiphenomenon of the other and by a partly shared genetic etiology. The aim of this paper is to evaluate the theory of Gray and McNaughton using the results of genetic epidemiological studies. METHOD: Twenty-three twin studies and 12 family studies on co-morbidity are reviewed. To compare the outcomes systematically, genetic and environmental correlations between disorders are calculated for the twin studies and the results from the family studies are summarized according to the method of Klein and Riso. RESULTS: Twin studies show that co-morbidity within anxiety disorders and between anxiety disorders and depression is explained by a shared genetic vulnerability for both disorders. Some family studies support this conclusion, but others suggest that co-morbidity is due to one disorder being an epiphenomenon of the other. CONCLUSIONS: Discrepancies between the twin and family studies seem partly due to differences in used methodology. The theory of Gray and McNaughton that neuroticism is a shared risk factor for anxiety and depression is supported. Further research should reveal the role of recursive interconnections linking brain regions. A model is proposed to simultaneously investigate the influence of neuroticism and recursive interconnections on co-morbidit

The most storage economical Runge-Kutta methods for the solution of large systems of coupled first-order differential equations

AbstractIt is shown how the attainable minimum for the memory requirements of Runge-Kutta methods can be realised for methods of the third order. These economisable third order methods belong to a one parameter sub-family from which two particular members with low error bound are selected