Business cycle synchronisation between the V4 countries and the euro area

Business cycle synchronisation between the V4 countries and the euro area is important in regard to the costs of the common monetary policy. This paper addresses the issue of business cycle synchronisation by directly calculating cross correlations, by calculating cross correlations from primary impulses, and finally by calculating output gap component correlations from common and country-specific shocks. In regard to the output gap, the results of all three methods are approximately the same: before 2001, the business cycles of the V4 countries were not synchronised with the euro area (low or negative correlations); between 2001 and 2007, the correlations entered positive territory as the V4 countries joined the EU and trade between the V4 countries and the euro area increased; and during the economic crisis of 2008–2009, synchronisation increased still further.optimum currency area, business cycle, autoregressive model, SVAR

Visual adaptation to convexity in macaque area V4

Aftereffects are perceptual illusions caused by visual adaptation to one or more stimulus attribute, such as orientation, motion, or shape. Neurophysiological studies seeking to understand the basis of visual adaptation have observed firing rate reduction and changes in tuning of stimulus-selective neurons following periods of prolonged visual stimulation. In the domain of shape, recent psychophysical work has shown that adaptation to a convex pattern induces a subsequently seen rectangle to appear slightly concave. In the present study, we investigate the possible contribution of V4 neurons of rhesus monkeys, which are thought to be involved in the coding of convexity, to shape-specific adaptation. Visually responsive neurons were monitored during the brief presentation of simple shapes varying in their convexity level. Each test presentation was preceded by either a blank period or several seconds of adaptation to a convex or concave stimulus, presented in two different sizes. Adaptation consistently shifted the tuning of neurons away from the convex or concave adapter, including shifting response to the neutral rectangle in the direction of the opposite convexity. This repulsive shift resembled the known perceptual distortion associated with adaptation to such stimuli. In addition, adaptation caused a nonspecific response decrease, as well as a specific decrease for repeated stimuli. The latter effects were observed whether or not the adapting and test stimuli matched closely in their size. Taken together, these results provide evidence for shape-specific adaptation of neurons in area V4, which may contribute to the perception of the convexity aftereffect

The Neural Representation Benchmark and its Evaluation on Brain and Machine

A key requirement for the development of effective learning representations is their evaluation and comparison to representations we know to be effective. In natural sensory domains, the community has viewed the brain as a source of inspiration and as an implicit benchmark for success. However, it has not been possible to directly test representational learning algorithms directly against the representations contained in neural systems. Here, we propose a new benchmark for visual representations on which we have directly tested the neural representation in multiple visual cortical areas in macaque (utilizing data from [Majaj et al., 2012]), and on which any computer vision algorithm that produces a feature space can be tested. The benchmark measures the effectiveness of the neural or machine representation by computing the classification loss on the ordered eigendecomposition of a kernel matrix [Montavon et al., 2011]. In our analysis we find that the neural representation in visual area IT is superior to visual area V4. In our analysis of representational learning algorithms, we find that three-layer models approach the representational performance of V4 and the algorithm in [Le et al., 2012] surpasses the performance of V4. Impressively, we find that a recent supervised algorithm [Krizhevsky et al., 2012] achieves performance comparable to that of IT for an intermediate level of image variation difficulty, and surpasses IT at a higher difficulty level. We believe this result represents a major milestone: it is the first learning algorithm we have found that exceeds our current estimate of IT representation performance. We hope that this benchmark will assist the community in matching the representational performance of visual cortex and will serve as an initial rallying point for further correspondence between representations derived in brains and machines.Comment: The v1 version contained incorrectly computed kernel analysis curves and KA-AUC values for V4, IT, and the HT-L3 models. They have been corrected in this versio

Unconventional Gravitational Excitation of a Schwarzschild Black Hole

Besides the well-known quasinormal modes, the gravitational spectrum of a Schwarzschild black hole also has a continuum part on the negative imaginary frequency axis. The latter is studied numerically for quadrupole waves. The results show unexpected striking behavior near the algebraically special frequency $\Omega=-4i$. This reveals a pair of unconventional damped modes very near $\Omega$, confirmed analytically.Comment: REVTeX4, 4pp, 6 EPS figure files. N.B.: "Alec" is my first, and "Maassen van den Brink" my family name. v2: better pole placement in Fig. 1. v3: fixed Refs. [9,20]. v4: added context on "area quantum" research; trimmed one Fig.; textual clarification

Magnetic flux loop in high-energy heavy-ion collisions

We consider the expectation value of a magnetic flux loop in the immediate forward light cone of collisions of heavy nuclei at high energies. Such collisions are characterized by a non-linear scale Q_s where color fields become strong. We find that loops of area greater than ~2/Q_s^2 exhibit area law behavior, which determines the scale of elementary flux excitations ("vortices"). We also estimate the magnetic string tension, sigma_M = 0.12 Q_s^2. By the time t ~ 1/Q_s even small loops satisfy an area law. We describe corrections to the propagator of semi-hard gluons at very early times in the background of fluctuating magnetic fields.Comment: 4 pages, 5 figures; v2: added plot of Z(N) part of loop in fig.1; v3: added magnetic loop for asymmetric projectile/target saturation momenta, estimate of vortex density, and an appendix; v4: corrected outline of perturbative calculation of Wilson loop; to appear in PR

On a Minkowski-like inequality for asymptotically flat static manifolds

The Minkowski inequality is a classical inequality in differential geometry, giving a bound from below, on the total mean curvature of a convex surface in Euclidean space, in terms of its area. Recently there has been interest in proving versions of this inequality for manifolds other than R^n; for example, such an inequality holds for surfaces in spatial Schwarzschild and AdS-Schwarzschild manifolds. In this note, we adapt a recent analysis of Y. Wei to prove a Minkowski-like inequality for general static asymptotically flat manifolds.Comment: 10 pages. Proc. Amer. Math. Soc. V4: Fixed typo in eq (1.1