The cross-entropy method is an efficient and general optimization algorithm. However, its applicability in reinforcement learning (RL) seems to be limited because it often converges to suboptimal policies. We apply noise for preventing early convergence of the cross-entropy method, using Tetris, a computer game, for demonstration. The resulting policy outperforms previous RL algorithms by almost two orders of magnitude.

It has long been known that macaque inferior temporal (IT) neurons tend to fire more strongly to some shapes than to others, and that different IT neurons can show markedly different shape preferences. Beyond the discovery that these preferences can be elicited by features of moderate complexity, no general principle of (nonface) object recognition had emerged by which this enormous variation in selectivity could be understood. Psychophysical, as well as computational work, suggests that one such principle is the difference between viewpoint-invariant, nonaccidental (NAP) and view-dependent, metric shape properties (MPs). We measured the responses of single IT neurons to objects differing in either a NAP (namely, a change in a geon) or an MP of a single part, shown at two orientations in depth. The cells were more sensitive to changes in NAPs than in MPs, even though the image variation (as assessed by wavelet-like measures) produced by the former were smaller than the latter. The magnitude of the response modulation from the rotation itself was, on average, similar to that produced by the NAP differences, although the image changes from the rotation were much greater than that produced by NAP differences. Multidimensional scaling of the neural responses indicated a NAP/MP dimension, independent of an orientation dimension. The present results thus demonstrate that a significant portion of the neural code of IT cells represents differences in NAPs rather than MPs. This code may enable immediate recognition of novel objects at new views.

We report new measurements of the rotational state dependence of the initial fluorescence decay of the vibrationless ¹B_3u state of pyrazine. Through examination of excited state dynamics from a Green’s function point of view the apparently inconsistent data from several sources are reconciled.

Research on optimal control of quantum systems has been severely restricted by the lack of experimentally feasible control pulses. Here, to overcome this obstacle, optimal control is considered with the help of chirped pulses. Simulated annealing is used as the optimizing procedure. The examples treated are pulsed population inversion between electronic levels, and optimization of vibronic excitation in the presence of another electronic level. In the problem of population inversion effective potentials of displaced harmonic oscillators are used. For optimizing vibronic excitation the CsI model potential is used.

We describe a single‐sideband frequency demodulation scheme with large dynamic range which gives linear response and is capable of shot‐noise‐limited performance when used with time‐resolved pump and probe, polarization, and three‐ and four‐wave mixing spectroscopies.