Control of coherent backscattering by breaking optical reciprocity

Reciprocity is a universal principle that has a profound impact on many areas of physics. A fundamental phenomenon in condensed-matter physics, optical physics and acoustics, arising from reciprocity, is the constructive interference of quantum or classical waves which propagate along time-reversed paths in disordered media, leading to, for example, weak localization and metal-insulator transition. Previous studies have shown that such coherent effects are suppressed when reciprocity is broken. Here we show that by breaking reciprocity in a controlled manner, we can tune, rather than simply suppress, these phenomena. In particular, we manipulate coherent backscattering of light, also known as weak localization. By utilizing a non-reciprocal magneto-optical effect, we control the interference between time-reversed paths inside a multimode fiber with strong mode mixing, and realize a continuous transition from the well-known peak to a dip in the backscattered intensity. Our results may open new possibilities for coherent control of classical and quantum waves in complex systemsComment: Comments are welcom

The IBMAP approach for Markov networks structure learning

In this work we consider the problem of learning the structure of Markov networks from data. We present an approach for tackling this problem called IBMAP, together with an efficient instantiation of the approach: the IBMAP-HC algorithm, designed for avoiding important limitations of existing independence-based algorithms. These algorithms proceed by performing statistical independence tests on data, trusting completely the outcome of each test. In practice tests may be incorrect, resulting in potential cascading errors and the consequent reduction in the quality of the structures learned. IBMAP contemplates this uncertainty in the outcome of the tests through a probabilistic maximum-a-posteriori approach. The approach is instantiated in the IBMAP-HC algorithm, a structure selection strategy that performs a polynomial heuristic local search in the space of possible structures. We present an extensive empirical evaluation on synthetic and real data, showing that our algorithm outperforms significantly the current independence-based algorithms, in terms of data efficiency and quality of learned structures, with equivalent computational complexities. We also show the performance of IBMAP-HC in a real-world application of knowledge discovery: EDAs, which are evolutionary algorithms that use structure learning on each generation for modeling the distribution of populations. The experiments show that when IBMAP-HC is used to learn the structure, EDAs improve the convergence to the optimum

Thermal collapse of a granular gas under gravity

Free cooling of a gas of inelastically colliding hard spheres represents a central paradigm of kinetic theory of granular gases. At zero gravity the temperature of a freely cooling homogeneous granular gas follows a power law in time. How does gravity, which brings inhomogeneity, affect the cooling? We combine molecular dynamics simulations, a numerical solution of hydrodynamic equations and an analytic theory to show that a granular gas cooling under gravity undergoes thermal collapse: it cools down to zero temperature and condenses on the bottom of the container in a finite time.Comment: 4 pages, 12 eps figures, to appear in PR

Failed Gamma-Ray Bursts: Thermal UV/Soft X-ray Emission Accompanied by Peculiar Afterglows

We show that the photospheres of "failed" Gamma-Ray Bursts (GRBs), whose bulk Lorentz factors are much lower than 100, can be outside of internal shocks. The resulting radiation from the photospheres is thermal and bright in UV/Soft X-ray band. The photospheric emission lasts for about one thousand seconds with luminosity about several times 10^46 erg/s. These events can be observed by current and future satellites. It is also shown that the afterglows of failed GRBs are peculiar at the early stage, which makes it possible to distinguish failed GRBs from ordinary GRBs and beaming-induced orphan afterglows.Comment: 19 pages, 7 figures, accepted for publication in the Astrophysical Journa

H-function extension of the NBD in the light of experimental data

The recently introduced H-function extension of the Negative Binomial Distribution is investigated. The analytic form of P(n) is rederived by means of the Mellin transform. Applications of the HNBD are provided using experimental data for P(n) in e+e-, e+p, inelastic pp and non-diffractive p-pbar reactions.Comment: 8 pages REVTeX, 1 eps figure embedde

Dopamine in Motivational Control: Rewarding, Aversive, and Alerting

Midbrain dopamine neurons are well known for their strong responses to rewards and their critical role in positive motivation. It has become increasingly clear, however, that dopamine neurons also transmit signals related to salient but nonrewarding experiences such as aversive and alerting events. Here we review recent advances in understanding the reward and nonreward functions of dopamine. Based on this data, we propose that dopamine neurons come in multiple types that are connected with distinct brain networks and have distinct roles in motivational control. Some dopamine neurons encode motivational value, supporting brain networks for seeking, evaluation, and value learning. Others encode motivational salience, supporting brain networks for orienting, cognition, and general motivation. Both types of dopamine neurons are augmented by an alerting signal involved in rapid detection of potentially important sensory cues. We hypothesize that these dopaminergic pathways for value, salience, and alerting cooperate to support adaptive behavior

Valuation of knowledge and ignorance in mesolimbic reward circuitry

The pursuit of knowledge is a basic feature of human nature. However, in domains ranging from health to finance people sometimes choose to remain ignorant. Here, we show that valence is central to the process by which the human brain evaluates the opportunity to gain information, explaining why knowledge may not always be preferred. We reveal that the mesolimbic reward circuitry selectively treats the opportunity to gain knowledge about future favorable outcomes, but not unfavorable outcomes, as if it has positive utility. This neural coding predicts participants’ tendency to choose knowledge about future desirable outcomes more often than undesirable ones, and to choose ignorance about future undesirable outcomes more often than desirable ones. Strikingly, participants are willing to pay both for knowledge and ignorance as a function of the expected valence of knowledge. The orbitofrontal cortex (OFC), however, responds to the opportunity to receive knowledge over ignorance regardless of the valence of the information. Connectivity between the OFC and mesolimbic circuitry could contribute to a general preference for knowledge that is also modulated by valence. Our findings characterize the importance of valence in information seeking and its underlying neural computation. This mechanism could lead to suboptimal behavior, such as when people reject medical screenings or monitor investments more during bull than bear markets

Montreal Protocol

DATE: Signed September 16, 1987; took effect January 1, 1989; amended 1990, 1992, 1995, 1997, and 1999 The Montreal Protocol was created to help preserve the Earth’s ozone layer by severely limiting the production and use of chlorofluorocarbons (CFCs ) and other halogenated compounds