2,672 research outputs found

    Evolution of robustness in digital organisms

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    We study the evolution of robustness in digital organisms adapting to a high mutation rate. As genomes adjust to the harsh mutational environment, the mean effect of single Imitations decreases, up until the point where a sizable fraction (up to 30% in many cases) of the Imitations are neutral. We correlate the changes in robustness along the line of descent to changes in directional epistasis, and find that increased robustness is achieved by moving from antagonistic epistasis between mutations towards codes where mutations are, on average, independent. We interpret this recoding as a breakup of linkage between vital sections of the genome, up to the point where instructions are maximally independent of each other. While such a recoding often requires sacrificing some replication speed, it is the best strategy for withstanding high rates of mutation

    Neural Prediction Errors Reveal a Risk-Sensitive Reinforcement-Learning Process in the Human Brain

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    Humans and animals are exquisitely, though idiosyncratically, sensitive to risk or variance in the outcomes of their actions. Economic, psychological, and neural aspects of this are well studied when information about risk is provided explicitly. However, we must normally learn about outcomes from experience, through trial and error. Traditional models of such reinforcement learning focus on learning about the mean reward value of cues and ignore higher order moments such as variance. We used fMRI to test whether the neural correlates of human reinforcement learning are sensitive to experienced risk. Our analysis focused on anatomically delineated regions of a priori interest in the nucleus accumbens, where blood oxygenation level-dependent (BOLD) signals have been suggested as correlating with quantities derived from reinforcement learning. We first provide unbiased evidence that the raw BOLD signal in these regions corresponds closely to a reward prediction error. We then derive from this signal the learned values of cues that predict rewards of equal mean but different variance and show that these values are indeed modulated by experienced risk. Moreover, a close neurometric–psychometric coupling exists between the fluctuations of the experience-based evaluations of risky options that we measured neurally and the fluctuations in behavioral risk aversion. This suggests that risk sensitivity is integral to human learning, illuminating economic models of choice, neuroscientific models of affective learning, and the workings of the underlying neural mechanisms

    The White Dwarf -- White Dwarf galactic background in the LISA data

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    LISA (Laser Interferometer Space Antenna) is a proposed space mission, which will use coherent laser beams exchanged between three remote spacecraft to detect and study low-frequency cosmic gravitational radiation. In the low-part of its frequency band, the LISA strain sensitivity will be dominated by the incoherent superposition of hundreds of millions of gravitational wave signals radiated by inspiraling white-dwarf binaries present in our own galaxy. In order to estimate the magnitude of the LISA response to this background, we have simulated a synthesized population that recently appeared in the literature. We find the amplitude of the galactic white-dwarf binary background in the LISA data to be modulated in time, reaching a minimum equal to about twice that of the LISA noise for a period of about two months around the time when the Sun-LISA direction is roughly oriented towards the Autumn equinox. Since the galactic white-dwarfs background will be observed by LISA not as a stationary but rather as a cyclostationary random process with a period of one year, we summarize the theory of cyclostationary random processes, present the corresponding generalized spectral method needed to characterize such process, and make a comparison between our analytic results and those obtained by applying our method to the simulated data. We find that, by measuring the generalized spectral components of the white-dwarf background, LISA will be able to infer properties of the distribution of the white-dwarfs binary systems present in our Galaxy.Comment: 36 pages, 15 figure

    Simulation of the White Dwarf -- White Dwarf galactic background in the LISA data

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    LISA (Laser Interferometer Space Antenna) is a proposed space mission, which will use coherent laser beams exchanged between three remote spacecraft to detect and study low-frequency cosmic gravitational radiation. In the low-part of its frequency band, the LISA strain sensitivity will be dominated by the incoherent superposition of hundreds of millions of gravitational wave signals radiated by inspiraling white-dwarf binaries present in our own galaxy. In order to estimate the magnitude of the LISA response to this background, we have simulated a synthesized population that recently appeared in the literature. We find the amplitude of the galactic white-dwarf binary background in the LISA data to be modulated in time, reaching a minimum equal to about twice that of the LISA noise for a period of about two months around the time when the Sun-LISA direction is roughly oriented towards the Autumn equinox. Since the galactic white-dwarfs background will be observed by LISA not as a stationary but rather as a cyclostationary random process with a period of one year, we summarize the theory of cyclostationary random processes and present the corresponding generalized spectral method needed to characterize such process. We find that, by measuring the generalized spectral components of the white-dwarf background, LISA will be able to infer properties of the distribution of the white-dwarfs binary systems present in our Galaxy.Comment: 14 pages and 6 figures. Submitted to Classical and Quantum Gravity (Proceedings of GWDAW9

    Pluripolarity of Graphs of Denjoy Quasianalytic Functions of Several Variables

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    In this paper we prove pluripolarity of graphs of Denjoy quasianalytic functions of several variables on the spanning se

    Novel self-assembled morphologies from isotropic interactions

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    We present results from particle simulations with isotropic medium range interactions in two dimensions. At low temperature novel types of aggregated structures appear. We show that these structures can be explained by spontaneous symmetry breaking in analytic solutions to an adaptation of the spherical spin model. We predict the critical particle number where the symmetry breaking occurs and show that the resulting phase diagram agrees well with results from particle simulations.Comment: 4 pages, 4 figure

    Axel: A Minimalist Tethered Rover for Exploration of Extreme Planetary Terrains

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    Recent scientific findings suggest that some of the most interesting sites for future exploration of planetary surfaces lie in terrains that are currently inaccessible to conventional robotic rovers. To provide robust and flexible access to these terrains, we have been developing Axel, the robotic rover. Axel is a lightweight two-wheeled vehicle that can access steep terrains and negotiate relatively large obstacles because of its actively managed tether and novel wheel design. This article reviews the Axel system and focuses on those system components that affect Axel's steep terrain mobility. Experimental demonstrations of Axel on sloped and rocky terrains are presented
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