WASP-1: A lithium- and metal-rich star with an oversized planet

In this paper we present our results of a comprehensive spectroscopicanalysis of WASP-1, the host star to the exoplanet WASP-1b. We derive T_eff = 6110 +/- 45 K, log g = 4.28 +/- 0.15, and [M/H] = 0.23 +/- 0.08, and also a high abundance of lithium, log n(Li) = 2.91 +/- 0.05. These parameters suggests an age for the system of 1-3 Gyr and a stellar mass of 1.25-1.35 M_sun. This means that WASP-1 has properties very similar to those of HD 149026, the host star for the highest density planet yet detected. Moreover, their planets orbit at comparable distances and receive comparable irradiating fluxes from their host stars. However, despite the similarity of WASP-1 with HD 149026, their planets have strongly different densities. This suggests that gas-giant planet density is not a simple function of host-star metallicity or of radiation environment at ages of ~2 Gyr.Comment: Accepted for publication in MNRAS. 6 pages, 4 figure

Possible geopotential improvement from satellite altimetry

Possible geopotential improvement from satellite altimetr

Spontaneous structure formation in a network of chaotic units with variable connection strengths

As a model of temporally evolving networks, we consider a globally coupled logistic map with variable connection weights. The model exhibits self-organization of network structure, reflected by the collective behavior of units. Structural order emerges even without any inter-unit synchronization of dynamics. Within this structure, units spontaneously separate into two groups whose distinguishing feature is that the first group possesses many outwardly-directed connections to the second group, while the second group possesses only few outwardly-directed connections to the first. The relevance of the results to structure formation in neural networks is briefly discussed.Comment: 4 pages, 3 figures, REVTe

A Search for Planets Transiting the M Dwarf Debris Disk Host, AU Microscopii

We present high cadence, high precision multi-band photometry of the young, M1Ve, debris disk star, AU Microscopii. The data were obtained in three continuum filters spanning a wavelength range from 4500\AA to 6600\AA, plus H$\alpha$, over 28 nights in 2005. The lightcurves show intrinsic stellar variability due to starspots with an amplitude in the blue band of 0.051 magnitudes and a period of 4.847 days. In addition, three large flares were detected in the data which all occur near the minimum brightness of the star. We remove the intrinsic stellar variability and combine the lightcurves of all the filters in order to search for transits by possible planetary companions orbiting in the plane of the nearly edge-on debris disk. The combined final lightcurve has a sampling of 0.35 minutes and a standard deviation of 6.8 millimags (mmag). We performed Monte Carlo simulations by adding fake transits to the observed lightcurve and find with 95% significance that there are no Jupiter mass planets orbiting in the plane of the debris disk on circular orbits with periods, P $\le 5$ days. In addition, there are no young Neptune-like planets (with radii 2.5$\times$ smaller than the young Jupiter) on circular orbits with periods, P $\le 3$ days.Comment: accepted to MNRA

A Heterosynaptic Learning Rule for Neural Networks

In this article we intoduce a novel stochastic Hebb-like learning rule for neural networks that is neurobiologically motivated. This learning rule combines features of unsupervised (Hebbian) and supervised (reinforcement) learning and is stochastic with respect to the selection of the time points when a synapse is modified. Moreover, the learning rule does not only affect the synapse between pre- and postsynaptic neuron, which is called homosynaptic plasticity, but effects also further remote synapses of the pre- and postsynaptic neuron. This more complex form of synaptic plasticity has recently come under investigations in neurobiology and is called heterosynaptic plasticity. We demonstrate that this learning rule is useful in training neural networks by learning parity functions including the exclusive-or (XOR) mapping in a multilayer feed-forward network. We find, that our stochastic learning rule works well, even in the presence of noise. Importantly, the mean learning time increases with the number of patterns to be learned polynomially, indicating efficient learning.Comment: 19 page

About the ergodic regime in the analogical Hopfield neural networks. Moments of the partition function

In this paper we introduce and exploit the real replica approach for a minimal generalization of the Hopfield model, by assuming the learned patterns to be distributed accordingly to a standard unit Gaussian. We consider the high storage case, when the number of patterns is linearly diverging with the number of neurons. We study the infinite volume behavior of the normalized momenta of the partition function. We find a region in the parameter space where the free energy density in the infinite volume limit is self-averaging around its annealed approximation, as well as the entropy and the internal energy density. Moreover, we evaluate the corrections to their extensive counterparts with respect to their annealed expressions. The fluctuations of properly introduced overlaps, which act as order parameters, are also discussed.Comment: 15 page

Thirty New Low-Mass Spectroscopic Binaries

As part of our search for young M dwarfs within 25 pc, we acquired high-resolution spectra of 185 low-mass stars compiled by the NStars project that have strong X-ray emission. By cross-correlating these spectra with radial velocity standard stars, we are sensitive to finding multi-lined spectroscopic binaries. We find a low-mass spectroscopic binary fraction of 16% consisting of 27 SB2s, 2 SB3s and 1 SB4, increasing the number of known low-mass SBs by 50% and proving that strong X-ray emission is an extremely efficient way to find M-dwarf SBs. WASP photometry of 23 of these systems revealed two low-mass EBs, bringing the count of known M dwarf EBs to 15. BD -22 5866, the SB4, is fully described in Shkolnik et al. 2008 and CCDM J04404+3127 B consists of a two mid-M stars orbiting each other every 2.048 days. WASP also provided rotation periods for 12 systems, and in the cases where the synchronization time scales are short, we used P_rot to determine the true orbital parameters. For those with no P_rot, we use differential radial velocities to set upper limits on orbital periods and semi-major axes. More than half of our sample has near-equal-mass components (q > 0.8). This is expected since our sample is biased towards tight orbits where saturated X-ray emission is due to tidal spin-up rather than stellar youth. Increasing the samples of M dwarf SBs and EBs is extremely valuable in setting constraints on current theories of stellar multiplicity and evolution scenarios for low-mass multiple systems.Comment: Accepted to Ap

Neuronal assembly dynamics in supervised and unsupervised learning scenarios

The dynamic formation of groups of neurons—neuronal assemblies—is believed to mediate cognitive phenomena at many levels, but their detailed operation and mechanisms of interaction are still to be uncovered. One hypothesis suggests that synchronized oscillations underpin their formation and functioning, with a focus on the temporal structure of neuronal signals. In this context, we investigate neuronal assembly dynamics in two complementary scenarios: the first, a supervised spike pattern classification task, in which noisy variations of a collection of spikes have to be correctly labeled; the second, an unsupervised, minimally cognitive evolutionary robotics tasks, in which an evolved agent has to cope with multiple, possibly conflicting, objectives. In both cases, the more traditional dynamical analysis of the system’s variables is paired with information-theoretic techniques in order to get a broader picture of the ongoing interactions with and within the network. The neural network model is inspired by the Kuramoto model of coupled phase oscillators and allows one to fine-tune the network synchronization dynamics and assembly configuration. The experiments explore the computational power, redundancy, and generalization capability of neuronal circuits, demonstrating that performance depends nonlinearly on the number of assemblies and neurons in the network and showing that the framework can be exploited to generate minimally cognitive behaviors, with dynamic assembly formation accounting for varying degrees of stimuli modulation of the sensorimotor interactions

Learning by message-passing in networks of discrete synapses

We show that a message-passing process allows to store in binary "material" synapses a number of random patterns which almost saturates the information theoretic bounds. We apply the learning algorithm to networks characterized by a wide range of different connection topologies and of size comparable with that of biological systems (e.g. $n\simeq10^{5}-10^{6}$). The algorithm can be turned into an on-line --fault tolerant-- learning protocol of potential interest in modeling aspects of synaptic plasticity and in building neuromorphic devices.Comment: 4 pages, 3 figures; references updated and minor corrections; accepted in PR

The 0.5MJ transiting exoplanet WASP-13b

We report the discovery of WASP-13b, a low-mass M_{\rm p} = 0.46 ^_~M_J transiting exoplanet with an orbital period of 4.35298 $\pm$ 0.00004 days. The transit has a depth of 9 mmag, and although our follow-up photometry does not allow us to constrain the impact parameter well (0 < b < 0.46), with radius in the range $R_{\rm p}$ ~ 1.06-1.21 RJ the location of WASP-13b in the mass-radius plane is nevertheless consistent with H/He-dominated, irradiated, low core mass and core-free theoretical models. The G1V host star is similar to the Sun in mass (M__ ~M_{\odot}) and metallicity ([M/H] = 0.0$\pm$0.2), but is possibly older ( 8.5^_{\rm -4.9} Gyr)