An eclipsing post common-envelope system consisting of a pulsating hot subdwarf B star and a brown dwarf companion

Hot subdwarf B stars (sdBs) are evolved, core helium-burning objects located on the extreme horizontal branch. Their formation history is still puzzling as the sdB progenitors must lose nearly all of their hydrogen envelope during the red-giant phase. About half of the known sdBs are in close binaries with periods from 1.2 h to a few days, a fact that implies they experienced a common-envelope phase. Eclipsing hot subdwarf binaries (also called HW Virginis systems) are rare but important objects for determining fundamental stellar parameters. Even more significant and uncommon are those binaries containing a pulsating sdB, as the mass can be determined independently by asteroseismology. Here we present a first analysis of the eclipsing hot subdwarf binary V2008-1753. The light curve shows a total eclipse, a prominent reflection effect, and low--amplitude pulsations with periods from 150 to 180 s. An analysis of the light-- and radial velocity (RV) curves indicates a mass ratio close to $q = 0.146$, an RV semi-amplitude of $K=54.6 \,\rm kms^{-1}$, and an inclination of $i=86.8^\circ$. Combining these results with our spectroscopic determination of the surface gravity, $\log \,g = 5.83$, the best--fitting model yields an sdB mass of 0.47$M_{\rm \odot}$ and a companion mass of $69 M_{\rm Jup}$. As the latter mass is below the hydrogen-burning limit, V2008-1753 represents the first HW Vir system known consisting of a pulsating sdB and a brown dwarf companion. Consequently, it holds great potential for better constraining models of sdB binary evolution and asteroseismology.Comment: 9 pages, 8 figures, accepted for A&

Resolution of Nested Neuronal Representations Can Be Exponential in the Number of Neurons

Collective computation is typically polynomial in the number of computational elements, such as transistors or neurons, whether one considers the storage capacity of a memory device or the number of floating-point operations per second of a CPU. However, we show here that the capacity of a computational network to resolve real-valued signals of arbitrary dimensions can be exponential in N, even if the individual elements are noisy and unreliable. Nested, modular codes that achieve such high resolutions mirror the properties of grid cells in vertebrates, which underlie spatial navigation

Optimizing information flow in small genetic networks. II: Feed forward interactions

Central to the functioning of a living cell is its ability to control the readout or expression of information encoded in the genome. In many cases, a single transcription factor protein activates or represses the expression of many genes. As the concentration of the transcription factor varies, the target genes thus undergo correlated changes, and this redundancy limits the ability of the cell to transmit information about input signals. We explore how interactions among the target genes can reduce this redundancy and optimize information transmission. Our discussion builds on recent work [Tkacik et al, Phys Rev E 80, 031920 (2009)], and there are connections to much earlier work on the role of lateral inhibition in enhancing the efficiency of information transmission in neural circuits; for simplicity we consider here the case where the interactions have a feed forward structure, with no loops. Even with this limitation, the networks that optimize information transmission have a structure reminiscent of the networks found in real biological systems

The dimension of loop-erased random walk in 3D

We measure the fractal dimension of loop-erased random walk (LERW) in 3 dimensions, and estimate that it is 1.62400 +- 0.00005. LERW is closely related to the uniform spanning tree and the abelian sandpile model. We simulated LERW on both the cubic and face-centered cubic lattices; the corrections to scaling are slightly smaller for the face-centered cubic lattice.Comment: 4 pages, 4 figures. v2 has more data, minor additional change

Entropy and information in neural spike trains: Progress on the sampling problem

The major problem in information theoretic analysis of neural responses and other biological data is the reliable estimation of entropy--like quantities from small samples. We apply a recently introduced Bayesian entropy estimator to synthetic data inspired by experiments, and to real experimental spike trains. The estimator performs admirably even very deep in the undersampled regime, where other techniques fail. This opens new possibilities for the information theoretic analysis of experiments, and may be of general interest as an example of learning from limited data.Comment: 7 pages, 4 figures; referee suggested changes, accepted versio

Complete Experimental Structure Determination of the p(3x2)pg Phase of Glycine on Cu{110}

We present a quantitative low energy electron diffraction (LEED) surface-crystallograpic study of the complete adsorption geometry of glycine adsorbed on Cu{110} in the ordered p(3×2) phase. The glycine molecules form bonds to the surface through the N atoms of the amino group and the two O atoms of the de-protonated carboxylate group, each with separate Cu atoms such that every Cu atom in the first layer is involved in a bond. Laterally, N atoms are nearest to the atop site (displacement 0.41 Å). The O atoms are asymmetrically displaced from the atop site by 0.54 Å and 1.18 Å with two very different O-Cu bond lengths of 1.93 Å and 2.18 Å. The atom positions of the upper-most Cu layers show small relaxations within 0.07 Å of the bulk-truncated surface geometry. The unit cell of the adsorbate layer consists of two glycine molecules, which are related by a glide-line symmetry operation. This study clearly shows that a significant coverage of adsorbate structures without this glide-line symmetry must be rejected, both on the grounds of the energy dependence of the spot intensities (LEED-IV curves) and of systematic absences in the LEED pattern