Joint halo-mass function for modified gravity and massive neutrinos - I. Simulations and cosmological forecasts

We present a halo-mass function accurate over the full relevant Hu-Sawicki f(R) parameter space based on spherical collapse calculations and calibrated to a suite of modified gravity N-body simulations that include massive neutrinos. We investigate the ability of current and forthcoming galaxy cluster observations to detect deviations from general relativity while constraining the total neutrino mass and including systematic uncertainties. Our results indicate that the degeneracy between massive neutrino and modify gravity effects is a limiting factor for the current searches for new gravitational physics with clusters of galaxies, but future surveys will be able to break the degeneracy

Spatial Optical Solitons due to Multistep Cascading

We introduce a novel class of parametric optical solitons supported simultaneously by two second-order nonlinear cascading processes, second-harmonic generation and sum-frequency mixing. We obtain, analytically and numerically, the solutions for three-wave spatial solitons and show that the presence of an additional cascading mechanism can change dramatically the properties and stability of two-wave quadratic solitary waves.Comment: 6 pages, 4 figure

Mass accretion rates of clusters of galaxies: CIRS and HeCS

We use a new spherical accretion recipe tested on N-body simulations to measure the observed mass accretion rate (MAR) of 129 clusters in the Cluster Infall Regions in the Sloan Digital Sky Survey (CIRS) and in the Hectospec Cluster Survey (HeCS). The observed clusters cover the redshift range of $0.01<z<0.30$ and the mass range of $\sim 10^{14}-10^{15} {h^{-1}~\rm{M_\odot}}$. Based on three-dimensional mass profiles of simulated clusters reaching beyond the virial radius, our recipe returns MARs that agree with MARs based on merger trees. We adopt this recipe to estimate the MAR of real clusters based on measurements of the mass profile out to $\sim 3R_{200}$. We use the caustic method to measure the mass profiles to these large radii. We demonstrate the validity of our estimates by applying the same approach to a set of mock redshift surveys of a sample of 2000 simulated clusters with a median mass of $M_{200}= 10^{14} {h^{-1}~\rm{M_{\odot}}}$ as well as a sample of 50 simulated clusters with a median mass of $M_{200}= 10^{15} {h^{-1}~\rm{M_{\odot}}}$: the median MARs based on the caustic mass profiles of the simulated clusters are unbiased and agree within $19\%$ with the median MARs based on the real mass profile of the clusters. The MAR of the CIRS and HeCS clusters increases with the mass and the redshift of the accreting cluster, which is in excellent agreement with the growth of clusters in the $\Lambda$CDM model.Comment: 25 pages, 19 figures, 7 table

Large scale study of multiple-molecule queries

<p>Abstract</p> <p>Background</p> <p>In ligand-based screening, as well as in other chemoinformatics applications, one seeks to effectively search large repositories of molecules in order to retrieve molecules that are similar typically to a single molecule lead. However, in some case, multiple molecules from the same family are available to seed the query and search for other members of the same family.</p> <p>Multiple-molecule query methods have been less studied than single-molecule query methods. Furthermore, the previous studies have relied on proprietary data and sometimes have not used proper cross-validation methods to assess the results. In contrast, here we develop and compare multiple-molecule query methods using several large publicly available data sets and background. We also create a framework based on a strict cross-validation protocol to allow unbiased benchmarking for direct comparison in future studies across several performance metrics.</p> <p>Results</p> <p>Fourteen different multiple-molecule query methods were defined and benchmarked using: (1) 41 publicly available data sets of related molecules with similar biological activity; and (2) publicly available background data sets consisting of up to 175,000 molecules randomly extracted from the ChemDB database and other sources. Eight of the fourteen methods were parameter free, and six of them fit one or two free parameters to the data using a careful cross-validation protocol. All the methods were assessed and compared for their ability to retrieve members of the same family against the background data set by using several performance metrics including the Area Under the Accumulation Curve (AUAC), Area Under the Curve (AUC), F1-measure, and BEDROC metrics.</p> <p>Consistent with the previous literature, the best parameter-free methods are the MAX-SIM and MIN-RANK methods, which score a molecule to a family by the maximum similarity, or minimum ranking, obtained across the family. One new parameterized method introduced in this study and two previously defined methods, the Exponential Tanimoto Discriminant (ETD), the Tanimoto Power Discriminant (TPD), and the Binary Kernel Discriminant (<b>BKD</b>), outperform most other methods but are more complex, requiring one or two parameters to be fit to the data.</p> <p>Conclusion</p> <p>Fourteen methods for multiple-molecule querying of chemical databases, including novel methods, (ETD) and (TPD), are validated using publicly available data sets, standard cross-validation protocols, and established metrics. The best results are obtained with ETD, TPD, BKD, MAX-SIM, and MIN-RANK. These results can be replicated and compared with the results of future studies using data freely downloadable from <url>http://cdb.ics.uci.edu/</url>.</p

A fixed-time second order sliding mode observer for a class of nonlinear systems

This paper presents a second order fixed time sliding mode observer based on an extension of the super-twisting algorithm. This observer can be applied to a class of nonlinear system with a block-wise representation. The block structure provides a straightforward form to the application of the proposed second order sliding mode algorithm, yielding to finite-time convergence with a settling time independent to the system initial conditions. Finally, as numerical simulation example, the case of a linear induction motor is studied, exposing the efficiency and feasibility of the proposal

A deep Chandra, VLA and Spitzer IRAC study of the very low luminosity nucleus of the elliptical NGC821

The relatively nearby (distance=24.1 Mpc) elliptical galaxy NGC821 hosts an extreme example of a quiescent central massive black hole, for which deep Chandra observations revealed a nuclear source for the first time (with L(2-10 keV)/L_Edd ~ 3.6X10^{-8}). We present here a multiwavelength study of this nucleus, including VLA observations that detect a radio counterpart to the Chandra nuclear source at 1.4 GHz, with a flux density of 127 $\mu$m and possibly a flat spectral shape; we also consider new Spitzer IRAC observations and archival HST images. With these data we discuss possible scenarios for the accretion modalities of the sole material that is available for fuelling, i.e., the stellar mass losses steadily replenishing the circumnuclear region. The final stages of accretion could be radiatively inefficient and coupled to a compact nuclear jet/outfow. The stellar mass losses could instead end up in a standard disc only if a Compton-thick AGN is present. Two extended sources detected by Chandra close to the nucleus could be due to several unresolved knots in a jet. If a jet is present, though, its kinetic energy would be only a very small fraction of the energy associated with the rest mass of the material being accreted. Starformation close to the nucleus is not shown by the available data. Deeper NICMOS, radio and far-IR observations should further constrain the accretion process.Comment: 32 pages, 4 figures; minor changes; accepted for publication in ApJ. This is the companion paper of astro-ph/070163

Predicting Secondary Structures, Contact Numbers, and Residue-wise Contact Orders of Native Protein Structure from Amino Acid Sequence by Critical Random Networks

Prediction of one-dimensional protein structures such as secondary structures and contact numbers is useful for the three-dimensional structure prediction and important for the understanding of sequence-structure relationship. Here we present a new machine-learning method, critical random networks (CRNs), for predicting one-dimensional structures, and apply it, with position-specific scoring matrices, to the prediction of secondary structures (SS), contact numbers (CN), and residue-wise contact orders (RWCO). The present method achieves, on average, $Q_3$ accuracy of 77.8% for SS, correlation coefficients of 0.726 and 0.601 for CN and RWCO, respectively. The accuracy of the SS prediction is comparable to other state-of-the-art methods, and that of the CN prediction is a significant improvement over previous methods. We give a detailed formulation of critical random networks-based prediction scheme, and examine the context-dependence of prediction accuracies. In order to study the nonlinear and multi-body effects, we compare the CRNs-based method with a purely linear method based on position-specific scoring matrices. Although not superior to the CRNs-based method, the surprisingly good accuracy achieved by the linear method highlights the difficulty in extracting structural features of higher order from amino acid sequence beyond that provided by the position-specific scoring matrices.Comment: 20 pages, 1 figure, 5 tables; minor revision; accepted for publication in BIOPHYSIC

An optical spectroscopic survey of the 3CR sample of radio galaxies with z<0.3. III. Completing the sample

We present optical nuclear spectra for nine 3CR radio sources obtained with the Telescopio Nazionale Galileo, that complete our spectroscopic observations of the sample up to redshifts $<$ 0.3. We measure emission line luminosities and ratios, and derive a spectroscopic classification for these sources.Comment: Accepted for publication in A&A. We provide as additional material two tables presenting the main data for the whole sample, combining the results presented here with those of Paper I and Paper I

Fixed Points of Hopfield Type Neural Networks

The set of the fixed points of the Hopfield type network is under investigation. The connection matrix of the network is constructed according to the Hebb rule from the set of memorized patterns which are treated as distorted copies of the standard-vector. It is found that the dependence of the set of the fixed points on the value of the distortion parameter can be described analytically. The obtained results are interpreted in the terms of neural networks and the Ising model.Comment: RevTEX, 19 pages, 2 Postscript figures, the full version of the earler brief report (cond-mat/9901251