How general is a female mating preference for clustered males in lekking species? a meta-analysis

A principal hypothesis for the evolution of leks (rare and intensely competitive territorial aggregations) is that leks result from females preferring to mate with clustered males. This hypothesis predicts more female visits and higher mating success per male on larger leks. Evidence for and against this hypothesis has been presented by different studies, primarily of individual populations, but its generality has not yet been formally investigated. We took a meta-analytical approach towards formally examining the generality of such a female bias in lekking species. Using available published data and using female visits as an index of female mating bias, we estimated the shape of the relationship between lek size and total female visits to a lek, female visits per lekking male and, where available, per capita male mating success. Individual analyses showed that female visits generally increased with lek size across the majority of taxa surveyed; the meta-analysis indicated that this relationship with lek size was disproportionately positive. The findings from analysing per capita female visits were mixed, with an increase with lek size detected in half of the species, which were, however, widely distributed taxonomically. Taken together, these findings suggest that a female bias for clustered males may be a general process across lekking species. Nevertheless, the substantial variation seen in these relationships implies that other processes are also important. Analyses of per capita copulation success suggested that, more generally, increased per capita mating benefits may be an important selective factor in lek maintenance.

A general geomorphological recession flow model for river basins

Recession flows in a basin are controlled by the temporal evolution of its active drainage network (ADN). The geomorphological recession flow model (GRFM) assumes that both the rate of flow generation per unit ADN length (q) and the speed at which ADN heads move downstream (c) remain constant during a recession event. Thereby, it connects the power law exponent of -dQ/dt versus Q (discharge at the outlet at time t) curve, , with the structure of the drainage network, a fixed entity. In this study, we first reformulate the GRFM for Horton-Strahler networks and show that the geomorphic ((g)) is equal to D/(D-1), where D is the fractal dimension of the drainage network. We then propose a more general recession flow model by expressing both q and c as functions of Horton-Strahler stream order. We show that it is possible to have = (g) for a recession event even when q and c do not remain constant. The modified GRFM suggests that is controlled by the spatial distribution of subsurface storage within the basin. By analyzing streamflow data from 39 U.S. Geological Survey basins, we show that is having a power law relationship with recession curve peak, which indicates that the spatial distribution of subsurface storage varies across recession events. Key Points The GRFM is reformulated for Horton-Strahler networks. The GRFM is modified by allowing its parameters to vary along streams. Sub-surface storage distribution controls recession flow characteristics.

Classification calibration dimension for general multiclass losses

We study consistency properties of surrogate loss functions for general multiclass classification problems, defined by a general loss matrix. We extend the notion of classification calibration, which has been studied for binary and multiclass 0-1 classification problems (and for certain other specific learning problems), to the general multiclass setting, and derive necessary and sufficient conditions for a surrogate loss to be classification calibrated with respect to a loss matrix in this setting. We then introduce the notion of \emph{classification calibration dimension} of a multiclass loss matrix, which measures the smallest `size' of a prediction space for which it is possible to design a convex surrogate that is classification calibrated with respect to the loss matrix. We derive both upper and lower bounds on this quantity, and use these results to analyze various loss matrices. In particular, as one application, we provide a different route from the recent result of Duchi et al.\ (2010) for analyzing the difficulty of designing `low-dimensional' convex surrogates that are consistent with respect to pairwise subset ranking losses. We anticipate the classification calibration dimension may prove to be a useful tool in the study and design of surrogate losses for general multiclass learning problems.

Extension of TSVM to multi-class and hierarchical text classification problems With general losses

Transductive SVM (TSVM) is a well known semi-supervised large margin learning method for binary text classification. In this paper we extend this method to multi-class and hierarchical classification problems. We point out that the determination of labels of unlabeled examples with fixed classifier weights is a linear programming problem. We devise an efficient technique for solving it. The method is applicable to general loss functions. We demonstrate the value of the new method using large margin loss on a number of multi-class and hierarchical classification datasets. For maxent loss we show empirically that our method is better than expectation regularization/constraint and posterior regularization methods, and competitive with the version of entropy regularization method which uses label constraints.

Combining ``chimie douce'' and green principles for the developing world: Improving industrial viability of photocatalytic water remediation

We highlight the need for a comprehensive, multi-disciplinary approach for the development of cost-effective water remediation methods. Combining ``chimie douce'' and green chemical principles seems essential for making these technologies economically viable and socially relevant (especially in the developing world). A comprehensive approach to water remediation will take into account issues such as nanotoxicity, chemical yield, cost, and ease of deployment in reactors. By considering technological challenges that lie ahead, we will attempt to identify directions that are likely to make photocatalytic water remediation a more global technology than it currently is. (C) 2013 Elsevier Ltd. All rights reserved

New insights into designing metallacarborane based room temperature hydrogen storage media

Metallacarboranes are promising towards realizing room temperature hydrogen storage media because of the presence of both transition metal and carbon atoms. In metallacarborane clusters, the transition metal adsorbs hydrogen molecules and carbon can link these clusters to form metal organic framework, which can serve as a complete storage medium. Using first principles density functional calculations, we chalk out the underlying principles of designing an efficient metallacarborane based hydrogen storage media. The storage capacity of hydrogen depends upon the number of available transition metal d-orbitals, number of carbons, and dopant atoms in the cluster. These factors control the amount of charge transfer from metal to the cluster, thereby affecting the number of adsorbed hydrogen molecules. This correlation between the charge transfer and storage capacity is general in nature, and can be applied to designing efficient hydrogen storage systems. Following this strategy, a search for the best metallacarborane was carried out in which Sc based monocarborane was found to be the most promising H-2 sorbent material with a 9 wt.% of reversible storage at ambient pressure and temperature. (C) 2013 AIP Publishing LLC.

ENTANGLEMENT ENTROPY FROM SURFACE TERMS IN GENERAL RELATIVITY

Entanglement entropy in local quantum field theories is typically ultraviolet divergent due to short distance effects in the neighborhood of the entangling region. In the context of gauge/gravity duality, we show that surface terms in general relativity are able to capture this entanglement entropy. In particular, we demonstrate that for 1+1-dimensional (1 + 1d) conformal field theories (CFTs) at finite temperature whose gravity dual is Banados-Teitelboim-Zanelli (BTZ) black hole, the Gibbons-Hawking-York term precisely reproduces the entanglement entropy which can be computed independently in the field theory.

Common recognition principles across diverse sequence and structural families of sialic acid binding proteins

Sialic acids form a large family of 9-carbon monosaccharides and are integral components of glycoconjugates. They are known to bind to a wide range of receptors belonging to diverse sequence families and fold classes and are key mediators in a plethora of cellular processes. Thus, it is of great interest to understand the features that give rise to such a recognition capability. Structural analyses using a non-redundant data set of known sialic acid binding proteins was carried out, which included exhaustive binding site comparisons and site alignments using in-house algorithms, followed by clustering and tree computation, which has led to derivation of sialic acid recognition principles. Although the proteins in the data set belong to several sequence and structure families, their binding sites could be grouped into only six types. Structural comparison of the binding sites indicates that all sites contain one or more different combinations of key structural features over a common scaffold. The six binding site types thus serve as structural motifs for recognizing sialic acid. Scanning the motifs against a non-redundant set of binding sites from PDB indicated the motifs to be specific for sialic acid recognition. Knowledge of determinants obtained from this study will be useful for detecting function in unknown proteins. As an example analysis, a genome-wide scan for the motifs in structures of Mycobacterium tuberculosis proteome identified 17 hits that contain combinations of the features, suggesting a possible function of sialic acid binding by these proteins.

First-principles DFT plus GW study of oxygen vacancies in rutile TiO2

We perform first-principles calculations of the quasiparticle defect states, charge transition levels, and formation energies of oxygen vacancies in rutile titanium dioxide. The calculations are done within the recently developed combined DFT + GW formalism, including the necessary electrostatic corrections for the supercells with charged defects. We find the oxygen vacancy to be a negative U defect, where U is the defect electron addition energy. For Fermi level values below similar to 2.8 eV (relative to the valence-band maximum), we find the +2 charge state of the vacancy to be the most stable, while above 2.8 eV we find that the neutral charge state is the most stable.

Hysteresis current controller for a general n-level inverter fed drive with online current error boundary computation and nearly constant switching frequency

A space vector-based hysteresis current controller for any general n-level three phase inverter fed induction motor drive is proposed in this study. It offers fast dynamics, inherent overload protection and low harmonic distortion for the phase voltages and currents. The controller performs online current error boundary calculations and a nearly constant switching frequency is obtained throughout the linear modulation range. The proposed scheme uses only the adjacent voltage vectors of the present sector, similar to space vector pulse-width modulation and exhibits fast dynamic behaviour under different transient conditions. The steps involved in the boundary calculation include the estimation of phase voltages from the current ripple, computation of switching time and voltage error vectors. Experimental results are given to show the performance of the drive at various speeds, effect of sudden change of the load, acceleration, speed reversal and validate the proposed advantages.

FeatureMatch: A General ANNF Estimation Technique and its Applications

In this paper, we propose FeatureMatch, a generalised approximate nearest-neighbour field (ANNF) computation framework, between a source and target image. The proposed algorithm can estimate ANNF maps between any image pairs, not necessarily related. This generalisation is achieved through appropriate spatial-range transforms. To compute ANNF maps, global colour adaptation is applied as a range transform on the source image. Image patches from the pair of images are approximated using low-dimensional features, which are used along with KD-tree to estimate the ANNF map. This ANNF map is further improved based on image coherency and spatial transforms. The proposed generalisation, enables us to handle a wider range of vision applications, which have not been tackled using the ANNF framework. We illustrate two such applications namely: 1) optic disk detection and 2) super resolution. The first application deals with medical imaging, where we locate optic disks in retinal images using a healthy optic disk image as common target image. The second application deals with super resolution of synthetic images using a common source image as dictionary. We make use of ANNF mappings in both these applications and show experimentally that our proposed approaches are faster and accurate, compared with the state-of-the-art techniques.

First-Principles Study of Structure, Vibrational, and Elastic Properties of Stoichiometric and Calcium-Deficient Hydroxyapatite

Hydroxyapatite (HAp), a primary constituent of human bone, is usually nonstoichiometric with varying Ca/P molar ratios, with the well-known fact that Ca deficiency can cause marked reductions in its mechanical properties. To gain insights into the mechanism of this degradation, we employ first-principles calculations based on density functional theory and determine the effects of Ca deficiency on structure, vibrational, and elastic properties of HAp. Our simulation results confirm a considerable reduction in the elastic constants of HAp due to Ca deficiency, which was experimentally reported earlier. Stress-induced transformation of the Ca-deficient defected structure into a metastable state upon the application of stress could be a reason for this. Local structural stability of HAp and Ca-deficient HAp structures is assessed with full phonon dispersion studies. Further, specific signatures in the computed vibrational spectra for Ca deficiency in HAp can be utilized in experimental characterization of different types of defected HAp.

Temperature-dependent stability of stacking faults in Al, Cu and Ni: first-principles analysis

We present comparative analysis of microscopic mechanisms relevant to plastic deformation of the face-centered cubic (FCC) metals Al, Cu, and Ni, through determination of the temperature-dependent free energies of intrinsic and unstable stacking faults along 1 (1) over bar 0] and 1 (2) over bar 1] on the (1 1 1) plane using first-principles density-functional-theory-based calculations. We show that vibrational contribution results in significant decrease in the free energy of barriers and intrinsic stacking faults (ISFs) of Al, Cu, and Ni with temperature, confirming an important role of thermal fluctuations in the stability of stacking faults (SFs) and deformation at elevated temperatures. In contrast to Al and Ni, the vibrational spectrum of the unstable stacking fault (USF1 (2) over bar 1]) in Cu reveals structural instabilities, indicating that the energy barrier (gamma(usf)) along the (1 1 1)1 (2) over bar 1] slip system in Cu, determined by typical first-principles calculations, is an overestimate, and its commonly used interpretation as the energy release rate needed for dislocation nucleation, as proposed by Rice (1992 J. Mech. Phys. Solids 40 239), should be taken with caution.