Feature Search in the Grassmanian in Online Reinforcement Learning

We consider the problem of finding the best features for value function approximation in reinforcement learning and develop an online algorithm to optimize the mean square Bellman error objective. For any given feature value, our algorithm performs gradient search in the parameter space via a residual gradient scheme and, on a slower timescale, also performs gradient search in the Grassman manifold of features. We present a proof of convergence of our algorithm. We show empirical results using our algorithm as well as a similar algorithm that uses temporal difference learning in place of the residual gradient scheme for the faster timescale updates.

Cluster labeling for multilingual scatter/gather using comparable corpora

Scatter/Gather systems are increasingly becoming useful in browsing document corpora. Usability of the present-day systems are restricted to monolingual corpora, and their methods for clustering and labeling do not easily extend to the multilingual setting, especially in the absence of dictionaries/machine translation. In this paper, we study the cluster labeling problem for multilingual corpora in the absence of machine translation, but using comparable corpora. Using a variational approach, we show that multilingual topic models can effectively handle the cluster labeling problem, which in turn allows us to design a novel Scatter/Gather system ShoBha. Experimental results on three datasets, namely the Canadian Hansards corpus, the entire overlapping Wikipedia of English, Hindi and Bengali articles, and a trilingual news corpus containing 41,000 articles, confirm the utility of the proposed system.

Simultaneous feature selection and clustering using particle swarm optimization

Data clustering groups data so that data which are similar to each other are in the same group and data which are dissimilar to each other are in different groups. Since generally clustering is a subjective activity, it is possible to get different clusterings of the same data depending on the need. This paper attempts to find the best clustering of the data by first carrying out feature selection and using only the selected features, for clustering. A PSO (Particle Swarm Optimization)has been used for clustering but feature selection has also been carried out simultaneously. The performance of the above proposed algorithm is evaluated on some benchmark data sets. The experimental results shows the proposed methodology outperforms the previous approaches such as basic PSO and Kmeans for the clustering problem.

Call intensity is a repeatable and dominant acoustic feature determining male call attractiveness in a field cricket

Acoustic signal variation and female preference for different signal components constitute the prerequisite framework to study the mechanisms of sexual selection that shape acoustic communication. Despite several studies of acoustic communication in crickets, information on both male calling song variation in the field and female preference in the same system is lacking for most species. Previous studies on acoustic signal variation either were carried out on populations maintained in the laboratory or did not investigate signal repeatability. We therefore used repeatability analysis to quantify variation in the spectral, temporal and amplitudinal characteristics of the male calling song of the field cricket Plebeiogryllus guttiventris in a wild population, at two temporal scales, within and across nights. Carrier frequency (CF) was the most repeatable character across nights, whereas chirp period (CP) had low repeatability across nights. We investigated whether female preferences were more likely to be based on features with high (CF) or low (CP) repeatability. Females showed no consistent preferences for CF but were significantly more attracted towards signals with short CPs. The attractiveness of lower CP calls disappeared, however, when traded off with sound pressure level (SPL). SPL was the only acoustic feature that was significantly positively correlated with male body size. Since relative SPL affects female phonotaxis strongly and can vary unpredictably based on male spacing, our results suggest that even strong female preferences for acoustic features may not necessarily translate into greater advantage for males possessing these features in the field. (C) 2013 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.

Examining the Effectiveness of Discriminant Function Analysis and Cluster Analysis in Species Identification of Male Field Crickets Based on Their Calling Songs

Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA) and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6-7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species classification and identification.

Electronic structure of Nd1-xYxMnO3 from Mn K edge absorption spectroscopy and DFT methods

The electronic structure of Nd1-xYxMnO3 (x-0-0.5) is studied using x-ray absorption near-edge structure (XANES) spectroscopy at the Mn K-edge along with the DFT-based LSDA+U and real space cluster calculations. The main edge of the spectra does not show any variation with doping. The pre-edge shows two distinct features which appear well-separated with doping. The intensity of the pre-edge decreases with doping. The theoretical XANES were calculated using real space multiple scattering methods which reproduces the entire experimental spectra at the main edge as well as the pre-edge. Density functional theory calculations are used to obtain the Mn 4p, Mn 3d and O 2p density of states. For x=0, the site-projected density of states at 1.7 eV above Fermi energy shows a singular peak of unoccupied e(g) (spin-up) states which is hybridized Mn 4p and O 2p states. For x=0.5, this feature develops at a higher energy and is highly delocalized and overlaps with the 3d spin-down states which changes the pre-edge intensity. The Mn 4p DOS for both compositions, show considerable difference between the individual p(x), p(y) and p(z)), states. For x=0.5, there is a considerable change in the 4p orbital polarization suggesting changes in the Jahn-Teller effect with doping. (C) 2013 Elsevier Ltd. All rights reserved.

Thermal conduction and multiphase gas in cluster cores

We examine the role of thermal conduction and magnetic fields in cores of galaxy clusters through global simulations of the intracluster medium (ICM). In particular, we study the influence of thermal conduction, both isotropic and anisotropic, on the condensation of multiphase gas in cluster cores. Previous hydrodynamic simulations have shown that cold gas condenses out of the hot ICM in thermal balance only when the ratio of the cooling time (t(cool)) and the free-fall time (t(ff)) is less than approximate to 10. Since thermal conduction is significant in the ICM and it suppresses local cooling at small scales, it is imperative to include thermal conduction in such studies. We find that anisotropic (along local magnetic field lines) thermal conduction does not influence the condensation criterion for a general magnetic geometry, even if thermal conductivity is large. However, with isotropic thermal conduction cold gas condenses only if conduction is suppressed (by a factor less than or similar to 0.3) with respect to the Spitzer value.

The Presence of Multiple Cellular Defects Associated with a Novel G50E Iron-Sulfur Cluster Scaffold Protein ( ISCU) Mutation Leads to Development of Mitochondrial Myopathy

Background: Muscle-specific deficiency of iron-sulfur (Fe-S) cluster scaffold protein (ISCU) leads to myopathy. Results: Cells carrying the myopathy-associated G50E ISCU mutation demonstrate impaired Fe-S cluster biogenesis and mitochondrial dysfunction. Conclusion: Reduced mitochondrial respiration as a result of diminished Fe-S cluster synthesis results in muscle weakness in myopathy patients. Significance: The molecular mechanism behind disease progression should provide invaluable information to combat ISCU myopathy. Iron-sulfur (Fe-S) clusters are versatile cofactors involved in regulating multiple physiological activities, including energy generation through cellular respiration. Initially, the Fe-S clusters are assembled on a conserved scaffold protein, iron-sulfur cluster scaffold protein (ISCU), in coordination with iron and sulfur donor proteins in human mitochondria. Loss of ISCU function leads to myopathy, characterized by muscle wasting and cardiac hypertrophy. In addition to the homozygous ISCU mutation (g.7044GC), compound heterozygous patients with severe myopathy have been identified to carry the c.149GA missense mutation converting the glycine 50 residue to glutamate. However, the physiological defects and molecular mechanism associated with G50E mutation have not been elucidated. In this report, we uncover mechanistic insights concerning how the G50E ISCU mutation in humans leads to the development of severe ISCU myopathy, using a human cell line and yeast as the model systems. The biochemical results highlight that the G50E mutation results in compromised interaction with the sulfur donor NFS1 and the J-protein HSCB, thus impairing the rate of Fe-S cluster synthesis. As a result, electron transport chain complexes show significant reduction in their redox properties, leading to loss of cellular respiration. Furthermore, the G50E mutant mitochondria display enhancement in iron level and reactive oxygen species, thereby causing oxidative stress leading to impairment in the mitochondrial functions. Thus, our findings provide compelling evidence that the respiration defect due to impaired biogenesis of Fe-S clusters in myopathy patients leads to manifestation of complex clinical symptoms.

Fluctuating micro-heterogeneity in water-tert-butyl alcohol mixtures and lambda-type divergence of the mean cluster size with phase transition-like multiple anomalies

Water-tert-butyl alcohol (TBA) binary mixture exhibits a large number of thermodynamic and dynamic anomalies. These anomalies are observed at surprisingly low TBA mole fraction, with x(TBA) approximate to 0.03-0.07. We demonstrate here that the origin of the anomalies lies in the local structural changes that occur due to self-aggregation of TBA molecules. We observe a percolation transition of the TBA molecules at x(TBA) approximate to 0.05. We note that ``islands'' of TBA clusters form even below this mole fraction, while a large spanning cluster emerges above that mole fraction. At this percolation threshold, we observe a lambda-type divergence in the fluctuation of the size of the largest TBA cluster, reminiscent of a critical point. Alongside, the structure of water is also perturbed, albeit weakly, by the aggregation of TBA molecules. There is a monotonic decrease in the tetrahedral order parameter of water, while the dipole moment correlation shows a weak nonlinearity. Interestingly, water molecules themselves exhibit a reverse percolation transition at higher TBA concentration, x(TBA) approximate to 0.45, where large spanning water clusters now break-up into small clusters. This is accompanied by significant divergence of the fluctuations in the size of largest water cluster. This second transition gives rise to another set of anomalies around. Both the percolation transitions can be regarded as manifestations of Janus effect at small molecular level. (C) 2014 AIP Publishing LLC.

Turbulence and cooling in galaxy cluster cores

We study the interplay between turbulent heating, mixing, and radiative cooling in an idealized model of cool cluster cores. Active galactic nuclei (AGN) jets are expected to drive turbulence and heat cluster cores. Cooling of the intracluster medium (ICM) and stirring by AGN jets are tightly coupled in a feedback loop. We impose the feedback loop by balancing radiative cooling with turbulent heating. In addition to heating the plasma, turbulence also mixes it, suppressing the formation of cold gas at small scales. In this regard, the effect of turbulence is analogous to thermal conduction. For uniform plasma in thermal balance (turbulent heating balancing radiative cooling), cold gas condenses only if the cooling time is shorter than the mixing time. This condition requires the turbulent kinetic energy to be a parts per thousand(3) the plasma internal energy; such high velocities in cool cores are ruled out by observations. The results with realistic magnetic fields and thermal conduction are qualitatively similar to the hydrodynamic simulations. Simulations where the runaway cooling of the cool core is prevented due to mixing with the hot ICM show cold gas even with subsonic turbulence, consistent with observations. Thus, turbulent mixing is the likely mechanism via which AGN jets heat cluster cores. The thermal instability growth rates observed in simulations with turbulence are consistent with the local thermal instability interpretation of cold gas in cluster cores.

Subexponential Algorithm for d-Cluster Edge Deletion: Exception or Rule?

The correlation clustering problem is a fundamental problem in both theory and practice, and it involves identifying clusters of objects in a data set based on their similarity. A traditional modeling of this question as a graph theoretic problem involves associating vertices with data points and indicating similarity by adjacency. Clusters then correspond to cliques in the graph. The resulting optimization problem, Cluster Editing (and several variants) are very well-studied algorithmically. In many situations, however, translating clusters to cliques can be somewhat restrictive. A more flexible notion would be that of a structure where the vertices are mutually ``not too far apart'', without necessarily being adjacent. One such generalization is realized by structures called s-clubs, which are graphs of diameter at most s. In this work, we study the question of finding a set of at most k edges whose removal leaves us with a graph whose components are s-clubs. Recently, it has been shown that unless Exponential Time Hypothesis fail (ETH) fails Cluster Editing (whose components are 1-clubs) does not admit sub-exponential time algorithm STACS, 2013]. That is, there is no algorithm solving the problem in time 2 degrees((k))n(O(1)). However, surprisingly they show that when the number of cliques in the output graph is restricted to d, then the problem can be solved in time O(2(O(root dk)) + m + n). We show that this sub-exponential time algorithm for the fixed number of cliques is rather an exception than a rule. Our first result shows that assuming the ETH, there is no algorithm solving the s-Club Cluster Edge Deletion problem in time 2 degrees((k))n(O(1)). We show, further, that even the problem of deleting edges to obtain a graph with d s-clubs cannot be solved in time 2 degrees((k))n(O)(1) for any fixed s, d >= 2. This is a radical contrast from the situation established for cliques, where sub-exponential algorithms are known.

Large carbon cluster thin film gauges for measuring aerodynamic heat transfer rates in hypersonic shock tunnels

Different types of Large Carbon Cluster (LCC) layers are synthesized by a single-step pyrolysis technique at various ratios of precursor mixture. The aim is to develop a fast responsive and stable thermal gauge based on a LCC layer which has relatively good electrical conduction in order to use it in the hypersonic flow field. The thermoelectric property of the LCC layer has been studied. It is found that these carbon clusters are sensitive to temperature changes. Therefore suitable thermal gauges were developed for blunt cone bodies and were tested in hypersonic shock tunnels at a flow Mach number of 6.8 to measure aerodynamic heating. The LCC layer of this thermal gauge encounters high shear forces and a hostile environment for test duration in the range of a millisecond. The results are favorable to use large carbon clusters as a better sensor than a conventional platinum thin film gauge in view of fast responsiveness and stability.

DEEP WASHINGTON PHOTOMETRY OF INCONSPICUOUS STAR CLUSTER CANDIDATES IN THE LARGE MAGELLANIC CLOUD

We present deep Washington photometry of 45 poorly populated star cluster candidates in the Large Magellanic Cloud (LMC). We have performed a systematic study to estimate the parameters of the cluster candidates by matching theoretical isochrones to the cleaned and dereddened cluster color-magnitude diagrams. We were able to estimate the basic parameters for 33 clusters, out of which 23 are identified as single clusters and 10 are found to be members of double clusters. The other 12 cluster candidates have been classified as possible clusters/asterisms. About 50% of the true clusters are in the 100-300 Myr age range, whereas some are older or younger. We have discussed the distribution of age, location, and reddening with respect to field, as well as the size of true clusters. The sizes and masses of the studied sample are found to be similar to that of open clusters in the Milky Way. Our study adds to the lower end of cluster mass distribution in the LMC, suggesting that the LMC, apart from hosting rich clusters, also has formed small, less massive open clusters in the 100-300 Myr age range.

Remarkable enhancement in hydrogen storage on free-standing Ti3B and BC3 supported Ti-3 clusters

Hydrogen storage capacity of Tin-1B (n = 3-7) clusters is studied and compared with that of the pristine Ti-n (n = 3-7), using density functional theory (DFT) based calculations. Among these clusters, Ti3B shows the most significant enhancement in the storage capacity by adsorbing 12 H-2, out of which three are dissociated and the other nine are stored as dihydrogen via Kubas-interaction. The best storage in Ti3B is owed to a large charge transfer from Ti to B along with the largest distance of Ti empty d-states above the Fermi level, which is a distinct feature of this particular cluster. Furthermore, the effect of substrates on the storage capacity of Ti3B was assessed by calculating the number of adsorbed H-2 on Ti-3 cluster anchored onto B atoms in the B-doped graphene, BC3, and BN substrates. Similar to free-standing Ti3B, Ti-3 anchored onto boron atom in BC3, stores nine di-hydrogen via Kubas interaction, at the same time eliminating the total number of non-useful dissociated hydrogen. Gibbs energy of adsorption as a function of H-2 partial pressure, indicated that at 250 K and 300 K the di-hydrogens on Ti-3@BC3 adsorb and desorb at ambient pressures. Importantly, Ti-3@BC3 avoids the clustering, hence meeting the criteria for efficient and reversible hydrogen storage media. Copyright (C) 2014, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

An Optical How Feature and McFIS Based Approach for 3-Dimensional Human Action Recognition

We propose to develop a 3-D optical flow features based human action recognition system. Optical flow based features are employed here since they can capture the apparent movement in object, by design. Moreover, they can represent information hierarchically from local pixel level to global object level. In this work, 3-D optical flow based features a re extracted by combining the 2-1) optical flow based features with the depth flow features obtained from depth camera. In order to develop an action recognition system, we employ a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). The m of McFIS is to find the decision boundary separating different classes based on their respective optical flow based features. McFIS consists of a neuro-fuzzy inference system (cognitive component) and a self-regulatory learning mechanism (meta-cognitive component). During the supervised learning, self-regulatory learning mechanism monitors the knowledge of the current sample with respect to the existing knowledge in the network and controls the learning by deciding on sample deletion, sample learning or sample reserve strategies. The performance of the proposed action recognition system was evaluated on a proprietary data set consisting of eight subjects. The performance evaluation with standard support vector machine classifier and extreme learning machine indicates improved performance of McFIS is recognizing actions based of 3-D optical flow based features.