Observation of pseudo-gap and Kondo behaviour in Al70Pd30−xMnx quasicrystals

We report here the electrical and magnetic properties of Al70Pd30−xMnx quasicrystals (x=9 and 11), from resistivity and point contact spectroscopy measurements. Electrical resistivity shows a resistivity maximum for both of these compositions. The positive TCR at lower temperature is attributed to spin–orbit scattering. For x=11, we observe an upturn in the resistivity below 20 K, which follows a lnT dependence indicating Kondo-like behaviour. In the point contact spectroscopy studies we observe two regimes showing a V2 dependence at low bias voltages (for V<10 meV) crossing over to the V0.5 dependence at higher voltages. This is attributed to the signature of a pseudo-gap in the density of states at zero bias. We suggest that this V2 dependence can also arise due to magnetic scattering effects, which are signatures of the Kondo-like behaviour.

Observation of high pressure o-GeTe phase at ambient pressure in Si-Te-Ge glasses

Te-rich Si15Te85-xGex (1 <= x <= 11) glasses are found to exhibit an anomalous phase separations with germanium composition. The structural transformation of o-GeTe crystalline phase from o-GeTe with a = 11.76 angstrom, b = 16.59 angstrom, c = 17.44 angstrom, to high pressure o-GeTe with a new reduced lattice parameters a = 10.95 angstrom, b = 4.03 angstrom, c = 4.45 angstrom, is observed at T-c3 in the composition range 6 <= x <= 11. Raman studies support the possible existence of high pressure o-GeTe phase which is observed in X-ray diffraction experiments. Copyright 2012 Author(s). This article is distributed under a Creative Commons Attribution 3.0 Unported License. http://dx.doi.org/10.1063/1.3696862]

Human action recognition using a fast learning fully complex-valued classifier

In this paper, we use optical flow based complex-valued features extracted from video sequences to recognize human actions. The optical flow features between two image planes can be appropriately represented in the Complex plane. Therefore, we argue that motion information that is used to model the human actions should be represented as complex-valued features and propose a fast learning fully complex-valued neural classifier to solve the action recognition task. The classifier, termed as, ``fast learning fully complex-valued neural (FLFCN) classifier'' is a single hidden layer fully complex-valued neural network. The neurons in the hidden layer employ the fully complex-valued activation function of the type of a hyperbolic secant function. The parameters of the hidden layer are chosen randomly and the output weights are estimated as the minimum norm least square solution to a set of linear equations. The results indicate the superior performance of FLFCN classifier in recognizing the actions compared to real-valued support vector machines and other existing results in the literature. Complex valued representation of 2D motion and orthogonal decision boundaries boost the classification performance of FLFCN classifier. (c) 2012 Elsevier B.V. All rights reserved.

Constraints on axino warm dark matter from X-ray observation at the Chandra telescope and SPI

A sufficiently long lived warm dark matter could be a source of X-rays observed by satellite based X-ray telescopes. We consider axinos and gravitinos with masses between 1 keV and 100 keV in supersymmetric models with sin all R-parity violation. We show that axino dark matter receives significant constraints from X-ray observations of Chandra and SPI, especially for the lower end of the allowed range of the axino decay constant f(a), while the gravitino dark matter remains unconstrained.

Direct Observation of Valley Hybridization and Universal Symmetry of Graphene with Mesoscopic Conductance Fluctuations

In graphene, the valleys represent spinlike quantities and can act as a physical resource in valley-based electronics to produce novel quantum computation schemes. Here we demonstrate a direct route to tune and read the valley quantum states of disordered graphene by measuring the mesoscopic conductance fluctuations. We show that the conductance fluctuations in graphene at low temperatures are reduced by a factor of 4 when valley triplet states are gapped in the presence of short-range potential scatterers at high carrier densities. We also show that this implies a gate tunable universal symmetry class that outlines a fundamental feature arising from graphene's unique crystal structure.

Observation of the nuclear magnetic octupole moment of Yb-173 from precise measurements of the hyperfine structure in the P-3(2) state

We measure hyperfine structure in the metastable P-3(2) state of Yb-173 and extract the nuclear magnetic octupole moment. We populate the state using dipole-allowed transitions through the P-3(1) and S-3(1) states. We measure frequencies of hyperfine transitions of the P-3(2) -> S-3(1) line at 770 nm using a Rb-stabilized ring cavity resonator with a precision of 200 kHz. Second-order corrections due to perturbations from the nearby P-3(1) and P-1(1) states are below 30 kHz. We obtain the hyperfine coefficients as A = -742.11(2) MHz and B = 1339.2(2) MHz, which represent a two orders-of-magnitude improvement in precision, and C = 0.54(2) MHz. From atomic structure calculations, we obtain the nuclear moments quadrupole Q = 2.46(12) b and octupole Omega = -34.4(21) b x mu(N). DOI: 10.1103/PhysRevA.87.012512

Fully complex-valued ELM classifiers for human action recognition

In this paper, we present a fast learning neural network classifier for human action recognition. The proposed classifier is a fully complex-valued neural network with a single hidden layer. The neurons in the hidden layer employ the fully complex-valued hyperbolic secant as an activation function. The parameters of the hidden layer are chosen randomly and the output weights are estimated analytically as a minimum norm least square solution to a set of linear equations. The fast leaning fully complex-valued neural classifier is used for recognizing human actions accurately. Optical flow-based features extracted from the video sequences are utilized to recognize 10 different human actions. The feature vectors are computationally simple first order statistics of the optical flow vectors, obtained from coarse to fine rectangular patches centered around the object. The results indicate the superior performance of the complex-valued neural classifier for action recognition. The superior performance of the complex neural network for action recognition stems from the fact that motion, by nature, consists of two components, one along each of the axes.

Room temperature synthesis of Mn2+ doped ZnS d-dots and observation of tunable dual emission: Effects of doping concentration, temperature, and ultraviolet light illumination

Mn2+ doped (0-50.0 molar %) ZnS d-dots have been synthesized in water medium by using an environment friendly low cost chemical technique. Tunable dual emission in UV and yellow-orange regions is achieved by tailoring the Mn2+ doping concentration in the host ZnS nanocrystal. The optimum doping concentration for achieving efficient photoluminescence (PL) emission is determined to be similar to 1.10 (at. %) corresponding to 40.0 (molar %) of Mn2+ doping concentration used during synthesis. The mechanism of charge transfer from the host to the dopant leading to the intensity modulated tunable (594-610 nm) yellow-orange PL emission is straightforwardly understood as no capping agent is used. The temperature dependent PL emission measurements are carried out, viz., in 1.10 at. % Mn2+ doped sample and the experimental results are explained by using a theoretical PL emission model. It is found that the ratio of non-radiative to radiative recombination rates is temperature dependent and this phenomenon has not been reported, so far, in Mn2+ doped ZnS system. The colour tuning of the emitted light from the samples are evident from the calculated chromaticity coordinates. UV light irradiation for 150 min in 40.0 (molar %) Mn2+ doped sample shows an enhancement of 33% in PL emission intensity. (C) 2013 American Institute of Physics. http://dx.doi.org/10.1063/1.4795779]

Mycobacterium tuberculosis mutT1 (Rv2985) and ADPRase (Rv1700) proteins constitute a two-stage mechanism of 8-Oxo-dGTP and 8-Oxo-GTP detoxification and adenosine to cytidine mutation avoidance

Approximately one third of the world population is infected with Mycobacterium tuberculosis, the causative agent of tuberculosis. A better understanding of the pathogen biology is crucial to develop new tools/strategies to tackle its spread and treatment. In the host macrophages, the pathogen is exposed to reactive oxygen species, known to damage dGTP and GTP to 8-oxo-dGTP and 8-oxo-GTP, respectively. Incorporation of the damaged nucleotides in nucleic acids is detrimental to organisms. MutT proteins, belonging to a class of Nudix hydrolases, hydrolyze 8-oxo-G nucleoside triphosphates/diphosphates to the corresponding nucleoside monophosphates and sanitize the nucleotide pool. Mycobacteria possess several MutT proteins. However, a functional homolog of Escherichia coli MutT has not been identified. Here, we characterized MtuMutT1 and Rv1700 proteins of M. tuberculosis. Unlike other MutT proteins, MtuMutT1 converts 8-oxo-dGTP to 8-oxo-dGDP, and 8-oxo-GTP to 8-oxo-GDP. Rv1700 then converts them to the corresponding nucleoside monophosphates. This observation suggests the presence of a two-stage mechanism of 8-oxo-dGTP/8-oxo-GTP detoxification in mycobacteria. MtuMutT1 converts 8-oxo-dGTP to 8-oxo-dGDP with a K-m of similar to 50 mu M and V-max of similar to 0.9 pmol/min per ng of protein, and Rv1700 converts 8-oxo-dGDP to 8-oxo-dGMP with a K-m of similar to 9.5 mu M and V-max of similar to 0.04 pmol/min per ng of protein. Together, MtuMutT1 and Rv1700 offer maximal rescue to E. coli for its MutT deficiency by decreasing A to C mutations (a hallmark of MutT deficiency). We suggest that the concerted action of MtuMutT1 and Rv1700 plays a crucial role in survival of bacteria against oxidative stress.

Subject independent human action recognition using spatio-depth information and meta-cognitive RBF network

In this paper, we present a machine learning approach for subject independent human action recognition using depth camera, emphasizing the importance of depth in recognition of actions. The proposed approach uses the flow information of all 3 dimensions to classify an action. In our approach, we have obtained the 2-D optical flow and used it along with the depth image to obtain the depth flow (Z motion vectors). The obtained flow captures the dynamics of the actions in space time. Feature vectors are obtained by averaging the 3-D motion over a grid laid over the silhouette in a hierarchical fashion. These hierarchical fine to coarse windows capture the motion dynamics of the object at various scales. The extracted features are used to train a Meta-cognitive Radial Basis Function Network (McRBFN) that uses a Projection Based Learning (PBL) algorithm, referred to as PBL-McRBFN, henceforth. PBL-McRBFN begins with zero hidden neurons and builds the network based on the best human learning strategy, namely, self-regulated learning in a meta-cognitive environment. When a sample is used for learning, PBLMcRBFN uses the sample overlapping conditions, and a projection based learning algorithm to estimate the parameters of the network. The performance of PBL-McRBFN is compared to that of a Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers with representation of every person and action in the training and testing datasets. Performance study shows that PBL-McRBFN outperforms these classifiers in recognizing actions in 3-D. Further, a subject-independent study is conducted by leave-one-subject-out strategy and its generalization performance is tested. It is observed from the subject-independent study that McRBFN is capable of generalizing actions accurately. The performance of the proposed approach is benchmarked with Video Analytics Lab (VAL) dataset and Berkeley Multimodal Human Action Database (MHAD). (C) 2013 Elsevier Ltd. All rights reserved.

Observation of trap-assisted space charge limited conductivity in short channel MoS2 transistor

We present temperature dependent I-V measurements of short channel MoS2 field effect devices at high source-drain bias. We find that, although the I-V characteristics are ohmic at low bias, the conduction becomes space charge limited at high V-DS, and existence of an exponential distribution of trap states was observed. The temperature independent critical drain-source voltage (V-c) was also determined. The density of trap states was quantitatively calculated from V-c. The possible origin of exponential trap distribution in these devices is also discussed. (C) 2013 AIP Publishing LLC.

The Role of Weak Interactions in the Phase Transition and Distinct Mechanical Behavior of Two Structurally Similar Caffeine Co-crystal Polymorphs Studied by Nanoindentation

Although weak interactions, such as C-H center dot center dot center dot O and pi-stacking, are generally considered to be insignificant, it is their reorganization that holds the key for many a solid-state phenomenon, such as phase transitions, plastic deformation, elastic flexibility, and mechanochromic luminescence in solid-state fluorophores. Despite this, the role of weak interactions in these dynamic phenomena is poorly understood. In this study, we investigate two co-crystal polymorphs of caffeine:4-chloro-3-nitrobenzoic acid, which have close structural similarity (2D layered structures), but surprisingly show distinct mechanical behavior. Form I is brittle, but shows shear-induced phase instability and, upon grinding, converts to Form II, which is soft and plastically shearable. This observation is in contrast to those reported in earlier studies on aspirin, wherein the metastable drug forms are softer and convert to stable and harder forms upon stressing To establish a molecular level understanding, have investigated the two co-crystal polymorphs I and II by single crystal X-ray diffraction, nanoindentation to quantify mechanical properties, and theoretical calculations. The lower hardness (from nanoindentation) and smooth potential surfaces (from theoretical studies) for shearing of layers in Form II allowed us to rationalize the role of stronger intralayer (sp(2))C-H center dot center dot center dot O and nonspecific interlayer pi-stacking interactions in the structure of II. Although the Form I also possesses the same type of interactions, its strength is clearly opposite, that is, weaker intralayer (sp(3))C-H center dot center dot center dot O and specific interlayer pi-stacking interactions. Hence, Form I is harder than Form IL Theoretical calculations and indentation on (111) of Form I suggested the low resistance of this face to mechanical stress; thus, Form I converts to II upon mechanical action. Hence, our approach demonstrates the usefulness of multiple techniques for establishing the role of weak noncovalent interactions in solid-state dynamic phenomena, such as stress induced phase transformation, and hence is important in the context of solid-state pharmaceutical chemistry and crystal engineering.