3,046 research outputs found

    Excitable Patterns in Active Nematics

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    We analyze a model of mutually-propelled filaments suspended in a two-dimensional solvent. The system undergoes a mean-field isotropic-nematic transition for large enough filament concentrations and the nematic order parameter is allowed to vary in space and time. We show that the interplay between non-uniform nematic order, activity and flow results in spatially modulated relaxation oscillations, similar to those seen in excitable media. In this regime the dynamics consists of nearly stationary periods separated by "bursts" of activity in which the system is elastically distorted and solvent is pumped throughout. At even higher activity the dynamics becomes chaotic.Comment: 4 pages, 4 figure

    Reduction and reconstruction of stochastic differential equations via symmetries

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    An algorithmic method to exploit a general class of infinitesimal symmetries for reducing stochastic differential equations is presented and a natural definition of reconstruction, inspired by the classical reconstruction by quadratures, is proposed. As a side result the well-known solution formula for linear one-dimensional stochastic differential equations is obtained within this symmetry approach. The complete procedure is applied to several examples with both theoretical and applied relevance

    Hybrid Method for Digits Recognition using Fixed-Frame Scores and Derived Pitch

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    This paper presents a procedure of frame normalization based on the traditional dynamic time warping (DTW) using the LPC coefficients. The redefined method is called as the DTW frame-fixing method (DTW-FF), it works by normalizing the word frames of the input against the reference frames. The enthusiasm to this study is due to neural network limitation that entails a fix number of input nodes for when processing multiple inputs in parallel. Due to this problem, this research is initiated to reduce the amount of computation and complexity in a neural network by reducing the number of inputs into the network. In this study, dynamic warping process is used, in which local distance scores of the warping path are fixed and collected so that their scores are of equal number of frames. Also studied in this paper is the consideration of pitch as a contributing feature to the speech recognition. Results showed a good performance and improvement when using pitch along with DTW-FF feature. The convergence rate between using the steepest gradient descent is also compared to another method namely conjugate gradient method. Convergence rate is also improved when conjugate gradient method is introduced in the back-propagation algorithm

    Spatially Resolved Mapping of Local Polarization Dynamics in an Ergodic Phase of Ferroelectric Relaxor

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    Spatial variability of polarization relaxation kinetics in relaxor ferroelectric 0.9Pb(Mg1/3Nb2/3)O3-0.1PbTiO3 is studied using time-resolved Piezoresponse Force Microscopy. Local relaxation attributed to the reorientation of polar nanoregions is shown to follow stretched exponential dependence, exp(-(t/tau)^beta), with beta~~0.4, much larger than the macroscopic value determined from dielectric spectra (beta~~0.09). The spatial inhomogeneity of relaxation time distributions with the presence of 100-200 nm "fast" and "slow" regions is observed. The results are analyzed to map the Vogel-Fulcher temperatures on the nanoscale.Comment: 23 pages, 4 figures, supplementary materials attached; to be submitted to Phys. Rev. Let

    Artificial Neural Network-based error compensation procedure for low-cost encoders

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    An Artificial Neural Network-based error compensation method is proposed for improving the accuracy of resolver-based 16-bit encoders by compensating for their respective systematic error profiles. The error compensation procedure, for a particular encoder, involves obtaining its error profile by calibrating it on a precision rotary table, training the neural network by using a part of this data and then determining the corrected encoder angle by subtracting the ANN-predicted error from the measured value of the encoder angle. Since it is not guaranteed that all the resolvers will have exactly similar error profiles because of the inherent differences in their construction on a micro scale, the ANN has been trained on one error profile at a time and the corresponding weight file is then used only for compensating the systematic error of this particular encoder. The systematic nature of the error profile for each of the encoders has also been validated by repeated calibration of the encoders over a period of time and it was found that the error profiles of a particular encoder recorded at different epochs show near reproducible behavior. The ANN-based error compensation procedure has been implemented for 4 encoders by training the ANN with their respective error profiles and the results indicate that the accuracy of encoders can be improved by nearly an order of magnitude from quoted values of ~6 arc-min to ~0.65 arc-min when their corresponding ANN-generated weight files are used for determining the corrected encoder angle.Comment: 16 pages, 4 figures. Accepted for Publication in Measurement Science and Technology (MST

    Consumer palatability scores and volatile beef flavor compounds of five USDA quality grades and four muscles

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    Proximate data, consumer palatability scores and volatile compounds were investigated for four beef muscles (Longissimus lumborum, Psoas major, Semimembranosus and Gluteus medius) and five USDA quality grades (Prime, Upper 2/3 Choice, Low Choice, Select, and Standard). Quality grade did not directly affect consumer scores or volatiles but interactions (P < 0.05) between muscle and grade were determined. Consumer scores and volatiles differed (P < 0.05) between muscles. Consumers scored Psoas major highest for tenderness, juiciness, flavor liking and overall liking, followed by Longissimus lumborum, Gluteus medius, and Semimembranosus (P < 0.05). Principal component analysis revealed clustering of compound classes, formed by related mechanisms. Volatile n-aldehydes were inversely related to percent fat. Increases in lipid oxidation compounds were associated with Gluteus medius and Semimembranosus, while greater quantities of sulfur-containing compounds were associated with Psoas major. Relationships between palatability scores and volatile compound classes suggest that differences in the pattern of volatile compounds may play a valuable role in explaining consumer liking

    Quantum effects in linguistic endeavors

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    Classifying the information content of neural spike trains in a linguistic endeavor, an uncertainty relation emerges between the bit size of a word and its duration. This uncertainty is associated with the task of synchronizing the spike trains of different duration representing different words. The uncertainty involves peculiar quantum features, so that word comparison amounts to measurement-based-quantum computation. Such a quantum behavior explains the onset and decay of the memory window connecting successive pieces of a linguistic text. The behavior here discussed is applicable to other reported evidences of quantum effects in human linguistic processes, so far lacking a plausible framework, since either no efforts to assign an appropriate quantum constant had been associated or speculating on microscopic processes dependent on Planck's constant resulted in unrealistic decoherence times
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