Can Self-Organizing Maps accurately predict photometric redshifts?

We present an unsupervised machine learning approach that can be employed for estimating photometric redshifts. The proposed method is based on a vector quantization approach called Self--Organizing Mapping (SOM). A variety of photometrically derived input values were utilized from the Sloan Digital Sky Survey's Main Galaxy Sample, Luminous Red Galaxy, and Quasar samples along with the PHAT0 data set from the PHoto-z Accuracy Testing project. Regression results obtained with this new approach were evaluated in terms of root mean square error (RMSE) to estimate the accuracy of the photometric redshift estimates. The results demonstrate competitive RMSE and outlier percentages when compared with several other popular approaches such as Artificial Neural Networks and Gaussian Process Regression. SOM RMSE--results (using $\Delta$z=z$_{phot}$--z$_{spec}$) for the Main Galaxy Sample are 0.023, for the Luminous Red Galaxy sample 0.027, Quasars are 0.418, and PHAT0 synthetic data are 0.022. The results demonstrate that there are non--unique solutions for estimating SOM RMSEs. Further research is needed in order to find more robust estimation techniques using SOMs, but the results herein are a positive indication of their capabilities when compared with other well-known methods.Comment: 5 pages, 3 figures, submitted to PAS

Quantum pattern recognition with liquid-state nuclear magnetic resonance

A novel quantum pattern recognition scheme is presented, which combines the idea of a classic Hopfield neural network with adiabatic quantum computation. Both the input and the memorized patterns are represented by means of the problem Hamiltonian. In contrast to classic neural networks, the algorithm can return a quantum superposition of multiple recognized patterns. A proof of principle for the algorithm for two qubits is provided using a liquid state NMR quantum computer.Comment: updated version, Journal-ref adde

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

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

Depth of Field Analysis for Multilayer Automultiscopic Displays

With the re-emergence of stereoscopic displays, through polarized glasses for theatrical presentations and shuttered liquid crystal eyewear in the home, automultiscopic displays have received increased attention. Commercial efforts have predominantly focused on parallax barrier and lenticular architectures applied to LCD panels. Such designs suffer from reduced resolution and brightness. Recently, multilayer LCDs have emerged as an alternative supporting full-resolution imagery with enhanced brightness and depth of field. We present a signal-processing framework for comparing the depth of field for conventional automultiscopic displays and emerging architectures comprising multiple light-attenuating layers. We derive upper bounds for the depths of field, indicating the potential of multilayer configurations to significantly enhance resolution and depth of field, relative to conventional designs.Massachusetts Institute of Technology. Media LaboratoryMIT Camera Culture GroupNational Science Foundation (U.S.) (Grant IIS-1116452)United States. Defense Advanced Research Projects Agency. MOSAIC ProgramUnited States. Defense Advanced Research Projects Agency. SCENICC ProgramAlfred P. Sloan Foundation (Research Fellowship)United States. Defense Advanced Research Projects Agency. (Young Faculty Award

The dynamical response to the node defect in thermally activated remagnetization of magnetic dot array

The influence of nonmagnetic central node defect on dynamical properties of regular square-shaped 5 x 5 segment of magnetic dot array under the thermal activation is investigated via computer simulations. Using stochastic Landau-Lifshitz-Gilbert equation we simulate hysteresis and relaxation processes. The remarkable quantitative and qualitative differences between magnetic dot arrays with nonmagnetic central node defect and magnetic dot arrays without defects have been found.Comment: 4 pages,5 figures, submitted to J. Magn. Magn. Matte

An Assessment of Students’ Satisfaction in Higher Education

Student’s Satisfaction (SS) with a particular subject may impact the learning process, being the figure of attentiveness of the utmost importance over time, and also a very difficult undertaking to accomplish. To go forward with such exercise, a workable methodology for problem solving had to be built and tested. It is based on a thermodynamic approach to Knowledge Representation and Reasoning, which is the ultimate goal of SS assessment when working on a particular topic

An improved constraint satisfaction adaptive neural network for job-shop scheduling

Copyright @ Springer Science + Business Media, LLC 2009This paper presents an improved constraint satisfaction adaptive neural network for job-shop scheduling problems. The neural network is constructed based on the constraint conditions of a job-shop scheduling problem. Its structure and neuron connections can change adaptively according to the real-time constraint satisfaction situations that arise during the solving process. Several heuristics are also integrated within the neural network to enhance its convergence, accelerate its convergence, and improve the quality of the solutions produced. An experimental study based on a set of benchmark job-shop scheduling problems shows that the improved constraint satisfaction adaptive neural network outperforms the original constraint satisfaction adaptive neural network in terms of computational time and the quality of schedules it produces. The neural network approach is also experimentally validated to outperform three classical heuristic algorithms that are widely used as the basis of many state-of-the-art scheduling systems. Hence, it may also be used to construct advanced job-shop scheduling systems.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and in part by the National Nature Science Fundation of China under Grant 60821063 and National Basic Research Program of China under Grant 2009CB320601

On line power spectra identification and whitening for the noise in interferometric gravitational wave detectors

In this paper we address both to the problem of identifying the noise Power Spectral Density of interferometric detectors by parametric techniques and to the problem of the whitening procedure of the sequence of data. We will concentrate the study on a Power Spectral Density like the one of the Italian-French detector VIRGO and we show that with a reasonable finite number of parameters we succeed in modeling a spectrum like the theoretical one of VIRGO, reproducing all its features. We propose also the use of adaptive techniques to identify and to whiten on line the data of interferometric detectors. We analyze the behavior of the adaptive techniques in the field of stochastic gradient and in the Least Squares ones.Comment: 28 pages, 21 figures, uses iopart.cls accepted for pubblication on Classical and Quantum Gravit

Using Neural Networks for Relation Extraction from Biomedical Literature

Using different sources of information to support automated extracting of relations between biomedical concepts contributes to the development of our understanding of biological systems. The primary comprehensive source of these relations is biomedical literature. Several relation extraction approaches have been proposed to identify relations between concepts in biomedical literature, namely, using neural networks algorithms. The use of multichannel architectures composed of multiple data representations, as in deep neural networks, is leading to state-of-the-art results. The right combination of data representations can eventually lead us to even higher evaluation scores in relation extraction tasks. Thus, biomedical ontologies play a fundamental role by providing semantic and ancestry information about an entity. The incorporation of biomedical ontologies has already been proved to enhance previous state-of-the-art results.Comment: Artificial Neural Networks book (Springer) - Chapter 1