Adaptive Evolutionary Clustering

In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term trends while being robust to short-term variations. Several evolutionary clustering algorithms have recently been proposed, often by adding a temporal smoothness penalty to the cost function of a static clustering method. In this paper, we introduce a different approach to evolutionary clustering by accurately tracking the time-varying proximities between objects followed by static clustering. We present an evolutionary clustering framework that adaptively estimates the optimal smoothing parameter using shrinkage estimation, a statistical approach that improves a naive estimate using additional information. The proposed framework can be used to extend a variety of static clustering algorithms, including hierarchical, k-means, and spectral clustering, into evolutionary clustering algorithms. Experiments on synthetic and real data sets indicate that the proposed framework outperforms static clustering and existing evolutionary clustering algorithms in many scenarios.Comment: To appear in Data Mining and Knowledge Discovery, MATLAB toolbox available at http://tbayes.eecs.umich.edu/xukevin/affec

Intelligent Learning Algorithms for Active Vibration Control

YesThis correspondence presents an investigation into the comparative performance of an active vibration control (AVC) system using a number of intelligent learning algorithms. Recursive least square (RLS), evolutionary genetic algorithms (GAs), general regression neural network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS) algorithms are proposed to develop the mechanisms of an AVC system. The controller is designed on the basis of optimal vibration suppression using a plant model. A simulation platform of a flexible beam system in transverse vibration using a finite difference method is considered to demonstrate the capabilities of the AVC system using RLS, GAs, GRNN, and ANFIS. The simulation model of the AVC system is implemented, tested, and its performance is assessed for the system identification models using the proposed algorithms. Finally, a comparative performance of the algorithms in implementing the model of the AVC system is presented and discussed through a set of experiments

Near-infrared integral-field spectra of the planet/brown dwarf companion AB Pic b

Chauvin et al. 2005 imaged a co-moving companion at ~260 AU from the young star AB Pic A. Evolutionary models predictions based on J H K photometry of AB Pic b suggested a mass of ~13 - 14 MJup, placing the object at the deuterium-burning boundary. We used the adaptive-optics-fed integral field spectrograph SINFONI to obtain high quality medium-resolution spectra of AB Pic b (R = 1500-2000) over the 1.1 - 2.5 microns range. Our analysis relies on the comparison of our spectra to young standard templates and to the latest libraries of synthetic spectra developed by the Lyon's Group. AB Pic b is confirmed to be a young early-L dwarf companion. We derive a spectral type L0-L1 and find several features indicative of intermediate gravity atmosphere. A comparison to synthetic spectra yields Teff = 2000+100-300 K and log(g) = 4 +- 0.5 dex. The determination of the derived atmospheric parameters of AB Pic b is limited by a non-perfect match of current atmosphere spectra with our near-infrared observations of AB Pic b. The current treatment of dust settling and missing molecular opacity lines in the atmosphere models could be responsible. By combining the observed photometry, the surface fluxes from atmosphere models and the known distance of the system, we derive new mass, luminosity and radius estimates of AB Pic b. They confirm independently the evolutionary model predictions. We finally review the current methods used to characterize planetary mass companions and discuss them in the perspective of future planet deep imaging surveys.Comment: 8 pages, 8 figure

The spectral analysis of nonstationary categorical time series using local spectral envelope

Most classical methods for the spectral analysis are based on the assumption that the time series is stationary. However, many time series in practical problems shows nonstationary behaviors. The data from some fields are huge and have variance and spectrum which changes over time. Sometimes,we are interested in the cyclic behavior of the categorical-valued time series such as EEG sleep state data or DNA sequence, the general method is to scale the data, that is, assign numerical values to the categories and then use the periodogram to find the cyclic behavior. But there exists numerous possible scaling. If we arbitrarily assign the numerical values to the categories and proceed with a spectral analysis, then the results will depend on the particular assignment. We would like to find the all possible scaling that bring out all of the interesting features in the data. To overcome these problems, there have been many approaches in the spectral analysis. Our goal is to develop a statistical methodology for analyzing nonstationary categorical time series in the frequency domain. In this dissertation, the spectral envelope methodology is introduced for spectral analysis of categorical time series. This provides the general framework for the spectral analysis of the categorical time series and summarizes information from the spectrum matrix. To apply this method to nonstationary process, I used the TBAS(Tree-Based Adaptive Segmentation) and local spectral envelope based on the piecewise stationary process. In this dissertation,the TBAS(Tree-Based Adpative Segmentation) using distance function based on the Kullback-Leibler divergence was proposed to find the best segmentation

Photometric characterization of exoplanets using angular and spectral differential imaging

The direct detection of exoplanets has been the subject of intensive research in the recent years. Data obtained with future high-contrast imaging instruments optimized for giant planets direct detection are strongly limited by the speckle noise. Specific observing strategies and data analysis methods, such as angular and spectral differential imaging, are required to attenuate the noise level and possibly detect the faint planet flux. Even though these methods are very efficient at suppressing the speckles, the photometry of the faint planets is dominated by the speckle residuals. The determination of the effective temperature and surface gravity of the detected planets from photometric measurements in different bands is then limited by the photometric error on the planet flux. In this work we investigate this photometric error and the consequences on the determination of the physical parameters of the detected planets. We perform detailed end-to-end simulation with the CAOS-based Software Package for SPHERE to obtain realistic data representing typical observing sequences in Y, J, H and Ks bands with a high contrast imager. The simulated data are used to measure the photometric accuracy as a function of contrast for planets detected with angular and spectral+angular differential methods. We apply this empirical accuracy to study the characterization capabilities of a high-contrast differential imager. We show that the expected photometric performances will allow the detection and characterization of exoplanets down to the Jupiter mass at angular separations of 1.0" and 0.2" respectively around high mass and low mass stars with 2 observations in different filter pairs. We also show that the determination of the planets physical parameters from photometric measurements in different filter pairs is essentialy limited by the error on the determination of the surface gravity.Comment: 13 pages, 7 figures, 4 tables. Accepted for publication in MNRA

High Spatial Resolution Thermal-Infrared Spectroscopy with ALES: Resolved Spectra of the Benchmark Brown Dwarf Binary HD 130948BC

We present 2.9-4.1 micron integral field spectroscopy of the L4+L4 brown dwarf binary HD 130948BC, obtained with the Arizona Lenslets for Exoplanet Spectroscopy (ALES) mode of the Large Binocular Telescope Interferometer (LBTI). The HD 130948 system is a hierarchical triple system, in which the G2V primary is joined by two co-orbiting brown dwarfs. By combining the age of the system with the dynamical masses and luminosities of the substellar companions, we can test evolutionary models of cool brown dwarfs and extra-solar giant planets. Previous near-infrared studies suggest a disagreement between HD 130948BC luminosities and those derived from evolutionary models. We obtained spatially-resolved, low-resolution (R~20) L-band spectra of HD 130948B and C to extend the wavelength coverage into the thermal infrared. Jointly using JHK photometry and ALES L-band spectra for HD 130948BC, we derive atmospheric parameters that are consistent with parameters derived from evolutionary models. We leverage the consistency of these atmospheric quantities to favor a younger age (0.50 \pm 0.07 Gyr) of the system compared to the older age (0.79 \pm 0.22 Gyr) determined with gyrochronology in order to address the luminosity discrepancy.Comment: 17 pages, 9 figures, Accepted to Ap