A psychology based approach for longitudinal development in cognitive robotics.

A major challenge in robotics is the ability to learn, from novel experiences, new behavior that is useful for achieving new goals and skills. Autonomous systems must be able to learn solely through the environment, thus ruling out a priori task knowledge, tuning, extensive training, or other forms of pre-programming. Learning must also be cumulative and incremental, as complex skills are built on top of primitive skills. Additionally, it must be driven by intrinsic motivation because formative experience is gained through autonomous activity, even in the absence of extrinsic goals or tasks. This paper presents an approach to these issues through robotic implementations inspired by the learning behavior of human infants. We describe an approach to developmental learning and present results from a demonstration of longitudinal development on an iCub humanoid robot. The results cover the rapid emergence of staged behavior, the role of constraints in development, the effect of bootstrapping between stages, and the use of a schema memory of experiential fragments in learning new skills. The context is a longitudinal experiment in which the robot advanced from uncontrolled motor babbling to skilled hand/eye integrated reaching and basic manipulation of objects. This approach offers promise for further fast and effective sensory-motor learning techniques for robotic learning

Cross-correlation Weak Lensing of SDSS Galaxy Clusters III: Mass-to-light Ratios

We present measurements of the excess mass-to-light ratio measured aroundMaxBCG galaxy clusters observed in the SDSS. This red sequence cluster sample includes objects from small groups with masses ranging from ~5x10^{12} to ~10^{15} M_{sun}/h. Using cross-correlation weak lensing, we measure the excess mass density profile above the universal mean \Delta \rho(r) = \rho(r) - \bar{\rho} for clusters in bins of richness and optical luminosity. We also measure the excess luminosity density \Delta l(r) = l(r) - \bar{l} measured in the z=0.25 i-band. For both mass and light, we de-project the profiles to produce 3D mass and light profiles over scales from 25 kpc/ to 22 Mpc/h. From these profiles we calculate the cumulative excess mass M(r) and excess light L(r) as a function of separation from the BCG. On small scales, where \rho(r) >> \bar{\rho}, the integrated mass-to-light profile may be interpreted as the cluster mass-to-light ratio. We find the M/L_{200}, the mass-to-light ratio within r_{200}, scales with cluster mass as a power law with index 0.33+/-0.02. On large scales, where \rho(r) ~ \bar{\rho}, the M/L approaches an asymptotic value independent of cluster richness. For small groups, the mean M/L_{200} is much smaller than the asymptotic value, while for large clusters it is consistent with the asymptotic value. This asymptotic value should be proportional to the mean mass-to-light ratio of the universe . We find /b^2_{ml} = 362+/-54 h (statistical). There is additional uncertainty in the overall calibration at the ~10% level. The parameter b_{ml} is primarily a function of the bias of the L <~ L_* galaxies used as light tracers, and should be of order unity. Multiplying by the luminosity density in the same bandpass we find \Omega_m/b^2_{ml} = 0.02+/-0.03, independent of the Hubble parameter.Comment: Third paper in a series; v2.0 incorporates ApJ referee's suggestion

An Ensemble Approach for Annotating Source Code Identifiers with Part-of-speech Tags

This paper presents an ensemble part-of-speech tagging approach for source code identifiers. Ensemble tagging is a technique that uses machine-learning and the output from multiple part-of-speech taggers to annotate natural language text at a higher quality than the part-of-speech taggers are able to obtain independently. Our ensemble uses three state-of-the-art part-of-speech taggers: SWUM, POSSE, and Stanford. We study the quality of the ensemble\u27s annotations on five different types of identifier names: function, class, attribute, parameter, and declaration statement at the level of both individual words and full identifier names. We also study and discuss the weaknesses of our tagger to promote the future amelioration of these problems through further research. Our results show that the ensemble achieves 75\% accuracy at the identifier level and 84-86\% accuracy at the word level. This is an increase of +17\% points at the identifier level from the closest independent part-of-speech tagger

A GMBCG Galaxy Cluster Catalog of 55,424 Rich Clusters from SDSS DR7

We present a large catalog of optically selected galaxy clusters from the application of a new Gaussian Mixture Brightest Cluster Galaxy (GMBCG) algorithm to SDSS Data Release 7 data. The algorithm detects clusters by identifying the red sequence plus Brightest Cluster Galaxy (BCG) feature, which is unique for galaxy clusters and does not exist among field galaxies. Red sequence clustering in color space is detected using an Error Corrected Gaussian Mixture Model. We run GMBCG on 8240 square degrees of photometric data from SDSS DR7 to assemble the largest ever optical galaxy cluster catalog, consisting of over 55,000 rich clusters across the redshift range from 0.1 < z < 0.55. We present Monte Carlo tests of completeness and purity and perform cross-matching with X-ray clusters and with the maxBCG sample at low redshift. These tests indicate high completeness and purity across the full redshift range for clusters with 15 or more members.Comment: Updated to match the published version. The catalog can be accessed from: http://home.fnal.gov/~jghao/gmbcg_sdss_catalog.htm

Precision Measurements of the Cluster Red Sequence using an Error Corrected Gaussian Mixture Model

The red sequence is an important feature of galaxy clusters and plays a crucial role in optical cluster detection. Measurement of the slope and scatter of the red sequence are affected both by selection of red sequence galaxies and measurement errors. In this paper, we describe a new error corrected Gaussian Mixture Model for red sequence galaxy identification. Using this technique, we can remove the effects of measurement error and extract unbiased information about the intrinsic properties of the red sequence. We use this method to select red sequence galaxies in each of the 13,823 clusters in the maxBCG catalog, and measure the red sequence ridgeline location and scatter of each. These measurements provide precise constraints on the variation of the average red galaxy populations in the observed frame with redshift. We find that the scatter of the red sequence ridgeline increases mildly with redshift, and that the slope decreases with redshift. We also observe that the slope does not strongly depend on cluster richness. Using similar methods, we show that this behavior is mirrored in a spectroscopic sample of field galaxies, further emphasizing that ridgeline properties are independent of environment.Comment: 33 pages, 14 Figures; A typo in Eq.A11 is fixed. The C++/Python codes for ECGMM can be downloaded from: https://sites.google.com/site/jiangangecgmm

Weak Lensing with SDSS Commissioning Data: The Galaxy-Mass Correlation Function To 1/h Mpc

(abridged) We present measurements of galaxy-galaxy lensing from early commissioning imaging data from the Sloan Digital Sky Survey (SDSS). We measure a mean tangential shear around a stacked sample of foreground galaxies in three bandpasses out to angular radii of 600'', detecting the shear signal at very high statistical significance. The shear profile is well described by a power-law. A variety of rigorous tests demonstrate the reality of the gravitational lensing signal and confirm the uncertainty estimates. We interpret our results by modeling the mass distributions of the foreground galaxies as approximately isothermal spheres characterized by a velocity dispersion and a truncation radius. The velocity dispersion is constrained to be 150-190 km/s at 95% confidence (145-195 km/s including systematic uncertainties), consistent with previous determinations but with smaller error bars. Our detection of shear at large angular radii sets a 95% confidence lower limit $s>140^{\prime\prime}$, corresponding to a physical radius of $260h^{-1}$ kpc, implying that galaxy halos extend to very large radii. However, it is likely that this is being biased high by diffuse matter in the halos of groups and clusters. We also present a preliminary determination of the galaxy-mass correlation function finding a correlation length similar to the galaxy autocorrelation function and consistency with a low matter density universe with modest bias. The full SDSS will cover an area 44 times larger and provide spectroscopic redshifts for the foreground galaxies, making it possible to greatly improve the precision of these constraints, measure additional parameters such as halo shape, and measure the properties of dark matter halos separately for many different classes of galaxies.Comment: 28 pages, 11 figures, submitted to A

Prediction of cardiovascular outcomes with machine learning techniques: application to the Cardiovascular Outcomes in Renal Atherosclerotic Lesions (CORAL) study.

Background: Data derived from the Cardiovascular Outcomes in Renal Atherosclerotic Lesions (CORAL) study were analyzed in an effort to employ machine learning methods to predict the composite endpoint described in the original study. Methods: We identified 573 CORAL subjects with complete baseline data and the presence or absence of a composite endpoint for the study. These data were subjected to several models including a generalized linear (logistic-linear) model, support vector machine, decision tree, feed-forward neural network, and random forest, in an effort to attempt to predict the composite endpoint. The subjects were arbitrarily divided into training and testing subsets according to an 80%:20% distribution with various seeds. Prediction models were optimized within the CARET package of R. Results: The best performance of the different machine learning techniques was that of the random forest method which yielded a receiver operator curve (ROC) area of 68.1%±4.2% (mean ± SD) on the testing subset with ten different seed values used to separate training and testing subsets. The four most important variables in the random forest method were SBP, serum creatinine, glycosylated hemoglobin, and DBP. Each of these variables was also important in at least some of the other methods. The treatment assignment group was not consistently an important determinant in any of the models. Conclusion: Prediction of a composite cardiovascular outcome was difficult in the CORAL population, even when employing machine learning methods. Assignment to either the stenting or best medical therapy group did not serve as an important predictor of composite outcome. Clinical Trial Registration: ClinicalTrials.gov, NCT00081731

Orientation bias of optically selected galaxy clusters and its impact on stacked weak-lensing analyses

Weak-lensing measurements of the averaged shear profiles of galaxy clusters binned by some proxy for cluster mass are commonly converted to cluster mass estimates under the assumption that these cluster stacks have spherical symmetry. In this paper, we test whether this assumption holds for optically selected clusters binned by estimated optical richness. Using mock catalogues created from N-body simulations populated realistically with galaxies, we ran a suite of optical cluster finders and estimated their optical richness. We binned galaxy clusters by true cluster mass and estimated optical richness and measure the ellipticity of these stacks. We find that the processes of optical cluster selection and richness estimation are biased, leading to stacked structures that are elongated along the line of sight. We show that weak-lensing alone cannot measure the size of this orientation bias. Weak-lensing masses of stacked optically selected clusters are overestimated by up to 3–6 per cent when clusters can be uniquely associated with haloes. This effect is large enough to lead to significant biases in the cosmological parameters derived from large surveys like the Dark Energy Survey, if not calibrated via simulations or fitted simultaneously. This bias probably also contributes to the observed discrepancy between the observed and predicted Sunyaev–Zel’dovich signal of optically selected clusters