Learning Opposites with Evolving Rules

The idea of opposition-based learning was introduced 10 years ago. Since then a noteworthy group of researchers has used some notions of oppositeness to improve existing optimization and learning algorithms. Among others, evolutionary algorithms, reinforcement agents, and neural networks have been reportedly extended into their opposition-based version to become faster and/or more accurate. However, most works still use a simple notion of opposites, namely linear (or type- I) opposition, that for each $x\in[a,b]$ assigns its opposite as $\breve{x}_I=a+b-x$. This, of course, is a very naive estimate of the actual or true (non-linear) opposite $\breve{x}_{II}$, which has been called type-II opposite in literature. In absence of any knowledge about a function $y=f(\mathbf{x})$ that we need to approximate, there seems to be no alternative to the naivety of type-I opposition if one intents to utilize oppositional concepts. But the question is if we can receive some level of accuracy increase and time savings by using the naive opposite estimate $\breve{x}_I$ according to all reports in literature, what would we be able to gain, in terms of even higher accuracies and more reduction in computational complexity, if we would generate and employ true opposites? This work introduces an approach to approximate type-II opposites using evolving fuzzy rules when we first perform opposition mining. We show with multiple examples that learning true opposites is possible when we mine the opposites from the training data to subsequently approximate $\breve{x}_{II}=f(\mathbf{x},y)$.Comment: Accepted for publication in The 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015), August 2-5, 2015, Istanbul, Turke

Learning Opposites Using Neural Networks

Many research works have successfully extended algorithms such as evolutionary algorithms, reinforcement agents and neural networks using "opposition-based learning" (OBL). Two types of the "opposites" have been defined in the literature, namely \textit{type-I} and \textit{type-II}. The former are linear in nature and applicable to the variable space, hence easy to calculate. On the other hand, type-II opposites capture the "oppositeness" in the output space. In fact, type-I opposites are considered a special case of type-II opposites where inputs and outputs have a linear relationship. However, in many real-world problems, inputs and outputs do in fact exhibit a nonlinear relationship. Therefore, type-II opposites are expected to be better in capturing the sense of "opposition" in terms of the input-output relation. In the absence of any knowledge about the problem at hand, there seems to be no intuitive way to calculate the type-II opposites. In this paper, we introduce an approach to learn type-II opposites from the given inputs and their outputs using the artificial neural networks (ANNs). We first perform \emph{opposition mining} on the sample data, and then use the mined data to learn the relationship between input $x$ and its opposite $\breve{x}$. We have validated our algorithm using various benchmark functions to compare it against an evolving fuzzy inference approach that has been recently introduced. The results show the better performance of a neural approach to learn the opposites. This will create new possibilities for integrating oppositional schemes within existing algorithms promising a potential increase in convergence speed and/or accuracy.Comment: To appear in proceedings of the 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, December 201

Asteroid lightcurves from the Palomar Transient Factory survey: Rotation periods and phase functions from sparse photometry

We fit 54,296 sparsely-sampled asteroid lightcurves in the Palomar Transient Factory to a combined rotation plus phase-function model. Each lightcurve consists of 20+ observations acquired in a single opposition. Using 805 asteroids in our sample that have reference periods in the literature, we find the reliability of our fitted periods is a complicated function of the period, amplitude, apparent magnitude and other attributes. Using the 805-asteroid ground-truth sample, we train an automated classifier to estimate (along with manual inspection) the validity of the remaining 53,000 fitted periods. By this method we find 9,033 of our lightcurves (of 8,300 unique asteroids) have reliable periods. Subsequent consideration of asteroids with multiple lightcurve fits indicate 4% contamination in these reliable periods. For 3,902 lightcurves with sufficient phase-angle coverage and either a reliably-fit period or low amplitude, we examine the distribution of several phase-function parameters, none of which are bimodal though all correlate with the bond albedo and with visible-band colors. Comparing the theoretical maximal spin rate of a fluid body with our amplitude versus spin-rate distribution suggests that, if held together only by self-gravity, most asteroids are in general less dense than 2 g/cm$^3$, while C types have a lower limit of between 1 and 2 g/cm$^3$, in agreement with previous density estimates. For 5-20km diameters, S types rotate faster and have lower amplitudes than C types. If both populations share the same angular momentum, this may indicate the two types' differing ability to deform under rotational stress. Lastly, we compare our absolute magnitudes and apparent-magnitude residuals to those of the Minor Planet Center's nominal $G=0.15$, rotation-neglecting model; our phase-function plus Fourier-series fitting reduces asteroid photometric RMS scatter by a factor of 3.Comment: 35 pages, 29 figures. Accepted 15-Apr-2015 to The Astronomical Journal (AJ). Supplementary material including ASCII data tables will be available through the publishing journal's websit

Observational Constraints on the Catastrophic Disruption Rate of Small Main Belt Asteroids

We have calculated 90% confidence limits on the steady-state rate of catastrophic disruptions of main belt asteroids in terms of the absolute magnitude at which one catastrophic disruption occurs per year (HCL) as a function of the post-disruption increase in brightness (delta m) and subsequent brightness decay rate (tau). The confidence limits were calculated using the brightest unknown main belt asteroid (V = 18.5) detected with the Pan-STARRS1 (Pan-STARRS1) telescope. We measured the Pan-STARRS1's catastrophic disruption detection efficiency over a 453-day interval using the Pan-STARRS moving object processing system (MOPS) and a simple model for the catastrophic disruption event's photometric behavior in a small aperture centered on the catastrophic disruption event. Our simplistic catastrophic disruption model suggests that delta m = 20 mag and 0.01 mag d-1 < tau < 0.1 mag d-1 which would imply that H0 = 28 -- strongly inconsistent with H0,B2005 = 23.26 +/- 0.02 predicted by Bottke et al. (2005) using purely collisional models. We postulate that the solution to the discrepancy is that > 99% of main belt catastrophic disruptions in the size range to which this study was sensitive (100 m) are not impact-generated, but are instead due to fainter rotational breakups, of which the recent discoveries of disrupted asteroids P/2013 P5 and P/2013 R3 are probable examples. We estimate that current and upcoming asteroid surveys may discover up to 10 catastrophic disruptions/year brighter than V = 18.5.Comment: 61 Pages, 10 Figures, 3 Table

Properties of the Distant Kuiper Belt: Results from the Palomar Distant Solar System Survey

We present the results of a wide-field survey using the 1.2-m Samuel Oschin Telescope at Palomar Observatory. This survey was designed to find the most distant members of the Kuiper belt and beyond. We searched ~12,000 deg2 down to a mean limiting magnitude of 21.3 in R. A total number of 52 KBOs and Centaurs have been detected, 25 of which were discovered in this survey. Except for the re-detection of Sedna, no additional Sedna-like bodies with perihelia greater than 45 AU were detected despite sensitivity out to distances of 1000 AU. We discuss the implications for a distant Sedna- like population beyond the Kuiper belt, focusing on the constraints we can place on the embedded stellar cluster environment the early Sun may be have been born in, where the location and distribution of Sedna-like orbits sculpted by multiple stellar encounters is indicative of the birth cluster size. We also report our observed latitude distribution and implications for the size of the plutino population.Comment: 40 pages, 12 figures, 3 tables Accepted by Ap