An intelligent assistant for exploratory data analysis

In this paper we present an account of the main features of SNOUT, an intelligent assistant for exploratory data analysis (EDA) of social science survey data that incorporates a range of data mining techniques. EDA has much in common with existing data mining techniques: its main objective is to help an investigator reach an understanding of the important relationships ina data set rather than simply develop predictive models for selectd variables. Brief descriptions of a number of novel techniques developed for use in SNOUT are presented. These include heuristic variable level inference and classification, automatic category formation, the use of similarity trees to identify groups of related variables, interactive decision tree construction and model selection using a genetic algorithm

Sub-femtosecond absolute timing precision with a 10 GHz hybrid photonic-microwave oscillator

We present an optical-electronic approach to generating microwave signals with high spectral purity. By circumventing shot noise and operating near fundamental thermal limits, we demonstrate 10 GHz signals with an absolute timing jitter for a single hybrid oscillator of 420 attoseconds (1Hz - 5 GHz)

A Simple Method for Rise-Time Discrimination of Slow Pulses from Charge-Sensitive Preamplifiers

Performance of a simple method of particle identification via pulse rise time discrimination is demonstrated for slow pulses from charge-sensitive preamplifiers with rise times ranging from 10 ns to 500 ns. The method is based on a comparison of the amplitudes of two pulses, derived from each raw preamplifier pulse with two amplifiers with largely differing shaping times, using a fast peak-sensing ADC. For the injected charges corresponding to energy deposits in silicon detectors of a few tens of MeV, a rise time resolution of the order of 1 ns can be achieved. The identification method is applicable in particle experiments involving large-area silicon detectors, but is easily adaptable to other detectors with a response corresponding to significantly different pulse rise times for different particle species.Comment: 10 pages, 7 figure

An artificial immune system for fuzzy-rule induction in data mining

This work proposes a classification-rule discovery algorithm integrating artificial immune systems and fuzzy systems. The algorithm consists of two parts: a sequential covering procedure and a rule evolution procedure. Each antibody (candidate solution) corresponds to a classification rule. The classification of new examples (antigens) considers not only the fitness of a fuzzy rule based on the entire training set, but also the affinity between the rule and the new example. This affinity must be greater than a threshold in order for the fuzzy rule to be activated, and it is proposed an adaptive procedure for computing this threshold for each rule. This paper reports results for the proposed algorithm in several data sets. Results are analyzed with respect to both predictive accuracy and rule set simplicity, and are compared with C4.5rules, a very popular data mining algorithm

New evidence for a massive black hole at the centre of the quiescent galaxy M32

Massive black holes are thought to reside at the centres of many galaxies, where they power quasars and active galactic nuclei. But most galaxies are quiescent, indicating that any central massive black hole present will be starved of fuel and therefore detectable only through its gravitational influence on the motions of the surrounding stars. M32 is a nearby, quiescent elliptical galaxy in which the presence of a black hole has been suspected; however, the limited resolution of the observational data and the restricted classes of models used to interpret this data have made it difficult to rule out alternative explanations, such as models with an anisotropic stellar velocity distribution and no dark mass or models with a central concentration of dark objects (for example, stellar remnants or brown dwarfs). Here we present high-resolution optical HST spectra of M32, which show that the stellar velocities near the centre of this galaxy exceed those inferred from previous ground-based observations. We use a range of general dynamical models to determine a central dark mass concentration of (3.4 +/- 1.6) x 10^6 solar masses, contained within a region only 0.3 pc across. This leaves a massive black hole as the most plausible explanation of the data, thereby strengthening the view that such black holes exist even in quiescent galaxies.Comment: 8 pages, LaTeX, 3 figures; mpeg animation of the stellar motions in M32 available at http://oposite.stsci.edu/pubinfo/Anim.htm

Performance of Alcohol and Safer Sex Change Rulers Compared With Readiness to Change Questionnaires

As part of a larger intervention study, the authors hypothesized that change rulers created for alcohol and safer sex would be equivalent to longer questionnaires. Ninety-six male college students completed rulers and questionnaires for assessing behavior change readiness. Participants\u27 scores on the rulers significantly correlated with their scores on the questionnaires (r = .77 for alcohol; r = .77 for safer sex). In both domains, the rulers outperformed the questionnaires in predicting behavioral intentions, suggesting that the rulers had at least comparable concurrent criterion validity. This finding is the first of its kind in the safe sex literature and suggests that quick assessments of readiness to change are possible. Because the rulers are a continuous measure, the results are consistent with the idea that the change process is continuous rather than a series of discrete stages