Persisting randomness in randomly growing discrete structures: graphs and search trees

The successive discrete structures generated by a sequential algorithm from random input constitute a Markov chain that may exhibit long term dependence on its first few input values. Using examples from random graph theory and search algorithms we show how such persistence of randomness can be detected and quantified with techniques from discrete potential theory. We also show that this approach can be used to obtain strong limit theorems in cases where previously only distributional convergence was known.Comment: Official journal fil

Task planning and control synthesis for robotic manipulation in space applications

Space-based robotic systems for diagnosis, repair and assembly of systems will require new techniques of planning and manipulation to accomplish these complex tasks. Results of work in assembly task representation, discrete task planning, and control synthesis which provide a design environment for flexible assembly systems in manufacturing applications, and which extend to planning of manipulatiuon operations in unstructured environments are summarized. Assembly planning is carried out using the AND/OR graph representation which encompasses all possible partial orders of operations and may be used to plan assembly sequences. Discrete task planning uses the configuration map which facilitates search over a space of discrete operations parameters in sequential operations in order to achieve required goals in the space of bounded configuration sets

Rapid Automatized Naming and Reading Ability

The Rapid Automatized Naming test (RAN) has been shown to be a strong predictor of reading ability (Katzir et al., 2006), however the nature of this relationship remains unclear. The purpose of this study was to evaluate the underlying components of RAN, and to then determine whether these components partially account for the relationship between RAN and reading ability. The sample consisted of 100 undergraduate students. The underlying components of RAN that were evaluated included, visual search and scanning, auditory and visual sequencing, discrete naming, confrontation naming, executive functioning and phonological processing. The findings suggest that visual search and scanning, auditory sequential processing, discrete naming and executive functioning are all significant underlying components of RAN. Additionally, the findings suggest that visual scanning and auditory sequential processing partially mediate the relationship between RAN and reading fluency

A Discrete Choice Model for Web Site Work Results

Currently a corporate web site is not considered as a necessary business attribute, but as a marketing tool which should yield results. In this study we consider a web site as an instrument for attraction of new partners (customers, suppliers). Web site outputs are a number of visitors interested in contact information (reached the contact info page) and a number of visitors who sent a request via a special form on the web site. We build a sequential discrete choice model for web site outputs. Explanatory variables set includes a number of visited pages, seconds spent on the web site, and dummy variables for specific pages visited (a page with prices information, a portfolio page). Also we investigate an influence of search engines (Google, Yahoo, MSN), which refer a visitor to a corporate web site and keywords used for pay-per-click advertising campaigns. We estimate model parameters on the base of a small UK-based web development company's web site statistical data and discover strong dependencies, which allow improving web site organisation and its search engine positioning.sequential discrete choice model; corporate web site

A statistical multiresolution approach for face recognition using structural hidden Markov models

This paper introduces a novel methodology that combines the multiresolution feature of the discrete wavelet transform (DWT) with the local interactions of the facial structures expressed through the structural hidden Markov model (SHMM). A range of wavelet filters such as Haar, biorthogonal 9/7, and Coiflet, as well as Gabor, have been implemented in order to search for the best performance. SHMMs perform a thorough probabilistic analysis of any sequential pattern by revealing both its inner and outer structures simultaneously. Unlike traditional HMMs, the SHMMs do not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the SHMMs. Therefore, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. SHMMs have not previously been applied to the problem of face identification. The results reported in this application have shown that SHMM outperforms the traditional hidden Markov model with a 73% increase in accuracy

A New Approach to Time Domain Classification of Broadband Noise in Gravitational Wave Data

Broadband noise in gravitational wave (GW) detectors, also known as triggers, can often be a deterrant to the efficiency with which astrophysical search pipelines detect sources. It is important to understand their instrumental or environmental origin so that they could be eliminated or accounted for in the data. Since the number of triggers is large, data mining approaches such as clustering and classification are useful tools for this task. Classification of triggers based on a handful of discrete properties has been done in the past. A rich information content is available in the waveform or 'shape' of the triggers that has had a rather restricted exploration so far. This paper presents a new way to classify triggers deriving information from both trigger waveforms as well as their discrete physical properties using a sequential combination of the Longest Common Sub-Sequence (LCSS) and LCSS coupled with Fast Time Series Evaluation (FTSE) for waveform classification and the multidimensional hierarchical classification (MHC) analysis for the grouping based on physical properties. A generalized k-means algorithm is used with the LCSS (and LCSS+FTSE) for clustering the triggers using a validity measure to determine the correct number of clusters in absence of any prior knowledge. The results have been demonstrated by simulations and by application to a segment of real LIGO data from the sixth science run.Comment: 16 pages, 16 figure