Structural alphabets derived from attractors in conformational space

Background: The hierarchical and partially redundant nature of protein structures justifies the definition of frequently occurring conformations of short fragments as 'states'. Collections of selected representatives for these states define Structural Alphabets, describing the most typical local conformations within protein structures. These alphabets form a bridge between the string-oriented methods of sequence analysis and the coordinate-oriented methods of protein structure analysis.Results: A Structural Alphabet has been derived by clustering all four-residue fragments of a high-resolution subset of the protein data bank and extracting the high-density states as representative conformational states. Each fragment is uniquely defined by a set of three independent angles corresponding to its degrees of freedom, capturing in simple and intuitive terms the properties of the conformational space. The fragments of the Structural Alphabet are equivalent to the conformational attractors and therefore yield a most informative encoding of proteins. Proteins can be reconstructed within the experimental uncertainty in structure determination and ensembles of structures can be encoded with accuracy and robustness.Conclusions: The density-based Structural Alphabet provides a novel tool to describe local conformations and it is specifically suitable for application in studies of protein dynamics. © 2010 Pandini et al; licensee BioMed Central Ltd

De-Trending Time Series for Astronomical Variability Surveys

We present a de-trending algorithm for the removal of trends in time series. Trends in time series could be caused by various systematic and random noise sources such as cloud passages, changes of airmass, telescope vibration or CCD noise. Those trends undermine the intrinsic signals of stars and should be removed. We determine the trends from subsets of stars that are highly correlated among themselves. These subsets are selected based on a hierarchical tree clustering algorithm. A bottom-up merging algorithm based on the departure from normal distribution in the correlation is developed to identify subsets, which we call clusters. After identification of clusters, we determine a trend per cluster by weighted sum of normalized light-curves. We then use quadratic programming to de-trend all individual light-curves based on these determined trends. Experimental results with synthetic light-curves containing artificial trends and events are presented. Results from other de-trending methods are also compared. The developed algorithm can be applied to time series for trend removal in both narrow and wide field astronomy.Comment: Revised version according to the referee's second revie

Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure

Cluster analysis on radio product integration testing faults

Abstract. Nowadays, when the different software systems keep getting larger and more complex, integration testing is necessary to ensure that the different components of the system work together correctly. With the large and complex systems the analysis of the test faults can be difficult, as there are so many components that can cause the failure. Also with the increased usage of automated tests, the faults can often be caused by test environment or test automation issues. Testing data and logs collected during the test executions are usually the main source of information that are used for test fault analysis. With the usage of text mining, natural language processing and machine learning methods, the fault analysis process is possible to be automated using the data and logs collected from the tests, as multiple studies have shown in the recent years. In this thesis, an exploratory data study is done on data collected from radio product integration tests done at Nokia. Cluster analysis is used to find the different fault types that can be found from each of the collected file types. Different feature extraction methods are used and evaluated in terms of how well they separate the data for fault analysis. The study done on this thesis paves the way for automated fault analysis in the future. The introduced methods can be applied for classifying the faults and the results and findings can be used to determine what are the next steps that can be taken to enable future implementations for automated fault analysis applications.Radiotuotteiden integraatiotestauksen vikojen klusterianalyysi. Tiivistelmä. Nykypäivänä, kun erilaiset ohjelmistojärjestelmät jatkavat kasvamista ja muuttuvat monimutkaisimmaksi, integraatiotestaus on välttämätöntä, jotta voidaan varmistua siitä, että järjestelmän eri komponentit toimivat yhdessä oikein. Suurien ja monimutkaisten järjestelmien testivikojen analysointi voi olla vaikeaa, koska järjestelmissä on mukana niin monta komponenttia, jotka voivat aiheuttaa testien epäonnistumisen. Testien automatisoinnin lisääntymisen myötä testit voivat usein epäonnistua myös johtuen testiympäristön tai testiautomaation ongelmista. Testien aikana kerätty testidata ja testilogit ovat yleensä tärkein tiedonlähde testivikojen analyysissä. Hyödyntämällä tekstinlouhinnan, luonnollisen kielen käsittelyn sekä koneoppimisen menetelmiä, testivikojen analyysiprosessi on mahdollista automatisoida käyttämällä testien aikana kerättyä testidataa ja testilogeja, kuten monet tutkimukset ovat viime vuosina osoittaneet. Tässä tutkielmassa tehdään eksploratiivinen tutkimus Nokian radiotuotteiden integraatiotesteistä kerätyllä datalla. Erilaiset vikatyypit, jotka voidaan löytää kustakin kerätystä tiedostotyypistä, löydetään käyttämällä klusterianalyysiä. Ominaisuusvektorien laskentaan käytetään eri menetelmiä ja näiden menetelmien kykyä erotella dataa vika-analyysin näkökulmasta arvioidaan. Tutkielmassa tehty tutkimus avaa tietä vika-analyysien automatisoinnille tulevaisuudessa. Esitettyjä menetelmiä voidaan käyttää vikojen luokittelussa ja tuloksien perusteella voidaan määritellä, mitkä ovat seuraavia askelia, jotta vika-analyysiprosessia voidaan automatisoida tulevaisuudessa

FSL-BM: Fuzzy Supervised Learning with Binary Meta-Feature for Classification

This paper introduces a novel real-time Fuzzy Supervised Learning with Binary Meta-Feature (FSL-BM) for big data classification task. The study of real-time algorithms addresses several major concerns, which are namely: accuracy, memory consumption, and ability to stretch assumptions and time complexity. Attaining a fast computational model providing fuzzy logic and supervised learning is one of the main challenges in the machine learning. In this research paper, we present FSL-BM algorithm as an efficient solution of supervised learning with fuzzy logic processing using binary meta-feature representation using Hamming Distance and Hash function to relax assumptions. While many studies focused on reducing time complexity and increasing accuracy during the last decade, the novel contribution of this proposed solution comes through integration of Hamming Distance, Hash function, binary meta-features, binary classification to provide real time supervised method. Hash Tables (HT) component gives a fast access to existing indices; and therefore, the generation of new indices in a constant time complexity, which supersedes existing fuzzy supervised algorithms with better or comparable results. To summarize, the main contribution of this technique for real-time Fuzzy Supervised Learning is to represent hypothesis through binary input as meta-feature space and creating the Fuzzy Supervised Hash table to train and validate model.Comment: FICC201