The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Data Mining Practical Machine Learning Tools and Techniques

The PASCAL Visual Object Classes Challenge

IEEE Transactions on Pattern Analysis and Machine Intelligence

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Ontology construction in OWL is an important and yet time-consuming task even for knowledge engineers and thus a (semi-) automatic approach will greatly assist in constructing ontologies. In this paper, we propose a novel approach to learning concept definitions in $\ensuremath{\ensuremath{\cal E}\ensuremath{\cal L}^{++}} $ from a collection of assertions. Our approach is based on both refinement operator in inductive logic programming and reinforcement learning algorithm. The use of reinforcement learning significantly reduces the search space of candidate concepts. Besides, we present an experimental evaluation of constructing a family ontology. The results show that our approach is competitive with an existing learning system for $\ensuremath{\cal E}\ensuremath{\cal L}$.

Concept learning for EL ++ by refinement and reinforcement

Probabilistic modelling is one of the most prominent techniques in bridge deterioration forecast. It can be classified into two types, namely, state- and time-based models. Reliability of both modelling techniques in forecasting long-term performance rely heavily on sufficient amount of bridge condition rating data being available together with well-distributed deterioration pattern over the age of bridge. However, it can be problematic when the available condition rating records are insufficient. In order to overcome this problem, an integrated deterioration method incorporating both the state- and time-based models has recently been developed. Despite such development and advancement, certain issues still remain with some cases of given condition data that cannot be used to produce reliable long-term performance curve. Aiming to achieve enhanced prediction performance, an Elman neural networks (ENN) technique is incorporated in the integrated method to replace the third-order polynomial regression function, the latter being the core component for long-term prediction in the state-based model. In the present study, the ENN are able to generate more reliable deterioration patterns than a typical deterministic method. The results demonstrate that the integrated method incorporating ENN are more effective in handling various situations of condition data quantities and distributions for generating long-term performance curves.

Implementation of Elman Neural Networks for Enhancing Reliability of Integrated Bridge Deterioration Model

In this paper, design of a neural network for a domain-specific problem is described. The problem of concern is forecasting flood events where data is contaminated heavily by noise, training examples have different importance levels and noisy data coincides with the most important ones. To this end, two ideas are explored namely, changing the loss function and integrating a coefficient that reflects on the relative importance of training examples. To this end, backpropagation is re-derived considering implication of having a more general objective function. Independently, inclusion of scores associated with each training examples and its implication of overall loss function and the way weights are optimized is explored. The derived model is implemented in MATLAB and flood data from Talebudgera, Australia is considered for investigations. Compared to the base case being backpropagation, the results suggest that inclusion of scored for training examples corresponds to visible improvement when predicting peaks.

Towards robust flood forecasts using neural networks

In the last decade, adaptive biometrics has become an emerging field of research. Considering the fact that limited work has been undertaken on adaptive biometrics using machine learning techniques, in this chapter we list and discuss a few out of many potential learning techniques that can be applied to build an adaptive biometric system. In order to illustrate the efficacy of one of the incremental learning techniques from the literature, we built an adaptive biometric system. For experimentation, we have used multi-modal ocular (sclera and iris) data. The preliminary results have been reported in the results section, which are very promising.

An Online Learning-Based Adaptive Biometric System

Use of handwriting words for person identification in contrast to biometric features is gaining importance in the field of forensic applications. As a result, forging handwriting is a part of crime applications and hence is challenging for the researchers. This paper presents a new work for detecting forged handwriting words because width and amplitude of spectral distributions have the ability to exhibit unique properties for forged handwriting words compared to blurred, noisy and normal handwriting words. The proposed method studies spectral density and variation of input handwriting images through clustering of high and low frequency coefficients. The extracted features, which are invariant to rotation and scaling, are passed to a neural network classifier for the classification for forged handwriting words from other types of handwriting words (like blurred, noisy and normal handwriting words). Experimental results on our own dataset, which consists of four handwriting word classes, and two benchmark datasets, namely, caption and scene text classification and forged IMEI number dataset, show that the proposed method outperforms the existing methods in terms of classification rate.

/pdf/a-new-forged-handwriting-detection-method-based-on-fourier-265qopl8lw.pdf

Michael Blumenstein

Papers

Concept learning for EL ++ by refinement and reinforcement

Implementation of Elman Neural Networks for Enhancing Reliability of Integrated Bridge Deterioration Model

Towards robust flood forecasts using neural networks

An Online Learning-Based Adaptive Biometric System

A New Forged Handwriting Detection Method Based on Fourier Spectral Density and Variation