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

A backward prediction model (BPM) has been developed to generate the missing bridge condition ratings in past years, thereby ensuring adequate condition data as required in long-term performance modelling. The BPM establishes a correlation between the known condition ratings and the non-bridge factors, including climate condition, traffic volume and population growth. The aim of this study is to confirm the ability of BPM in improving the prediction accuracy using the existing bridge deterioration models. The prediction accuracies of typical deterministic and stochastic bridge deterioration models are compared when different sets of BPM-generated historical condition ratings are used as input. Comparisons indicate that the prediction error decreases as more historical condition ratings are made available. Notwithstanding the above findings, several limitations of the current deterministic and stochastic bridge deterioration models are also worth noting and further research is essential to improve the prediction accuracy of bridge deterioration modelling.

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Typical deterministic and stochastic bridge deterioration modelling incorporating backward prediction model

The segmentation of cursive and mixed scripts persists to be a difficult problem in the area of handwriting recognition. This research details advances for segmenting characters in off-line cursive script. Specifically, a heuristic algorithm and a neural network-based technique, which uses a structural feature vector representation, are proposed and combined for identifying incorrect segmentation points. Following the location of appropriate anchorage points, a character extraction technique, using segmentation paths, is employed to complete the segmentation process. Results are presented for neural-based heuristic segmentation, segmentation point validation, character recognition, segmentation path detection and overall segmentation accuracy.

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Cursive Character Segmentation Using Neural Network Techniques

Bridge Management Systems (BMSs) have been developed since the early 1990s as a decision support system (DSS) for effective Maintenance, Repair and Rehabilitation (MR&R) activities in a large bridge network Historical condition ratings obtained from biennial bridge inspections are major resources for predicting future bridge deteriorations via BMSs However, available historical condition ratings are very limited in all bridge agencies This constitutes the major barrier for predicting reliably future structural performances To alleviate this problem, the Backward Prediction Model (BPM) technique for generating the missing historical condition ratings has been developed, and its reliability has been verified using existing condition ratings available from the Maryland Department of Transportation, USA The function of the BPM is to establish the correlations between the known condition ratings and non-bridge factors including climate, traffic volumes and population growth Such correlations can then be used to obtain the bridge condition ratings of the missing years Based on these generated datasets, the currently available bridge deterioration model can be used to predict future bridge conditions The existing 4 National Bridge Inventory and 9 BPM-generated historical condition ratings are used as input data to compare the prediction accuracy using deterministic bridge deterioration models The comparison results show that prediction error decreases as more historical data become available This suggests that the BPM can be used to generate additional historical condition ratings, which is essential for bridge deterioration models to achieve more accurate prediction results However, there are still significant limitations identified in the current bridge deterioration models Hence, further research is necessary to improve the prediction accuracy of bridge deterioration models

/pdf/generating-historical-condition-ratings-for-the-reliable-1dm90mk5iv.pdf

Generating Historical Condition Ratings for the Reliable Prediction of Bridge Deteriorations

The recovery of handwriting’s dynamic stroke is an effective method to help improve the accuracy of any handwriting’s authentication or verification system. The recovered trajectory can be considered as a dynamic feature of any static handwritten images. Capitalising on this temporal information can significantly increase the accuracy of the verification phase. Extraction of dynamic features from static handwritings remains a challenge due to the lack of temporal information as compared to the online methods. Previously, there are two typical approaches to recover the handwriting’s stroke. The first approach is based on the script’s skeleton. The skeletonisation method has highly computational efficiency whereas it often produces noisy artifacts and mismatches on the resulted skeleton. The second approach deals with the handwriting’s contour, crossing areas and overlaps using parametric representations of lines and thickness of strokes. This method can avoid the artifacts, but it requires complicated mathematical models and may lead to computational explosion. Our paper is based on the script’s extracted skeleton and provides an approach to processing static handwriting’s objects, including edges, vertices and loops, as the important aspects of any handwritten image. Our paper is also to provide analysing and classifying loops types and human’s natural writing behavior to improve the global construction of stroke order. Then, a detailed tracing algorithm on global stroke reconstruction is presented. The experimental results reveal the superiority of our method as compared with the existing ones.

/pdf/extraction-of-dynamic-trajectory-on-multi-stroke-static-3bt4867uxb.pdf

Extraction of Dynamic Trajectory on Multi-Stroke Static Handwriting Images Using Loop Analysis and Skeletal Graph Model

Use of multiple scripts for information communication through various media is quite common in a multilingual country. Optical character recognition of such document images or videos assists in indexing them for effective information retrieval. Hence, script identification from multi-lingual documents/images is a necessary step for selecting the appropriate OCR, due the absence of a single OCR system capable of handling multiple scripts. Script identification from printed as well as handwritten documents is a well-researched area, but script identification from video frames has not been explored much. Low resolution, blur, noisy background, to mention a few are the major bottle necks when processing video frames, and makes script identification from video images a challenging task. This paper examines the potential of Bag-of-Visual Words based techniques for word-wise script identification from video frames. Two different approaches namely, Bag-Of-Features (BoF) and Spatial Pyramid Matching (SPM), using patch based SIFT descriptors were considered for the current study. SVM Classifier was used for analysing the three popular south Indian scripts, namely Tamil, Telugu and Kannada in combination with English and Hindi. A comparative study of Bag-of-Visual words with traditional script identification techniques involving gradient based features (e.g. HoG) and texture based features (e.g. LBP) is presented. Experimental results shows that patch-based features along with SPM outperformed the traditional techniques and promising accuracies were achieved on 2534 words from the five scripts. The study reveals that patch-based feature can be used for scripts identification in-order to overcome the inherent problems with video frames.

Michael Blumenstein

Papers

Typical deterministic and stochastic bridge deterioration modelling incorporating backward prediction model

Cursive Character Segmentation Using Neural Network Techniques

Generating Historical Condition Ratings for the Reliable Prediction of Bridge Deteriorations

Extraction of Dynamic Trajectory on Multi-Stroke Static Handwriting Images Using Loop Analysis and Skeletal Graph Model

Bag-of-Visual Words for word-wise video script identification: A study