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

In the past decade, artificial neural networks (ANNs) have been widely applied to the engineering problems with a complicated system. ANNs are becoming an important alternative option for solving problems in comparison to traditional engineering solutions, which are usually involved in complicated mathematical theories. In this study, we apply an ANN model to the wave-induced seabed liquefaction problem, which is a key issue in the area of coastal and ocean engineering. Furthermore, we adopted an ANN model with preprocessing (MIN-MAX) on difficult training data. This paper demonstrates the capacity of the proposed ANN model using MIN-MAX pre-processing to provide coastal engineers with another effective tool to analyse the stability of seabed sediment.

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Improvement of an Artificial Neural Network Model using Min-Max Preprocessing for the Prediction of Wave-induced Seabed Liquefaction

Due to the presence of struck-out handwritten words in document images, the performance of different methods degrades for several important applications, such as handwriting recognition, writer, gender, fraudulent document identification, document age estimation, writer age estimation, normal/abnormal behavior of person analysis, and descriptive answer evaluation. This work proposes a new method which combines connected component analysis for text component detection and deep learning for classification of struck-out and non-struck-out words. For text component detection, the proposed method finds the stroke width to detect edges of texts in images, and then performs smoothing operations to remove noise. Furthermore, morphological operations are performed on smoothed images to label connected components as text by fixing bounding boxes. Inspired by the great success of deep learning models, we explore DenseNet for classifying struck-out and non-struck-out handwritten components by considering text components as input. Experimental results on our dataset demonstrate the proposed method outperforms the existing methods in terms of classification rate.

A Connected Component-Based Deep Learning Model for Multi-type Struck-Out Component Classification

Automatic document interpretation and retrieval is an important task to access handwritten digitized document repositories. In documents, the date is an important field and it has various applications such as date-wise document indexing/retrieval. In this paper a framework has been proposed for automatic date field extraction from handwritten documents. In order to design the system, sliding window-wise Local Gradient Histogram (LGH)-based features and a character-level Hidden Markov Model (HMM)-based approach have been applied for segmentation and recognition. Individual date components such as month-word (month written in word form i.e. January, Jan, etc.), numeral, punctuation and contraction categories are segmented and labelled from a text line. Next, a Histogram of Gradient (HoG)-based features and a Support Vector Machine (SVM)- based classifier have been used to improve the results obtained from the HMM-based recognition system. Subsequently, both numeric and semi-numeric regular expressions of date patterns have been considered for undertaking date pattern extraction in labelled components. The experiments are performed on an English document dataset and the encouraging results obtained from the approach indicate the effectiveness of the proposed system.

/pdf/date-field-extraction-from-handwritten-documents-using-hmms-11f1nmeb7i.pdf

Date field extraction from handwritten documents using HMMs

Recognizing human actions from the video streams has become one of the very popular research areas in computer vision and deep learning in the recent years. Action recognition is wildly used in different scenarios in real life, such as surveillance, robotics, healthcare, video indexing and human-computer interaction. The challenges and complexity involved in developing a video-based human action recognition system are manifold. In particular, recognizing actions with similar gestures and describing complex actions is a very challenging problem. To address these issues, we study the problem of classifying human actions using Convolutional Neural Networks (CNN) and develop a hierarchical 3DCNN architecture for similar gesture recognition. The proposed model firstly combines similar gesture pairs into one class, and classify them along with all other class, as a stage-1 classification. In stage-2, similar gesture pairs are classified individually, which reduces the problem to binary classification. We apply and evaluate the developed models to recognize the similar human actions on the HMDB51 dataset. The result shows that the proposed model can achieve high performance in comparison to the state-of-the-art methods.

/pdf/similar-gesture-recognition-using-hierarchical-obh8a9bxuq.pdf

Similar Gesture Recognition using Hierarchical Classification Approach in RGB Videos

In almost the last two decades, commercial Bridge Management System (BMS) packages have been remarkably developed. However, inconsistency between BMS inputs and bridge agencies’ existing data is an obstacle to implement and to operate a BMS software application. A large number of bridge datasets for a BMS database is an essential requirement to analyze a bridge network. Among many requirements, historical structural datasets are vital to compute the prioritization of bridge stock for maintenance and repair activities and are mostly unavailable for bridges of more than 20 years in age. This study focuses on the abovementioned difficulty to overcome the lacking historical data problem faced by bridge agencies to effectively use BMS applications. This paper proposes an artificial neural network (ANN) technique to predict missing components of time-series datasets to estimate historical bridge element condition ratings. Although this study only estimates historical condition ratings, the proposed concept can be used to compute other historical dataset requirements in the BMS database and hence improving the reliability of various BMS analysis modules.

Michael Blumenstein

Papers

Improvement of an Artificial Neural Network Model using Min-Max Preprocessing for the Prediction of Wave-induced Seabed Liquefaction

A Connected Component-Based Deep Learning Model for Multi-type Struck-Out Component Classification

Date field extraction from handwritten documents using HMMs

Similar Gesture Recognition using Hierarchical Classification Approach in RGB Videos

Development of a Backward Prediction Model Based on Limited Historical Datasets