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

Text recognition from natural scene images is an active research area because of its important real world applications, including multimedia search and retrieval, and scene understanding through computer vision. It is often the case that portions of text in images are missed due to occlusion with objects in the background. Therefore, this paper presents a method for restoring occluded text to improve text recognition performance. The proposed method uses the GOOGLE Vision API for obtaining labels for input images. We propose to use PixelLink-E2E methods for detecting text and obtaining recognition results. Using these results, the proposed method generates candidate words based on distance measures employing lexicons created through natural scene text recognition. We extract the semantic similarity between labels and recognition results, which results in a Global Context Score (GCS). Next, we use the Natural Language Processing (NLP) system known as BERT for extracting semantics between candidate words, which results in a Local Context Score (LCS). Global and local context scores are then fused for estimating the ranking for each candidate word. The word that gets the highest ranking is taken as the correction for text which is occluded in the image. Experimental results on a dataset assembled from standard natural scene datasets and our resources show that our approach helps to improve the text recognition performance significantly.

/pdf/a-new-context-based-method-for-restoring-occluded-text-in-ipgj51y4fy.pdf

A New Context-Based Method for Restoring Occluded Text in Natural Scene Images

In this article we show how a network of cooperatively updated semi-formal knowledge bases with adequate knowledge valuation, organization and filtering mechanisms can solve the numerous problems of Wikipedia (lack of structure and evaluations of the information, limitation to overviews, edit wars, etc.) and be a good support to learning, research and more generally information sharing and retrieval.

A Network of Semantically Structured Wikipedia to Bind Information

In this paper, we work on intra-variable handwriting, where the writing samples of an individual can vary significantly. Such within-writer variation throws a challenge for automatic writer inspection, where the state-of-the-art methods do not perform well. To deal with intra-variability, we analyze the idiosyncrasy in individual handwriting. We identify/verify the writer from highly idiosyncratic text-patches. Such patches are detected using a deep recurrent reinforcement learning-based architecture. An idiosyncratic score is assigned to every patch, which is predicted by employing deep regression analysis. For writer identification, we propose a deep neural architecture, which makes the final decision by the idiosyncratic score-induced weighted average of patch-based decisions. For writer verification, we propose two algorithms for patch-fed deep feature aggregation, which assist in authentication using a triplet network. The experiments were performed on two databases, where we obtained encouraging results.

Intra-Variable Handwriting Inspection Reinforced with Idiosyncrasy Analysis.

This paper describes the experiences in coordinating a first year programming course at Griffith University since Semester 1, 2000. In this time, the course structure and content have evolved to implement and evaluate an "objects-as-needed" approach to first year programming with the recent return to an "objects-early" approach. A variety of assessment strategies have also been employed in order to maximize student-learning outcomes. The success of the revised course has continuously been measured by evaluating student feedback and performance. Finally, a focus group-based strategy of evaluation was adopted to determine students' attitudes to the most recently implemented changes.

/pdf/experience-in-teaching-object-oriented-concepts-to-first-xkkjyettqs.pdf

Experience in teaching object-oriented concepts to first year students with diverse backgrounds

Determining the end uses of water in residential properties can facilitate a more proactive approach to water literacy, awareness and demand management. This type of information enables the public, government and water businesses to implement more cost-effective and targeted demand management and customer engagement strategies. This study sought to develop a next-generation water management system that combines advanced digital metering technology with machine learning to provide customers and water utilities with a breakthrough in household-scale water management. This breakthrough system (Autoflow) provides a range of functions including autonomous water end-use disaggregation, demand forecasting and customer-specific efficiency recommendations.

/pdf/next-generation-machine-learning-for-urban-water-management-d6au3s2h26.pdf

Michael Blumenstein

Papers

A New Context-Based Method for Restoring Occluded Text in Natural Scene Images

A Network of Semantically Structured Wikipedia to Bind Information

Intra-Variable Handwriting Inspection Reinforced with Idiosyncrasy Analysis.

Experience in teaching object-oriented concepts to first year students with diverse backgrounds

Next Generation Machine Learning for Urban Water Management