Comparative analysis of environmental sequences: potential and challenges

Environmental sequencing, also dubbed metagenomics, is increasingly being used to obtain insights into organismal communities in diverse habitats, and has a variety of potential applications foreseeable in biotechnology and medicine. The first public large-scale data provide already a wealth of information hidden in vast amounts of fragmented pieces of DNA from unknown species residing in these environments. Comparative sequence analysis is essential for the interpretation of such data. However, different layers of complexity that are intrinsic to each sample require the establishment of some baselines for comparison: how to normalize for the differences in phylogenetic and functional diversity, how to avoid biases from incomplete data, and how to deal with differences in species dominance or genome sizes? Here we discuss a few of these items and delineate some simple discriminative sequence properties for four distinct habitats.

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A Deep Multiview Active Learning for Large-Scale Image Classification

Mathematical Problems in Engineering ◽

10.1155/2020/6639503 ◽

2020 ◽

Vol 2020 ◽

pp. 1-7

Author(s):

Tuozhong Yao ◽

Wenfeng Wang ◽

Yuhong Gu

Keyword(s):

Active Learning ◽

Large Scale ◽

State Of The Art ◽

Feature Representation ◽

Deep Convolutional Neural Networks ◽

Large Scale Data ◽

Version Space ◽

Potential Applications ◽

Effort Reduction ◽

Scale Data

Multiview active learning (MAL) is a technique which can achieve a large decrease in the size of the version space than traditional active learning and has great potential applications in large-scale data analysis. In this paper, we present a new deep multiview active learning (DMAL) framework which is the first to combine multiview active learning and deep learning for annotation effort reduction. In this framework, our approach advances the existing active learning methods in two aspects. First, we incorporate two different deep convolutional neural networks into active learning which uses multiview complementary information to improve the feature learnings. Second, through the properly designed framework, the feature representation and the classifier can be simultaneously updated with progressively annotated informative samples. The experiments with two challenging image datasets demonstrate that our proposed DMAL algorithm can achieve promising results than several state-of-the-art active learning algorithms.

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Spatial and Geographic Patterns of Building Energy Performance: A Cross-City Comparative Analysis of Large-Scale Data

International Conference on Sustainable Infrastructure 2017 ◽

10.1061/9780784481219.030 ◽

2017 ◽

Cited By ~ 3

Author(s):

Sokratis Papadopoulos ◽

Bartosz Bonczak ◽

Constantine E. Kontokosta

Keyword(s):

Comparative Analysis ◽

Large Scale ◽

Building Energy ◽

Energy Performance ◽

Building Energy Performance ◽

Large Scale Data ◽

Geographic Patterns ◽

Scale Data

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Comparative Analysis of a Systematic Coherent Encryption Scheme for Large-Scale Data Management Using Cryptographic Encryption Technique

Smart Intelligent Computing and Applications - Smart Innovation, Systems and Technologies ◽

10.1007/978-981-13-1927-3_46 ◽

2018 ◽

pp. 427-437

Author(s):

A. Stephen Dass ◽

J. Prabhu

Keyword(s):

Comparative Analysis ◽

Data Management ◽

Large Scale ◽

Encryption Scheme ◽

Large Scale Data ◽

Scale Data

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Multiview Active Learning for Scene Classification with High-Level Semantic-Based Hypothesis Generation

Scientific Programming ◽

10.1155/2020/3878153 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Tuozhong Yao ◽

Wenfeng Wang ◽

Yuhong Gu ◽

Qiuguo Zhu

Keyword(s):

Active Learning ◽

Large Scale ◽

Autonomous Driving ◽

Complex Environments ◽

Scene Classification ◽

Large Scale Data ◽

Potential Applications ◽

Modeling Strategy ◽

High Level ◽

Scale Data

Multiview active learning (MVAL) is a technique which can result in a large decrease in the size of the version space than traditional active learning and has great potential applications in large-scale data analysis. This paper made research on MVAL-based scene classification for helping the computer accurately understand diverse and complex environments macroscopically, which has been widely used in many fields such as image retrieval and autonomous driving. The main contribution of this paper is that different high-level image semantics are used for replacing the traditional low-level features to generate more independent and diverse hypotheses in MVAL. First, our algorithm uses different object detectors to achieve local object responses in the scenes. Furthermore, we design a cascaded online LDA model for mining the theme semantic of an image. The experimental results demonstrate that our proposed theme modeling strategy fits the large-scale data learning, and our MVAL algorithm with both high-level semantic views can achieve significant improvement in the scene classification than traditional active learning-based algorithms.

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