Ocean Waste Detection Model

2021 ◽  
Vol 7 (3) ◽  
Author(s):  
Sujata Khandaskar ◽  
Siddharth Tayde ◽  
Aditya Sawant ◽  
Nikhil Masand ◽  
Barun Singh
Keyword(s):  
Visual Recognition ◽  
Marine Debris ◽  
Second Step ◽  
Specific Information ◽  
Monitoring Methods ◽  
Detection Model ◽  
Domain Specific ◽  
Glass Metal ◽  
Marine Waste ◽  
Accurate Quantification

The number of marine debris is excellent in understanding the diagnosis of debris from all oceans of the world and the identification of the highest levels of waste disposal that is most necessary for the removal of waste. Currently, the standard for floating waste management requires the use of a manta trawl. Techniques that require manta trawls (or similar ground-collection devices) that use the physical removal of marine debris as a first step and then analyze the collected samples as a second step. The need for pre-analysis removal is very costly and requires significant oversight - preventing the safe transfer of marine waste monitoring services to all Earth's marine bodies. Without better monitoring methods and samples, the overall impact of water pollution on the entire environment. This study revealed an unusual flow of activity that used images taken from aquatic debris as roots. Produces quantification of marine plastic or waste incorporated into photographs to perform accurate quantification and body removal. This model is trained in the ImageNet Large Visual Recognition Challenge using the 2012 data and can distinguish between many different classes such as cardboard, glass, metal, paper, and plastic. This program uses the transfer of learning from the existing model and then returns it to separate a new set of images. Workflow involves creating and processing domain-specific information, building an object acquisition model using a deep neural network.

Featural and Configural Processing of Faces and Houses in Matched Dyslexic and Typical Readers

2020 ◽  
Author(s):  
Bahareh Jozranjbar ◽  
Arni Kristjansson ◽  
Heida Maria Sigurdardottir
Keyword(s):  
Object Recognition ◽  
Visual Processing ◽  
Reading Ability ◽  
Visual Recognition ◽  
Reading Deficits ◽  
Domain Specific ◽  
Configural Information ◽  
Lower House ◽  
Single Process ◽  
Processing Mechanisms

While dyslexia is typically described as a phonological deficit, recent evidence suggests that ventral stream regions, important for visual categorization and object recognition, are hypoactive in dyslexic readers who might accordingly show visual recognition deficits. By manipulating featural and configural information of faces and houses, we investigated whether dyslexic readers are disadvantaged at recognizing certain object classes or utilizing particular visual processing mechanisms. Dyslexic readers found it harder to recognize objects (houses), suggesting that visual problems in dyslexia are not completely domain-specific. Mean accuracy for faces was equivalent in the two groups, compatible with domain-specificity in face processing. While face recognition abilities correlated with reading ability, lower house accuracy was nonetheless related to reading difficulties even when accuracy for faces was kept constant, suggesting a specific relationship between visual word recognition and the recognition of non-face objects. Representational similarity analyses (RSA) revealed that featural and configural processes were clearly separable in typical readers, while dyslexic readers appeared to rely on a single process. This occurred for both faces and houses and was not restricted to particular visual categories. We speculate that reading deficits in some dyslexic readers reflect their reliance on a single process for object recognition.

Knowledge Enhanced LSTM for Coreference Resolution on Biomedical Texts

2021 ◽  
Author(s):  
Yufei Li ◽  
Xiaoyong Ma ◽  
Xiangyu Zhou ◽  
Pengzhen Cheng ◽  
Kai He ◽  
...  
Keyword(s):  
Information Integration ◽  
Short Term Memory ◽  
Superior Performance ◽  
Supplementary Information ◽  
Specific Information ◽  
Coreference Resolution ◽  
Fine Grained ◽  
Domain Specific ◽  
Memory Network ◽  
Biomedical Texts

Abstract Motivation Bio-entity Coreference Resolution focuses on identifying the coreferential links in biomedical texts, which is crucial to complete bio-events’ attributes and interconnect events into bio-networks. Previously, as one of the most powerful tools, deep neural network-based general domain systems are applied to the biomedical domain with domain-specific information integration. However, such methods may raise much noise due to its insufficiency of combining context and complex domain-specific information. Results In this paper, we explore how to leverage the external knowledge base in a fine-grained way to better resolve coreference by introducing a knowledge-enhanced Long Short Term Memory network (LSTM), which is more flexible to encode the knowledge information inside the LSTM. Moreover, we further propose a knowledge attention module to extract informative knowledge effectively based on contexts. The experimental results on the BioNLP and CRAFT datasets achieve state-of-the-art performance, with a gain of 7.5 F1 on BioNLP and 10.6 F1 on CRAFT. Additional experiments also demonstrate superior performance on the cross-sentence coreferences. Supplementary information Supplementary data are available at Bioinformatics online.

PROTEIN STRUCTURE ALIGNMENT AND FAST SIMILARITY SEARCH USING LOCAL SHAPE SIGNATURES

2004 ◽  
Vol 02 (01) ◽  
pp. 215-239 ◽  
Author(s):  
TOLGA CAN ◽  
YUAN-FANG WANG
Keyword(s):  
Protein Structure ◽  
Protein Structures ◽  
Structure Alignment ◽  
Protein Structure Alignment ◽  
Specific Information ◽  
Alignment Algorithm ◽  
Screening Process ◽  
Domain Specific ◽  
Local Sequence ◽  
Shape Signatures

We present a new method for conducting protein structure similarity searches, which improves on the efficiency of some existing techniques. Our method is grounded in the theory of differential geometry on 3D space curve matching. We generate shape signatures for proteins that are invariant, localized, robust, compact, and biologically meaningful. The invariancy of the shape signatures allows us to improve similarity searching efficiency by adopting a hierarchical coarse-to-fine strategy. We index the shape signatures using an efficient hashing-based technique. With the help of this technique we screen out unlikely candidates and perform detailed pairwise alignments only for a small number of candidates that survive the screening process. Contrary to other hashing based techniques, our technique employs domain specific information (not just geometric information) in constructing the hash key, and hence, is more tuned to the domain of biology. Furthermore, the invariancy, localization, and compactness of the shape signatures allow us to utilize a well-known local sequence alignment algorithm for aligning two protein structures. One measure of the efficacy of the proposed technique is that we were able to perform structure alignment queries 36 times faster (on the average) than a well-known method while keeping the quality of the query results at an approximately similar level.

Marine Debris: Sources, Impacts and Reduction Programs

2016 ◽  
Author(s):  
Peter Bishop
Keyword(s):  
Human Health ◽  
Marine Debris ◽  
Fishing Gear ◽  
Ecological Economic ◽  
Monitoring Programs ◽  
Management Plans ◽  
Glass Metal

Marine debris is a major problem worldwide with ecological, economic, and human health impacts.Plastics, glass, metal, Styrofoam, rubber, and old fishing gear form the majority of the debris. Thepollution comes largely from land sources, although a portion of it is released by vessels. The litter affectswildlife, habitats, fisheries, and vessels that travel through the polluted areas. Recently, the severity ofthe problem is being recognized, and governments are acting. Monitoring programs have beenestablished to examine the extent of the problem, and management plans have been introduced to cleanup affected areas and to reduce the sources of the debris. Governments face technical, political, andfinancial barriers when tackling this issue. This report reviews the sources and impacts of the marinelitter, and examines the methods adopted by various countries and regions to tackle the problem.  

scholarly journals DISSECT: DISentangle SharablE ConTent for Multimodal Integration and Crosswise-mapping

2020 ◽  
Author(s):  
Geoffrey Schau ◽  
Erik Burlingame ◽  
Young Hwan Chang
Keyword(s):  
Deep Learning ◽  
Complete Information ◽  
Specific Information ◽  
Multimodal Integration ◽  
Specific Data ◽  
Domain Specific ◽  
Cross Domain ◽  
Input Feature ◽  
Novel Approach ◽  
Latent Representations

AbstractDeep learning systems have emerged as powerful mechanisms for learning domain translation models. However, in many cases, complete information in one domain is assumed to be necessary for sufficient cross-domain prediction. In this work, we motivate a formal justification for domain-specific information separation in a simple linear case and illustrate that a self-supervised approach enables domain translation between data domains while filtering out domain-specific data features. We introduce a novel approach to identify domainspecific information from sets of unpaired measurements in complementary data domains by considering a deep learning cross-domain autoencoder architecture designed to learn shared latent representations of data while enabling domain translation. We introduce an orthogonal gate block designed to enforce orthogonality of input feature sets by explicitly removing non-sharable information specific to each domain and illustrate separability of domain-specific information on a toy dataset.

scholarly journals Measuring Models

2009 ◽  
pp. 147-169 ◽  
Author(s):  
Martin Monperrus ◽  
Jean-Marc Jézéquel ◽  
Joël Champeau ◽  
Brigitte Hoeltzener
Keyword(s):  
Quality Assurance ◽  
Software Development ◽  
Point Of View ◽  
Specific Information ◽  
Model Driven Engineering ◽  
Text Documents ◽  
Model Driven ◽  
Domain Specific ◽  
Wide Range

Model-Driven Engineering (MDE) is an approach to software development that uses models as primary artifacts, from which code, documentation and tests are derived. One way of assessing quality assurance in a given domain is to define domain metrics. We show that some of these metrics are supported by models. As text documents, models can be considered from a syntactic point of view i.e., thought of as graphs. We can readily apply graph-based metrics to them, such as the number of nodes, the number of edges or the fan-in/fan-out distributions. However, these metrics cannot leverage the semantic structuring enforced by each specific metamodel to give domain specific information. Contrary to graph-based metrics, more specific metrics do exist for given domains (such as LOC for programs), but they lack genericity. Our contribution is to propose one metric, called s, that is generic over metamodels and allows the easy specification of an open-ended wide range of model metrics.

scholarly journals The Repository Chemotion: Infrastructure for Sustainable Research in Chemistry

2020 ◽  
Author(s):  
Pierre Tremouilhac ◽  
Chia-Lin Lin ◽  
Pei-Chi Huang ◽  
Yu-Chieh Huang ◽  
An Nguyen ◽  
...  
Keyword(s):  
Analytical Data ◽  
Research Data ◽  
Digital Object Identifier ◽  
Specific Information ◽  
Data Infrastructure ◽  
Domain Specific ◽  
Chemistry Research ◽  
Peer Reviewing ◽  
Institute Of Technology ◽  
New Community

<p>We describe the development of a repository for chemistry research data (called Chemotion) that provides solutions for current challenges to store research data in a feasible manner, allowing the conservation of domain specific information in a machine readable format. A main advantage of the repository Chemotion is the comprehensive functionality, which offers options to collect, prepare and reuse data using discipline specific methods and data processing tools. For selected analytical data, automated procedures are implemented to facilitate the curation of the data. Chemotion provides functions to facilitate the publishing process of data and the citation of the deposited data. It supports automated Digital Object Identifier (DOI) generation, the comparison of the submissions with PubChem instances, and workflows for peer reviewing of the submissions including embargo settings. The described developments were used to establish a research data infrastructure that is hosted at the Karlsruhe Institute of Technology (KIT), including the necessary storage and support to build a new community-driven repository as a comprehensive alternative to commercial databases. </p>

Development of a Faster R-CNN-based Marine Debris Detection Model for an Embedded System

2021 ◽  
Vol 27 (12) ◽  
pp. 1038-1043
Author(s):  
Younghun Byeon ◽  
Eunju Kim ◽  
Hyeon Jun Lim ◽  
Han Sol Kim
Keyword(s):  
Embedded System ◽  
Marine Debris ◽  
Detection Model
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