scholarly journals MELODI Presto: a fast and agile tool to explore semantic triples derived from biomedical literature

2020 ◽  
Author(s):  
Benjamin Elsworth ◽  
Tom R Gaunt
Keyword(s):  
Language Processing ◽  
Application Programming Interface ◽  
Biomedical Literature ◽  
Supplementary Information ◽  
Modern Natural ◽  
Application Programming ◽  
The Subject ◽  
Processing Algorithms ◽  
Literature Based Discovery ◽  
Programming Interface

ABSTRACT Summary The field of literature-based discovery is growing in step with the volume of literature being produced. From modern natural language processing algorithms to high quality entity tagging, the methods and their impact are developing rapidly. One annotation object that arises from these approaches, the subject–predicate–object triple, is proving to be very useful in representing knowledge. We have implemented efficient search methods and an application programming interface, to create fast and convenient functions to utilize triples extracted from the biomedical literature by SemMedDB. By refining these data, we have identified a set of triples that focus on the mechanistic aspects of the literature, and provide simple methods to explore both enriched triples from single queries, and overlapping triples across two query lists. Availability and Implementation: https://melodi-presto.mrcieu.ac.uk/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Diplomatic documents data for international relations: the Freedom of Information Archive Database

2020 ◽  
pp. 073889422093032
Author(s):  
Matthew J Connelly ◽  
Raymond Hicks ◽  
Robert Jervis ◽  
Arthur Spirling ◽  
Clara H Suong
Keyword(s):  
International Relations ◽  
Language Processing ◽  
Application Programming Interface ◽  
Us Foreign Policy ◽  
Freedom Of Information ◽  
Domain Specific ◽  
Government Documents ◽  
The Usa ◽  
Application Programming ◽  
Programming Interface

We introduce the Freedom of Information Archive (FOIArchive) Database, a collection of over 3 million documents about state diplomacy. Substantively, our database focusses on the USA and provides opportunities to analyze previously classified (or publicly unavailable) corpora of internal government documents which include the raw—often full—text of those documents. We also provide within-country diplomatic records for the USA, UK, and Brazil. The full span of the data is 1620–2013, but it is mainly from the twentieth century. Our database allows scholars to view text and associated statistics online and to download and view customized datasets via an application programming interface. We provide extensive metadata about the documents, including the countries and persons they mention, and their topics and classification levels. The metadata includes information we extracted with domain-specific, customized natural language processing tools. To demonstrate the potential of this data, we use it to design and validate a new index for “country importance” in the context of US foreign policy priorities.

scholarly journals EpyNN: Educational python for Neural Networks

2021 ◽  
Author(s):  
Florian Malard ◽  
Laura Danner ◽  
Emilie Rouzies ◽  
Jesse G Meyer ◽  
Ewen Lescop ◽  
...  
Keyword(s):  
Neural Networks ◽  
Graphical Representation ◽  
Source Code ◽  
Application Programming Interface ◽  
Supplementary Information ◽  
Practical Implementation ◽  
Data Preparation ◽  
Link Type ◽  
Application Programming ◽  
Programming Interface

AbstractSummaryArtificial Neural Networks (ANNs) have achieved unequaled performance for numerous problems in many areas of Science, Business, Public Policy, and more. While experts are familiar with performance-oriented software and underlying theory, ANNs are difficult to comprehend for non-experts because it requires skills in programming, background in mathematics and knowledge of terminology and concepts. In this work, we release EpyNN, an educational python resource meant for a public willing to understand key concepts and practical implementation of scalable ANN architectures from concise, homogeneous and idiomatic source code. EpyNN contains an educational Application Programming Interface (API), educational workflows from data preparation to ANN training and a documentation website setting side-by-side code, mathematics, graphical representation and text to facilitate learning and provide teaching material. Overall, EpyNN provides basics for python-fluent individuals who wish to learn, teach or develop from scratch.AvailabilityEpyNN documentation is available at https://epynn.net and repository can be retrieved from https://github.com/synthaze/epynn.ContactStéphanie Olivier-Van-Stichelen, [email protected] InformationSupplementary files and listings.

scholarly journals Tibanna: software for scalable execution of portable pipelines on the cloud

2019 ◽  
Vol 35 (21) ◽  
pp. 4424-4426 ◽  
Author(s):  
Soohyun Lee ◽  
Jeremy Johnson ◽  
Carl Vitzthum ◽  
Koray Kırlı ◽  
Burak H Alver ◽  
...  
Keyword(s):  
Open Source Software ◽  
Source Code ◽  
Software Tool ◽  
Application Programming Interface ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Description Language ◽  
Amazon Web Services ◽  
Application Programming ◽  
Programming Interface

Abstract Summary We introduce Tibanna, an open-source software tool for automated execution of bioinformatics pipelines on Amazon Web Services (AWS). Tibanna accepts reproducible and portable pipeline standards including Common Workflow Language (CWL), Workflow Description Language (WDL) and Docker. It adopts a strategy of isolation and optimization of individual executions, combined with a serverless scheduling approach. Pipelines are executed and monitored using local commands or the Python Application Programming Interface (API) and cloud configuration is automatically handled. Tibanna is well suited for projects with a range of computational requirements, including those with large and widely fluctuating loads. Notably, it has been used to process terabytes of data for the 4D Nucleome (4DN) Network. Availability and implementation Source code is available on GitHub at https://github.com/4dn-dcic/tibanna. Supplementary information Supplementary data are available at Bioinformatics online.

Sistem Pengawasan Aktifitas Penggunaan Smartphone Android

2018 ◽  
Vol 9 (1) ◽  
pp. 24-31
Author(s):  
Rudianto Rudianto ◽  
Eko Budi Setiawan
Keyword(s):  
Application Programming Interface ◽  
Third Party ◽  
Image Content ◽  
Image File ◽  
Application Programming ◽  
Index Terms ◽  
Programming Interface

Availability the Application Programming Interface (API) for third-party applications on Android devices provides an opportunity to monitor Android devices with each other. This is used to create an application that can facilitate parents in child supervision through Android devices owned. In this study, some features added to the classification of image content on Android devices related to negative content. In this case, researchers using Clarifai API. The result of this research is to produce a system which has feature, give a report of image file contained in target smartphone and can do deletion on the image file, receive browser history report and can directly visit in the application, receive a report of child location and can be directly contacted via this application. This application works well on the Android Lollipop (API Level 22). Index Terms— Application Programming Interface(API), Monitoring, Negative Content, Children, Parent.

scholarly journals Advanced soft robot modeling in ChainQueen

Robotica ◽  
2021 ◽  
pp. 1-31
Author(s):  
Andrew Spielberg ◽  
Tao Du ◽  
Yuanming Hu ◽  
Daniela Rus ◽  
Wojciech Matusik
Keyword(s):  
Application Programming Interface ◽  
Material Point ◽  
Soft Robotics ◽  
Soft Robot ◽  
Inference Control ◽  
Simulation And Optimization ◽  
Efficient Simulation ◽  
Application Programming ◽  
Robot Modeling ◽  
Programming Interface

Abstract We present extensions to ChainQueen, an open source, fully differentiable material point method simulator for soft robotics. Previous work established ChainQueen as a powerful tool for inference, control, and co-design for soft robotics. We detail enhancements to ChainQueen, allowing for more efficient simulation and optimization and expressive co-optimization over material properties and geometric parameters. We package our simulator extensions in an easy-to-use, modular application programming interface (API) with predefined observation models, controllers, actuators, optimizers, and geometric processing tools, making it simple to prototype complex experiments in 50 lines or fewer. We demonstrate the power of our simulator extensions in over nine simulated experiments.

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