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.

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.

A Layered Mapping of Ada 202X to OpenMP

2021 ◽  
Vol 40 (2) ◽  
pp. 55-58
Author(s):  
S. Tucker Taft
Keyword(s):  
Operating System ◽  
Application Programming Interface ◽  
Light Weight ◽  
Main Interest ◽  
Thread Scheduling ◽  
Environment Variables ◽  
Application Programming ◽  
Parallel Loops ◽  
Programming Interface

The OpenMP specification defines a set of compiler directives, library routines, and environment variables that together represent the OpenMP Application Programming Interface, and is currently defined for C, C++, and Fortran. The forthcoming version of Ada, currently dubbed Ada 202X, includes lightweight parallelism features, in particular parallel blocks and parallel loops. All versions of Ada, since its inception in 1983, have included "tasking," which corresponds to what are traditionally considered "heavyweight" parallelism features, or simply "concurrency" features. Ada "tasks" typically map to what are called "kernel threads," in that the operating system manages them and schedules them. However, one of the goals of lightweight parallelism is to reduce overhead by doing more of the management outside the kernel of the operating system, using a light-weight-thread (LWT) scheduler. The OpenMP library routines support both levels of threading, but for Ada 202X, the main interest is in making use of OpenMP for its lightweight thread scheduling capabilities.

Nonprobability Sampling and Twitter

2017 ◽  
Vol 36 (2) ◽  
pp. 195-211 ◽  
Author(s):  
Patrick Rafail
Keyword(s):  
Social Sciences ◽  
Application Programming Interface ◽  
Sampling Strategies ◽  
Twitter Data ◽  
Large Databases ◽  
The Social ◽  
Application Programming ◽  
Data Source ◽  
Twitter Activity ◽  
Programming Interface

Twitter data are widely used in the social sciences. The Twitter Application Programming Interface (API) allows researchers to build large databases of user activity efficiently. Despite the potential of Twitter as a data source, less attention has been paid to issues of sampling, and in particular, the implications of different sampling strategies on overall data quality. This research proposes a set of conceptual distinctions between four types of populations that emerge when analyzing Twitter data and suggests sampling strategies that facilitate more comprehensive data collection from the Twitter API. Using three applications drawn from large databases of Twitter activity, this research also compares the results from the proposed sampling strategies, which provide defensible representations of the population of activity, to those collected with more frequently used hashtag samples. The results suggest that hashtag samples misrepresent important aspects of Twitter activity and may lead researchers to erroneous conclusions.

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