SpinSPJ: a novel NMR scripting system to implement artificial intelligence and advanced applications

Background Software for nuclear magnetic resonance (NMR) spectrometers offer general functionality of instrument control and data processing; these applications are often developed with non-scripting languages. NMR users need to flexibly integrate rapidly developing NMR applications with emerging technologies. Scripting systems offer open environments for NMR users to write custom programs. However, existing scripting systems have limited capabilities for both extending the functionality of NMR software’s non-script main program and using advanced native script libraries to support specialized application domains (e.g., biomacromolecules and metabolomics). Therefore, it is essential to design a novel scripting system to address both of these needs. Result Here, a novel NMR scripting system named SpinSPJ is proposed. It works as a plug-in in the Java based NMR spectrometer software SpinStudioJ. In the scripting system, both Java based NMR methods and original CPython based libraries are supported. A module has been developed as a bridge to integrate the runtime environments of Java and CPython. The module works as an extension in the CPython environment and interacts with Java via the Java Native Interface. Leveraging this bridge, Java based instrument control and data processing methods of SpinStudioJ can be called with the CPython style. Compared with traditional scripting systems, SpinSPJ better supports both extending the non-script main program and implementing advanced NMR applications with a rich variety of script libraries. NMR researchers can easily call functions of instrument control and data processing as well as developing complex functionality (such as multivariate statistical analysis, deep learning, etc.) with CPython native libraries. Conclusion SpinSPJ offers a user-friendly environment to implement custom functionality leveraging its powerful basic NMR and rich CPython libraries. NMR applications with emerging technologies can be easily integrated. The scripting system is free of charge and can be downloaded by visiting http://www.spinstudioj.net/spinspj.

Executing native Java code in R: an approach based on a local server

The R language is widely used for data analysis. However, it does not allow for complex object-oriented implementation and it tends to be slower than other languages such as Java, C and C++. Consequently, it can be more computationally efficient to run native Java code in R. To do this, there exist at least two approaches. One is based on the Java Native Interface (JNI) and it has been successfully implemented in the rJava package. An alternative approach consists of running a local server in Java and linking it to an R environment through a socket connection. This alternative approach has been implemented in an R package called J4R. This article shows how this approach makes it possible to simplify the calls to Java methods and to integrate the R vectorization. The downside is a loss of performance. However, if the vectorization is used in conjunction with multithreading, this loss of performance can be compensated for.

The design of JVM and native libraries in ScalaLab for efficient scientific computation

ScalaLab is a MATLAB-like environment for the Java Virtual Machine (JVM). ScalaLab is based on the Scala programming language. It utilizes an extensive set of Java and Scala scientific libraries and also has access to many native C/C[Formula: see text] scientific libraries by using mainly the Java Native Interface (JNI). The performance of the JVM platform is continuously improved at a fast pace. Today JVM can effectively support demanding high-performance computing and scales well on multicore platforms. However, sometimes optimized native C/[Formula: see text] code can yield even better performance, by exploiting low-level programming issues, such as optimization of caches and architecture-dependent instruction sets. The present work reports some of the experiences that we gained with experiments with both Just in Time (JIT) JVM code and native code. We compare some aspects of Scala and C[Formula: see text] that concern the requirements of scientific computing and highlight some strong features of the Scala language that facilitate the implementation of scientific scripting. This paper describes how ScalaLab tries to combine the best features of the JVM with those of the C/C[Formula: see text] technology, in order to implement an effective scientific computing environment.

Evaluating the Java Native Interface (JNI)

This article describes how the java native interface (JNI) is a powerful feature of the java platform that started to draw attention in the latter years as an efficient programming framework for building and delivering innovative technological applications based on disruptive technologies such as mobile, Internet of Things and embedded systems. Developers use it to incorporate native code written in programming languages such as C, C++, python etc., into java. JNI is particularly useful when java applications need to access existing native libraries or code blocks written in other languages to increase performance, avoid recoding and expand interoperability between programming languages for processes that reside in the same process. This article aims to explore JNI features and to discover fundamental operations of the Java programming language, such as arrays, objects, classes, threads and exception handling, and to illustrate these by using various algorithms and code samples.

Evaluating the Java Native Interface (JNI)

This article aims to explore JNI features and to discover fundamental operations of the Java programming language, such as arrays, objects, classes, threads and exception handling, and to illustrate these by using various algorithms and code samples. The authors also investigate the JNI Invocation API that allows native applications to interact with the Java Virtual Machine (JVM). They focus on attaching native threads to a running JVM and on leveraging existing native code using one-to-one mapping and shared stubs.

SPICE TOOLS SUPPORTING PLANETARY REMOTE SENSING

NASA's "SPICE"<sup>*</sup> ancillary information system has gradually become the de facto international standard for providing scientists the fundamental observation geometry needed to perform photogrammetry, map making and other kinds of planetary science data analysis. SPICE provides position and orientation ephemerides of both the robotic spacecraft and the target body; target body size and shape data; instrument mounting alignment and field-of-view geometry; reference frame specifications; and underlying time system conversions. <br><br> SPICE comprises not only data, but also a large suite of software, known as the SPICE Toolkit, used to access those data and subsequently compute derived quantities–items such as instrument viewing latitude/longitude, lighting angles, altitude, etc. <br><br> In existence since the days of the Magellan mission to Venus, the SPICE system has continuously grown to better meet the needs of scientists and engineers. For example, originally the SPICE Toolkit was offered only in Fortran 77, but is now available in C, IDL, MATLAB, and Java Native Interface. SPICE calculations were originally available only using APIs (subroutines), but can now be executed using a client-server interface to a geometry engine. Originally SPICE "products" were only available in numeric form, but now SPICE data visualization is also available. <br><br> The SPICE components are free of cost, license and export restrictions. Substantial tutorials and programming lessons help new users learn to employ SPICE calculations in their own programs. The SPICE system is implemented and maintained by the Navigation and Ancillary Information Facility (NAIF)–a component of NASA's Planetary Data System (PDS). <br><br> <sup>*</sup> Spacecraft, Planet, Instrument, Camera-matrix, Events

SPICE TOOLS SUPPORTING PLANETARY REMOTE SENSING

NASA's "SPICE"^* ancillary information system has gradually become the de facto international standard for providing scientists the fundamental observation geometry needed to perform photogrammetry, map making and other kinds of planetary science data analysis. SPICE provides position and orientation ephemerides of both the robotic spacecraft and the target body; target body size and shape data; instrument mounting alignment and field-of-view geometry; reference frame specifications; and underlying time system conversions. <br><br> SPICE comprises not only data, but also a large suite of software, known as the SPICE Toolkit, used to access those data and subsequently compute derived quantities–items such as instrument viewing latitude/longitude, lighting angles, altitude, etc. <br><br> In existence since the days of the Magellan mission to Venus, the SPICE system has continuously grown to better meet the needs of scientists and engineers. For example, originally the SPICE Toolkit was offered only in Fortran 77, but is now available in C, IDL, MATLAB, and Java Native Interface. SPICE calculations were originally available only using APIs (subroutines), but can now be executed using a client-server interface to a geometry engine. Originally SPICE "products" were only available in numeric form, but now SPICE data visualization is also available. <br><br> The SPICE components are free of cost, license and export restrictions. Substantial tutorials and programming lessons help new users learn to employ SPICE calculations in their own programs. The SPICE system is implemented and maintained by the Navigation and Ancillary Information Facility (NAIF)–a component of NASA's Planetary Data System (PDS). <br><br> ^* Spacecraft, Planet, Instrument, Camera-matrix, Events