Software

About: Software is a research topic. Over the lifetime, 130577 publications have been published within this topic receiving 2028987 citations. The topic is also known as: computer software & computational tool.

The Chroma software system for lattice QCD

Abstract: We describe aspects of the Chroma software for lattice QCD calculations. Chroma is an open source C++ based software system developed using the software infrastructure of the US SciDAC initiative. Chroma interfaces with output from the BAGEL assembly generator for optimised lattice fermion kernels on some architectures. It can be run on workstations, clusters and the QCDOC supercomputer.

Software engineering for machine learning: a case study

Abstract: Recent advances in machine learning have stimulated widespread interest within the Information Technology sector on integrating AI capabilities into software and services. This goal has forced organizations to evolve their development processes. We report on a study that we conducted on observing software teams at Microsoft as they develop AI-based applications. We consider a nine-stage workflow process informed by prior experiences developing AI applications (e.g., search and NLP) and data science tools (e.g. application diagnostics and bug reporting). We found that various Microsoft teams have united this workflow into preexisting, well-evolved, Agile-like software engineering processes, providing insights about several essential engineering challenges that organizations may face in creating large-scale AI solutions for the marketplace. We collected some best practices from Microsoft teams to address these challenges. In addition, we have identified three aspects of the AI domain that make it fundamentally different from prior software application domains: 1) discovering, managing, and versioning the data needed for machine learning applications is much more complex and difficult than other types of software engineering, 2) model customization and model reuse require very different skills than are typically found in software teams, and 3) AI components are more difficult to handle as distinct modules than traditional software components --- models may be "entangled" in complex ways and experience non-monotonic error behavior. We believe that the lessons learned by Microsoft teams will be valuable to other organizations.

Populating a Release History Database from version control and bug tracking systems

Abstract: Version control and bug tracking systems contain large amounts of historical information that can give deep insight into the evolution of a software project. Unfortunately, these systems provide only insufficient support for a detailed analysis of software evolution aspects. We address this problem and introduce an approach for populating a release history database that combines version data with bug tracking data and adds missing data not covered by version control systems such as merge points. Then simple queries can be applied to the structured data to obtain meaningful views showing the evolution of a software project. Such views enable more accurate reasoning of evolutionary aspects and facilitate the anticipation of software evolution. We demonstrate our approach on the large open source project Mozilla that offers great opportunities to compare results and validate our approach.

Encapsulated, streaming media automation and distribution system

Abstract: Disclosed are systems and methods for creating and distributing programming content carried by a digital streaming media to be a plurality of remote nodes located over a large geographic area to create customized broadcast quality programming at the remote nodes. At the remote nodes, a multi-window screen display simultaneously shows different programming including national programming and local programming content. The remote nodes utilize a remote channel origination device to assemble the customized programming at the remote location that can be controlled from a central location. An encapsulated IP and IP encryption system is used to transport the digital streaming media to the appropriate remote nodes. Also disclosed is a graphical user interface (“GUI”) providing a software control interface for creating and editing shows or programs that can be aired or played on a remote display device having a multi-window display. The intuitive GUI Software provides the user the ability to easily manage and assemble a series of images, animations and transitions as a single broadcast quality program to be displayed on a remote display device. Another application software system is capable of automating the production of audio narration reports. The disclosed audio concatenation engine automates the creation of audio narration using prerecorded audio segments to minimize the requirement for live, on-air personnel to record audio narration segments.

Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python

Abstract: Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze large and often diverse (highly multi-dimensional) data. However, this heterogeneous collection of specialized applications creates several issues that hinder replicable, efficient and optimal use of neuroimaging analysis approaches: 1) No uniform access to neuroimaging analysis software and usage information; 2) No framework for comparative algorithm development and dissemination; 3) Personnel turnover in laboratories often limits methodological continuity and training new personnel takes time; 4) Neuroimaging software packages do not address computational efficiency; and 5) Methods sections in journal articles are inadequate for reproducing results. To address these issues, we present Nipype (Neuroimaging in Python: Pipelines and Interfaces; http://nipy.org/nipype), an open-source, community-developed, software package and scriptable library. Nipype solves the issues by providing Interfaces to existing neuroimaging software with uniform usage semantics and by facilitating interaction between these packages using Workflows. Nipype provides an environment that encourages interactive exploration of algorithms, eases the design of Workflows within and between packages, allows rapid comparative development of algorithms and reduces the learning curve necessary to use different packages. Nipype supports both local and remote execution on multi-core machines and clusters, without additional scripting. Nipype is BSD licensed, allowing anyone unrestricted usage. An open, community-driven development philosophy allows the software to quickly adapt and address the varied needs of the evolving neuroimaging community, especially in the context of increasing demand for reproducible research.