Modernizing PHCpack through phcpy

PHCpack is a large software package for solving systems of polynomial equations. The executable phc is menu driven and file oriented. This paper describes the development of phcpy, a Python interface to PHCpack. Instead of navigating through menus, users of phcpy solve systems in the Python shell or via scripts. Persistent objects replace intermediate files.Comment: Part of the Proceedings of the 6th European Conference on Python in Science (EuroSciPy 2013), Pierre de Buyl and Nelle Varoquaux editors, (2014

A study of the use of abstract types for the representation of engineering units in integration and test applications

Physical quantities using various units of measurement can be well represented in Ada by the use of abstract types. Computation involving these quantities (electric potential, mass, volume) can also automatically invoke the computation and checking of some of the implicitly associable attributes of measurements. Quantities can be held internally in SI units, transparently to the user, with automatic conversion. Through dimensional analysis, the type of the derived quantity resulting from a computation is known, thereby allowing dynamic checks of the equations used. The impact of the possible implementation of these techniques in integration and test applications is discussed. The overhead of computing and transporting measurement attributes is weighed against the advantages gained by their use. The construction of a run time interpreter using physical quantities in equations can be aided by the dynamic equation checks provided by dimensional analysis. The effects of high levels of abstraction on the generation and maintenance of software used in integration and test applications are also discussed

ada: An R Package for Stochastic Boosting

Boosting is an iterative algorithm that combines simple classification rules with "mediocre" performance in terms of misclassification error rate to produce a highly accurate classification rule. Stochastic gradient boosting provides an enhancement which incorporates a random mechanism at each boosting step showing an improvement in performance and speed in generating the ensemble. ada is an R package that implements three popular variants of boosting, together with a version of stochastic gradient boosting. In addition, useful plots for data analytic purposes are provided along with an extension to the multi-class case. The algorithms are illustrated with synthetic and real data sets.

Development and Verification of a Flight Stack for a High-Altitude Glider in Ada/SPARK 2014

SPARK 2014 is a modern programming language and a new state-of-the-art tool set for development and verification of high-integrity software. In this paper, we explore the capabilities and limitations of its latest version in the context of building a flight stack for a high-altitude unmanned glider. Towards that, we deliberately applied static analysis early and continuously during implementation, to give verification the possibility to steer the software design. In this process we have identified several limitations and pitfalls of software design and verification in SPARK, for which we give workarounds and protective actions to avoid them. Finally, we give design recommendations that have proven effective for verification, and summarize our experiences with this new language

Space station operating system study

The current phase of the Space Station Operating System study is based on the analysis, evaluation, and comparison of the operating systems implemented on the computer systems and workstations in the software development laboratory. Primary emphasis has been placed on the DEC MicroVMS operating system as implemented on the MicroVax II computer, with comparative analysis of the SUN UNIX system on the SUN 3/260 workstation computer, and to a limited extent, the IBM PC/AT microcomputer running PC-DOS. Some benchmark development and testing was also done for the Motorola MC68010 (VM03 system) before the system was taken from the laboratory. These systems were studied with the objective of determining their capability to support Space Station software development requirements, specifically for multi-tasking and real-time applications. The methodology utilized consisted of development, execution, and analysis of benchmark programs and test software, and the experimentation and analysis of specific features of the system or compilers in the study

Uplift Modeling with Multiple Treatments and General Response Types

Randomized experiments have been used to assist decision-making in many areas. They help people select the optimal treatment for the test population with certain statistical guarantee. However, subjects can show significant heterogeneity in response to treatments. The problem of customizing treatment assignment based on subject characteristics is known as uplift modeling, differential response analysis, or personalized treatment learning in literature. A key feature for uplift modeling is that the data is unlabeled. It is impossible to know whether the chosen treatment is optimal for an individual subject because response under alternative treatments is unobserved. This presents a challenge to both the training and the evaluation of uplift models. In this paper we describe how to obtain an unbiased estimate of the key performance metric of an uplift model, the expected response. We present a new uplift algorithm which creates a forest of randomized trees. The trees are built with a splitting criterion designed to directly optimize their uplift performance based on the proposed evaluation method. Both the evaluation method and the algorithm apply to arbitrary number of treatments and general response types. Experimental results on synthetic data and industry-provided data show that our algorithm leads to significant performance improvement over other applicable methods

Intelligent multi-sensor integrations

Growth in the intelligence of space systems requires the use and integration of data from multiple sensors. Generic tools are being developed for extracting and integrating information obtained from multiple sources. The full spectrum is addressed for issues ranging from data acquisition, to characterization of sensor data, to adaptive systems for utilizing the data. In particular, there are three major aspects to the project, multisensor processing, an adaptive approach to object recognition, and distributed sensor system integration

Multilabel Classification with R Package mlr

We implemented several multilabel classification algorithms in the machine learning package mlr. The implemented methods are binary relevance, classifier chains, nested stacking, dependent binary relevance and stacking, which can be used with any base learner that is accessible in mlr. Moreover, there is access to the multilabel classification versions of randomForestSRC and rFerns. All these methods can be easily compared by different implemented multilabel performance measures and resampling methods in the standardized mlr framework. In a benchmark experiment with several multilabel datasets, the performance of the different methods is evaluated.Comment: 18 pages, 2 figures, to be published in R Journal; reference correcte