API design for machine learning software: experiences from the scikit-learn project

Scikit-learn is an increasingly popular machine learning li- brary. Written in Python, it is designed to be simple and efficient, accessible to non-experts, and reusable in various contexts. In this paper, we present and discuss our design choices for the application programming interface (API) of the project. In particular, we describe the simple and elegant interface shared by all learning and processing units in the library and then discuss its advantages in terms of composition and reusability. The paper also comments on implementation details specific to the Python ecosystem and analyzes obstacles faced by users and developers of the library

LDR: A Package for Likelihood-Based Sufficient Dimension Reduction

We introduce a new MATLAB software package that implements several recently proposed likelihood-based methods for sufficient dimension reduction. Current capabilities include estimation of reduced subspaces with a fixed dimension d, as well as estimation of d by use of likelihood-ratio testing, permutation testing and information criteria. The methods are suitable for preprocessing data for both regression and classification. Implementations of related estimators are also available. Although the software is more oriented to command-line operation, a graphical user interface is also provided for prototype computations

Implementation of Control Design Methods into Matlab Environment

The main aim of this chapter is to present two simple and freely downloadable Matlab programs which allow user-friendly work for two selected specific control design issues by means of Graphical User Interface (GUI).P(ED2.1.00/03.0089

An Exploratory Study of Patient Falls

Debate continues between the contribution of education level and clinical expertise in the nursing practice environment. Research suggests a link between Baccalaureate of Science in Nursing (BSN) nurses and positive patient outcomes such as lower mortality, decreased falls, and fewer medication errors. Purpose: To examine if there a negative correlation between patient falls and the level of nurse education at an urban hospital located in Midwest Illinois during the years 2010-2014? Methods: A retrospective crosssectional cohort analysis was conducted using data from the National Database of Nursing Quality Indicators (NDNQI) from the years 2010-2014. Sample: Inpatients aged ≥ 18 years who experienced a unintentional sudden descent, with or without injury that resulted in the patient striking the floor or object and occurred on inpatient nursing units. Results: The regression model was constructed with annual patient falls as the dependent variable and formal education and a log transformed variable for percentage of certified nurses as the independent variables. The model overall is a good fit, F (2,22) = 9.014, p = .001, adj. R2 = .40. Conclusion: Annual patient falls will decrease by increasing the number of nurses with baccalaureate degrees and/or certifications from a professional nursing board-governing body

Demodulation of Fiber-Optic Chirped Fiber Bragg Grating Sensors for Thermal Pattern Detection

This report focuses on development of demodulation software for ultra-dense distributed chirped fiber Bragg grating optical sensors, which measure temperature and strain with 75 micron resolution over 1.5 cm for advanced medical applications. Initially, addressed problem is stated, thermal ablation technology is discussed, and basic background of optics is considered. Next, old and new reconstruction algorithms are reviewed in detail. It follows with discussion of MATLAB code to LabVIEW environment transition. Real spectrum measurements, which are used for verification of proposed reconstruction, were obtained during experiments conducted under supervision of Professor Daniele Tosi. The obtained data is used as software inputs to obtain thermal distribution. Moreover, significance and opportunities of temperature prediction for the proposed technology is discussed. Finally, the results of prediction based on linear regression model are analyzed and further improvements of technology are proposed