Symbolic-numeric interface: A review

A survey of the use of a combination of symbolic and numerical calculations is presented. Symbolic calculations primarily refer to the computer processing of procedures from classical algebra, analysis, and calculus. Numerical calculations refer to both numerical mathematics research and scientific computation. This survey is intended to point out a large number of problem areas where a cooperation of symbolic and numerical methods is likely to bear many fruits. These areas include such classical operations as differentiation and integration, such diverse activities as function approximations and qualitative analysis, and such contemporary topics as finite element calculations and computation complexity. It is contended that other less obvious topics such as the fast Fourier transform, linear algebra, nonlinear analysis and error analysis would also benefit from a synergistic approach

Functional Structure and Approximation in Econometrics (book front matter)

This is the front matter from the book, William A. Barnett and Jane Binner (eds.), Functional Structure and Approximation in Econometrics, published in 2004 by Elsevier in its Contributions to Economic Analysis monograph series. The front matter includes the Table of Contents, Volume Introduction, and Section Introductions by Barnett and Binner and the Preface by W. Erwin Diewert. The volume contains a unified collection and discussion of W. A. Barnett's most important published papers on applied and theoretical econometric modelling.consumer demand, production, flexible functional form, functional structure, asymptotics, nonlinearity, systemwide models

Interest rate models with Markov chains

Imperial Users onl

A comparative study of different strategies of batch effect removal in microarray data: a case study of three datasets

Batch effects refer to the systematic non-biological variability that is introduced by experimental design and sample processing in microarray experiments. It is a common issue in microarray data and could introduce bias into the analysis, if ignored. Many batch effect removal methods have been developed. Previous comparative work has been focused on their effectiveness of batch effects removal and impact on downstream classification analysis. The most common type of analysis for microarray data is differential expression (DE) analysis, yet no study has examined the impact of these methods on downstream DE analysis, which identifies markers that are significantly associated with the outcome of interest. In this project, we investigated the performance of five popular batch effect removal methods, mean-centering, ComBat_p, ComBat_n, SVA, and ratio based methods, on batch effects reduction and their impact on DE analysis using three experimental datasets with different sources of batch effects. We found that the performance of these methods is data-dependent: simple mean-centering method performed reasonably well in all three datasets, but the more complicated algorithms such as ComBat method’s performance could be unstable for certain dataset and should be applied with caution. Given a new dataset, we recommend either using the mean-centering method or carefully investigating a few different batch removal methods and choosing the one that is the best for the data, if possible. This study has important public health significance because better handling of batch effect in microarray data can reduce biased results and lead to improved biomarker identification

A Personalized System for Conversational Recommendations

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system

The matching relaxation for a class of generalized set partitioning problems

This paper introduces a discrete relaxation for the class of combinatorial optimization problems which can be described by a set partitioning formulation under packing constraints. We present two combinatorial relaxations based on computing maximum weighted matchings in suitable graphs. Besides providing dual bounds, the relaxations are also used on a variable reduction technique and a matheuristic. We show how that general method can be tailored to sample applications, and also perform a successful computational evaluation with benchmark instances of a problem in maritime logistics.Comment: 33 pages. A preliminary (4-page) version of this paper was presented at CTW 2016 (Cologne-Twente Workshop on Graphs and Combinatorial Optimization), with proceedings on Electronic Notes in Discrete Mathematic

Evaluating probabilistic forecasts with scoringRules

Probabilistic forecasts in the form of probability distributions over future events have become popular in several fields including meteorology, hydrology, economics, and demography. In typical applications, many alternative statistical models and data sources can be used to produce probabilistic forecasts. Hence, evaluating and selecting among competing methods is an important task. The scoringRules package for R provides functionality for comparative evaluation of probabilistic models based on proper scoring rules, covering a wide range of situations in applied work. This paper discusses implementation and usage details, presents case studies from meteorology and economics, and points to the relevant background literature

USSR Space Life Sciences Digest, issue 1

The first issue of the bimonthly digest of USSR Space Life Sciences is presented. Abstracts are included for 49 Soviet periodical articles in 19 areas of aerospace medicine and space biology, published in Russian during the first quarter of 1985. Translated introductions and table of contents for nine Russian books on topics related to NASA's life science concerns are presented. Areas covered include: botany, cardiovascular and respiratory systems, cybernetics and biomedical data processing, endocrinology, gastrointestinal system, genetics, group dynamics, habitability and environmental effects, health and medicine, hematology, immunology, life support systems, man machine systems, metabolism, musculoskeletal system, neurophysiology, perception, personnel selection, psychology, radiobiology, reproductive system, and space biology. This issue concentrates on aerospace medicine and space biology