Human Resource Planning: A Business Necessity

Abstract

Information-based organizations are structured to function with as small and efficient a staff as possible. To this end, executives at The Promus Companies are using IT to spread decision-making authority and responsibility for customer satisfaction and customer service throughout the organization.     

A Parallel Method for Earth Mover’s Distance

We propose a new algorithm to approximate the Earth Mover’s distance (EMD). Our main idea is motivated by the theory of optimal transport, in which EMD can be reformulated as a familiar \(L_1\) type minimization. We use a regularization which gives us a unique solution for this \(L_1\) type problem. The new regularized minimization is very similar to problems which have been solved in the fields of compressed sensing and image processing, where several fast methods are available. In this paper, we adopt a primal-dual algorithm designed there, which uses very simple updates at each iteration and is shown to converge very rapidly. Several numerical examples are provided.     

Adaptive spatiotemporal receptive field estimation in the visual pathway

The encoding properties of the visual pathway are under constant control from mechanisms of adaptation and systems-level plasticity. In all but the most artificial experimental conditions, these mechanisms serve to continuously modulate the spatial and temporal receptive field (RF) dynamics. Conventional reverse-correlation techniques designed to capture spatiotemporal RF properties assume invariant stimulus-response relationships over experimental trials and are thus limited in their applicability to more natural experimental conditions. Presented here is an approach to tracking time-varying encoding dynamics in the early visual pathway based on adaptive estimation of the spatiotemporal RF in the time domain. Simulations and experimental data from the lateral geniculate nucleus reveal that subtle features of encoding properties can be captured by the adaptive approach that would otherwise be undetected. Capturing the role of dynamically varying encoding mechanisms is vital to our understanding of vision on the natural setting, where there is absence of a true steady state.     

Level Set Based Simulations of Two-Phase Oil–Water Flows in Pipes

We simulate the axisymmetric pipeline transportation of oil and water numerically under the assumption that the densities of the two fluids are different and that the viscosity of the oil core is very large. We develop the appropriate equations for core-annular flows using the level set methodology. Our method consists of a finite difference scheme for solving the model equations, and a level set approach for capturing the interface between two liquids (oil and water). A variable density projection method combined with a TVD Runge–Kutta scheme is used to advance the computed solution in time. The simulations succeed in predicting the spatially periodic waves called bamboo waves, which have been observed in the experiments of [Bai et al. (1992) J. Fluid Mech. 240, 97–142.] on up-flow in vertical core flow. In contrast to the stable case, our simulations succeed in cases where the oil breaks up in the water, and then merging occurs. Comparisons are made with other numerical methods and with both theoretical and experimental results.     

Exact Low-Rank Matrix Completion from Sparsely Corrupted Entries Via Adaptive Outlier Pursuit

Recovering a low-rank matrix from some of its linear measurements is a popular problem in many areas of science and engineering. One special case of it is the matrix completion problem, where we need to reconstruct a low-rank matrix from incomplete samples of its entries. A lot of efficient algorithms have been proposed to solve this problem and they perform well when Gaussian noise with a small variance is added to the given data. But they can not deal with the sparse random-valued noise in the measurements. In this paper, we propose a robust method for recovering the low-rank matrix with adaptive outlier pursuit when part of the measurements are damaged by outliers. This method will detect the positions where the data is completely ruined and recover the matrix using correct measurements. Numerical experiments show the accuracy of noise detection and high performance of matrix completion for our algorithms compared with other algorithms.     

Dynamic setup scheduling and flow control in manufacturing systems

We propose a method for flow control of parts in a manufacturing system with machines that require setups. The setup scheduling problem is investigated in the context of a multilevel hierarchy of discrete events with distinct frequencies. The higher level of the hierarchy calculates a target trajectory in the surplus/backlog space of the part types which must be tracked at the level of setups. We consider a feedback setup scheduling policy which usescorridors in the surplus/backlog space of the part types to determine the timing of the set-up changes in order to guide the trajectory in the desired direction. An interesting case in which the trajectory leads to a target point (e.g., a hedging point) is investigated in detail. It is shown that in this case the surplus/backlog trajectory at the setup level can lead to a limit cycle. Conditions for linear corridors which result in a stable limit cycle are determined.     

The design and implementation of K: a high-level knowledge-base programming language of OSAM*.KBMS

The OSAM*.KBMS is a knowledge-base management system, or the so-called next-generation database management system, for non-traditional data/knowledge-intensive applications. In order to define, query, and manipulate a knowledge base, as well as to write codes to implement any application system, we have developed an object-oriented knowledge-base programming language called K to serve as the high-level interface of OSAM*.KBMS. This paper presents the design of K, its implementation, and its supporting KBMS developed at the Database Systems Research and Development Center of the University of Florida. Edited by Dennis McLeod. Received July 1992 / Accepted August 1995     

A Web-Based Knowledge Network for Supporting Emerging Internet Applications

Although the Internet and the World Wide Web technologies have gained a tremendous amount of popularity among people and organizations, the network that these technologies created is not much more than a multimedia data network. It provides tools and services for people to browse and search for data but does not provide the facilities for automatically delivering the relevant information for supporting decision–making to the right people or applications at the right time. Nor does it provide the means for users to enter and share their knowledge that would be useful for making the right decisions. In this work, we introduce the concept of a Web–based knowledge network, which allows users and organizations to publish, not only their multimedia data, but also their knowledge in terms of events, parameterized event filters, customizable rules and triggers that are associated with their data and application systems. Operations on the data and application systems may post events over the Internet to trigger the processing of rules defined by both information providers and consumers. The knowledge network is constructed by a number of replicable software components, which can be installed at various network sites. They, together with the existing Web servers, form a network of knowledge Web servers.     

Goodness-of-fit tests for logistic regression models when data are collected using a complex sampling design

Logistic regression models are frequently used in epidemiological studies for estimating associations that demographic, behavioral, and risk factor variables have on a dichotomous outcome, such as disease being present versus absent. After the coefficients in a logistic regression model have been estimated, goodness-of-fit of the resulting model should be examined, particularly if the purpose of the model is to estimate probabilities of event occurrences. While various goodness-of-fit tests have been proposed, the properties of these tests have been studied under the assumption that observations selected were independent and identically distributed. Increasingly, epidemiologists are using large-scale sample survey data when fitting logistic regression models, such as the National Health Interview Survey or the National Health and Nutrition Examination Survey. Unfortunately, for such situations no goodness-of-fit testing procedures have been developed or implemented in available software. To address this problem, goodness-of-fit tests for logistic regression models when data are collected using complex sampling designs are proposed. Properties of the proposed tests were examined using extensive simulation studies and results were compared to traditional goodness-of-fit tests. A Stata ado function svylogitgof for estimating the F-adjusted mean residual test after svylogit fit is available at the author's website http://www.people.vcu.edu/~kjarcher/Research/Data.htm.