The Antecedents of Creativity Revisited: A Process Perspective

This study invokes a process view on employee creativity to uncover how the different stages of the creative process are associated with different antecedents. Specifically, we explore the role of five previously identified antecedents of organizational creativity in the different phases of the creative process within organizations: (1) personality; (2) rewards; (3) the role of co‐workers; (4) leadership; and (5) organizational resources. In an analysis of 22 case studies we found that antecedents of creativity indeed have different roles in different stages of the creative process and that antecedents that are helpful in one stage of the creative process, can be detrimental for another stage. Such results highlight the importance of conceptualizing creativity as a process, rather than as an outcome variable.     

Capturing Design Space From a User Perspective: The Repertory Grid Technique Revisited

The design of an artifact (e.g., software system, household appliance) requires a multitude of decisions. In the course of narrowing down the design process, "good ideas" have to be divided from "bad ideas." To accomplish this, user perceptions and evaluations are of great value. The individual way people perceive and evaluate a set of prototypes designed in parallel may shed light on their general needs and concerns. The Repertory Grid Technique (RGT) is a method of elucidating the so-called personal constructs (e.g., friendly-hostile, bad-good, playful-expert-like) people employ when confronted with other individuals, events, or artifacts. We assume that the personal constructs (and the underlying topics) generated as a reaction to a set of artifacts mark the artifacts' design space from a user's perspective and that this information may be helpful in separating valuable ideas from the not so valuable. This article explores the practical value of the RGT in gathering design-relevant information about the design space of early artifact prototypes designed in parallel. Ways of treating the information gathered, its quality and general advantages, and limitations of the RGT are presented and discussed. In general, the RGT proved to be a valuable tool in exploring a set of artifact's design space from a user's perspective.     

A New Parallel Skeleton for General Accumulative Computations

Skeletal parallel programming enables programmers to build a parallel program from ready-made components (parallel primitives) for which efficient implementations are known to exist, making both the parallel program development and the parallelization process easier. Constructing efficient parallel programs is often difficult, however, due to difficulties in selecting a proper combination of parallel primitives and in implementing this combination without having unnecessary creations and exchanges of data among parallel primitives and processors. To overcome these difficulties, we propose a powerful and general parallel skeleton, accumulate, which can be used to naturally code efficient solutions to problems as well as be efficiently implemented in parallel using Message Passing Interface (MPI).     

A New Poisson Noise Filter Based on Weights Optimization

We propose a new image denoising algorithm when the data is contaminated by a Poisson noise. As in the Non-Local Means filter, the proposed algorithm is based on a weighted linear combination of the observed image. But in contrast to the latter where the weights are defined by a Gaussian kernel, we propose to choose them in an optimal way. First some “oracle” weights are defined by minimizing a very tight upper bound of the Mean Square Error. For a practical application the weights are estimated from the observed image. We prove that the proposed filter converges at the usual optimal rate to the true image. Simulation results are presented to compare the performance of the presented filter with conventional filtering methods.     

Completion-of-squares: Revisited and extended

Due to its simplicity, the completion-of-squares technique is quite popular in linear optimal control. However, this simple technique is limited to linear quadratic Gaussian systems. In this note, by interpreting the completion-of-squares from a new angle, we extend this technique to performance optimization of general Markov systems with the long run average criterion, leading to a new approach to performance optimization based on direct comparisons of the performance of two policies.     

一种运用势能理论的骨架特征提取方法

一种好的特征提取方法可以使模式识别和目标跟踪系统的性能得到较大的提高.二值图像势能理论和方法是一种利用像素所具有的势能来表现图像特征的新方法.运用图像势能的方法把目标图像骨架的特征提取出来在特征提取领域是一种创新.经仿真实验,图像骨架的势能方法可以较好的表现出图像的特征,计算速度快,占用存储空间小,准确性高.骨架势能的方法可以应用到目标分类,目标识别,特征提取等多个领域.     

基于骨架的血细胞图像分离算法

文章从细胞图像骨架的角度出发,研究和发展了一种新的粘连细胞分离算法。其主要思路是:首先采用细化算法提取细胞图像的骨架,并计算骨架各点的边界距离值,然后利用骨架边界距离函数波谷的特性将相互粘结的细胞分离。新算法提出了一种计算骨架边界距离值的新方法用于分析骨架波谷的特性,并在理论和实践的基础上,研究和发展了细胞图像中粘结细胞的分离准则。为证明该算法的有效性,选取了上百种不同的细胞图像进行了测试。实验结果表明,新算法能有效、快速地对细胞图像中相互粘结的细胞进行合理分割。     

Mathematical expression recognition: a survey

Abstract. Automatic recognition of mathematical expressions is one of the key vehicles in the drive towards transcribing documents in scientific and engineering disciplines into electronic form. This problem typically consists of two major stages, namely, symbol recognition and structural analysis. In this survey paper, we will review most of the existing work with respect to each of the two major stages of the recognition process. In particular, we try to put emphasis on the similarities and differences between systems. Moreover, some important issues in mathematical expression recognition will be addressed in depth. All these together serve to provide a clear overall picture of how this research area has been developed to date. Received February 22, 2000 / Revised June 12, 2000     

Poisson Hypothesis: Combinatorial Aspect

We describe our results concerning the proof of the Poisson hypothesis. We explain the probabilistic aspect of our results and present the main combinatorial step of our proof. That combinatorial statement deals with counting the number of rod placements on a line.     

Nonlinear Poisson regression using neural networks: a simulation study

We describe a novel extension of the Poisson regression model to be based on a multi-layer perceptron, a type of neural network. This relaxes the assumptions of the traditional Poisson regression model, while including it as a special case. In this paper, we describe neural network regression models with six different schemes and compare their performances in three simulated data sets, namely one linear and two nonlinear cases. From the simulation study it is found that the Poisson regression models work well when the linearity assumption is correct, but the neural network models can largely improve the prediction in nonlinear situations.     

Scientometric Methods for Identifying Scientific Leaders: New Mathematical Models

A new approach to the multidimensional assessment of research organizations is introduced. Based on the use of quantitative data, various types of research and development results are considered in the approach.     

Why Event Observation: Observability Revisited

In this paper, we revisit observability defined in Lin and Wonham (1988a). We show that although the original definition is intended for event observation with projection, its significance goes beyond its original intent. Specifically, we show that (1) supervisory control with state observation can be studied in terms of event observation; (2) supervisory control problems with event mask can be converted to problems with event projection; and (3) nondeterminism is closely related to unobservability.     

Skeleton growing: an algorithm to extract a curve skeleton from a pseudonormal vector field

A curve skeleton is used to represent a 3D object in many different applications. It is a 1D curve that captures topology of the 3D object. The proposed method extracts a curve skeleton from the vector field inside the 3D object. A vector at each voxel of the 3D object is calculated using a pseudonormal vector. By using such a calculation, the computation time is significantly reduced compared with using a typical potential field. A curve skeleton is then extracted from the pseudonormal vector field by using a skeleton-growing algorithm. The proposed algorithm uses high-curvature boundary voxels to search for a set of critical points and skeleton branches near high-curvature areas. The set of detected critical points is then used to grow a curve skeleton in the next step. All parameters of our algorithms are calculated from the 3D object itself, without user intervention. The effectiveness of our method is demonstrated in our experiments.     

Using geometric Poisson exponentially weighted moving average control schemes in a compound Poisson production environment

In this study, the variable to be controlled over time is the number of defects. Meanwhile, the underlying distribution of defects is the geometric Poisson distribution, a Poisson distribution compounded by a geometric distribution. For production process control, the exponentially weighted moving average (EWMA) control scheme based on the geometric Poisson process is addressed. Performance of the EWMA control scheme is assessed not only by both in-control and out-of-control average run lengths (ARL’s), but also by higher moments of the run length (RL) distribution. The run length distribution properties can be obtained from the probability transition matrix and implemented using the computer programs developed in this study. With proper ARL and variance of RL selected, any small shift in mean can be detected via the geometric Poisson EWMA control scheme.     

The FastICA Algorithm Revisited: Convergence Analysis

The fast independent component analysis (FastICA) algorithm is one of the most popular methods to solve problems in ICA and blind source separation. It has been shown experimentally that it outperforms most of the commonly used ICA algorithms in convergence speed. A rigorous local convergence analysis has been presented only for the so-called one-unit case, in which just one of the rows of the separating matrix is considered. However, in the FastICA algorithm, there is also an explicit normalization step, and it may be questioned whether the extra rotation caused by the normalization will affect the convergence speed. The purpose of this paper is to show that this is not the case and the good convergence properties of the one-unit case are also shared by the full algorithm with symmetrical normalization. A local convergence analysis is given for the general case, and the global behavior is illustrated numerically for two sources and two mixtures in several typical cases     

Dynamic Programming Revisited: Improving Knapsack Algorithms

The contribution of this paper is twofold: At first an improved dynamic programming algorithm for the bounded knapsack problem is given. It decreases the running time for an instance with n items and capacity c from to , which is the same pseudopolynomial complexity as usually given for the 0--1 knapsack problem. In the second part a general approach based on dynamic programming is presented to reduce the storage requirements for combinatorial optimization problems where it is computationally more expensive to compute the explicit solution structure than the optimal solution value. Among other applications of this scheme it is shown that the 0--1 knapsack problem as well as the bounded knapsack problem can be solved in time and space. Received: October 15, 1998; revised March 10, 1999