Rensselaer Polytechnic Institute

About: Rensselaer Polytechnic Institute is a education organization based out in Troy, New York, United States. It is known for research contribution in the topics: Terahertz radiation & Finite element method. The organization has 19024 authors who have published 39922 publications receiving 1414699 citations. The organization is also known as: RPI & Rensselaer Institute.

On the Use of the Adjusted Rand Index as a Metric for Evaluating Supervised Classification

Abstract: The Adjusted Rand Index (ARI) is frequently used in cluster validation since it is a measure of agreement between two partitions: one given by the clustering process and the other defined by external criteria. In this paper we investigate the usability of this clustering validation measure in supervised classification problems by two different approaches: as a performance measure and in feature selection. Since ARI measures the relation between pairs of dataset elements not using information from classes (labels) it can be used to detect problems with the classification algorithm specially when combined with conventional performance measures. Instead, if we use the class information, we can apply ARI also to perform feature selection. We present the results of several experiments where we have applied ARI both as a performance measure and for feature selection showing the validity of this index for the given tasks.

Constrained K-Means Clustering

Abstract: We consider practical methods for adding constraints to the K-Means clustering algorithm in order to avoid local solutions with empty clusters or clusters having very few points. We often observe this phenomena when applying K-Means to datasets where the number of dimensions is n 10 and the number of desired clusters is k 20. We propose explicitly adding k constraints to the underlying clustering optimization problem requiring that each cluster have at least a minimum number of points in it. We then investigate the resulting cluster assignment step. Preliminary numerical tests on real datasets indicate the constrained approach is less prone to poor local solutions, producing a better summary of the underlying data. Contrained K-Means Clustering 1

Progressive Transverse Cracking In Composite Laminates

Abstract: HE DEVELOPMENT OF a satisfactory theory for cross-ply laminates which have been damaged by transverse matrix cracking under monotonic loading has attracted a substantial number of investigators. The formulation of a shear lag model appears to have been first proposed in a series of papers by Bailey and his co-workers [1,2,3,4,5,6]. This work, in turn, relies on some studies of unidirectional composites by Aveston and Kelly [6]. Subsequent contributions to the theory have geen given by Wang [7], Highsmith and Reifsnider [8], Flaggs and Kural [9], Nuismer and Tan [10], Manders, Chou, Jones and Rock [11], Fukunaga, Chou, Peters and Schulte [12], Flaggs [13], Ohira [14] and Ogin, Smith and Beaumont [15,16]. Doubtless a diligent search of the literature would disclose other related work.

A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks

Abstract: Background Automated segmentation of fluorescently-labeled cell nuclei in 3D confocal microscope images is essential to many studies involving morphological and functional analysis. A common source of segmentation error is tight clustering of nuclei. There is a compelling need to minimize these errors for constructing highly automated scoring systems. Methods A combination of two approaches is presented. First, an improved distance transform combining intensity gradients and geometric distance is used for the watershed step. Second, an explicit mathematical model for the anatomic characteristics of cell nuclei such as size and shape measures is incorporated. This model is constructed automatically from the data. Deliberate initial over-segmentation of the image data is performed, followed by statistical model-based merging. A confidence score is computed for each detected nucleus, measuring how well the nucleus fits the model. This is used in combination with the intensity gradient to control the merge decisions. Results Experimental validation on a set of rodent brain cell images showed 97% concordance with the human observer and significant improvement over prior methods. Conclusions Combining a gradient-weighted distance transform with a richer morphometric model significantly improves the accuracy of automated segmentation and FISH analysis. Cytometry Part A 56A:23–36, 2003. © 2003 Wiley-Liss, Inc.

Gender Differences in Financial Reporting Decision-Making: Evidence from Accounting Conservatism

Abstract: ​This paper investigates the effect of CFO gender on corporate financial reporting decision-making. Focusing on firms that experience changes of CFO from male to female, the paper compares the firms’ degree of accounting conservatism between pre- and post-transition periods. We find that female CFOs are more conservative in their financial reporting. In addition, we find that the relation between CFO gender and conservatism varies with the levels of various firm risks such as litigation risk, default risk, systematic risk, and CFO specific risk such as job security risk. We further find that risk-aversion of female CFOs is associated with less equity-based compensation, lower firm risk, higher tangibility level, and lower dividend payout level. Overall, the study provides strong support for the notion that female CFOs are more risk averse than male CFOs, which leads female CFOs to adopt more conservative financial reporting policies.