Enrichment Procedures for Soft Clusters: A Statistical Test and its Applications

Clusters, typically mined by modeling locality of attribute spaces, are often evaluated for their ability to demonstrate ‘enrichment’ of categorical features. A cluster enrichment procedure evaluates the membership of a cluster for significant representation in pre-defined categories of interest. While classical enrichment procedures assume a hard clustering definition, in this paper we introduce a new statistical test that computes enrichments for soft clusters. We demonstrate an application of this test in refining and evaluating soft clusters for classification of remotely sensed images

An SMP Soft Classification Algorithm for Remote Sensing

This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative guided spectral class rejection (CIGSCR) algorithm, a semiautomated classification algorithm for remote sensing (multispectral) images. The algorithm uses soft data clusters to produce a soft classification containing inherently more information than a comparable hard classification at an increased computational cost. Previous work suggests that similar algorithms achieve good parallel scalability, motivating the parallel algorithm development work here. Experimental results of applying parallel CIGSCR to an image with approximately 10^8 pixels and six bands demonstrate superlinear speedup. A soft two class classification is generated in just over four minutes using 32 processors

Continuous Iterative Guided Spectral Class Rejection Classification Algorithm: Part 1

This paper outlines the changes necessary to convert the iterative guided spectral class rejection (IGSCR) classification algorithm to a soft classification algorithm. IGSCR uses a hypothesis test to select clusters to use in classification and iteratively refines clusters not yet selected for classification. Both steps assume that cluster and class memberships are crisp (either zero or one). In order to make soft cluster and class assignments (between zero and one), a new hypothesis test and iterative refinement technique are introduced that are suitable for soft clusters. The new hypothesis test, called the (class) association significance test, is based on the normal distribution, and a proof is supplied to show that the assumption of normality is reasonable. Soft clusters are iteratively refined by creating new clusters using information contained in a targeted soft cluster. Soft cluster evaluation and refinement can then be combined to form a soft classification algorithm, continuous iterative guided spectral class rejection (CIGSCR)

Continuous Iterative Guided Spectral Class Rejection Classification Algorithm: Part 2

This paper describes in detail the continuous iterative guided spectral class rejection (CIGSCR) classification method based on the iterative guided spectral class rejection (IGSCR) classification method for remotely sensed data. Both CIGSCR and IGSCR use semisupervised clustering to locate clusters that are associated with classes in a classification scheme. In CIGSCR and IGSCR, training data are used to evaluate the strength of the association between a particular cluster and a class, and a statistical hypothesis test is used to determine which clusters should be associated with a class and used for classification and which clusters should be rejected and possibly refined. Experimental results indicate that the soft classification output by CIGSCR is reasonably accurate (when compared to IGSCR), and the fundamental algorithmic changes in CIGSCR (from IGSCR) result in CIGSCR being less sensitive to input parameters that influence iterations. Furthermore, evidence is presented that the semisupervised clustering in CIGSCR produces more accurate classifications than classification based on clustering without supervision

Note On The Effectiveness OF Stochastic Optimization Algorithms For Robust Design

Robust design optimization (RDO) uses statistical decision theory and optimization techniques to optimize a design over a range of uncertainty (introduced by the manufacturing process and unintended uses). Since engineering ob jective functions tend to be costly to evaluate and prohibitively expensive to integrate (required within RDO), surrogates are introduced to allow the use of traditional optimization methods to find solutions. This paper explores the suitability of radically different (deterministic and stochastic) optimization methods to solve prototypical robust design problems. The algorithms include a genetic algorithm using a penalty function formulation, the simultaneous perturbation stochastic approximation (SPSA) method, and two gradient-based constrained nonlinear optimizers (method of feasible directions and sequential quadratic programming). The results show that the fully deterministic standard optimization algorithms are consistently more accurate, consistently more likely to terminate at feasible points, and consistently considerably less expensive than the fully nondeterministic algorithms

Feature Reduction using a Singular Value Decomposition for the Iterative Guided Spectral Class Rejection Hybrid Classifier

Feature reduction in a remote sensing dataset is often desirable to decrease the processing time required to perform a classification and improve overall classification accuracy. This work introduces a feature reduction method based on the singular value decomposition (SVD). This feature reduction technique was applied to training data from two multitemporal datasets of Landsat TM/ETM+ imagery acquired over a forested area in Virginia, USA and Rondonia, Brazil. Subsequent parallel iterative guided spectral class rejection (pIGSCR) forest/nonforest classifications were performed to determine the quality of the feature reduction. The classifications of the Virginia data were five times faster using SVDbased feature reduction without affecting the classification accuracy. Feature reduction using the SVD was also compared to feature reduction using principal components analysis (PCA). The highest average accuracies for the Virginia dataset (88.34%) and for the Rondonia dataset (93.31%) were achieved using the SVD. The results presented here indicate that SVDbased feature reduction can produce statistically significantly better classifications than PCA

An Adaptive Noise Filtering Algorithm for AVIRIS Data with Implications for Classification Accuracy

This paper describes a new algorithm used to adaptively filter a remote sensing dataset based on signal-to-noise ratios (SNRs) once the maximum noise fraction (MNF) has been applied. This algorithm uses Hermite splines to calculate the approximate area underneath the SNR curve as a function of band number, and that area is used to place bands into “bins” with other bands having similar SNRs. A median filter with a variable sized kernel is then applied to each band, with the same size kernel used for each band in a particular bin. The proposed adaptive filters are applied to a hyperspectral image generated by the AVIRIS sensor, and results are given for the identification of three different pine species located within the study area. The adaptive filtering scheme improves image quality as shown by estimated SNRs, and classification accuracies improved by more than 10% on the sample study area, indicating that the proposed methods improve the image quality, thereby aiding in species discrimination

CD24 Expression Identifies Teratogen-Sensitive Fetal Neural Stem Cell Subpopulations: Evidence from Developmental Ethanol Exposure and Orthotopic Cell Transfer Models

Ethanol is a potent teratogen. Its adverse neural effects are partly mediated by disrupting fetal neurogenesis. The teratogenic process is poorly understood, and vulnerable neurogenic stages have not been identified. Identifying these is a prerequisite for therapeutic interventions to mitigate effects of teratogen exposures.We used flow cytometry and qRT-PCR to screen fetal mouse-derived neurosphere cultures for ethanol-sensitive neural stem cell (NSC) subpopulations, to study NSC renewal and differentiation. The identity of vulnerable NSC populations was validated in vivo, using a maternal ethanol exposure model. Finally, the effect of ethanol exposure on the ability of vulnerable NSC subpopulations to integrate into the fetal neurogenic environment was assessed following ultrasound guided, adoptive transfer.Ethanol decreased NSC mRNAs for c-kit, Musashi-1and GFAP. The CD24(+) NSC population, specifically the CD24(+)CD15(+) double-positive subpopulation, was selectively decreased by ethanol. Maternal ethanol exposure also resulted in decreased fetal forebrain CD24 expression. Ethanol pre-exposed CD24(+) cells exhibited increased proliferation, and deficits in cell-autonomous and cue-directed neuronal differentiation, and following orthotopic transplantation into naïve fetuses, were unable to integrate into neurogenic niches. CD24(depleted) cells retained neurosphere regeneration capacity, but following ethanol exposure, generated increased numbers of CD24(+) cells relative to controls.Neuronal lineage committed CD24(+) cells exhibit specific vulnerability, and ethanol exposure persistently impairs this population's cell-autonomous differentiation capacity. CD24(+) cells may additionally serve as quorum sensors within neurogenic niches; their loss, leading to compensatory NSC activation, perhaps depleting renewal capacity. These data collectively advance a mechanistic hypothesis for teratogenesis leading to microencephaly

The Vehicle, Fall 1987

Table of Contents Sketches in the SunRodger L. Patiencepage 3 Reflecting PoolRob Montgomerypage 5 Grandpa\u27s Porcelain DollRichard E. Hallpage 6 Tintype 1837Catherine Friemannpage 6 PhotographSteven M. Beamerpage 7 Washerwoman\u27s SongBob Zordanipage 8 Scrambled Eggs for D.O.Lynne A. Rafoolpage 8 my mother would sayMonica Grothpage 9 Retired by His ChildrenDan Von Holtenpage 10 I am the oldestMonica Grothpage 11 Ice on WheatRob Montgomerypage 12 The Nature of the RoseTroy Mayfieldpage 12 Past NebraskaDan Hornbostelpage 13 Five Minute Jamaican VacationChristy Dunphypage 14 PhotographSteven M. Beamerpage 14 The Angry PoemChristy Dunphypage 15 Road UnfamiliarChristy Dunphypage 15 raised voicesMonica Grothpage 16 Old Ladies & MiniskirtsKara Shannonpage 17 FreakspeakBob Zordanipage 18 PortraitDan Von Holtenpage 18 Mobile VacuumKathleen L. Fairfieldpage 19 Rev. Fermus DickSteve Hagemannpage 20 PhotographSteven M. Beamerpage 21 What\u27s the Name of That Flower?Richard Jesse Davispage 22 RequestChristy Dunphypage 23 SketchPaul Seabaughpage 24 ExperiencedMarilyn Wilsonpage 26 Leaving: Two ViewsTina Phillipspage 27 AntaeusDan Von Holtenpage 28 Misogyny at 19J. D. Finfrockpage 29 A Mental CrippleSteve Hagemannpage 32 AssociationsRhonda Ealypage 33 Banana BreadGail Bowerpage 34 Bill and JackBradford B. Autenpage 35 After Image No. 2Rob Montgomerypage 35 VrrooomBeth Goodmanpage 36 Mr. Modern LoverMolly Maddenpage 36 TravelogueRodger L. Patiencepage 37 Down the HighwayJoan Sebastianpage 38 A Retread HeavenRob Montgomerypage 41 StuporDan Von Holtenpage 42 Love Poem After a Seizure in Your BedBob Zordanipage 43 PalsyChristy Dunphypage 44 Interview with Mr. MatthewsBob Zordanipage 45 Chasing Down Hot Air Balloons on a Sunday MorningRob Montgomerypage 48https://thekeep.eiu.edu/vehicle/1049/thumbnail.jp

A rhetoric-in-context approach to building commitment to multiple strategic goals

There are still few explanations of the micro-level practices by which top managers influence employee commitment to multiple strategic goals. This paper argues that, through their language, top managers can construct a context for commitment to multiple strategic goals. We therefore propose a rhetoric-in-context approach to illuminate some of the micro practices through which top managers influence employee commitment. Based upon an empirical study of the rhetorical practices through which top managers influence academic commitment to multiple strategic goals in university contexts, we demonstrate relationships between rhetoric and context. Specifically, we show that rhetorical influences over commitment to multiple goals are associated with the historical context for multiple goals, the degree to which top managers' rhetoric instantiates a change in that context, and the internal consistency of the rhetorical practices used by top managers. Copyright © 2007 SAGE Publications