Doing Business with Theory: Communities of Practice in Knowledge Management

We explore how the notion of communities of practice (CoPs) was translated and popularized from its original inception by Lave and Wenger in 1991. We argue that the Institute for Research on Learning (IRL), a spin-off of Xerox PARC, proved instrumental in enrolling CoPs into the knowledge management (KM) discipline. IRL objectified, packaged, and made a business out of CoPs. CoPs in KM are now a formalized process coupled with technological artifacts to build groups of people who effectively share knowledge across boundaries. Drawing from participant observations, archival documents, and interviews with KM practitioners in the aerospace industry as well as key players of IRL, our research seeks to unveil the invisible history that the popularization of a theory can often obscure. We argue that CoPs provide a case study for understanding how abstract concepts in science are strategically and subconsciously reified, or made objects of inquiry, and appropriated by actors. This reification of a “soft” science blurs the line between theory and technology.     

Improving the Parameterization of Approximate Subdivision Surfaces

We provide a method for improving the parameterization of patching schemes that approximate Catmull‐Clark subdivision surfaces, such that the new parameterization conforms better to that of the original subdivision surface. We create this reparameterization in real‐time using a method that only depends on the topology of the surface and is independent of the surface's geometry. Our method can handle patches with more than one extraordinary vertex and avoids the combinatorial increase in both complexity and storage associated with multiple extraordinary vertices. Moreover, the reparameterization function is easy to implement and fast.     

Deletion, replacement and mean-shift for diagnostics in linear mixed models

Deletion, replacement and mean-shift model are three approaches frequently used to detect influential observations and outliers. For general linear model with known covariance matrix, it is known that these three approaches lead to the same update formulae for the estimates of the regression coefficients. However if the covariance matrix is indexed by some unknown parameters which also need to be estimated, the situation is unclear. In this paper, we show under a common subclass of linear mixed models that the three approaches are no longer equivalent. For maximum likelihood estimation, replacement is equivalent to mean-shift model but both are not equivalent to case deletion. For restricted maximum likelihood estimation, mean-shift model is equivalent to case deletion but both are not equivalent to replacement. We also demonstrate with real data that misuse of replacement and mean-shift model in place of case deletion can lead to incorrect results.     

Impact of pulmonary vascular stiffness and vasodilator treatment in pediatric pulmonary hypertension: 21 patient-specific fluid-structure interaction studies

Recent clinical studies of pulmonary arterial hypertension (PAH) have found correlations between increased pulmonary vascular stiffness (PVS) and poorer disease outcomes. However, mechanistic questions remain about the relationships amongst PVS, RV power, and vascular hemodynamics in the setting of progressive PAH that are difficult or impossible to answer using direct measurements. Clinically validated patient-specific computational modeling may allow exploration of these issues through perturbation-based predictive testing. Here we use a simple patient-specific model to answer four questions: how do hemodynamics change as PAH worsens? How does increasing PVS impact hemodynamics and RV power? For a patient with moderate PAH, what are the consequences if the pressures increase modestly yet sufficiently to engage collagen in those vessels? What impact does pressure-reducing vasodilator treatment have on hemodynamics? Twenty-one sets of model-predicted impedance and mean PA pressure (mPAP) show good agreement with clinical measurements, thereby validating the model. Worsening was modeled using data from three PAH outcomes groups; these show not only the expected increase in mPAP, but also an increase in pressure pulsatility. Interestingly, chronically increasing mPAP decreased WSS, suggesting that increased PA cross-sectional area affected WSS greater than increased PVS. For a patient with moderately high PVR (12.7 WU) with elastin-based upstream vascular remodeling, moving from elastin-dominant vessel behavior to collagen-dominant behavior caused substantial increases in mPAP, pressure and WSS pulsatility. For the same patient, reducing PVR through a simulated vasodilator to a value equivalent to mild PAH did not decrease pressure pulsatility and dramatically increased WSS pulsatility. Overall, these results suggest a close association between PVS and hemodynamics and that hemodynamics may play an important role in progressing PAH. These support the hypothesis that treatments should target decreasing or reversing upstream vascular remodeling in addition to decreasing mean pressures.     

Histology image analysis for carcinoma detection and grading

This paper presents an overview of the image analysis techniques in the domain of histopathology, specifically, for the objective of automated carcinoma detection and classification. As in other biomedical imaging areas such as radiology, many computer assisted diagnosis (CAD) systems have been implemented to aid histopathologists and clinicians in cancer diagnosis and research, which have been attempted to significantly reduce the labor and subjectivity of traditional manual intervention with histology images. The task of automated histology image analysis is usually not simple due to the unique characteristics of histology imaging, including the variability in image preparation techniques, clinical interpretation protocols, and the complex structures and very large size of the images themselves. In this paper we discuss those characteristics, provide relevant background information about slide preparation and interpretation, and review the application of digital image processing techniques to the field of histology image analysis. In particular, emphasis is given to state-of-the-art image segmentation methods for feature extraction and disease classification. Four major carcinomas of cervix, prostate, breast, and lung are selected to illustrate the functions and capabilities of existing CAD systems.     

3D navigation and monitoring for spinal milling operation based on registration between multiplanar fluoroscopy and CT images

Milling operations in spinal surgery demand much experience and skill for the surgeon to perform the procedure safely. A 3D navigation method is introduced aiming at providing a monitoring system with enhanced safety and minimal intraoperative interaction. An automatic registration method is presented to establish the 3D-3D transformation between the preoperative CT images and a common reference system in the surgical space, and an intensity-based similarity metric adapted for the multi-planar configuration is introduced in the registration procedure. A critical region is defined for real-time monitoring in order to prevent penetration of the lamina and avoid violation of nerve structures. The contour of the spinal canal is reconstructed as the critical region, and different levels of warning limits are defined. During the milling procedure, the position of the surgical instrument relative to the critical region is provided with augmented display and audio warnings. Timely alarm is provided for surgeons to prevent surgical failure when the mill approaches the critical region. Our validation experiment shows that real-time 3D navigation and monitoring is advantageous for improving the safety of the milling operation.     

Pinning a stochastic neural network to the synchronous state

In this paper, the asymptotic stability of the pinning synchronous solution of stochastic neural networks with and without time-delays is analyzed. The delays are time-varying, and the uncertainties are norm-bounded that enter into all the parameters of network and control. The aim of this paper is not only to establish easily verifiable conditions under which the pinning synchronous solution of stochastic neural network is globally asymptotically stable but also to give a feasible way to offset the limitation of network itself in order to reach synchronization. In addition, a specific neurobiological network is also introduced, and some numerical examples are provided to illustrate the applicability of the proposed criteria.     

Hoeffding bound based evolutionary algorithm for symbolic regression

In symbolic regression area, it is difficult for evolutionary algorithms to construct a regression model when the number of sample points is very large. Much time will be spent in calculating the fitness of the individuals and in selecting the best individuals within the population. Hoeffding bound is a probability bound for sums of independent random variables. As a statistical result, it can be used to exactly decide how many samples are necessary for choosing i individuals from a population in evolutionary algorithms without calculating the fitness completely. This paper presents a Hoeffding bound based evolutionary algorithm (HEA) for regression or approximation problems when the number of the given learning samples is very large. In HEA, the original fitness function is used in every k generations to update the approximate fitness obtained by Hoeffding bound. The parameter 1?δ is the probability of correctly selecting i best individuals from population P, which can be tuned to avoid an unstable evolution process caused by a large discrepancy between the approximate model and the original fitness function. The major advantage of the proposed HEA algorithm is that it can guarantee that the solution discovered has performance matching what would be discovered with a traditional genetic programming (GP) selection operator with a determinate probability and the running time can be reduced largely. We examine the performance of the proposed algorithm with several regression problems and the results indicate that with the similar accuracy, the HEA algorithm can find the solution more efficiently than tradition EA. It is very useful for regression problems with large number of training samples.