A level-set approach for the metamorphosis of solid models

We present a new approach to 3D shape metamorphosis. We express the interpolation of two shapes as a process where one shape deforms to maximize its similarity with another shape. The process incrementally optimizes an objective function while deforming an implicit surface model. We represent the deformable surface as a level set (iso-surface) of a densely sampled scalar function of three dimensions. Such level-set models have been shown to mimic conventional parametric deformable surface models by encoding surface movements as changes in the grayscale values of a volume data set. Thus, a well-founded mathematical structure leads to a set of procedures that describes how voxel values can be manipulated to create deformations that are represented as a sequence of volumes. The result is a 3D morphing method that offers several advantages over previous methods, including minimal need for user input, no model parameterization, flexible topology, and subvoxel accuracy     

3D Metamorphosis Between Different Types of Geometric Models

We present a powerful morphing technique based on level set methods, that can be combined with a variety of scan conversion/model processing techniques. Bringing these techniques together creates a general morphing approach that allows a user to morph a number of geometric model types in a single animation. We have developed techniques for converting several types of geometric models (polygonal meshes, CSG models and MRI scans) into distance volumes, the volumetric representation required by our level set morphing approach. The combination of these two capabilities allows a user to create a morphing sequence regardless of the model type of the source and target objects, freeing him/her to use whatever model type is appropriate for a particular animation.     

The peak in the thermal conductivity of Cu-O superconductors: Electronic or phononic origin?

The thermal conductivity of hole-doped Cu-O plane high-T _c perovskites exhibits a dramatic increase belowT _c which results in a pronounced peak nearT _c /2. The origin of this peak was initially thought to arise from an enhancement in the mean-free path of phonons as the charge carriers undergo condensation. Indeed, excellent fits to the data can be obtained with physically reasonable parameters using the conventional theory of lattice conduction in superconductors. In contrast, a recently observed sharp decrease in the quasiparticle scattering rate of YBCO single crystals belowT _c has motivated proposals for an electronic origin of the thermal conductivity peak. We shall critically examine experimental evidence and highlight relative advantages and shortcomings of the two contrasting interpretations. Furthermore, we shall draw attention to recently available data on the relaxation time of out-of-equilibrium carriers in Cu-O superconductors obtained using pump-probe femtosecond laser studies and what new light they shed on the controversy.     

A case-study on naïve labelling for the nearest mean and the linear discriminant classifiers

The abundance of unlabelled data alongside limited labelled data has provoked significant interest in semi-supervised learning methods. “Naïve labelling” refers to the following simple strategy for using unlabelled data in on-line classification. A new data point is first labelled by the current classifier and then added to the training set together with the assigned label. The classifier is updated before seeing the subsequent data point. Although the danger of a run-away classifier is obvious, versions of naïve labelling pervade in on-line adaptive learning. We study the asymptotic behaviour of naïve labelling in the case of two Gaussian classes and one variable. The analysis shows that if the classifier model assumes correctly the underlying distribution of the problem, naïve labelling will drive the parameters of the classifier towards their optimal values. However, if the model is not guessed correctly, the benefits are outweighed by the instability of the labelling strategy (run-away behaviour of the classifier). The results are based on exact calculations of the point of convergence, simulations, and experiments with 25 real data sets. The findings in our study are consistent with concerns about general use of unlabelled data, flagged up in the recent literature.     

Visual exploration of high dimensional scalar functions

An important goal of scientific data analysis is to understand the behavior of a system or process based on a sample of the system. In many instances it is possible to observe both input parameters and system outputs, and characterize the system as a high-dimensional function. Such data sets arise, for instance, in large numerical simulations, as energy landscapes in optimization problems, or in the analysis of image data relating to biological or medical parameters. This paper proposes an approach to analyze and visualizing such data sets. The proposed method combines topological and geometric techniques to provide interactive visualizations of discretely sampled high-dimensional scalar fields. The method relies on a segmentation of the parameter space using an approximate Morse-Smale complex on the cloud of point samples. For each crystal of the Morse-Smale complex, a regression of the system parameters with respect to the output yields a curve in the parameter space. The result is a simplified geometric representation of the Morse-Smale complex in the high dimensional input domain. Finally, the geometric representation is embedded in 2D, using dimension reduction, to provide a visualization platform. The geometric properties of the regression curves enable the visualization of additional information about each crystal such as local and global shape, width, length, and sampling densities. The method is illustrated on several synthetic examples of two dimensional functions. Two use cases, using data sets from the UCI machine learning repository, demonstrate the utility of the proposed approach on real data. Finally, in collaboration with domain experts the proposed method is applied to two scientific challenges. The analysis of parameters of climate simulations and their relationship to predicted global energy flux and the concentrations of chemical species in a combustion simulation and their integration with temperature.     

Rethinking the design of virtual machine monitors

A virtual machine monitor is a software system that partitions a single physical machine into multiple virtual machines. Traditionally, VMMs have created a precise replica of the underlying physical machine. Through faithful emulation, VMMs support the execution of legacy guest operating systems such as Windows or Linux without modifications. However, traditional VMMs suffer from poor scalability and extensibility. To overcome the poor scalability and extensibility of traditional virtual machine monitors that partition a single physical machine into multiple virtual machines, the Denali VMM uses paravirtualization to promote scalability and hardware interposition to promote extensibility.     

Modeling deformable surfaces with level sets

This article describes how to use level sets to represent and compute deformable surfaces. A deformable surface is a sequence of surface models obtained by taking an initial model and incrementally modifying its shape. Typically, we can parameterize the deformation over time, and thus we can imagine that a surface moves or flows under the influence of a vector field. The surface flow, v, can be determined as a function of spatial position (and time), or it can depend on the shape of the surface itself. The latter is called a geometric flow. Deformable surfaces have been used to solve a variety of problems in image processing, computer vision, visualization, and graphics. In graphics, for instance, deformable surface models have been used to form sequences of shapes that animate the morphing of one object into another. They have also been used to denoise or smooth surface models derived from a set of noisy 3D measurements.     

Growth support and metabolism of phytosterols inParamecium tetraurelia

The basis of the growth requirement ofParamecium for one of several structurally similar phytosterols is not known. Previous research has indicated that selective esterification of only growth-promoting sterols may be a key. In this study, it was found that under certain conditions sterols that fail to support growth (e.g., cholesterol) can be esterified in large amounts inParamecium. We found no compelling evidence to support the hypothesis that steryl esters serve a specialized role in the fatty acid metabolism of the cell. Octadecenoic acid, essential for cell growth, was the major fatty acid in both steryl esters and triglycerides. It was also shown thatP. tetraurelia can dehydrogenate Δ⁰ and Δ⁷, as well as Δ⁵-3β-hydroxy sterols, to yield the conjugated 5,7-diene derivative. These results indicate the presence of a Δ⁵, in addition to a Δ⁷, desaturase of the sterol nucleus in this ciliate. Two C₂₄ α-ethyl sterols, Δ²²-stigmasterol (Δ²²) and stigmastanol (Δ⁰), were shown for the first time to promote growth. Finally, we found that non-growth-promoting sterols may compose a high percentage of the free sterols of the surface membrane without adversely affecting cell growth or viability. These data support the conclusion that the growth requirement for select phytosterols inParamecium does not involve the structural or functional role of “bulk” sterols in cell membranes.     

利用环境产生电能创造无电池无线传感器

本文介绍了基于最新半导体器件提供极低的静态电流和一种高效率能量收集解决方案,使分布式传感器网络更易于部署。传感器现在可以用在远程地点,而无需担忧电池寿命。     

A multicomponent intervention to prevent partner violence within an existing service intervention.

Intimate partner violence (IPV) is an enormous public health problem that results in injury, health problems, and substantial cost to society. Despite having a grasp of the scope of IPV, public health officials and workers know little about how to prevent it. The few empirically established primary prevention programs consist of school-based curricula targeting high school students. Additional venues for IPV prevention are needed, especially for women at elevated risk. This article describes a preventive intervention for IPV consisting of three components: (a) a structured assessment for IPV; (b) a brochure-driven intervention for women experiencing IPV, including safety planning, referrals, and advocacy; and (c) a skills-based curriculum delivered to all participants that focuses on improving relationship decisions and outcomes. While this intervention could potentially be delivered in a multitude of clinical settings, this article focuses on its delivery within a home visitation program for young, disadvantaged new mothers, a population known to be at increased risk for IPV. If found to be effective, this intervention could be incorporated into many service delivery systems, with broad-based clinical implications for IPV prevention. (PsycINFO Database Record (c) 2010 APA, all rights reserved)