Preparation of cross-sectional specimens of ceramic thermal barrier coatings for transmission electron microscopy

During the microstructural examination of ceramic thermal barrier coatings by transmission electron microscopy (TEM), initial efforts for the preparation of cross-sectional thin foils from interface regions by conventional means were mostly failures. Delamination of the Y₂O₃-stabilized ZrO₂ ceramic coating from the nickel-base alloy substrate sometimes occurred during fine polishing at around 80 μm thickness but mostly occurred during dimpling. Because of this sensitivity, special techniques for mechanical handling were developed so that ion milling could give thin enough regions of the metal-ceramic interface. TEM showed convincingly that the highly fragile nature of the coatings is in fact due to the extensive porosity at the interface developed as a result of heat treatment.     

Looking Beyond Informality

It is all too easy to be optimistic about the economic and social future of Latin America. Daniela Fabricius , the author of 100% Favela: The Informal Geographies of Rio de Janeiro (forthcoming), calls into question architects working within the realpolitik of a globalised, post-nationalist world. Could an all too ready acceptance of existing conditions and the adoption of informality leave inhabitants short-changed? For to live informally is also to live precariously - no substitute for secure and prosperous living.     

Two-dimensionally quantized friction observed with two-dimensional frictional force microscope

Using a two-dimensional frictional force microscope, we studied the two-dimensional nature of the atomic scale friction between a Si₃N₄ sharp tip and a cleaved graphite surface, which is composed of only C atoms and is a good conductor. As a result, we observed the two-dimensionally quantized friction with the lattice periodicity of the graphite surface, similarly to mica, MoS₂ and NaF surfaces. Thus quantized friction occurs at material surfaces which are composed of not only some elements but also of a single element, and the quantized friction does not depend on the conductivity of the surface.     

Modeling and Exploring Co‐variations in the Geometry and Configuration of Man‐made 3D Shape Families

We introduce co‐variation analysis as a tool for modeling the way part geometries and configurations co‐vary across a family of man‐made 3D shapes. While man‐made 3D objects exhibit large geometric and structural variations, the geometry, structure, and configuration of their individual components usually do not vary independently from each other but in a correlated fashion. The size of the body of an airplane, for example, constrains the range of deformations its wings can undergo to ensure that the entire object remains a functionally‐valid airplane. These co‐variation constraints, which are often non‐linear, can be either physical, and thus they can be explicitly enumerated, or implicit to the design and style of the shape family. In this article, we propose a data‐driven approach, which takes pre‐segmented 3D shapes with known component‐wise correspondences and learns how various geometric and structural properties of their components co‐vary across the set. We demonstrate, using a variety of 3D shape families, the utility of the proposed co‐variation analysis in various applications including 3D shape repositories exploration and shape editing where the propagation of deformations is guided by the co‐variation analysis. We also show that the framework can be used for context‐guided orientation of objects in 3D scenes.     

Graffinity: Visualizing Connectivity in Large Graphs

Multivariate graphs are prolific across many fields, including transportation and neuroscience. A key task in graph analysis is the exploration of connectivity, to, for example, analyze how signals flow through neurons, or to explore how well different cities are connected by flights. While standard node‐link diagrams are helpful in judging connectivity, they do not scale to large networks. Adjacency matrices also do not scale to large networks and are only suitable to judge connectivity of adjacent nodes. A key approach to realize scalable graph visualization are queries: instead of displaying the whole network, only a relevant subset is shown. Query‐based techniques for analyzing connectivity in graphs, however, can also easily suffer from cluttering if the query result is big enough. To remedy this, we introduce techniques that provide an overview of the connectivity and reveal details on demand. We have two main contributions: (1) two novel visualization techniques that work in concert for summarizing graph connectivity; and (2) Graffinity, an open‐source implementation of these visualizations supplemented by detail views to enable a complete analysis workflow. Graffinity was designed in a close collaboration with neuroscientists and is optimized for connectomics data analysis, yet the technique is applicable across domains. We validate the connectivity overview and our open‐source tool with illustrative examples using flight and connectomics data.     

Carbon micro-ribbon and strip polarimeter targets for the AGS and RHIC

Current techniques used to produce carbon micro-ribbon targets 5 μm wide×3.7–4.5 μg/cm²×25-mm long will be described. Developmental emphasis was to provide nearly identical micro-ribbons with the minimum number of atoms per unit of length, and to position them within ±0.5 mm of the desired location on C-shaped frames.The foil strip targets to be described were 200–600 μm wide×3.7–4.5 μg/cm²×51 mm long. These were produced from 25-mm-wide carbon film deposits that were scribed using a jig prior to dissolving the betaine/sucrose release agent under ethanol.Both types of targets required methods and devices that differed significantly from those reported previously for substrate texturing, masking, vacuum deposition, releasing from the substrate, and mounting. Sets of 12–24 of the targets have been made for the 2006 run period at BNL.     

Mean-field phase transitions and correlation functions for Gibbs random fields

The use of Gibbs random fields (GRF) to model images poses the important problem of the dependence of the patterns sampled from the Gibbs distribution on its parameters. Sudden changes in these patterns as the parameters are varied are known asphase transitions. In this paper we concentrate on developing a general deterministic theory for the study of phase transitions when a single parameter, namely, the temperature, is varied. This deterministic framework is based on a technique known as themean-field approximation, which is widely used in statistical physics. Our mean-field theory is general in that it is valid for any number of gray levels, any pairwise interaction potential, any neighborhood structure or size, and any set of constraints imposed on the desired images. The mean-field approximation is used to compute closed-form estimates of the critical temperatures at which phase transitions occur for two texture models widely used in the image modeling literature: the Potts model and the autobinomial model. The mean-field model allows us to gain insight into the Gibbs model behavior in the neighborhood of these temperatures. These analytical results are verified by computer simulations that use a novel mean-field descent algorithm. An important spinoff of our mean-field theory is that it allows us to compute approximations for the correlation functions of GRF models, thus bridging the gap between neighborhood-based and correlation-baseda priori image models.The work of I.M. Elfadel was supported in part by the National Science Foundation under grant MIP-91-17724. The work of A.L. Yuille was supported by the Brown, Harvard, and MIT Center for Intelligent Control Systems under U.S. Army Research Office grant DAAL03-86-C-0171, by the Defense Advanced Research Projects Agency under contract AFOSR-89-0506, and by the National Science Foundation under grant IRI-9003306.     

Bijective Composite Mean Value Mappings

We introduce the novel concept of composite barycentric mappings and give theoretical conditions under which they are guaranteed to be bijective. We then focus on mean value mappings and derive a simple procedure for computing their Jacobians, leading to an efficient GPU‐assisted implementation for interactively designing composite mean value mappings which are bijective up to pixel resolution. We provide a number of examples of 2D image deformation and an example of 3D shape deformation based on a natural extension of the concept to spatial mappings.     

Boundary‐Aware Extinction Mapping

We introduce Boundary‐Aware Extinction Maps for interactive rendering of massive heterogeneous volumetric datasets. Our approach is based on the projection of the extinction along light rays into a boundary‐aware function space, focusing on the most relevant sections of the light paths. This technique also provides an alternative representation of the set of participating media, allowing scattering simulation methods to be applied on arbitrary volume representations. Combined with a simple out‐of‐core rendering framework, Boundary‐Aware Extinction Maps are valuable tools for interactive applications as well as production previsualization and rendering.     

Adaptive Ray‐bundle Tracing with Memory Usage Prediction: Efficient Global Illumination in Large Scenes

This paper proposes an adaptive rendering technique for ray‐bundle tracing. Ray‐bundle tracing can be done by per‐pixel linked‐list construction on a GPU rasterization pipeline. This rasterization based approach offers significant benefits for the efficient generation of light maps (e.g., hardware acceleration, tessellation, and recycling of shaders used in real‐time graphics). However, it is inapplicable to large and complex scenes due to the limited capacity of the GPU memory because it requires a high‐resolution frame buffer and high‐capacity node buffer for the linked‐lists. In addition, memory overflow can potentially occur on the per‐pixel linked‐list since the memory usage of the lists is usually unknown before the rendering process. We introduce an adaptive tiling technique with memory usage prediction. Our method uses an appropriately tiled frame buffer, thus eliminating almost all of the overflow risks thanks to our adaptive tile subdivision scheme. Using this technique, we are able to render high‐quality light maps of large and complex scenes which cannot be computed using previous ray‐bundle based methods.