Variable Resolution & Dimensional Mapping For 3d Model Optimization

Three-dimensional computer models, especially geospatial architectural data sets, can be visualized in the same way humans experience the world, providing a realistic, interactive experience. Scene familiarization, architectural analysis, scientific visualization, and many other applications would benefit from finely detailed, high resolution, 3D models. Automated methods to construct these 3D models traditionally has produced data sets that are often low fidelity or inaccurate; otherwise, they are initially highly detailed, but are very labor and time intensive to construct. Such data sets are often not practical for common real-time usage and are not easily updated. This thesis proposes Variable Resolution & Dimensional Mapping (VRDM), a methodology that has been developed to address some of the limitations of existing approaches to model construction from images. Key components of VRDM are texture palettes, which enable variable and ultra-high resolution images to be easily composited; texture features, which allow image features to integrated as image or geometry, and have the ability to modify the geometric model structure to add detail. These components support a primary VRDM objective of facilitating model refinement with additional data. This can be done until the desired fidelity is achieved as practical limits of infinite detail are approached. Texture Levels, the third component, enable real-time interaction with a very detailed model, along with the flexibility of having alternate pixel data for a given area of the model and this is achieved through extra dimensions. Together these techniques have been used to construct models that can contain GBs of imagery data

Effective Visualization Approaches For Ultra-High Dimensional Datasets

Multivariate informational data, which are abstract as well as complex, are becoming increasingly common in many areas such as scientific, medical, social, business, and so on. Displaying and analyzing large amounts of multivariate data with more than three variables of different types is quite challenging. Visualization of such multivariate data suffers from a high degree of clutter when the numbers of dimensions/variables and data observations become too large. We propose multiple approaches to effectively visualize large datasets of ultrahigh number of dimensions by generalizing two standard multivariate visualization methods, namely star plot and parallel coordinates plot. We refine three variants of the star plot, which include overlapped star plot, shifted origin plot, and multilevel star plot by embedding distribution plots, displaying dataset in groups, and supporting adjustable positioning of the star axes. We introduce a bifocal parallel coordinates plot (BPCP) based on the focus + context approach. BPCP splits vertically the overall rendering area into the focus and context regions. The focus area maps a few selected dimensions of interest at sufficiently wide spacing. The remaining dimensions are represented in the context area in a compact way to retain useful information and provide the data continuity. The focus display can be further enriched with various options, such as axes overlays, scatterplot, and nested PCPs. In order to accommodate an arbitrarily large number of dimensions, the context display supports the multi-level stacked view. Finally, we present two innovative ways of enhancing parallel coordinates axes to better understand all variables and their interrelationships in high-dimensional datasets. Histogram and circle/ellipse plots based on uniform and non-uniform frequency/density mappings are adopted to visualize distributions of numerical and categorical data values. Color-mapped axis stripes are designed in the parallel coordinates layout so that correlations can be fully realized in the same display plot irrespective of axes locations. These colors are also propagated to histograms as stacked bars and categorical values as pie charts to further facilitate data exploration. By using the datasets consisting of 25 to 130 variables of different data types we have demonstrated effectiveness of the proposed multivariate visualization enhancements

Microtubule rescue at midzone edges promotes overlap stability and prevents spindle collapse during anaphase B

During anaphase B, molecular motors slide interpolar microtubules to elongate the mitotic spindle, contributing to the separation of chromosomes. However, sliding of antiparallel microtubules reduces their overlap, which may lead to spindle breakage, unless microtubules grow to compensate sliding. How sliding and growth are coordinated is still poorly understood. In this study, we have used the 1ssion yeast S. pombe to measure microtubule dynamics during anaphase B. We report that the coordination of microtubule growth and sliding relies on promoting rescues at the midzone edges. This makes microtubules stable from pole to midzone, while their distal parts including the plus ends alternate between assembly and disassembly. Consequently, the midzone keeps a constant length throughout anaphase, enabling sustained sliding without the need for a precise regulation of microtubule growth speed. Additionally, we found that in S. pombe, which undergoes closed mitosis, microtubule growth speed decreases when the nuclear 22 membrane wraps around the spindle midzone

Data visualizing popular science fiction movies with use of circular hierarchical edge bundling

In this article, a specific type of data visualization method called Circular Hierarchical Edge Bundling has been utilized to investigate a subjective discussion on determining the most commonly observed themes in the popular Sci-Fi Movies. To reflect people’s opinions on the subject, a website (www.dystopia-utopia.com) has been designed to invite larger communities to participate in with filling an online form to deliver their judgments. Data Visualization methods and the research results are elaborated in further details

Visual analytics of multidimensional time-dependent trails:with applications in shape tracking

Lots of data collected for both scientific and non-scientific purposes have similar characteristics: changing over time with many different properties. For example, consider the trajectory of an airplane travelling from one location to the other. Not only does the airplane itself move over time, but its heading, height and speed are changing at the same time. During this research, we investigated different ways to collect and visualze data with these characteristics. One practical application being for an automated milking device which needs to be able to determine the position of a cow's teats. By visualizing all data which is generated during the tracking process we can acquire insights in the working of the tracking system and identify possibilites for improvement which should lead to better recognition of the teats by the machine. Another important result of the research is a method which can be used to efficiently process a large amount of trajectory data and visualize this in a simplified manner. This has lead to a system which can be used to show the movement of all airplanes around the world for a period of multiple weeks

Visualizing multidimensional data similarities:Improvements and applications

Multidimensional data is increasingly more prominent and important in many application domains. Such data typically consist of a large set of elements, each of which described by several measurements (dimensions). During the design of techniques and tools to process this data, a key component is to gather insights into their structure and patterns, which can be described by the notion of similarity between elements. Among these techniques, multidimensional projections and similarity trees can effectively capture similarity patterns and handle a large number of data elements and dimensions. However, understanding and interpreting these patterns in terms of the original data dimensions is still hard. This thesis addresses the development of visual explanatory techniques for the easy interpretation of similarity patterns present in multidimensional projections and similarity trees, by several contributions. First, we propose methods that make the computation of similarity trees efficient for large datasets, and also enhance its visual representation to allow the exploration of more data in a limited screen. Secondly, we propose methods for the visual explanation of multidimensional projections in terms of groups of similar elements. These are automatically annotated to describe which dimensions are more important to define their notion of group similarity. We show next how these explanatory mechanisms can be adapted to handle both static and time-dependent data. Our proposed techniques are designed to be easy to use, work nearly automatically, and are demonstrated on a variety of real-world large data obtained from image collections, text archives, scientific measurements, and software engineering

LIPIcs, Volume 258, SoCG 2023, Complete Volume

LIPIcs, Volume 258, SoCG 2023, Complete Volum

Contributions to the analysis of proteins

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 131-145).Proteins are essential to organisms and play a central role in almost every biological process. The analysis of the conformational dynamics and mechanics of proteins using numerical methods, such as normal mode analysis (NMA), provides insight into their functional mechanisms. However, despite the fact that much effort has been focused on improving NMA over the last few decades, the analysis of large-scale protein motions is still infeasible due to computational limitations. In this work, first, we identify the usefulness and effectiveness of the subspace iteration (SSI) procedure, otherwise widely used in structural engineering, for the analysis of proteins. We also develop a novel technique for the selection of iteration vectors in protein NMA, which significantly increases the effectiveness of the method. The SSI procedure also lends itself naturally to efficient NMA of multiple neighboring macromolecular conformations, as demonstrated in a conformational change pathway analysis of adenylate kinase. Next, we present a new algorithm to account for the effects of solvent-damping on slow protein conformational dynamics. The algorithm proves to be an effective approach to calculating the diffusion coefficients of proteins with various molecular weights, as well as their Langevin modes and corresponding relaxation times, as demonstrated for the small molecule crambin. Finally, the structure of Homo sapiens fascin-1, an actin-binding protein that is present predominantly in filopodia, is examined and described in detail. Application of a sequence conservation analysis to the protein indicates highly conserved surface patches near the putative actin-binding domains of fascin. A novel conformational dynamics analysis suggests that these domains are coupled via an allosteric mechanism that may have important functional implications for F-actin bundling by fascin.by Reza Sharifi Sedeh.Ph.D