7 research outputs found

    LIPIcs, Volume 244, ESA 2022, Complete Volume

    Get PDF
    LIPIcs, Volume 244, ESA 2022, Complete Volum

    Navigating Weighted Regions with Scattered Skinny Tetrahedra

    No full text
    We propose an algorithm for finding a (1 + ??)-approximate shortest path through a weighted 3D simplicial complex T. The weights are integers from the range [1,W] and the vertices have integral coordinates. Let N be the largest vertex coordinate magnitude, and let n be the number of tetrahedra in T. Let ?? be some arbitrary constant. Let ?? be the size of the largest connected component of tetrahedra whose aspect ratios exceed ??. There exists a constant C dependent on ?? but independent of T such that if ?? ??? 1 C log log n + O(1), the running time of our algorithm is polynomial in n, 1/?? and log(NW). If ?? = O(1), the running time reduces to O(n??−O(1)(log(NW))O(1))

    Navigating weighted regions with scattered skinny tetrahedra

    No full text
    We propose an algorithm for finding a (1 + ??)-approximate shortest path through a weighted 3D simplicial complex ??. The weights are integers from the range [1,W] and the vertices have integral coordinates. Let N be the largest vertex coordinate magnitude, and let n be the number of tetrahedra in ??. Let ?? be some arbitrary constant. Let ?? be the size of the largest connected component of tetrahedra whose aspect ratios exceed ??. There exists a constant C dependent on ?? but independent of such that if ?? ??? 1 Cloglog n + O(1), the running time of our algorithm is polynomial in n, 1/ and log(NW). If ?? = O(1), the running time reduces to O(n??-O(1)(log(NW))O(1))

    Proceedings, MSVSCC 2014

    Get PDF
    Proceedings of the 8th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 17, 2014 at VMASC in Suffolk, Virginia

    Deep Model for Improved Operator Function State Assessment

    Get PDF
    A deep learning framework is presented for engagement assessment using EEG signals. Deep learning is a recently developed machine learning technique and has been applied to many applications. In this paper, we proposed a deep learning strategy for operator function state (OFS) assessment. Fifteen pilots participated in a flight simulation from Seattle to Chicago. During the four-hour simulation, EEG signals were recorded for each pilot. We labeled 20- minute data as engaged and disengaged to fine-tune the deep network and utilized the remaining vast amount of unlabeled data to initialize the network. The trained deep network was then used to assess if a pilot was engaged during the four-hour simulation

    Gridfields: Model-Driven Data Transformation in the Physical Sciences

    Get PDF
    Scientists\u27 ability to generate and store simulation results is outpacing their ability to analyze them via ad hoc programs. We observe that these programs exhibit an algebraic structure that can be used to facilitate reasoning and improve performance. In this dissertation, we present a formal data model that exposes this algebraic structure, then implement the model, evaluate it, and use it to express, optimize, and reason about data transformations in a variety of scientific domains. Simulation results are defined over a logical grid structure that allows a continuous domain to be represented discretely in the computer. Existing approaches for manipulating these gridded datasets are incomplete. The performance of SQL queries that manipulate large numeric datasets is not competitive with that of specialized tools, and the up-front effort required to deploy a relational database makes them unpopular for dynamic scientific applications. Tools for processing multidimensional arrays can only capture regular, rectilinear grids. Visualization libraries accommodate arbitrary grids, but no algebra has been developed to simplify their use and afford optimization. Further, these libraries are data dependent—physical changes to data characteristics break user programs. We adopt the grid as a first-class citizen, separating topology from geometry and separating structure from data. Our model is agnostic with respect to dimension, uniformly capturing, for example, particle trajectories (1-D), sea-surface temperatures (2-D), and blood flow in the heart (3-D). Equipped with data, a grid becomes a gridfield. We provide operators for constructing, transforming, and aggregating gridfields that admit algebraic laws useful for optimization. We implement the model by analyzing several candidate data structures and incorporating their best features. We then show how to deploy gridfields in practice by injecting the model as middleware between heterogeneous, ad hoc file formats and a popular visualization library. In this dissertation, we define, develop, implement, evaluate and deploy a model of gridded datasets that accommodates a variety of complex grid structures and a variety of complex data products. We evaluate the applicability and performance of the model using datasets from oceanography, seismology, and medicine and conclude that our model-driven approach offers significant advantages over the status quo
    corecore