36 research outputs found

    LIPIcs, Volume 274, ESA 2023, Complete Volume

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    LIPIcs, Volume 274, ESA 2023, Complete Volum

    LIPIcs, Volume 258, SoCG 2023, Complete Volume

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    LIPIcs, Volume 258, SoCG 2023, Complete Volum

    Multi feature-rich synthetic colour to improve human visual perception of point clouds

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    Although point features have shown their usefulness in classification with Machine Learning, point cloud visualization enhancement methods focus mainly on lighting. The visualization of point features helps to improve the perception of the 3D environment. This paper proposes Multi Feature-Rich Synthetic Colour (MFRSC) as an alternative non-photorealistic colour approach of natural-coloured point clouds. The method is based on the selection of nine features (reflectance, return number, inclination, depth, height, point density, linearity, planarity, and scattering) associated with five human perception descriptors (edges, texture, shape, size, depth, orientation). The features are reduced to fit the RGB display channels. All feature permutations are analysed according to colour distance with the natural-coloured point cloud and Image Quality Assessment. As a result, the selected feature permutations allow a clear visualization of the scene's rendering objects, highlighting edges, planes, and volumetric objects. MFRSC effectively replaces natural colour, even with less distorted visualization according to BRISQUE, NIQUE and PIQE. In addition, the assignment of features in RGB channels enables the use of MFRSC in software that does not support colorization based on point attributes (most commercially available software). MFRSC can be combined with other non-photorealistic techniques such as Eye-Dome Lighting or Ambient Occlusion.Xunta de Galicia | Ref. ED481B-2019-061Xunta de Galicia | Ref. ED431F 2022/08Agencia Estatal de Investigación | Ref. PID2019-105221RB-C43Universidade de Vigo/CISU

    LIPIcs, Volume 244, ESA 2022, Complete Volume

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    LIPIcs, Volume 244, ESA 2022, Complete Volum

    Towards Performance Portable Graph Algorithms

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    In today's data-driven world, our computational resources have become heterogeneous, making the processing of large-scale graphs in an architecture agnostic manner crucial. Traditionally, hand-optimized high-performance computing (HPC) solutions have been studied and used to implement highly efficient and scalable graph algorithms. In recent years, several graph processing and management systems have also been proposed. Hand optimized HPC approaches require high levels of expertise and graph processing frameworks suffer from expressibility and performance. Portability is a major concern for both approaches. The main thesis of this work is that block-based graph algorithms offer a compromise between efficient parallelism and architecture agnostic algorithm design for a wide class of graph problems. This dissertation seeks to prove this thesis by focusing the work on the three pillars; data/computation partitioning, block-based algorithm design, and performance portability. In this dissertation, we first show how we can partition the computation and the data to design efficient block-based algorithms for solving graph merging and triangle counting problems. Then, generalizing from our experiences, we propose an algorithmic framework, for shared-memory, heterogeneous machines for implementing block-based graph algorithms; PGAbB. PGAbB aims to maximally leverage different architectures by implementing a task-based execution on top of a block-based programming model. In this talk we will discuss PGAbB's programming model, algorithmic optimizations for scheduling, and load-balancing strategies for graph problems on real-world and synthetic inputs.Ph.D

    The Stellar decomposition: A compact representation for simplicial complexes and beyond

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    We introduce the Stellar decomposition, a model for efficient topological data structures over a broad range of simplicial and cell complexes. A Stellar decomposition of a complex is a collection of regions indexing the complex’s vertices and cells such that each region has sufficient information to locally reconstruct the star of its vertices, i.e., the cells incident in the region’s vertices. Stellar decompositions are general in that they can compactly represent and efficiently traverse arbitrary complexes with a manifold or non-manifold domain. They are scalable to complexes in high dimension and of large size, and they enable users to easily construct tailored application-dependent data structures using a fraction of the memory required by a corresponding global topological data structure on the complex. As a concrete realization of this model for spatially embedded complexes, we introduce the Stellar tree, which combines a nested spatial tree with a simple tuning parameter to control the number of vertices in a region. Stellar trees exploit the complex’s spatial locality by reordering vertex and cell indices according to the spatial decomposition and by compressing sequential ranges of indices. Stellar trees are competitive with state-of-the-art topological data structures for manifold simplicial complexes and offer significant improvements for cell complexes and non-manifold simplicial complexes. We conclude with a high-level description of several mesh processing and analysis applications that utilize Stellar trees to process large datasets

    Compressing and Performing Algorithms on Massively Large Networks

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    Networks are represented as a set of nodes (vertices) and the arcs (links) connecting them. Such networks can model various real-world structures such as social networks (e.g., Facebook), information networks (e.g., citation networks), technological networks (e.g., the Internet), and biological networks (e.g., gene-phenotype network). Analysis of such structures is a heavily studied area with many applications. However, in this era of big data, we find ourselves with networks so massive that the space requirements inhibit network analysis. Since many of these networks have nodes and arcs on the order of billions to trillions, even basic data structures such as adjacency lists could cost petabytes to zettabytes of storage. Storing these networks in secondary memory would require I/O access (i.e., disk access) during analysis, thus drastically slowing analysis time. To perform analysis efficiently on such extensive data, we either need enough main memory for the data structures and algorithms, or we need to develop compressions that require much less space while still being able to answer queries efficiently. In this dissertation, we develop several compression techniques that succinctly represent these real-world networks while still being able to efficiently query the network (e.g., check if an arc exists between two nodes). Furthermore, since many of these networks continue to grow over time, our compression techniques also support the ability to add and remove nodes and edges directly on the compressed structure. We also provide a way to compress the data quickly without any intermediate structure, thus giving minimal memory overhead. We provide detailed analysis and prove that our compression is indeed succinct (i.e., achieves the information-theoretic lower bound). Also, we empirically show that our compression rates outperform or are equal to existing compression algorithms on many benchmark datasets. We also extend our technique to time-evolving networks. That is, we store the entire state of the network at each time frame. Studying time-evolving networks allows us to find patterns throughout the time that would not be available in regular, static network analysis. A succinct representation for time-evolving networks is arguably more important than static graphs, due to the extra dimension inflating the space requirements of basic data structures even more. Again, we manage to achieve succinctness while also providing fast encoding, minimal memory overhead during encoding, fast queries, and fast, direct modification. We also compare against several benchmarks and empirically show that we achieve compression rates better than or equal to the best performing benchmark for each dataset. Finally, we also develop both static and time-evolving algorithms that run directly on our compressed structures. Using our static graph compression combined with our differential technique, we find that we can speed up matrix-vector multiplication by reusing previously computed products. We compare our results against a similar technique using the Webgraph Framework, and we see that not only are our base query speeds faster, but we also gain a more significant speed-up from reusing products. Then, we use our time-evolving compression to solve the earliest arrival paths problem and time-evolving transitive closure. We found that not only were we the first to run such algorithms directly on compressed data, but that our technique was particularly efficient at doing so

    Machine learning and privacy preserving algorithms for spatial and temporal sensing

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    Sensing physical and social environments are ubiquitous in modern mobile phones, IoT devices, and infrastructure-based settings. Information engraved in such data, especially the time and location attributes have unprecedented potential to characterize individual and crowd behaviour, natural and technological processes. However, it is challenging to extract abstract knowledge from the data due to its massive size, sequential structure, asynchronous operation, noisy characteristics, privacy concerns, and real time analysis requirements. Therefore, the primary goal of this thesis is to propose theoretically grounded and practically useful algorithms to learn from location and time stamps in sensor data. The proposed methods are inspired by tools from geometry, topology, and statistics. They leverage structures in the temporal and spatial data by probabilistically modeling noise, exploring topological structures embedded, and utilizing statistical structure to protect personal information and simultaneously learn aggregate information. Proposed algorithms are geared towards streaming and distributed operation for efficiency. The usefulness of the methods is argued using mathematical analysis and empirical experiments on real and artificial datasets
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