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PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Distributed Computing for Localized and Multilayer Visual...
Mark Burgin, Walter Karplus, Damon Liu · 2001-11-09 · via cs.DS updates on arXiv.org

The aim of this paper is to develop an approach to visualizations that benefits from distributed computing. Three schemes of process distribution are considered: parallel, pipeline, and expanding pipeline computations. Expanding pipeline structure synthesizes the advantages and traits of both parallel and pipeline computations. In expanding pipeline computing, a novel approach presented in this paper, a multiplicity of processes are concurrently developed in parallel and knotted processor pipelines. The theoretical foundations for expanding pipeline computing as a computational process are in the domains of alternating Turing machines, molecular computing, and E-machines. Expanding pipeline computing constitutes the development of the conventional pipeline architecture aimed at utilization of implicit parallel structures existing in algorithms. Such structures appear in various kinds of visualization. Image deriving and processing is a field that provides diverse opportunities for utilization of the advantages of distributed computing. The most relevant to the distributed architecture is stratified visualization with its two cases based on data localization and layer separation. Visualization is treated here as a special case of simulation. The conceptual approach to distributed computing developed in this paper have been applied to visualization in a computer support system, which is utilized in radiology and namely, for the noninvasive treatment of brain aneurysms.