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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
New Clocks, Optimal Line Formation and Self-Replication P...
Leszek Gasieniec, Paul Spirakis, Grzegorz Stachowiak · 2021-11-21 · via cs.DS updates on arXiv.org

In this paper we consider a variant of population protocols in which agents are allowed to be connected by edges, known as the constructors model. During an interaction between two agents the relevant connecting edge can be formed, maintained or eliminated by the transition function. The contributions of this paper are manifold. -- We propose and analyse a novel type of phase clocks allowing to count parallel time $Θ(n\log n)$ in the constructors model. This new type of clocks can be also implemented in the standard population protocol model assuming a unique leader is available. -- The new clock enables an optimal $O(n\log n)$ parallel time spanning line construction which improves dramatically on the best previously known $O(n^2)$ parallel time solution. -- We define a probabilistic version of bubble-sort in which random comparisons are allowed only between adjacent numbers in the sequence being sorted. We show that rather surprisingly this probabilistic bubble-sort requires $O(n^2)$ comparisons in expectation, i.e., on the same level as its deterministic counterpart. -- We propose the first self-replication protocol allowing to reproduce a strand (line-segment carrying information) of length $k$ in parallel time $O(n(k+\log n)).$ This result is based on the probabilistic bubble-sort argument. This protocol permits also simultaneous replication where $l$ copies of the strand can be obtained in time $O(n(k+\log n)\log l).$ All protocols in this paper operate with high probability.