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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
One Tile to Rule Them All: Simulating Any Turing Machine,...
Erik D. Demaine, Martin L. Demaine, Sándor P. Fekete, Matthew J. · 2012-12-20 · via cs.DS updates on arXiv.org

In this paper we explore the power of tile self-assembly models that extend the well-studied abstract Tile Assembly Model (aTAM) by permitting tiles of shapes beyond unit squares. Our main result shows the surprising fact that any aTAM system, consisting of many different tile types, can be simulated by a single tile type of a general shape. As a consequence, we obtain a single universal tile type of a single (constant-size) shape that serves as a "universal tile machine": the single universal tile type can simulate any desired aTAM system when given a single seed assembly that encodes the desired aTAM system. We also show how to adapt this result to convert any of a variety of plane tiling systems (such as Wang tiles) into a "nearly" plane tiling system with a single tile (but with small gaps between the tiles). All of these results rely on the ability to both rotate and translate tiles; by contrast, we show that a single nonrotatable tile, of arbitrary shape, can produce assemblies which either grow infinitely or cannot grow at all, implying drastically limited computational power. On the positive side, we show how to simulate arbitrary cellular automata for a limited number of steps using a single nonrotatable tile and a linear-size seed assembly.