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cs.DS updates on arXiv.org

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
ColorFloat: Constant space token coloring
Ryan Zarick, Bryan Pellegrino, Isaac Zhang, Thomas Kim, Caleb Ba · 2023-11-14 · via cs.DS updates on arXiv.org

We present ColorFloat, a family of O(1) space complexity algorithms that solve the problem of attributing (coloring) fungible tokens to the entity that minted them (minter). Tagging fungible tokens with metadata is not a new problem and was first formalized in the Colored Coins protocol. In certain contexts, practical solutions to this challenge have been implemented and deployed such as NFT. We define the fungible token coloring problem, one specific aspect of the Colored Coins problem, to be the problem of retaining fungible characteristics of the underlying token while accurately tracking the attribution of fungible tokens to their respective minters. Fungible token coloring has a wide range of Web3 applications. One application which we highlight in this paper is the onchain yield-sharing collateral-based stablecoin.