惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

P
Proofpoint News Feed
V
V2EX
WordPress大学
WordPress大学
Google DeepMind News
Google DeepMind News
Martin Fowler
Martin Fowler
小众软件
小众软件
Blog — PlanetScale
Blog — PlanetScale
月光博客
月光博客
The Cloudflare Blog
T
Tailwind CSS Blog
H
Help Net Security
腾讯CDC
爱范儿
爱范儿
人人都是产品经理
人人都是产品经理
H
Hackread – Cybersecurity News, Data Breaches, AI and More
The GitHub Blog
The GitHub Blog
Microsoft Security Blog
Microsoft Security Blog
Stack Overflow Blog
Stack Overflow Blog
D
DataBreaches.Net
C
Check Point Blog
量子位
酷 壳 – CoolShell
酷 壳 – CoolShell
美团技术团队
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com

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
Inferring an Indeterminate String from a Prefix Graph
Ali Alatabbi, M. Sohel Rahman, W. F. Smyth · 2015-02-27 · via cs.DS updates on arXiv.org

An \itbf{indeterminate string} (or, more simply, just a \itbf{string}) $\s{x} = \s{x}[1..n]$ on an alphabet $Σ$ is a sequence of nonempty subsets of $Σ$. We say that $\s{x}[i_1]$ and $\s{x}[i_2]$ \itbf{match} (written $\s{x}[i_1] \match \s{x}[i_2]$) if and only if $\s{x}[i_1] \cap \s{x}[i_2] \ne \emptyset$. A \itbf{feasible array} is an array $\s{y} = \s{y}[1..n]$ of integers such that $\s{y}[1] = n$ and for every $i \in 2..n$, $\s{y}[i] \in 0..n\- i\+ 1$. A \itbf{prefix table} of a string $\s{x}$ is an array $\sπ = \sπ[1..n]$ of integers such that, for every $i \in 1..n$, $\sπ[i] = j$ if and only if $\s{x}[i..i\+ j\- 1]$ is the longest substring at position $i$ of \s{x} that matches a prefix of \s{x}. It is known from \cite{CRSW13} that every feasible array is a prefix table of some indetermintate string. A \itbf{prefix graph} $\mathcal{P} = \mathcal{P}_{\s{y}}$ is a labelled simple graph whose structure is determined by a feasible array \s{y}. In this paper we show, given a feasible array \s{y}, how to use $\mathcal{P}_{\s{y}}$ to construct a lexicographically least indeterminate string on a minimum alphabet whose prefix table $\sπ = \s{y}$.