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Pseudo-Boolean and Cardinality Constraints
Handbook of Satisfiability, 2021Pseudo-Boolean and cardinality constraints are a natural generalization of clauses. While a clause expresses that at least one literal must be true, a cardinality constraint expresses that at least n literals must be true and a pseudo-Boolean constraint ...
Olivier Roussel, Vasco M. Manquinho
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FACE: A Normalizing Flow based Cardinality Estimator
Proceedings of the VLDB Endowment, 2021Cardinality estimation is one of the most important problems in query optimization. Recently, machine learning based techniques have been proposed to effectively estimate cardinality, which can be broadly classified into query-driven and data-driven ...
Jiayi Wang +3 more
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Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation
Proceedings of the VLDB Endowment, 2021Cardinality estimation is core to the query optimizers of DBMSs. Non-learned methods, especially based on histograms and samplings, have been widely used in commercial and open-source DBMSs.
Ji Sun +4 more
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Cardinality-constrained portfolio selection based on collaborative neurodynamic optimization
Neural Networks, 2021Portfolio optimization is one of the most important investment strategies in financial markets. It is practically desirable for investors, especially high-frequency traders, to consider cardinality constraints in portfolio selection, to avoid odd lots ...
Man-Fai Leung, Jun Wang
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On Deploying Secure Computing: Private Intersection-Sum-with-Cardinality
European Symposium on Security and Privacy, 2020In this work, we discuss our successful efforts for industry deployment of a cryptographic secure computation protocol. The problem we consider is privately computing aggregate conversion rate of advertising campaigns.
Mihaela Ion +8 more
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Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation
Proceedings of the VLDB Endowment, 2021Cardinality estimation is a fundamental and critical problem in databases. Recently, many estimators based on deep learning have been proposed to solve this problem and they have achieved promising results.
Jie Liu +3 more
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Learned Cardinality Estimation for Similarity Queries
SIGMOD Conference, 2021In this paper, we study the problem of using deep neural networks (DNNs) for estimating the cardinality of similarity queries. Intuitively, DNNs can capture the distribution of data points, and learn to predict the number of data points that are similar ...
Ji Sun, Guoliang Li, N. Tang
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Unbalanced private set intersection cardinality protocol with low communication cost
Future Generation Computer Systems, 2020Private set intersection cardinality (PSI-CA) allows two parties, the sender and receiver, to compute the cardinality of the intersection, without revealing anything more to the other party.
Siyi Lv, Xiaochun Cheng, Zheli Liu
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007
A Bayesian probability density for an interpolating function is developed, and its desirable properties and practical potential are demonstrated. This density has an often needed but previously unachieved property, here called cardinal interpolation, which ensures extrapolation to the density of the least squares linear model.
Steven C. Gustafson +2 more
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A Bayesian probability density for an interpolating function is developed, and its desirable properties and practical potential are demonstrated. This density has an often needed but previously unachieved property, here called cardinal interpolation, which ensures extrapolation to the density of the least squares linear model.
Steven C. Gustafson +2 more
openaire +2 more sources
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation
Proceedings of the VLDB Endowment, 2020Query optimizers rely on accurate cardinality estimation (CardEst) to produce good execution plans. The core problem of CardEst is how to model the rich joint distribution of attributes in an accurate and compact manner.
Rong Zhu +7 more
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