Relevant bibliographies by topics / Cardinality

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Cardinality'

Author: Grafiati
Published: 4 June 2021
Last updated: 4 February 2022

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Journal articles on the topic "Cardinality"

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Harmouch, Hazar, and Felix Naumann. "Cardinality estimation." Proceedings of the VLDB Endowment 11, no. 4 (December 2017): 499–512. http://dx.doi.org/10.1145/3186728.3164145.

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Luce, Lila. "Frege on Cardinality." Philosophy and Phenomenological Research 48, no. 3 (March 1988): 415. http://dx.doi.org/10.2307/2107471.

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Roblot, Tania, Miika Hannula, and Sebastian Link. "Probabilistic Cardinality Constraints." VLDB Journal 27, no. 6 (July 2, 2018): 771–95. http://dx.doi.org/10.1007/s00778-018-0511-z.

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Jornsten, Kurt O., and Soren Holm. "Cardinality Constrained Decomposition." Journal of Information and Optimization Sciences 11, no. 3 (September 1990): 425–42. http://dx.doi.org/10.1080/02522667.1990.10699034.

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Nolan, D., and A. Sandgren. "Creationism and cardinality." Analysis 74, no. 4 (September 1, 2014): 615–22. http://dx.doi.org/10.1093/analys/anu089.

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Tantau, Till. "Weak cardinality theorems." Journal of Symbolic Logic 70, no. 3 (September 2005): 861–78. http://dx.doi.org/10.2178/jsl/1122038917.

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AbstractKummer's Cardinality Theorem states that a language A must be recursive if a Turing machine can exclude for any n words , …, one of the n + 1 possibilities for the cardinality of {, …, }⋂ A. There was good reason to believe that this theorem is a peculiarity of recursion theory: neither the Cardinality Theorem nor weak forms of it hold for resource-bounded computational models like polynomial time. This belief may be flawed. In this paper it is shown that weak cardinality theorems hold for finite automata and also for other models. An explanation is proposed as to why recursion-theoretic and automata-theoretic weak cardinality theorems hold, but not corresponding 'middle-ground theorems': The recursion- and automata-theoretic weak cardinality theorems are instantiations of purely logical weak cardinality theorems. The logical theorems can be instantiated for logical structures characterizing recursive computations and finite automata computations. A corresponding structure characterizing polynomial time computations does not exist.
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Carrara, Massimiliano, and Elisabetta Sacchi. "Cardinality and Identity." Journal of Philosophical Logic 36, no. 5 (April 18, 2007): 539–56. http://dx.doi.org/10.1007/s10992-007-9047-1.

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Tzouvaras, Athanassios. "Cardinality without Enumeration." Studia Logica 80, no. 1 (June 2005): 121–41. http://dx.doi.org/10.1007/s11225-005-6780-8.

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Levin, Oscar, and Tyler Markkanen. "Puzzles of Cardinality." College Mathematics Journal 52, no. 4 (August 8, 2021): 243–53. http://dx.doi.org/10.1080/07468342.2021.1941539.

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Gao, Jintao, Zhanhuai Li, and Wenjie Liu. "A Strategy of Efficient and Accurate Cardinality Estimation Based on Query Result." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 4 (August 2018): 768–77. http://dx.doi.org/10.1051/jnwpu/20183640768.

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Cardinality estimation is an important component of query optimization. Its accuracy and efficiency directly decide effect of query optimization. Traditional cardinality estimation strategy is based on original table or sample to collect statistics, then inferring cardinality by collected statistics. It will be low-efficiency when handling big data; Statistics exist update latency and are gotten by inferring, which can not guarantee correctness; Some strategies can get the actual cardinality by executing some subqueries, but they do not keep the result, leading to low efficiency of fetching statistics. Against these problems, this paper proposes a novel cardinality estimation strategy, called cardinality estimation based on query result(CEQR). For keeping correctness of cardinality, CEQR directly gets statistics from query results, which is not related with data size; we build a cardinality table to store the statistics of basic tables and middle results under specific predicates. Cardinality table can provide cardinality services for subsequent queries, and we build a suit of rules to maintain cardinality table; To improve the efficiency of fetching statistics, we introduce the source aware strategy, which hashes cardinality item to appropriate cache. This paper gives the adaptability and deviation analytic of CEQR, and proves that CEQR is more efficient than traditional cardinality estimation strategy by experiments.
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Dissertations / Theses on the topic "Cardinality"

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Abdi, Mohammad Javad. "Cardinality optimization problems." Thesis, University of Birmingham, 2013. http://etheses.bham.ac.uk//id/eprint/4620/.

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In this thesis, we discuss the cardinality minimization problem (CMP) and the cardinality constraint problem. Due to the NP-hardness of these problems, we discuss different computational and relaxation techniques for finding an approximate solution to these problems. We also discuss the l\(_1\)-minimization as one of the most efficient methods for solving CMPs, and we demonstrate that the l\(_1\)-minimization uses a kind of weighted l\(_2\)-minimization. We show that the reweighted l\(_j\)-minimization (j≥1) is very effective to locate a sparse solution to a linear system. Next, we show how to introduce different merit functions for sparsity, and how proper weights may reduce the gap between the performances of these functions for finding a sparse solution to an undetermined linear system. Furthermore, we introduce some effective computational approaches to locate a sparse solution for an underdetermined linear system. These approaches are based on reweighted l\(_j\)-minimization (j≥1) algorithms. We focus on the reweighted l\(_1\)-minimization, and introduce several new concave approximations to the l\(_0\)-norm function. These approximations can be employed to define new weights for reweighted l\(_1\)-minimization algorithms. We show how the change of parameters in reweighted algorithms may affect the performance of the algorithms for finding the solution of the cardinality minimization problem. In our experiments, the problem data were generated according to different statistical distributions, and we test the algorithms on different sparsity level of the solution of the problem. As a special case of cardinality constrained problems, we also discuss compressed sensing, restricted isometry property (RIP), and restricted isometry constant (RIC).
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Aslan, Murat. "The Cardinality Constrained Multiple Knapsack Problem." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12610131/index.pdf.

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The classical multiple knapsack problem selects a set of items and assigns each to one of the knapsacks so as to maximize the total profit. The knapsacks have limited capacities. The cardinality constrained multiple knapsack problem assumes limits on the number of items that are to be put in each knapsack, as well. Despite many efforts on the classical multiple knapsack problem, the research on the cardinality constrained multiple knapsack problem is scarce. In this study we consider the cardinality constrained multiple knapsack problem. We propose heuristic and optimization procedures that rely on the optimal solutions of the linear programming relaxation problem. Our computational results on the large-sized problem instances have shown the satisfactory performances of our algorithms.
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Petridis, Giorgis. "Plünnecke's inequality and the cardinality of sumsets." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609666.

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Falgén, Enqvist Olle. "Cardinality estimation with a machine learning approach." Thesis, KTH, Optimeringslära och systemteori, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288909.

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This thesis investigates how three different machine learning models perform on cardinalty estimation for sql queries. All three models were evaluated on three different data sets. The models were tested on both estimating cardinalities when the query just takes information from one table and also a two way join case. Postgresql's own cardinality estimator was used as a baseline. The evaluated models were: Artificial neural networks, random forests and extreme gradient boosted trees. What was found is that the model that performs best is the extreme gradient boosted tree with a tweedie regression loss function. To the authors knowledge, this is the first time an extreme gradient boosted tree has been used in this context.
Denna uppsats undersöker hur tre olika maskininlärningsmodeller presterar på kardinalitetsuppskattning för sql förfrågningar till en databas. Alla tre modeller utvärderades på tre olika datauppsättningar. Modellerna fick både behandla förfrågningar från en tabell, samt en sammanslagning mellan två tabeller. Postgresql's egna kardinalitetsestimerare användes som referenspunkt. De utvärderade modellerna var följande: artificiella neurala nätverk, random forests och extreme gradient boosted trees. En slutsats var att den modellen som utförde uppgiften bäst var extreme gradient boosted trees med en tweedie-regression förlustfunktion. Såvitt författaren vet är det här första gången den här typen av extreme gradient boosted tree används på denna typ av problem.
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Maryokhin, Tymur. "Data dissemination in large-cardinality social graphs." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-48268.

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Near real-time event streams are a key feature in many popular social media applications. These types of applications allow users to selectively follow event streams to receive a curated list of real-time events from various sources. Due to the emphasis on recency, relevance, personalization of content, and the highly variable cardinality of social subgraphs, it is extremely difficult to implement feed following at the scale of major social media applications. This leads to multiple architectural approaches, but no consensus has been reached as to what is considered to be an idiomatic solution. As of today, there are various theoretical approaches exploiting the dynamic nature of social graphs, but not all of them have been applied in practice. In this paper, large-cardinality graphs are placed in the context of existing research to highlight the exceptional data management challenges that are posed for large-scale real-time social media applications. This work outlines the key characteristics of data dissemination in large-cardinality social graphs, and overviews existing research and state-of-the-art approaches in industry, with the goal of stimulating further research in this direction.
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Tessaro, Amanda E. "The relationship between cardinality and understanding written number." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ39239.pdf.

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Twyford, Helen Elizabeth. "A cognitive-developmental profile of cardinality in preschoolers." Thesis, University of Bristol, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.393949.

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Qian, Chen. "Efficient cardinality counting for large-scale RFID systems /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?CSED%202008%20QIAN.

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Lo, Bianco Accou Giovanni Christian. "Estimating the number of solutions on cardinality constraints." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2019. http://www.theses.fr/2019IMTA0155/document.

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La richesse de la programmation par contraintes repose sur la très large variété des algorithmes qu’elle utilise en puisant dans les grands domaines de l’Intelligence Artificielle, de la Programmation Logique et de la Recherche Opérationnelle. Cependant, cette richesse, qui offre aux spécialistes une palette quasi-illimitée de configurations possibles pour attaquer des problèmes combinatoires, devient une frein à la diffusion plus large du paradigme, car les outils actuels sont très loin d’une boîte noire, et leur utilisation suppose une bonne connaissance du domaine, notamment en ce qui concerne leur paramétrage. Dans cette thèse, nous proposons d’analyser le comportement des contraintes de cardinalité avec des modèles probabilistes et des outils de dénombrement, pour paramétrer automatiquement les solveurs de contraintes : heuristiques de choix de variables et de choix de valeurs et stratégies de recherche
The main asset of constraint programming is its wide variety of algorithms that comes from the major areas of artificial intelligence, logic programming and operational research. It offers specialists a limitless range of possible configurations to tackle combinatorial problems, but it becomes an obstacle to the wider diffusion of the paradigm. The current tools are very far from being used as a black-box tool, and it assumes a good knowledge of the field, in particular regarding the parametrization of solvers.In this thesis, we propose to analyze the behavior of cardinality constraints with probabilistic models and counting tools, to automatically parameterize constraint solvers: heuristics of choice of variables and choice of values and search strategies
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Silvanus, Jannik [Verfasser]. "Improved Cardinality Bounds for Rectangle Packing Representations / Jannik Silvanus." Bonn : Universitäts- und Landesbibliothek Bonn, 2019. http://d-nb.info/1188726226/34.

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Books on the topic "Cardinality"

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Cakir, Kadir. Counting ability and the understanding of cardinality in preschool children. [s.l.]: typescript, 1997.

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Wygralak, Maciej. Vaguely defined objects: Representations, fuzzy sets, and nonclassical cardinality theory. Dordrecht: Kluwer Academic Publishers, 1996.

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Cantone, D. A decision procedure for set-theoretic formulae involving rank and cardinality comparison. New York: Courant Institute of Mathematical Sciences, New York University, 1989.

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Linnell, Margaret Elizabeth. The influence of social interaction on the development of cardinality in pre-school children. Portsmouth: University of Portsmouth, Dept. of Psychology, 1998.

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Allais, Maurice, and Ole Hagen, eds. Cardinalism. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-0888-1.

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Delogu, Marco. Cardinali. [Milano]: B. Mondadori, 2001.

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Rossi, Agnelo. Cardinali santi. Roma: Pontifica Universitas Urbaniana, 1994.

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Dracs, Ofèlia. Boccato di cardinali. València: E. Climent, 1985.

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Pius. Lettere scritte durante il cardinalato. [Brescia]: Marco Serra Tarantola, 2007.

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Sicari, Giovanni. Stemmi cardinalizi: (secoli XV-XVII). Roma: Alma Roma, 1996.

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Book chapters on the topic "Cardinality"

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Andreescu, Titu, Cristinel Mortici, and Marian Tetiva. "Cardinality." In Mathematical Bridges, 25–42. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-0-8176-4629-5_2.

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Mynard, Frédéric. "Cardinality." In An Introduction to the Language of Mathematics, 131–43. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00641-9_4.

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Monk, J. Donald. "Cardinality." In Cardinal Invariants on Boolean Algebras, 311–34. Basel: Springer Basel, 2014. http://dx.doi.org/10.1007/978-3-0348-0730-2_10.

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Pepperberg, Irene M. "Cardinality." In Encyclopedia of Animal Cognition and Behavior, 1–4. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47829-6_1614-1.

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Nicholson, Neil R. "Cardinality." In A Transition to Proof, 327–70. Boca Raton : CRC Press, Taylor & Francis Group, 2018.: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429259838-7.

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Just, Winfried, and Martin Weese. "Cardinality." In Graduate Studies in Mathematics, 29–42. Providence, Rhode Island: American Mathematical Society, 1995. http://dx.doi.org/10.1090/gsm/008/04.

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Beck, Matthias, and Ross Geoghegan. "Cardinality." In The Art of Proof, 121–29. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7023-7_13.

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Monk, J. Donald. "Cardinality." In Cardinal Invariants on Boolean Algebras, 145–46. Basel: Birkhäuser Basel, 1996. http://dx.doi.org/10.1007/978-3-0346-0334-8_10.

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Monk, J. Donald. "Cardinality." In Cardinal Functions on Boolean Algebras, 66–68. Basel: Birkhäuser Basel, 1990. http://dx.doi.org/10.1007/978-3-0348-6381-0_8.

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Ehrgott, Matthias. "K-Cardinality Subgraphs." In Operations Research Proceedings, 86–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-79459-9_17.

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Conference papers on the topic "Cardinality"

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Cai, Walter, Magdalena Balazinska, and Dan Suciu. "Pessimistic Cardinality Estimation." In SIGMOD/PODS '19: International Conference on Management of Data. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3299869.3319894.

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Pham, Anh T., Raviv Raich, and Xiaoli Z. Fern. "Discriminative Clustering with Cardinality Constraints." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8461842.

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Cao, J., Y. Jin, A. Chen, T. Bu, and Z. L. Zhang. "Identifying High Cardinality Internet Hosts." In 2009 Proceedings IEEE INFOCOM. IEEE, 2009. http://dx.doi.org/10.1109/infcom.2009.5061990.

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Zhang, Zhenjie, Yin Yang, Ruichu Cai, Dimitris Papadias, and Anthony Tung. "Kernel-based skyline cardinality estimation." In the 35th SIGMOD international conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1559845.1559899.

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Thiele, Maik, Tim Kiefer, and Wolfgang Lehner. "Cardinality estimation in ETL processes." In Proceeding of the ACM twelfth international workshop. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1651291.1651302.

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Zhang, Jiawei, Jianhui Chen, Junxing Zhu, Yi Chang, and Philip S. Yu. "Link Prediction with Cardinality Constraint." In WSDM 2017: Tenth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3018661.3018734.

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Biswas, Arpita, and Siddharth Barman. "Fair Division Under Cardinality Constraints." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/13.

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We consider the problem of fairly allocating indivisible goods, among agents, under cardinality constraints and additive valuations. In this setting, we are given a partition of the entire set of goods---i.e., the goods are categorized---and a limit is specified on the number of goods that can be allocated from each category to any agent. The objective here is to find a fair allocation in which the subset of goods assigned to any agent satisfies the given cardinality constraints. This problem naturally captures a number of resource-allocation applications, and is a generalization of the well-studied unconstrained fair division problem. The two central notions of fairness, in the context of fair division of indivisible goods, are envy freeness up to one good (EF1) and the (approximate) maximin share guarantee (MMS). We show that the existence and algorithmic guarantees established for these solution concepts in the unconstrained setting can essentially be achieved under cardinality constraints. Furthermore, focusing on the case wherein all the agents have the same additive valuation, we establish that EF1 allocations exist even under matroid constraints.
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Buchbinder, Niv, Moran Feldman, Joseph (Seffi) Naor, and Roy Schwartz. "Submodular Maximization with Cardinality Constraints." In Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2013. http://dx.doi.org/10.1137/1.9781611973402.106.

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Pilipchuk, Nina I. "Cardinality of Subspace Multicomponent Codes." In 2017 IVth International Conference on Engineering and Telecommunication (EnT). IEEE, 2017. http://dx.doi.org/10.1109/icent.2017.9.

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Lucchese, Riccardo, and Damiano Varagnolo. "Networks cardinality estimation using order statistics." In 2015 American Control Conference (ACC). IEEE, 2015. http://dx.doi.org/10.1109/acc.2015.7171924.

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Reports on the topic "Cardinality"

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Ozkaya, Yusuf, Erdem Sariyuce, Umit Catalyurek, and Ali Pinar. Active Betweenness Cardinality: Algorithms and Applications. Office of Scientific and Technical Information (OSTI), November 2017. http://dx.doi.org/10.2172/1733256.

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Rotem, Doron, Kurt Stockinger, and Kesheng Wu. Efficient binning for bitmap indices on high-cardinality attributes. Office of Scientific and Technical Information (OSTI), November 2004. http://dx.doi.org/10.2172/841113.

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