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Tuple

About: Tuple is a research topic. Over the lifetime, 6513 publications have been published within this topic receiving 146057 citations. The topic is also known as: tuple & ordered tuplet.


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Journal ArticleDOI
TL;DR: It is shown that 1-region membrane computing systems which only use rules of the form Ca → Cv are equivalent to communication-free Petri nets, which are also equivalent to commutative context-free grammars, and that systems of the first type define precisely the semilinear sets.

47 citations

Proceedings ArticleDOI
20 May 2015
TL;DR: It is shown that for any self-join-free Boolean conjunctive query q, it can be decided whether or not CERTAINTY(q) is in FO, P, or coNP-complete, and the complexity dichotomy is effective.
Abstract: A relational database is said to be uncertain if primary key constraints can possibly be violated A repair (or possible world) of an uncertain database is obtained by selecting a maximal number of tuples without ever selecting two distinct tuples with the same primary key value For any Boolean query q, CERTAINTY(q) is the problem that takes an uncertain database db as input, and asks whether q is true in every repair of db The complexity of this problem has been particularly studied for q ranging over the class of self-join-free Boolean conjunctive queries A research challenge is to determine, given q, whether CERTAINTY(q) belongs to complexity classes FO, P, or coNP-complete In this paper, we combine existing techniques for studying the above complexity classification task We show that for any self-join-free Boolean conjunctive query q, it can be decided whether or not CERTAINTY(q) is in FO Further, for any self-join-free Boolean conjunctive query q, CERTAINTY(q) is either in P or coNP-complete, and the complexity dichotomy is effective This settles a research question that has been open for ten years

47 citations

Proceedings ArticleDOI
23 Jun 2013
TL;DR: The novel contribution is to extend standard object detection by introducing separate classes for tuples of objects into the detection process, and it is shown that this formulation can be learned within the structured output SVM framework, and that the inference can be accomplished using dynamic programming on a tree structured region graph.
Abstract: The objective of this work is to detect all instances of a class (such as cells or people) in an image. The instances may be partially overlapping and clustered, and hence quite challenging for traditional detectors, which aim at localizing individual instances. Our approach is to propose a set of candidate regions, and then select regions based on optimizing a global classification score, subject to the constraint that the selected regions are non-overlapping. Our novel contribution is to extend standard object detection by introducing separate classes for tuples of objects into the detection process. For example, our detector can pick a region containing two or three object instances, while assigning such region an appropriate label. We show that this formulation can be learned within the structured output SVM framework, and that the inference in such model can be accomplished using dynamic programming on a tree structured region graph. Furthermore, the learning only requires weak annotations - a dot on each instance. The improvement resulting from the addition of the capability to detect tuples of objects is demonstrated on quite disparate data sets: fluorescence microscopy images and UCSD pedestrians.

47 citations

01 Jan 1999
TL;DR: In this article, the authors developed standard models for commuting tuples of bounded linear operators on a Hilbert space under certain polynomial positivity con- ditions, generalizing the work of V. Muller and F.-H. Vasilescu in (6), (14).
Abstract: We develop standard models for commuting tuples of bounded linear operators on a Hilbert space under certain polynomial positivity con- ditions, generalizing the work of V. Muller and F.-H. Vasilescu in (6), (14). As a consequence of the model, we prove a von Neumann-type inequal- ity for such tuples. Up to similarity, we obtain the existence of in a certain sense "unitary" dilations.

47 citations

Proceedings ArticleDOI
05 Feb 1986
TL;DR: The basic model is homogeneous in the sense that the periods of validity of all the attributes in a given tuple of a temporal relation are identical and generalizes to a multihomogeneous model which allows the model to model a significant part of the real world.
Abstract: In a conventional database, out of date information is deleted from time to time to keep the database up-to-date. In some applications it is not appropriate to discard old information. In a temporal database, time values are associated with each data item to indicate its period of validity. We propose a model for temporal databases within the framework of classical relational database theory. Our basic model is homogeneous in the sense that the periods of validity of all the attributes in a given tuple of a temporal relation are identical. The model is realized as a temporal parameterization of static relations. The concepts of normal forms, dependencies, etc., can be extended to our model, allowing the proper initial structuring of the database. We develop relational algebra and tuple calculus for our model and prove their equivalence. We generalize the homogeneous model to a multihomogeneous model which allows us to model a significant part of the real world.

47 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023203
2022459
2021210
2020285
2019306
2018266