Range-Efficient Counting of Distinct Elements in a Massive Data
... A. PAVAN† AND SRIKANTA TIRTHAPURA‡ Abstract. Efficient one-pass estimation of F0 , the number of distinct elements in a data stream, is a fundamental problem arising in various contexts in databases and networking. We consider rangeefficient estimation of F0 : estimation of the number of distinct elemen ...
... A. PAVAN† AND SRIKANTA TIRTHAPURA‡ Abstract. Efficient one-pass estimation of F0 , the number of distinct elements in a data stream, is a fundamental problem arising in various contexts in databases and networking. We consider rangeefficient estimation of F0 : estimation of the number of distinct elemen ...
Likelihood inference for generalized Pareto distribution
... Davison, A. C.; Smith, R. L. (1990). Models for exceedances over high thresholds. With discussion and a reply by the authors. JRSS-B Embrechts, P. Klüppelberg, C. and Mikosch, T. (1997). Modelling Extremal Events for Insurance and Finance. Springer-Verlag, Berlin. McNeil, A. J., Frey, R. and Embrec ...
... Davison, A. C.; Smith, R. L. (1990). Models for exceedances over high thresholds. With discussion and a reply by the authors. JRSS-B Embrechts, P. Klüppelberg, C. and Mikosch, T. (1997). Modelling Extremal Events for Insurance and Finance. Springer-Verlag, Berlin. McNeil, A. J., Frey, R. and Embrec ...
Automatic Verification of Multi-Agent Systems in Parameterised Grid
... Thus, agents should be designed so that they operate correctly on all possible grids that are consistent with their information [44, 43, 7, 31, 19, 25, 3]. In this paper we study the verification problem for such multiagent systems that have partial information about the environment. Model checking ...
... Thus, agents should be designed so that they operate correctly on all possible grids that are consistent with their information [44, 43, 7, 31, 19, 25, 3]. In this paper we study the verification problem for such multiagent systems that have partial information about the environment. Model checking ...
Undecidability of the unification and admissibility problems for
... set of equations axiomatising the variety of Boolean algebras with operators and additional equations corresponding the axioms of L. A close algorithmic problem for L is the admissibility problem for inference rules: given an inference rule ϕ1 , . . . , ϕn /ϕ, decide whether it is admissible in L, t ...
... set of equations axiomatising the variety of Boolean algebras with operators and additional equations corresponding the axioms of L. A close algorithmic problem for L is the admissibility problem for inference rules: given an inference rule ϕ1 , . . . , ϕn /ϕ, decide whether it is admissible in L, t ...
Document
... A, by going through array A and counting the number of occurrences of value x in A; if successful output x; otherwise null Lectures on Recursive Algorithms ...
... A, by going through array A and counting the number of occurrences of value x in A; if successful output x; otherwise null Lectures on Recursive Algorithms ...
Exact discovery of length-range motifs
... The algorithm starts by selecting a random set of Nr reference points. The algorithm works in two phases: The first phase (called hereafter referencing phase) is used to calculate both the upper limit on best motif distance and a lower limit on distances of all possible pairs. During this phase, dis ...
... The algorithm starts by selecting a random set of Nr reference points. The algorithm works in two phases: The first phase (called hereafter referencing phase) is used to calculate both the upper limit on best motif distance and a lower limit on distances of all possible pairs. During this phase, dis ...
Generalised Integer Programming Based on Logically Defined
... t1 , t2 , . . . , tk ∈ R, the n-tuple f (t1 , t2 , . . . , tk ) is defined as follows: f (t1 , . . . , tk ) = (f (t1 [1], . . . , tk [1]), . . . , f (t1 [n], . . . , tk [n])) where tj [i] is the i-th component in tuple tj . If f is an operation such that for all t1 , t2 , . . . , tk ∈ R f (t1 , t2 ...
... t1 , t2 , . . . , tk ∈ R, the n-tuple f (t1 , t2 , . . . , tk ) is defined as follows: f (t1 , . . . , tk ) = (f (t1 [1], . . . , tk [1]), . . . , f (t1 [n], . . . , tk [n])) where tj [i] is the i-th component in tuple tj . If f is an operation such that for all t1 , t2 , . . . , tk ∈ R f (t1 , t2 ...
Stochastic Search and Surveillance Strategies for
... rely on team objective as well as cognitive performance of the human operator. In the context of information aggregation, we consider two particular missions. First, we consider information aggregation for a multiple alternative decision making task and pose it as a sensor selection problem in seque ...
... rely on team objective as well as cognitive performance of the human operator. In the context of information aggregation, we consider two particular missions. First, we consider information aggregation for a multiple alternative decision making task and pose it as a sensor selection problem in seque ...
Knapsack problem
The knapsack problem or rucksack problem is a problem in combinatorial optimization: Given a set of items, each with a mass and a value, determine the number of each item to include in a collection so that the total weight is less than or equal to a given limit and the total value is as large as possible. It derives its name from the problem faced by someone who is constrained by a fixed-size knapsack and must fill it with the most valuable items.The problem often arises in resource allocation where there are financial constraints and is studied in fields such as combinatorics, computer science, complexity theory, cryptography and applied mathematics.The knapsack problem has been studied for more than a century, with early works dating as far back as 1897. It is not known how the name ""knapsack problem"" originated, though the problem was referred to as such in the early works of mathematician Tobias Dantzig (1884–1956), suggesting that the name could have existed in folklore before a mathematical problem had been fully defined.