scholarly journals Tracking of the expected time to reinforcement in temporal conditioning procedures

2003 ◽  
Vol 31 (1) ◽  
pp. 3-21 ◽  
Author(s):  
Kimberly Kirkpatrick ◽  
Russell M. Church
Keyword(s):  
Expected Time ◽  
Temporal Conditioning

TEMPORAL CONDITIONING: NEGATIVE RESULTS

1960 ◽  
Vol 7 (7) ◽  
pp. 539 ◽  
Author(s):  
WILLIAM F. PROKASY
Keyword(s):  
Negative Results ◽  
Temporal Conditioning

scholarly journals Asymptotic Expansions and Strategies in the Online Increasing Subsequence Problem

2021 ◽  
Vol 174 (1) ◽  
Author(s):  
Amirlan Seksenbayev
Keyword(s):  
Dynamic Programming ◽  
Asymptotic Expansions ◽  
Dual Problem ◽  
Infinite Length ◽  
Expected Time ◽  
Selection Strategies ◽  
Dynamic Programming Equation ◽  
Optimal Values ◽  
Design Selection ◽  
Selection Of

AbstractWe study two closely related problems in the online selection of increasing subsequence. In the first problem, introduced by Samuels and Steele (Ann. Probab. 9(6):937–947, 1981), the objective is to maximise the length of a subsequence selected by a nonanticipating strategy from a random sample of given size $n$ n . In the dual problem, recently studied by Arlotto et al. (Random Struct. Algorithms 49:235–252, 2016), the objective is to minimise the expected time needed to choose an increasing subsequence of given length $k$ k from a sequence of infinite length. Developing a method based on the monotonicity of the dynamic programming equation, we derive the two-term asymptotic expansions for the optimal values, with $O(1)$ O ( 1 ) remainder in the first problem and $O(k)$ O ( k ) in the second. Settling a conjecture in Arlotto et al. (Random Struct. Algorithms 52:41–53, 2018), we also design selection strategies to achieve optimality within these bounds, that are, in a sense, best possible.

scholarly journals The Rate of Compensatory Evolution

Genetics ◽  
1996 ◽  
Vol 144 (1) ◽  
pp. 419-426 ◽  
Author(s):  
Wolfgang Stephan
Keyword(s):  
Diffusion Theory ◽  
Secondary Structures ◽  
Compensatory Mutation ◽  
Weak Selection ◽  
Mutation Pressure ◽  
Theory Of Evolution ◽  
Compensatory Evolution ◽  
Expected Time ◽  
Individual Fitness ◽  
Shifting Balance

Abstract A two-locus model is presented to analyze the evolution of compensatory mutations occurring in stems of RNA secondary structures. Single mutations are assumed to be deleterious but harmless (neutral) in appropriate combinations. In proceeding under mutation pressure, natural selection and genetic drift from one fitness peak to another one, a population must therefore pass through a valley of intermediate deleterious states of individual fitness. The expected time for this transition is calculated using diffusion theory. The rate of compensatory evolution, kc, is then defined as the inverse of the expected transition time. When selection against deleterious single mutations is strong, kc, depends on the recombination fraction r between the two loci. Recombination generally reduces the rate of compensatory evolution because it breaks up favorable combinations of double mutants. For complete linkage, kc, is given by the rate at which favorable combinations of double mutantS are produced by compensatory mutation. For r > 0, kc, decreases exponentially with r. In contrast, kc, becomes independent of r for weak selection. We discuss the dynamics of evolutionary substitutions of compensatory mutants in relation to Wright'S shifting balance theory of evolution and use our results to analyze the substitution process in helices of mRNA secondary structures.

Ovarian and endocrine responses to prostaglandin F2α (PGF2α) given at the expected time of the endogenous FSH peak at mid-cycle in ewes

2006 ◽  
Vol 66 (4) ◽  
pp. 811-821 ◽  
Author(s):  
Xinyu Liu ◽  
Qiao Dai ◽  
Ezra J. Hart ◽  
Rajesha Duggavathi ◽  
David M.W. Barrett ◽  
...  
Keyword(s):  
Prostaglandin F2α ◽  
Expected Time

Temporal conditioning of GSR.

1966 ◽  
Vol 71 (3) ◽  
pp. 438-446 ◽  
Author(s):  
Russell A. Lockhart
Keyword(s):  
Temporal Conditioning

scholarly journals An Efficient Motion Strategy to Compute Expected-Time Locally Optimal Continuous Search Paths in Known Environments

2009 ◽  
Vol 23 (12-13) ◽  
pp. 1533-1560 ◽  
Author(s):  
Alejandro Sarmiento ◽  
Rafael Murrieta-Cid ◽  
Seth Hutchinson
Keyword(s):  
Expected Time ◽  
Motion Strategy
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