Results 131 to 140 of about 4,928,656 (192)
A simple compound prioritization method for drug discovery considering multi-target binding.
Digit DiscovKubincová A, Mobley DL.
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2013
Multiple sequence alignment involves alignment of more than two sequences and is an NP-complete problem. Therefore, heuristic algorithms that use different criteria to find an approximation to the optimal solution are employed. At the heart of these approaches lie the scoring and objective functions that a given algorithm uses to compare competing ...
Haluk, Doğan, Hasan H, Otu
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Multiple sequence alignment involves alignment of more than two sequences and is an NP-complete problem. Therefore, heuristic algorithms that use different criteria to find an approximation to the optimal solution are employed. At the heart of these approaches lie the scoring and objective functions that a given algorithm uses to compare competing ...
Haluk, Doğan, Hasan H, Otu
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Object Shape, Object Function, and Object Name
Journal of Memory and Language, 1998Abstract We investigated the roles of shape and function in object naming. Two-, three-, and five-year-olds and adults heard novel or familiar objects named; some participants also were instructed about the objects' functions. Then they were asked to generalize the names to new objects that preserved shape or functional capability; some participants ...
Barbara Landau, Linda Smith, Susan Jones
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Greedoids and Linear Objective Functions
SIAM Journal on Algebraic Discrete Methods, 1984A greedoid is a generalization of a matroid, defined as a framework for the greedy algorithm for finding an optimal basis. For a given greedoid, the authors give a class of weightings of the elements for which the ''best-in'' greedy algorithm (i.e., starting from \(\emptyset\), successively include the best possible element) is optimal.
Korte, Bernhard, Lovász, László
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2019
In this chapter we discuss some classical approaches for dealing with randomness in the objective function: expected utility maximization (von Neumann and Morgenstern) and the mean-variance model (Markowitz).
Willem K. Klein Haneveld +2 more
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In this chapter we discuss some classical approaches for dealing with randomness in the objective function: expected utility maximization (von Neumann and Morgenstern) and the mean-variance model (Markowitz).
Willem K. Klein Haneveld +2 more
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Learning objective functions for manipulation
2013 IEEE International Conference on Robotics and Automation, 2013We present an approach to learning objective functions for robotic manipulation based on inverse reinforcement learning. Our path integral inverse reinforcement learning algorithm can deal with high-dimensional continuous state-action spaces, and only requires local optimality of demonstrated trajectories.
Kalakrishnan, M. +3 more
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The Objectivity of Organizational Functions
Acta Biotheoretica, 2019We critique the organizational account of biological functions by showing how its basis in the closure of constraints fails to be objective. While the account treats constraints as objective features of physical systems, the number and relationship of potential constraints are subject to potentially arbitrary redescription by investigators. For example,
Samuel Cusimano, Beckett Sterner
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Objective analysis of finger function
Hand Clinics, 2003Hand motor tasks, even those commonly required by daily life activities, entail complex muscle activation. This article describes a self-contained experimental set-up for the objective kinetic and kinematic analysis of each finger function under several working conditions.
Claudia, Giacomozzi +4 more
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2019
When we say functions are objects in Dart, it may seem confusing if you’re a beginner. Basically, because Dart is an object-oriented language, even functions are objects and have a type called Function.
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When we say functions are objects in Dart, it may seem confusing if you’re a beginner. Basically, because Dart is an object-oriented language, even functions are objects and have a type called Function.
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Subjectively biased objective functions
EURO Journal on Decision Processes, 2016The maximization of an objective function is a cornerstone of OR/MS modeling. How can we integrate subjective values within these models without weakening their scientific objectivity? This paper proposes a methodological answer that maintains the objective function and relaxes the maximization principle.
Marc Le Menestrel, Luk N. Van Wassenhove
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