Results 181 to 190 of about 36,960 (271)

Learning Cut Generating Functions for Integer Programming

open access: yes
The branch-and-cut algorithm is the method of choice to solve large scale integer programming problems in practice. A key ingredient of branch-and-cut is the use of cutting planes which are derived constraints that reduce the search space for an optimal ...
Cheng, Hongyu, Basu, Amitabh
core  

Comparative cranial biomechanics reveal macroevolutionary trends in theropod dinosaurs, with emphasis on Tyrannosauroidea

open access: yesThe Anatomical Record, EarlyView.
Abstract Tyrannosaurus is viewed as a model organism in vertebrate paleontology, with numerous studies analyzing its feeding biomechanics. Nonetheless, the evolution of this feeding performance has been under‐addressed in Tyrannosauroidea, especially in basal tyrannosauroids. Here we used muscle‐force reconstruction and finite element analysis (FEA) to
Evan Johnson‐Ransom   +4 more
wiley   +1 more source

Show Me the Brain!!: A modern approach to neuroanatomy education

open access: yesAnatomical Sciences Education, EarlyView.
Abstract Show Me the Brain!! (SMtB) is a digital system for interactive graphics that is designed to support instruction in neuroanatomy and neuroscience. It will soon be made open‐source and freely available. SMtB bridges medical and traditional neuroanatomy instruction with the computational systems and representational conventions common in ...
Nicholas C. Hindy   +3 more
wiley   +1 more source

A compatibility criterion for optimal control and information aggregation in hierarchical network systems

open access: yesAsian Journal of Control, EarlyView.
Abstract Large swarms often adopt a hierarchical network structure that incorporates information aggregation. Although this approach offers significant advantages in terms of communication efficiency and computational complexity, it can also lead to degradation due to information constraints.
Kento Fujita, Daisuke Tsubakino
wiley   +1 more source

Risk‐aware safe reinforcement learning for control of stochastic linear systems

open access: yesAsian Journal of Control, EarlyView.
Abstract This paper presents a risk‐aware safe reinforcement learning (RL) control design for stochastic discrete‐time linear systems. Rather than using a safety certifier to myopically intervene with the RL controller, a risk‐informed safe controller is also learned besides the RL controller, and the RL and safe controllers are combined together ...
Babak Esmaeili   +2 more
wiley   +1 more source

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