Computationally efficient design of optimal strategies for controllable damping devices

2014 ◽  
Vol 22 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Mahmoud Kamalzare ◽  
Erik A. Johnson ◽  
Steven F. Wojtkiewicz
Keyword(s):  
Optimal Strategies ◽  
Efficient Design ◽  
Computationally Efficient

scholarly journals Perturbation Based Learning for Structured NLP Tasks with Application to Dependency Parsing

2019 ◽  
Vol 7 ◽  
pp. 643-659
Author(s):  
Amichay Doitch ◽  
Ram Yazdi ◽  
Tamir Hazan ◽  
Roi Reichart
Keyword(s):  
Prediction Models ◽  
Learning Algorithm ◽  
Solution Space ◽  
Optimal Strategies ◽  
Expressive Power ◽  
Entire Solution ◽  
Dependency Parsing ◽  
Computationally Efficient ◽  
Cross Lingual

The best solution of structured prediction models in NLP is often inaccurate because of limited expressive power of the model or to non-exact parameter estimation. One way to mitigate this problem is sampling candidate solutions from the model’s solution space, reasoning that effective exploration of this space should yield high-quality solutions. Unfortunately, sampling is often computationally hard and many works hence back-off to sub-optimal strategies, such as extraction of the best scoring solutions of the model, which are not as diverse as sampled solutions. In this paper we propose a perturbation-based approach where sampling from a probabilistic model is computationally efficient. We present a learning algorithm for the variance of the perturbations, and empirically demonstrate its importance. Moreover, while finding the argmax in our model is intractable, we propose an efficient and effective approximation. We apply our framework to cross-lingual dependency parsing across 72 corpora from 42 languages and to lightly supervised dependency parsing across 13 corpora from 12 languages, and demonstrate strong results in terms of both the quality of the entire solution list and of the final solution. 1

scholarly journals Computationally efficient design of directionally compliant metamaterials

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Lucas A. Shaw ◽  
Frederick Sun ◽  
Carlos M. Portela ◽  
Rodolfo I. Barranco ◽  
Julia R. Greer ◽  
...  
Keyword(s):  
Efficient Design ◽  
Computationally Efficient

scholarly journals Optimal Design Strategies for Sibling Studies with Binary Exposures

2014 ◽  
Vol 10 (2) ◽  
Author(s):  
Zhigang Li ◽  
Ian W. McKeague ◽  
Lambert H. Lumey
Keyword(s):  
Optimal Strategy ◽  
Optimal Strategies ◽  
Binary Outcomes ◽  
Binary Response ◽  
Efficient Design ◽  
Design Strategies ◽  
Sibling Pairs ◽  
Family Level ◽  
Continuous Outcomes ◽  
Study Power

AbstractSibling studies have become increasingly popular because they provide better control over confounding by unmeasured family-level risk factors than can be obtained in standard cohort studies. However, little attention has been devoted to the development of efficient design strategies for sibling studies in terms of optimizing power. We here address this issue in commonly encountered types of sibling studies, allowing for continuous and binary outcomes and varying numbers of exposed and unexposed siblings. For continuous outcomes, we show that in families with sibling pairs, optimal study power is obtained by recruiting discordant (exposed–control) pairs of siblings. More generally, balancing the exposure status within each family as evenly as possible is shown to be optimal. For binary outcomes, we elucidate how the optimal strategy depends on the variation of the binary response; as the within-family correlation increases, the optimal strategy tends toward only recruiting discordant sibling pairs (as in the case of continuous outcomes). R code for obtaining the optimal strategies is included.

Derivational Economy in Syntax and Semantics

2017 ◽  
Author(s):  
Željko Bošković ◽  
Troy Messick
Keyword(s):  
Syntactic Structure ◽  
Universal Grammar ◽  
Minimalist Program ◽  
Efficient Design ◽  
Computationally Efficient ◽  
Computational System ◽  
Language Faculty ◽  
Generative Theory ◽  
Guiding Principles ◽  
Syntactic Dependencies

Economy considerations have always played an important role in the generative theory of grammar. They are particularly prominent in the most recent instantiation of this approach, the Minimalist Program, which explores the possibility that Universal Grammar is an optimal way of satisfying requirements that are imposed on the language faculty by the external systems that interface with the language faculty which is also characterized by optimal, computationally efficient design. In this respect, the operations of the computational system that produce linguistic expressions must be optimal in that they must satisfy general considerations of simplicity and efficient design. Simply put, the guiding principles here are (a) do something only if you need to and (b) if you do need to, do it in the most economical/efficient way. These considerations ban superfluous steps in derivations and superfluous symbols in representations. Under economy guidelines, movement takes place only when there is a need for it (with both syntactic and semantic considerations playing a role here), and when it does take place, it takes place in the most economical way: it is as short as possible and carries as little material as possible. Furthermore, economy is evaluated locally, on the basis of immediately available structure. The locality of syntactic dependencies is also enforced by minimal search and by limiting the number of syntactic objects and the amount of structure accessible in the derivation. This is achieved by transferring parts of syntactic structure to the interfaces during the derivation, the transferred parts not being accessible for further syntactic operations.

Computationally Efficient Design and Optimization Approach of PMa-SynRM in Frequent Operating Torque–Speed Range

2018 ◽  
Vol 33 (4) ◽  
pp. 1776-1786 ◽  
Author(s):  
Carlos Lopez-Torres ◽  
Antonio Garcia ◽  
Jordi-Roger Riba ◽  
Gerhard Lux ◽  
Luis Romeral
Keyword(s):  
Optimization Approach ◽  
Efficient Design ◽  
Computationally Efficient ◽  
Design And Optimization ◽  
Speed Range
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