Spaces for which the first uncountable ordinal space is a remainder

1988 ◽  
Vol 19 (2) ◽  
pp. 107-112
Author(s):  
J. Hatzenbuhler ◽  
D. A. Mattson
Keyword(s):  
Ordinal Space

scholarly journals On Russell’s 1927 Book The Analysis of Matter

Philosophies ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 40
Author(s):  
Said Mikki
Keyword(s):  
General Relativity ◽  
Quantum Mechanics ◽  
Building Blocks ◽  
Bertrand Russell ◽  
Important Work ◽  
Philosophy Of Nature ◽  
Foundations Of Physics ◽  
The World ◽  
Ordinal Space ◽  
Historical Fact

The goal of this article is to bring into wider attention the often neglected important work by Bertrand Russell on the philosophy of nature and the foundations of physics, published in the year 1927. It is suggested that the idea of what could be named Russell space, introduced in Part III of that book, may be viewed as more fundamental than many other types of spaces since the highly abstract nature of the topological ordinal space proposed by Russell there would incorporate into its very fabric the emergent nature of spacetime by deploying event assemblages, but not spacetime or particles, as the fundamental building blocks of the world. We also point out the curious historical fact that the book The Analysis of Matter can be chronologically considered the earliest book-length generic attempt to reflect on the relation between quantum mechanics, just emerging by that time, and general relativity.

Ordinal space projection learning via neighbor classes representation

2018 ◽  
Vol 174 ◽  
pp. 24-32 ◽  
Author(s):  
Qing Tian ◽  
Songcan Chen ◽  
Tinghuai Ma
Keyword(s):  
Ordinal Space ◽  
Space Projection

scholarly journals The Cech number of Cp(X) when X is an ordinal space

2008 ◽  
Vol 9 (1) ◽  
pp. 67-76
Author(s):  
Ofelia T. Alas ◽  
Ángel Tamariz-Mascarúa
Keyword(s):  
Ordinal Space

MO2TOS: Multi-Fidelity Optimization with Ordinal Transformation and Optimal Sampling

2016 ◽  
Vol 33 (03) ◽  
pp. 1650017 ◽  
Author(s):  
Jie Xu ◽  
Si Zhang ◽  
Edward Huang ◽  
Chun-Hung Chen ◽  
Loo Hay Lee ◽  
...  
Keyword(s):  
Job Scheduling ◽  
Optimization Problems ◽  
Simulation Optimization ◽  
Optimal Solution ◽  
Solution Space ◽  
Simulation Models ◽  
Optimal Sampling ◽  
Original Solution ◽  
Ordinal Space ◽  
Fidelity Model

Simulation optimization can be used to solve many complex optimization problems in automation applications such as job scheduling and inventory control. We propose a new framework to perform efficient simulation optimization when simulation models with different fidelity levels are available. The framework consists of two novel methodologies: ordinal transformation (OT) and optimal sampling (OS). The OT methodology uses the low-fidelity simulations to transform the original solution space into an ordinal space that encapsulates useful information from the low-fidelity model. The OS methodology efficiently uses high-fidelity simulations to sample the transformed space in search of the optimal solution. Through theoretical analysis and numerical experiments, we demonstrate the promising performance of the multi-fidelity optimization with ordinal transformation and optimal sampling (MO2TOS) framework.

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