From the Secondary Section: Is That Your Final Answer?

2000 ◽  
Vol 90 (2) ◽  
pp. 20
Author(s):  
Paul R. Hirth
Keyword(s):  
Final Answer

Migraine and the TMJ: the final answer?

BDJ ◽  
1986 ◽  
Vol 161 (11) ◽  
pp. 393-393
Keyword(s):  
Final Answer

scholarly journals Scale Invariance, Horizons, and Inflation

2021 ◽  
Author(s):  
Andre Maeder ◽  
Vesselin G Gueorguiev
Keyword(s):  
Scalar Field ◽  
Scale Invariance ◽  
Maxwell Equations ◽  
High Redshift ◽  
Empty Space ◽  
Causal Connection ◽  
Cosmological Constant Problem ◽  
Scale Invariant ◽  
Starting Point ◽  
Final Answer

Abstract Maxwell equations and the equations of General Relativity are scale invariant in empty space. The presence of charge or currents in electromagnetism or the presence of matter in cosmology are preventing scale invariance. The question arises on how much matter within the horizon is necessary to kill scale invariance. The scale invariant field equation, first written by Dirac in 1973 and then revisited by Canuto et al. in 1977, provides the starting point to address this question. The resulting cosmological models show that, as soon as matter is present, the effects of scale invariance rapidly decline from ϱ = 0 to ϱc, and are forbidden for densities above ϱc. The absence of scale invariance in this case is consistent with considerations about causal connection. Below ϱc, scale invariance appears as an open possibility, which also depends on the occurrence of in the scale invariant context. In the present approach, we identify the scalar field of the empty space in the Scale Invariant Vacuum (SIV) context to the scalar field ϕ in the energy density $\varrho = \frac{1}{2} \dot{\varphi }^2 + V(\varphi )$ of the vacuum at inflation. This leads to some constraints on the potential. This identification also solves the so-called “cosmological constant problem”. In the framework of scale invariance, an inflation with a large number of e-foldings is also predicted. We conclude that scale invariance for models with densities below ϱc is an open possibility; the final answer may come from high redshift observations, where differences from the ΛCDM models appear.

What is the carbon market: Is there a final answer?

2009 ◽  
Vol 64 (1) ◽  
pp. 27A-35A ◽  
Author(s):  
J. R. Williams ◽  
S. Mooney ◽  
J. M. Peterson
Keyword(s):  
Carbon Market ◽  
Final Answer

scholarly journals Myth in the Context of African Traditional Histories: Can it be Called “Applied History”?

2005 ◽  
Vol 32 ◽  
pp. 485-491 ◽  
Author(s):  
S.A. Shokpeka
Keyword(s):  
Subject Matter ◽  
Religious Life ◽  
Moral Order ◽  
Inanimate Object ◽  
Cultural Artifacts ◽  
Comprehensive Examination ◽  
The World ◽  
Definition Of ◽  
Final Answer

For the reconstruction of history from oral sources, four broad types are usually distinguishable. These are myth, legend, songs, and what Phillips Stevens calls “popular history.” All of them fall under the generic heading of “folklore”—a term which is so broad in its application that it could include nearly all expressive aspects of culture. The only type that we will concern ourselves with in this study is myth. A comprehensive examination of the issue in question in the study requires a definition of the word myth; an examination of the characteristics of “applied history;” and the application of these characteristics to myth with a view to finding out any point of agreement between them, before a final answer will be given to the question whether “myth in the context of African traditional histories,” can be called applied history.The Advanced Learner's Dictionary of Current English defines myth as a “story handed down from olden time, containing the early beliefs of a race.” Vansina identifies myths by their subject matter and talks about them as those stories which “deal with and interpret the relations between the natural and the supernatural and are concerned with all that part of religious life that lies beyond the moral order. “ He says that they “attempt to explain the world, the culture, the society … in terms of religious causes.” McCall, for his part, refers to myths as “stories concerning the supernatural, the activities of deities, spirits and semi-divine heroes on the origin of the world, mankind and cultural artifacts and institutions which usually are said to have been achieved through the instrumentality of these sacred beings.” Afigbo, in turn, considers myths as having the “tendency to explain historical institutions and development by appeal to non-historic factors and forces”—as stories that see “the supernatural acting at times through the agency of man, at times through the agency of the lower animals and other times even through the agency of inanimate object, as the original and continuing causes of motion in a society.”

MEMS Challenge: Can This Technology Catch the Internet Lightwave?

2000 ◽  
Author(s):  
Ken Gilleo
Keyword(s):  
Optical Switch ◽  
The Internet ◽  
Underwater Communications ◽  
Optical Mems ◽  
Phone Calls ◽  
Micro Bubble ◽  
Mechanical Devices ◽  
Final Answer ◽  
The Right ◽  
The Ideal

Abstract The pundits of the money world tell us to be a “dotcom” or enable them for excitement and rewards. Traffic on the Internet Highway is certainly stepping up the pace as “slow” electrons make way for ultimate-speed photons creating major hardware opportunities. The “Copper Road” has become the “Glass Super Highway” as long-haul terrestrial and underwater communications links move up to Advanced Photonics. Nothing can be faster than light, but more important, no other medium can offer wider bandwidth when wave-multiplexing strategies are used. Photonics, employing dense wave division multiplexing (DWDM) can carry the equivalent of 12,000 encyclopedias or 5-million phone calls on a single fiber. Recent advances in photonics hardware, including higher-powered lasers, more efficient amplifiers and cleaner optical fiber are enabling incredible bandwidth for the Internet and general communications services. But how do we route a light beam? The long-haul segments of the Internet, now mostly fiberoptics, have been converting modulated light to electronic signals, routing with conventional electronic hardware and then re-converting back to light. Yes, O-E-O (Opto-electro-opto) works, but with cost and time-delay penalties. The communications industry has decreed that the double conversion process must go, but what technology will be the replacement? Enter optical MEMS, or MOEMS (micro-opto-electro-mechanical systems). The MOEMS switch/router approach was endorsed by the Internet carrier and hardware industry that paid billions of dollars in 2000 to acquire MEMS companies, some that had not even shipped a product. But what are the issues and are there competing technologies that could win? Micro-mirror technology is at the top of the popularity chart right now. Can MOEMS mirror routers solve cost problems and can they even switch at the rates demanded. What is the ideal mirror switch strategy: binary “off/on” or point-to-light pipe arrays? What about other MEMS approaches such as micro-bubble fluid beam refraction that appears to offer a much simpler construction? Maybe the mechanical devices are only an interim destined to obsolescence by a future solid state optics switch. The optical switch, powered totally by photons, is already in the lab and could be the final answer. This paper will survey MOEMS inside the Internet to seek answers to the billion dollar questions. The focus will be on micro-mirrors and their packaging issues both inside and out. We will deal with selecting the ideal optical MEMS package and choosing the right atmosphere control. Certain in-package contaminants are death to mirrors, but they can be controlled even if generated after the package is sealed. So tune in to find out if MEMS can catch the WAVE!

scholarly journals Quantitative Literacy: A Tool for Survival

Numeracy ◽  
2020 ◽  
Vol 13 (2) ◽  
Author(s):  
Gizem Karaali
Keyword(s):  
Decision Maker ◽  
Habits Of Mind ◽  
Quantitative Literacy ◽  
Individual Decision ◽  
Global Pandemic ◽  
Final Answer ◽  
Changing World

In the context of a global pandemic, the need for quantitative literacy has become more urgent. QL and QL-adjacent habits of mind, such as awareness of the limitations of data and modeling, are vital tools of survival that can help people understand today's fast-changing world and make decisions about next actions, in particular in relation to the ongoing protests supporting the #BlackLivesMatter movement. QL does not provide a final answer to most human questions, but it can be an invaluable guide for each individual decision maker.

g: Is This Your Final Answer?

2002 ◽  
pp. 123-142
Author(s):  
Harold W. Goldstein ◽  
Sheldon Zedeck ◽  
Irwin L. Goldstein
Keyword(s):  
Final Answer

scholarly journals Shortened therapy for genotype 1 hepatitis C virus. The final answer?

2010 ◽  
Vol 9 (1) ◽  
pp. 112-113 ◽  
Author(s):  
Francisco Barrera M ◽  
Alejandro Soza
Keyword(s):  
Hepatitis C Virus ◽  
Hepatitis C ◽  
Genotype 1 ◽  
Final Answer

scholarly journals Multi-Task Learning with Generative Adversarial Training for Multi-Passage Machine Reading Comprehension

2020 ◽  
Vol 34 (05) ◽  
pp. 8705-8712
Author(s):  
Qiyu Ren ◽  
Xiang Cheng ◽  
Sen Su
Keyword(s):  
Reading Comprehension ◽  
Open Domain ◽  
Training Process ◽  
Task Learning ◽  
Novel Approach ◽  
Adversarial Training ◽  
Model Training ◽  
Final Answer ◽  
Machine Reading ◽  
Candidate Set

Multi-passage machine reading comprehension (MRC) aims to answer a question by multiple passages. Existing multi-passage MRC approaches have shown that employing passages with and without golden answers (i.e. labeled and unlabeled passages) for model training can improve prediction accuracy. In this paper, we present MG-MRC, a novel approach for multi-passage MRC via multi-task learning with generative adversarial training. MG-MRC adopts the extract-then-select framework, where an extractor is first used to predict answer candidates, then a selector is used to choose the final answer. In MG-MRC, we adopt multi-task learning to train the extractor by using both labeled and unlabeled passages. In particular, we use labeled passages to train the extractor by supervised learning, while using unlabeled passages to train the extractor by generative adversarial training, where the extractor is regarded as the generator and a discriminator is introduced to evaluate the generated answer candidates. Moreover, to train the extractor by backpropagation in the generative adversarial training process, we propose a hybrid method which combines boundary-based and content-based extracting methods to produce the answer candidate set and its representation. The experimental results on three open-domain QA datasets confirm the effectiveness of our approach.

Sign in / Sign up

Export Citation Format

Share Document