One flavor assumption and gamma-acyclicity for universal relation views

1986 ◽  
Author(s):  
H Biskup ◽  
L Schnetgoke
Keyword(s):  
Universal Relation

scholarly journals Universal Relation between Instantaneous Diffusivity and Radius of Gyration of Proteins in Aqueous Solution

2021 ◽  
Vol 126 (12) ◽  
Author(s):  
Eiji Yamamoto ◽  
Takuma Akimoto ◽  
Ayori Mitsutake ◽  
Ralf Metzler
Keyword(s):  
Aqueous Solution ◽  
Radius Of Gyration ◽  
Universal Relation

Multi-Graph Cooperative Learning Towards Distant Supervised Relation Extraction

2021 ◽  
Vol 12 (5) ◽  
pp. 1-21
Author(s):  
Changsen Yuan ◽  
Heyan Huang ◽  
Chong Feng
Keyword(s):  
Cooperative Learning ◽  
State Of The Art ◽  
Relation Extraction ◽  
Sentence Length ◽  
Universal Relation ◽  
Dependency Parsing ◽  
Convolutional Network ◽  
Syntactic Features ◽  
Use Dependency

The Graph Convolutional Network (GCN) is a universal relation extraction method that can predict relations of entity pairs by capturing sentences’ syntactic features. However, existing GCN methods often use dependency parsing to generate graph matrices and learn syntactic features. The quality of the dependency parsing will directly affect the accuracy of the graph matrix and change the whole GCN’s performance. Because of the influence of noisy words and sentence length in the distant supervised dataset, using dependency parsing on sentences causes errors and leads to unreliable information. Therefore, it is difficult to obtain credible graph matrices and relational features for some special sentences. In this article, we present a Multi-Graph Cooperative Learning model (MGCL), which focuses on extracting the reliable syntactic features of relations by different graphs and harnessing them to improve the representations of sentences. We conduct experiments on a widely used real-world dataset, and the experimental results show that our model achieves the state-of-the-art performance of relation extraction.

scholarly journals Measuring the H i Content of Individual Galaxies Out to the Epoch of Reionization with [C ii]

2021 ◽  
Vol 922 (2) ◽  
pp. 147
Author(s):  
Kasper E. Heintz ◽  
Darach Watson ◽  
Pascal A. Oesch ◽  
Desika Narayanan ◽  
Suzanne C. Madden
Keyword(s):  
Galaxy Evolution ◽  
Conversion Factor ◽  
Mass Density ◽  
Cosmic Time ◽  
Gas Content ◽  
Universal Relation ◽  
New Approach ◽  
Star Forming ◽  
Baryonic Mass ◽  
Individual Galaxies

Abstract The H i gas content is a key ingredient in galaxy evolution, the study of which has been limited to moderate cosmological distances for individual galaxies due to the weakness of the hyperfine H i 21 cm transition. Here we present a new approach that allows us to infer the H i gas mass M HI of individual galaxies up to z ≈ 6, based on a direct measurement of the [C ii]-to-H i conversion factor in star-forming galaxies at z ≳ 2 using γ-ray burst afterglows. By compiling recent [C ii]-158 μm emission line measurements we quantify the evolution of the H i content in galaxies through cosmic time. We find that M HI starts to exceed the stellar mass M ⋆ at z ≳ 1, and increases as a function of redshift. The H i fraction of the total baryonic mass increases from around 20% at z = 0 to about 60% at z ∼ 6. We further uncover a universal relation between the H i gas fraction M HI/M ⋆ and the gas-phase metallicity, which seems to hold from z ≈ 6 to z = 0. The majority of galaxies at z > 2 are observed to have H i depletion times, t dep,HI = M HI/SFR, less than ≈2 Gyr, substantially shorter than for z ∼ 0 galaxies. Finally, we use the [C ii]-to-H i conversion factor to determine the cosmic mass density of H i in galaxies, ρ HI, at three distinct epochs: z ≈ 0, z ≈ 2, and z ∼ 4–6. These measurements are consistent with previous estimates based on 21 cm H i observations in the local universe and with damped Lyα absorbers (DLAs) at z ≳ 2, suggesting an overall decrease by a factor of ≈5 in ρ HI(z) from the end of the reionization epoch to the present.

scholarly journals Universal patterns of long-distance commuting and social assortativity in cities

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eszter Bokányi ◽  
Sándor Juhász ◽  
Márton Karsai ◽  
Balázs Lengyel
Keyword(s):  
Social Networks ◽  
Low Income ◽  
Social Inclusion ◽  
The United States ◽  
Universal Relation ◽  
Long Distance ◽  
Low Income Groups ◽  
Twitter Users ◽  
Diverse Individual ◽  
Commuting Distance

AbstractMillions commute to work every day in cities and interact with colleagues, partners, friends, and strangers. Commuting facilitates the mixing of people from distant and diverse neighborhoods, but whether this has an imprint on social inclusion or instead, connections remain assortative is less explored. In this paper, we aim to better understand income sorting in social networks inside cities and investigate how commuting distance conditions the online social ties of Twitter users in the 50 largest metropolitan areas of the United States. An above-median commuting distance in cities is linked to more diverse individual networks, moreover, we find that longer commutes are associated with a nearly uniform, moderate reduction of overall social tie assortativity across all cities. This suggests a universal relation between long-distance commutes and the integration of social networks. Our results inform policy that facilitating access across distant neighborhoods can advance the social inclusion of low-income groups.

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