Dual Distribution Matching GAN

2022 ◽  
Author(s):  
Zhiwen Zuo ◽  
Lei Zhao ◽  
Ailin Li ◽  
Zhizhong Wang ◽  
Haibo Chen ◽  
...  
Keyword(s):  
Dual Distribution ◽  
Distribution Matching

scholarly journals Image Segmentation using Dual Distribution Matching

2012 ◽  
Author(s):  
Tatsunori Taniai ◽  
Viet-quoc Pham ◽  
Keita Takahashi ◽  
Takeshi Naemura
Keyword(s):  
Image Segmentation ◽  
Dual Distribution ◽  
Distribution Matching

scholarly journals Non-random sampling and association tests on realized returns and risk proxies

2021 ◽  
Author(s):  
Frank Ecker ◽  
Jennifer Francis ◽  
Per Olsson ◽  
Katherine Schipper
Keyword(s):  
Random Sampling ◽  
Reference Sample ◽  
Positive Association ◽  
Cost Of Equity ◽  
Association Tests ◽  
Random Samples ◽  
Distribution Matching ◽  
Matched Samples ◽  
Data Requirements ◽  
Selection Of

AbstractThis paper investigates how data requirements often encountered in archival accounting research can produce a data-restricted sample that is a non-random selection of observations from the reference sample to which the researcher wishes to generalize results. We illustrate the effects of non-random sampling on results of association tests in a setting with data on one variable of interest for all observations and frequently-missing data on another variable of interest. We develop and validate a resampling approach that uses only observations from the data-restricted sample to construct distribution-matched samples that approximate randomly-drawn samples from the reference sample. Our simulation tests provide evidence that distribution-matched samples yield generalizable results. We demonstrate the effects of non-random sampling in tests of the association between realized returns and five implied cost of equity metrics. In this setting, the reference sample has full information on realized returns, while on average only 16% of reference sample observations have data on cost of equity metrics. Consistent with prior research (e.g., Easton and Monahan The Accounting Review 80, 501–538, 2005), analysis using the unadjusted (non-random) cost of equity sample reveals weak or negative associations between realized returns and cost of equity metrics. In contrast, using distribution-matched samples, we find reliable evidence of the theoretically-predicted positive association. We also conceptually and empirically compare distribution-matching with multiple imputation and selection models, two other approaches to dealing with non-random samples.

Stochastic Late Accretion to Earth, the Moon, and Mars

Science ◽  
2010 ◽  
Vol 330 (6010) ◽  
pp. 1527-1530 ◽  
Author(s):  
William F. Bottke ◽  
Richard J. Walker ◽  
James M. D. Day ◽  
David Nesvorny ◽  
Linda Elkins-Tanton
Keyword(s):  
Size Distribution ◽  
Asteroid Belt ◽  
The Moon ◽  
Core Formation ◽  
Highly Siderophile Elements ◽  
Earth’S Mantle ◽  
Distribution Matching ◽  
Siderophile Elements ◽  
Impact Basins

Core formation should have stripped the terrestrial, lunar, and martian mantles of highly siderophile elements (HSEs). Instead, each world has disparate, yet elevated HSE abundances. Late accretion may offer a solution, provided that ≥0.5% Earth masses of broadly chondritic planetesimals reach Earth’s mantle and that ~10 and ~1200 times less mass goes to Mars and the Moon, respectively. We show that leftover planetesimal populations dominated by massive projectiles can explain these additions, with our inferred size distribution matching those derived from the inner asteroid belt, ancient martian impact basins, and planetary accretion models. The largest late terrestrial impactors, at 2500 to 3000 kilometers in diameter, potentially modified Earth’s obliquity by ~10°, whereas those for the Moon, at ~250 to 300 kilometers, may have delivered water to its mantle.

scholarly journals CHIMERA: Clustering of Heterogeneous Disease Effects via Distribution Matching of Imaging Patterns

2016 ◽  
Vol 35 (2) ◽  
pp. 612-621 ◽  
Author(s):  
Aoyan Dong ◽  
Nicolas Honnorat ◽  
Bilwaj Gaonkar ◽  
Christos Davatzikos
Keyword(s):  
Distribution Matching

scholarly journals Molecular generation by Fast Assembly of (Deep)SMILES fragments

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Francois Berenger ◽  
Koji Tsuda
Keyword(s):  
High Frequency ◽  
Molecular Diversity ◽  
Molecular Design ◽  
Peak Performance ◽  
Scoring Functions ◽  
Training Set ◽  
Simple Method ◽  
Distribution Matching ◽  
Property Profile ◽  
Speed Training

Abstract Background In recent years, in silico molecular design is regaining interest. To generate on a computer molecules with optimized properties, scoring functions can be coupled with a molecular generator to design novel molecules with a desired property profile. Results In this article, a simple method is described to generate only valid molecules at high frequency ($$>300,000$$ > 300 , 000 molecule/s using a single CPU core), given a molecular training set. The proposed method generates diverse SMILES (or DeepSMILES) encoded molecules while also showing some propensity at training set distribution matching. When working with DeepSMILES, the method reaches peak performance ($$>340,000$$ > 340 , 000 molecule/s) because it relies almost exclusively on string operations. The “Fast Assembly of SMILES Fragments” software is released as open-source at https://github.com/UnixJunkie/FASMIFRA. Experiments regarding speed, training set distribution matching, molecular diversity and benchmark against several other methods are also shown.

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