Data synthesis method preserving correlation of features

2022 ◽  
Vol 122 ◽  
pp. 108241 ◽  
Author(s):  
Wonseok Yang ◽  
Woochul Nam
Keyword(s):  
Synthesis Method ◽  
Data Synthesis

scholarly journals Controllable data synthesis method for grammatical error correction

2021 ◽  
Vol 16 (4) ◽  
Author(s):  
Liner Yang ◽  
Chengcheng Wang ◽  
Yun Chen ◽  
Yongping Du ◽  
Erhong Yang
Keyword(s):  
Error Correction ◽  
Synthesis Method ◽  
Data Synthesis ◽  
Grammatical Error

scholarly journals Growing synthetic data through differentially-private vine copulas

2021 ◽  
Vol 2021 (3) ◽  
pp. 122-141
Author(s):  
Sébastien Gambs ◽  
Frédéric Ladouceur ◽  
Antoine Laurent ◽  
Alexandre Roy-Gaumond
Keyword(s):  
Synthesis Method ◽  
Synthetic Data ◽  
Data Synthesis ◽  
Protection Level ◽  
Novel Approach ◽  
Inference Attack ◽  
Arbitrary Dimensions ◽  
Vine Copulas

Abstract In this work, we propose a novel approach for the synthetization of data based on copulas, which are interpretable and robust models, extensively used in the actuarial domain. More precisely, our method COPULA-SHIRLEY is based on the differentially-private training of vine copulas, which are a family of copulas allowing to model and generate data of arbitrary dimensions. The framework of COPULA-SHIRLEY is simple yet flexible, as it can be applied to many types of data while preserving the utility as demonstrated by experiments conducted on real datasets. We also evaluate the protection level of our data synthesis method through a membership inference attack recently proposed in the literature.

scholarly journals BAGAN: Effective Data Generation Based on GAN Augmented 3D Synthesizing

2018 ◽  
Author(s):  
Yan Ma ◽  
Kang Liu ◽  
Zhi-Bin Guan ◽  
Xin-Kai Xu ◽  
Xu Qian ◽  
...  
Keyword(s):  
Object Recognition ◽  
Synthesis Method ◽  
Recognition Task ◽  
Image Database ◽  
Interactive Systems ◽  
Training Data ◽  
Natural Image ◽  
Data Generation ◽  
Data Synthesis ◽  
Augment Reality

Augment reality (AR) is crucial for immersive human-computer interaction (HCI) and vision of artificial intelligence (AI). Labeled data drove object recognition in AR. However, manual annotating data is expensive and labor-intensive, and furthermore, scanty labeled data limits the application of AR. Aiming at solving the problem of insufficient training data in AR object recognition, an automated vision data synthesis method called BAGAN is proposed in this paper based on the 3D modeling and GAN algorithm. Our approach has been validated to have better performance than other methods through image recognition task on natural image database ObjectNet3D. This study can shorten the algorithm development time of AR and expand the application scope of AR, which is of great significance to immersive interactive systems.

What causes the anti-Flynn effect? A data synthesis and analysis of predictors.

2018 ◽  
Vol 12 (4) ◽  
pp. 276-295 ◽  
Author(s):  
Michael A. Woodley of Menie ◽  
Mateo Peñaherrera-Aguirre ◽  
Heitor B. F. Fernandes ◽  
Aurelio-José Figueredo
Keyword(s):  
Data Synthesis ◽  
Flynn Effect

Enhanced catalytic activity of low-Pt content nanocatalysts supported on hollow carbon spheres for the ORR in alkaline media

MRS Advances ◽  
2020 ◽  
Vol 5 (57-58) ◽  
pp. 2961-2972
Author(s):  
P.C. Meléndez-González ◽  
E. Garza-Duran ◽  
J.C. Martínez-Loyola ◽  
P. Quintana-Owen ◽  
I.L. Alonso-Lemus ◽  
...  
Keyword(s):  
Catalytic Activity ◽  
Anion Exchange ◽  
Average Particle Size ◽  
Synthesis Method ◽  
Alkaline Media ◽  
High Catalytic Activity ◽  
Average Particle ◽  
Carbon Spheres ◽  
Hollow Carbon ◽  
Hollow Carbon Spheres

In this work, low-Pt content nanocatalysts (≈ 5 wt. %) supported on Hollow Carbon Spheres (HCS) were synthesized by two routes: i) colloidal conventional polyol, and ii) surfactant-free Bromide Anion Exchange (BAE). The nanocatalysts were labelled as Pt/HCS-P and Pt/HCS-B for polyol and BAE, respectively. The physicochemical characterization of the nanocatalysts showed that by following both methods, a good control of chemical composition was achieved, obtaining in addition well dispersed nanoparticles of less than 3 nm TEM average particle size (d) on the HCS. Pt/HCS-B contained more Pt0 species than Pt/HCS-P, an effect of the synthesis method. In addition, the structure of the HCS remains more ordered after BAE synthesis, compared to polyol. Regarding the catalytic activity for the Oxygen Reduction Reaction (ORR) in 0.5 M KOH, Pt/HCS-P and Pt/HCS-B showed a similar performance in terms of current density (j) at 0.9 V vs. RHE than the benchmark commercial 20 wt. % Pt/C. However, Pt/HCS-P and Pt/HCS-B demonstrated a 6 and 5-fold increase in mass catalytic activity compared to Pt/C, respectively. A positive effect of the high specific surface area of the HCS and its interactions with metal nanoparticles and electrolyte, which promoted the mass transfer, increased the performance of Pt/HCS-P and Pt/HCS-B. The high catalytic activity showed by Pt/HCS-B and Pt/HCS-P for the ORR, even with a low-Pt content, make them promising cathode nanocatalysts for Anion Exchange Membrane Fuel Cells (AEMFC).

Discovery of a Difluoroglycine Synthesis Method through Quantum Chemical Calculations

2020 ◽  
Author(s):  
Tsuyoshi Mita ◽  
Yu Harabuchi ◽  
Satoshi Maeda
Keyword(s):  
Quantum Chemical Calculations ◽  
Experimental Verification ◽  
Quantum Chemical ◽  
Synthesis Method ◽  
Target Product ◽  
Systematic Exploration ◽  
Hands On ◽  
Retrosynthetic Analysis ◽  
Synthetic Routes ◽  
Verification Process

The systematic exploration of synthetic pathways to afford a desired product through quantum chemical calculations remains a considerable challenge. In 2013, Maeda et al. introduced ‘quantum chemistry aided retrosynthetic analysis’ (QCaRA), which uses quantum chemical calculations to search systematically for decomposition paths of the target product and propose a synthesis method. However, until now, no new reactions suggested by QCaRA have been reported to lead to experimental discoveries. Using a difluoroglycine derivative as a target, this study investigated the ability of QCaRA to suggest various synthetic paths to the target without relying on previous data or the knowledge and experience of chemists. Furthermore, experimental verification of the seemingly most promising path led to the discovery of a synthesis method for the difluoroglycine derivative. The extent of the hands-on expertise of chemists required during the verification process was also evaluated. These insights are expected to advance the applicability of QCaRA to the discovery of viable experimental synthetic routes.

Discovery of a Difluoroglycine Synthesis Method through Quantum Chemical Calculations

2020 ◽  
Author(s):  
Tsuyoshi Mita ◽  
Yu Harabuchi ◽  
Satoshi Maeda
Keyword(s):  
Quantum Chemical Calculations ◽  
Experimental Verification ◽  
Quantum Chemical ◽  
Synthesis Method ◽  
Target Product ◽  
Systematic Exploration ◽  
Hands On ◽  
Retrosynthetic Analysis ◽  
Synthetic Routes ◽  
Verification Process

The systematic exploration of synthetic pathways to afford a desired product through quantum chemical calculations remains a considerable challenge. In 2013, Maeda et al. introduced ‘quantum chemistry aided retrosynthetic analysis’ (QCaRA), which uses quantum chemical calculations to search systematically for decomposition paths of the target product and propose a synthesis method. However, until now, no new reactions suggested by QCaRA have been reported to lead to experimental discoveries. Using a difluoroglycine derivative as a target, this study investigated the ability of QCaRA to suggest various synthetic paths to the target without relying on previous data or the knowledge and experience of chemists. Furthermore, experimental verification of the seemingly most promising path led to the discovery of a synthesis method for the difluoroglycine derivative. The extent of the hands-on expertise of chemists required during the verification process was also evaluated. These insights are expected to advance the applicability of QCaRA to the discovery of viable experimental synthetic routes.

Rapid Crystallization and Kinetic Freezing of Site-Disorder in the Lithium Superionic Argyrodite Li6PS5Br

2019 ◽  
Author(s):  
Ajay Gautam ◽  
Marcel Sadowski ◽  
Nils Prinz ◽  
Henrik Eickhoff ◽  
Nicolo Minafra ◽  
...  
Keyword(s):  
Elevated Temperatures ◽  
Synthesis Method ◽  
Reaction Times ◽  
Ionic Conductivities ◽  
Ionic Transport ◽  
High Energy ◽  
Superionic Conductors ◽  
Rapid Crystallization ◽  
Site Disorder ◽  
Fast Cooling

<p>Lithium argyrodite superionic conductors are currently being investigated as solid electrolytes for all-solid-state batteries. Recently, in the lithium argyrodite Li<sub>6</sub>PS<sub>5</sub>X (X = Cl, Br, I), a site-disorder between the anionsS<sup>2–</sup>and X<sup>–</sup>has been observed, which strongly affects the ionic transport and appears to be a function of the halide present. In this work, we show how such disorder in Li<sub>6</sub>PS<sub>5</sub>Br can be engineered <i>via</i>the synthesis method. By comparing fast cooling (<i>i.e. </i>quenching) to more slowly cooled samples, we find that anion site-disorder is higher at elevated temperatures, and that fast cooling can be used to kinetically trap the desired disorder, leading to higher ionic conductivities as shown by impedance spectroscopy in combination with <i>ab-initio</i>molecular dynamics. Furthermore, we observe that after milling, a crystalline lithium argyrodite can be obtained within one minute of heat treatment. This rapid crystallization highlights the reactive nature of mechanical milling and shows that long reaction times with high energy consumption are not needed in this class of materials. The fact that site-disorder induced <i>via</i>quenching is beneficial for ionic transport provides an additional approach for the optimization and design of lithium superionic conductors.</p>

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