scholarly journals Generating Training Sets for the Automatic Recognition of Handwritten Documents

10.5772/52074 ◽  
2012 ◽  
Author(s):  
Gabriel Pereira e Silva ◽  
Rafael Dueire
Keyword(s):  
Automatic Recognition ◽  
Handwritten Documents ◽  
Training Sets

Molecular Generation Targeting Desired Electronic Properties via Deep Generative Models

2019 ◽  
Author(s):  
Qi Yuan ◽  
Alejandro Santana-Bonilla ◽  
Martijn Zwijnenburg ◽  
Kim Jelfs
Keyword(s):  
Neural Network ◽  
Electronic Properties ◽  
Transfer Learning ◽  
Recurrent Neural Network ◽  
Chemical Space ◽  
Generative Models ◽  
Molecular Features ◽  
Donor Acceptor ◽  
Homo Lumo ◽  
Training Sets

<p>The chemical space for novel electronic donor-acceptor oligomers with targeted properties was explored using deep generative models and transfer learning. A General Recurrent Neural Network model was trained from the ChEMBL database to generate chemically valid SMILES strings. The parameters of the General Recurrent Neural Network were fine-tuned via transfer learning using the electronic donor-acceptor database from the Computational Material Repository to generate novel donor-acceptor oligomers. Six different transfer learning models were developed with different subsets of the donor-acceptor database as training sets. We concluded that electronic properties such as HOMO-LUMO gaps and dipole moments of the training sets can be learned using the SMILES representation with deep generative models, and that the chemical space of the training sets can be efficiently explored. This approach identified approximately 1700 new molecules that have promising electronic properties (HOMO-LUMO gap <2 eV and dipole moment <2 Debye), 6-times more than in the original database. Amongst the molecular transformations, the deep generative model has learned how to produce novel molecules by trading off between selected atomic substitutions (such as halogenation or methylation) and molecular features such as the spatial extension of the oligomer. The method can be extended as a plausible source of new chemical combinations to effectively explore the chemical space for targeted properties.</p>

Unknown Documents of 1734 About Swedish Citizens Taken Into Serfdom

2019 ◽  
pp. 12-19
Author(s):  
Serhii I. Degtyarev ◽  
Violetta S. Molchanova
Keyword(s):  
Social History ◽  
Russian Empire ◽  
District Court ◽  
Peace Agreement ◽  
The Caucasus ◽  
Handwritten Documents ◽  
Ancient Times ◽  
Historical Phenomenon ◽  
State Archives ◽  
The Russian Empire

This work is devoted to the publication and analysis of two previously unknown handwritten documents of 1734. These documents contain information on several persons of Swedish nationality, which were illegally taken out by the Russian nobleman I. Popov during the Northern War from the territory of Sweden. Materials are stored in the State Archives of the Sumy region. They are part of the archival case of Okhtyrka District Court, but they are not thematically connected with it. These documents were once part of a much larger complex of materials. They refer to the request of former Swedish nationals to release them from serfdom from the Belgorod and Kursk landlords Popov and Dolgintsev. The further fate of these people remained unknown. But it is known that they were mistreated by their masters. Russian legislation at the time prohibited such treatment of persons of Swedish nationality. This was discussed in terms of the peace agreement Nishtadskoyi 1721. The two documents revealed illustrate the episodes of the lives of several foreigners who were captured. The analyzed materials give an opportunity to look at a historical phenomenon like a serfdom in the territory of the Russian Empire under a new angle. They allow us to study one of the ways to replenish the serfs. Documents can also be used as a source for the study of some aspects of social history, in biographical studies. The authors noted that the conversion to the property of the enslaved people of other nationalities was a very common practice in the XVII-XIX centuries. This source of replenishment of the dependent population groups were popular in many nations in Europe, Asia and Africa since ancient times. For example, in the Crimean Khanate, Turkey, Italy, Egypt, the nations of the Caucasus and many others. Кeywords: Sweden, Russian Empire, historical source, documents, Russo-Swedish War, Nistadt Treaty, Viborg, Swedish citizens, enslavement, serfdom.

scholarly journals Automatic Recognition of Fetal Facial Standard Plane in Ultrasound Image via Fisher Vector

PLoS ONE ◽  
2015 ◽  
Vol 10 (5) ◽  
pp. e0121838 ◽  
Author(s):  
Baiying Lei ◽  
Ee-Leng Tan ◽  
Siping Chen ◽  
Liu Zhuo ◽  
Shengli Li ◽  
...  
Keyword(s):  
Ultrasound Image ◽  
Automatic Recognition ◽  
Fisher Vector

scholarly journals Smart Care Using a DNN-Based Approach for Activities of Daily Living (ADL) Recognition

2020 ◽  
Vol 11 (1) ◽  
pp. 10
Author(s):  
Muchun Su ◽  
Diana Wahyu Hayati ◽  
Shaowu Tseng ◽  
Jiehhaur Chen ◽  
Hsihsien Wei
Keyword(s):  
Neural Network ◽  
Health Care ◽  
Activities Of Daily Living ◽  
Video Processing ◽  
Daily Living ◽  
Deep Neural Network ◽  
Recognition System ◽  
Automatic Recognition ◽  
Data Filtering ◽  
Accuracy Rate

Health care for independently living elders is more important than ever. Automatic recognition of their Activities of Daily Living (ADL) is the first step to solving the health care issues faced by seniors in an efficient way. The paper describes a Deep Neural Network (DNN)-based recognition system aimed at facilitating smart care, which combines ADL recognition, image/video processing, movement calculation, and DNN. An algorithm is developed for processing skeletal data, filtering noise, and pattern recognition for identification of the 10 most common ADL including standing, bending, squatting, sitting, eating, hand holding, hand raising, sitting plus drinking, standing plus drinking, and falling. The evaluation results show that this DNN-based system is suitable method for dealing with ADL recognition with an accuracy rate of over 95%. The findings support the feasibility of this system that is efficient enough for both practical and academic applications.

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