scholarly journals Wasserstein Generative Models for Patch-Based Texture Synthesis

2021 ◽  
pp. 269-280
Author(s):  
Antoine Houdard ◽  
Arthur Leclaire ◽  
Nicolas Papadakis ◽  
Julien Rabin
Keyword(s):  
Texture Synthesis ◽  
Generative Models

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>

Wang Tile-based improved texture synthesis

2010 ◽  
Vol 30 (8) ◽  
pp. 2098-2100 ◽  
Author(s):  
Feng XUE ◽  
Cheng CHENG ◽  
Ju-lang JIANG
Keyword(s):  
Texture Synthesis ◽  
Wang Tile

Review on Recent Patents in Texture Synthesis

2010 ◽  
Vol 2 (2) ◽  
pp. 111-115
Author(s):  
Weiming Dong ◽  
Jean-Claude Paul
Keyword(s):  
Texture Synthesis

Networks

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Biological Networks ◽  
Generative Models ◽  
The Internet ◽  
Network Data ◽  
Computer Algorithms ◽  
Graph Models ◽  
Random Graph Models ◽  
The Social ◽  
Spectral Algorithms ◽  
Wide Availability

The study of networks, including computer networks, social networks, and biological networks, has attracted enormous interest in recent years. The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyse network data on an unprecendented scale, and the development of new theoretical tools has allowed us to extract knowledge from networks of many different kinds. The study of networks is broadly interdisciplinary and developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social science. This book brings together the most important breakthroughts in each of these fields and presents them in a unified fashion, highlighting the strong interconnections between work in different areas. Topics covered include the measurement of networks; methods for analysing network data, including methods developed in physics, statistics, and sociology; fundamentals of graph theory; computer algorithms, including spectral algorithms and community detection; mathematical models of networks such as random graph models and generative models; and models of processes taking place on networks.

scholarly journals Automatic Labeled Dialogue Generation for Nursing Record Systems

2020 ◽  
Vol 10 (3) ◽  
pp. 62
Author(s):  
Tittaya Mairittha ◽  
Nattaya Mairittha ◽  
Sozo Inoue
Keyword(s):  
Data Augmentation ◽  
Short Term Memory ◽  
Generative Models ◽  
Abstract Knowledge ◽  
Augmentation Techniques ◽  
Nursing Record ◽  
Long Short Term Memory ◽  
The Individual ◽  
High Level ◽  
Embedding Methods

The integration of digital voice assistants in nursing residences is becoming increasingly important to facilitate nursing productivity with documentation. A key idea behind this system is training natural language understanding (NLU) modules that enable the machine to classify the purpose of the user utterance (intent) and extract pieces of valuable information present in the utterance (entity). One of the main obstacles when creating robust NLU is the lack of sufficient labeled data, which generally relies on human labeling. This process is cost-intensive and time-consuming, particularly in the high-level nursing care domain, which requires abstract knowledge. In this paper, we propose an automatic dialogue labeling framework of NLU tasks, specifically for nursing record systems. First, we apply data augmentation techniques to create a collection of variant sample utterances. The individual evaluation result strongly shows a stratification rate, with regard to both fluency and accuracy in utterances. We also investigate the possibility of applying deep generative models for our augmented dataset. The preliminary character-based model based on long short-term memory (LSTM) obtains an accuracy of 90% and generates various reasonable texts with BLEU scores of 0.76. Secondly, we introduce an idea for intent and entity labeling by using feature embeddings and semantic similarity-based clustering. We also empirically evaluate different embedding methods for learning good representations that are most suitable to use with our data and clustering tasks. Experimental results show that fastText embeddings produce strong performances both for intent labeling and on entity labeling, which achieves an accuracy level of 0.79 and 0.78 f1-scores and 0.67 and 0.61 silhouette scores, respectively.

scholarly journals Contextualizing genetic risk score for disease screening and rare variant discovery

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Dan Zhou ◽  
Dongmei Yu ◽  
Jeremiah M. Scharf ◽  
Carol A. Mathews ◽  
Lauren McGrath ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Basis ◽  
Clinical Decision Making ◽  
Genetic Risk Score ◽  
Sufficient Conditions ◽  
Clinical Decision ◽  
Generative Models ◽  
Variant Discovery ◽  
Models Of Disease ◽  
The Uk

AbstractStudies of the genetic basis of complex traits have demonstrated a substantial role for common, small-effect variant polygenic burden (PB) as well as large-effect variants (LEV, primarily rare). We identify sufficient conditions in which GWAS-derived PB may be used for well-powered rare pathogenic variant discovery or as a sample prioritization tool for whole-genome or exome sequencing. Through extensive simulations of genetic architectures and generative models of disease liability with parameters informed by empirical data, we quantify the power to detect, among cases, a lower PB in LEV carriers than in non-carriers. Furthermore, we uncover clinically useful conditions wherein the risk derived from the PB is comparable to the LEV-derived risk. The resulting summary-statistics-based methodology (with publicly available software, PB-LEV-SCAN) makes predictions on PB-based LEV screening for 36 complex traits, which we confirm in several disease datasets with available LEV information in the UK Biobank, with important implications on clinical decision-making.

scholarly journals Calcium functional imaging with high-resolution CT in the inner ear

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hisaya Tanioka ◽  
Sayaka Tanioka
Keyword(s):  
Internal State ◽  
Texture Synthesis ◽  
Biological Information ◽  
Endolymphatic Sac ◽  
Otolith Organs ◽  
Synthesis Algorithm ◽  
Utricular Macula ◽  
Positional Vertigo ◽  
Paroxysmal Positional Vertigo

AbstractAlthough the otolith and otolith organs correlate with vertigo and instability, there is no method to investigate them without harmful procedures. We will create the technique for 3D microanatomical images of them, and investigate the in vivo internal state and metabolisms. The otolith and otolith organs images were reconstructed from a texture synthesis algorithm under the skull volume rendering algorithm using a cutting-plane method. The utricular macula was elongated pea-shaped. The saccular macula was almost bud-shaped. The changes in the amount of CaCO3 in the maculae and the endolymphatic sac showed various morphologies, reflecting the balance status of each subject. Both shapes and volumes were not always constant depending on time. In Meniere’s disease (MD), the saccular macula was larger and the utricular macula was smaller. In benign paroxysmal positional vertigo (BPPV), the otolith increased in the utricular macula but did not change much in the saccular macula. The saccule, utricle, and endolymphatic sac were not constantly shaped according to their conditions. These created 3D microanatomical images can allow detailed observations of changes in physiological and biological information. This imaging technique will contribute to our understanding of pathology and calcium metabolism in the in vivo vestibulum.

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