Search results for "NEURAL NETWORK"

Defects in glasses

1995

Abstract The absence of long range order in the glass structure allows to define only point defects in these materials. They are: 1) intrinsic defects—atomic size local deviation from short range order; 2) impurity defects—isolated impurity atoms or ions in the glass network; 3) intrinsic impurity defects—complexes consisting of the impurity atoms chemically bonded to one of the intrinsic defect atoms. The latter defects are characteristic for the doped glasses. Presence of point defects in glasses introduces new spectroscopic properties of these solid materials. Defect generation, interaction and recombination reactions resulting from the external influence causes the glass spectroscopic p…

Long-Term Behavioral Programming Induced by Peripuberty Stress in Rats Is Accompanied by GABAergic-Related Alterations in the Amygdala

2014

Stress during childhood and adolescence is a risk factor for psychopathology. Alterations in γ-aminobutyric acid (GABA), the main inhibitory neurotransmitter in the brain, have been found following stress exposure and fear experiences and are often implicated in anxiety and mood disorders. Abnormal amygdala functioning has also been detected following stress exposure and is also implicated in anxiety and social disorders. However, the amygdala is not a unitary structure; it includes several nuclei with different functions and little is known on the potential differences the impact of early life stress may have on this system within different amygdaloid nuclei. We aimed here to evaluate pote…

Improving Small Molecule pKa Prediction Using Transfer Learning With Graph Neural Networks

2022

Enumerating protonation states and calculating microstate pKa values of small molecules is an important yet challenging task for lead optimization and molecular modeling. Commercial and non-commercial solutions have notable limitations such as restrictive and expensive licenses, high CPU/GPU hour requirements, or the need for expert knowledge to set up and use. We present a graph neural network model that is trained on 714,906 calculated microstate pKa predictions from molecules obtained from the ChEMBL database. The model is fine-tuned on a set of 5,994 experimental pKa values significantly improving its performance on two challenging test sets. Combining the graph neural network model wit…

Domain Adaptation of Landsat-8 and Proba-V Data Using Generative Adversarial Networks for Cloud Detection

2019

Training machine learning algorithms for new satellites requires collecting new data. This is a critical drawback for most remote sensing applications and specially for cloud detection. A sensible strategy to mitigate this problem is to exploit available data from a similar sensor, which involves transforming this data to resemble the new sensor data. However, even taking into account the technical characteristics of both sensors to transform the images, statistical differences between data distributions still remain. This results in a poor performance of the methods trained on one sensor and applied to the new one. In this this work, we propose to use the generative adversarial networks (G…

A 3D Deep Neural Network for Liver Volumetry in 3T Contrast-Enhanced MRI.

2020

 To create a fully automated, reliable, and fast segmentation tool for Gd-EOB-DTPA-enhanced MRI scans using deep learning. Datasets of Gd-EOB-DTPA-enhanced liver MR images of 100 patients were assembled. Ground truth segmentation of the hepatobiliary phase images was performed manually. Automatic image segmentation was achieved with a deep convolutional neural network. Our neural network achieves an intraclass correlation coefficient (ICC) of 0.987, a Sørensen-Dice coefficient of 96.7 ± 1.9 % (mean ± std), an overlap of 92 ± 3.5 %, and a Hausdorff distance of 24.9 ± 14.7 mm compared with two expert readers who corresponded to an ICC of 0.973, a Sørensen-Dice coefficient of 95.2 ± 2.8 %, and…

Blood vessel segmentation and width estimation in ultra-wide field scanning laser ophthalmoscopy.

2014

Features of the retinal vasculature, such as vessel widths, are considered biomarkers for systemic disease. The aim of this work is to present a supervised approach to vessel segmentation in ultra-wide field of view scanning laser ophthalmoscope (UWFoV SLO) images and to evaluate its performance in terms of segmentation and vessel width estimation accuracy. The results of the proposed method are compared with ground truth measurements from human observers and with existing state-of-the-art techniques developed for fundus camera images that we optimized for UWFoV SLO images. Our algorithm is based on multi-scale matched filters, a neural network classifier and hysteresis thresholding. After …

Modelling Electricity Price Expectations in a Day-Ahead Market: A Case of Latvia

2016

Abstract The paper aims at modelling the electricity generator’s expectations about price development in the Latvian day-ahead electricity market. Correlation and sensitivity analysis methods are used to identify the key determinants of electricity price expectations. A neural network approach is employed to model electricity price expectations. The research results demonstrate that electricity price expectations depend on the historical electricity prices. The price a day ago is the key determinant of price expectations and the importance of the lagged prices reduces as the time backwards lengthens. Nine models of electricity price expectations are prepared for different natural seasons an…

Discussion of “Soil Water Retention Characteristics of Vertisols and Pedotransfer Functions Based on Nearest Neighbor and Neural Networks Approaches …

2013

Deep Convolutional Neural Network Based Object Detection Inference Acceleration Using FPGA

2022

Object detection is one of the most challenging yet essential computer vision research areas. It means labeling and localizing all known objects of interest on an input image using tightly fit rectangular bounding boxes around the objects. Object detection, having passed through several evolutions and progressions, nowadays relies on the successes of image classification networks based on deep convolutional neural networks. However, as the depth and complication of convolutional neural networks increased, detection speed reduced, and accuracy increased. Unfortunately, most computer vision applications, such as real-time object tracking on an embedded system, requires lightweight, fast and a…

From Arithmetic to Logic based AI: A Comparative Analysis of Neural Networks and Tsetlin Machine

2020

Neural networks constitute a well-established design method for current and future generations of artificial intelligence. They depends on regressed arithmetic between perceptrons organized in multiple layers to derive a set of weights that can be used for classification or prediction. Over the past few decades, significant progress has been made in low-complexity designs enabled by powerful hardware/software ecosystems. Built on the foundations of finite-state automata and game theory, Tsetlin Machine is increasingly gaining momentum as an emerging artificial intelligence design method. It is fundamentally based on propositional logic based formulation using booleanized input features. Rec…