Search results for "Neural Networks"
showing 10 items of 599 documents
Effect of the milling conditions on the degree of amorphization of selenium by milling in a planetary ball mill
2007
The effect of the milling parameters (rotation speed of the milling device and duration of milling) on the phase composition of the products of milling of fully crystalline selenium has been investigated. The milling was conducted using a planetary micromill and the phase composition of the milling products was determined by differential thermal analysis. It has been found that ball milling leads to the partial amorphization of the starting crystalline material. The content of amorphous phase in the milling products depends, in a rather complicated way, on the milling parameters. At the milling parameters adopted in the present study, the milling product was never fully amorphous. The compl…
Devitrification of the Kob-Andersen glass former: Competition with the locally favored structure
2018
Abstract Supercooled liquids are kinetically trapped materials in which the transition to a thermodynamically more stable state with long-range order is strongly suppressed. To assess the glass-forming abilities of a liquid empirical rules exist, but a comprehensive microscopic picture of devitrification is still missing. Here we study the crystallization of a popular model glass former, the binary Kob-Andersen mixture, in small systems. We perform trajectory sampling employing the population of the locally favored structure as order parameter. While for large population a dynamical phase transition has been reported, here we show that biasing towards a small population of locally favored s…
Critical phenomena without “hyper scaling”: How is the finite-size scaling analysis of Monte Carlo data affected?
2010
Abstract The finite size scaling analysis of Monte Carlo data is discussed for two models for which hyperscaling is violated: (i) the random field Ising model (using a model for a colloid-polymer mixture in a random matrix as a representative) (ii) The Ising bi-pyramid in computing surface fields.
Generating Hyperspectral Skin Cancer Imagery using Generative Adversarial Neural Network
2020
In this study we develop a proof of concept of using generative adversarial neural networks in hyperspectral skin cancer imagery production. Generative adversarial neural network is a neural network, where two neural networks compete. The generator tries to produce data that is similar to the measured data, and the discriminator tries to correctly classify the data as fake or real. This is a reinforcement learning model, where both models get reinforcement based on their performance. In the training of the discriminator we use data measured from skin cancer patients. The aim for the study is to develop a generator for augmenting hyperspectral skin cancer imagery. peerReviewed
Neural Networks to Determine the Relationships Between Business Innovation and Gender Aspects
2021
Gender aspects of management, innovation and entrepreneurship are gaining more and more importance as cross-cutting issues for researchers, practitioners and decision makers. Extant literature pays a growing attention to the hypothesis that there exists a correlation between the gender diversity of corporate boards of directors and the business attitude to innovation. In this paper we introduce a working framework to test the aforementioned hypothesis and to examine the correlation between board diversity and innovation perception of a business. This framework is based on correlation computation and feed-forward neural networks, and it is used to evaluate whether the gender component may be…
Slow and fast methyl group rotations in fragile glass-formers studied by NMR
2000
Abstract The spin-lattice relaxation times of the selectively ring deuterated, fragile glass-formers propylene carbonate and toluene were compared with those measured for species which were specifically labeled at the methyl groups. It was found that the dynamics of the CD 3 group is strongly decoupled from that associated with the primary response of toluene, while for propylene carbonate the degree of decoupling is relatively weak. The experimental results could be described successfully using a model which takes into account the ring dynamics as well as those of the methyl group.
Machine learning techniques to estimate the degree of binder activity of reclaimed asphalt pavement
2022
Part of this research was funded by the project RTI2018-096224-J-I00 that has been cofounded by the Spanish Ministry of Science and Innovation, inside the National Program for Fostering Excellence in Scientific and Technical Research, National Subprogram of Knowledge Generation, 2018 call, in the framework of the Spanish National Plan for Scientific and Technical Research and Innovation 2017-2020, and by the European Union, through the European Regional Development Fund, with the main objective of Promoting technological development, innovation and quality research. Part of this work was financially supported by the Italian Ministry of University and Research with the research Grant PRIN 20…
Lung CT Image Registration through Landmark-constrained Learning with Convolutional Neural Network
2020
Accurate registration of lung computed tomography (CT) image is a significant task in thorax image analysis. Recently deep learning-based medical image registration methods develop fast and achieve promising performance on accuracy and speed. However, most of them learned the deformation field through intensity similarity but ignored the importance of aligning anatomical landmarks (e.g., the branch points of airway and vessels). Accurate alignment of anatomical landmarks is essential for obtaining anatomically correct registration. In this work, we propose landmark constrained learning with a convolutional neural network (CNN) for lung CT registration. Experimental results of 40 lung 3D CT …
Multi-Path U-Net Architecture for Cell and Colony-Forming Unit Image Segmentation
2022
U-Net is the most cited and widely-used deep learning model for biomedical image segmentation. In this paper, we propose a new enhanced version of a ubiquitous U-Net architecture, which improves upon the original one in terms of generalization capabilities, while addressing several immanent shortcomings, such as constrained resolution and non-resilient receptive fields of the main pathway. Our novel multi-path architecture introduces a notion of an individual receptive field pathway, which is merged with other pathways at the bottom-most layer by concatenation and subsequent application of Layer Normalization and Spatial Dropout, which can improve generalization performance for small datase…
A Benchmarking Platform for Atomic Learned Indexes
2022
This repository provides a benchmarking platform to evaluate how Feed Forward Neural Networks can be effectively used as index data structures.