EDITORIAL article Front. Earth Sci., 30 September 2022Sec. Environmental Informatics and Remote Sensing Volume 10 - 2022 | https://doi.org/10.3389/feart.2022.1043314

We propose a highly efficient and faster Single Image Super-Resolution (SISR) model with Deep Convolutional neural networks (Deep CNN). Deep CNN have recently shown that they have a significant reconstruction performance on single-image super-resolution. Current trend is using deeper CNN layers to improve performance. However, deep models demand larger computation resources and is not suitable ...

Hashing is one of the most popular and powerful approximate nearest neighbor search techniques for large-scale image retrieval. Most traditional hashing methods first represent images as off-the-shelf visual features and then produce hash codes in a separate stage. However, off-the-shelf visual features may not be optimally compatible with the hash code learning procedure, which may result in s...

In recent years, all winning architectures that achieved state of the art results have been very deep and had parameters ranging from tens to hundreds of millions. While an optimal depth has yet to be discovered, it is known that these deep architectures are far from being optimal. Very deep and heavy architecture such as VGGNet, GoogleNet, ResNet and the likes, are very demanding in terms of h...

The harnessing of modern computational abilities for many-body wave-function representations is naturally placed as a prominent avenue in contemporary condensed matter physics. Specifically, highly expressive computational schemes that are able to efficiently represent the entanglement properties which characterize many-particle quantum systems are of interest. In the seemingly unrelated field ...

Hashing is one of the most popular and powerful approximate nearest neighbor search techniques for large-scale image retrieval. Most traditional hashing methods first represent images as off-the-shelf visual features and then produce hashing codes in a separate stage. However, off-the-shelf visual features may not be optimally compatible with the hash code learning procedure, which may result i...

Introduction: Deep AVMs are more difficult to operate compared with other supratentorial locations due to the long working corridor through the brain, location in eloquent areas and vicinity with brain critical structures. Material and Method: We report a series of 7 cases with deep AVMs operated in the Fourth Department of Neurosurgery, Emergency Clinical Hospital BagdasarArseni, between 2009 ...

The working group on two photon physics concentrated on three main subtopics: modelling the hadronic final state of deep inelastic scattering on a photon; unfolding the deep inelastic scattering data to obtain the photon structure function; and resonant production of exclusive final states, particularly of glueball candidates. In all three areas, new results were presented.

Deep learning has been one of the most prominent machine learning techniques nowadays, being the state-of-the-art on a broad range of applications where automatic feature extraction is needed. Many such applications also demand varying costs for different types of mis-classification errors, but it is not clear whether or how such cost information can be incorporated into deep learning to improv...

Recent measurements of the diffractive deep-inelastic cross section are used to extract diffrac-tive parton densities of the proton. These are subsequently applied in models to predict the production of jets and open charm in the final state. A rapidity gap suppression factor for dijet production in diffractive photoproduction relative to diffractive deep-inelastic scattering is obtained using ...