scholarly journals Deep learning: a new tool for photonic nanostructure design

2020 ◽  
Vol 2 (3) ◽  
pp. 1007-1023 ◽  
Author(s):  
Ravi S. Hegde
Keyword(s):  
Deep Learning ◽  
Recent Progress ◽  
Further Development

We review recent progress in the application of Deep Learning (DL) techniques for photonic nanostructure design and provide a perspective on current limitations and fruitful directions for further development.

scholarly journals The Application and Development of Deep Learning in Radiotherapy: A Systematic Review

2021 ◽  
Vol 20 ◽  
pp. 153303382110163
Author(s):  
Danju Huang ◽  
Han Bai ◽  
Li Wang ◽  
Yu Hou ◽  
Lan Li ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Neural Networks ◽  
Decision Making ◽  
Deep Learning ◽  
Radiation Oncology ◽  
Clinical Work ◽  
Radiation Oncologists ◽  
Basic Concepts ◽  
Further Development

With the massive use of computers, the growth and explosion of data has greatly promoted the development of artificial intelligence (AI). The rise of deep learning (DL) algorithms, such as convolutional neural networks (CNN), has provided radiation oncologists with many promising tools that can simplify the complex radiotherapy process in the clinical work of radiation oncology, improve the accuracy and objectivity of diagnosis, and reduce the workload, thus enabling clinicians to spend more time on advanced decision-making tasks. As the development of DL gets closer to clinical practice, radiation oncologists will need to be more familiar with its principles to properly evaluate and use this powerful tool. In this paper, we explain the development and basic concepts of AI and discuss its application in radiation oncology based on different task categories of DL algorithms. This work clarifies the possibility of further development of DL in radiation oncology.

scholarly journals Deep learning-enabled medical computer vision

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Andre Esteva ◽  
Katherine Chou ◽  
Serena Yeung ◽  
Nikhil Naik ◽  
Ali Madani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Medical Imaging ◽  
Real World ◽  
Recent Progress ◽  
Medical Applications ◽  
Modern Computer ◽  
Medical Computer

AbstractA decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

scholarly journals Application of neuron networks of deep learning for exposures editing of digital phonograms

2020 ◽  
Vol 65 (4) ◽  
pp. 506-512
Author(s):  
V. I. Solovyov ◽  
O. V. Rybalskiy ◽  
V. V. Zhuravel ◽  
V. K. Zheleznyak
Keyword(s):  
Deep Learning ◽  
Binary Classification ◽  
Maximum Size ◽  
Maximum Efficiency ◽  
Correct Classification ◽  
Digital Audio ◽  
Neuron Networks ◽  
Effective System ◽  
Further Development

Possibility of creation of effective system, which is intended for exposure of tracks of editing in digital phonograms and is built on the basis of neuron networks of the deep learning, is experimentally proven. Sense of experiment consisted in research of ability of the systems on the basis of such networks to expose pauses with tracks of editing. The experimental array of data is created in a voice editor from phonograms written on the different apparatus of the digital audio recording (at frequency of discretisation 44,1 kHz). A preselection of pauses was produced from it, having duration from 100 мs to a few seconds. From 1000 selected pauses the array of fragments of pauses is formed in the automatic (computer) mode, from which the arrays of fragments of pauses of different duration are generated by a dimension about 100 000. For forming of array of fragments of pauses with editing, the chosen pauses were divided into casual character parts in arbitrary correlation. Afterwards, the new pauses were created from it with the fixed place of editing. The general array of all fragments of pauses was broken into training and test arrays. The maximum efficiency, achieved on a test array in the process of educating, was determined. In general case this efficiency is determined by the maximum size of probability of correct classification of fragments with editing and fragments without editing. Scientifically reasonable methodology of exposure of signs of editing in digital phonograms is offered on the basis of neuron networks of the deep learning. The conducted experiments showed that the construction of the effective system is possible for the exposure of such tracks. Further development of methodology must be directed to find the ways to increase the probability of correct binary classification of investigated pauses.

scholarly journals Recent Advances in Stimuli-Responsive Photofunctional Materials Based on Accommodation of Chromophore into Layered Double Hydroxide Nanogallery

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Wu Li ◽  
Dongpeng Yan ◽  
Rui Gao ◽  
Jun Lu ◽  
Min Wei ◽  
...  
Keyword(s):  
Thermal Stability ◽  
Metal Ion ◽  
Recent Progress ◽  
Stimuli Responsive ◽  
Responsive Materials ◽  
Chemical Factors ◽  
Host Matrices ◽  
Double Hydroxide ◽  
Double Hydroxides ◽  
Further Development

The assembly of photofunctional molecules into host matrices has become an important strategy to achieve tunable fluorescence and to develop intelligent materials. The stimuli-responsive photofunctional materials based on chromophores-assembled layered double hydroxides (LDHs) have received much attention from both academic and industry fields as a result of their advantages, such as high photo/thermal stability, easy processing, and well reversibility, which can construct new types of smart luminescent nanomaterials (e.g., ultrathin film and nanocomposite) for sensor and switch applications. In this paper, external environmental stimuli have mainly involved physical (such as temperature, pressure, light, and electricity) and chemical factors (such as pH and metal ion); recent progress on the LDH-based organic-inorganic stimuli-responsive materials has been summarized. Moreover, perspectives on further development of these materials are also discussed.

Recent Progress on Radical Decarboxylative Alkylation for Csp3–C Bond Formation

Synthesis ◽  
2017 ◽  
Vol 49 (24) ◽  
pp. 5263-5284 ◽  
Author(s):  
Hongli Bao ◽  
Yajun Li ◽  
Liang Ge ◽  
Munira Muhammad
Keyword(s):  
Carboxylic Acids ◽  
Recent Progress ◽  
Bond Formation ◽  
Stepping Stone ◽  
Recent Advances ◽  
Aliphatic Carboxylic Acids ◽  
Further Development

Radical decarboxylation has emerged as an attractive method for the formation of C–C bonds starting from easily accessible carboxylic acids. In this review, we attempt to bring the readers up to date in this rapidly expanding field. Specifically, we will cover recent advances in Csp3–C bond formation via the radical decarboxylation of aliphatic carboxylic acids and their activated forms, such as N-hydroxyphthalimide esters (NHP esters), alkyl diacyl peroxides, alkyl peresters, and aryliodine(III) dicarboxylates. The scope and limitation of these transformations will be discussed, highlighting gaps in knowledge and research and examining the mechanisms underlying radical decarboxylation. We aim to make this review a stepping stone for further development in this field.1 Introduction2 Aliphatic Carboxylic Acids3 N-Hydroxyphthalimide Esters (NHP Esters)4 Alkyl Diacyl Peroxides5 Alkyl Peresters6 Aryliodine(III) Dicarboxylates7 Conclusion

scholarly journals Nature Inspired Meta-heuristic Algorithms for Deep Learning: Recent Progress and Novel Perspective

2019 ◽  
pp. 59-70 ◽  
Author(s):  
Haruna Chiroma ◽  
Abdulsalam Ya’u Gital ◽  
Nadim Rana ◽  
Shafi’i M. Abdulhamid ◽  
Amina N. Muhammad ◽  
...  
Keyword(s):  
Deep Learning ◽  
Recent Progress ◽  
Heuristic Algorithms

scholarly journals Using Deep Learning to Estimate Tropical Cyclone Intensity from Satellite Passive Microwave Imagery

2019 ◽  
Vol 147 (6) ◽  
pp. 2261-2282 ◽  
Author(s):  
Anthony Wimmers ◽  
Christopher Velden ◽  
Joshua H. Cossuth
Keyword(s):  
Deep Learning ◽  
Tropical Cyclone ◽  
Satellite Images ◽  
Initial Study ◽  
Intensity Analysis ◽  
Marginal Benefit ◽  
Cyclone Intensity ◽  
Testing Data ◽  
Data Source ◽  
Further Development

Abstract A deep learning convolutional neural network model is used to explore the possibilities of estimating tropical cyclone (TC) intensity from satellite images in the 37- and 85–92-GHz bands. The model, called “DeepMicroNet,” has unique properties such as a probabilistic output, the ability to operate from partial scans, and resiliency to imprecise TC center fixes. The 85–92-GHz band is the more influential data source in the model, with 37 GHz adding a marginal benefit. Training the model on global best track intensities produces model estimates precise enough to replicate known best track intensity biases when compared to aircraft reconnaissance observations. Model root-mean-square error (RMSE) is 14.3 kt (1 kt ≈ 0.5144 m s−1) compared to two years of independent best track records, but this improves to an RMSE of 10.6 kt when compared to the higher-standard aircraft reconnaissance-aided best track dataset, and to 9.6 kt compared to the reconnaissance-aided best track when using the higher-resolution TRMM TMI and Aqua AMSR-E microwave observations only. A shortage of training and independent testing data for category 5 TCs leaves the results at this intensity range inconclusive. Based on this initial study, the application of deep learning to TC intensity analysis holds tremendous promise for further development with more advanced methodologies and expanded training datasets.

scholarly journals Deep Learning Enabled Nanophotonics

2020 ◽  
Author(s):  
Lujun Huang ◽  
Lei Xu ◽  
Andrey E. Miroshnichenko
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
High Performance ◽  
Recent Progress ◽  
Design Method ◽  
Computation Time ◽  
Chiral Metamaterials ◽  
Nanophotonic Devices ◽  
Optical Media ◽  
Optical Neural Networks

Deep learning has become a vital approach to solving a big-data-driven problem. It has found tremendous applications in computer vision and natural language processing. More recently, deep learning has been widely used in optimising the performance of nanophotonic devices, where the conventional computational approach may require much computation time and significant computation source. In this chapter, we briefly review the recent progress of deep learning in nanophotonics. We overview the applications of the deep learning approach to optimising the various nanophotonic devices. It includes multilayer structures, plasmonic/dielectric metasurfaces and plasmonic chiral metamaterials. Also, nanophotonic can directly serve as an ideal platform to mimic optical neural networks based on nonlinear optical media, which in turn help to achieve high-performance photonic chips that may not be realised based on conventional design method.

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