scholarly journals PhyCRNet: Physics-informed convolutional-recurrent network for solving spatiotemporal PDEs

2022 ◽  
Vol 389 ◽  
pp. 114399
Author(s):  
Pu Ren ◽  
Chengping Rao ◽  
Yang Liu ◽  
Jian-Xun Wang ◽  
Hao Sun
Keyword(s):  
Recurrent Network

Lateral inhibition in a recurrent network

1997 ◽  
Author(s):  
William T. Farrar ◽  
Guy C. Van Orden
Keyword(s):  
Lateral Inhibition ◽  
Recurrent Network

Prediction of momentum distribution of supercooled atoms in optical lattice using convolutional-recurrent network

2020 ◽  
Vol 28 (7) ◽  
pp. 1480-1484
Author(s):  
Yun-hong LI ◽  
◽  
Hong-hao LI ◽  
Da WEN ◽  
Fan-su WEI ◽  
...  
Keyword(s):  
Momentum Distribution ◽  
Optical Lattice ◽  
Recurrent Network

scholarly journals Restoring Raindrops Using Attentive Generative Adversarial Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 7034
Author(s):  
Hee-Deok Yang
Keyword(s):  
Weather Conditions ◽  
Recurrent Network ◽  
Generative Adversarial Networks ◽  
Navigation Systems ◽  
Vision Systems ◽  
Generative Adversarial Network ◽  
Network Layers ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Outdoor Vision

Artificial intelligence technologies and vision systems are used in various devices, such as automotive navigation systems, object-tracking systems, and intelligent closed-circuit televisions. In particular, outdoor vision systems have been applied across numerous fields of analysis. Despite their widespread use, current systems work well under good weather conditions. They cannot account for inclement conditions, such as rain, fog, mist, and snow. Images captured under inclement conditions degrade the performance of vision systems. Vision systems need to detect, recognize, and remove noise because of rain, snow, and mist to boost the performance of the algorithms employed in image processing. Several studies have targeted the removal of noise resulting from inclement conditions. We focused on eliminating the effects of raindrops on images captured with outdoor vision systems in which the camera was exposed to rain. An attentive generative adversarial network (ATTGAN) was used to remove raindrops from the images. This network was composed of two parts: an attentive-recurrent network and a contextual autoencoder. The ATTGAN generated an attention map to detect rain droplets. A de-rained image was generated by increasing the number of attentive-recurrent network layers. We increased the number of visual attentive-recurrent network layers in order to prevent gradient sparsity so that the entire generation was more stable against the network without preventing the network from converging. The experimental results confirmed that the extended ATTGAN could effectively remove various types of raindrops from images.

scholarly journals Attention-based hierarchical recurrent neural networks for MOOC forum posts analysis

2020 ◽  
Author(s):  
Nicola Capuano ◽  
Santi Caballé ◽  
Jordi Conesa ◽  
Antonio Greco
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Text Categorization ◽  
Online Courses ◽  
Recurrent Network ◽  
Subject Area ◽  
Conversational Agents ◽  
Collaborative Tools ◽  
Experimental Findings ◽  
Productive Talk

AbstractMassive open online courses (MOOCs) allow students and instructors to discuss through messages posted on a forum. However, the instructors should limit their interaction to the most critical tasks during MOOC delivery so, teacher-led scaffolding activities, such as forum-based support, can be very limited, even impossible in such environments. In addition, students who try to clarify the concepts through such collaborative tools could not receive useful answers, and the lack of interactivity may cause a permanent abandonment of the course. The purpose of this paper is to report the experimental findings obtained evaluating the performance of a text categorization tool capable of detecting the intent, the subject area, the domain topics, the sentiment polarity, and the level of confusion and urgency of a forum post, so that the result may be exploited by instructors to carefully plan their interventions. The proposed approach is based on the application of attention-based hierarchical recurrent neural networks, in which both a recurrent network for word encoding and an attention mechanism for word aggregation at sentence and document levels are used before classification. The integration of the developed classifier inside an existing tool for conversational agents, based on the academically productive talk framework, is also presented as well as the accuracy of the proposed method in the classification of forum posts.

scholarly journals Recurrent neural network-based volumetric fluorescence microscopy

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Luzhe Huang ◽  
Hanlong Chen ◽  
Yilin Luo ◽  
Yair Rivenson ◽  
Aydogan Ozcan
Keyword(s):  
Neural Network ◽  
Image Reconstruction ◽  
Fluorescence Microscopy ◽  
Recurrent Network ◽  
Sample Volume ◽  
Depth Of Field ◽  
Objective Lens ◽  
Wide Field ◽  
Volumetric Imaging ◽  
3D Image Reconstruction

AbstractVolumetric imaging of samples using fluorescence microscopy plays an important role in various fields including physical, medical and life sciences. Here we report a deep learning-based volumetric image inference framework that uses 2D images that are sparsely captured by a standard wide-field fluorescence microscope at arbitrary axial positions within the sample volume. Through a recurrent convolutional neural network, which we term as Recurrent-MZ, 2D fluorescence information from a few axial planes within the sample is explicitly incorporated to digitally reconstruct the sample volume over an extended depth-of-field. Using experiments on C. elegans and nanobead samples, Recurrent-MZ is demonstrated to significantly increase the depth-of-field of a 63×/1.4NA objective lens, also providing a 30-fold reduction in the number of axial scans required to image the same sample volume. We further illustrated the generalization of this recurrent network for 3D imaging by showing its resilience to varying imaging conditions, including e.g., different sequences of input images, covering various axial permutations and unknown axial positioning errors. We also demonstrated wide-field to confocal cross-modality image transformations using Recurrent-MZ framework and performed 3D image reconstruction of a sample using a few wide-field 2D fluorescence images as input, matching confocal microscopy images of the same sample volume. Recurrent-MZ demonstrates the first application of recurrent neural networks in microscopic image reconstruction and provides a flexible and rapid volumetric imaging framework, overcoming the limitations of current 3D scanning microscopy tools.

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