scholarly journals The Inverse Fundamental Operator Marching Method for Cauchy Problems in Range-Dependent Stratified Waveguides

2011 ◽  
Vol 2011 ◽  
pp. 1-15
Author(s):  
Peng Li ◽  
Weizhou Zhong
Keyword(s):  
Wave Propagation ◽  
Helmholtz Equation ◽  
Long Range ◽  
Error Estimation ◽  
Large Scale ◽  
Cauchy Problems ◽  
Computationally Efficient ◽  
Fundamental Operator ◽  
Marching Method ◽  
Application Scope

The inverse fundamental operator marching method (IFOMM) is suggested to solve Cauchy problems associated with the Helmholtz equation in stratified waveguides. It is observed that the method for large-scale Cauchy problems is computationally efficient, highly accurate, and stable with respect to the noise in the data for the propagating part of a starting field. In further, the application scope of the IFOMM is discussed through providing an error estimation for the method. The estimation indicates that the IFOMM particularly suits to compute wave propagation in long-range and slowly varying stratified waveguides.

Marching schemes for Cauchy wave propagation problems in laterally varying waveguides

2018 ◽  
Vol 26 (2) ◽  
pp. 259-276
Author(s):  
Peng Li ◽  
Keying Liu ◽  
Weizhou Zhong
Keyword(s):  
Wave Propagation ◽  
Helmholtz Equation ◽  
Second Order ◽  
Cauchy Problems ◽  
Stable Representation ◽  
Ill Conditioning ◽  
Wave Propagation In Waveguides ◽  
Marching Scheme ◽  
Application Scope

AbstractThis paper intends to develop practical marching schemes for Cauchy problems of the Helmholtz equation in laterally varying waveguides. We arrive at a stable representation of the marching solutions in waveguides. Based on the representation, a second-order marching scheme is then constructed to eliminate the ill-conditioning and compute the wave propagation in waveguides with laterally variable mediums. In the end, extensive experiments are implemented to verify the efficiency and accuracy of the marching scheme in various waveguides, and we also point out the application scope of the scheme.

Cloud-Based Multimedia Content Protection System

2020 ◽  
pp. 164-169
Author(s):  
B. Aparna ◽  
S. Madhavi ◽  
G. Mounika ◽  
P. Avinash ◽  
S. Chakravarthi
Keyword(s):  
Large Scale ◽  
Protection System ◽  
Multimedia Content ◽  
Computationally Efficient ◽  
Content Protection ◽  
Protection Systems ◽  
Multimedia Content Protection ◽  
High Scalability ◽  
Cloud Infrastructures ◽  
Rapid Deployment

We propose a new design for large-scale multimedia content protection systems. Our design leverages cloud infrastructures to provide cost efficiency, rapid deployment, scalability, and elasticity to accommodate varying workloads. The proposed system can be used to protect different multimedia content types, including videos, images, audio clips, songs, and music clips. The system can be deployed on private and/or public clouds. Our system has two novel components: (i) method to create signatures of videos, and (ii) distributed matching engine for multimedia objects. The signature method creates robust and representative signatures of videos that capture the depth signals in these videos and it is computationally efficient to compute and compare as well as it requires small storage. The distributed matching engine achieves high scalability and it is designed to support different multimedia objects. We implemented the proposed system and deployed it on two clouds: Amazon cloud and our private cloud. Our experiments with more than 11,000 videos and 1 million images show the high accuracy and scalability of the proposed system. In addition, we compared our system to the protection system used by YouTube and our results show that the YouTube protection system fails to detect most copies of videos, while our system detects more than 98% of them.

scholarly journals GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hongyi Zhang ◽  
Xiaowei Zhan ◽  
Bo Li
Keyword(s):  
T Cell ◽  
T Cell Receptor ◽  
Large Scale ◽  
Cell Receptor ◽  
Alignment Algorithm ◽  
Computationally Efficient ◽  
Antigen Specificity ◽  
Non Invasive ◽  
Isometric Transformation ◽  
Specific Receptors

AbstractSimilarity in T-cell receptor (TCR) sequences implies shared antigen specificity between receptors, and could be used to discover novel therapeutic targets. However, existing methods that cluster T-cell receptor sequences by similarity are computationally inefficient, making them impractical to use on the ever-expanding datasets of the immune repertoire. Here, we developed GIANA (Geometric Isometry-based TCR AligNment Algorithm) a computationally efficient tool for this task that provides the same level of clustering specificity as TCRdist at 600 times its speed, and without sacrificing accuracy. GIANA also allows the rapid query of large reference cohorts within minutes. Using GIANA to cluster large-scale TCR datasets provides candidate disease-specific receptors, and provides a new solution to repertoire classification. Querying unseen TCR-seq samples against an existing reference differentiates samples from patients across various cohorts associated with cancer, infectious and autoimmune disease. Our results demonstrate how GIANA could be used as the basis for a TCR-based non-invasive multi-disease diagnostic platform.

Imaging the Chicxulub Central Crater Zone from Large-Scale Seismic Acoustic Wave Propagation and Gravity Modeling

2021 ◽  
Author(s):  
Carlos Ortiz-Aleman ◽  
Ronald Martin ◽  
Jaime Urrutia-Fucugauchi ◽  
Mauricio Orozco del Castillo ◽  
Mauricio Nava-Flores
Keyword(s):  
Wave Propagation ◽  
Acoustic Wave ◽  
Large Scale ◽  
Gravity Modeling ◽  
Acoustic Wave Propagation ◽  
Central Crater

Fast Large-Scale Algorithm for Electromagnetic Wave Propagation in 3D Media

2019 ◽  
Author(s):  
Mitchell Tong Harris ◽  
M. Harper Langston ◽  
Pierre-David Letourneau ◽  
George Papanicolaou ◽  
James Ezick ◽  
...  
Keyword(s):  
Wave Propagation ◽  
Electromagnetic Wave ◽  
Large Scale ◽  
Electromagnetic Wave Propagation

scholarly journals TransMed: Transformers Advance Multi-Modal Medical Image Classification

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1384
Author(s):  
Yin Dai ◽  
Yifan Gao ◽  
Fayu Liu
Keyword(s):  
Image Analysis ◽  
Image Classification ◽  
Long Range ◽  
Medical Image ◽  
Large Scale ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Lesion Detection ◽  
Tumor Segmentation ◽  
Medical Image Classification

Over the past decade, convolutional neural networks (CNN) have shown very competitive performance in medical image analysis tasks, such as disease classification, tumor segmentation, and lesion detection. CNN has great advantages in extracting local features of images. However, due to the locality of convolution operation, it cannot deal with long-range relationships well. Recently, transformers have been applied to computer vision and achieved remarkable success in large-scale datasets. Compared with natural images, multi-modal medical images have explicit and important long-range dependencies, and effective multi-modal fusion strategies can greatly improve the performance of deep models. This prompts us to study transformer-based structures and apply them to multi-modal medical images. Existing transformer-based network architectures require large-scale datasets to achieve better performance. However, medical imaging datasets are relatively small, which makes it difficult to apply pure transformers to medical image analysis. Therefore, we propose TransMed for multi-modal medical image classification. TransMed combines the advantages of CNN and transformer to efficiently extract low-level features of images and establish long-range dependencies between modalities. We evaluated our model on two datasets, parotid gland tumors classification and knee injury classification. Combining our contributions, we achieve an improvement of 10.1% and 1.9% in average accuracy, respectively, outperforming other state-of-the-art CNN-based models. The results of the proposed method are promising and have tremendous potential to be applied to a large number of medical image analysis tasks. To our best knowledge, this is the first work to apply transformers to multi-modal medical image classification.

An Efficient Preconditioner for Linear System Solution in Multi-Domain Modeling of the Circulatory System

2013 ◽  
Author(s):  
Mahdi Esmaily Moghadam ◽  
Yuri Bazilevs ◽  
Tain-Yen Hsia ◽  
Alison Marsden
Keyword(s):  
Strong Coupling ◽  
Large Scale ◽  
Computational Cost ◽  
Circulatory System ◽  
Flow Simulation ◽  
Global Dynamics ◽  
Domain Modeling ◽  
Computationally Efficient ◽  
Lumped Parameter ◽  
System Solution

A closed-loop lumped parameter network (LPN) coupled to a 3D domain is a powerful tool that can be used to model the global dynamics of the circulatory system. Coupling a 0D LPN to a 3D CFD domain is a numerically challenging problem, often associated with instabilities, extra computational cost, and loss of modularity. A computationally efficient finite element framework has been recently proposed that achieves numerical stability without sacrificing modularity [1]. This type of coupling introduces new challenges in the linear algebraic equation solver (LS), producing an strong coupling between flow and pressure that leads to an ill-conditioned tangent matrix. In this paper we exploit this strong coupling to obtain a novel and efficient algorithm for the linear solver (LS). We illustrate the efficiency of this method on several large-scale cardiovascular blood flow simulation problems.

TrafficBERT: Pre-trained model with large-scale data for long-range traffic flow forecasting

2021 ◽  
pp. 115738
Author(s):  
KyoHoon Jin ◽  
JeongA Wi ◽  
EunJu Lee ◽  
ShinJin Kang ◽  
SooKyun Kim ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Long Range ◽  
Large Scale ◽  
Traffic Flow Forecasting ◽  
Large Scale Data ◽  
Scale Data
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