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Stochastic Modeling of Inhomogeneities in the Aortic Wall and Uncertainty Quantification using a Bayesian Encoder-Decoder Surrogate

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arXiv 2022年

作者： Ranftl, Sascha Rolf-Pissarczyk, Malte Wolkerstorfer, Gloria Pepe, Antonio Egger, Jan von der Linden, Wolfgang Holzapfel, Gerhard A. Graz University of Technology Institute of Theoretical and Computational Physics Austria Graz Center for Computational Engineering Graz University of Technology Austria Graz University of Technology Institute of Biomechanics Austria Graz University of Technology Institute of Computer Graphics and Vision Austria University Medicine Essen Institute for AI in Medicine Essen Germany Department of Structural Analysis Trondheim Norway

Inhomogeneities in the aortic wall can lead to localized stress accumulations, possibly initiating dissection. In many cases, a dissection results from pathological changes such as fragmentation or loss of elastic fibers. But it has been shown that even the healthy aortic wall has an inherent heterogeneous microstructure. Some parts of the aorta are particularly susceptible to the development of inhomogeneities due to pathological changes, however, the distribution in the aortic wall and the spatial extent, such as size, shape, and type, are difficult to predict. Motivated by this observation, we describe the heterogeneous distribution of elastic fiber degradation in the dissected aortic wall using a stochastic constitutive model. For this purpose, random field realizations, which model the stochastic distribution of degraded elastic fibers, are generated over a non-equidistant grid. The random field then serves as input for a uniaxial extension test of the pathological aortic wall, solved with the finite-element (FE) method. To include the microstructure of the dissected aortic wall, a constitutive model developed in a previous study is applied, which also includes an approach to model the degradation of interlamellar elastic fibers. Then to assess the uncertainty in the output stress distribution due to this stochastic constitutive model, a convolutional neural network, specifically a Bayesian encoder-decoder, was used as a surrogate model that maps the random input fields to the output stress distribution obtained from the FE analysis. The results show that the neural network is able to predict the stress distribution of the FE analysis while significantly reducing the computational time. In addition, it provides the probability for exceeding critical stresses within the aortic wall, which could allow for the prediction of delamination or fatal rupture. Copyright © 2022, The Authors. All rights reserved.

关键词： Finite element method

Improved cross-media relevance model for automatic image annotation

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ICIC Express Letters, Part B: Applications 2012年第5期3卷 1231-1237页

作者： Song, Haiyu Li, Xiongfei Wang, Pengjie Bao, Cuizhu Liu, Yongkui Xu, Lu Institute of Computer Graphics and Image Processing Dalian Nationalities University No. 18 Liaohe Road Economic and Technological Development Zone-Dalian 116600 China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Jilin University No. 2699 Qianjin Avenue Changchun 130012 China

Automatic image annotation has been an active research topic in the last decade due to its potentially large impact on image retrieval, object recognition and image understanding. Many approaches have been proposed for image annotation in recent years. The most influential annotation model is relevance model, whose representative algorithm is Cross-Media Relevance Model (CMRM). In this paper, to overcome the shortcomings of the original CMRM, we propose improved CMRM. In the improved CMRM, the annotation word distribution is estimated using binary probability, the significance of blob is discriminately depicted using TF-IDF (term frequency-inverse document frequency) schemes, and the similarity measurement of blob is based on blob and continuousvalued feature vector. The experimental results on the standard Corel5K dataset show that our proposed approach outperforms the original CMRM by about 111% and 73% in terms of F1 and N+ measures respectively. The improved CMRM can get better performance in specific domains such as cultural heritage and special necessities digitization for ethnic minorities. © 2012 ICIC International.

关键词： Image retrieval

A Survey on the E-learning platforms used during COVID-19 11

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A Survey on the E-learning platforms used during COVID-19

11th Annual IEEE Information Technology, Electronics and Mobile Communication Conference, IEMCON 2020

作者： Wang, Xianping Chen, Weiru Qiu, Hao Eldurssi, Awad Xie, Fei Shen, Jiayue Purdue University Northwest Dept of Computer Information Science and Graphics HammondIN46323 United States Old Dominion University Dept of Information Technology and Decision Sciences NorfolkVA23529 United States Fort Valley State University Dept of Engineering Technology Fort ValleyGA31030 United States Fort Valley State University Dept of Engineering Technology Fort ValleyGA31030 United States School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAR86011 United States College of Engineering Suny Polytechnic Institute UticaNY13502 United States

ISBN: (纸本)9781728184166

E-learning is globalized in a short period of time due to the physical distancing requirement for curbing the spread of COVID-19. There are two major challenges imposed by the migration from face-To-face learning to E-learning: live lecturing and lab sessions. In this survey, we studied the E-learning platforms used in the COVID-19 pandemic: learning management systems or course management systems, tools for live-lecturing and tools for lab sessions. We found that this migration is quite smooth with contemporary technologies and the Internet. Prescriptive suggestions are also provided for this migration. For the future trend of education, it is expected that E-learning will become a preferable education and learning style in the future, especially for self-study and life-long learning. © 2020 IEEE.

关键词： COVID-19

Experimental realization of phonon demultiplexing in three-dimensions

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arXiv 2020年

作者： Bilal, Osama R. Yee, Chern Hwee Rys, Jan Schumacher, Christian Daraio, Chiara Department of Mechanical engineering University of Connecticut Storrs United States Department of Mechanical and Process Engineering ETH Zürich Zürich8092 Switzerland Computer Graphics Laboratory ETH Zürich Zürich8092 Switzerland Division of Engineering and Applied Science California Institute of Technology PasadenaCA91125 United States

Phononic metamaterials enabled the realization of many acoustic components analogous to their electronic counterparts, such as transistors, logic gates and calculators. A key component among these is the demultiplexer, a device that receives multiple signals and sorts them based on their frequencies into separate channels. Previous experimental realizations of acoustic and elastic multiplexers have employed plates with pillars or holes to demultiplex frequencies. However, existing realizations are confined to two-dimensions, which can limit potential acoustic or elastic circuit design. Here we show the first experimental realization of a three-dimensional, four channel phononic demultiplexer. Our design methodology is based on bundles of pass-bands within a large band gap that can easily be tuned for multi-channel frequency demultiplexing. The proposed design can be utilized in acoustic and elastic information processing, nondestructive evaluation and communication applications among others. Copyright © 2020, The Authors. All rights reserved.

关键词： Demultiplexing

Memorizing all for implicit discourse relation recognition

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arXiv 2019年

作者： Bai, Hongxiao Zhao, Hai Zhao, Junhan Department of Computer Science and Engineering Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence Ai Institute Shanghai Jiao Tong University Computer Graphics Technology Purdue University West LafayetteIN United States

Implicit discourse relation recognition is a challenging task due to the absence of the nec-essary informative clue from explicit connec-tives. The prediction of relations requires a deep understanding of the semantic meanings of sentence pairs. As implicit discourse relation recognizer has to carefully tackle the se-mantic similarity of the given sentence pairs and the severe data sparsity issue exists in the meantime, it is supposed to be beneficial from mastering the entire training data. Thus in this paper, we propose a novel memory mecha-nism to tackle the challenges for further performance improvement. The memory mecha-nism is adequately memorizing information by pairing representations and discourse relations of all training instances, which right fills the slot of the data-hungry issue in the current im-plicit discourse relation recognizer. Our experiments show that our full model with memo-rizing the entire training set reaches new state-of-the-art against strong baselines, which es-pecially for the first time exceeds the milestone of 60% accuracy in the 4-way task. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

Deep learning methods for parallel magnetic resonance image reconstruction

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arXiv 2019年

作者： Knoll, Florian Hammernik, Kerstin Zhang, Chi Moeller, Steen Pock, Thomas Sodickson, Daniel K. Akçakaya, Mehmet Center for Biomedical Imaging Department of Radiology New York University Institute of Computer Vision and Graphics Graz University of Technology Department of Electrical and Computer Engineering Center for Magnetic Resonance Research University of Minnesota MinneapolisMN United States Center for Magnetic Resonance Research University of Minnesota MinneapolisMN United States

Following the success of deep learning in a wide range of applications, neural network-based machine learning techniques have received interest as a means of accelerating magnetic resonance imaging (MRI). A number of ideas inspired by deep learning techniques from computer vision and image processing have been successfully applied to non-linear image reconstruction in the spirit of compressed sensing for both low dose computed tomography and accelerated MRI. The additional integration of multi-coil information to recover missing k-space lines in the MRI reconstruction process, is still studied less frequently, even though it is the de-facto standard for currently used accelerated MR acquisitions. This manuscript provides an overview of the recent machine learning approaches that have been proposed specifically for improving parallel imaging. A general background introduction to parallel MRI is given that is structured around the classical view of image space and k-space based methods. Both linear and non-linear methods are covered, followed by a discussion of recent efforts to further improve parallel imaging using machine learning, and specifically using artificial neural networks. Image-domain based techniques that introduce improved regularizers are covered as well as k-space based methods, where the focus is on better interpolation strategies using neural networks. Issues and open problems are discussed as well as recent efforts for producing open datasets and benchmarks for the community. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

PFMRC: A Unified MRC Framework for Chinese Named Entity Recognition via Semantic Feature Fusion

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PFMRC: A Unified MRC Framework for Chinese Named Entity Reco...

Artificial Intelligence, Robotics, and Communication (ICAIRC), IEEE International Conference on

作者： Zhenrong Deng Jinglin Zhang Yannan Xiao Shiwei Wei Guangxi Key Laboratory of Images and Graphics Intelligent Processing Guilin University of Electronic Technology Guilin China Nanning Research Institute Guilin University of Electronic Technology Guilin China School of Computer Science and Engineering Guilin University of Aerospace Technology Guilin China

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

The landscape of natural language processing (NLP) has been revolutionized by advancements that enable the upstream training of knowledge-rich models for downstream tasks, particularly named entity recognition (NER). Traditional NER approaches, treating it as a sequence labeling problem, often fail to deeply understand the whole text and to capture syntactic nuances of text. To address these issues, we introduce PFMRC, a novel machine reading comprehension (MRC) framework that leverages the strengths of both contextual semantics and syntactic information for enhanced NER performance. PFMRC employs a dual-pronged feature extraction strategy, utilizing RoBERTa and BiGRU to encode text features for contextual semantic information and representing them as node features of the graph structure, and a weighted embedding representation to translate syntactic dependencies into graph-structured edge features. Our model further incorporates Graph Attention Networks (GAT) to harmonize node and edge features, facilitating a nuanced understanding of text. The integration of prior knowledge and the modeling of intricate syntactic relationships through Graph Attention Networks (GAT) not only improves entity recognition accuracy but also enriches the feature representation, leading to more robust NER outcomes. Extensive experiments on three Chinese datasets demonstrate PFMRC's superior performance over state-of-the-art models.

关键词： Training Accuracy Translation Semantics Refining Named entity recognition Syntactics Feature extraction Solids Robots

Parametric shape optimization for combined additive?subtractive Manufacturing

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arXiv 2019年

作者： Altenhofen, Christian Attene, Marco Barrowclough, Oliver Chiumenti, Michele Livesu, Marco Marini, Federico Martinelli, Massimiliano Skytt, Vibeke Tamellini, Lorenzo Fraunhofer Institute for Computer Graphics Research IGD Interactive Engineering Technologies Darmstadt Germany Interactive Graphics Systems Group Technische Universität Darmstadt Darmstadt Germany Consiglio Nazionale Delle Ricerche Genova Italy SINTEF Digital Mathematics and Cybernetics Oslo Norway Universidad Politécnica de Catalunã Barcelona Spain Consiglio Nazionale Delle Ricerche Pavia Italy

In the industrial practice, additive manufacturing processes are often followed by post-processing operations such as subtractive machining, milling, etc. to achieve the desired surface quality and dimensional accuracy. Hence, a given part must be 3D printed with extra material to enable such finishing phase. This combined additive/subtractive technique can be optimized to reduce manufacturing costs by saving printing time and reducing material and energy usage. In this work, a numerical methodology based on parametric shape optimization is proposed for optimizing the thickness of the extra material, allowing for minimal machining operations while ensuring the finishing requirements. Moreover, the proposed approach is complemented by a novel algorithm for generating inner structures leading to reduced distortion and improved weight reduction. The computational effort induced by classical constrained optimization methods is alleviated by replacing both the objective and constraint functions by their sparse-grid surrogates. Numerical results showcase the effectiveness of the proposed approach. Copyright © 2019, The Authors. All rights reserved.

关键词： 3D printers

Iris Liveness Detection Competition (LivDet-Iris) – The 2023 Edition

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Iris Liveness Detection Competition (LivDet-Iris) – The 202...

IEEE International Joint Conference on Biometrics (IJCB)

作者： Patrick Tinsley Sandip Purnapatra Mahsa Mitcheff Aidan Boyd Colton Crum Kevin Bowyer Patrick Flynn Stephanie Schuckers Adam Czajka Meiling Fang Naser Damer Xingyu Liu Caiyong Wang Xianyun Sun Zhaohua Chang Xinyue Li Guangzhe Zhao Juan Tapia Christoph Busch Carlos Aravena Daniel Schulz University of Notre Dame IN USA Clarkson University NY USA Fraunhofer Institute for Computer Graphics Research IGD Germany School of Electrical and Information Eng. Beijing University of Civil Engineering and Architecture (BUCEA) China Hochschule Darmstadt (HDA) Germany IDVisionCenter Germany

This paper describes the results of the 2023 edition of the “LivDet” series of iris presentation attack detection (PAD) competitions. New elements in this fifth competition include (1) GAN-generated iris images as a category of presentation attack instruments (PAI), and (2) an evaluation of human accuracy at detecting PAI as a reference benchmark. Clarkson University and the University of Notre Dame contributed image datasets for the competition, composed of samples representing seven different PAI categories, as well as baseline PAD algorithms. Fraunhofer IGD, Beijing University of Civil engineering and Architecture, and Hochschule Darmstadt contributed results for a total of eight PAD algorithms to the competition. Accuracy results are analyzed by different PAI types, and compared to human accuracy. Overall, the Fraunhofer IGD algorithm, using an attention-based pixel-wise binary supervision network, showed the best-weighted accuracy results (average classification error rate of 37.31%), while the Beijing University of Civil engineering and Architecture’s algorithm won when equal weights for each PAI were given (average classification rate of 22.15%). These results suggest that iris PAD is still a challenging problem.

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