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Soft Tissue Deformation Model Based on Marquardt Algorithm and Enrichment Function

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computer Modeling in Engineering & Sciences 2020年第9期124卷 1131-1147页

作者： Xiaorui Zhang Xuefeng Yu Wei Sun Aiguo Song Jiangsu Engineering Center of Network Monitoring Engineering Research Center of Digital ForensicsMinistry of EducationSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory of Bioelectronics Jiangsu Key Lab of Remote Measurement and ControlSchool of Instrument Science and EngineeringSoutheast UniversityNanjing210096China

In order to solve the problem of high computing cost and low simulation accuracy caused by discontinuity of incision in traditional meshless model,this paper proposes a soft tissue deformation model based on the Marquardt algorithm and enrichment *** model is based on the element-free Galerkin method,in which Kelvin viscoelastic model and adjustment function are *** algorithm is applied to fit the relation between force and displacement caused by surface deformation,and the enrichment function is applied to deal with the discontinuity in the meshless *** verify the validity of the model,the Sensable Phantom Omni force tactile interactive device is used to simulate the deformations of stomach and *** results show that the proposed model improves the real-time performance and accuracy of soft tissue deformation simulation,which provides a new perspective for the application of the meshless method in virtual surgery.

关键词： Virtual surgery meshless model Marquardt algorithm enrichment function soft tissue simulation

4D-CBCT Reconstruction via Motion Compensataion Learning Induced Sparse Tensor Constraint 12

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4D-CBCT Reconstruction via Motion Compensataion Learning Ind...

12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019

作者： Liu, Jin Kang, Yanqin Hu, Dianlin Chen, Yang College of Computer and Information Anhui Polytechnic University Wuhu China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9781728148526

The challenge in 4D-CBCT reconstruction is the lacked of projections at each phase, which result in a reconstruction image full of streak artifacts and motion blurring with the conventional analytical algorithms. Aiming at improving the overall quality of 4D-CBCT images, we propose a motion compensation learning induced sparse tensor constraint reconstruction (MCL-STCR) method which tries to explore the high correlation in 4D-CBCT images for different phases. In this method, we additionally conduct motion compensation on the 4D-CBCT volume by using trained image motion compensation convolutional neural network (CNN). Then the compensated 4D-CBCT volume is viewed as a pseudostatic sequence, of which the sparse tensor constraint was imposed on. The cost function of proposed MCL-STCR is optimized by a variable splitting algorithm. Its comparison to other methods through simulated dynamic phantom and lung data experiments demonstrated that the proposed method can lead to a promising improvement of 4D-CBCT reconstruction. © 2019 IEEE.

关键词： Image reconstruction

Cascade ResUnet with Noise Power Spectrum Loss for Low Dose CT Imaging

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Cascade ResUnet with Noise Power Spectrum Loss for Low Dose ...

International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Jin Liu Yanqin Kang Dianlin Hu Yang Chen College of Computer and Information Anhui Polytechnic University Wuhu China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (数字)9780738105451

ISBN: (纸本)9781665422994

The suppression of noise-artifacts in computed tomography (CT) with a low dose scan protocol is a well-known challenge. Conventional statistical iterative algorithms are able to provide improved reconstructions, but do not work well at eliminating large streaks and strong noise. In this paper, we present a 3D cascade ResUnet neural network (Ca-ResUnet) strategy with modified noise power spectrum loss for the reduction of noise-artifacts in low dose CT reconstruction. The reconstruction workflow consists of four components: the first component is FBP (filtered backprojection) reconstruction module, the second is a ResUnet neural network with operating in the CT image, the third is image update module to compensated the loss of tiny structure, and the last is a ResUnet neural network with modified noise power spectrum loss for reconstruction image fine tuning. Verification results based on AAPM clinical data confirm that the proposed method can significantly reduce serious noise-artifacts.

关键词： Image reconstruction Computed tomography Image restoration Training Imaging X-ray imaging TV

Parallel Sketch Based Super Node Detection with Traceability

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Chinese Journal of Electronics 2018年第6期27卷 1133-1140页

作者： ZHOU Aiping LIU Lijun ZHU Huisheng ZHU Chen'gang CHEN Lin School of Computer Science and Technology Taizhou UniversityTaizhou 225300 China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education

Traffic measurement and monitoring is crucial for network applications, such as network security, network management and so on. One central problem is to detect super nodes, which have significant change of connection degree between consecutive measurement *** to weakness in massive network traffic processing for the centralized algorithm and low detection accuracy, space efficiency for super node detection algorithm based on flow sampling, we propose Parallel sketch based super node detection with traceability(PSD). It constructs parallel sketch and estimates connection degree of nodes by probabilistic counting approach, so that super nodes are identified using connection degree change between consecutive measurement periods. Moreover, IP addresses of super nodes are reconstructed by simple computing to trace attacker or victims. The experimental results illustrate that the proposed method outperforms the Compact spread estimator(CSE) and Data streaming and sampling(DSS) in terms of detection accuracy and storage utilization.

关键词： network measurement Super node detection Parallel sketch network data stream

Trusted AI in Multi-agent Systems: An Overview of Privacy and Security for Distributed Learning

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arXiv 2022年

作者： Ma, Chuan Li, Jun Wei, Kang Liu, Bo Ding, Ming Yuan, Long Han, Zhu Poor, H. Vincent Zhejiang Laboratory Hangzhou311121 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China The School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210096 China The Department of Computing Hong Kong Polytechnic University 999077 Hong Kong The University of Technology SydneyNSW2007 Australia Data61 CSIRO NSW2015 Australia The School of Computer Science Nanjing University of Science and Technology Nanjing210096 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul Korea Republic of The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Motivated by the advancing computational capacity of distributed end-user equipment (UE), as well as the increasing concerns about sharing private data, there has been considerable recent interest in machine learning (ML) and artificial intelligence (AI) that can be processed on distributed UEs. Specifically, in this paradigm, parts of a ML process are outsourced to multiple distributed UEs. Then the processed information is aggregated on a certain level at a central server, which turns a centralized ML process into a distributed one, and brings about significant benefits. However, this new distributed ML paradigm raises new risks of privacy and security issues. In this paper, we provide a survey on the emerging security and privacy risks of distributed ML from a unique perspective of information exchange levels, which are defined according to the key steps of a ML process, i.e.: i) the level of pre-processed data, ii) the level of learning models, iii) the level of extracted knowledge and, iv) the level of intermediate results. We explore and analyze the potential of threats for each information exchange level based on an overview of the current state-of-the-art attack mechanisms, and then discuss the possible defense methods against such threats. Finally, we complete the survey by providing an outlook on the challenges and possible directions for future research in this critical area. Copyright © 2022, The Authors. All rights reserved.

关键词： Multi agent systems

Less is More: Data-Efficient Complex Question Answering over Knowledge Bases

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arXiv 2020年

作者： Hua, Yuncheng Li, Yuan-Fang Qi, Guilin Wu, Wei Zhang, Jingyao Qi, Daiqing School of Computer Science and Engineering Southeast University Nanjing China Faculty of Information Technology Monash University Melbourne Australia Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China Southeast University Monash University Joint Research Institute Suzhou China

Question answering is an effective method for obtaining information from knowledge bases (KB). In this paper, we propose the Neural-Symbolic Complex Question Answering (NS-CQA) model, a data-efficient reinforcement learning framework for complex question answering by using only a modest number of training samples. Our framework consists of a neural generator and a symbolic executor that, respectively, transforms a natural-language question into a sequence of primitive actions, and executes them over the knowledge base to compute the answer. We carefully formulate a set of primitive symbolic actions that allows us to not only simplify our neural network design but also accelerate model convergence. To reduce search space, we employ the copy and masking mechanisms in our encoder-decoder architecture to drastically reduce the decoder output vocabulary and improve model generalizability. We equip our model with a memory buffer that stores high-reward promising programs. Besides, we propose an adaptive reward function. By comparing the generated trial with the trials stored in the memory buffer, we derive the curriculum-guided reward bonus, i.e., the proximity and the novelty. To mitigate the sparse reward problem, we combine the adaptive reward and the reward bonus, reshaping the sparse reward into dense feedback. Also, we encourage the model to generate new trials to avoid imitating the spurious trials while making the model remember the past high-reward trials to improve data efficiency. Our NS-CQA model is evaluated on two datasets: CQA, a recent large-scale complex question answering dataset, and WebQuestionsSP, a multi-hop question answering dataset. On both datasets, our model outperforms the state-of-the-art models. Notably, on CQA, NS-CQA performs well on questions with higher complexity, while only using approximately 1% of the total training samples. Copyright © 2020, The Authors. All rights reserved.

关键词： Reinforcement learning

Maximum entropy subspace clustering network

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arXiv 2020年

作者： Peng, Zhihao Jia, Yuheng Liu, Hui Hou, Junhui Zhang, Qingfu The Department of Computer Science City University of Hong Kong Kowloon999077 Hong Kong The School of Computer Science and Engineering Southeast University China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing211189 China

Deep subspace clustering networks have attracted much attention in subspace clustering, in which an auto-encoder non-linearly maps the input data into a latent space, and a fully connected layer named self-expressiveness module is introduced to learn the affinity matrix via a typical regularization term (e.g., sparse or low-rank). However, the adopted regularization terms ignore the connectivity within each subspace, limiting their clustering performance. In addition, the adopted framework suffers from the coupling issue between the auto-encoder module and the self-expressiveness module, making the network training non-trivial. To tackle these two issues, we propose a novel deep subspace clustering method named Maximum Entropy Subspace Clustering network (MESC-Net). Specifically, MESC-Net maximizes the entropy of the affinity matrix to promote the connectivity within each subspace, in which its elements corresponding to the same subspace are uniformly and densely distributed. Meanwhile, we design a novel framework to explicitly decouple the auto-encoder module and the self-expressiveness module. Besides, we also theoretically prove that the learned affinity matrix satisfies the block-diagonal property under the assumption of independent subspaces. Extensive quantitative and qualitative results on commonly used benchmark datasets validate MESC-Net significantly outperforms state-of-the-art methods. The code is publicly available at https://***/ZhihaoPENG-CityU/ MESC. © 2020, CC BY.

关键词： Deep learning

Self-Supervised Speech Denoising Using Only Noisy Audio Signals

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arXiv 2021年

作者： Wu, Jiasong Li, Qingchun Yang, Guanyu Li, Lei Senhadji, Lotfi Shu, Huazhong LIST Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University Nanjing210096 China RennesF-35042 France The 28th Research Institute of China Electronics Technology Group Corporation Nanjing210007 China Univ Rennes INSERM LTSI UMR 1099 RennesF-35042 France

In traditional speech denoising tasks, clean audio signals are often used as the training target, but absolutely clean signals are collected from expensive recording equipment or in studios with the strict environments. To overcome this drawback, we propose an end-to-end self-supervised speech denoising training scheme using only noisy audio signals, named Only-Noisy Training (ONT), without extra training conditions. The proposed ONT strategy constructs training pairs only from each single noisy audio, and it contains two modules: training audio pairs generated module and speech denoising module. The first module adopts a random audio sub-sampler on each noisy audio to generate training pairs. The sub-sampled pairs are then fed into a novel complex-valued speech denoising module. Experimental results show that the proposed method not only eliminates the high dependence on clean targets of traditional audio denoising tasks, but also achieves on-par or better performance than other training strategies. Availability-ONT is available at https://***/liqingchunnnn/Only-Noisy-Training. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

A Novel Deep Hybrid Recommender System Based on Auto-encoder with Neural Collaborative Filtering

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Big Data Mining and Analytics 2018年第3期1卷 211-221页

作者： Yu Liu Shuai Wang M.Shahrukh Khan Jieyu He School of Computer Science and Engineering and also with MOE Key Laboratory of Computer Network and Information Integration Southeast University

Due to the widespread availability of implicit feedback(e.g., clicks and purchases), some researchers have endeavored to design recommender systems based on implicit feedback. However, unlike explicit feedback,implicit feedback cannot directly reflect user preferences. Therefore, although more challenging, it is also more practical to use implicit feedback for recommender systems. Traditional collaborative filtering methods such as matrix factorization, which regards user preferences as a linear combination of user and item latent vectors, have limited learning capacities and suffer from data sparsity and the cold-start problem. To tackle these problems,some authors have considered the integration of a deep neural network to learn user and item features with traditional collaborative filtering. However, there is as yet no research combining collaborative filtering and contentbased recommendation with deep learning. In this paper, we propose a novel deep hybrid recommender system framework based on auto-encoders(DHA-RS) by integrating user and item side information to construct a hybrid recommender system and enhance performance. DHA-RS combines stacked denoising auto-encoders with neural collaborative filtering, which corresponds to the process of learning user and item features from auxiliary information to predict user preferences. Experiments performed on the real-world dataset reveal that DHA-RS performs better than state-of-the-art methods.

关键词： hybrid recommender system neural collaborative filtering auto-encoder implicit feedback