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arXiv 2021年

作者： Feng, Chun-Mei Yan, Yunlu Chen, Geng Xu, Yong Hu, Ying Shao, Ling Fu, Huazhu Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Terminus Group China Institute of High Performance Computing A*STAR Singapore138632 Singapore

Accelerated multi-modal magnetic resonance (MR) imaging is a new and effective solution for fast MR imaging, providing superior performance in restoring the target modality from its undersampled counterpart with guidance from an auxiliary modality. However, existing works simply combine the auxiliary modality as prior information, lacking in-depth investigations on the potential mechanisms for fusing different modalities. Further, they usually rely on the convolutional neural networks (CNNs), which is limited by the intrinsic locality in capturing the long-distance dependency. To this end, we propose a multi-modal transformer (MTrans), which is capable of transferring multi-scale features from the target modality to the auxiliary modality, for accelerated MR imaging. To capture deep multi-modal information, our MTrans utilizes an improved multi-head attention mechanism, named cross attention module, which absorbs features from the auxiliary modality that contribute to the target modality. Our framework provides three appealing benefits: (i) Our MTrans use an improved transformers for multi-modal MR imaging, affording more global information compared with existing CNN-based methods. (ii) A new cross attention module is proposed to exploit the useful information in each modality at different scales. The small patch in the target modality aims to keep more fine details, the large patch in the auxiliary modality aims to obtain high-level context features from the larger region and supplement the target modality effectively. (iii) We evaluate MTrans with various accelerated multi-modal MR imaging tasks, e.g., MR image reconstruction and super-resolution, where MTrans outperforms state-of-the-art methods on fastMRI and real-world clinical datasets. © 2021, CC BY.

关键词： Magnetic resonance imaging

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A convolutional neural network-based diagnostic method using resting-state electroencephalograph signals for major depressive and bipolar disorders

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Biomedical Signal Processing and Control 2022年第PartB期72卷 103370-103370页

作者： Lei, Yu Belkacem, Abdelkader Nasreddine Wang, Xiaotian Sha, Sha Wang, Changming Chen, Chao Beijing Institute of Basic Medical Sciences Beijing China Department of Computer and Network Engineering College of Information Technology United Arab Emirates University Al Ain United Arab Emirates School of Artificial Intelligence Xidian University Xian China National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital Capital Medical University Beijing China Brain-inspired Intelligence and Clinical Translational Research Center/Department of Neurosurgery Xuanwu Hospital Capitap Medical University Beijing China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology China Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin China

Background: Early and accurate diagnosis of bipolar and major depressive disorders is important in clinical practice. However, no diagnostic biomarkers can discriminate bipolar from major depressive disorder with high accuracy at present. Methods: We propose a novel convolutional neural network architecture using multichannel raw resting-state electroencephalograph signals to differentiate bipolar disorder from major depressive disorder. This method has great potential in diagnosing mental disorders. In total, 101 patients with major depressive disorder, 82 patients with bipolar disorder, and 81 healthy controls were assessed. Clinical diagnosis was performed by psychiatrists based on the Diagnostic and Statistical Manual of Mental Disorders, fifth edition. Participants were instructed to fix their eyes on a cross on the monitor during the collection of resting-state electroencephalograph signals. Results: A classification mean accuracy of 96.88% was achieved using the resting-state electroencephalograph signals with the 10-fold cross-validation method. The results indicate that deep neural networks could learn efficient feature patterns automatically without manual feature selections. Both statistical analysis and feature visualization on different brain regions showed that the classification performance and the learned features of the proposed model were consistent with that obtained in neurobiological studies on bipolar and major depressive disorders. Conclusions: The combined use of deep neural networks and electroencephalograph signals is an effective approach for the computer-aided diagnosis of bipolar and major depressive disorders. © 2021 Elsevier Ltd

关键词： Deep neural networks

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Ultrasonic backscatter communication for implantable medical devices

arXiv

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arXiv 2022年

作者： Wang, Qianqian Guan, Quansheng Cheng, Julian Tang, Yuankun School of Electronic and Information Engineering South China University of Technology Guangzhou510640 China School of Engineering The University of British Columbia KelownaBCV1V 1V7 Canada Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural Resources Guangzhou510300 China Southampton Wireless Group School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

This paper proposes an ultrasonic backscatter communication (UsBC) system for passive implantable medical devices (IMDs) that can operate without batteries, enabling versatile revolutionary applications for future healthcare. The proposed UsBC system consists of a reader and a tag. The reader sends interrogation pulses to the tag. The tag backscatters the pulses based on the piezoelectric effect of a piezo transducer. We present several basic modulation schemes for UsBC by impedance matching of the piezo transducer. To mitigate the interference of other scatters in the human body, the tag transmits information bits by codeword mapping, and the reader performs codeword matching before energy detection in the reader. We further derive the theoretical bit-error rate (BER) expression. Monte Carlo simulations verify the theoretical analysis and show that passive UsBC can achieve low BER and low complexity, which is desirable for size-and energy-constrained IMDs. Copyright © 2022, The Authors. All rights reserved.

关键词： Piezoelectricity

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Motif Graph Neural Network

arXiv

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arXiv 2021年

作者： Chen, Xuexin Cai, Ruichu Fang, Yuan Wu, Min Li, Zijian Hao, Zhifeng The School of Computer Science Guangdong University of Technology Guangzhou510006 China The School of Computer Science Guangdong University of Technology Guangdong Provincial Key Laboratory of Public Finance and Taxation with Big Data Application Guangzhou China Peng Cheng Laboratory Shenzhen518066 China The School of Computing and Information Systems Singapore Management University 178902 Singapore A*STAR 138632 Singapore The College of Science Shantou University Shantou515063 China

Graphs can model complicated interactions between entities, which naturally emerge in many important applications. These applications can often be cast into standard graph learning tasks, in which a crucial step is to learn low-dimensional graph representations. Graph neural networks (GNNs) are currently the most popular model in graph embedding approaches. However, standard GNNs in the neighborhood aggregation paradigm suffer from limited discriminative power in distinguishing high-order graph structures as opposed to low-order structures. To capture high-order structures, researchers have resorted to motifs and developed motif-based GNNs. However, existing motif-based GNNs still often suffer from less discriminative power on high-order structures. To overcome the above limitations, we propose Motif Graph Neural Network (MGNN), a novel framework to better capture high-order structures, hinging on our proposed motif redundancy minimization operator and injective motif combination. First, MGNN produces a set of node representations w.r.t. each motif. The next phase is our proposed redundancy minimization among motifs which compares the motifs with each other and distills the features unique to each motif. Finally, MGNN performs the updating of node representations by combining multiple representations from different motifs. In particular, to enhance the discriminative power, MGNN utilizes an injective function to combine the representations w.r.t. different motifs. We further show that our proposed architecture increases the expressive power of GNNs with a theoretical analysis. We demonstrate that MGNN outperforms state-of-the-art methods on seven public benchmarks on both node classification and graph classification tasks. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph neural networks

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Quality Meets Diversity: A Model-Agnostic Framework for computerized Adaptive Testing

Quality Meets Diversity: A Model-Agnostic Framework for Comp...

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IEEE International Conference on Data Mining (ICDM)

作者： Haoyang Bi Haiping Ma Zhenya Huang Yu Yin Qi Liu Enhong Chen Yu Su Shijin Wang Anhui Province Key Laboratory of Big Data Analysis and ApplicationSchool of Computer Science and Technology University of Science and Technology of China CN Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Anhui University CN IFLYTEK Research CN

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

computerized Adaptive Testing (CAT) is emerging as a promising testing application in many scenarios, such as education, game and recruitment, which targets at diagnosing the knowledge mastery levels of examinees on required concepts. It shows the advantage of tailoring a personalized testing procedure for each examinee, which selects questions step by step, depending on her performance. While there are many efforts on developing CAT systems, existing solutions generally follow an inflexible model-specific fashion. That is, they need to observe a specific cognitive model which can estimate examinee's knowledge levels and design the selection strategy according to the model estimation. In this paper, we study a novel model-agnostic CAT problem, where we aim to propose a flexible framework that can adapt to different cognitive models. Meanwhile, this work also figures out CAT solution with addressing the problem of how to generate both high-quality and diverse questions simultaneously, which can give a comprehensive knowledge diagnosis for each examinee. Inspired by Active Learning, we propose a novel framework, namely Model-Agnostic Adaptive Testing (MAAT) for CAT solution, where we design three sophisticated modules including Quality Module, Diversity Module and Importance Module. Specifically, at one CAT selection step, Quality Module first quantifies the informativeness of questions and generates candidate subset with the highest quality. Then, Diversity Module selects one question at each step that maximizes the concept coverage. Additionally, we propose Importance Module to estimate the importance of concepts that optimizes the CAT selection. Under MAAT, we prove that the goal of maximizing both quality and diversity is NP-hard, but we provide efficient algorithms by exploiting the inherent submodular property. Extensive experimental results on two real-world datasets clearly demonstrate that our MAAT can support CAT with guaranteeing both quality and diversity

关键词： Adaptation models Cats Computational modeling Estimation Games Testing Recruitment

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Exploiting Knowledge Hierarchy for Finding Similar Exercises in Online Education Systems

Exploiting Knowledge Hierarchy for Finding Similar Exercises...

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IEEE International Conference on Data Mining (ICDM)

作者： Wei Tong Shiwei Tong Wei Hunag Liyang He Jianhui Ma Qi Liu Enhong Chen School of Computer Science and Technology Anhui Province Key Laboratory of Big Data Analysis and Application & School of Data Science University of Science and Technology of China University of Science and Technology of China China

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

In education systems, Finding Similar Exercises (FSE) is the key step for both exercise retrieval and duplicate detection. Recently, more and more attention has been drawn into this area and several works have been proposed, to utilize the exercise content (e.g., texts or images) or the labeled knowledge concepts. Such approaches, however, have failed to take knowledge hierarchy into account. To this end, we advance a novel knowledge-aware multimodal network, namely KnowNet, for finding similar exercises in large-scale online education systems by integrating the knowledge hierarchy into the heterogeneous exercise data and learning a relation-aware semantic representation. Specifically, we first propose a Content Representation Layer (CRL) to learn a unified semantic representation of the heterogeneous exercise content. Then, we design a Hierarchy Fusion Layer (HFL) to exploit the knowledge hierarchy. By combining the knowledge hierarchy, HFL can not only retrieve the relation-aware semantic representation but also provide an interpretable view to investigate the similarity of exercises. Finally, we adopt a Similarity Score Layer (SSL) for returning similar exercises. Extensive experiments demonstrate the effectiveness and interpretability of KnowNet.

关键词： Knowledge engineering Conferences Semantics Education Data mining

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Corrosion behavior of Mg-3Gd-1Zn-0.4Zr alloy with and without stacking faults

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Journal of Magnesium and Alloys 2019年第2期7卷 240-248页

作者： Xiaobo Zhang Jianwei Dai Ruifeng Zhang Zhixin Ba Nick Birbilis Jiangsu Key Laboratory of Advanced Structural Materials and Application Technology School of Materials Science and EngineeringNanjing Institute of TechnologyNanjing 211167China School of Materials Science and Engineering Central South UniversityChangsha 410083China College of Engineering and Computer Science Australian National UniversityActon ACT 2000Australia

To develop biodegradable magnesium alloy with desirable corrosion properties,a low Gd-containing Mg-3Gd-1Zn-0.4Zr(wt%,GZ31K)alloy was *** as-cast ingot was solution treated and then hot *** were characterized by scanning electron microscopy(SEM).Corrosion behavior of the alloy under each condition was studied by hydrogen evolution and quasi in-situ corrosion *** has been found that the as-cast alloy is composed ofα-Mg,stacking faults(SFs)at the outer edge of the matrix grains,and eutectic phase along the grain *** solution treatment,the SFs disappear and precipitates rich in Zn and Zr elements form in the grain interior and *** microstructure is significantly refined after *** evolution tests show that the as-cast alloy exhibits the best corrosion resistance,and the solution-treated alloy has the worst corrosion *** rate of the alloy under each condition decreases first and then increases with prolonging immersion *** experiments demonstrate thatα-Mg was corroded preferentially,the eutectic phase and precipitates exhibit better corrosion *** as-extruded alloy demonstrates uniform corrosion due to fine and homogeneous microstructure.

关键词： Magnesium alloys Stacking faults Corrosion resistance Uniform corrosion

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A new classification method of SAR image based on OGMRF-RC model 5

A new classification method of SAR image based on OGMRF-RC m...

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IET International Radar Conference 2020, IET IRC 2020

作者： Li, Yuan Guo, Zhishun Wu, Lin Guo, Zhengwei College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng475000 China College of Environment and Planning Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

Synthetic Aperture Radar (SAR) image classification is a key technique and research focus in the application of remote sensing. This paper proposes a new classification method of SAR image based on Object-based Gaussian-Markov Random Field model with Region Coefficients (OGMRF-RC), the new method use Fuzzy Probability (FP) represents the region's category probability, FP is obtained by regional posterior probability and edge information of neighborhood. Use FP to replace the unique class label of the region, which avoids the parameters estimation inaccurate of the feature field caused by the unique initial class label. And the parameter calculation method of feature field takes into account the feature information of all regions in the image. In this paper, we take the area around Baliwan in Kaifeng as study area, Sentinel-1 is used as the remote sensing data sources. FCM, K-means, classic MRF and OGMRF-RC are used as contrast methods, the experimental result demonstrates the accuracy of our method is better than these methods. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Synthetic aperture radar

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O2iJoin： An Efficient Index-Based Algorithm for Overlap Interval Join

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Journal of computer Science & technology 2018年第5期33卷 1023-1038页

作者： Ji-Zhou Luo Sheng-Fei Shi Guang Yang Hong-Zhi Wang Jian-Zhong Li School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China Guangdong Key Laboratory of Popular High Performance Computers Key Laboratory of Service Computing and Application Shenzhen 518000 China

Time intervals are often associated with tuples to represent their valid time in temporal relations, where overlap join is crucial for various kinds of queries. Many existing overlap join algorithms use indices based on tree structures such as quad-tree, B＋-tree and interval tree. These algorithms usually have high CPU cost since deep path traversals are unavoidable, which makes them not so competitive as data-partition or plane-sweep based algorithms. This paper proposes an efficient overlap join algorithm based on a new two-layer flat index named as Overlap Interval Inverted Index （i.e., O2i Index）. It uses an array to record the end points of intervals and approximates the nesting structures of intervals via two functions in the first layer, and the second layer uses inverted lists to trace all intervals satisfying the approximated nesting structures. With the help of the new index, the join algorithm only visits the must-be-scanned lists and skips all others. Analyses and experiments on both real and synthetic datasets show that the proposed algorithm is as competitive as the state-of-the-art algorithms.

关键词： overlap interval join temporal relation overlap inverted index join algorithm

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On the value of imbalance loss functions in enhancing deep learning-based vulnerability detection

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Expert Systems with applications 2025年 291卷

作者： Xiaoxue Ma Yanzhong He Jacky Keung Cheng Tan Chuanxiang Ma Wenhua Hu Fuyang Li School of Science and Technology Hong Kong Metropolitan University Hong Kong China School of Computer Science Wuhan University Wuhan Hubei China School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan Hubei China Department of Computer Science City University of Hong Kong Hong Kong China School of Computer Science Hubei University Wuhan Hubei China Hubei Key Laboratory of Big Data Intelligent Analysis and Application (Hubei University) Wuhan Hubei China

Software vulnerability detection is crucial in software engineering and information security, and deep learning has been demonstrated to be effective in this domain. However, the class imbalance issue, where non-vulnerable code snippets vastly outnumber vulnerable ones, hinders the performance of deep learning-based vulnerability detection (DLVD) models. Although some recent research has explored the use of imbalance loss functions to address this issue and enhance model efficacy, they have primarily focused on a limited selection of imbalance loss functions, leaving many others unexplored. Therefore, their conclusions about the most effective imbalance loss function may be biased and inconclusive. To fill this gap, we first conduct a comprehensive literature review of 119 DLVD studies, focusing on the loss functions used by these models. We then assess the effectiveness of nine imbalance loss functions alongside cross entropy (CE) loss (the standard balanced loss function) on two DLVD models across four public vulnerability datasets. Our evaluation incorporates six performance metrics, with results analyzed using the Scott-Knott effect size difference (ESD) test. Furthermore, we employ interpretable analysis to elucidate the impact of loss functions on model performance. Our findings provide key insights for DLVD, which mainly include the following: the LineVul model consistently outperforms the ReVeal model; label distribution aware margin (LDAM) loss achieves the highest Precision, while logit adjustment (LA) loss yields the best Recall; Class balanced focal (CB-Focal) loss excels in comprehensive performance on extremely imbalanced datasets; and LA loss is optimal for nearly balanced datasets. We recommend using LineVul with either CB-Focal loss or LA loss to enhance DLVD outcomes. Our source code and datasets are available at https://***/YanzhongHe/DLVD-ImbalanceLossEmpirical .

关键词： Error detection

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