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Optical Fiber Communication (OFC) Conference

作者： Xuefeng Wang Yifan Zeng Ruolin Liao Can Zhao Hao Wu Ming Tang Wuhan National Lab for Optoelectronics (WNLO) & National Engineering Laboratory for Next Generation Internet Access System School of Optics and Electronic Information Huazhong University of Science and Technology Wuhan China

We experimentally demonstrated an endlessly adaptive polarization controller based on a Mach-Zehnder Interferometer on the Silicon photonic integrated circuit. A polarization tracking speed up to hundreds of rad/s was... 详细信息

ISBN: (纸本)9781943580866

关键词： Mach-Zehnder interferometers Integrated circuits Fading channels Phase shifters Couplers Silicon Optical fiber communication

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A High-Fidelity Virtual Liver Model Incorporating Biological Characteristics

SSRN

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SSRN 2022年

作者： Zhang, Xiaorui Zhang, Wenzheng Sun, Wei Song, Aiguo Xu, Tong Wuxi Research Institute Nanjing University of Information Science & Technology Wuxi214100 China Engineering Research Center of Digital Forensics Ministry of Education Jiangsu Engineering Center of Network Monitoring School of Computer and Software Nanjing University of Information Science & Technology Nanjing210044 China School of Automation Nanjing University of Information Science & Technology Nanjing210044 China State Key Laboratory of Bioelectronics Jiangsu Key Lab of Remote Measurement and Control School of Instrument Science and Engineering Southeast University Nanjing210096 China University of Southern California Los AngelesCA United States

Flexible tissue modeling plays an important role in the field of telemedicine. It is related to whether the soft tissue deformation process can be accurately, real-time and vividly simulated during surgery. Most existing models lack the unique biological characteristics. To enhance visual perception ability, a high-fidelity virtual liver model incorporating biological characteristics, such as the viscoelastic, anisotropic and nonlinear biological characteristics, is proposed based on the finite element model with the fourth-order Runge-Kutta method. When simulating the resection of diseased livers, the model undergoes a plastic deformation process where the model loses elasticity due to excessive deformation, but does not reach the cutting fracture threshold. To improve the fidelity of the liver model, this study is the first to introduce the viscoplasticity of biological tissues to the liver model. The 3DMax2020 and OpenGL4.6 were used to build a liver surgery simulation platform, and the PHANTOM OMNI manual controller was used to sense the feedback force during the operation. The proposed model was verified from three aspects of accuracy, fidelity and real-time performance. The experimental results show that the proposed virtual liver model can achieve real visual reproduction, with natural deformation effect, accurate simulation and high fidelity. © 2022, The Authors. All rights reserved.

关键词： Simulation platform

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Continuous-time dynamic network node classification algorithm based on autoencoder 9

Continuous-time dynamic network node classification algorith...

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9th IEEE Joint International information technology and Artificial Intelligence Conference, ITAIC 2020

作者： Huang, Xin Li, Yun Xiong, Jinyu Liang, Jie Wu, Jie Jing, Jie PLA Strategic Support Force Information Engineering University College of Information System Engineering Zhengzhou China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China Officers College of PAP Chengdu China

Node classification is an important issue in network researches. Existing methods lay more emphasis on the classification of static network nodes, while there are insufficient researches on network dynamic characteristics. At the same time, the existing adjacency matrix representation cannot reflect the potential relationship of indirectly connected nodes. To better describe the network structure and utilize network time information, the author proposes a new Node Classification Algorithm based on Autoencoder, namely NCAA. It adopts the random walk in limited spatio-temporal and the matrix representation of node relationship. The low-dimensional representation vector of the node is generated by the autoencoder model, and the LogicRegression classifier is used to classify the representation vectors, thereby achieving node classification. Experiments show that, compared to some classic algorithms, NCAA can better represent the network, and its two-dimensional visualization of node vectors has a superior effect. Under the same conditions, the F1 value of the classification result is significantly higher than that of baseline algorithms. © 2020 IEEE.

关键词： Classification (of information)

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Ambient Brightness Issues in AR-BCI Applications: Phenomenon and Solutions

TechRxiv

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TechRxiv 2022年

作者： Zhang, Rui Yao, Dezhong Cao, Lijun Xu, Zongxin Zhang, Yangsong Zhang, Lipeng Hu, Yuxia Chen, Mingming Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China School of Computer Science and Technology Southwest University of Science and Technology Mianyang621010 China Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation University of Electronic Science and Technology of China Chengdu611731 China

Augmented reality-based brain-computer interface (AR-BCI) system is one of the important ways to promote BCI technology outside of the laboratory due to its portability and mobility, but its performance in real-world scenarios has not been fully studied. In current study, we first investigated the effect of ambient brightness on AR-BCI performance. 5 different light intensities were set as experimental conditions to simulate typical brightness in real scenes, while the same steady-state visual evoked potentials (SSVEP) stimulus was displayed in the AR glass. The data analysis results showed that SSVEP can be evoked under all 5 light intensities, but the response intensity became weaker when the brightness increased. The recognition accuracies of SSVEP were negatively correlated to light intensity, the highest accuracies were 89.35% with FBCCA and 83.33% with CCA under 0 lux light intensity, while they decreased to 62.53% and 49.24% under 1200 lux. In addition, we designed a new SSVEP recognition algorithm, named ensemble online adaptive CCA (eOACCA), to solve the accuracy loss problem in high ambient brightness. The main strategy of eOACCA is to provide initial filters for high-intensity data by iteratively learning low-light-intensity SSVEP data. The experimental results showed that the eOACCA algorithm had significant advantages under higher light intensities (>600 lux). Compared with FBCCA, the accuracies of eOACCA under 1200 lux was increased by 12.83%. In conclusion, current study contributed to the in-depth understanding of the performance variations of AR-BCI under different lighting conditions, and was helpful to promote the AR-BCI application in complex lighting environments. © 2022-11-30, CC BY.

关键词： Augmented reality

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Trash to Treasure: Using text-to-image models to inform the design of physical artefacts

arXiv

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arXiv 2023年

作者： Smith, Amy Schroeder, Hope Epstein, Ziv Cook, Michael Colton, Simon Lippman, Andrew School of Electronic Engineering and Computer Science Queen Mary University London United Kingdom MIT Media Lab United States Department of Informatics King's College London United Kingdom SensiLab Faculty of Information Technology Monash University Australia

Text-to-image generative models have recently exploded in popularity and accessibility. Yet so far, use of these models in creative tasks that bridge the 2D digital world and the creation of physical artefacts has been understudied. We conduct a pilot study to investigate if and how text-to-image models can be used to assist in upstream tasks within the creative process, such as ideation and visualization, prior to a sculpture-making activity. Thirty participants selected sculpture-making materials and generated three images using the Stable Diffusion text-to-image generator, each with text prompts of their choice, with the aim of informing and then creating a physical sculpture. The majority of participants (23/30) reported that the generated images informed their sculptures, and 28/30 reported interest in using text-to-image models to help them in a creative task in the future. We identify several prompt engineering strategies and find that a participant's prompting strategy relates to their stage in the creative process. We discuss how our findings can inform support for users at different stages of the design process and for using text-to-image models for physical artefact design. © 2023, CC BY.

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Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks

arXiv

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arXiv 2025年

作者： Dong, Xuewen Li, Jiachen Li, Shujun You, Zhichao Qu, Qiang Kholodov, Yaroslav Shen, Yulong School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China University of Kent CanterburyCT2 7NP United Kingdom Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Intelligent Transportation Systems Lab Innopolis University Innopolis Russia School of Computer Science and Technology Xidian University China The Shaanxi Key Laboratory of Network and System Security Xi’an710071 China

Recent studies show that graph neural networks (GNNs) are vulnerable to backdoor attacks. Existing backdoor attacks against GNNs use fixed-pattern triggers and lack reasonable trigger constraints, overlooking individual graph characteristics and rendering insufficient evasiveness. To tackle the above issues, we propose ABARC, the first Adaptive Backdoor Attack with Reasonable Constraints, applying to both graph-level and node-level tasks in GNNs. For graph-level tasks, we propose a subgraph backdoor attack independent of the graph’s topology. It dynamically selects trigger nodes for each target graph and modifies node features with constraints based on graph similarity, feature range, and feature type. For node-level tasks, our attack begins with an analysis of node features, followed by selecting and modifying trigger features, which are then constrained by node similarity, feature range, and feature type. Furthermore, an adaptive edge-pruning mechanism is designed to reduce the impact of neighbors on target nodes, ensuring a high attack success rate (ASR). Experimental results show that even with reasonable constraints for attack evasiveness, our attack achieves a high ASR while incurring a marginal clean accuracy drop (CAD). When combined with the state-of-the-art defense randomized smoothing (RS) method, our attack maintains an ASR over 94%, surpassing existing attacks by more than 7%. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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Discovery of novel antagonists targeting the DNA binding domain of androgen receptor by integrated docking-based virtual screening and bioassays

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中国药理学报（英文版） 2022年第1期43卷 229-239页

作者： Jin-ping Pang Chao Shen Wen-fang Zhou Yun-xia Wang Lu-hu Shan Xin Chai Ying Shao Xue-ping Hu Feng Zhu Dan-yan Zhu Li Xiao Lei Xu Xiao-hong Xu Dan Li Ting-jun Hou Hangzhou Institute of Innovative Medicine College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China Institute of Cancer Research and Basic Medical Sciences of Chinese Academy of Sciences Cancer Hospital of University of Chinese Academy of SciencesZhejiang Cancer HospitalHangzhou 310022China School of Life Science Huzhou UniversityHuzhou 313000China Institute of Bioinformatics and Medical Engineering School of Electrical and Information EngineeringJiangsu University of TechnologyChangzhou 213001China Hangzhou Institute of Innovative Medicine College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China State Key Lab of CAD&CG Zhejiang UniversityHangzhou 310058China

Androgen receptor(AR),a ligand-activated transcription factor,is a master regulator in the development and progress of prostate cancer(PCa).A major challenge for the clinically used AR antagonists is the rapid emergence of resistance induced by the mutations at AR ligand binding domain(LBD),and therefore the discovery of novel anti-AR therapeutics that can combat mutation-induced resistance is quite ***,blocking the interaction between AR and DNA represents an innovative ***,the hits confirmed targeting on it so far are all structurally based on a sole chemical *** this study,an integrated docking-based virtual screening(VS)strategy based on the crystal structure of the DNA binding domain(DBD)of AR was conducted to search for novel AR antagonists with new scaffolds and 2-(2-butyl-1,3-dioxoisoindoline-5-carboxamido)-4,5-dimethoxybenzoicacid(Cpd39)was identified as a potential hit,which was competent to block the binding of AR DBD to DNA and showed decent potency against AR transcriptional ***,Cpd39 was safe and capable of effectively inhibiting the proliferation of PCa cell lines(i.e.,LNCaP,PC3,DU 145,and 22RV1)and reducing the expression of the genes regulated by not only the full-length AR but also the splice variant *** novel AR DBD-ARE blocker Cpd39 could serve as a starting point for the development of new therapeutics for castration-resistant PCa.

关键词： androgen receptor DNA binding domain antagonist prostate cancer virtual screening molecular docking

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Predictive Monitoring Algorithm Based on Global Feature Encoding

Predictive Monitoring Algorithm Based on Global Feature Enco...

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2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020

作者： Jin, Mengsi Ye, JianHong Luo, JiLiang Lin, Yan College of Computer Science and Technology Huaqiao University Xiamen361021 China College of Information Science and Engineering Huaqiao University Fujian Engineering Research Center of Motor Control and System Optimal Schedule Xiamen361021 China

ISBN: (纸本)9781728168531

Predictive monitoring is a branch of process mining to provide some valuable information that enables proactive corrective actions to mitigate risks. This paper proposes a new predictive monitoring algorithm, which divides the data processing into three parts: prefix extraction, prefix bucketing and prefix encoding. The presented encoding methods are based on the data structure of event log, and it will cause the loss of information in the raw data. Our main contribution is to define a new global feature encoding method, which keeps more information in raw data and has better scalability. Experiments are presented to demonstrate the proposed approach. © 2020 IEEE.

关键词： Signal encoding

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Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization

arXiv

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arXiv 2023年

作者： Huang, Zhuo Zhu, Miaoxi Xia, Xiaobo Shen, Li Yu, Jun Gong, Chen Han, Bo Du, Bo Liu, Tongliang Syndey Ai Centre The University of Sydney Australia National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China Jd Explore Academy China Department of Automation University of Science and Technology of China China Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computer Science Hong Kong Baptist University Hong Kong

Robust generalization aims to tackle the most challenging data distributions which are rare in the training set and contain severe noises, i.e., photon-limited corruptions. Common solutions such as distributionally robust optimization (DRO) focus on the worst-case empirical risk to ensure low training error on the uncommon noisy distributions. However, due to the over-parameterized model being optimized on scarce worst-case data, DRO fails to produce a smooth loss landscape, thus struggling on generalizing well to the test set. Therefore, instead of focusing on the worst-case risk minimization, we propose SharpDRO by penalizing the sharpness of the worst-case distribution, which measures the loss changes around the neighbor of learning parameters. Through worst-case sharpness minimization, the proposed method successfully produces a flat loss curve on the corrupted distributions, thus achieving robust generalization. Moreover, by considering whether the distribution annotation is available, we apply SharpDRO to two problem settings and design a worst-case selection process for robust generalization. Theoretically, we show that SharpDRO has a great convergence guarantee. Experimentally, we simulate photon-limited corruptions using CIFAR10/100 and ImageNet30 datasets and show that SharpDRO exhibits a strong generalization ability against severe corruptions and exceeds well-known baseline methods with large performance gains. Copyright © 2023, The Authors. All rights reserved.

关键词： Optimization

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DNA Methylation Biomarkers-Based Human Age Prediction Using Machine Learning

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Computational Intelligence and Neuroscience 2022年 2022卷 8393498页

作者： Zaguia, Atef Pandey, Deepak Painuly, Sandeep Pal, Saurabh Kumar Garg, Vivek Kumar Goel, Neelam Department of Computer Science College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Information Technology University Institute of Engineering and Technology Panjab University Chandigarh160014 India Department of Medical Lab Technology University Institute of Applied Health Sciences Chandigarh University Gharuan Punjab Mohali140413 India

Purpose. Age can be an important clue in uncovering the identity of persons that left biological evidence at crime scenes. With the availability of DNA methylation data, several age prediction models are developed by using statistical and machine learning methods. From epigenetic studies, it has been demonstrated that there is a close association between aging and DNA methylation. Most of the existing studies focused on healthy samples, whereas diseases may have a significant impact on human age. Therefore, in this article, an age prediction model is proposed using DNA methylation biomarkers for healthy and diseased samples. Methods. The dataset contains 454 healthy samples and 400 diseased samples from publicly available sources with age (1-89 years old). Six CpG sites are identified from this data having a high correlation with age using Pearson's correlation coefficient. In this work, the age prediction model is developed using four different machine learning techniques, namely, Multiple Linear Regression, Support Vector Regression, Gradient Boosting Regression, and Random Forest Regression. Separate models are designed for healthy and diseased data. The data are split randomly into 80: 20 ratios for training and testing, respectively. Results. Among all the techniques, the model designed using Random Forest Regression shows the best performance, and Gradient Boosting Regression is the second best model. In the case of healthy samples, the model achieved a MAD of 2.51 years for training data and 4.85 for testing data. Also, for diseased samples, a MAD of 3.83 years is obtained for training and 9.53 years for testing. Conclusion. These results showed that the proposed model can predict age for healthy and diseased samples. © 2022 Atef Zaguia et al.

关键词： Decision trees

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