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Bi-layered coded metasurface for multi-functional hologram with broadband transmission and efficient reflection

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Chinese Optics Letters 2024年第5期22卷 8-16页

作者：朱磊李绪胜董亮谢文静尚关玉 Shah Nawaz Burokur 丁旭旻 School of Computer and Control Engineering Qiqihar UniversityQiqihar 161006China Communication and Electronics Engineering Institute Qiqihar UniversityQiqihar 161006China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar UniversityQiqihar 161000China Department of Microwave Engineering Harbin Institute of TechnologyHarbin 150001China LEME UPLUniversitéParis NanterreF92410 Ville d’AvrayFrance Advanced Microscopy and Instrumentation Research Center Harbin Institute of TechnologyHarbin 150080China Key Laboratory of Millimeter Waves Southeast UniversityNanjing 210096China

A multi-functional full-space metasurface based on frequency and polarization multiplexing is *** metasurface unit consists of metallic patterns printed on the two faces of a single-layered dielectric *** unit cell can control electromagnetic wavefronts to achieve a broadband transmission with amplitudes greater than 0.4 from 4.4 to 10.4 ***,at 11.7 GHz and 15.4 GHz,four high-efficiency reflection channels with a reflection amplitude greater than 0.8 are also *** illuminated by linearly polarized waves,five different functions can be realized at five different frequencies,which are demonstrated by theoretical calculations,full-wave simulations,and experimental measurements.

关键词： multi-functional metasurface broadband transmission efficient reflection full space

Detection and classification of breast lesions using multiple information on contrast-enhanced mammography by a multiprocess deep-learning system: A multicenter study

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Chinese Journal of Cancer Research 2023年第4期35卷 408-423页

作者： Yuqian Chen Zhen Hua Fan Lin Tiantian Zheng Heng Zhou Shijie Zhang Jing Gao Zhongyi Wang Huafei Shao Wenjuan Li Fengjie Liu Simin Wang Yan Zhang Feng Zhao Hao Liu Haizhu Xie Heng Ma Haicheng Zhang Ning Mao School of Information and Electronic Engineering Shandong Technology and Business UniversityYantai 264005China Department of Radiology Yantai Yuhuangding HospitalAffiliated Hospital of Qingdao UniversityYantai 264000China School of Medical Imaging Binzhou Medical UniversityYantai 264003China Department of Radiology Fudan University Cancer CenterShanghai 200433China Department of Radiology Guangdong Maternal and Child Health HospitalGuangzhou 510010China School of Computer Science and Technology Shandong Technology and Business UniversityYantai 264005China Yizhun Medical AI Co.Ltd. Beijing 100080China Big Data and Artificial Intelligence Laboratory Yantai Yuhuangding HospitalAffiliated Hospital of Qingdao UniversityYantai 264000China

Objective: Accurate detection and classification of breast lesions in early stage is crucial to timely formulate effective treatments for patients. We aim to develop a fully automatic system to detect and classify breast lesions using multiple contrast-enhanced mammography(CEM) ***: In this study, a total of 1,903 females who underwent CEM examination from three hospitals were enrolled as the training set, internal testing set, pooled external testing set and prospective testing set. Here we developed a CEM-based multiprocess detection and classification system(MDCS) to perform the task of detection and classification of breast lesions. In this system, we introduced an innovative auxiliary feature fusion(AFF)algorithm that could intelligently incorporates multiple types of information from CEM images. The average freeresponse receiver operating characteristic score(AFROC-Score) was presented to validate system’s detection performance, and the performance of classification was evaluated by area under the receiver operating characteristic curve(AUC). Furthermore, we assessed the diagnostic value of MDCS through visual analysis of disputed cases,comparing its performance and efficiency with that of radiologists and exploring whether it could augment radiologists’ ***: On the pooled external and prospective testing sets, MDCS always maintained a high standalone performance, with AFROC-Scores of 0.953 and 0.963 for detection task, and AUCs for classification were 0.909[95% confidence interval(95% CI): 0.822-0.996] and 0.912(95% CI: 0.840-0.985), respectively. It also achieved higher sensitivity than all senior radiologists and higher specificity than all junior radiologists on pooled external and prospective testing sets. Moreover, MDCS performed superior diagnostic efficiency with an average reading time of 5 seconds, compared to the radiologists’ average reading time of 3.2 min. The average performance of all radiologists was also improved to

关键词： Deep learning contrast-enhanced mammography breast lesions detection classification

GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud Detection

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arXiv 2023年

作者： Hu, Xinxin Chen, Haotian Zhang, Junjie Chen, Hongchang Liu, Shuxin Li, Xing Wang, Yahui Xue, Xiangyang National Digital Switching System Engineering and Technological Research Center Zhengzhou450002 China Department of Electrical & Computer Engineering University of Toronto TorontoM5S 3G4 Canada Institute of Big Data Fudan University Shanghai China

Along with the rapid evolution of mobile communication technologies, such as 5G, there has been a drastically increase in telecom fraud, which significantly dissipates individual fortune and social wealth. In recent years, graph mining techniques are gradually becoming a mainstream solution for detecting telecom fraud. However, the graph imbalance problem, caused by the Pareto principle, brings severe challenges to graph data mining. This is a new and challenging problem, but little previous work has been noticed. In this paper, we propose a Graph ATtention network with COst-sensitive BOosting (GAT-COBO) for the graph imbalance problem. First, we design a GAT-based base classifier to learn the embeddings of all nodes in the graph. Then, we feed the embeddings into a well-designed cost-sensitive learner for imbalanced learning. Next, we update the weights according to the misclassification cost to make the model focus more on the minority class. Finally, we sum the node embeddings obtained by multiple cost-sensitive learners to obtain a comprehensive node representation, which is used for the downstream anomaly detection task. Extensive experiments on two real-world telecom fraud detection datasets demonstrate that our proposed method is effective for the graph imbalance problem, outperforming the state-of-the-art GNNs and GNN-based fraud detectors. In addition, our model is also helpful for solving the widespread over-smoothing problem in GNNs. The GAT-COBO code and datasets are available at https://***/xxhu94/GAT-COBO. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization

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arXiv 2023年

作者： Yuan, Haonan Sun, Qingyun Fu, Xingcheng Zhang, Ziwei Ji, Cheng Peng, Hao Li, Jianxin Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China School of Computer Science and Engineering Beihang University China Key Lab of Education Blockchain and Intelligent Technology Guangxi Normal University China Department of Computer Science and Technology Tsinghua University China

Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-temporal patterns on dynamic graphs. However, existing works fail to generalize under distribution shifts, which are common in real-world scenarios. As the generation of dynamic graphs is heavily influenced by latent environments, investigating their impacts on the out-of-distribution (OOD) generalization is critical. However, it remains unexplored with the following two major challenges: (1) How to properly model and infer the complex environments on dynamic graphs with distribution shifts? (2) How to discover invariant patterns given inferred spatio-temporal environments? To solve these challenges, we propose a novel Environment-Aware dynamic Graph LEarning (EAGLE) framework for OOD generalization by modeling complex coupled environments and exploiting spatiotemporal invariant patterns. Specifically, we first design the environment-aware EA-DGNN to model environments by multi-channel environments disentangling. Then, we propose an environment instantiation mechanism for environment diversification with inferred distributions. Finally, we discriminate spatio-temporal invariant patterns for out-of-distribution prediction by the invariant pattern recognition mechanism and perform fine-grained causal interventions node-wisely with a mixture of instantiated environment samples. Experiments on real-world and synthetic dynamic graph datasets demonstrate the superiority of our method against state-of-the-art baselines under distribution shifts. To the best of our knowledge, we are the first to study OOD generalization on dynamic graphs from the environment learning perspective. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

Exploring the Influence of Dimensionality Reduction on Anomaly Detection Performance in Multivariate Time Series

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arXiv 2024年

作者： Altin, Mahsun Cakir, Altan Department of Computer Engineering Istanbul Technical University Sariyer Istanbul34467 Turkey Department of Physics Engineering Istanbul Technical University Sariyer Istanbul34467 Turkey Artificial Intelligence Data Science Research and Application Center Istanbul Technical University Sariyer Istanbul34467 Turkey Parton Big Data Analytics and Consulting ITU Ariteknokent Sariyer Istanbul34467 Turkey Adin.Ai WilmingtonDE19806 United States

This paper presents an extensive empirical study on the integration of dimensionality reduction techniques with advanced unsupervised time series anomaly detection models, focusing on the MUTANT and Anomaly-Transformer models. The study involves a comprehensive evaluation across three different datasets: MSL, SMAP, and SWaT. Each dataset poses unique challenges, allowing for a robust assessment of the models’ capabilities in varied contexts. The dimensionality reduction techniques examined include PCA, UMAP, Random Projection, and t-SNE, each offering distinct advantages in simplifying high-dimensional data. Our findings reveal that dimensionality reduction not only aids in reducing computational complexity but also significantly enhances anomaly detection performance in certain scenarios. Moreover, a remarkable reduction in training times was observed, with reductions by approximately 300% and 650% when dimensionality was halved and minimized to the lowest dimensions, respectively. This efficiency gain underscores the dual benefit of dimensionality reduction in both performance enhancement and operational efficiency. The MUTANT model exhibits notable adaptability, especially with UMAP reduction, while the Anomaly-Transformer demonstrates versatility across various reduction techniques. These insights provide a deeper understanding of the synergistic effects of dimensionality reduction and anomaly detection, contributing valuable perspectives to the field of time series analysis. The study underscores the importance of selecting appropriate dimensionality reduction strategies based on specific model requirements and dataset characteristics, paving the way for more efficient, accurate, and scalable solutions in anomaly detection. © 2024, CC BY.

关键词： Anomaly detection

Deep Multi-kernel Clustering Network

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Deep Multi-kernel Clustering Network

IEEE International Conference on data Mining (ICDM)

作者： Lina Ren Ruizhang Huang Shengwei Ma Yongbin Qin Yanping Chen Chuan Lin State Key Laboratory of Public Big Data Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and Technology Guizhou University Guiyang China Department of Information Engineering Guizhou Light Industry Technical College Guiyang China

In this paper, a deep multi-kernel clustering network, named DMKCN, is proposed to learn a high-quality and structurally separable kernel representation for the clustering task. Specifically, a multi-kernel learner is proposed to choose a suitable kernel function by learning a suitable combination of kernel functions automatically. A kernel-aid encoder module, consisting of a series of multi-kernel learners, is proposed to learn the structurally separable kernel representation. Besides, a dual self-supervised mechanism, consisting of a kernel self-supervised strategy and a representation self-supervised strategy, is designed to uniformly optimize the kernel representation learning and structural partition. The kernel self-supervised strategy is developed to supervise the multi-kernel learners with the consideration of an objective of clustering task, the representation self-supervised strategy is developed to guide the optimization of kernel representation learning by reconstructing the raw data. Extensive experiments on six real-world datasets demonstrate the outstanding performance of our proposed DMKCN.

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Evaluating the Application of ChatGPT in Outpatient Triage Guidance: A Comparative Study

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arXiv 2024年

作者： Liu, Dou Han, Ying Wang, Xiandi Tan, Xiaomei Liu, Di Qian, Guangwu Li, Kang Pu, Dan Yin, Rong Department of Industrial Engineering Pittsburgh Institute Sichuan University Chengdu610207 China Department of Medical Simulation Center West China Hospital Sichuan University Chengdu610041 China Department of Computer Science Pittsburgh Institute Sichuan University Chengdu610207 China West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu610041 China

The integration of Artificial Intelligence (AI) in healthcare presents a transformative potential for enhancing operational efficiency and health outcomes. Large Language Models (LLMs), such as ChatGPT, have shown their capabilities in supporting medical decision-making. Embedding LLMs in medical systems is becoming a promising trend in healthcare development. The potential of ChatGPT to address the triage problem in emergency departments has been examined, while few studies have explored its application in outpatient departments. With a focus on streamlining workflows and enhancing efficiency for outpatient triage, this study specifically aims to evaluate the consistency of responses provided by ChatGPT in outpatient guidance, including both within-version response analysis and between-version comparisons. For within-version, the results indicate that the internal response consistency for ChatGPT-4.0 is significantly higher than ChatGPT-3.5 (p=0.03) and both have a moderate consistency (71.2% for 4.0 and 59.6% for 3.5) in their top recommendation. However, the between-version consistency is relatively low (mean consistency score=1.43/3, median=1), indicating few recommendations match between the two versions. Also, only 50% top recommendations match perfectly in the comparisons. Interestingly, ChatGPT-3.5 responses are more likely to be complete than those from ChatGPT-4.0 (p=0.02), suggesting possible differences in information processing and response generation between the two versions. The findings offer insights into AI-assisted outpatient operations, while also facilitating the exploration of potentials and limitations of LLMs in healthcare utilization. Future research may focus on carefully optimizing LLMs and AI integration in healthcare systems based on ergonomic and human factors principles, precisely aligning with the specific needs of effective outpatient triage. Copyright © 2024, The Authors. All rights reserved.

关键词： Efficiency

AppleQSM:Geometry-Based 3D Characterization of Apple Tree Architecture in Orchards

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Plant Phenomics 2024年第3期6卷 737-754页

作者： Tian Qiu Tao Wang Tao Han Kaspar Kuehn Lailiang Cheng Cheng Meng Xiangtao Xu Kenong Xu Jiang Yu School of Electrical and Computer Engineering Cornell UniversityIthacaNYUSA Institute of Statistics and Big Data Renming University of ChinaBeijingChina Department of Ecology and Evolutionary Biology Cornell UniversityIthacaNYUSA School of Integrative Plant Science Cornell UniversityIthacaNYUSA Center for Applied Statistics Renmin University of ChinaBeijingChina School of Integrative Plant Science Cornell University.IthacaNYUSA

The architecture of apple trees plays a pivotal role in shaping their growth and fruit-bearing potential,forming the foundation for precision apple ***,2D imaging technologies were employed to delineate the architectural traits of apple trees,but their accuracy was hampered by occlusion and perspective *** study aimed to surmount these constraints by devising a 3D geometry-based processing pipeline for apple tree structure segmentation and architectural trait characterization,utilizing point clouds collected by a terrestrial laser scanner(TLS).The pipeline consisted of four modules:(a)data preprocessing module,(b)tree instance segmentation module,(c)tree structure segmentation module,and(d)architectural trait extraction *** developed pipeline was used to analyze 84 trees of two representative apple cultivars,characterizing architectural traits such as tree height,trunk diameter,branch count,branch diameter,and branch *** results indicated that the established pipeline attained an R^(2)of 0.92 and 0.83,and a mean absolute error(MAE)of 6.1cm and 4.71mm for tree height and trunk diameter at the tree level,***,at the branch level,it achieved an R^(2)of 0.77 and 0.69,and a MAE of 6.86 mm and 7.48°for branch diameter and angle,*** accurate measurement of these architectural traits can enable precision management in high-density apple orchards and bolster phenotyping endeavors in breeding ***,bottlenecks of 3D tree characterization in general were comprehensively analyzed to reveal future development.

关键词： architecture tree tion appleqsm character geometry-based orchards

Physics-guided machine learning for wind-farm power prediction: Toward interpretability and generalizability

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arXiv 2022年

作者： Zehtabiyan-Rezaie, Navid Iosifidis, Alexandros Abkar, Mahdi Department of Mechanical and Production Engineering Aarhus University Aarhus N8200 Denmark Department of Electrical and Computer Engineering Aarhus University Aarhus N8200 Denmark Center for Digitalization Big Data and Data Analytics Aarhus University Aarhus N8200 Denmark

With the increasing amount of available data from simulations and experiments, research for the development of data-driven models for wind-farm power prediction has increased significantly. While the data-driven models can successfully predict the power of a wind farm with similar characteristics as those in the training ensemble, they generally do not have a high degree of flexibility for extrapolation to unseen cases in contrast to the physics-based models. In this paper, we focus on data-driven models with improved interpretability and generalizability levels that can predict the performance of turbines in wind farms. To prepare the datasets, several cases are defined based on the layouts of operational wind farms, and massive computational fluid dynamics simulations are performed. The extreme gradient boosting algorithm is used afterward to build models, which have turbine-level geometric inputs in combination with the efficiency from physics-based models as the features. After training, to analyze the models’ capability in generalization, their predictions for the unseen cases with different operating conditions, inflow turbulence levels, and wind-farm layouts are compared to the Park model and an empirical-analytical Gaussian wake model. Results show that the physics-guided machine-learning models outperform both physics-based models showing a high degree of generalizability, and the machine is not sensitive to the choice of the physics-based guide model. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning