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Harnessing Graphene Plasmons by Accessing the Retardation Regime

ACS Applied Materials and Interfaces 2025年第22期17卷 32782-32789页

作者： Zhang, Jiasheng Xing, Qiaoxia Zhao, Tuoyu Wang, Shunjia Ma, Junwei Wang, Chong Wang, Jiajun Xie, Yuangang Huang, Shenyang Song, Chaoyu Wan, Quan Shi, Lei Tao, Zhensheng Shi, Wu Li, Xuesong Zhou, Lei Yan, Hugen State Key Laboratory of Surface Physics Key Laboratory of Micro- and Nano-Photonic Structures Ministry of Education Shanghai Key Laboratory of Metasurfaces for Light Manipulation Department of Physics Fudan University Shanghai 200433 China Inner Mongolia Key Laboratory of Microscale Physics and Atom Innovation School of Physical Science and Technology Inner Mongolia University Hohhot 010021 China State Key Laboratory of Surface Physics Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai 200433 China Zhangjiang Fudan International Innovation Center Fudan University Shanghai 201210 China Centre for Quantum Physics Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE) School of Physics Beijing Institute of Technology Beijing 100081 China Beijing Key Lab of Nanophotonics & Ultrafine Optoelectronic Systems School of Physics Beijing Institute of Technology Beijing 100081 China School of Integrated Circuit Science and Engineering Exemplary School of Microelectronics University of Electronic Science and Technology of China Chengdu 611731 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen 518110 China

Plasmons in graphene are highly tunable, mainly by leveraging the carrier density and Drude scattering. Here, we introduce another scheme, i.e., accessing the retardation regime (originated from the finite speed of light), to engineer the lifetime and dispersion of plasmons in graphene in the terahertz regime. We find that the retardation regime can be approached by reducing plasmon momentum in artificially stacked multilayer graphene systems with large Drude weight, and this can significantly increase the plasmon lifetime. In addition, an explicit theoretical model along with finite-element simulation was given, consistent with the experimental findings. Our work opens another avenue to manipulate terahertz plasmons in graphene. © 2025 American Chemical Society.

关键词： Drude weight graphene plasmon retardation effects terahertz

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ThicknesVAE: Learning a Latera Prior for Clothed Human Body Reconsuction

ThicknesVAE: Learning a Latera Prior for Clothed Human Body ...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wu, Xiaotao Fan, Zhaoxin He, Huiguang Shen, Dinggang School of Biomedical Engineering State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai201210 China NeuBCI Group State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology Institute of Automation Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Institute of Artificial Intelligence Beihang University Beijing100191 China Beijing Academy of Blockchain and Edge Computing China Shanghai United Imaging Intelligence Co. Ltd. Shanghai200232 China Shanghai Clnical Research and Trial Center Shanghai201210 China

ISBN: (纸本)9798350368741

Sandwich-like structures have shown remarkable efficacy in clothed human reconstruction. However, these approaches often generate unrealistic side geometries due to inadequate handling of lateral regions. This paper addresses this limitation by incorporating the side geometry of clothed humans as a prior. We propose ThicknessVAE, a novel two-stage method that makes two key contributions: (1) We learn a prototype from point clouds for the lateral regions of clothed humans to extract common and detailed geometric features. (2) We utilize this prototype as a prior to transform geometric features into a thickness map associated with clothed human images, enabling refined normal integration for sandwich-like reconstruction methods. By seamlessly integrating our model into the sandwich-like reconstruction pipeline, we achieve highly realistic side views. Both qualitative and quantitative experiments demonstrate that our approach is comparable to state-of-the-art methods in terms of side-view realism. © 2025 IEEE.

关键词： 3D Human Reconsruction Normal Integraton Prior Learning

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Extraction of Secret Message Based on Uniformly Most Powerful Test

SSRN

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SSRN 2022年

作者： Du, Hansong Liu, Jiufen Luo, Xiangyang Zhang, Yi The State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

As the ultimate goal of steganalysis, secret message extraction is a bottleneck and difficulty that has long plagued the development of steganalysis technology. Existing pioneering work on secret message extraction for adaptive steganography based on STC (Syndrome-Trellis Codes): the method based on randomness test under plaintext embedding may misjudge incorrect steganographic key as correct steganographic key, resulting in the failure of extraction. For this reason, a method for extracting message with 100% accuracy for plaintext embedding is proposed in this manuscript. First, by analyzing the probability distribution of stego images, a theorem is given: For STC-based adaptive steganography algorithm, the sequences extracted by correct and incorrect steganographic keys are statistically different. Then based on this theorem, a uniformly most powerful test model is designed to recover the correct steganographic key. Finally, given the probability of type I and II errors, the sample size and threshold in the hypothesis test are derived. Classic adaptive steganography such as HUGO (Highly Undetectable Steganography) and J-UNIWARD (JPEG Universal Wavelet Relative Distortion) have been conducted experiment, showing that the proposed method can extract message with 100% accuracy and 44 bits sample size, which verifies the correctness of the theorem and the effectiveness of the method. © 2022, The Authors. All rights reserved.

关键词： Steganography

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A Novel Feature Selection Strategy Based on Salp Swarm Algorithm for Plant Disease Detection

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Plant Phenomics 2023年第2期5卷 209-225页

作者： Xiaojun Xie Fei Xia Yufeng Wu Shouyang Liu Ke Yan Huanliang Xu Zhiwei Ji College of Artificial Intelligence Nanjing Agricultural UniversityNanjingJiangsu 210095China Center for Data Science and Intelligent Computing Nanjing Agricultural UniversityNanjingJiangsu 210095China State Key Laboratory for Crop Genetics and Germplasm Enhancement Bioinformatics CenterAcademy for Advanced Interdisciplinary StudiesNanjing Agricultural UniversityNanjingJiangsu 210095China Academy for Advanced Interdisciplinary Studies Nanjing Agricultural UniversityNanjingJiangsu 210095China Department of the Built Environment College of Design and EngineeringNational University of Singapore4 Architecture DriveSingapore 117566Singapore

Deep learning has been widely used for plant disease recognition in smart agriculture and has proven to be a powerful tool for image classification and pattern ***,it has limited interpretability for deep *** the transfer of expert knowledge,handcrafted features provide a new way for personalized diagnosis of plant ***,irrelevant and redundant features lead to high *** this study,we proposed a swarm intelligence algorithm for feature selection[salp swarm algorithm for feature selection(SSAFS)]in image-based plant disease *** is employed to determine the ideal combination of handcrafted features to maximize classification success while minimizing the number of *** verify the effectiveness of the developed SSAFS algorithm,we conducted experimental studies using SSAFS and 5 metaheuristic *** evaluation metrics were used to evaluate and analyze the performance of these methods on 4 datasets from the UCI machine learning repository and 6 plant phenomics datasets from *** results and statistical analyses validated the outstanding performance of SSAFS compared to existing state-of-the-art algorithms,confirming the superiority of SSAFS in exploring the feature space and identifying the most valuable features for diseased plant image *** computational tool will allow us to explore an optimal combination of handcrafted features to improve plant disease recognition accuracy and processing time.

关键词： Plant image redundant

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spotFuzzer: Static Instrument and Fuzzing Windows COTs

arXiv

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arXiv 2022年

作者： Gu, Yeming Shu, Hui Ma, Rongkuan Yan, Lin Zhu, Lei State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450000 China

The security research on Windows has received little attention in the academic circle. Most of the new methods are usually designed for Linux system, and are difficult to transplant to Windows. Fuzzing for Windows programs always suffering from its closed source. Therefore, we need to find an appropriate way to achieve feedback from Windows programs. To our knowledge, there are no stable and scalable static instrumentation tools for Windows yet, and dynamic tools, such as DynamoRIO, have been criticized for their performance. To make matters worse, dynamic instrumentation tools have very limited usage scenarios and are impotent for many system services or large commercial software. In this paper, we proposed spotInstr, a novel static tool for instrumenting Windows binaries. It is lightweight and can instrument most Windows PE programs in a very short time. At the same time, spotInstr provides a set of filters, which can be used to select instrumentation points or restrict the target regions. Based on these filters, we propose a novel memory-sensitive instrumentation method which can speed up both instrumentation and fuzzing. After that, we design a system called spotFuzzer, which leverage the ability of spotInstr and can fuzz most Windows binaries. We tested spotInstr and spotFuzzer in multiple dimensions to show their superior performance and stability. Copyright © 2022, The Authors. All rights reserved.

关键词： Computer operating systems

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A Graph Reasoning Model Based on Multiple Types of Nodes 7

A Graph Reasoning Model Based on Multiple Types of Nodes

引用

7th International Conference on Big Data and Information Analytics, BigDIA 2021

作者： Liu, Yibing Huo, Lijun Zhou, Gang Information Engineering University State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China National University of Defense Technology Key Laboratory of Information System Engineering Changsha China

ISBN: (纸本)9781665424660

Multi-hop machine reading comprehension (MRC) depends on the model to answer a semantically related question based on the given supporting text. In this study, inspired by previous research about Graph Neural Network(GNN), we develop a new graph covering three kinds of nodes: Sentence, Entity, Candidate. A corresponding MRC method based on graph inference is proposed, called SEC reasoner. The advantage of SECr is that it adopts different granular information as graph nodes, which can execute a more comprehensive reasoning. Our proposed model first initialize the node representations by co-Attention, multi-head attention and self-Attention based on context embedding. SECr then employs GNN based information transfer algorithms to collect evidences from the node encoding, and calculates a score for choosing the answer. In experiments, we evaluate the model on two popular benchmark datasets-WikiHop and MedHop. SECr reachs accuracy rates of 71.6%, 63.1% respectively, which is a competitive result. In addition, a comprehensive analysis proves the effectiveness and interpretability of the SECr. © 2021 IEEE.

关键词： Semantics

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A Cross-Platform Instant Messaging User Association Method Based on Spatio-temporal Trajectory 8th

A Cross-Platform Instant Messaging User Association Method B...

引用

8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Zhou, Pei Luo, Xiangyang Du, Shaoyong Li, Lingling Yang, Yang Liu, Fenlin School of Cyber Science and Engineering Zhengzhou University Zhengzhou450000 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450000 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Intelligent Engineering Zhengzhou University of Aeronautics Zhengzhou450000 China School of Computing and Information Systems Singapore Management University Singapore188065 Singapore

ISBN: (纸本)9783031067600

The current research on cross-platform instant messaging user association is mainly divided into two categories: based on user attributes and based on user behavior. Methods based on user attributes mainly identify users through multiple attributes such as user name and multi-platform user information association based on cell phone number, but user association is not possible when multi-platform user information is inconsistent and users do not grant their address book permissions. Methods based on user behavior mainly calculate the similarity between user trajectories features such as geographic location frequency and co-occurrence, but this method lacks the user’s information, which leads to the inability to fully excavate the sequential features of the trajectory and affects the accuracy of trajectory matching. In order to solve this problem, this paper proposes a cross-platform instant messaging user association algorithm based on temporal trajectories (CPTrajst). We firstly place probes in the area where the target may appear so as to obtain user information, gets user trajectory, then processes the trajectory and performs two trajectory matches, finally associate users of different platforms whose trajectories match, thus increasing the accuracy and reliability of cross-platform instant messaging user association. We conducts specific experiments for users of WeChat and Momo, the most commonly used instant messengers in China. The results show that we can achieve reliable association for these two types of instant messaging users and the user association accuracy can reach 99.5%, which is better than the existing user association algorithms based on trajectory matching. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Trajectories

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Preparation and Simulation of Quantum states of Matrices 22

Preparation and Simulation of Quantum States of Matrices

引用

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer engineering

作者： Zhengyu Liu Xiaonan Liu Haoshan Xie Chenyan Zhao School of Computer and Artificial Intelligence of Zhengzhou University China State Key Laboratory of Mathematical Engineering and advanced computing Information Engineering University China

ISBN: (纸本)9781450397148

A basic problem of quantum computing is how to effectively express classical data in quantum systems. This problem is called state preparation problem, and the process of preparing quantum states is called coding. In this paper, the classical data of matrix is transformed into quantum states in the form of amplitude coding and simulated. This paper first introduces the detailed process of matrix transformation into the linear combination of quantum ground state, then designs a quantum circuit model to realize amplitude coding according to the idea of binary tree model and quantum random walk algorithm, and finally simulates the three matrices on IBM quantum cloud platform, and analyzes and introduces many situations. The experimental results verify the feasibility of converting the matrix into quantum states by amplitude coding, analyze the causes of errors in the experimental process, and put forward the corresponding solutions. This paper introduces in detail the whole process of matrix conversion to quantum states and the construction of quantum circuits for simulation. Compared with the existing experiments, the quantum circuits constructed in this paper are shorter in length, less in number of quantum gates, and more versatile.

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Circuit Simulation and Optimization of Quantum Search Algorithm 22

Circuit Simulation and Optimization of Quantum Search Algori...

引用

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer engineering

作者： Xiaonan Liu Chenyan Zhao Haoshan Xie Zhengyu Liu State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University China School of Computer and Artificial Intelligence ZhengZhou University China

ISBN: (纸本)9781450397148

At present, the scale of quantum computers in the real sense is still small, and quantum simulation has become one of the important ways of quantum theory research, grover quantum search algorithm is suitable for the search problem of disordered database. Firstly, according to the implementation principle of Grover algorithm and Boolean logic relationship, the design idea of multi-objective Oracle is analyzed, based on IBMQ quantum cloud platform, the quantum circuit of Grover algorithm with multi-objective items is simulated. Based on the characteristics of Grover algorithm and the simulation process of quantum gate, the action of multiple identical quantum gates is combined to reduce the update times of probability amplitude and improve the simulation efficiency. The libquantum quantum simulator is used for experiments and the target item is successfully searched, which proves the feasibility of the optimization method and provides reference for the simulation and optimization of other quantum algorithms.

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Expert with Outlier Exposure for Continual Learning of New Diseases

Expert with Outlier Exposure for Continual Learning of New D...

引用

2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Xu, Zhengjing Chen, Kanghao Zheng, Wei-Shi Tan, Zhijun Yang, Xiaobo Wang, Ruixuan Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing MOE China State Key Laboratory of Dampness Syndrome of Chinese Medicine Guangzhou China Peng Cheng Laboratory Department of Network Intelligence Shenzhen China

ISBN: (纸本)9781665468190

Current intelligent diagnosis systems struggle to continually learn to diagnose more and more diseases due to catastrophic forgetting of old knowledge when learning new knowledge. Although storing small old data for subsequent continual learning can effectively help alleviate the forgetting issue, the heavy data imbalance between old classes and to-be-learned new classes in classifier training often causes biased prediction towards the new classes just learned by the updated classifier. In this study, an outlier detection technique is novelly applied to train an additional expert classifier for new classes to help alleviate the class imbalance issue and discriminate the learned new classes from old classes during inference (instead of the training phase). Specially, the stored small data of old classes are considered as outliers during training the expert classifier, such that the output probability distributions from the expert classifier are expected to be obviously different between test data of the old classes and those of the new classes. Such difference between old classes and new classes can be used to fine-tune the original output from the updated classifier which is responsible for prediction of all learned (old and new) classes. During inference, a novel ensemble strategy is proposed to combine the predictions from the updated classifier, the expert classifier, and the previously learned old classifier. The proposed learning and inference framework can be easily combined with existing continual learning strategies. Empirical evaluations on three medical image datasets and one natural image dataset show that the proposed framework can effectively improve continual learning performance. © 2022 IEEE.

关键词： Forecasting

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