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arXiv 2025年

作者： Gao, Yuan Wu, Hao Shu, Ruiqi Dong, Huanshuo Xu, Fan Chen, Rui Yan, Yibo Wen, Qingsong Hu, Xuming Wang, Kun Wu, Jiahao Li, Qing Xiong, Hui Huang, Xiaomeng Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University China TEG Tencent China Department and Computer and Science University of Science and Technology of China China Institute for Interdisciplinary Information Sciences Tsinghua University China Department of Computer Science and Engineering The HongKong University of Science and Technology Hong Kong AI Thrust The Hong Kong University of Science and Technology Guangzhou China Squirrel Ai Learning China School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computing The Hong Kong Polytechnic University Hong Kong

Accurate weather forecasts are important for disaster prevention, agricultural planning, and water resource management. Traditional numerical weather prediction (NWP) methods offer physically interpretable high-accuracy predictions but are computationally expensive and fail to fully leverage rapidly growing historical data. In recent years, deep learning methods have made significant progress in weather forecasting, but challenges remain, such as balancing global and regional high-resolution forecasts, excessive smoothing in extreme event predictions, and insufficient dynamic system modeling. To address these issues, this paper proposes a global-regional nested weather forecasting framework based on graph neural networks (GNNs). By combining a dynamic system perspective with multi-grid theory, we construct a multi-scale graph structure and densify the target region to capture local high-frequency features. We introduce an adaptive information propagation mechanism, using dynamic gating units to deeply integrate node and edge features for more accurate extreme event forecasting. For high-resolution regional forecasts, we propose a neural nested grid method to mitigate boundary information loss. Experimental results show that the proposed method performs excellently across global to regional scales and short-term to long-term forecasts, especially in extreme event predictions (e.g., typhoons), significantly improving forecast accuracy. Our codes are available at https://***/YuanGao-YG/OneForecast. Copyright © 2025, The Authors. All rights reserved.

关键词： Weather forecasting

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A 3D Memristive Cubic Map with Dual Discrete Memristors: Design, Implementation, and Application in Image Encryption

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IEEE Transactions on Circuits and systems for Video technology 2025年

作者： Gao, Suo Iu, Herbert Ho-Ching Erkan, Ugur Simsek, Cemaleddin Toktas, Abdurrahim Cao, Yinghong Wu, Rui Mou, Jun Li, Qi Wang, Chunpeng Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering Perth6009 Australia Faculty of Engineering Electrical and Electronics Engineering Respectively Karamanoglu Mehmetbey University Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system's potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme for image encryption with the *** results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks. © 1991-2012 IEEE.

关键词： Bifurcation (mathematics)

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A Novel Image Recognition Method Based on CNN Algorithm 5th

A Novel Image Recognition Method Based on CNN Algorithm

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5th International Conference on Smart Computing and Communication, SmartCom 2020

作者： Guo, Yuwen Zhang, Xin Yang, Qi Guo, Hong College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Beijing China

ISBN: (纸本)9783030747169

Handwritten digital image recognition is a special problem in digital recognition, which is much more difficult due to uncertainties such as writer and writing environment. But the accuracy of handwritten digital recognition is particularly important in some critical areas such as public security and finance. In this paper, we used machine learning and deep learning technology, to identify MNIST handwritten digital data sets. And the support vector machine and convolutional neural network were used to image recognition, among them, the support vector machine compares the accuracy of the identification of the two kernel functions. Convolutional neural networks are improved based on the classic LeNet-5 model. The Experiments result shown that the identification accuracy of Gaussian kernel function model and linear kernel function model is 94.03% and 90.47% for support vector machines, respectively. In contrast, Gaussian kernel function performs better, but the overall accuracy is low. The best performance is based on the classic convolutional neural network LeNet-5 improved model, the test set recognition rate can reach 99.05%. © 2021, Springer Nature Switzerland AG.

关键词： Support vector machines

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PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimization

arXiv

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arXiv 2024年

作者： Ye, Yuyang Tang, Lu-An Wang, Haoyu Yu, Runlong Yu, Wenchao He, Erhu Chen, Haifeng Xiong, Hui Department of Management Science and Information Systems Rutgers Business School Rutgers University NewarkNJ United States Department of Data Science and System Security NEC Laboratories PrincetonNJ United States Department of Computer Science University of Pittsburgh PittsburghPA United States Department of Computer Science and Engineering The Hong Kong University of Science and Technology Guangzhou China

Achieving carbon neutrality within industrial operations has become increasingly imperative for sustainable development. It is both a significant challenge and a key opportunity for operational optimization in industry 4.0. In recent years, Deep Reinforcement Learning (DRL) based methods offer promising enhancements for sequential optimization processes and can be used for reducing carbon emissions. However, existing DRL methods need a pre-defined reward function to assess the impact of each action on the final sustainable development goals (SDG). In many real applications, such a reward function cannot be given in advance. To address the problem, this study proposes a Performance based Adversarial Imitation Learning (PAIL) engine. It is a novel method to acquire optimal operational policies for carbon neutrality without any pre-defined action rewards. Specifically, PAIL employs a Transformer-based policy generator to encode historical information and predict following actions within a multi-dimensional space. The entire action sequence will be iteratively updated by an environmental simulator. Then PAIL uses a discriminator to minimize the discrepancy between generated sequences and real-world samples of high SDG. In parallel, a Q-learning framework based performance estimator is designed to estimate the impact of each action on SDG. Based on these estimations, PAIL refines generated policies with the rewards from both discriminator and performance estimator. PAIL is evaluated on multiple real-world application cases and datasets. The experiment results demonstrate the effectiveness of PAIL comparing to other state-of-the-art baselines. In addition, PAIL offers meaningful interpretability for the optimization in carbon neutrality. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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Multi-View Incongruity Learning for Multimodal Sarcasm Detection

arXiv

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arXiv 2024年

作者： Guo, Diandian Cao, Cong Yuan, Fangfang Liu, Yanbing Zeng, Guangjie Yu, Xiaoyan Peng, Hao Yu, Philip S. Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science University of Illinois Chicago United States

Multimodal sarcasm detection (MSD) is essential for various downstream tasks. Existing MSD methods tend to rely on spurious correlations. These methods often mistakenly prioritize non-essential features yet still make correct predictions, demonstrating poor generalizability beyond training environments. Regarding this phenomenon, this paper undertakes several initiatives. Firstly, we identify two primary causes that lead to the reliance of spurious correlations. Secondly, we address these challenges by proposing a novel method that integrate Multimodal Incongruities via Contrastive Learning (MICL) for multimodal sarcasm detection. Specifically, we first leverage incongruity to drive multi-view learning from three views: token-patch, entity-object, and sentiment. Then, we introduce extensive data augmentation to mitigate the biased learning of the textual modality. Additionally, we construct a test set, SPMSD, which consists potential spurious correlations to evaluate the the model’s generalizability. Experimental results demonstrate the superiority of MICL on benchmark datasets, along with the analyses showcasing MICL’s advancement in mitigating the effect of spurious correlation. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Robust Phase Retrieval from Quantized and Noisy Measurements

Robust Phase Retrieval from Quantized and Noisy Measurements

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Yihong Gao Luteng Zhu Zhuoyang An Mingjie Shao Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong SAR China School of Information Science and Engineering Shandong University Qingdao China Computer School Beijing Information Science and Technology University Beijing China Key Laboratory of System and Control AMSS Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331519315

ISBN: (纸本)9798331519322

The phase retrieval (PR) problem involves recovering a signal from amplitude-only measurements and has been extensively studied in various physical measurement and signal processing systems. Recently, there has been increasing interest in the quantized phase retrieval (QPR) problem, where the observed amplitude is quantized to binary values. The existing studies have focused on the noiseless case. In this paper, we address a robust QPR problem that accounts for both quantization and noise. We formulate the robust QPR problem using maximum-likelihood estimation (MLE), which results in a non-convex and challenging optimization problem due to the complexity of its likelihood function. To obtain a high-quality solution, we develop an expectation-maximization (EM) algorithm combined with the Wirtinger flow, allowing us to convert the problem into a more manageable form at each iteration. Our method is implemented on both randomly generated synthetic data and image reconstruction tasks. The results demonstrate that our approach significantly enhances signal recovery performance under the combined effects of quantization and noise. Additionally, the proposed method exhibits better runtime efficiency compared to existing approaches.

关键词： Maximum likelihood estimation Phase measurement Quantization (signal) Runtime Noise Signal processing algorithms Noise measurement Speech processing Optimization Synthetic data

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Domain decomposition based on the spectral element method for frequency-domain computational elastodynamics

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science China Earth sciences 2021年第3期64卷 388-403页

作者： Linlin SHI Mingwei ZHUANG Yuanguo ZHOU Na LIU Qinghuo LIU Institute of Electromagnetics and Acoustics and Key Laboratory of Electromagnetic Wave Science and Detection Technology Xiamen UniversityXiamen 361005China The Fifth Electronics Research Institute of Ministry of Industry and Information Technology Guangzhou 510610China College of Communication and Information Engineering Xi’an University of Science and TechnologyXi’an 710054China Department of Electrical and Computer Engineering Duke UniversityDurhamNC 27708USA

We propose a domain decomposition method based on the spectral element method(DDM-SEM)for elastic wave computation in frequency *** combines the high accuracy of the spectral element method and the high degree of parallelism of a domain decomposition technique,which makes this method suitable for accurate and efficient simulations of large scale problems in *** the DDM-SEM,the original large-scale problem is divided into a number of well designed *** use the spectral element method independently for each subdomain,and the neighboring subdomains are connected by a frequency-domain version of Riemann transmission condition(RTC)for elastic *** the proposed method,we can employ the non-conforming meshes and different interpolation orders in different subdomains to maximize the *** separating the internal and boundary unknowns of each subdomain,an efficient and naturally parallelizable block LDU direct solver is developed to solve the final system *** experiments verify its accuracy and efficiency,and show that the proposed DDM-SEM can be a promising numerical tool for accurately and effectively solving large and multi-scale problems of elastic *** is potentially valuable for the frequency domain seismic inversion where multiple source illuminations are required.

关键词： Elastic waves Domain decomposition Spectral element method Frequency domain Numerical simulations

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Physically Secure and Privacy-Preserving Authentication Scheme for VANET

Physically Secure and Privacy-Preserving Authentication Sche...

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Artificial Intelligence of Things and systems (AIoTSys), International Conference on

作者： Dongmei Chen Yangfan Liang Lihui Li Yining Liu School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing University Jiaxing China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou China

ISBN: (数字)9798331507886

ISBN: (纸本)9798331507893

The communication between vehicles and other entities occurs over open wireless networks, making them susceptible to various security attacks In Vehicular Ad Hoc Network (VANET). Consequently, many researchers have proposed numerous data transmission and key exchange protocols to ensure secure communication within VANET. The keys and sensitive information involved in these protocols need to be stored long-term in the tamper-resistant devices of vehicles. However, due to the limited resources of these devices, many encryption schemes are difficult to embed. Even if effective encryption schemes can be embedded in VANET, the long-term storage of data in vehicles still carries a significant risk of being attacked. To tackle this problem, we propose an authentication protocol designed for VANET that emphasizes both physical security and privacy protection. The protocol primarily leverages the properties of Physical Unclonable Functions to achieve enhanced security. This approach eliminates the need for vehicles to store keys and sensitive messages long-term, reducing the storage burden on vehicles while enhancing the system's security and efficiency. Detailed security analyses demonstrate that our proposed scheme achieves the goals of physical security and privacy protection. A comparison with similar approaches demonstrates that our solution outperforms them in both time efficiency and communication overhead.

关键词： Privacy Protocols Wireless networks Vehicular ad hoc networks Authentication Physical unclonable function Encryption security Protection Secure storage

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AUTOMATIC CHINESE NATIONAL PENTATONIC MODES RECOGNITION USING CONVOLUTIONAL NEURAL NETWORK 23

AUTOMATIC CHINESE NATIONAL PENTATONIC MODES RECOGNITION USIN...

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23rd International Society for Music information Retrieval Conference, ISMIR 2022

作者： Wang, Zhaowen Che, Mingjin Yang, Yue Meng, Wenwu Li, Qinyu Xia, Fan Li, Wei Department of Music AI and Information Technology Central Conservatory of Music China College of Experimental Art Sichuan Conservatory of Music China School of Computer Science and Technology Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China

ISBN: (纸本)9781732729926

Chinese national pentatonic modes, with five tones of Gong, Shang, Jue, Zhi and Yu as the core, play an essential role in traditional Chinese music culture. After the early twentieth century, with the development of new Chinese music, the ancient Chinese theory of scales gradually developed into a new pentatonic modes theory under the influence of western music. In this paper, we briefly introduce our self-built CNPM (Chinese National Pentatonic Modes) Dataset, then design residual convolutional neural network models to identify which TongGong system the mode belongs, the pitch of tonic, the mode pattern and the mode type from audio signals, in combination with musical domain knowledge. We use both single-task and multi-task models with three strategies for identification, and compare them with a simple template-based baseline method. In experiments, we use seven accuracy metrics to evaluate the models. The results on identifying both the tonic pitch and the pattern of mode correctly achieve an average accuracy of 69.65%. As an initial research on automatic Chinese national pentatonic modes recognition, this work will contribute to the development of multicultural music information retrieval, computational ethnomusicology and five-tone music therapy. © Z. Wang, M. Che, Y. Yang, W. Meng, Q. Li, F. Xia and W. Li.

关键词： Convolutional neural networks

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E2: Entropy Discrimination and Energy Optimization for Source-free Universal Domain Adaptation

E2: Entropy Discrimination and Energy Optimization for Sourc...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Meng Shen Andy J. Ma Pong C. Yuen School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Department of Computer Science Hong Kong Baptist University Hong Kong

Universal domain adaptation (UniDA) transfers knowledge under both distribution and category shifts. Most UniDA methods accessible to source-domain data during model adaptation may result in privacy policy violation and source-data transfer inefficiency. To address this issue, we propose a novel source-free UniDA method coupling confidence-guided entropy discrimination and likelihood-induced energy optimization. The entropy-based separation of target-known and unknown classes is too conservative for known-class prediction. Thus, we derive the confidence-guided entropy by scaling the normalized prediction score with the known-class confidence, that more known-class samples are correctly predicted. Due to difficult estimation of the marginal distribution without source-domain data, we constrain the target-domain marginal distribution by maximizing (minimizing) the known (unknown)-class likelihood, which equals free energy optimization. Theoretically, the overall optimization amounts to decreasing and increasing internal energy of known and unknown classes in physics, respectively. Extensive experiments demonstrate the superiority of the proposed method.

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