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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Yueqing Zhang, Peng Liu, Yushuang Zhao, Jianing Lin, Jie Chen, Yi State Key Laboratory of Aerodynamics Sichuan China Computational Aerodynamics Institute China Aerodynamics Research and Development Center Sichuan China Sichuan Tianfu Fluid Big Data Research Center Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)157735897X

Aerodynamic coefficient prediction is pivotal in aircraft and vehicles’ design, performance evaluation, and motion control. Integrating artificial neural networks into aerodynamic coefficient prediction offers a promising alternative to traditional numerical methods burdened by extensive computations and high costs. Nevertheless, this data-driven approach faces several critical challenges, which limit its further performance enhancement: i) The current research lacks a profound understanding of the complex interplay between the shape of an object and its aerodynamic characteristics. ii) The scarcity of high-quality aerodynamic data poses a significant barrier. The models trained on limited datasets lack generalization ability, struggling to accurately predict and adapt to diverse aerodynamic performance under new shapes or conditions. To overcome these challenges, we introduce an innovative framework that employs cross-attention to capture the intimate interplay between shape and flow conditions and allows for the direct utilization of pre-trained models on general shape datasets to mitigate the scarcity of aerodynamic data. Furthermore, to bolster the inference capabilities of this data-driven approach, we integrate physical information constraints into the model, leveraging them as guiding principles to enhance the model’s predictive power under unknown conditions. Experimental validation demonstrates that our proposed method performs excellently in multiple aerodynamic prediction tasks. This achievement brings a new technological breakthrough to the field of aerodynamic prediction and provides robust support for the design optimization of complex systems such as aircraft and vehicles. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Technological forecasting

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PERSON RETRIEVAL IN PHYSICAL WORLD

PERSON RETRIEVAL IN PHYSICAL WORLD

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Huang, Wenxin Li, Dongyang Hu, Ruimin Liang, Chao Zhong, Xian School of Computer and Information Engineering Hubei University China National Engineering Research Center for Multimedia Software Wuhan University China Research Institute of Wuhan University Shenzhen China School of Computer Science and Technology Wuhan University of Technology China

ISBN: (纸本)9781665438643

Person re-identification (re-ID) gains plenty of achievements as a retrieval problem in constrained camera networks. However, most of the researches are concentrated on visual appearance, they still suffer from the complicated environments in unconstrained urban/campus surveillance scenario due to unreliable visual representations with extremely challenging problems as lack of training samples, amounts of irrelevant crowds, etc. Besides, most of the existing person re-ID datasets neglect the physical truth of realistic investigation application: 1) investigators search only few suspects among amounts of crowds. Moreover, he may not go through by every camera in the surveillance area and may appear in the same camera several times;and 2) the corresponding characteristic in multi-space of the same ID can be verified with each other. Therefore, we propose a person retrieval in physical world (PRPW) dataset with large-scale unconstrained surveillance scenario. It contains over 1.4 million bounding boxes, including 20 labeled IDs and numerous irrelevant crowds captured by 86 cameras. Furthermore, over 30,000 records of 20 mobile trajectories are collected in this dataset, and the 20 mobile trajectories are partially overlapped while passing by 86 cameras. Finally, based on two common senses and a verification experiment, we provide a proposal to tackle with PRPW task on the basis of trajectory association which utilizes global optimization to compensate for the errors caused by visual expression on local observation points. The comparison experiments with two typical unsupervised person re-ID methods are implemented on the constructed dataset. © 2021 IEEE

关键词： Cameras

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A Surveillance System Utilizing mmWave-FMCW Radar for Restricted Control Area

A Surveillance System Utilizing mmWave-FMCW Radar for Restri...

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International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON

作者： Nattakarn Shutimarrungson Seksan Eiadkaew Akkarat Boonpoonga Krit Athikulwongse Suramate Chalermwisutkul Department of Business Computer Mahasarakham Business School Mahasarakham University Maha Sarakham Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering Research Center of Innovation Digital and Electromagnetic Technology (iDEMT) King Mongkut's University of Technology North Bangkok Bangkok Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathumthani Thailand Communication and Smart System Engineering The Sirindhorn International Thai- German Graduate School of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

In this paper, we introduce a surveillance system utilizing mmWave-FMCW radar designed for restricted control areas. The experiment, conducted in our laboratory, defines the restricted control area parameters. Employing the TI-IWR6843AOPEVM model, operating at 60–64 GHz, and providing point cloud and Doppler information, we assumed walking paths for testing, partitioning scenarios into four distinct cases to simulate real-world scenarios. System performance evaluation, assessed through confusion matrices, demonstrates an achieved accuracy of up to 76.9%. Notably, errors are mainly caused by multipath effects due to static objects within the testing environment.

关键词： Point cloud compression Measurement Accuracy Surveillance System performance Radar tracking Doppler radar

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SSAN: A Symbol Spatial-Aware Network for Handwritten Mathematical Expression Recognition 39

SSAN: A Symbol Spatial-Aware Network for Handwritten Mathema...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Haoran Su, Xiangdong Zhou, Xingxiang Gao, Guanglai College of Computer Science Inner Mongolia University China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology China

ISBN: (纸本)157735897X

The great challenge of handwritten mathematical expression recognition (HMER) is the complex structures of the expressions, which are directly related to the symbol spatial positions. Existing HMER methods typically employ attention mechanisms in the decoder of their models to implicitly perceive the symbol positions, or employ symbol counting and tree-based strategies to model the symbol spatial relation. However, these methods still cannot effectively capture the structural information of formulas, thus negatively impacting the symbol decoding in HMER. To deal with this problem and enhance the HMER performance, this paper proposes a novel auxiliary task, namely predicting the symbol spatial distribution map of handwritten expression images. On such basis, this paper designs a symbol spatial-aware network (SSAN) for this task, which is jointly optimized with the HMER model. Specifically, considering the similarity of the symbol spatial positions between the handwritten mathematical expression images and their corresponding printed templates, we obtain the symbol spatial distribution map by first generating printed templates from LaTeX ground-truth for handwritten formula images and then replacing the connected components of printed templates with 2D Gaussian distribution maps of the same size. Meanwhile, due to the loose alignment of the symbol spatial positions between handwritten and printed formula images, and misclassification of similar symbols, we further propose a coarse-to-fine alignment strategy and an attention-guided symbol masking strategy in SSAN to tackle these issues. Extensive experiments demonstrate that SSAN significantly improves the recognition performance of the HMER models, and the proposed auxiliary tasks are more effective in enhancing HMER performance than existing auxiliary tasks. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Population distribution

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Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability

Why Does Little Robustness Help? A Further Step Towards Unde...

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IEEE Symposium on Security and Privacy

作者： Yechao Zhang Shengshan Hu Leo Yu Zhang Junyu Shi Minghui Li Xiaogeng Liu Wei Wan Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University School of Software Engineering Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology

ISBN: (数字)9798350331301

ISBN: (纸本)9798350331318

Adversarial examples for deep neural networks (DNNs) are transferable: examples that successfully fool one white-box surrogate model can also deceive other black-box models with different architectures. Although a bunch of empirical studies have provided guidance on generating highly transferable adversarial examples, many of these findings fail to be well explained and even lead to confusing or inconsistent advice for practical *** this paper, we take a further step towards understanding adversarial transferability, with a particular focus on surrogate aspects. Starting from the intriguing "little robustness" phenomenon, where models adversarially trained with mildly perturbed adversarial samples can serve as better surrogates for transfer attacks, we attribute it to a trade-off between two dominant factors: model smoothness and gradient similarity. Our research focuses on their joint effects on transferability, rather than demonstrating the separate relationships alone. Through a combination of theoretical and empirical analyses, we hypothesize that the data distribution shift induced by off-manifold samples in adversarial training is the reason that impairs gradient *** on these insights, we further explore the impacts of prevalent data augmentation and gradient regularization on transferability and analyze how the trade-off manifests in various training methods, thus building a comprehensive blueprint for the regulation mechanisms behind transferability. Finally, we provide a general route for constructing superior surrogates to boost transferability, which optimizes both model smoothness and gradient similarity simultaneously, e.g., the combination of input gradient regularization and sharpness-aware minimization (SAM), validated by extensive experiments. In summary, we call for attention to the united impacts of these two factors for launching effective transfer attacks, rather than optimizing one while ignoring the other, and emphasize the

关键词： Training Privacy Buildings Closed box Minimization Data augmentation Robustness

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Source-free Semantic Regularization Learning for Semi-supervised Domain Adaptation

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IEEE Transactions on Multimedia 2025年

作者： Huang, Xinyang Zhu, Chuang Ren, Ruiying Liu, Shengjie Huang, Tiejun Beijing University of Posts and Telecommunications School of Artificial Intelligence Beijing China Peking University BAAI and Institute for Artificial Intelligence Beijing China Peking University National Engineering Research Center of Visual Technology School of Computer Science China

Semi-supervised domain adaptation (SSDA) has been extensively researched due to its ability to improve classification performance and generalization ability of models by using a small amount of labeled data on the target domain. However, existing methods cannot effectively adapt to the target domain due to difficulty in fully learning rich and complex target semantic information and relationships. In this paper, we propose a novel SSDA learning framework called semantic regularization learning (SERL), which captures the target semantic information from multiple perspectives of regularization learning to achieve adaptive fine-tuning of the source pre-trained model on the target domain. SERL includes three robust semantic regularization techniques. Firstly, semantic probability contrastive regularization (SPCR) helps the model learn more discriminative feature representations from a probabilistic perspective, using semantic information on the target domain to understand the similarities and differences between samples. Additionally, adaptive weights in SPCR can help the model learn the semantic distribution correctly through the probabilities of different samples. To further comprehensively understand the target semantic distribution, we introduce hard-sample mixup regularization (HMR), which uses easy samples as guidance to mine the latent target knowledge contained in hard samples, thereby learning more complete and complex target semantic knowledge. Finally, target prediction regularization (TPR) regularizes the target predictions of the model by maximizing the correlation between the current prediction and the past learned objective, thereby mitigating the misleading of semantic information caused by erroneous pseudo-labels. Extensive experiments on three benchmark datasets demonstrate that our SERL method achieves state-of-the-art performance. The code for our method will be available at https://***/bupt-ai-cz/SERL. © 2025 IEEE.

关键词： Latent semantic analysis

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An Optimized Variational Bayesian Point Set Matching Approach using Coordinate Ascent and Simulated Annealing

An Optimized Variational Bayesian Point Set Matching Approac...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Fangjun Luan Shitong Liu Shuai Yuan Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang China School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

Aiming at the interference problem of outliers in point set matching, we propose an optimized variational Bayesian point set matching algorithm using coordinate ascent and simulated annealing. For the complex terms in the objective function, we introduce the coordinate ascending strategy and use the Monte Carlo integral stochastic approximation and control variable method to directly optimize the parameters. To enhance the generalization ability of the algorithm and avoid falling into the local optimal solution, we use the simulated annealing scheme to optimize the spatial mapping of the point set. Finally, the affine transform point set matching experiment and the non-rigid point set matching experiment represent our algorithm have a good matching effect and high accuracy.

关键词： Monte Carlo methods Annealing Integral equations Simulated annealing Transforms Interference Approximation algorithms

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A Different Approach Design for Higher-Order Highly Selective Wideband Bandpass Filters Using Cascaded Building Blocks

A Different Approach Design for Higher-Order Highly Selectiv...

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Asia-Pacific Conference on Microwave

作者： Adnan Nadeem Nosherwan Shoaib Symeon Nikolaou Dimitra Psychogiou Photos Vryonides Department of Electrical Engineering Frederick University and Frederick Research Center Nicosia Cyprus School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan School of Engineering University College Cork Tyndall National Institute Cork Ireland

ISBN: (数字)9798350363548

ISBN: (纸本)9798350363555

This paper presents a novel design approach for creating higher-order wideband bandpass filters (BPFs). The technique builds on a conventional filter topology, featuring shunt short-circuited stubs connected by transmission lines. In this method, the traditional $\lambda_{\mathrm{g}}/4$ interconnecting lines and $\lambda_{\mathrm{g}}/4$ short-circuited stubs are replaced by a building block comprising $\lambda_{\mathrm{g}}/8$ lines separated by $\lambda_{\mathrm{g}}/8$ short-circuited coupled-line sections. To enhance selectivity, $)_{\vee \mathrm{g}}/2$ open-and short-circuited coupled-line segments are used at the filter's input and output. The paper provides design guidelines and formulas through ABCD analysis for practical filter implementations. Additionally, a design example is demonstrated, along with its practical validation through the manufacturing and testing of a fifth-order microstrip-type BPF.

关键词： Band-pass filters Microwave measurement Power transmission lines Prototypes Transmission line measurements Reflection Topology Microwave filters Wideband Testing

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Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples

Securely Fine-tuning Pre-trained Encoders Against Adversaria...

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IEEE Symposium on Security and Privacy

作者： Ziqi Zhou Minghui Li Wei Liu Shengshan Hu Yechao Zhang Wei Wan Lulu Xue Leo Yu Zhang Dezhong Yao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University

ISBN: (数字)9798350331301

ISBN: (纸本)9798350331318

With the evolution of self-supervised learning, the pre-training paradigm has emerged as a predominant solution within the deep learning landscape. Model providers furnish pre-trained encoders designed to function as versatile feature extractors, enabling downstream users to harness the benefits of expansive models with minimal effort through fine-tuning. Nevertheless, recent works have exposed a vulnerability in pre-trained encoders, highlighting their susceptibility to downstream-agnostic adversarial examples (DAEs) meticulously crafted by attackers. The lingering question pertains to the feasibility of fortifying the robustness of downstream models against DAEs, particularly in scenarios where the pre-trained encoders are publicly accessible to the *** this paper, we initially delve into existing defensive mechanisms against adversarial examples within the pre-training paradigm. Our findings reveal that the failure of current defenses stems from the domain shift between pre-training data and downstream tasks, as well as the sensitivity of encoder parameters. In response to these challenges, we propose Genetic Evolution-Nurtured Adversarial Fine-tuning (Gen-AF), a two-stage adversarial fine-tuning approach aimed at enhancing the robustness of downstream models. Gen-AF employs a genetic-directed dual-track adversarial fine-tuning strategy in its first stage to effectively inherit the pre-trained encoder. This involves optimizing the pre-trained encoder and classifier separately while incorporating genetic regularization to preserve the model’s topology. In the second stage, Gen-AF assesses the robust sensitivity of each layer and creates a dictionary, based on which the top-k robust redundant layers are selected with the remaining layers held fixed. Upon this foundation, we conduct evolutionary adaptability fine-tuning to further enhance the model’s generalizability. Our extensive experiments, conducted across ten self-supervised training methods and six d

关键词： Training Adaptation models Sensitivity Accuracy Self-supervised learning Genetics Feature extraction

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Asymmetric Electrode Structure Induces Dual-Channel Phenomenon Under Hot-Carrier Stress in Organic Thin-Film Transistors

IEEE Electron Device Letters

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IEEE Electron Device Letters 2023年第9期44卷 1496-1499页

作者： Tu, Yu-Fa Huang, Jen-Wei Chang, Ting-Chang Hung, Yang-Hao Lu, I-Nien Zhou, Kuan-Ju Sun, Li-Chuan Chen, Yu-An Wu, Chia-Chuan Hung, Wei-Chieh Lee, Jason Lien, Chen-Hsin The Institute of Electronics Engineering National Tsing Hua University Hsinchu30013 Taiwan Republic of China Military Academy The Department of Physics Kaohsiung83059 Taiwan National Sun Yat-sen University The Center of Crystal Research College of Semiconductor and Advanced Technology Research The Department of Physics Kaohsiung80424 Taiwan National Sun Yat-sen University The Department of Physics Kaohsiung80424 Taiwan National Sun Yat-sen University The Department of Photonics Kaohsiung80424 Taiwan National Sun Yat-sen University The Department of Materials and Optoelectronic Science Kaohsiung80424 Taiwan National Sun Yat-sen University The Department of Electrical Engineering Kaohsiung80424 Taiwan Boston University The Department of Computer Science BostonMA02215 United States

This study investigates the electrical mechanisms of the organic thin-film transistor (OTFT) with an asymmetric U-I electrode structure under hot-carrier stress (HCS). The threshold voltage shifts negatively and a dual-channel phenomenon occurs. In addition, the degradation behaviors of the linear and saturated ID-VG transfer curves are different. These are attributed to the non-uniform electric-field and heat distribution, which results in the non-uniform trapped holes in the organic-gate insulator-1 by charge trapping model during HCS. Finally, physical mechanisms based on COMSOL simulations and energy bands are proposed to clarify the degradation phenomena. © 1980-2012 IEEE.

关键词： Threshold voltage

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