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38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Pan, Jiadong Gao, Hongcheng Wu, Zongyu Hu, Taihang Su, Li Huang, Qingming Li, Liang Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS China University of Chinese Academy of Sciences China The Pennsylvania State University United States Nankai University China

Diffusion models (DMs) have demonstrated remarkable proficiency in producing images based on textual prompts. Numerous methods have been proposed to ensure these models generate safe images. Early methods attempt to incorporate safety filters into models to mitigate the risk of generating harmful images but such external filters do not inherently detoxify the model and can be easily bypassed. Hence, model unlearning and data cleaning are the most essential methods for maintaining the safety of models, given their impact on model parameters. However, malicious fine-tuning can still make models prone to generating harmful or undesirable images even with these methods. Inspired by the phenomenon of catastrophic forgetting, we propose a training policy using contrastive learning to increase the latent space distance between clean and harmful data distribution, thereby protecting models from being fine-tuned to generate harmful images due to forgetting. The experimental results demonstrate that our methods not only maintain clean image generation capabilities before malicious fine-tuning but also effectively prevent DMs from producing harmful images after malicious fine-tuning. Our method can also be combined with other safety methods to maintain their safety against malicious fine-tuning further. WARNING: This paper contains offensive images generated by models. © 2024 Neural information processing systems foundation. All rights reserved.

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Transverse Velocity Field Measurement of Solar High-resolution Images Based on Unsupervised Deep Learning

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Research in Astronomy and Astrophysics 2025年第3期25卷 236-248页

作者： Zhen-Hong Shang Long Chen Zhen-Ping Qiang Yi Bi Run-Xin Li Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology College of Big Data and Intelligent Engineering Southwest Forestry University Yunnan Observatories Chinese Academy of Sciences

Measuring the transverse velocity field in high-resolution solar images is essential for understanding solar *** paper introduces an innovative unsupervised deep learning optical flow model designed to calculate the transverse velocity field,addressing the challenges of missing optical flow labels and the limited accuracy of velocity field measurements in high-resolution solar *** proposed method converts the transverse velocity field computation problem into an optical flow computation problem,using two forward propagations of features to get rid of the reliance on optical flow ***,it reduces the impact of the“Brightness Consistency”constraint on optical flow accuracy by identifying and handling optical flow *** apply this method to compute the transverse velocity fields of high-resolution solar image sequences from the Hαand TiO bands,observed by the New Vacuum Solar *** experiments with several wellestablished optical flow methods,including those based on supervised deep learning models,show that our approach outperforms the comparison methods according to key evaluation metrics such as Residual Map Mean,Residual Map Variance,Cross Correlation,and Structural Similarity Index ***,since optical flow captures the fundamental motion information in image sequences,the proposed method can be applied to a variety of research areas,including solar image registration,sequence alignment,image super-resolution,magnetic field calibration,and solar activity *** code is available at https://***/jackie-willianm/Transverse-Velocity-Field-Measurement-of-Solar-High-Resolution-Images.

关键词： methods: data analysis Sun: fundamental parameters techniques: image processing

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A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of information

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 1045-1075页

作者： Hao Jiang Yuerong Liao Dongdong Zhao Wenjian Luo Xingyi Zhang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei230601China Chongqing Research Institute School of Computer Science and Artificial IntelligenceWuhan University of TechnologyWuhan430070China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and TechnologyHarbin Institute of TechnologyShenzhen518055China

Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many *** by the self-nonself discrimination paradigmin the biological immune system,the negative representation of information indicates features such as simplicity and efficiency,which is very suitable for preserving social network ***,we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks,called ***,a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept ***,a negative database-based method is proposed to hide node attributes,so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes,which is crucial to the analysis of social *** evaluate the performance of the AttNetNRI,empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social *** experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding *** experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.

关键词： Attributed social network topology privacy node attribute privacy negative representation of information negative survey negative database

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Safe Q-Learning for Data-Driven Nonlinear Optimal Control With Asymmetric State Constraints

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2408-2422页

作者： Mingming Zhao Ding Wang Shijie Song Junfei Qiao the School of Information Science and Technology the Beijing Key Laboratory of Computational Intelligence and Intelligent Systemthe Beijing Laboratory of Smart Environmental Protectionand the Beijing Institute of Artificial IntelligenceBeijing University of Technology.Beijing 100124China IEEE the School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

This article develops a novel data-driven safe Q-learning method to design the safe optimal controller which can guarantee constrained states of nonlinear systems always stay in the safe region while providing an optimal ***,we design an augmented utility function consisting of an adjustable positive definite control obstacle function and a quadratic form of the next state to ensure the safety and ***,by exploiting a pre-designed admissible policy for initialization,an off-policy stabilizing value iteration Q-learning(SVIQL)algorithm is presented to seek the safe optimal policy by using offline data within the safe region rather than the mathematical ***,the monotonicity,safety,and optimality of the SVIQL algorithm are theoretically *** obtain the initial admissible policy for SVIQL,an offline VIQL algorithm with zero initialization is constructed and a new admissibility criterion is established for immature iterative ***,the critic and action networks with precise approximation ability are established to promote the operation of VIQL and SVIQL ***,three simulation experiments are conducted to demonstrate the virtue and superiority of the developed safe Q-learning method.

关键词： Adaptive critic control adaptive dynamic programming(ADP) control barrier functions(CBF) stabilizing value iteration Q-learning(SVIQL) state constraints

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Regulating atomic Fe-Rh site distance for efficient oxygen reduction reaction

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Science China Chemistry 2024年第4期67卷 1352-1359页

作者： Tong Liu Yudan Chen Airong Xu Xiaokang Liu Dong Liu Sicheng Li Hui Huang Li Xu Shuaiwei Jiang Qiquan Luo Tao Ding Tao Yao National Synchrotron Radiation Laboratory University of Science and Technology of ChinaHefei 230029China Key Laboratory of Precision and Intelligent Chemistry Hefei National Research Center for Physical Sciences at the MicroscaleUniversity of Science and Technology of ChinaHefei 230026China Institutes of Physical Science and Information Technology Anhui UniversityHefei 230601China

Exploring the atomic interaction mechanisms of dense single-atom catalysts(SACs)is of great significance for their application in oxygen reduction reaction(ORR).However,the intrinsic mechanism of the site-distance effect on the catalytic performance has been largely ***,we demonstrate the site-distance effect of Fe-Rh_(x)@NC catalysts in ORR theoretically and *** charge analysis reveals that the strong interaction between Fe and Rh atoms at a certain atomic distance(dFe-Rh)alters the catalytic electronic structure,facilitating the optimization of catalyst adsorption *** by the theoretical calculations,we designed and synthesized the Fe-Rhx@NC catalysts through a spatial confinement *** characterization results prove that the Fe-Rh_(2)@NC has the optimal d_(Fe-Rh),which improves its intrinsic ORR activity,providing a half wave potential of 0.91 V,higher than that of the commercial Pt/C(0.86 V).This study emphasizes the importance of determining the basic mechanism of the site-distance effect in dissimilar metal atoms catalysts,which is conducive to the design of efficient catalyst systems for practical applications.

关键词： Density functional theory single-atom catalyst in-situ encapsulating site-distance effect oxygen reduction reaction

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Tunable terahertz narrowband filter based on heterostructure waveguides filled with liquid-crystals 2

Tunable terahertz narrowband filter based on heterostructure...

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2nd International Academic Conference on Optics and Photonics, IACOP 2024

作者： Zhang, Ya-Yun Liu, Huan Zhang, Huan Li, Yong Wang, Xiang-Yu Ma, Jing Fan, Ya-Xian Tao, Zhi-Yong Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin541004 China Key Laboratory of Cognitive Radio and Information Processing Ministry of Education Guilin University of Electronic Technology Guilin541004 China Academy of Marine Information Technology Guilin University of Electronic Technology Beihai536000 China

ISBN: (数字)9781510688827

ISBN: (纸本)9781510688810

We propose a tunable waveguide-type terahertz (THz) narrowband filter based on heterostructure waveguide filled with liquid-crystals (LCs). This filter consists of two periodic corrugated waveguide structures with different duty cycles, which produces a narrow linewidth transmission peak within the original stop band. By adjusting the effective refractive index of LC through external fields, the center frequency of the transmission peak can be tuned. The simulated electric field proves that the narrow linewidth transmission peak is mainly attributed to the local resonance occurred at the interface. Furthermore, an increase in the number of periods can greatly reduce bandwidth and improve the Q-factor of the filter. The proposed electrically tunable THz narrowband filter based on the heterostructure waveguide provide an effective method for the design and development of THz narrowband filter. © 2025 SPIE.

关键词： Terahertz waves

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Dual-band tunable terahertz absorber based on all-dielectric InSb metamaterials 2

Dual-band tunable terahertz absorber based on all-dielectric...

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2nd International Academic Conference on Optics and Photonics, IACOP 2024

作者： Zhang, Huan Liu, Huan Ma, Jing Li, Yong Zhang, Ya-Yun Wang, Xiang-Yu Tao, Zhi-Yong Fan, Ya-Xian Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin541004 China Academy of Marine Information Technology Guilin University of Electronic Technology Beihai536000 China Key Laboratory of Cognitive Radio and Information Processing Ministry of Education Guilin University of Electronic Technology Guilin541004 China

ISBN: (数字)9781510688827

ISBN: (纸本)9781510688810

We propose a dual-band tunable terahertz (THz) absorber based on all-dielectric InSb metamaterials. This absorber consists of a resonant structure at the top and a dielectric substrate at the bottom. The resonant structure layer interacts with THz waves to achieve high-efficiency absorption, while the dielectric substrate layer not only provides support but also effectively prevents THz waves from penetrating the absorber. Simulation results show that under room temperature conditions (T=300 K), the absorber achieves absorption rates of 96.85% and 98.76% at the frequencies of 0.366 THz and 0.991 THz, respectively. As the temperature increases, these two absorption peaks shift towards a higher frequency range. Based on the unique properties of InSb, the frequency shift range of the proposed absorber can be tuned through external thermal excitation, with a maximum of 0.24 THz. Meanwhile, the absorber maintains a high absorption rate of over 85%. The incorporation of InSb metamaterials enables temperature tunability, allowing the absorber to exhibit enhanced performance over a broader temperature range, making it suitable for sensor devices that require precise control over both absorption efficiency and temperature tuning. © 2025 SPIE.

关键词： Terahertz waves

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Faster and More Robust Low-Resource Nearest Neighbor Machine Translation 12th

Faster and More Robust Low-Resource Nearest Neighbor Machin...

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12th National CCF Conference on Natural Language processing and Chinese Computing, NLPCC 2023

作者： Sun, Shuo Hou, Hongxu Yang, Zongheng Wang, Yisong College of Computer Science Inner Mongolia University National and Local Joint Engineering Research Center of Intelligent Information Hohhot China Processing Technology for Mongolian Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Hohhot China

ISBN: (纸本)9783031446955

Transformer-based neural machine translation (NMT) models have achieved performance close to human-level on some languages, but still suffer from poor interpretability and scalability of the models. Many advanced studies enhance the model’s translation ability by building external memory modules and utilizing retrieval operations, however, it suffers from poor robustness and low decoding efficiency while improving the model performance, especially for low-resource translation tasks. In this paper, we propose a confidence-based gating mechanism to optimize the decoding efficiency by building a sub-network to determine the confidence of the model’s own translation capability and then decide whether the current translation needs to be retrieved from the memory module. By reducing the number of retrievals to improve the model’s translation speed without degrading the translation quality as much as possible. In addition, we use a nonparametric dynamic Monte Carlo-based algorithm to fuse retrieval probabilities and model predictions to improve the generalization and robustness of the model. Extensive experiments on different datasets demonstrate the effectiveness of our method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Neural machine translation

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Attentive Neighborhood Feature Augmentation for Semi-supervised Learning

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intelligent Automation & Soft Computing 2023年第8期37卷 1753-1771页

作者： Qi Liu Jing Li Xianmin Wang Wenpeng Zhao School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510002China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang UniversityFuzhou350121China

Recent state-of-the-art semi-supervised learning(SSL)methods usually use data augmentations as core *** methods,however,are limited to simple transformations such as the augmentations under the instance’s naive representations or the augmentations under the instance’s semantic *** tackle this problem,we offer a unique insight into data augmentations and propose a novel data-augmentation-based semi-supervised learning method,called Attentive Neighborhood Feature Aug-mentation(ANFA).The motivation of our method lies in the observation that the relationship between the given feature and its neighborhood may contribute to constructing more reliable transformations for the data,and further facilitating the classifier to distinguish the ambiguous features from the low-dense ***,we first project the labeled and unlabeled data points into an embedding space and then construct a neighbor graph that serves as a similarity measure based on the similar representations in the embedding ***,we employ an attention mechanism to transform the target features into augmented ones based on the neighbor ***,we formulate a novel semi-supervised loss by encouraging the predictions of the interpolations of augmented features to be consistent with the corresponding interpolations of the predictions of the target *** carried out exper-iments on SVHN and CIFAR-10 benchmark datasets and the experimental results demonstrate that our method outperforms the state-of-the-art methods when the number of labeled examples is limited.

关键词： Semi-supervised learning attention mechanism feature augmentation consistency regularization

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MULTI-SCALE RESNET INTEGRATED WITH ATTENTION MECHANISM FOR ENHANCED WHEAT FRESHNESS DIAGNOSIS BASED ON BIOPHOTONICS

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Acta Scientiarum Polonorum, Technologia Alimentaria 2024年第4期23卷 525-535页

作者： Shi, Weiya Chen, Liang Key Laboratory of Grain Information Processing and Control Ministry of Education Henan 450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Henan 450001 China College of Artificial Intelligence and Big Data Henan University of Technology Henan 450001 China

Background. Accurate detection of wheat freshness is important for ensuring the quality and safety of wheat products, thereby protecting the health and interests of consumers. Material and methods. This study integrates biophoton emission technology with advanced deep learning frameworks to transform the process of wheat freshness assessment. Leveraging the powerful feature extraction capabilities of the ResNet architecture, we employ a multi-scale framework integrated with the Gaussian Context Transformer (GCT) attention mechanism. The MS-GCT-ResNet method presents a groundbreaking approach that not only enhances the accuracy and efficiency of wheat freshness discrimination but also demonstrates the potential for combining biophysical phenomena with cutting-edge Artificial Intelligence (AI) technologies for precision agriculture and food quality control. Results. This model enhances detection accuracy and adaptability, offering a powerful technique for the rapid and precise evaluation of wheat freshness. Its effectiveness is validated using years of wheat sample data. Based on the experimental results, MS-GCT-ResNet achieves a recognition accuracy of 93.6%. Compared with traditional CNN and ResNet models, the recognition accuracy increases by 1.9% and 1.1%, respectively. Conclusion. MS-GCT-ResNet is a highly promising, non-invasive, and efficient technological advancement capable of quickly and accurately assessing wheat freshness. This method holds immense potential to revolutionize the agriculture and food processing industries. © (2024), (Wydawnictwo Uniwersytetu Przyrodniczego w Poznaniu). All rights reserved.

关键词： attention mechanism biophoton emission detection multi-scale wheat freshness

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