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Tunable all-dielectric metasurfaces based on quasi-BIC for broadband infrared refractive index sensing

Optical Materials 2025年 165卷

作者： Lv, Jinwen Xu, Zhengji Liu, Yiqing Zhang, Shubin Zong, Meixue School of Microelectronics Science and Technology Sun Yat-sen University Zhuhai519082 China Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-sen University Zhuhai519082 China

The mid-infrared (MIR) band has attracted extensive attention in the field of label-free refractive index sensing due to its characteristic absorption frequencies of various biomolecules and gas molecules. However, most sensors operate in the visible, near-infrared (NIR), and terahertz bands with static operating wavelengths, which do not meet the flexible regulation needs in the MIR band. We propose and fabricate a Ge-based metasurface with a high quality factor (8973.08) based on quasi-bound states in the continuum (BIC), which significantly enhances the interaction between light and matter. By adjusting the geometric parameters of the resonator, we can linearly control the position of the quasi-BIC resonance peak, covering a wide spectral range from NIR to MIR. We numerically simulate the response of the device to the refractive index change of the surrounding medium, and the maximum sensitivity and FOM are 1037.1 nm/RIU and 2171.08 RIU−1, respectively. Due to the tunability of the quasi-BIC formant, the sensor can operate across a wide infrared spectral range. This high-resolution dielectric metasurfaces, compatible with CMOS processing, offer new opportunities for developing miniaturized, integrable broadband infrared refractive index sensors. © 2025 Elsevier B.V.

关键词： Q factor measurement

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Multimodal depression recognition and analysis: Facial expression and body posture changes via emotional stimuli

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Journal of Affective Disorders 2025年 381卷 44-54页

作者： Liu, Yang Li, Xingyun Wang, Mengqi Bi, Jianlu Lin, Shaoqin Wang, Qingxiang Yu, Yanhong Ye, Jiayu Zheng, Yunshao Endocrinology The Fifth Clinical College of Guangzhou University of Chinese Medicine Guangzhou 510095 China Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou 730000 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Science) Jinan 250353 China Shandong Mental Health Center Shandong University Jinan 250014 China College of Traditional Chinese Medicine Shandong University of Traditional Chinese Medicine Jinan 250355 China School of Computer Science Guangdong University of Technology Guangzhou 510006 China Endocrinology Guangdong Provincial Second Hospital of Traditional Chinese Medicine Guangzhou 510095 China

Background: Clinical studies have shown that facial expressions and body posture in depressed patients differ significantly from those of healthy individuals. Combining relevant behavioral features with artificial intelligence technology can effectively improve the efficiency of depression detection, thereby assisting doctors in early identification of patients. This study aims to develop an end-to-end multimodal recognition model combining facial expressions and body posture via deep learning techniques, enabling rapid preliminary screening of depression. Methods: We invited 146 subjects (73 in the patient group and 73 in the control group) to participate in an emotion-stimulus experiment for depression recognition. We focused on differentiating depression patients from the control group by analyzing changes in body posture and facial expressions under emotional stimuli. We first extracted images of body position and facial emotions from the video, then used a pre-trained ResNet-50 network to extract features. Additionally, we analyzed facial expression features using OpenFace for sequence analysis. Subsequently, various deep learning frameworks were combined to assess the severity of depression. Results: We found that under different stimuli, facial expression units AU04, AU07, AU10, AU12, AU17, and AU26 had significant effects in the emotion-stimulus experiment, with these features generally being negative. The decision-level fusion model based on facial expressions and body posture achieved excellent results, with the highest accuracy of 0.904 and an F1 score of 0.901. Conclusions: The experimental results suggest that depression patients exhibit predominantly negative facial expressions. This study validates the emotion-stimulus experiment, demonstrating that combining facial expressions and body posture enables accurate preliminary depression screening. © 2025 Elsevier B.V.

关键词： Body posture Depression recognition Emotional stimuli Facial expressions

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Anomize: Better Open Vocabulary Video Anomaly Detection

arXiv

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arXiv 2025年

作者： Li, Fei Liu, Wenxuan Chen, Jingjing Zhang, Ruixu Wang, Yuran Zhong, Xian Wang, Zheng National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China State Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China Hubei Key Laboratory of Transportation Internet of Things Wuhan University of Technology China

Open Vocabulary Video Anomaly Detection (OVVAD) seeks to detect and classify both base and novel anomalies. However, existing methods face two specific challenges related to novel anomalies. The first challenge is detection ambiguity, where the model struggles to assign accurate anomaly scores to unfamiliar anomalies. The second challenge is categorization confusion, where novel anomalies are often misclassified as visually similar base instances. To address these challenges, we explore supplementary information from multiple sources to mitigate detection ambiguity by leveraging multiple levels of visual data alongside matching textual information. Furthermore, we propose incorporating label relations to guide the encoding of new labels, thereby improving alignment between novel videos and their corresponding labels, which helps reduce categorization confusion. The resulting Anomize framework effectively tackles these issues, achieving superior performance on UCF-CRIME and XD-VIOLENCE datasets, demonstrating its effectiveness in OVVAD. Copyright © 2025, The Authors. All rights reserved.

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MEFA-Net: A mask enhanced feature aggregation network for polyp segmentation

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computers in Biology and Medicine 2025年 186卷 109601-109601页

作者： Ke, Xiao Chen, Guanhong Liu, Hao Guo, Wenzhong Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350116 China

Accurate polyp segmentation is crucial for early diagnosis and treatment of colorectal cancer. This is a challenging task for three main reasons: (i) the problem of model overfitting and weak generalization due to the multi-center distribution of data;(ii) the problem of interclass ambiguity caused by motion blur and overexposure to endoscopic light;and (iii) the problem of intraclass inconsistency caused by the variety of morphologies and sizes of the same type of polyps. To address these challenges, we propose a new high-precision polyp segmentation framework, MEFA-Net, which consists of three modules, including the plug-and-play Mask Enhancement Module (MEG), Separable Path Attention Enhancement Module (SPAE), and Dynamic Global Attention Pool Module (DGAP). Specifically, firstly, the MEG module regionally masks the high-energy regions of the environment and polyps through a mask, which guides the model to rely on only a small amount of information to distinguish between polyps and background features, avoiding the model from overfitting the environmental information, and improving the robustness of the model. At the same time, this module can effectively counteract the "dark corner phenomenon" in the dataset and further improve the generalization performance of the model. Next, the SPAE module can effectively alleviate the inter-class fuzzy problem by strengthening the feature expression. Then, the DGAP module solves the intra-class inconsistency problem by extracting the invariance of scale, shape and position. Finally, we propose a new evaluation metric, MultiColoScore, for comprehensively evaluating the segmentation performance of the model on five datasets with different domains. We evaluated the new method quantitatively and qualitatively on five datasets using four metrics. Experimental results show that MEFA-Net significantly improves the accuracy of polyp segmentation and outperforms current state-of-the-art algorithms. Code posted on https://***/

关键词： Endoscopy

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Long and Recent Preference Learning with Recent-k Items Distribution for Recommender System

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IEEE Transactions on Multimedia 2025年

作者： Gao, Yongbiao Niu, Sijie Lv, Guohua Ling, Miaogen Geng, Xin Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China Southeast University Ministry of Education Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing School of Information Science and Engineering China Nanjing University of Information Science and Technology School of Computer Science China Southeast University School of Computer Science and Engineering China

Reinforcement learning (RL) aims to formulate the recommendation task as a Markov decision process (MDP) and trains an agent to automatically learn the optimal recommendation policy from interaction trajectories through trial-and-error and reward mechanisms. However, most existing RL-based approaches overlook the correlation between items and the dynamics of user interests implied in temporally close interactions. Therefore, in this paper, we propose a reinforcement learning method that incorporates a "recent-k items"distribution to capture users' local preferences. Specifically, we model the output layer as two distinct branches. The "recent-k items"branch, formulated with a Kullback-Leibler divergence loss, learns the recent interests of users, whereas the other branch utilizes a one-step temporal difference error to capture long-term preferences. The proposed structure is integrated into deep Q-learning and actor-critics, resulting in two enhanced methods named R k Q and R k AC, respectively. Furthermore, a novel soft inter-reward is carefully designed to enhance the proposed method, and we theoretically prove the convergence of the proposed algorithm. We perform extensive experiments on two large real-world datasets and conduct further analysis of the influences of different action sequences, time intervals, and enhancement capabilities for state-of-the-art models. The experimental results demonstrate the efficacy of our proposed methods. © 1999-2012 IEEE.

关键词： Deep reinforcement learning

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An Enhancing Stability Control Strategy of VSG-Based Power Source Under PWM Rectifier Load

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Gaoxiang Liu, Bowen Song, Wenhao Jining, Chen Liu, Xiao Guangxi University Guangxi Key Laboratory of Power System Optimization and Energy Technology School of Electrical Engineering Nanning530004 China Guangxi Zhuang Autonomous Region Information Center Guangxi Key Laboratory of Digital Infrastructure Nanning530200 China Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing School of Computer Electronics and Information Nanning530004 China

Under PWM rectifier load, the power source based on virtual synchronous generator (VSG) is prone to cause mid-frequency oscillation. Firstly, the system oscillation mechanism and characteristic of the VSG-based power source are analyzed based on impedance method. Due to the negative resistance and capacitive characteristic in the middle frequency range, the PWM rectifier load is prone to interact with the VSG-based power source, which can lead to impedance resonance. Secondly, to improve the stability of the VSG-based power source under the PWM rectifier load, a voltage differentiation feedforward control is proposed. In addition, since the mid-frequency impedance magnitude decrease obviously, the improved VSG-based power source is closer to the ideal voltage source, which can automatically suppress the middle harmonics induced by uncontrollable rectifier load. Finally, the proposed control and the corresponding theoretical analysis are verified by simulations and experiments. © 1975-2011 IEEE.

关键词： Synchronous generators

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A Polarization-Diversity Omnidirectional Glass Dielectric Resonator Antenna with Gain and Bandwidth Enhancements

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IEEE Transactions on Antennas and Propagation 2025年

作者： Yu, Bo Hao Leung, Kwok Wa Hu, Peng Fei Lee, Mike W. K. Luk, Kwai Man City University of Hong Kong State Key Laboratory of Terahertz and Millimeter Waves Department of Electrical Engineering Hong Kong CityU Shenzhen Research Institute Shenzhen Key Laboratory of Millimeter Wave and Wideband Wireless Communications Information and Communication Technology Centre Shenzhen518057 China Sun Yat-sen University School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou510006 China

A polarization-diversity omnidirectional glass dielectric resonator antenna (DRA) with gain and bandwidth enhancements is presented in this communication. It employs high-order TM01δ+2 and TE01δ+3 modes to improve the antenna gains of the vertical and horizontal polarizations, respectively. However, their high quality factors result in narrow bandwidths. When the modes of feeding circuits are combined with them, the overlapping bandwidth of the two polarizations is only 6.5%. To overcome this, an air gap is introduced into the dielectric resonator (DR), modifying its shape from cylindrical to I-shaped. This reduces the effective dielectric constant and quality factor, increasing the overlapped bandwidth from 6.5% to 16.0%. In addition, the peak gain is increased from 2.66 to 4.62 dBi by reducing the back lobe. To verify the idea, a DRA made of K9 glass is designed for the 3.3-3.6 GHz WiMAX band. The prototype antenna has a measured overlapping bandwidth of 13.8% and a peak gain of 4.14 dBi. © 2025 IEEE.

关键词： Bandwidth

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Low-light Image Enhancement through Learning a Simplified Inverse Rendering Model

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Wu, Wenhui Pang, Jia Gao, Shuaibo Zhang, Pingping Chen, Zhe Qiu, Guoping Shenzhen University College of Electronics and Information Engineering China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen China City University of Hong Kong Department of Computer Science Hong Kong University of Nottingham School of Computer Science United Kingdom

It remains to be extremely difficult to capture high quality photographs of low-light scenes. Low light causes the low signal-to-noise ratio (SNR) problem which makes the image noisy. Such scenes almost always have the high dynamic range (HDR) problem caused by uneven lighting where a small area surrounding the light source is very bright while the rest of the scene is very dark, making it very difficult to simultaneously obtain high quality signals in both the dark and bright areas. This paper presents a new image restoration method for tackling the problems in low-light scenes. Fundamentally differing from existing approaches, the new method borrows ideas from inverse graphics rendering and re-renders the image with a canonical light source thus correcting the image from first principle. A deep learning based simplified inverse rendering model (SIRM) featuring implicit regularization is first developed for correcting uneven lighting and then an end-to-end convolutional neural network is constructed for reducing noise. Extensive experimental results are presented to demonstrate that the new method outperforms state-of-the-art methods, and is capable of effectively brightening up dark image regions while at the same time preserving details and color consistency. Our code is available at: https://***/pj0927/SIRNet. © 2025 IEEE.

关键词： Rendering (computer graphics)

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TEMPO: Temporal Preference Optimization of Video LLMs via Difficulty Scheduling and Pre-SFT Alignment

arXiv

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arXiv 2025年

作者： Li, Shicheng Li, Lei Ouyang, Kun Ren, Shuhuai Liu, Yuanxin Zhang, Yuanxing Zhang, Fuzheng Kong, Lingpeng Liu, Qi Sun, Xu National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China The University of Hong Kong Hong Kong Kuaishou Technology China

Video Large Language Models (Video LLMs) have achieved significant success by leveraging a two-stage paradigm: pretraining on large-scale video-text data for vision-language alignment, followed by supervised fine-tuning (SFT) for task-specific capabilities. However, existing approaches struggle with temporal reasoning due to weak temporal correspondence in the data and reliance on the next-token prediction paradigm during training. To address these limitations, we propose TEMPO (TEMporal Preference Optimization), a systematic framework that enhances Video LLMs’ temporal reasoning capabilities through Direct Preference Optimization (DPO). To facilitate this, we introduce an automated preference data generation pipeline that systematically constructs preference pairs by selecting videos that are rich in temporal information, designing video-specific perturbation strategies, and finally evaluating model responses on clean and perturbed video inputs. Our temporal alignment features two key innovations: curriculum learning which that progressively increases perturbation difficulty to improve model robustness and adaptability;and "Pre-SFT Alignment", applying preference optimization before instruction tuning to prioritize fine-grained temporal comprehension. Extensive experiments demonstrate that our approach consistently improves Video LLM performance across multiple benchmarks with a relatively small set of self-generated DPO data. We further analyze the transferability of DPO data across architectures and the role of difficulty scheduling in optimization. Our findings highlight our TEMPO as a scalable and efficient complement to SFT-based methods, paving the way for developing reliable Video LLMs. © 2025, CC BY.

关键词： Visual languages

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Towards Multi-Label Open Set Diagnosis for Medical Images

Towards Multi-Label Open Set Diagnosis for Medical Images

引用

IEEE International Symposium on Biomedical Imaging

作者： Shijie Pang Yuze Zhao Rui-Wei Zhao Rui Feng Academy for Engineering and Technology Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

In real clinical scenarios, diagnosis algorithms may face the complex concurrent presence of multiple common, rare, or even unknown disease conditions. In particular, rare disease categories may be underrepresented or even absent from the class-imbalanced training set, leaving safe and trustworthy diagnostic decision-making extremely challenging. In this work, we first formalize the medical image diagnosis as a multi-label open set recognition under class-imbalanced training data and propose a novel Multi-label Open set Diagnosis (MOSD) solution to address this problem. Our MOSD first employs a Balanced Multi-label Data Augmentation (BMDA) module to augment multi-label image data to alleviate the class imbalance in model training more effectively. Then it also boasts a Multi-label Evidential Deep Learning (MEDL) module with tailored learning objectives to better estimate the uncertainty for multi-label open set prediction. Experimental results on the ODIR-5K benchmark successfully demonstrate its superiority in both close set known class classification and open set unknown class detection.

关键词： Training Uncertainty Image recognition Face recognition Training data Multi label classification Robustness Medical diagnosis Medical diagnostic imaging Diseases

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