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Visible-thermal cross-modality class-incremental learning

Expert Systems with Applications 2025年 294卷

作者： Xinjie Yao Yingxue Wang Yu Wang Pengfei Zhu Ruipu Zhao Weihao Li Shenglei Pei Wanyu Lin College of Intelligence and Computing Tianjin University Tianjin 300350 China Engineering Research Center of City Intelligence and Digital Governance Ministry of Education of the People’s Republic of China Tianjin 300350 China Haihe Lab of ITAI Tianjin 300350 China National Engineering Research Center Public Safety Risk Perception and Control by Big Data Beijing 100041 China School of New Media and Communication Tianjin University Tianjin 300072 China School of Intelligence Science and Engineering Qinghai Minzu University Xining 810007 China Department of Computing The Hong Kong Polytechnic University Hong Kong 999077 China

Visible-thermal cross-modality learning has gained significant success by utilizing information from different sources to improve performance in downstream tasks. To make cross-modality learning methods more practical in open-ended environments, such as autonomous driving, a straightforward question arises on how we can incrementally update the model with such cross-modality data. In response, we propose a practical task, namely cross-modality class-incremental learning (CIL). We first provide a cross-modality CIL benchmark, which encompasses datasets abundant in scene diversity and environmental variability, including urban, residential, and diverse temporal settings. Then we thoroughly analyze its challenges and find that: (1) simply using cross-modality learning or CIL methods cannot achieve satisfactory performance in cross-modality CIL; (2) combining existing cross-modality learning methods and CIL strategies benefits cross-modality CIL to a very limited extent; (3) modality forgetting, i.e., neurons favoring a dominant modality and diminishing the weaker, is the significant factor that leads to forgetting in cross-modality CIL. To address the challenges of cross-modality CIL, we design a simple yet effective method, termed Hybrid-fused Forgetting Prevention (HFP). Specifically, HFP first introduces a block-diagonal structured sparsity on the channel dimension and implements the information interchange mechanism by shuffling the feature maps of different groups and changing the sequence of channels, thus fully exploiting the complementarity of cross-modality data. Then, with representation fusion and forgetting prevention strategies, more complementary features can be learned and exploited in retaining old knowledge, which can alleviate modality forgetting. Extensive experiments validate that HFP contributes a favorable framework to cross-modality CIL and alleviates forgetting effectively.

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Prediction of Mastitis in Dairy Cows Based on Milk Yield, Rumination Time, and Milk Electrical Conductivity Using Machine Learning Algorithms

SSRN

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SSRN 2023年

作者： Zhou, XiaoJing Wang, Hao Xu, Chuang Zhao, Zixuan Xu, Wei Deng, Zhaoju Department of Information and Computing Science Heilongjiang Bayi Agricultural University No. 5 Xinyang Road Heilongjiang Province Daqing163319 China Heilongjiang Provincial Key Laboratory of Prevention and Control of Bovine Diseases College of Animal Science and Veterinary Medicine Heilongjiang Bayi Agricultural University No. 5 Xinyang Road Heilongjiang Province Daqing163319 China Animal Husbandry and Veterinary Branch of Heilongjiang Academy of Agricultural Science Heilongjiang Province Qiqihar161005 China Department of Biosystems Division of Animal and Human Health Engineering KU Oude Markt 13 Leuven3000 Belgium College of Veterinary Medicine China Agricultural University Haidian District Tsinghua East road No.17 Beijing10010 China

In commercial dairy farms, mastitis is associated with potentially increased use of antimicrobials and associated resistance, thereby affecting the welfare of dairy cows and increasing the rate of culling and/or death. Therefore, accurate and efficient mastitis prediction is valuable for timely intervention. Machine learning algorithms have been successfully used to solve many biological problems such as predicting or detecting subclinical mastitis using high-dimensional and noisy data obtained from sensors used on the farm. Therefore, this study aimed to develop sensor-based prediction models for bovine mastitis using variables related to daily activity and rumination time generated from collars, coupled with daily composite milk yield, and milk electrical conductivity from rotary milking systems from five commercial farms in Northeast China. We employed nine machine learning algorithms to analyze original data of 447 diseased and 2146 healthy cows obtained over 2.5 years and the corresponding Z-standardized dataset of four specific stages of lactation to train and test the prediction models for recognizing and predicting naturally occurring mastitis in these dairy cows. The sensitivity, specificity, accuracy, precision, AUC, and Matthews’s correlation coefficient of these models were calculated to evaluate the model performance. For Z-standardized data of cows in the 0–28 DIM and 29–100 DIM subgroups, the multi-layer artificial neural net (MNET) algorithm showed the best performance, with the sensitivity, specificity, and accuracy of 91.89%, 93.69%, and 93.24% for data of cows in 0–28 DIM, and 91.84%, 96.73, and 95.44% for that in 29–100 DIM, respectively. For data of the subgroups 101–200 DIM and 201–305 DIM, random forest performed best on both the non-standardized and the Z-standardized datasets. Results of the Z-standardized dataset for all four subgroups were better than those of the non-standardized data. We also calculated the peak milk yield (PMY) and days

关键词： Forecasting

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Cavity-Quantum Electrodynamics with Moiré Flatband Photonic Crystals

arXiv

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arXiv 2024年

作者： Wang, Yu-Tong Ye, Qi-Hang Yan, Jun-Yong Qiao, Yufei Chen, Chen Cheng, Xiao-Tian Li, Chen-Hui Zhang, Zi-Jian Huang, Cheng-Nian Meng, Yun Zou, Kai Zhan, Wen-Kang Zhao, Chao Hu, Xiaolong Tee, Clarence Augustine T.H. Sha, Wei E.I. Huang, Zhixiang Liu, Huiyun Jin, Chao-Yuan Ying, Lei Liu, Feng State Key Laboratory of Extreme Photonics and Instrumentation College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Physics Zhejiang Key Laboratory of Micro-nano Quantum Chips and Quantum Control Zhejiang University Hangzhou310027 China School of Precision Instrument and Optoelectronic Engineering Tianjin University Tianjin300072 China Key Laboratory of Optoelectronic Information Science and Technology Ministry of Education Tianjin300072 China Laboratory of Solid State Optoelectronics Information Technology Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Science Beijing101804 China College of Physics and Electrical Information Engineering Zhejiang Normal University Hangzhou310058 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom International Joint Innovation Center Zhejiang University Haining314400 China

Quantum emitters are a key component in photonic quantum technologies. Enhancing their single-photon emission by engineering the photonic environment using cavities can improve the overall efficiency in quantum information processing. However, this enhancement is often limited by the need for precise nanoscale control over emitter’s position within micro/nano-cavities. Inspired by the fascinated physics of moiré pattern, we present an approach to strongly modify the spontaneous emission rate of a quantum emitter using fine-designed multilayer moiré photonic crystal with a robust isolate-flatband dispersion. Theoretical analysis reveals that due to its nearly infinite photonic density of states, the moiré cavity can simultaneously possess high Purcell factor and large tolerance over emitter’s position. We experimentally demonstrate the coupling between this moiré cavity and a quantum dot via the cavity-determined polarization of the dot’s emission. The radiative lifetime of the quantum dot can be tuned by a factor of 40 from 42 ps to 1692 ps, attributed to strong Purcell enhancement and Purcell inhibition effects. Our findings pave the way for moiré flatband cavity-enhanced quantum light sources, quantum optical switches and quantum nodes for quantum internet. Copyright © 2024, The Authors. All rights reserved.

关键词： Particle beams

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Adaptive fuzzy convolution networks for uncertainty-aware image analysis in ambiguous environments

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Expert Systems with Applications 2025年 292卷

作者： Saeed Iqbal Xiaopin Zhong Muhammad Attique Khan Zongze Wu Amir Hussain Shrooq Alsenan Weixiang Liu College of Mechatronics and Control Engineering Shenzhen University Shenzhen 518060 China Department of AI College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia School of Computing Merchiston Campus Edinburgh Napier University Edinburgh EH10 5DT Scotland UK Information Systems Department College of Computer and Information Sciences Princess Nourah bint Abdulrahman University PO Box 84428 Riyadh 11671 Saudi Arabia

In complex and ambiguous situations, effective image analysis is severely hampered by the instability and unpredictability of convolutional frameworks and fuzzy systems, especially when it comes to managing noise-induced uncertainties and nonlinear interactions. The innovative framework Adaptive Fuzzy Convolution Networks (AFCN) is presented in this paper to tackle the problems of nonlinear interactions and noise-induced uncertainties in image processing in ambiguous situations. To improve feature extraction and uncertainty management, the suggested technique combines context-aware fuzzy aggregation operators, adaptive kernel parameterization, and dynamic nonlinearity modulation. Stability in fuzzy systems under different noise situations is ensured by the framework’s use of a feedback-driven method to modify the nonlinearity parameter ( γ k ) depending on noise characterisation. Multiscale fuzzy convolution with adaptive kernel parameters (center ξ s , k ( x ) and spread σ s , k ( x ) ) that react to localized uncertainty gradients are also incorporated into the model, allowing for reliable feature extraction in noisy environments. A hybrid regularization approach that combines fuzzification and L 2 regularization is presented in order to smooth uncertainty propagation over multiscale convolutions and reduce overfitting. The capacity of AFCN to dynamically adjust to noise while maintaining structural integrity and reducing error measures like F-MAE and F-MSE is what makes it innovative. State-of-the-art performance is demonstrated by experimental assessments on several datasets, with an accuracy of 97.2 %, an F1 Score of 97.3 %, and exceptionally low error metrics like F-MAE of 0.005 and F-MSE of 0.010. These developments make AFCN a potent instrument for practical uses like autonomous driving, where resilience to background noise is crucial, and medical imaging, where accurate feature recognition is crucial.

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Mechanistic Investigation of Co2/Ch4/N2 Dynamic Adsorption Separation in Carbon Molecular Sieves Via High-Fidelity Modeling of Hierarchical Pore Structures and Functional Groups

SSRN

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SSRN 2025年

作者： Xu, Ran Song, Zhenlong Xiao, Zejun Gu, Jianhong Huang, Jie Xian, Xuefu School of Safety and Management Engineering Hunan Institute of Technology Hengyang421002 China High Performance Computing Department National Supercomputing Center in Shenzhen Guangdong Shenzhen518055 China Institute of Future Civil Engineering Science and Technology Chongqing Jiaotong University Chongqing400074 China State Key Laboratory of Coal Mine Disaster Dynamics and Control School of Resources and Safety Engineering Chongqing University Chongqing400044 China Southern University of Science and Technology China

Carbon molecular sieves (CMS) show promise in CO₂/CH₄ and CH₄/N₂ separations due to tunable pores and stability, yet lack systematic insights into hierarchical pore-functional group synergy and adsorption dynamics. Using coal-based CMS (AAC), we construct a high-fidelity macromolecular model (C₁₈₅O₁₈N₂S₂H₈₆) via multi-dimensional characterization, featuring hierarchical pores and functional groups. Molecular simulations reveal dynamic adsorption mechanisms governed by pore confinement and functional group interactions. Results show CMS separation efficiency relies on van der Waals energy differences between adsorbates and pores, where hierarchical pores dominate by amplifying this disparity, while functional groups modulate synergistically. Dynamic adsorption studies indicate all gases preferentially adsorb in 4-7 Å pores before diffusing to larger ones. CH₄ adsorption heavily depends on 4-7 Å ultramicropores, while CO₂ maintains stability in larger pores (>7 Å) due to stronger framework interactions, and N₂ shows low sensitivity to pore distribution. Pores >7 Å enhance CO₂/CH₄ separation, with C=O and C-O groups boosting CO₂ selectivity via enhanced adsorption. For CH₄/N₂ separation, 4-7 Å pores optimize performance, where quaternary N and aromatic S groups strengthen CH₄ adsorption. The developed CMS model and hierarchical pore adsorption theory offer new insights for targeted synthesis of high-performance CMS in low-concentration gas purification. © 2025, The Authors. All rights reserved.

关键词： Van der Waals forces

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PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminators

arXiv

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arXiv 2023年

作者： Cong, Runmin Yang, Wenyu Zhang, Wei Li, Chongyi Guo, Chun-Le Huang, Qingming Kwong, Sam Institute of Information Science Beijing Jiaotong University Beijing100044 China School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China College of Computer Science Nankai University Tianjin300350 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China Department of Computer Science City University of Hong Kong Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

Due to the light absorption and scattering induced by the water medium, underwater images usually suffer from some degradation problems, such as low contrast, color distortion, and blurring details, which aggravate the difficulty of downstream underwater understanding tasks. Therefore, how to obtain clear and visually pleasant images has become a common concern of people, and the task of underwater image enhancement (UIE) has also emerged as the times require. Among existing UIE methods, Generative Adversarial Networks (GANs) based methods perform well in visual aesthetics, while the physical model-based methods have better scene adaptability. Inheriting the advantages of the above two types of models, we propose a physical model-guided GAN model for UIE in this paper, referred to as PUGAN. The entire network is under the GAN architecture. On the one hand, we design a Parameters Estimation subnetwork (Par-subnet) to learn the parameters for physical model inversion, and use the generated color enhancement image as auxiliary information for the Two-Stream Interaction Enhancement subnetwork (TSIE-subnet). Meanwhile, we design a Degradation Quantization (DQ) module in TSIE-subnet to quantize scene degradation, thereby achieving reinforcing enhancement of key regions. On the other hand, we design the Dual-Discriminators for the style-content adversarial constraint, promoting the authenticity and visual aesthetics of the results. Extensive experiments on three benchmark datasets demonstrate that our PUGAN outperforms state-of-the-art methods in both qualitative and quantitative metrics. The code and results can be found from the link of https://***/proj_***. © 2023, CC BY-NC-SA.

关键词： Discriminators

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End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19

arXiv

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arXiv 2020年

作者： Belkacem, Abdelkader Nasreddine Ouhbi, Sofia Lakas, Abderrahmane Benkhelifa, Elhadj Chen, Chao Department of Computer and Network Engineering CIT UAEU UAE Department of Computer Science and Software Engineering CIT UAEU UAE Cloud Computing and Applications Reseach Lab Staffordshire University United Kingdom Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology China

Respiratory symptoms can be a caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms, including coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases such as the recent COVID-19 pandemic. One of the factors that contributed to the spread of the pandemic, was the late diagnosis or confusing it with regular flu-like symptoms. Science has proved that one of the possible differentiators of the underlying causes of these different respiratory diseases is coughing, which comes in different types and forms. Therefore, a reliable lab-free tool for early and more accurate diagnosis that can differentiate between different respiratory diseases is very much needed. This paper proposes an end-to-end portable system that can record data from patients with symptom, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. With the ongoing efforts to stop the spread of the COVID-19 disease everywhere today, and against similar diseases in the future, our proposed low cost and user-friendly solution can play an important part in the early diagnosis. Copyright © 2020, The Authors. All rights reserved.

关键词： Diagnosis

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Optimal control of linear Gaussian quantum systems via quantum learning control

arXiv

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arXiv 2024年

作者： Liu, Yu-Hong Zeng, Yexiong Tan, Qing-Shou Dong, Daoyi Nori, Franco Liao, Jie-Qiao Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Key Laboratory for Matter Microstructure and Function of Hunan Province Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China Theoretical Quantum Physics Laboratory Cluster for Pioneering Research RIKEN Wakoshi Saitama351-0198 Japan Quantum Computing Center RIKEN Wakoshi Saitama351-0198 Japan Key Laboratory of Hunan Province on Information Photonics and Freespace Optical Communication College of Physics and Electronics Hunan Institute of Science and Technology Yueyang414000 China School of Engineering and Information Technology University of New South Wales CanberraACT2600 Australia Department of Physics The University of Michigan Ann ArborMI48109-1040 United States Institute of Interdisciplinary Studies Hunan Normal University Changsha410081 China

Efficiently controlling linear Gaussian quantum (LGQ) systems is a significant task in both the study of fundamental quantum theory and the development of modern quantum technology. Here, we propose a general quantum-learning-control method for optimally controlling LGQ systems based on the gradient-descent algorithm. Our approach flexibly designs the loss function for diverse tasks by utilizing first- and second-order moments that completely describe the quantum state of LGQ systems. We demonstrate both deep optomechanical cooling and large optomechanical entanglement using this approach. Our approach enables the fast and deep ground-state cooling of a mechanical resonator within a short time, surpassing the limitations of sideband cooling in the continuous-wave driven strong-coupling regime. Furthermore, optomechanical entanglement could be generated remarkably fast and surpass several times the corresponding steady-state entanglement, even when the thermal phonon occupation reaches one hundred. This work will not only broaden the application of quantum learning control, but also open an avenue for optimal control of LGQ systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Cooling

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Disorder-induced pronounced magnetoresistive hysteresis in Josephson junctions

引用

Physical Review B 2025年第13期111卷 134513-134513页

作者： Yu Wu Daiqiang Huang Huanyu Zhang Anita Guarino Rosalba Fittipaldi Chao Ma Wenjie Hu Chang Niu Zhen Wang Weichao Yu Yuriy Yerin Antonio Vecchione Yang Liu Mario Cuoco Hangwen Guo Jian Shen State Key Laboratory of Surface Physics and Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai 200433 People's Republic of China Department of Physics Fudan University Shanghai 200433 People's Republic of China Shanghai Aerospace Control Technology Institute Shanghai 201109 People's Republic of China International Center for Quantum Materials Peking University Beijing 100871 People's Republic of China CNR-SPIN c/o Universitá di Salerno I-84084 Fisciano (SA) Italy College of Materials Science and Engineering Hunan University Changsha 410082 People's Republic of China Department of Physics University of Science and Technology of China Hefei Anhui 230026 Peoples Republic of China CNR-SPIN via del Fosso del Cavaliere 100 00133 Roma Italy Hefei National Laboratory Hefei 230088 People's Republic of China Zhangjiang Fudan International Innovation Center Fudan University Shanghai 201210 People's Republic of China Shanghai Research Center for Quantum Sciences Shanghai 201315 People's Republic of China Collaborative Innovation Center of Advanced Microstructures Nanjing 210093 People's Republic of China

The relation between superconductivity and time-reversal symmetry is one of the most fascinating problems in condensed-matter physics. Here we report an anomalous transport behavior in superconducting Josephson junctions induced by surface disorder. We fabricate vertical Josephson junctions using metallic superconductor (Al) and ion-bombarded Sr2RuO4. Below the superconducting transition temperature, we observe persistent magnetoresistive hysteresis behavior dependent on the disorder deposition time. Field- and temperature-dependent measurements suggest that the observed effects arise from disorder-induced anomalous flux in Sr2RuO4 which can be sensitively detected by superconducting Al. To account for the observed findings, we consider different physical scenarios such as interband surface pairing reconstruction, magnetic surface states, and vortex phases.

关键词： Hysteresis

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Autonomous Water Surface Vehicle Metaheuristic Mission Planning using Self-generated Goals and Environmental Forecasts

Autonomous Water Surface Vehicle Metaheuristic Mission Plann...

引用

American control Conference

作者： Evan Krell Scott A. King Luis Rodolfo Garcia Carrillo Control of Robots and Autonomous Agents Laboratory (CORAL) Department of Computing Sciences Texas A&M University Corpus Christi CORAL and the Unmanned Systems Laboratory (TAMUCC-USL) Department of Electrical Engineering Texas A&M University Corpus Christi

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

An unmanned surface vehicle takes on the role of an autonomous science agent, generating missions based on data. The vehicle explores over long durations and large regions, continually selecting and visiting target survey locations. Select system components are implemented and evaluated for automatic target generation, onboard mapping and path planning that optimizes energy use and data collection reward.

关键词： Planning History Cameras Satellites Path planning Collision avoidance Prototypes

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