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Quantum Reinforcement Learning for QoS-Aware Real-Time Job Scheduling in Cloud Systems

IEEE Systems Journal 2025年

作者： Dai, Shuhong Saurabh, Nishant Wang, Qingle Nian, Jiawei Kan, Shuwen Mao, Ying Cheng, Long North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources the School of Control and Computer Engineering Beijing102206 China Utrecht University Department of Information and Computing Sciences CS Utrecht 3584 Netherlands Fordham University Department of Computer and Information Science New YorkNY10458 United States

Effective cloud job scheduling is essential for enhancing the performance and operational efficiency of cloud-based services, directly impacting their quality of service (QoS). Among existing methodologies, deep reinforcement learning (DRL) has proven effective in addressing complex, multidimensional optimization challenges in real-time scheduling. With advancements in quantum computing, quantum neural networks (QNNs) are showing unique advantages in information representation and processing. This study is the first to explore quantum reinforcement learning (QRL) for real-time job scheduling in cloud systems. Specifically, we propose a QRL framework that utilizes variational and encoding layers to convert state information into quantum data, repeatedly embedded into a QNN to compute optimal value returns. This approach aims to enhance QoS by improving job execution success rates and reducing average response times with unpredictable job arrivals. We present the detailed design of our approach, and our simulation results demonstrate that the QRL method significantly exceeds established baselines, including those based on DRL, across a range of workload intensities and computational resource configurations. This is particularly evident under high-load conditions, where our approach can achieve 55.2% higher success rates, underscoring its significant potential in cloud job scheduling optimization. © 2007-2012 IEEE.

关键词： Qubits

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Urgency-aware optimal routing in repeated games through artificial currencies

arXiv

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arXiv 2020年

作者： Salazar, Mauro Paccagnan, Dario Agazzi, Andrea Heemels, W.P.M.H. Control Systems Technology Group Department of Mechanical Engineering Eindhoven University of Technology Netherlands Computational Optimization Group Department of Computing Imperial College London United Kingdom Department of Mathematics Duke University United States

When people choose routes minimizing their individual delay, the aggregate congestion can be much higher compared to that experienced by a centrally-imposed routing. Yet centralized routing is incompatible with the presence of self-interested agents. How can we reconcile the two? In this paper we address this question within a repeated game framework and propose a fair incentive mechanism based on artificial currencies that routes selfish agents in a system-optimal fashion, while accounting for their temporal preferences. We instantiate the framework in a parallel-network whereby agents commute repeatedly (e.g., daily) from a common start node to the end node. Thereafter, we focus on the specific two-arcs case whereby, based on an artificial currency, the agents are charged when traveling on the first, fast arc, whilst they are rewarded when traveling on the second, slower arc. We assume the agents to be rational and model their choices through a game where each agent aims at minimizing a combination of today’s discomfort, weighted by their urgency, and the average discomfort encountered for the rest of the period (e.g., a week). We show that, if prices of artificial currencies are judiciously chosen, the routing pattern converges to a system-optimal solution, while accommodating the agents’ urgency. We complement our study through numerical simulations. Our results show that it is possible to achieve a system-optimal solution whilst reducing the agents’ perceived discomfort by 14-20% when compared to a centralized optimal but urgency-unaware policy. Copyright © 2020, The Authors. All rights reserved.

关键词： Optimal systems

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Unpolarized transverse momentum dependent parton distributions of the nucleon from lattice QCD

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Physical Review D 2024年第11期109卷 114513-114513页

作者： Jin-Chen He Min-Huan Chu Jun Hua Xiangdong Ji Andreas Schäfer Yushan Su Wei Wang Yi-Bo Yang Jian-Hui Zhang Qi-An Zhang Yang Yuanqing Scientific Computering Center Tsung-Dao Lee Institute Shanghai Key Laboratory for Particle Physics and Cosmology Key Laboratory for Particle Astrophysics and Cosmology (MOE) School of Physics and Astronomy Department of Physics Key Laboratory of Atomic and Subatomic Structure and Quantum Control (MOE) Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter Institute of Quantum Matter Guangdong-Hong Kong Joint Laboratory of Quantum Matter Guangdong Provincial Key Laboratory of Nuclear Science Southern Nuclear Science Computing Center Institut für Theoretische Physik Southern Center for Nuclear-Science Theory (SCNT) CAS Key Laboratory of Theoretical Physics School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study UCAS Hangzhou 310024 China Beijing/Hangzhou China School of Physical Sciences School of Science and Engineering Center of Advanced Quantum Studies Department of Physics School of Physics

We present a first lattice QCD calculation of the unpolarized nucleon’s isovector transverse-momentum-dependent parton distribution functions (TMDPDFs), which are essential to predict observables of multiscale, semi-inclusive processes in the standard model. We use a Nf=2+1+1 MILC ensemble with valence clover fermions on a highly improved staggered quark (HISQ) sea to compute the quark momentum distributions in a large-momentum nucleon on the lattice. The state-of-the-art techniques in renormalization and extrapolation in the correlation distance on the lattice are adopted. The perturbative kernel up to next-to-next-to-leading order is taken into account, and the dependence on the pion mass and the hadron momentum is explored. Our results are qualitatively comparable with phenomenological TMDPDFs, which provide an opportunity to predict high energy scatterings from first principles.

关键词： Lattice QCD Nucleon distribution Parton distribution functions

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Networked Integrated Sensing and Communications for 6G Wireless Systems

arXiv

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arXiv 2024年

作者： Li, Jiapeng Shao, Xiaodan Chen, Feng Wan, Shaohua Liu, Chang Wei, Zhiqiang Ng, Derrick Wing Kwan The College of Artificial Intelligence Southwest University Chongqing400715 China The Brain-Inspired Computing and Intelligent Control Key Laboratory Southwest University Chongqing400715 China The Key Laboratory of Luminescence Analysis and Molecular Sensing Southwest University Chongqing400715 China The Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China The School of Mathematics and Statistics Xi'an Jiaotong University Xi'An710049 China The Peng Cheng Laboratory Guangdong Shenzhen518055 China Guangdong Guangzhou510555 China The Department of Computer Science and Information Technology La Trobe University MelbourneVIC3150 Australia The School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia

Integrated sensing and communication (ISAC) is envisioned as a key pillar for enabling the upcoming sixth generation (6G) communication systems, requiring not only reliable communication functionalities but also highly accurate environmental sensing capabilities. In this paper, we design a novel networked ISAC framework to explore the collaboration among multiple users for environmental sensing. Specifically, multiple users can serve as powerful sensors, capturing back scattered signals from a target at various angles to facilitate reliable computational imaging. Centralized sensing approaches are extremely sensitive to the capability of the leader node because it requires the leader node to process the signals sent by all the users. To this end, we propose a two-step distributed cooperative sensing algorithm that allows low-dimensional intermediate estimate exchange among neighboring users, thus eliminating the reliance on the centralized leader node and improving the robustness of sensing. This way, multiple users can cooperatively sense a target by exploiting the block-wise environment sparsity and the interference cancellation technique. Furthermore, we analyze the mean square error of the proposed distributed algorithm as a networked sensing performance metric and propose a beamforming design for the proposed network ISAC scheme to maximize the networked sensing accuracy and communication performance subject to a transmit power constraint. Simulation results validate the effectiveness of the proposed algorithm compared with the state-of-the-art algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

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Transverse-momentum-dependent wave functions of the pion from lattice QCD

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Physical Review D 2024年第9期109卷 L091503-L091503页

作者： Min-Huan Chu Jin-Chen He Jun Hua Jian Liang Xiangdong Ji Andreas Schäfer Hai-Tao Shu Yushan Su Ji-Hao Wang Wei Wang Yi-Bo Yang Jun Zeng Jian-Hui Zhang Qi-An Zhang INPAC Shanghai Key Laboratory for Particle Physics and Cosmology Key Laboratory for Particle Astrophysics and Cosmology (MOE) School of Physics and Astronomy Shanghai Jiao Tong University Shanghai 200240 China Yang Yuanqing Scientific Computering Center Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 200240 China Department of Physics University of Maryland College Park Maryland 20742 USA Key Laboratory of Atomic and Subatomic Structure and Quantum Control (MOE) Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter Institute of Quantum Matter South China Normal University Guangzhou 510006 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Guangdong Provincial Key Laboratory of Nuclear Science Southern Nuclear Science Computing Center South China Normal University Guangzhou 510006 China Institut für Theoretische Physik Universität Regensburg D-93040 Regensburg Germany CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing 100190 China School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study UCAS Hangzhou 310024 China Southern Center for Nuclear-Science Theory (SCNT) Institute of Modern Physics Chinese Academy of Sciences Huizhou 516000 Guangdong Province China International Centre for Theoretical Physics Asia-Pacific Beijing/Hangzhou China School of Physical Sciences University of Chinese Academy of Sciences Beijing 100049 China School of Science and Engineering The Chinese University of Hong Kong Shenzhen 518172 China Center of Advanced Quantum Studies Department of Physics Beijing Normal University Beijing 100875 China School of Physics Beihang University Beijing 102206 China

We present a lattice QCD calculation of the transverse-momentum-dependent wave function (TMDWF) of the pion using large-momentum effective theory. Numerical simulations are based on one ensemble with 2+1+1 flavors of highly improved staggered quarks with lattice spacing a=0.121 fm from the MILC collaboration, and one with 2+1 flavor clover fermions and tree-level Symanzik gauge action generated by the CLS collaboration with a=0.098 fm. As a key ingredient, the soft function is first obtained by incorporating the one-loop perturbative contributions and a proper normalization. Based on this and the equal-time quasi-TMDWF simulated on the lattice, we extract the TMDWF. The results for both lattice ensembles are compatible and a comparison with a phenomenological parametrization is made. Our studies provide a first attempt of ab initio calculation of TMDWFs which will eventually lead to crucial theory inputs for making predictions for exclusive processes under QCD factorization.

关键词： Lattice QCD Transverse momentum dependent distribution Pions

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Visible-Thermal Cross-Modality Class-Incremental Learning

引用

Expert Systems with Applications 2025年

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 300350 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.

关键词：

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Enhancing biodegradability of landfill leachate by hybrid treatment process: coagulation followed by catalytic ozonation with Z5Å-Fe-Zn for environmental sustainability

引用

Journal of Water Process engineering 2025年 75卷

作者： Javaid, Rahat Ikhlaq, Amir Rizvi, Osama Shaheen Abid, Maryam Qi, Fei Qazi, Umair Yaqub Molinaroli College of Engineering and Computing University of South Carolina Columbia 29208 SC United States Institute of Environmental Engineering and Research University of Engineering and Technology GT Road Punjab Lahore 54890 Pakistan Department of Chemistry and Chemical Engineering Syed Babar Ali School of Science & Engineering (SBASSE) Lahore University of Management Sciences (LUMS) Sector U DHA Lahore Cantt. 54792 Pakistan Beijing Key Lab for Source Control Technology of Water Pollution College of Environmental Science and Engineering Beijing Forestry University Beijing 100083 China Department of Chemistry Benedict College 1600 Harden Street Columbia 29204 SC United States

Treatment of real stabilized landfill leachate (RSLL) is extremely challenging due to its complex mixture containing high levels of organic pollutants, that pose severe environmental problems. Biological treatment is often ineffective due to low biodegradability and high toxicity which limits its application. Effective pre-treatment is the ultimate choice to make the RSLL biodegradable for secondary anaerobic treatment. A combined physicochemical and advanced treatment process was employed to make RSLL treatment effective and economically viable. To the best of our knowledge, in this study, we for the first time utilized multi-metal coated Z5Å-Fe-Zn in catalytic ozonation to study the improvements in the biodegradability (BOD₅/COD ratios) of RSLL. To use RSLL for recycling, this study may be beneficial to achieve United Nations' sustainable development goals. Coagulation-flocculation (CF) with two coagulants [alum and ferrous sulfate (FS)] followed by catalytic ozonation with novel catalyst Z5Å-Fe-Zn were studied in two combinations. Combined treatment processes CF(Alum)–Z5Å-Fe-Zn/O₃ and CF(FS)–Z5Å-Fe-Zn/O₃ demonstrated higher performance achieving COD removal of 91 % and 88 %, and BOD₅ removal of 72 % and 68 %, respectively at optimized working parameters (pH = 8.3 ± 0.3;coagulant = 3 g/L;O₃ = 11 g/h;Z5Å-Fe-Zn = 6 g/L;mixing time-CF = 25 min;treatment time-CF(Alum)–Z5Å-Fe-Zn/O₃ = 55 min). Notably, biodegradability (BOD₅/COD ratios) increased significantly from 0.14 to 0.58. Therefore, treated RSLL could be easily biodegradable and recycled for energy generation in anaerobic processes and could achieve recycling standards, facilitating the practical applications of the studied process. © 2025 Elsevier Ltd

关键词： Biodegradability Catalytic ozonation Combined treatment process Stabilized leachate treatment Zeolite 5Å-Fe-Zn

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

arXiv

引用

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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