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Multi-Modal Self-Supervised Learning AlgorithmBased Product Recommendation

IEEE Transactions on Automation Science and engineering 2025年 22卷 15371-15380页

作者： Gao, Li Fan, Haiping Chen, Qingkui Yang, Heyu School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

With the rise of e-commerce, personalized recommendation algorithms have received much attention in recent years. Meanwhile, multimodal recommendation algorithms have become the next competitive track of personalized recommendation. The current mainstream recommendation algorithms (e.g., NeuMF, NGCF) rely too much on empirical data and do not effectively utilize multimodal information to model user preferences, while there is the problem of sparsity of user preference data. Secondly, they are still limited to using the same weights to fuse the item features of different modalities, and have the problem of inaccurate recommendation. For this reason, this paper proposes Multi-Modal Self-Supervised Learning Algorithm Based Product Recommendation (MMSLRec). (1) Data augmentation by self-supervised adversarial perturbation generation learning is performed to supplement the missing interaction information of tailed products to solve the common data sparsity problem in product recommendation. (2) A multimodal representation learning module is introduced to capture deep features in image and text modalities using AlexNet and Bert, respectively, in order to avoid the recommendation accuracy degradation problem caused by insufficient data feature learning. (3) Learning the user's perceived strength in different modalities, using the user co-occurrence matrix to fuse the features and make recommendations. Experiments on Amazon-Electronics, Amazon-Clothing, and Amazon-Baby, with different degrees of data sparsity verify the effectiveness of our MMSLRec in solving the data sparsity problem and the multimodal user preference modeling problem, and compared to the state-of-the-art baseline recommendation model on the three datasets, the Recall@20 improved by 9.26%, 3.11% and 2.92%, respectively. © 2004-2012 IEEE.

关键词： Self-supervised learning

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Orbit-Orbit Interaction in Spatiotemporal optical Vortex

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engineering 2025年第2期45卷 44-51页

作者： Jian Chen Jie Zhao Xi Shen Dewei Mo Cheng-Wei Qiu Qiwen Zhan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Shanghai Key Laboratory of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

While spin-orbit interaction has been extensively studied,few investigations have reported on the interaction between orbital angular momenta(OAMs).In this work,we study a new type of orbit-orbit coupling between the longitudinal OAM and the transverse OAM carried by a three-dimensional(3D)spatiotemporal optical vortex(STOV)in the process of tight *** 3D STOV possesses orthogonal OAMs in the x-y,t-x,and y-t planes,and is preconditioned to overcome the spatiotemporal astigmatism effect.x,y,and t are the axes in the spatiotemporal *** corresponding focused wavepacket is calculated by employing the Debye diffraction theory,showing that a phase singularity ring is generated by the interactions among the transverse and longitudinal vortices in the highly confined *** Fourier-transform decomposition of the Debye integral is employed to analyze the mechanism of the orbit-orbit *** is the first revelation of coupling between the longitudinal OAM and the transverse OAM,paving the way for potential applications in optical trapping,laser machining,nonlinear light-matter interactions,and more.

关键词： Spatiotemporal optical vortex Orbital angular momentum Momentum interaction Highly confined wavepacket Diffraction

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Super-resolution reconstruction of off-axis digital holography based on Real-ESRGAN

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Applied Optics 2025年第9期64卷 2245-2254页

作者： Chen, Geng Chen, Jie Liao, Houzhang Liu, Keke Tang, Xin Kong, Yong School of Electric and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

Off-axis digital holography plays a crucial role in high-precision three-dimensional imaging. However, high-resolution phase images are often affected by the limited pixel size of the sensor. To address this issue, this study proposes, for the first time, to the best of our knowledge, the application of Real-ESRGAN for the super-resolution reconstruction of off-axis digital holography images. By directly applying super-resolution processing to the hologram, the limitations of the sensor's pixel size are overcome, followed by phase reconstruction to obtain high-resolution phase images. The Real-ESRGAN model enhances the texture information of the hologram through a deep network with multiple residual-in-residual dense blocks (RRDBs), restoring a clearer hologram. A U-Net architecture with spectral normalization, offering stronger discriminative capability, is used to provide detailed per-pixel feedback for the generator. The experimental results demonstrate that this method can be applied to multi-scale holograms, outperforming comparison networks in both visual quality and quantitative metrics, thus providing an innovative solution for the super-resolution reconstruction of off-axis digital holography. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Holograms

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More than just a name?From magnetic to optical skyrmions and the topology of light

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Light(Science & Applications) 2025年第1期14卷 26-29页

作者： Jian Chen Andrew Forbes Cheng-Wei Qiu School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China Department of Electrical and Computer Engineering National University of SingaporeSingapore117583Singapore School of Physics University of the WitwatersrandJohannesburgSouth Africa

Topology is usually perceived intrinsically immutable for a given *** argue that optical topologies do not immediately enjoy such ***'optical skyrmions'as an example,we show that they will exhibit varying textures and topological invariants(skyrmion numbers),depending on how to construct the skyrmion vector when projecting from real to parameter *** demonstrate the fragility of optical skyrmions under a ubiquitous scenario-simple reflection off an optical *** topology is not without benefit,but it must not be assumed.

关键词： numbers space. topology

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HGLFNet: Hybrid Global Semantic and Local Detail Feature Network for Lane Detection

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IEEE Access 2025年 13卷 72314-72328页

作者： Ding, Lei Tang, Chunhui Fang, Yi University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China

Lane detection is a crucial component of autonomous driving perception systems. Existing methods still encounter performance limitations in complex situations like occlusions and extreme illumination. To address these challenges, this paper introduces a novel Hybrid Global Semantic and Local Detail Feature Network (HGLFNet), designed to enhance lane detection accuracy and robustness in complex scenarios. Technically, we propose a Global Semantic Feature Extraction Module (GSFEM), integrating a Long-Range Information Capture Unit (LRICU) and a Multi-Scale Aggregation Unit (MSAU). GSFEM innovatively utilizes large-kernel convolutions to efficiently capture global contextual information, significantly improving the model’s perception of complete lane line shapes and overcoming the limited receptive field of conventional methods. Furthermore, we develop a Semantic Feature Fusion Module (SFFM) to effectively bridge the semantic gap between global semantic features and local detailed features. SFFM incorporates a Deformable Feature-based Attention Mechanism (DFAM) to adaptively enhance the representational power of fused features and highlight crucial lane line characteristics. HGLFNet effectively integrates global semantic context with local detailed information, enhancing the network’s ability to detect thin and occluded lane line structures. In quantitative evaluation, HGLFNet is compared with state-of-the-art methods. On the VIL-100 dataset, the F1 score and accuracy are improved by 0.75% and 0.38%, respectively, compared to ADNet. On the Tusimple dataset, the F1 score and accuracy are improved by 0.49% and 0.11%, respectively. On the more challenging CULane dataset, the F1 score is improved by 0.03%. The experimental results comprehensively demonstrate that HGLFNet surpasses the existing state-of-the-art techniques in both accuracy and efficiency, providing a novel and effective solution for lane detection in complex scenarios and showing significant potential in pra

关键词： Semantics Feature extraction Lane detection Shape Attention mechanisms Accuracy Kernel Lighting Autonomous vehicles Roads

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Enhancement Methods for Chiral optical Signals by Tailoring optical Fields and Nanostructures

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engineering 2025年第2期45卷 25-43页

作者： Hanqing Cai Liangliang Gu Haifeng Hu Qiwen Zhan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Zhangjiang Laboratory Shanghai 201204China Shanghai Key Laboratory of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China

The unique property of chirality is widely used in various *** the past few decades,a great deal of research has been conducted on the interactions between light and matter,resulting in significant technical advancements in the precise manipulation of light field *** this review,which focuses on current chiral optics research,we introduce the fundamental theory of chirality and highlight the latest achievements in enhancing chiral signals through artificial nano-manufacturing technology,with a particular focus on mechanisms such as light scattering and Mie resonance used to amplify chiral *** providing an overview of enhanced chiral signals,this review aims to provide researchers with an indepth understanding of chiral phenomena and its versatile applications in various domains.

关键词： Mie scattering optical chirality Circular dichroism Orbital angular momentum Bound states in the continuum Nonlinear optics

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Broadband single-polarization single-mode low confinement loss hollow core anti-resonant fiber with semi-elliptical nested tube

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optical and Quantum Electronics 2025年第4期57卷 1-12页

作者： Lin, Wen Fei, Yihong Wang, Ning Jia, Hongzhi Engineering Research Center of Optical Instruments and Systems Ministry of Education Shanghai Key Laboratory of Modern Optical Systems School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

In this paper, a hollow-core anti-resonant optical fibre containing a semi-elliptical nested tube is proposed, which has the characteristics of single-polarization, large bandwidth, single-mode and low confinement loss. By optimizing the structural design, the polarization extinction ratio (PER) of the fiber reaches 21,0183 at a wavelength of 1.55 μm, and the y-polarized fundamental mode loss is only 0.00086 dB/m. By changing the material and thickness of the semi-ellipsoidal nested tube, a large bandwidth of 280 nm was achieved in the wavelength range of 1.51-1.78 μm, with loss limited to less than 0.001 dB/m within the bandwidth range. The PER and higher-order mode extinction ratio of 4870, 1487, respectively. The loss remains below 0.01 dB/m when the bending radius exceeds 4.5 cm. Therefore, it is expected that this optical fiber can be used in optical devices such as fiber optic sensors, fiber optic lasers and fiber optic gyroscopes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Single mode fibers

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Exploration of New Dimensions for the Massive Internet of Things

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IEEE Access 2025年 13卷 89582-89599页

作者： Wang, Michael Wang, L. Zhang, J.J. You, X. Southeast University School of Information Science and Engineering School of Information Science and Engineering China Nanjing University of Science and Technology of Electrical and Computer Engineering School of Electronic and Optical Engineering China Nanjing University of Posts and Telecommunications School of Internet of Things China

Like all other types of wireless communications, massive Internet of Things (mIoT) communication is all about degrees of freedom. It holds the key to almost all mIoT challenging issues. However, it is also the most limited resource in mIoT due to its unique constraints. This paper derives a new class of waveforms, i.e., a canonical waveform, and explores the dimensions hidden in its underlying mathematical structure. The intrinsic characteristics and the degrees of freedom of the canonical waveform are studied and fully explored to reap the full potential of the waveform. Most significantly, a new dimension is uncovered and exploited as additional degrees of freedom to better address the mIoT challenges in energy efficiency, resource efficiency, and robustness under the mIoT constraint. The corresponding benefits are evaluated analytically and numerically in AWGN, frequency-flat, and frequency-selective channels. We demonstrate that no existing mIoT waveforms can fully address the mIoT challenges, whereas the new degrees of freedom from the canonical waveform offer a comprehensive solution. © 2013 IEEE.

关键词： Wireless communication Wireless sensor networks Temperature sensors Costs Monitoring Energy efficiency LoRa Complexity theory Temperature measurement Radio frequency

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Infrared spectroscopic analysis of O-H bond dynamics in one-dimensional confined water and bulk water

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红外与毫米波学报 2025年第1期44卷 78-85页

作者： ZHANG Lei WANG Tian-Qi FAN Yan-Ping School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China

In sub nanometer carbon nanotubes,water exhibits unique dynamic characteristics,and in the high-frequency region of the infrared spectrum,where the stretching vibrations of the internal oxygen-hydrogen(O-H)bonds are closely related to the hydrogen bonds(H-bonds)network between water ***,it is crucial to analyze the relationship between these two *** this paper,the infrared spectrum and motion characteristics of the stretching vibrations of the O-H bonds in one-dimensional confined water(1DCW)and bulk water(BW)in(6,6)single-walled carbon nanotubes(SWNT)are studied by molecular dynamics *** results show that the stretching vibrations of the two O-H bonds in 1DCW exhibit different frequencies in the infrared spectrum,while the O-H bonds in BW display two identical main frequency *** analysis using the spring oscillator model reveals that the difference in the stretching amplitude of the O-H bonds is the main factor causing the change in vibration frequency,where an increase in stretching amplitude leads to a decrease in spring stiffness and,consequently,a lower vibration frequency.A more in-depth study found that the interaction of H-bonds between water molecules is the fundamental cause of the increased stretching amplitude and decreased vibration frequency of the O-H ***,by analyzing the motion trajectory of the H atoms,the dynamic differences between 1DCW and BW are clearly *** findings provide a new perspective for understanding the behavior of water molecules at the nanoscale and are of significant importance in advancing the development of infrared spectroscopy detection technology.

关键词： one-dimensional confined water infrared spectroscopy hydrogen bonds

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SVTNet: Dual Branch of Swin Transformer and Vision Transformer for Monocular Depth Estimation

SVTNet: Dual Branch of Swin Transformer and Vision Transform...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jia, Shuwen Wang, Yongxiong Chen, Han Huang, Shuai School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

ISBN: (纸本)9798350368741

In monocular depth estimation, effective acquisition of global and local information is the key to improving accuracy. We introduce a novel dual branch network called Swin Vision Transformer Net (SVTNet), where the Swin Transformer and Vision Transformer are combined to learn features with global and local information. In Swin Transformer branch, we remove the Feature Pyramid Networks which reduces the noise introduced by interpolation and maintains the original characteristics of the hierarchical features extracted from each layer of Swin Transformer. In Vision Transformer Branch, we remove the multi-scale convolutional structure and keep the same-scale feature maps extracted by Vision Transformer for reducing the consumption of memory and extracting more global features. The experimental results on the NYU-Depth v2, KITTI, and CityScapes datasets show that compared with existing advanced methods, the SVTNet has achieved significant performance improvements in multiple evaluation metrics. © 2025 IEEE.

关键词： dual branch Feature Pyramid Networks monocular depth estimation Same-scale feature Swin Transformer Vision Transformer

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