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Polarization-directed nanophotonic routers based on twodimensional inorganic molecular crystals

InfoMat 2024年第8期6卷 111-118页

作者： Jiacheng Yao Xin Feng Tingting Zhang Fangqi Chen Zhenglong Zhang Hairong Zheng Tianyou Zhai Tao Ding Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education School of Physics and TechnologyWuhan UniversityWuhanthe People's Republic of China State Key Laboratory of Materials Processing and Die&Mould Technology and School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhanthe People's Republic of China Department of Mechanical Engineering The University of Texas at DallasRichardsonTexasUSA School of Physics and Information Technology Shaanxi Normal UniversityXi'anthe People's Republic of China

Photonic and plasmonic hybrid nanostructures are the key solution for integratednanophotonic circuits with ultracompact size but relative low loss. However,the poor tunability and modulability of conventional waveguides makesthem cumbersome for optical multiplexing. Here we make use of twodimensionalmolecular crystal, α-Sb_(2)O_(3) as a dielectric waveguide via totalinternal reflection, which shows polarization-sensitive modulation of the propagatingbeams due to its large polarization mode dispersion. Both experimentsand simulations are performed to verify such concept. These Sb_(2)O_(3) nanoflakescan be coupled with plasmonic nanowires to form nanophotonic beam splittersand routers which can be easily modulated by changing the polarization of theincidence. It thus provides a robust, exploitable and tunable platform for onchipnanophotonics.

关键词： nanoflakes plasmonics polarization Sb_(2)O_(3) waveguides

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Evaluation of the Illumination system of a High-End Handheld Dermatoscope 14

Evaluation of the Illumination System of a High-End Handheld...

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Optics in Health Care and Biomedical Optics XIV 2024

作者： Wei, Zhe Huang, Deliang Wang, Sen Cui, Shengdong Wang, Min Huang, Zheng MOE Key Laboratory of Medical Optoelectronics Science and Technology School of Optoelectronics and Information Engineering Fujian Normal University Fuzhou China

ISBN: (纸本)9781510682122

Background and objectives: Dermatoscope is an important optical tool for dermatologists. Its illumination system is a key component for high quality visual observation and digital photography. The aim of this study was to evaluate the illumination system of a high-end handhold dermatoscope. Materials and Methods: DermLite DL5 was used for this study. The dermatoscope equipped with sophisticated illumination system for visualization under white, yellow and ultraviolet (UV) light with or without the use of polarization. A fiber optic spectrometer was used to measure the spectra of each lighting mode. A handhold spectrometer was used to measure the color temperature, luminance and chromatic aberration of various lighting conditions under non-polarized, cross-polarized and parallel-polarized modes. The uniformity of each lighting condition was analyzed by pixel analysis of projected digital images. Results: The peak wavelength of the UV LEDs was 377 nm and the full width at half maximum (FWHM) was 21.27 nm. The peak wavelength of the yellow LEDs was 591 nm and the FWHM was 15.5 nm. The color temperature of white LEDs was over 9000 k in several modes, whereas the color temperature of the yellow LEDs varied widely and the color temperature of mixed lighting remained stable at 5000 k. The uniformity of white, yellow and mixed lighting was less than ±10%. Conclusions: The designed higher color temperature of the tested handhold dermatoscope can accommodate the physician's view of skin blood vessels. Good uniformity under different lighting and viewing modes not only satisfies the human eye but also ensure the high quality of dermatoscope digital image of the skin. © 2024 SPIE.

关键词： Illumination meters

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A Deep Neural Collaborative Filtering Based Service Recommendation Method with Multi-Source Data for Smart Cloud-Edge Collaboration Applications

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Tsinghua science and technology 2024年第3期29卷 897-910页

作者： Wenmin Lin Min Zhu Xinyi Zhou Ruowei Zhang Xiaoran Zhao Shigen Shen Lu Sun Alibaba Business School Hangzhou Normal UniversityHangzhou 311121China Blockchain Laboratory of Agricultural Vegetables Weifang University of Science and TechnologyShouguang 262700China School of Computer Science Qufu Normal UniversityRizhao 276827China School of Information Engineering Huzhou UniversityHuzhou 313000China

Service recommendation provides an effective solution to extract valuable information from the huge and ever-increasing volume of big data generated by the large cardinality of user ***,the distributed and rich multi-source big data resources raise challenges to the centralized cloud-based data storage and value mining approaches in terms of economic cost and effective service recommendation *** view of these challenges,we propose a deep neural collaborative filtering based service recommendation method with multi-source data(i.e.,NCF-MS)in this paper,which adopts the cloud-edge collaboration computing paradigm to build recommendation *** specifically,the Stacked Denoising Auto Encoder(SDAE)module is adopted to extract user/service features from auxiliary user profiles and service *** Multiple Layer Perceptron(MLP)module is adopted to integrate the auxiliary user/service features to train the recommendation ***,we evaluate the effectiveness of the NCF-MS method on three public *** experimental results show that our proposed method achieves better performance than existing methods.

关键词： deep neural collaborative filtering multi-source data cloud-edge collaboration application stackeddenoising auto encoder multiple layer perceptron

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Improved Image Matching Method Between SAR and Optical Based on Gaussian Vote by Ballot 11th

Improved Image Matching Method Between SAR and Optical Based...

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11th China Conference on Command and Control, C2 2023

作者： Yang, Yanjiao Wang, Ziwei Qu, Guangzhou Ma, Meng Liu, ShuaiShuai Chi, QingXi Beijing Electro-Mechanical Engineering Institute Beijing China Science and Technology on Complex System Control and Intelligent Agent Coopration Laboratory Beijing China

ISBN: (纸本)9789819990207

Aiming to obtain the high-precision matching position and reduce time and resource requirements, an image matching method between SAR and optical based on CSP-DenseNet and Gaussian vote by ballot is proposed. First, in order to detect stable interest points and extract deep feature in SAR image, we use FAST operator and CSP-DenseNet. Then, in order to save time and resource requirements, we use gaussian vote by ballot to determine the high-precision matching position between two *** simulation results reveal the validity of the algorithm. © Chinese Institute of Command and Control 2024.

关键词： Synthetic aperture radar

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Remote Sensing Monitoring of Pasture Growth Using the Coherence Characteristics of InSAR technology 2

Remote Sensing Monitoring of Pasture Growth Using the Cohere...

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2nd IEEE International Conference on Signal, information and Data Processing, ICSIDP 2024

作者： Gao, Xian Yang, Xiangli Huang, Pingping Tan, Weixian Wang, Zhiguo Chongqing Jiaotong University School of Information Science and Engineering Chongqing China Inner Mongolia University of Technology College of Information Engineering Hohhot China Inner Mongolia Key Laboratory of Radar Technology and Application Hohhot China

ISBN: (纸本)9798331515669

Grassland resources play a crucial role as significant assets on Earth, exerting vital functions in carbon cycling, soil conservation, water resource management, and climate regulation. However, in recent years, excessive human exploitation and various pest infestations have led to the gradual desertification of existing grasslands. Therefore, it is imperative to conduct real-time monitoring and effective management of these resources. This study employs synthetic aperture radar (SAR) images to monitor and analyze the pasture status in the steppe region. In contrast to conventional monitoring methods, this approach offers a wide monitoring range and an extended observation period. Additionally, it leverages the advantages of SAR data, such as its independence from weather conditions compared to optical imagery. By calculating the coherence index using SAR data and combining it with optical imagery, we analyze the growth conditions of pasture in the study area. The research demonstrates a strong correlation between the variations in coherence index regions and the growth trend of pasture. For the issue of weather interference in optical imagery acquisition, the coherence index derived from SAR imagery can fully substitute for optical imagery in steppe analysis. © 2024 IEEE.

关键词： Radar imaging

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Poly(lactic acid)/Poly(butylene adipate-co-terephthalate)films with simultaneous high oxygen barrier and fast degradation properties

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Green Energy & Environment 2025年第1期10卷 1-10页

作者： Mengjing Yang Yuxi Mao Penghui Zhang Jie Li Zeming Tong Zhenguo Liu Yanhui Chen Institute of Flexible Electronics Northwestern Polytechnical UniversityXi'an710072China Key Laboratory of Flexible Electronics of Zhejiang Province Ningbo Institute of Northwestern Polytechnical University218 Qingyi RoadNingbo315103China School of Chemistry and Chemical Engineering Shaanxi Key Laboratory of Macromolecular Science and TechnologyKey Laboratory of Special Functional and Smart Polymer Materials of Ministry of Industry and Information TechnologyNorthwestern Polytechnical UniversityXi'an 710072China

Although poly(lactic acid)(PLA)is a good environmentally-friendly bio-degradable polymer which is used to substitute traditional petrochemical-based polymer packaging films,the barrier properties of PLA films are still insufficient for high-barrier packaging *** this study,oxygen scavenger hydroxyl-terminated polybutadiene(HTPB)and cobalt salt catalyst were incorporated into the PLA/poly(butylene adipate-co-terephthalate)(PLA/PBAT),followed by melting extrusion and three-layer co-extrusion blown film process to prepare the composite *** oxygen permeability coefficient of the composite film combined with 6 wt%oxygen scavenger and 0.4 wt%catalyst was decreased significantly from 377.00 cc·mil·m^(-2)·day^(-1)·0.1 MPa^(-1) to 0.98 cc·mil·m^(-2)·day^(-1)·0.1 MPa^(-1),showing a remarkable enhancement of 384.69 times compared with the PLA/PBAT composite ***,the degradation behavior of the composite film was also accelerated,exhibiting a mass loss of nearly 60%of the original mass after seven days of degradation in an alkaline environment,whereas PLA/PBAT composite film only showed a mass loss of 32%.This work has successfully prepared PLA/PBAT composite films with simultaneously improved oxygen barrier property and degradation behavior,which has great potential for high-demanding green chemistry packaging industries,including food,agricultural,and military packaging.

关键词： Barrier property Oxygen scavenging Blow molding Eco-packaging Degradable composite film

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FairCF: fairness-aware collaborative filtering

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science China(information sciences) 2022年第12期65卷 127-141页

作者： Pengyang SHAO Le WU Lei CHEN Kun ZHANG Meng WANG School of Computer Science and Information Engineering Hefei University of Technology Intelligent Interconnected Systems Laboratory of Anhui Province Institute of Artifcial Intelligence Hefei Comprehensive National Science Center

Collaborative filtering(CF) techniques learn user and item embeddings from user-item interaction behaviors, and are commonly used in recommendation systems to help users find potentially desirable items. Most CF models optimize recommendation accuracy; however, they may lead to unwanted biases for particular demographic groups. Thus, we focus on learning fair representations of CF-based recommendations. We formulate this problem as an optimization task with two competing goals: embedding representations better meet accuracy requirements of recommendations, and simultaneously obfuscate information hidden in the embedding space, which is related to the users' sensitive attributes for fairness. Here,the intuitive idea is to use fair representation learning from machine learning to train a classifier with a sensitive attribute predictor from the user side to satisfy the fairness goal. However, such fair machine learning models assume entity independence, which differs greatly from CF because users and items are correlated collaboratively via user-item behaviors. Therefore, sensitive user information can be exposed from the users' preferred items. Consequently, defining only fairness constraints on users cannot achieve fairness in recommendation systems. In this paper, we propose FairCF framework for fairness-aware collaborative *** particular, we first define fairness constraints in a fair embedding space, where both a user classifier and an item classifier are employed to fit the fairness constraints. We then design an item classifier without item sensitive labels. The proposed framework can be trained in an end-to-end manner under most embedding based CF models. Extensive experiments conducted on three datasets(Movie Lens-100K, Movie Lens-1M,and Lastfm-360K) clearly demonstrate the superiority of the proposed FairCF framework relative to various fairness metrics(i.e., performance of newly-trained classifiers) than other state-of-the-art fairness-aware CF mode

关键词： recommendation systems fairness adversarial learning

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Unveiling the influential factors for customized bus service reopening from naturalistic observations in Shanghai

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International Journal of Transportation science and technology 2024年第1期13卷 106-121页

作者： Yu Shen Chenlong Xu Shengchuan Jiang Zhikang Zhai Yuxiong Ji Yuchuan Du Key Laboratory of Road and Traffic Engineering of the MOE Tongji Unviersity4800 Cao’an Hwy.Shanghai 201804China Department of Transportation System Engineering Business SchoolUniversity of Shanghai for Science and Technology516 Jungong Rd.Shanghai 200093China

This work attempts to understand how a customized bus(CB)operator decides to open or close a CB *** look into the changes in the operation status of CB lines(*** and closure)from one of the largest CB operators in Shanghai,China,with a 22-month con secutive observation ranging from January 2019 to October *** all CB services were totally suspended at the beginning of 2020 due to the COVID-19 travel restriction and then gradually recovered in March 2020,we utilize this study period as a naturalistic observa tion experiment to investigate the changes in the operation status of each CB line before and after the travel *** the operation status at each month as the binary alternatives,the mixed logit models and the tree-based models with explainable machine learning techniques are respectively adopted to explore the factors that influence the decision-making *** findings from both types of models are in general *** results show that the characteristics of each CB line including the ridership,the length of the line,the closeness to charging stations,and the overlap of CB lines significantly impact the *** addition,the land-use types around the CB stops and the market competition from alternative travel modes also play a key role in making the decisions.

关键词： Customized bus Decision-making Naturalistic observations Discrete choice models Explainable machine learning

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MACLSTM: A Weather Attributes Enabled Recurrent Approach to Appliance-Level Energy Consumption Forecasting

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Computers, Materials & Continua 2025年第2期82卷 2969-2984页

作者： Ruoxin Li Shaoxiong Wu Fengping Deng Zhongli Tian Hua Cai Xiang Li Xu Xu Qi Liu NARI-TECH Nanjing Control System Co. Ltd.Nanjing211106China School of Software Nanjing University of Information Science&TechnologyNanjing210044China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing210044China

Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management plan of the available resources at the power grids and consumer levels. A non-intrusive inference process can be adopted to predict the amount of energy required by appliances. In this study, an inference process of appliance consumption based on temporal and environmental factors used as a soft sensor is proposed. First, a study of the correlation between the electrical and environmental variables is presented. Then, a resampling process is applied to the initial data set to generate three other subsets of data. All the subsets were evaluated to deduce the adequate granularity for the prediction of the energy demand. Then, a cloud-assisted deep neural network model is designed to forecast short-term energy consumption in a residential area while preserving user privacy. The solution is applied to the consumption data of four appliances elected from a set of real household power data. The experiment results show that the proposed framework is effective for estimating consumption with convincing accuracy.

关键词： Electrical load forecasting cloud computing smart grid weather attributes energy consumption time-series analysis

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FastDrag: Manipulate Anything in One Step 38

FastDrag: Manipulate Anything in One Step

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38th Conference on Neural information Processing systems, NeurIPS 2024

作者： Zhao, Xuanjia Guan, Jian Fan, Congyi Xu, Dongli Lin, Youtian Pan, Haiwei Feng, Pengming College of Computer Science and Technology Harbin Engineering University China School of Intelligence Science and Technology Nanjing University China State Key Laboratory of Space-Ground Integrated Information Technology China

Drag-based image editing using generative models provides precise control over image contents, enabling users to manipulate anything in an image with a few clicks. However, prevailing methods typically adopt n-step iterations for latent semantic optimization to achieve drag-based image editing, which is time-consuming and limits practical applications. In this paper, we introduce a novel one-step drag-based image editing method, i.e., FastDrag, to accelerate the editing process. Central to our approach is a latent warpage function (LWF), which simulates the behavior of a stretched material to adjust the location of individual pixels within the latent space. This innovation achieves one-step latent semantic optimization and hence significantly promotes editing speeds. Meanwhile, null regions emerging after applying LWF are addressed by our proposed bilateral nearest neighbor interpolation (BNNI) strategy. This strategy interpolates these regions using similar features from neighboring areas, thus enhancing semantic integrity. Additionally, a consistency-preserving strategy is introduced to maintain the consistency between the edited and original images by adopting semantic information from the original image, saved as key and value pairs in self-attention module during diffusion inversion, to guide the diffusion sampling. Our FastDrag is validated on the DragBench dataset, demonstrating substantial improvements in processing time over existing methods, while achieving enhanced editing performance. Project page: https://***/. © 2024 Neural information processing systems foundation. All rights reserved.

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