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SSRN 2024年

作者： Wang, Miaomiao Ding, Shoujun Hu, Haitang Zhang, Chuancheng Zou, Yong Liu, Wenpeng School of Microelectronics and Data Science Anhui University of Technology Anhui Maanshan243002 China Anhui Provincial Joint Key Laboratory of Disciplines for Industrial Big Data Analysis and Intelligent Decision Anhui Maanshan243002 China Anhui Institute of Optics and Fine Mechanics Chinese Academy of Sciences Anhui Hefei230031 China

Under the excitation of a 980nm laser, the visible upconversion (UC) luminescence of Er3+ ions-doped Yb3+ ions-self-activated NaYb(MoO4)2 phosphor and crystal, as well as the Yb3+/Er3+ ions-codoped NaBi(MoO4)2 crystal, were investigated comprehensively. The results indicate that all three samples exhibit two significant green emission bands and a weak red emission band in the visible band corresponding to the transitions of 2H11/2/4S3/2 → 4I15/2 and 4F9/2 → 4I15/2 of Er3+ ions, respectively. Through the variable power spectra of three different samples, the relationship between the energy back transfer (EBT) process of Yb3+-Er3+ ions and the power point and Yb3+ ion concentration was investigated. The EBT process was observed in both the Er3+ ions-doped Yb3+ ions-self-activated NaYb(MoO4)2 phosphor and crystal, as confirmed by the luminescence image of the sample. At high power, the Yb3+ ion self-activated sample exhibited yellow luminescence, with the crystal appearing later than the phosphor. © 2024, The Authors. All rights reserved.

关键词： Sodium compounds

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Orange-Reddish Emitting of Trivalent Samarium Doped Double Perovskite Sr2lunbo6 with High Thermal Stability for White-Led Application

SSRN

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SSRN 2024年

作者： Su, Wenzhi Ding, Shoujun Zhang, Chuancheng Wang, Miaomiao Ren, Hao Hu, Haitang Zou, Yong Liu, Wenpeng School of Microelectronics and Data Science Anhui University of Technology Anhui Maanshan243002 China Anhui Provincial Joint Key Laboratory of Disciplines for Industrial Big Data Analysis and Intelligent Decision Anhui Maanshan243002 China Anhui Institute of Optics and Fine Mechanics Chinese Academy of Sciences Anhui Hefei230031 China

A series of trivalent samarium ions doped double perovskite Sr2LuNbO6 (x at% Sm3+:Sr2LuNbO6, x = 0.5-15) phosphors have been prepared via high-temperature solid-state method. The structural characterization of these phosphors was conducted employing X-ray diffraction (XRD), XRD Rietveld refinement, scanning electron microscope (SEM) and transmission electron microscope (TEM), respectively. Subsequently, density functional theory calculations utilizing the Vienna ab initio simulation package (VASP) were employed to unveil the electronic structures, encompassing the band structure and density of states (DOS). Photoluminescence (PL) spectroscopy was employed to scrutinize the luminescent properties, while the thermal stability of the PL spectra was also evaluated. The most prominent emission peak, exhibiting an orange-reddish hue, was observed at 601 nm upon excitation at 406 nm. The primary quenching mechanism of Sm3+ ions within the Sr2LuNbO6 host lattice was identified as dipole-dipole interaction, with the quenching concentration of Sm3+:Sr2LuNbO6 phosphors estimated to be approximately 3 at%. Notably, the fluorescence lifetime of the optimally doped sample was determined to be 0.723 ms, exhibiting a gradual reduction with increasing Sm3+ doping concentration. Moreover, the phosphors demonstrated excellent thermal stability, with an activation energy (Ea) value of 0.126 eV. Additionally, the CIE coordinates remained relatively constant with increasing temperature. These findings collectively underscore the promising potential of Sm3+:Sr2LuNbO6 phosphors for applications in white light-emitting diodes (w-LEDs). © 2024, The Authors. All rights reserved.

关键词： Phosphors

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Meta-Stock: Task-Difficulty-Adaptive Meta-learning for Sub-new Stock Price Prediction

arXiv

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arXiv 2023年

作者： Wang, Linghao Liu, Zhen Ma, Peitian Ma, Qianli School of Computer Science and Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China

Sub-new stock price prediction, forecasting the price trends of stocks listed less than one year, is crucial for effective quantitative trading. While deep learning methods have demonstrated effectiveness in predicting old stock prices, they require large training datasets unavailable for sub-new stocks. In this paper, we propose Meta-Stock: a task-difficulty-adaptive meta-learning approach for sub-new stock price prediction. Leveraging prediction tasks formulated by old stocks, our meta-learning method aims to acquire the fast generalization ability that can be further adapted to sub-new stock price prediction tasks, thereby solving the data scarcity of sub-new stocks. Moreover, we enhance the meta-learning process by incorporating an adaptive learning strategy sensitive to varying task difficulties. Through wavelet transform, we extract high-frequency coefficients to manifest stock price volatility. This allows the meta-learning model to assign gradient weights based on volatility-quantified task difficulty. Extensive experiments on datasets collected from three stock markets spanning twenty-two years prove that our Meta-Stock significantly outperforms previous methods and manifests strong applicability in real-world stock trading. Besides, we evaluate the reasonability of the task difficulty quantification and the effectiveness of the adaptive learning strategy. Copyright © 2023, The Authors. All rights reserved.

关键词： Wavelet transforms

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EDSep: An Effective Diffusion-Based Method for Speech Source Separation

EDSep: An Effective Diffusion-Based Method for Speech Source...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jinwei Dong Xinsheng Wang Qirong Mao School of Computer Science and Communication Engineering Jiangsu University Audio Speech and Language Processing Group School of Computer Science Northwestern Polytechnical University Xi’an China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Zhenjiang China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Generative models have attracted considerable attention for speech separation tasks, and among these, diffusion-based methods are being explored. Despite the notable success of diffusion techniques in generation tasks, their adaptation to speech separation has encountered challenges, notably slow convergence and suboptimal separation outcomes. To address these issues and enhance the efficacy of diffusion-based speech separation, we introduce EDSep, a novel single-channel method grounded in score matching via stochastic differential equation (SDE). This method enhances generative modeling for speech source separation by optimizing training and sampling efficiency. Specifically, a novel denoiser function is proposed to approximate data distributions, which obtains ideal denoiser outputs. Additionally, a stochastic sampler is carefully designed to resolve the reverse SDE during the sampling process, gradually separating speech from mixtures. Extensive experiments on databases such as WSJ0-2mix, LRS2-2mix, and VoxCeleb2-2mix demonstrate our proposed method’s superior performance over existing diffusion and discriminative models, validating its efficacy.

关键词： Training Adaptation models Technological innovation Source separation databases Stochastic processes Differential equations Speech enhancement Diffusion models Mathematical models

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A Survivable Routing Algorithm with Hybrid Protection in Elastic Optical Networks

A Survivable Routing Algorithm with Hybrid Protection in Ela...

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Mobile Internet, Cloud Computing and Information Security (MICCIS), International Conference on

作者： Hankun Zeng Xu Zhang Chuan Feng Xiaoxue Gong School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350389906

ISBN: (纸本)9798350389913

Spectral efficiency is a crucial factor in the survivability of multi-class traffic in elastic optical networks (EONs). In this paper, we propose a spectrum-efficient hybrid protection scheme, namely dedicated path protection (DPP) and shared backup path protection (SBPP), to address the survivability issue in EONs. Meanwhile, we design a survivable routing, modulation level, and spectrum assignment (RMLSA) algorithm to achieve hybrid protection in EONs. The simulation results demonstrate that the proposed algorithm has a lower blocking rate and Spectrum occupation ratio.

关键词： Time-frequency analysis Spectral efficiency Simulation Modulation Information security Optical fiber networks Routing

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Pixel-Level Polyp Segmentation Network Based on Parallel Feature Enhancement and Attention Mechanism

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SSRN 2024年

作者： Xu, Haiping Wang, Jie Li, Zuoyong Cheng, Xuesong Teng, Shenghua College of Computer and Data Science Minjiang University Fuzhou China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Key Laboratory of Medical Big Data Engineering Fujian Provincial Hospital Fuzhou China

Polyp segmentation, a crucial tool in medical diagnostics, effectively locates and segments polyps, thereby aiding clinicians in enhancing both detection efficiency and accuracy. To address the persistent challenges of imprecise polyp localization and ambiguous boundary segmentation, we propose a pixel-level polyp segmentation network based on a parallel feature enhancement and attention mechanism (PFEA-Net). The proposed PFEA-Net follows an enconder-decoder architecture. Given that polyps vary greatly in size and shape, PFEA-Net first utilizes the HarDNet68 as the encoder to extract multi-level feature representations. Then, a parallel feature enhancement module is designed to enrich the feature map of each level by integrating semantic information derived from higher-level features. Meanwhile, a parallel partial decoder module is used to introduce global contextual information. Finally, the reverse attention module refines the boundary features. PFEA-Net effectively extracts high-level representations to enhance feature depiction, capturing both detailed and semantic information in the images. Rigorous experiments conducted on five publicly available polyp datasets using six evaluation metrics conclusively demonstrate that the proposed network exhibits superior robustness and achieves more precise segmentation. Compared to other state-of-the-art algorithms, PFEA-Net distinguishes itself with exceptional performance. © 2024, The Authors. All rights reserved.

关键词： Pixels

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Kernel-Guided Texture-Aware Blind Super-Resolution via Uncertainty Learning

Kernel-Guided Texture-Aware Blind Super-Resolution via Uncer...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Tingting Zhao Zuoyong Li Li Quan Kun Zeng Shenghua Teng College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China College of Computer Science and Big Data Fuzhou University Fuzhou China

The accuracy of kernel estimation is critical to the performance of blind super-resolution (SR). Traditional kernel estimation methods usually use L1 or L2 loss function to minimize the difference between the estimated kernel and the ground-truth kernel. This tends to make the estimated kernel converge towards the average of all possible kernels, deviating from the ground-truth kernel. To improve the performance of kernel estimation, this paper proposes an uncertainty loss for training a kernel estimation network, focusing on regions with high uncertainty (variance) in the kernel. In addition, a texture-aware SR network is proposed that utilizes the Gumbel Softmax trick to pay more attention to the complex regions of the image texture, thus improving the SR performance. Extensive experiments on synthetic datasets show that our approach achieves promising performance.

关键词：

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CLDG: Contrastive Learning on Dynamic Graphs

arXiv

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arXiv 2024年

作者： Xu, Yiming Shi, Bin Ma, Teng Dong, Bo Zhou, Haoyi Zheng, Qinghua Department of Computer Science and Technology Xi’an Jiaotong University China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China Department of Distance Education Xi’an Jiaotong University China School of Software Beihang University China Advanced Innovation Center for Big Data and Brain Computing Beihang University China

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph’s augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG. © 2024, CC BY.

关键词： Contrastive Learning

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Query-oriented data Augmentation for Session Search

arXiv

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arXiv 2024年

作者： Chen, Haonan Dou, Zhicheng Zhu, Yutao Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China Engineering Research Center of Next-Generation Intelligent Search and Recommendation Ministry of Education Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing100872 China

Modeling contextual information in a search session has drawn more and more attention when understanding complex user intents. Recent methods are all data-driven, i.e., they train different models on large-scale search log data to identify the relevance between search contexts and candidate documents. The common training paradigm is to pair the search context with different candidate documents and train the model to rank the clicked documents higher than the unclicked ones. However, this paradigm neglects the symmetric nature of the relevance between the session context and document, i.e., the clicked documents can also be paired with different search contexts when training. In this work, we propose query-oriented data augmentation to enrich search logs and empower the modeling. We generate supplemental training pairs by altering the most important part of a search context, i.e., the current query, and train our model to rank the generated sequence along with the original sequence. This approach enables models to learn that the relevance of a document may vary as the session context changes, leading to a better understanding of users’ search patterns. We develop several strategies to alter the current query, resulting in new training data with varying degrees of difficulty. Through experimentation on two extensive public search logs, we have successfully demonstrated the effectiveness of our model. © 2024, CC BY.

关键词： Search engines

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Bimolybdate Single Crystals Codoping with Trivalent Ytterbium and Erbium Ions for High-Stable and High-Sensitive Optical Thermometry

SSRN

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SSRN 2024年

作者： Wang, Miaomiao Ding, Shoujun Hu, Haitang Zhang, Chuancheng Ren, Hao Zou, Yong Liu, Wenpeng School of Microelectronics and Data Science Anhui University of Technology Anhui Maanshan243002 China Anhui Provincial Joint Key Laboratory of Disciplines for Industrial Big Data Analysis and Intelligent Decision Anhui Maanshan243002 China Anhui Institute of Optics and Fine Mechanics Chinese Academy of Sciences Anhui Hefei230031 China

An effective strategy for enhancing the sensing sensitivity, responsiveness, and anti-interference capabilities of the fluorescence intensity ratio (FIR) based optical temperature sensing technology involves substituting the traditional powder polycrystalline materials with crystalline materials. In this work, Yb3+/Er3+ codoped bimolybdate single crystals, NaBi(MoO4)2 (NBM), featuring varying concentrations of Er3+ ions, were cultivated via the Czochralski method for prospective utilization in optical temperature sensing applications. The structure of the crystals were comprehensively characterized employing X-ray diffraction (XRD), Fourier transform infrared (FT-IR) spectroscopy, and transmission electron microscopy (TEM). Upon excitation with a 980 nm laser, the crystals demonstrated notable green and subdued red upconversion (UC) emissions, with a thorough investigation into the underlying UC luminescence mechanism. The luminescence intensity was found to be contingent upon the doping concentration, with a gradual attenuation observed with increasing Er3+ ions. The optical temperature sensing properties of Yb3+/Er3+ codoped NBM single crystals were assessed utilizing the FIR technique, predicated on the thermally coupled level of Er3+ ions (2H11/2/4S3/2). Experimental findings revealed that the 10 at% Yb3+, 10 at% Er3+: NBM crystal exhibited highest sensor sensitivity among the synthesized crystals, with a maximum absolute sensitivity of approximately 1.22% K-1 at 510 K. Furthermore, the temperature sensing characteristics of the crystals were contingent upon the doping concentration of Er3+ ions. In summary, the Yb3+/Er3+ codoped NBM single crystal showcased exemplary sensor sensitivity and holds considerable promise as a material for optical temperature sensing applications. © 2024, The Authors. All rights reserved.

关键词： Single crystals

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