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Eu3+ co-doped broadband yellow-emitting BaZnSiO4: Bi3+ phosphors for multifunctional applications

Ceramics International 2025年

作者： Li, Bin Cao, Bin Yang, Chong Ma, Rongbo Deng, Chaoyong Huang, Weichao Key Laboratory of Functional Composite Materials of Guizhou Province College of Big Data and Information Engineering Guizhou University Guizhou Guiyang550025 China

Bi3+-activated phosphors have received increasing attention due to their abundant emission properties. In this paper, a series of BaZnSiO4: Bi3+ phosphors have been synthesized and structurally characterized, and the correlations between their structure and luminescence were also studied systematically. Under 306 nm excitation, the phosphors show broadband yellow emission centered at 559 nm with a full width at half-maximum (FWHM) of 123 nm, resulting from the 3P1/1P1 → 1S0 transition of Bi3+. Thermal stability spectroscopy of BaZnSiO4: Bi3+ phosphor was found to have the potential for application in optical temperature sensing with a Sr value of 2.2 % K−1. BaZnSiO4: Bi3+ phosphors were also used to explore flexible display and lighting. In addition, the color change function is realized by Eu3+ co-doping, which shows good potential application value in the field of anti-counterfeiting. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Phosphors

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8th International Congress on Edge Computing, EDGE 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Tian, Yun Wu, Bin Shi, Jiaoli Zhang, Caicai Xu, Du School of Computer and Big Data Science Jiujiang University Jiujiang332005 China Jiujiang Key Laboratory of Network and Information Security Jiujiang332005 China School of Modern Information Technology Zhejiang Polytechnic University of Mechanical and Electrical Engineering Hangzhou310053 China

ISBN: (纸本)9783031770685

The development of cloud computing and the widespread application of cloud services have made outsourcing services more convenient. The need for individuals and businesses to store and manipulate the graph data they generate is growing rapidly. The unreliability and insecurity of cloud servers make outsourcing graph data a great risk of information leakage. To effectively protect data security, encrypting outsourced data is a useful method. The adjacent vertex query is a very commonly used and fundamental operation, and similarity search is a widely used and powerful tool to improve the scope and functionality of queries. After outsourcing encrypted sparse graph data to cloud servers, it becomes very inconvenient to use and manipulate the data. In this work, we present a scheme to realize the adjacent vertex query supporting similarity search on sparse graph data in cloud environment (SSAQ), which also protects the security of the information. This work uses edit distance and the searchable encryption principle to construct query index, and next implement the similar adjacent vertex query on cloud server. This work provides a formal security analysis, and also gives the experimental comparison and analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Information leakage

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Less is More: Token Context-aware Learning for Object Tracking

arXiv

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arXiv 2025年

作者： Xu, Chenlong Zhong, Bineng Liang, Qihua Zheng, Yaozong Li, Guorong Song, Shuxiang Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China

Recently, several studies have shown that utilizing contextual information to perceive target states is crucial for object tracking. They typically capture context by incorporating multiple video frames. However, these naive frame-context methods fail to consider the importance of each patch within a reference frame, making them susceptible to noise and redundant tokens, which deteriorates tracking performance. To address this challenge, we propose a new token context-aware tracking pipeline named LMTrack, designed to automatically learn high-quality reference tokens for efficient visual tracking. Embracing the principle of Less is More, the core idea of LMTrack is to analyze the importance distribution of all reference tokens, where important tokens are collected, continually attended to, and updated. Specifically, a novel Token Context Memory module is designed to dynamically collect high-quality spatio-temporal information of a target in an autoregressive manner, eliminating redundant background tokens from the reference frames. Furthermore, an effective Unidirectional Token Attention mechanism is designed to establish dependencies between reference tokens and search frame, enabling robust cross-frame association and target localization. Extensive experiments demonstrate the superiority of our tracker, achieving state-of-the-art results on tracking benchmarks such as GOT-10K, TrackingNet, and LaSOT. Code and models are available at https://***/XuChenLong/LMTrack. Copyright © 2025, The Authors. All rights reserved.

关键词： Object detection

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CoT-based Synthesizer: Enhancing LLM Performance through Answer Synthesis

arXiv

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arXiv 2025年

作者： Zhang, Bohan Zhang, Xiaokang Zhang, Jing Yu, Jifan Luo, Sijia Tang, Jie School of Information Renmin University of China China Tsinghua University China Key Laboratory of Data Engineering and Knowledge Engineering Beijing China

Current inference scaling methods, such as Self-consistency and Best-of-N, have proven effective in improving the accuracy of LLMs on complex reasoning tasks. However, these methods rely heavily on the quality of candidate responses and are unable to produce correct answers when all candidates are incorrect. In this paper, we propose a novel inference scaling strategy, CoT-based Synthesizer, which leverages CoT reasoning to synthesize superior answers by analyzing complementary information from multiple candidate responses, even when all candidate responses are flawed. To enable a lightweight and cost-effective implementation, we introduce an automated data generation pipeline that creates diverse training data. This allows smaller LLMs trained on this data to improve the inference accuracy of larger models, including API-based LLMs. Experimental results across four benchmark datasets with seven policy models demonstrate that our method significantly enhances performance, with gains of 11.8% for Llama3-8B and 10.3% for GPT-4o on the MATH dataset. The corresponding training data and code are publicly available on the repository. Copyright © 2025, The Authors. All rights reserved.

关键词： data accuracy

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Crop Classification Methods Based on Siamese CBMM-CNN Architectures Using Hyperspectral Remote Sensing data 24th

Crop Classification Methods Based on Siamese CBMM-CNN Archi...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xie, Bin Zhang, Jiahao Li, Yuling Li, Yusong Dong, Xinyu College of Computer and Cyber Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Hebei Normal University Hebei Shijiazhuang China

ISBN: (纸本)9789819615278

Amidst global population growth and escalating food demands, real-time agricultural monitoring is crucial for ensuring food security. During the initial stages of crop growth, however, it faces significant challenges in obtaining accurate ground truth data via remote sensing, thereby impeding effective crop classification. Traditional Convolutional Neural Network (CNN) models, although effective in image processing and feature extraction within computer vision, are notably limited in their ability to handle the high dimensionality and complex spectral information inherent in hyperspectral non-image remote sensing data. To address this problem, we designed an enhanced CNN model, CBM-CNN. The CBM-CNN incorporates additional convolutional layers (C), batch normalization (B), and MLP(M) to convert one-dimensional data into two-dimensional data for classification, thus improving the processing efficiency of highly nonlinear data features. Building on CBM-CNN, we add multiple attention mechanisms (CBMM-CNN) to enhance feature extraction capabilities. The Siamese CBMM-CNN (SCBMM-CNN) is employed for in-depth learning of data features, with supervised learning precisely adjusting the model to improve classification performance. Experimental results on hyperspectral remote sensing datasets demonstrate that SCBMM-CNN outperforms traditional machine learning models (PCA-RF, GBDT, GMO-SVM, Random Forest, Decision Tree) and deep learning models (CNN, CNN-LSTM) in terms of accuracy, recall rate, F1 score, and kappa coefficient. These results confirm that SCBMM-CNN significantly improves crop classification accuracy and provides a novel method for early crop growth classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Dynamic Scaling of Unit Tests for Code Reward Modeling

arXiv

引用

arXiv 2025年

作者： Ma, Zeyao Zhang, Xiaokang Zhang, Jing Yu, Jifan Luo, Sijia Tang, Jie School of Information Renmin University of China China Tsinghua University China Key Laboratory of Data Engineering and Knowledge Engineering Beijing China

Current large language models (LLMs) often struggle to produce accurate solutions on the first attempt for code generation. Prior research tackles this challenge by generating multiple candidate solutions and validating them with LLM-generated unit tests. The execution results of unit tests serve as reward signals to identify correct solutions. As LLMs always confidently make mistakes, these unit tests are not reliable, thereby diminishing the quality of reward signals. Motivated by the observation that scaling the number of solutions improves LLM performance, we explore the impact of scaling unit tests to enhance reward signal quality. Our pioneer experiment reveals a positive correlation between the number of unit tests and reward signal quality, with greater benefits observed in more challenging problems. Based on these insights, we propose CodeRM-8B, a lightweight yet effective unit test generator that enables efficient and high-quality unit test scaling. Additionally, we implement a dynamic scaling mechanism that adapts the number of unit tests based on problem difficulty, further improving efficiency. Experimental results show that our approach significantly improves performance across various models on three benchmarks (e.g., with gains of 18.43% for Llama3-8B and 3.42% for GPT-4o-mini on HumanEval Plus). © 2025, CC BY-SA.

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A leaded sine–cosine artificial protozoa optimizer algorithm for solving multi-objective optimization problems

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Cluster Computing 2025年第5期28卷 1-65页

作者： Liao, Junjie Melethil Sethumadhavan, Syam Ke, Rui Gao, Zheng-Ming Zhao, Juan College of Computer and Information Technology China Three Gorges University Yichang443002 China School of Computer Engineering Jingchu University of Technology Jingmen448000 China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University Yichang443002 China Big Data Research Center Jingchu University of Technology Jingmen448000 China School of Electronics and Information Engineering Jingchu University of Technology Jingmen448000 China

Many real-world engineering problems require simultaneous optimization of multiple objectives. The artificial protozoa optimizer (APO), a recently proposed and efficient optimization method, has demonstrated potential in solving multi-objective problems. In order to solve multi-objective problems, this paper proposes a novel algorithm based on the APO framework, the Leaded Sine Cosine Multi-objective APO (LSCMOAPO) algorithm. LSCMOAPO mainly integrates the sine–cosine algorithm (SCA) and a leader selection strategy to guide the population towards the true Pareto front. The algorithm was validated through extensive simulations involving 41 benchmark test functions (ZDT-series, UF-series, CF-series, and CEC2020-series) and five practical engineering problems. Performance was evaluated using the inverted generational distance, generational distance, Hypervolume, and spacing metrics. Comparative analysis with ten other multi-objective optimization algorithms (MOPSO, MOSSA, MOGWO, MODA, MOCryStAl, MOALO, MOGOA, MOSMA, MOGNDO, and MOAPO) also validated the better performance of LSCMOAPO in more than 86% occasions (The average of the evaluation indicators) in realizing high-quality solutions to all multi-objective problems, including linear, nonlinear, continuous, and discrete Pareto optimal front. It will set a new benchmark for new algorithm proposed. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Multiobjective optimization

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key Clues Guided Video Character Social Relationship Recognition Enhanced by LLM

Key Clues Guided Video Character Social Relationship Recogni...

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

作者： Dong, Wenlong Zhu, Qing Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

ISBN: (纸本)9798350368741

Video Character Social Relationship Recognition (VCSRR) requires a comprehensive consideration about spatio-temporal and multi-modal clues in videos. Most existing methods mainly focus on integrating multi-modal clues and modeling interactions among characters. However, they fail to discover key clues in the complex video data or fully understand the clues related to social relationships. In this article, we propose a novel Large Language Model Enhanced key Clues Selection (LEKCS) framework to address the aforementioned issues. The core of LE-KCS is to mine multi-scale key clues from the perspectives of time, space and multi-modality, then transfer the knowledge about social relationships of the Large Language Model to VCSRR for understanding the selected clues. We evaluated LEKCS on the MovieGraphs dataset and the experimental results indicate that our proposed LE-KCS achieves state-of-the-art performance. © 2025 IEEE.

关键词： large language model Social relationship recognition video understanding

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T3 Planner: Multi-Phase Planning Across Structure-Constrained Optical, IP, and Routing Topologies

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IEEE Journal on Selected Areas in Communications 2025年

作者： Hao, Yijun Yang, Shusen Li, Fang Zhang, Yifan Zhao, Cong Ren, Xuebin Zhao, Peng Xu, Chenren Wang, Shibo Xi'an Jiaotong University National Engineering Laboratory for Big Data Analytics China National Engineering Laboratory for Big Data Analytics Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University China Peking University China Chinese University of HongKong Xi'an Jiaotong University China

Network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46:6% cost, and supports 3:12× more demand when compared to related existing approaches. © 1983-2012 IEEE.

关键词： Network topology

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A Comparative Analysis of Three Multispectral Images in Classification with Different Methods 24

A Comparative Analysis of Three Multispectral Images in Clas...

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13th International Conference on Computing and Pattern Recognition, ICCPR 2024

作者： Chen, Feng Gao, Zhibin Song, Yuejun College of Computer and Information Engineering Big Data Institute of Digital Natural Disaster Monitoring in Fujian Xiamen University of Technology Xiamen China College of Computer and Information Engineering Xiamen University of Technology Xiamen China Jiangxi Key Laboratory of Watershed Soil and Water Conservation Jiangxi Academy of Water Science and Engineering Nanchang China

ISBN: (纸本)9798400717482

Benefited from the increased satellite observations with medium spatial resolution, land use/cover mapping at local to global scales has provided important supports for society and academy community. For specific purposes (e.g., disaster assessment, detecting surface abrupt change), it is needed to increase the observation frequency, through combining different satellite sensors (i.e. as virtual constellation). However, many factors challenge this combination in practice, in which the comparability issue in sensor settings should be considered among various payload platforms. In this study, from the perspective of practical application, a comparative analysis of land use/cover mapping was investigated, with focusing on three observations by the Gaofen-1 (GF-1) wide field of view (WFV), Sentinel-2A Multi-Spectral Instrument (MSI), and Landsat-8 Operational Land Imager (OLI) respectively. Four methods were implemented in classification experiments. Preliminary results showed that the Sentinal-2A MSI outperformed the GF-1 WFV and the Landsat-8 OLI in classification, no matter which classifier was considered. Meanwhile, the higher spatial resolution of the GF-1 WFV certainly compensated its lack of spectral channels compared against the Landsat-8 OLI. The choice of methods affected classification for a specific observation, while the more suitable classifier varied among sensors. This study underlines the compatibility issue in land use/cover mapping with different observations. © 2024 Copyright held by the owner/author(s).

关键词： Mapping

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