检索结果-内蒙古大学图书馆

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Rui Yang, Wanli Xiao, Kuntao Sun, Yi Sheng, Shurong Lv, Zhao Gao, Jiahong Auhui University Auhui Hefei China Hefei Comprehensive National Science Center Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence Hefei China Anhui Province Key Laboratory of Multimodal Cognitive Computation Auhui Hefei China Peking University Center for Mri Research Academy for Advanced Interdisciplinary Studies Beijing China Peking University Beijing City Key Lab for Medical Physics and Engineering Institute of Heavy Ion Physics School of Physics Beijing China Peking University McGovern Institute for BrainResearch Beijing China

ISBN: (纸本)9798350386226

Anatomical landmark detection, a pivotal research area in medical image processing, holds immense value in surgical navigation, image registration, and related fields. Traditional machine learning methods struggle with generalization and robustness. Current supervised end-to-end approaches lacks in-terpretability in capturing global information, and GPU memory constraints restrict their application to extensive 3D medical image datasets. Moreover, existing methods overlook intrinsic geometric cues within images and point sets. Herein, we introduce a novel anatomical landmark detection framework that integrates deep learning's representation capabilities with global geometric information derived from images and point sets (DL2G). This approach facilitates anatomical landmark localization in a local-to-global fashion. Initially, we train a deep feature descriptor based on self-supervised contrastive learning, which is further used to screen candidate points for the target one by comparing local patch embeddings. Subsequently, geometric constraints constructed from labeled template point sets enable the selection of geometrically consistent matching point sets. Ultimately, the framework utilizes the global mutual information of images to perform landmark localization through iterative optimization. Adhering to the AFID data annotation protocol, we evaluated our method on two public MRI head datasets, OASIS and HCP, for detecting 32 anatomical landmarks. The results convincingly demonstrate the superiority of our method over current state-of-the-art approaches in terms of both accuracy and computational efficiency. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Electrically Switchable Longitudinal Nonlinear Conductivity in Magnetic Semiconductors

引用

Physical Review Letters 2025年第4期134卷 046801-046801页

作者： Hong Jian Zhao Yuhao Fu Yurong Yang Yanchao Wang Laurent Bellaiche Yanming Ma Key Laboratory of Material Simulation Methods and Software of Ministry of Education College of Physics Jilin University Changchun 130012 China Key Laboratory of Physics and Technology for Advanced Batteries (Ministry of Education) College of Physics Jilin University Changchun 130012 China International Center of Future Science Jilin University Changchun 130012 China National Laboratory of Solid State Microstructures and Jiangsu Key Laboratory of Artificial Functional Materials Department of Materials Science and Engineering Nanjing University Nanjing 210093 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun 130012 China Smart Functional Materials Center Physics Department and Institute for Nanoscience and Engineering University of Arkansas Fayetteville Arkansas 72701 USA Department of Materials Science and Engineering Tel Aviv University Ramat Aviv Tel Aviv 6997801 Israel

Writing data by electric field (as opposed to electric current) offers promises for energy efficient memory devices. While this data writing scheme is enabled by the magnetoelectric effect, the narrow spectrum of room-temperature magnetoelectrics hinders the design of practical magnetoelectric memories, and the exploration of other mechanisms toward low-power memories is greatly demanding. Here, we propose a mechanism that allows the electric-field writing of data beyond the framework of magnetoelectric effect. By symmetry analysis, we show that electric field can induce longitudinal nonlinear conductivity (LNC) in a wide spectrum of magnetic materials, including ferromagnets, antiferromagnets, magnetoelectrics, and nonmagnetoelectrics. The LNC is electrically switchable by reversing the electric field, where the switched LNC is detectable by transport measurements. Our first-principles simulations combined with transport calculations further predict YFeO3 and CuFeS2 (room-temperature antiferromagnets) to showcase electrically switchable LNC. Our Letter helps enrich the research avenues in nonlinear charge transport, and offers a pathway for designing energy efficient devices based on LNC.

关键词： Magnetoelectric effects

来源：评论

学校读者我要写书评

暂无评论

Boosting Short Text Classification with Multi-Source Information Exploration and Dual-Level Contrastive Learning

arXiv

引用

arXiv 2025年

作者： Liu, Yonghao Li, Mengyu Pang, Wei Giunchiglia, Fausto Huang, Lan Feng, Xiaoyue Guan, Renchu Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Jilin University China Mathematical and Computer Sciences Heriot-Watt University United Kingdom University of Trento Italy

Short text classification, as a research subtopic in natural language processing, is more challenging due to its semantic sparsity and insufficient labeled samples in practical scenarios. We propose a novel model named MI-DELIGHT for short text classification in this work. Specifically, it first performs multi-source information (i.e., statistical information, linguistic information, and factual information) exploration to alleviate the sparsity issues. Then, the graph learning approach is adopted to learn the representation of short texts, which are presented in graph forms. Moreover, we introduce a dual-level (i.e., instance-level and cluster-level) contrastive learning auxiliary task to effectively capture different-grained contrastive information within massive unlabeled data. Meanwhile, previous models merely perform the main task and auxiliary tasks in parallel, without considering the relationship among tasks. Therefore, we introduce a hierarchical architecture to explicitly model the correlations between tasks. We conduct extensive experiments across various benchmark datasets, demonstrating that MI-DELIGHT significantly surpasses previous competitive models. It even outperforms popular large language models on several datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Deep Multi-modal Graph Clustering via Graph Transformer Network 39

Deep Multi-modal Graph Clustering via Graph Transformer Netw...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Qianqian Xu, Haiming Zhang, Zihao Feng, Wei Gao, Quanxue School of Telecommunications Engineering Xidian University Xi'an China School of Computer Science and Technology Xi'an Jiaotong University Xi'an China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University China

ISBN: (纸本)157735897X

Current deep multi-modal graph clustering methods primarily rely on Graph Neural Network (GNN) to fully exploit attribute features and graph structures, including message propagation and low-dimensional feature embedding. However, these methods lack further exploration of graph structural information, such as the relationship between nodes and shortest paths. Additionally, they may not sufficiently mine complementary information among multi-modal graph data. To address these issues, we propose a novel Deep Multi-modal Graph Clustering via Graph Transformer Network method, called DMGC-GTN. This method thoroughly dissects and utilizes graph structural information, applying graph smoothing to node features and incorporating various forms of embeddings into the transformer architecture. This achieves a unified embedding of graph structure and multi-modal feature attributes, fully exploiting the complementary information within multi-modal graph data. Extensive experiments demonstrate the effectiveness of our algorithm. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Pattern Analysis and Placement Optimization of the Four-horn Monopulse for Terahertz Communication

Pattern Analysis and Placement Optimization of the Four-horn...

引用

Cross Strait Quad-Regional Radio science and Wireless Technology Conference (CSRWTC)

作者： Yong-Han Lu Zhong-bo Zhu Jing Ma Bin Shi Yu-ting Zhang Lin Peng Key Laboratory of Microwave and Optical Wave Application Technology Guilin University of Electronic Technology Guilin Guangxi China National Key Laboratory of Science and Technology on Space Microwave Xi'an China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing China Guangxi Key Laboratory of Optoelectronic Information Processing School of Optoelectronic Engineering Guilin University of Electronic Technology Guilin China

The mobile node communication in terahertz monopulse system requires narrow beam to align with the mobile target precisely. In this paper, the radiation pattern of four-horn monopulse antenna is analyzed by use of the geometric optics (GO) and aperture field integration method (AFIM), and an efficient placement optimization to reduce the beamwidth of four-horn monopulse is presented. The optimization combines the genetic algorithm (GA) with GO-AFIM. The GA is employed to seek the optimal positions of the four horns, and the GO and AFIM are introduced into the fitness function of GA to evaluate the individual fitness. The optimization result has narrowed the beamwidth and enhanced gain of radiation pattern. Compared with the simulation in full wave software, the proposed method costs less time in pattern analysis and performs more efficiently in placement optimization.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Novel Dual-Stage Algorithm for Capacitated Arc Routing Problems with Time-Dependent Service Costs

SSRN

引用

SSRN 2024年

作者： Li, Qingya Liu, Shengcai Zou, Juan Tang, Ke Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Xiangtan411105 China The Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiangtan University Xiangtan411105 China

This paper focuses on solving the capacitated arc routing problem with time-dependent service costs (CARPTDSC), which is motivated by winter gritting applications. In the current literature, exact algorithms designed for CARPTDSC can only handle small-scale instances, while heuristic algorithms fail to obtain high-quality solutions. To overcome these limitations, we propose a novel dual-stage algorithm, called MAENS-GN, that consists of a routing stage and a vehicle departure time optimization stage. The former obtains the routing plan, while the the latter determines the vehicle departure time. Importantly, existing literature often ignores the characteristic information contained in the relationship between the route cost and the vehicle departure time. The most signiﬁcant innovation in this paper lies in the exploitation of this characteristic information during the vehicle departure time optimization stage. Speciﬁcally, we conduct a detailed analysis of this relationship under various scenarios and employ tailored methods to obtain the (approximately) optimal vehicle departure time. Furthermore, we propose an improved initialization strategy that considers time-dependent characteristics to achieve better solution quality. In addition to the modiﬁed benchmark test sets, we also experiment on a real-world test set. Experimental results demonstrate that MAENS-GN can obtain high-quality solutions on both small-scale and larger-scale instances of CARPTDSC. © 2024, The Authors. All rights reserved.

关键词： Combinatorial optimization

来源：评论

学校读者我要写书评

暂无评论

地球深处形成含硼金刚石的可行机制

引用

science Bulletin 2023年第13期68卷 1456-1461,M0004页

作者：刘思瑜陆文成张晓冉宋京岩吕健刘晓兵王彦超陈长风马琰铭 Key Laboratory of Material Simulation Methods and Software of Ministry of Education College of PhysicsJilin UniversityChangchun 130012China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchun 130012China Laboratory of High Pressure Physics and Material Science School of Physics and Physical EngineeringQufu Normal UniversityQufu 273100China Department of Physics and Astronomy University of NevadaLas VegasNV 89154USA International Center of Future Science Jilin UniversityChangchun 130012China

近年来,以“希望之钻”为代表天然蓝色钻石(蓝钻)的形成机制引起科学界和大众的广泛兴趣.天然蓝钻是形成于地球最深处的珍贵矿物,含约0.011~10μg/g棚杂质.现行地球化学模型表明地球内部硼元素主要富集于大陆和大洋地壳,极难进入地球内... 详细信息

近年来,以“希望之钻”为代表天然蓝色钻石(蓝钻)的形成机制引起科学界和大众的广泛兴趣.天然蓝钻是形成于地球最深处的珍贵矿物,含约0.011~10μg/g棚杂质.现行地球化学模型表明地球内部硼元素主要富集于大陆和大洋地壳,极难进入地球内部的下地幔深度.蓝钻的存在为地球下地幔环境中存在硼元素提供了有力证据,然而其地质起源与循环机制是当前领域内亟待解决的科学难题之一.在本研究中,我们提出金属硼化物随地球板块运动进入下地幔深处,在高温高压环境下与碳酸盐发生氧化还原反应,从而形成蓝钻的物理机制.通过第一性原理计算提出了多种可能形成蓝钻的化学反应途径,并在下地幔压力与温度条件下(22.5 GPa,2100 K),利用CaCO_(3)和FeB的直接氧化还原反应,成功制备“类天然”蓝钻.这一发现揭示了地球深处蓝钻形成之谜,并为进一步深入探索地球内部的物质循环和矿物形成机制提供了新思路.

关键词： Boron-bearing diamonds indeep Earth First-principlescalculations High-pressureandhigh-temperature experimental synthesis Redoxreactions

来源：评论

学校读者我要写书评

暂无评论

Addressing Intermediate Verification Latency in Online Learning Through Immediate Pseudo-labeling and Oriented Synthetic Correction

Addressing Intermediate Verification Latency in Online Learn...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Zixin Zhong Liyan Song Fengzhen Tang Bo Yuan Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Faculty of Computing Harbin Institute of Technology Harbin China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In non-stationary data streams, the challenges of concept drift are further compounded by the issue of Intermediate Verification Latency (IVL), which can impede timely model adaptation. IVL refers to the finite delay between the arrival of data features and their corresponding labels. This delay could pose a significant challenge in adapting models to new concepts, ultimately hindering predictive performance. However, existing IVL approaches exhibit certain limitations. Some approaches passively wait for delayed labels, thereby overlooking temporarily unlabeled data. Other approaches employ pseudo-labeling for immediate model updates, but may risk losing valuable information when reverting model states to rectify previous pseudo-labeling mistakes. To overcome these limitations, we propose a novel approach called Micro-cluster based Immediate Pseudo-Labeling with Oriented Synthetic Correction (MIPLOSC). MIPLOSC leverages micro-cluster systems to effectively capture data distributions, thus facilitating its two core components: immediate pseudo-labeling and oriented synthetic correction. The immediate pseudo-labeling mechanism facilitates immediate utilization of temporarily unlabeled data, and the oriented synthetic correction mechanism enables finergrained rectification from previous erroneous pseudo-labels and concept drift, minimizing the loss of learned information. Experimental studies validated the effectiveness of MIPLOSC in addressing IVL, demonstrating its superiority over competing methods in both space consumption and predictive performance across varying degrees of label delay.

关键词： Adaptation models Costs Concept drift Noise Neural networks Predictive models Robustness

来源：评论

学校读者我要写书评

暂无评论

Evolutionary computation for Large-scale Multi-objective Optimization: A Decade of Progresses

引用

International Journal of Automation and computing 2021年第2期18卷 155-169页

作者： Wen-Jing Hong Peng Yang Ke Tang Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China Department of Management Science University of Science and Technology of ChinaHefei 230027China Guangdong–Hong Kong–Macao Greater Bay Area Center for Brain Science and Brain–inspired Intelligence Guangzhou 510515China

Large-scale multi-objective optimization problems(MOPs)that involve a large number of decision variables,have emerged from many real-world *** evolutionary algorithms(EAs)have been widely acknowledged as a mainstream method for MOPs,most research progress and successful applications of EAs have been restricted to MOPs with small-scale decision *** recently,it has been reported that traditional multi-objective EAs(MOEAs)suffer severe deterioration with the increase of decision *** a result,and motivated by the emergence of real-world large-scale MOPs,investigation of MOEAs in this aspect has attracted much more attention in the past *** paper reviews the progress of evolutionary computation for large-scale multi-objective optimization from two *** the key difficulties of the large-scale MOPs,the scalability analysis is discussed by focusing on the performance of existing MOEAs and the challenges induced by the increase of the number of decision *** the perspective of methodology,the large-scale MOEAs are categorized into three classes and introduced respectively:divide and conquer based,dimensionality reduction based and enhanced search-based *** future research directions are also discussed.

关键词： Large-scale multi-objective optimization high-dimensional search space evolutionary computation evolutionary algorithms scalability

来源：评论

学校读者我要写书评

暂无评论

Tetrachromatic CDs based on carbonation degree gradient: natural/synthetic precursor synergistic modulation and luminescence mechanism

引用

Optics Express 2025年第12期33卷 25756-25766页

作者： Cong Li Mingyang Han Zheng Ren Zepeng Wu Yiming Chen Yueqi Sun Jialin Yang Jiajia Zhou Chunsheng Wang Xu Li Li Guan Hebei Key Laboratory of Photo-Electricity Information and Materials College of Physics Science and Technology Hebei University Baoding 071002 China School of Electrical Engineering Hebei University of Technology Tianjin 300131 China Key Laboratory of High-precision Computation and Application of Quantum Field Theory of Hebei Province Hebei University Baoding 071002 China wangchunsheng@*** lixcn@*** lguan@***

Carbon dots (CDs), known for their notable fluorescence, cost-effectiveness, and eco-friendliness, hold substantial promise in versatile applications. However, existing one-pot strategies using biomass precursors predominantly yield blue-green emissive CDs, and achieving multicolor emission (especially red) remains challenging. In this work, four distinct fluorescent CDs (blue, green, yellow, and red) were synthesized via a one-pot solvothermal method using a synergistic natural/synthetic precursor system (Radix Saposhnikoviae and p-phenylenediamine). Systematic characterization revealed that the emission red shift (450 nm to 626 nm) is primarily governed by increased carbonization degree, as evidenced by the gradual enhancement of graphitic nitrogen content and sp 2 domain ordering. The synthetic precursor (p-phenylenediamine) drives carbon core growth and graphitization, while the natural biomass (Radix Saposhnikoviae) facilitates multi-scale carbonization control, enabling color diversification in a single pot. By integrating the four CDs with silicone resin on a 395 nm UV chip, we fabricated a warm white LED with high-quality white light with CIE chromatic coordinates (0.36, 0.35) and correlated color temperature 4428 K, demonstrating their practical potential in solid-state lighting.

关键词： Fourier transform infrared spectroscopy Indoor lighting Luminescent materials Red shift Ultraviolet light emitting diodes White light emitting diodes

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：