检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Guo, Tianlin Zhang, Lingling Wang, Jiaxin Lei, Yunkuo Li, Yifei Wang, Haofen Liu, Jun School of Computer Science and Technology Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Xi’an710049 China School of Computer Science and Technology Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi’an710049 China College of Design and Innovation Tongji University Shanghai200092 China

Few-shot relation extraction with none-of-the-above (FsRE with NOTA) aims at predicting labels in few-shot scenarios with unknown classes. FsRE with NOTA is more challenging than the conventional few-shot relation extraction task, since the boundaries of unknown classes are complex and difficult to learn. Meta-learning based methods, especially prototype-based methods, are the mainstream solutions to this task. They obtain the classification boundary by learning the sample distribution of each class. However, their performance is limited because few-shot overfitting and NOTA boundary confusion lead to misclassification between known and unknown classes. To this end, we propose a novel framework based on Gaussian prototype and adaptive margin named GPAM for FsRE with NOTA, which includes three modules, semi-factual representation, GMM-prototype metric learning and decision boundary learning. The first two modules obtain better representations to solve the few-shot problem through debiased information enhancement and Gaussian space distance measurement. The third module learns more accurate classification boundaries and prototypes through adaptive margin and negative sampling. In the training procedure of GPAM, we use contrastive learning loss to comprehensively consider the effects of range and margin on the classification of known and unknown classes to ensure the model's stability and robustness. Sufficient experiments and ablations on the FewRel dataset show that GPAM surpasses previous prototype methods and achieves state-of-the-art performance. Copyright © 2024, 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

A Multi-Label Classification Algorithm Based on Self-paced Learning and LDA

A Multi-Label Classification Algorithm Based on Self-paced L...

引用

6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Qiangqiang Wang Shenming Gu School of Information Engineering Zhejiang Ocean University Zhoushan China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhoushan China

To address the problem that existing multi-label learning algorithms treat all samples equally during model training and ignore inter-sample variability, this paper proposes a multi-label classification algorithm based on self-paced learning and LDA (SPLDA for short). SPLDA first constructs an adaptive self-paced function based on the label prediction loss values in multi-label data, and introduces a self-paced learning framework. Secondly, the linear discriminant analysis regularization term in the low-dimensional space of the samples is constructed by combining the principles of minimizing the within-class distance and maximizing the between-class distance in this framework. Next, a multi-label classification model is trained using alternating optimization methods. Finally, the experimental results on 5 real datasets and 3 evaluation metrics show that SPLDA outperforms the existing multi-label classification algorithms.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The Applications of Artificial Intelligence in Digestive System Neoplasms:A Review

引用

Health data Science 2023年第1期3卷 1-16页

作者： Shuaitong Zhang Wei Mu Di Dong Jingwei Wei Mengjie Fang Lizhi Shao Yu Zhou Bingxi He Song Zhang Zhenyu Liu Jianhua Liu Jie Tian School of Engineering Medicine Beihang UniversityBeijingChina. Key Laboratory of Big Data-Based Precision Medicine Beihang UniversityMinistry of Industry and Information TechnologyBeijingChina. CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijingChina. Department of Oncology Guangdong Provincial People's Hospital/Second Clinical Medical College of Southern Medical University/Guangdong Academy of Medical SciencesGuangzhouGuangdongChina.

Importance:Digestive system neoplasms(DSNs)are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%.Subjective evaluation of medical images including endoscopic images,whole slide images,computed tomography images,and magnetic resonance images plays a vital role in the clinical practice of DSNs,but with limited performance and increased workload of radiologists or *** application of artificial intelligence(AI)in medical image analysis holds promise to augment the visual interpretation of medical images,which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor ***:We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis,assessment of treatment response,and prognosis prediction on 4 typical DSNs including esophageal cancer,gastric cancer,colorectal cancer,and hepatocellular ***:AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of *** technical issues should be overcome before its application into clinical practice of DSNs.

关键词： diagnosis neoplasms holds

来源：评论

学校读者我要写书评

暂无评论

FrustumFusionNets: A Three-Dimensional Object Detection Network Based on Tractor Road Scene

arXiv

引用

arXiv 2025年

作者： Yang, Lili Chang, Mengshuai Guo, Xiao Feng, Yuxin Mei, Yiwen Wu, Caicong College of Information and Electrical Engineering China Agricultural University Beijing100083 China Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications Ministry of Agriculture and Rural Affairs Beijing100083 China

To address the issues of the existing frustum-based methods' underutilization of image information in road three-dimensional object detection as well as the lack of research on agricultural scenes, we constructed an object detection dataset using an 80-line Light Detection And Ranging (LiDAR) and a camera in a complex tractor road scene and proposed a new network called FrustumFusionNets (FFNets). Initially, we utilize the results of image-based two-dimensional object detection to narrow down the search region in the three-dimensional space of the point cloud. Next, we introduce a Gaussian mask to enhance the point cloud information. Then, we extract the features from the frustum point cloud and the crop image using the point cloud feature extraction pipeline and the image feature extraction pipeline, respectively. Finally, we concatenate and fuse the data features from both modalities to achieve three-dimensional object detection. Experiments demonstrate that on the constructed test set of tractor road data, the FrustumFusionNetv2 achieves 82.28% and 95.68% accuracy in the three-dimensional object detection of the two main road objects, cars and people, respectively. This performance is 1.83% and 2.33% better than the original model. It offers a hybrid fusion-based multi-object, high-precision, real-time three-dimensional object detection technique for unmanned agricultural machines in tractor road scenarios. On the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) Benchmark Suite validation set, the FrustumFusionNetv2 also demonstrates significant superiority in detecting road pedestrian objects compared with other frustum-based three-dimensional object detection methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Tractors (truck)

来源：评论

学校读者我要写书评

暂无评论

Optimizing Random Access to Hierarchically-Compressed data on GPU

Optimizing Random Access to Hierarchically-Compressed Data o...

引用

Supercomputing Conference

作者： Feng Zhang Yihua Hu Haipeng Ding Zhiming Yao Zhewei Wei Xiao Zhang Xiaoyong Du Key Laboratory of Data Engineering and Knowledge Engineering (MOE) School of Information Renmin University of China Beijing China

ISBN: (纸本)9781665454452

GPU's powerful computational capacity holds great potentials for processing hierarchically-compressed data without decompression in data science domain. Unfortunately, existing GPU approaches offer only traversal-based data analytics; random access is extremely inefficient, substantially limiting their utility. To solve this problem, we develop a novel and broadly applicable optimization that enables efficient random access to hierarchically-compressed data without decompression in GPU memory. We address three major challenges for enabling efficient random access to compressed data on GPUs. The first challenge is designing GPU data structures that support random access. The second challenge is efficiently generating data structures on GPU. Generating data structures for random access is costly on the CPU, and the inefficiency increases dramatically when PCIe data transmission is incorporated. The third challenge is query processing on compressed data in GPU memory. Random accesses, including data updates, result in significant conflicts between massive threads. To solve the first challenge, we propose and modify a number of compressed data structures, including indexing within the complicated GPU memory hierarchy. To address the second challenge, we develop a two-phase process for generating these data structures on the GPU. To handle the third challenge, we propose a double-parsing design to avoid data conflicts. We evaluate our solution on two GPU platforms using five real-world datasets. Experiments show that the random access operations on GPU can achieve 65.04x average speedup compared to the state-of-the-art method.

关键词： Limiting Query processing Instruction sets High performance computing Memory management Graphics processing units data science

来源：评论

学校读者我要写书评

暂无评论

Label Enhanced Event Detection with Collective knowledge and Heterogeneous Graph

Label Enhanced Event Detection with Collective Knowledge and...

引用

IEEE International Conference on big knowledge (ICBK)

作者： Gongqing Wu Zhenya Lu Zhuochun Miao Xingrui Zhuo Zan Zhang Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Event Detection (ED) aims to recognize instances of specified types of event triggers in text. Existing graph neural network-based models have achieved promising progress to alleviate this problem by capturing different orders of syntactic information, but they are limited by two issues. First, the long-range syntactic information between words is not fully exploited. Second, they ignore the semantic information provided by dependency labels which provide linguistic knowledge that is useful to ED. As a result, we proposed a label-enhanced dense graph convolutional network that employs dense connectivity and Graph Transformer Networks (GTN) to learn a flexible selection of edge types and composite relations between the words. Each layer can make use of the collective knowledge of dense blocks in order to model syntactic dependencies over long distances through dense connectivity. The proposed model achieves state-of-the-art performance for ED on common datasets after extensive experiments are conducted to show its advantages.

关键词： knowledge engineering Event detection Text recognition Semantics Syntactics Linguistics Transformers

来源：评论

学校读者我要写书评

暂无评论

knowledge Enhanced Coreference Resolution via Gated Attention

Knowledge Enhanced Coreference Resolution via Gated Attentio...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kai He Bing Mao Xiangyu Zhou Yufei Li Tieliang Gong Chen Li Jialun Wu School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Coreference resolution aims at linking all mentions that refer to the same entity, which are widely adopted in many biomedical and bioinformatics tasks, such as biomedical knowledge graph construction and metabolic pathway integration. Many recent studies focus on improving neural model structures. However, we argue that a practical method that integrates commonsense knowledge can further improve coreference resolution performance, because commonsense delivers extra prior knowledge for reasoning and can enhance related representations, rather than naive mention-context occurrence modeling. In this work, we propose an effective method to integrate external commonsense knowledge into a neural coreference resolution model. Specially, a gated attention mechanism is employed in our method to leverage commonsense according to different contexts. By using ConceptNet as the knowledge base in three span-ranking backbone models, the models can yield significant performance gains on used datasets. We also achieve improvements in tasks of long-term mention detection and cross-sentence coreferences after incorporating knowledge.

关键词： Biological system modeling knowledge based systems Logic gates Performance gain Task analysis Bioinformatics Commonsense reasoning

来源：评论

学校读者我要写书评

暂无评论

Feature Aggregating Network with Inter-Frame Interaction for Efficient Video Super-Resolution

Feature Aggregating Network with Inter-Frame Interaction for...

引用

IEEE International Conference on data Mining (ICDM)

作者： Yawei Li Zhao Zhang Suiyi Zhao Jicong Fan Haijun Zhang Mingliang Xu School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Kunming China Shenzhen Research Institute of Big Data Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China Department of Computer Science Harbin Institute of Technology Shenzhen China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

Video super-resolution (VSR) on mobile devices aims to restore high-resolution frames from their low-resolution counterparts, satisfying the requirements of performance, FLOPs and latency. On one hand, partial feature processing, as a classic and acknowledged strategy, is developed in current studies to reach an appropriate trade-off between FLOPs and accuracy. However, the splitting of partial feature processing strategy are usually performed in a blind manner, thereby reducing the computational efficiency and performance gains. On the other hand, current methods for mobile platforms primarily treat VSR as an extension of single-image super-resolution to reduce model calculation and inference latency. However, lacking inter-frame information interaction in current methods results in a suboptimal latency and accuracy trade-off. To this end, we propose a novel architecture, termed Feature Aggregating Network with Inter-frame Interaction (FANI), a lightweight yet considering frame-wise correlation VSR network, which could achieve real-time inference while maintaining superior performance. Our FANI accepts adjacent multi-frame low-resolution images as input and generally consists of several fully-connection-embedded modules, i.e., Multi-stage Partial Feature Distillation (MPFD) for capturing multi-level feature representations. Moreover, considering the importance of inter-frame alignment, we further employ a tiny Attention-based Frame Alignment (AFA) module to promote inter-frame information flow and aggregation efficiently. Extensive experiments on the well-known dataset and real-world mobile device demonstrate the superiority of our proposed FANI, which means that our FANI could be well adapted to mobile devices and produce visually pleasing results.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Reducing Traffic Wastage in Video Streaming via Bandwidth-Efficient Bitrate Adaptation

arXiv

引用

arXiv 2024年

作者： Su, Hairong Wang, Shibo Yang, Shusen Huang, Tianchi Ren, Xuebin National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University China Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi'an Jiaotong University China R&D department Sony

Bitrate adaptation (also known as ABR) is a crucial technique to improve the quality of experience (QoE) for video streaming applications. However, existing ABR algorithms suffer from severe traffic wastage, which refers to the traffic cost of downloading the video segments that users do not finally consume, for example, due to early departure or video skipping. In this paper, we carefully formulate the dynamics of buffered data volume (BDV), a strongly correlated indicator of traffic wastage, which, to the best of our knowledge, is the first time to rigorously clarify the effect of downloading plans on potential wastage. To reduce wastage while keeping a high QoE, we present a bandwidth-efficient bitrate adaptation algorithm (named BEABR), achieving consistently low BDV without distinct QoE losses. Specifically, we design a precise, time-aware transmission delay prediction model over the Transformer architecture, and develop a fine-grained buffer control scheme. Through extensive experiments conducted on emulated and real network environments including WiFi, 4G, and 5G, we demonstrate that BE-ABR performs well in both QoE and bandwidth savings, enabling a 60.87% wastage reduction and a comparable, or even better, QoE, compared to the state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Bandwidth

来源：评论

学校读者我要写书评

暂无评论

Cooperative engineering the multiple radio-frequency fields to reduce the X-junction barrier for ion trap chips

引用

Chip 2024年第1期3卷 55-63页

作者： Yarui Liu Zhao Wang Zixuan Xiang Qikun Wang Tianyang Hu Xu Wang College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China International Quantum Academy Shenzhen 518048China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China

With the increasing number of ion qubits and improving performance of sophisticated quantum algorithms,more and more scalable complex ion trap electrodes have been developed and *** ion shuttling operations at the junction are more frequently used,such as in the areas of separation,merging,and *** studies have been conducted to optimize the geometries of the radio-frequency(RF)electrodes to generate ideal trapping electric fields with a lower junction barrier and an even ion height of the RF saddle ***,this iteration is time-consuming and commonly accompanied by complicated and sharp electrode ***,high-accuracy fabrication process and high electric breakdown voltage are *** the current work,an effective method was proposed to reduce the junction’s pseudo-potential barrier and ion height variation by setting several individual RF electrodes and adjusting each RF voltage amplitude without changing the geometry of the electrode *** simulation results show that this method shows the same effect on engineering the trapping potential and reducing the potential barrier,but requires fewer parameters and optimization *** combining this method with the geometrical shapeoptimizing,the pseudo-potential barrier and the ion height variation near the junction can be further *** addition,the geometry of the electrodes can be simplified to relax the fabrication precision and keep the ability to engineer the trapping electric field in real-time even after the fabrication of the electrodes,which provides a potential all-electric degree of freedom for the design and control of the two-dimensional ion crystals and investigation of their phase transition.

关键词： Ion trap Junction Ion shuttling Electrode optimization Pseudo-potential barrier

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：