检索结果-内蒙古大学图书馆

22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Ma, Xinrui Cheng, Jian Fan, Wenxin Wu, Ruoyou Ye, Yongquan Wang, Shanshan Beihang University State Key Laboratory of Software Development Environment Beijing China Institute of International Innovation Beihang University Key Laboratory of Data Science and Intelligent Computing Zhejiang Hangzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China University of Chinese Academy of Sciences Beijing China Pengcheng Laboratory Guangdong Shenzhen China United Imaging Healthcare Houston TX United States

ISBN: (纸本)9798331520526

Diffusion magnetic resonance imaging (dMRI) is a crucial non-invasive technique for exploring the microstructure of the living human brain. Traditional hand-crafted and model-based tissue microstructure reconstruction methods often require extensive diffusion gradient sampling, which can be time-consuming and limits the clinical applicability of tissue microstructure information. Recent advances in deep learning have shown promise in microstructure estimation;however, accurately estimating tissue microstructure from clinically feasible dMRI scans remains challenging without appropriate constraints. This paper introduces a novel framework that achieves high-fidelity and rapid diffusion microstructure imaging by simultaneously leveraging anatomical information from macro-level priors and mutual information across parameters. This approach enhances time efficiency while maintaining accuracy in microstructure estimation. Experimental results demonstrate that our method outperforms four state-of-the-art techniques, achieving a peak signal-to-noise ratio (PSNR) of 30.51±0.58 and a structural similarity index measure (SSIM) of 0.97±0.004 in estimating parametric maps of multiple diffusion models. Notably, our method achieves a 15× acceleration compared to the dense sampling approach, which typically utilizes 270 diffusion gradients. © 2025 IEEE.

关键词： deep learning diffusion MRI microstructural estimation multiple diffusion models

来源：评论

学校读者我要写书评

暂无评论

When Large Language Models Meet Citation: A Survey

arXiv

引用

arXiv 2023年

作者： Zhang, Yang Wang, Yufei Wang, Kai Sheng, Quan Z. Yao, Lina Mahmood, Adnan Zhang, Wei Emma Zhao, Rongying School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia School of Information Management Wuhan University Wuhan China School of Computer Science and Engineering Nanyang Technological University Singapore CSIRO’s Data61 Sydney Australia Computer Science and Engineering University of New South Wales Sydney Australia School of Computer and Mathematical Sciences The University of Adelaide Adelaide Australia

Citations in scholarly work serve the essential purpose of acknowledging and crediting the original sources of knowledge that have been incorporated or referenced. Depending on their surrounding textual context, these citations are used for different motivations and purposes. Large Language Models (LLMs) could be helpful in capturing these fine-grained citation information via the corresponding textual context, thereby enabling a better understanding towards the literature. Furthermore, these citations also establish connections among scientific papers, providing high-quality inter-document relationships and human-constructed knowledge. Such information could be incorporated into LLMs pre-training and improve the text representation in LLMs. Therefore, in this paper, we offer a preliminary review of the mutually beneficial relationship between LLMs and citation analysis. Specifically, we review the application of LLMs for in-text citation analysis tasks, including citation classification, citation-based summarization, and citation recommendation. We then summarize the research pertinent to leveraging citation linkage knowledge to improve text representations of LLMs via citation prediction, network structure information, and inter-document relationship. We finally provide an overview of these contemporary methods and put forth potential promising avenues in combining LLMs and citation analysis for further investigation. Copyright © 2023, The Authors. All rights reserved.

关键词： Text processing

来源：评论

学校读者我要写书评

暂无评论

YOLOO: You Only Learn from Others Once

arXiv

引用

arXiv 2024年

作者： Gu, Lipeng Wei, Mingqiang Yan, Xuefeng Zhu, Dingkun Zhao, Wei Xie, Haoran Liu, Yong-Jin School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Shenzhen Institute of Research Nanjing University of Aeronautics and Astronautics Shenzhen China The School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China The Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China The School of Computer Science Jiangsu University of Technology Changzhou China The Division of Artificial Intelligence School of Data Science Lingnan University New Territories Hong Kong The MOE-Key Laboratory of Pervasive Computing Department of Computer Science and Technology Tsinghua University Beijing China

Multi-modal 3D multi-object tracking (MOT) typically necessitates extensive computational costs of deep neural networks (DNNs) to extract multi-modal representations. In this paper, we propose an intriguing question: May we learn from multiple modalities only during training to avoid multi-modal input in the inference phase? To answer it, we propose YOLOO, a novel multi-modal 3D MOT paradigm: You Only Learn from Others Once. YOLOO empowers the point cloud encoder to learn a unified tri-modal representation (UTR) from point clouds and other modalities, such as images and textual cues, all at once. Leveraging this UTR, YOLOO achieves efficient tracking solely using the point cloud encoder without compromising its performance, fundamentally obviating the need for computationally intensive DNNs. Specifically, YOLOO includes two core components: a unified tri-modal encoder (UTEnc) and a flexible geometric constraint (F-GC) module. UTEnc integrates a point cloud encoder with image and text encoders adapted from pretrained CLIP. It seamlessly fuses point cloud information with rich visual-textual knowledge from CLIP into the point cloud encoder, yielding highly discriminative UTRs that facilitate the association between trajectories and detections. Additionally, FGC filters out mismatched associations with similar representations but significant positional discrepancies. It further enhances the robustness of UTRs without requiring any scene-specific tuning, addressing a key limitation of customized geometric constraints (e.g., 3D IoU). Lastly, high-quality 3D trajectories are generated by a traditional data association component. By integrating these advancements into a multi-modal 3D MOT scheme, our YOLOO achieves substantial gains in both robustness and efficiency. Comprehensive experiments on KITTI and Waymo demonstrate the superior performance of our YOLOO over its twenty competitors. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Hi-SAM: Marrying Segment Anything Model for Hierarchical Text Segmentation

arXiv

引用

arXiv 2024年

作者： Ye, Maoyuan Zhang, Jing Liu, Juhua Liu, Chenyu Yin, Baocai Liu, Cong Du, Bo Tao, Dacheng School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China IFLYTEK Research IFLYTEK CO. LTD. China College of Computing & Data Science Nanyang Technological University #32 Block N4 #02a-014 50 Nanyang Avenue 639798 Singapore

The Segment Anything Model (SAM), a profound vision foundation model pretrained on a large-scale dataset, breaks the boundaries of general segmentation and sparks various downstream applications. This paper introduces Hi-SAM, a unified model leveraging SAM for hierarchical text segmentation. Hi-SAM excels in segmentation across four hierarchies, including pixel-level text, word, text-line, and paragraph, while realizing layout analysis as well. Specifically, we first turn SAM into a high-quality pixel-level text segmentation (TS) model through a parameter-efficient fine-tuning approach. We use this TS model to iteratively generate the pixel-level text labels in a semi-automatical manner, unifying labels across the four text hierarchies in the HierText dataset. Subsequently, with these complete labels, we launch the end-to-end trainable Hi-SAM based on the TS architecture with a customized hierarchical mask decoder. During inference, Hi-SAM offers both automatic mask generation (AMG) mode and promptable segmentation (PS) mode. In the AMG mode, Hi-SAM segments pixel-level text foreground masks initially, then samples foreground points for hierarchical text mask generation and achieves layout analysis in passing. As for the PS mode, Hi-SAM provides word, text-line, and paragraph masks with a single point click. Experimental results show the state-of-the-art performance of our TS model: 84.86% fgIOU on Total-Text and 88.96% fgIOU on TextSeg for pixel-level text segmentation. Moreover, compared to the previous specialist for joint hierarchical detection and layout analysis on HierText, Hi-SAM achieves significant improvements: 4.73% PQ and 5.39% F1 on the text-line level, 5.49% PQ and 7.39% F1 on the paragraph level layout analysis, requiring 20× fewer training epochs. The code is available at Hi-SAM. Copyright © 2024, The Authors. All rights reserved.

关键词： Masks

来源：评论

学校读者我要写书评

暂无评论

Inventory models for short life cycle clothing products use a logistic growth model 5

Inventory models for short life cycle clothing products use ...

引用

5th International Conference on Mathematics: Pure, Applied and Computation, ICoMPAC 2019

作者： Lukitosari, V. Subriadi, A.P. Mathematics Department Faculty of Mathematics Computing and Data Science Sepuluh Nopember Institute of Technology [ITS Surabaya60111 Indonesia Surabaya60111 Indonesia

Clothes are products that follow short-life fashion and market demand. Products with a short lifetime occur due to technological developments and/or changes in market tastes. The clothing industry is one of the industries that has a short and obsolete sales period in stages. The excess number of products that accumulate in the warehouse due to obsolescence can cause the company to get a profit that is not optimal. One strategy to increase demand for the product is to apply discounts. There are two types of discounts used, namely a single discount and multiple discounts. The demand function of inventory model is considered analogous to the logistic growth model. The results show profit is maximized to get optimal order quantity and optimal discount price. © Published under licence by IOP Publishing Ltd.

关键词： Profitability

来源：评论

学校读者我要写书评

暂无评论

Hyte: A Hotness-Aware Hybrid DRAM-PM Native Table Storage Engine

引用

IEEE Transactions on Computers 2025年

作者： Fan, Xiaopeng Peng, Xiaoshuang Huang, Kaixin Weng, Chuliang East China Normal University School of Data Science and Engineering China Shanghai Jiao Tong University School of Computing China

Emerging persistent memory (PM) technologies offer data persistence at close-to-DRAM latency, causing the legacy multi-layer storage stack to become a bottleneck. On the other hand, PM improves I/O efficiency while also introducing unique hardware features that require software modifications to exploit them. In this paper, we propose HYTE, a native table storage engine for hybrid memory, which abstracts the data service for the database’s table. HYTE performs a cross-layer design across the file system and storage engine to avoid the pitfalls of layered abstraction and provides SQL-compatible APIs. Meanwhile, it co-designs with PM’s properties to unlock the hardware’s performance potential. Furthermore, HYTE is hotness-aware and equipped with a suite of lightweight, customized fetching and eviction strategies. Our evaluation shows that HYTE can perform up to 1.4-7.2× better than existing state-of-the-art PM-based storage systems in industry and academia. © 1968-2012 IEEE.

关键词： Dynamic random access storage

来源：评论

学校读者我要写书评

暂无评论

The importance of robust and reliable energy prediction models: Next generation of smart meters 10

The importance of robust and reliable energy prediction mode...

引用

10th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2020

作者： Jurado, Sergio Nebot, Àngela Mugica, Francisco Soft Computing Research Group Intelligent Data Science and Artificial Intelligence Research Center Universitat Politècnica de Catalunya Barcelona Spain

ISBN: (纸本)9789897584442

In this position paper a discussion is performed related to the importance of Energy Prediction Models (EPM) in the context of trading renewable energy in smart grids and the need to develop a new generation of Smart Meters (SMs) based on edge computing. If the electricity currently produced and consumed in the low voltage is expected to grow due to installations of PVs and the electrification of the system, we need to incentivise local energy trading and provide tools to make it possible. Currently, all energy trading mechanisms in the literature heavily rely on predictions, therefore, inaccuracy would be translated in low profitability of prosumer's investments or even worse, disappointment with the local energy markets. To guarantee robustness and reliability of predictions, we propose Flexible FIR as EPM to be used during the trading process and to integrate them in a Next Generation of SMs (NGSMs). In this document some reflections about new functionalities of NGSMs and first steps of a prototype are also addressed. © 2020 by SCITEPRESS - science and Technology Publications, Lda. All rights reserved

关键词： Smart meters

来源：评论

学校读者我要写书评

暂无评论

Reversible data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

引用

IEEE Internet of Things Journal 2025年

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： adaptive coding chinese remainder theorem for polynomials encrypted images Reversible data hiding secret sharing

来源：评论

学校读者我要写书评

暂无评论

Curating corpora with classifiers: A case study of clean energy sentiment online

arXiv

引用

arXiv 2023年

作者： Arnold, Michael V. Dodds, Peter Sheridan Danforth, Christopher M. Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core University of Vermont BurlingtonVT United States Department of Computer Science University of Vermont BurlingtonVT United States Department of Mathematics & Statistics University of Vermont BurlingtonVT United States

Well curated, large-scale corpora of social media posts containing broad public opinion offer an alternative data source to complement traditional surveys. While surveys are effective at collecting representative samples and are capable of achieving high accuracy, they can be both expensive to run and lag public opinion by days or weeks. Both of these drawbacks could be overcome with a real-time, high volume data stream and fast analysis pipeline. A central challenge in orchestrating such a data pipeline is devising an effective method for rapidly selecting the best corpus of relevant documents for analysis. Querying with keywords alone often includes irrelevant documents that are not easily disambiguated with bag-of-words natural language processing methods. Here, we explore methods of corpus curation to filter irrelevant tweets using pre-trained transformer-based models, fine-tuned for our binary classification task on hand-labeled tweets. We are able to achieve F1 scores of up to 0.95. The low cost and high performance of fine-tuning such a model suggests that our approach could be of broad benefit as a pre-processing step for social media datasets with uncertain corpus boundaries. © 2023, CC BY.

关键词： Tweets

来源：评论

学校读者我要写书评

暂无评论

Stock Price Prediction Using Transformers

Stock Price Prediction Using Transformers

引用

Advancement in Electronics & Communication Engineering (AECE), International Conference on

作者： Shailendra Singh Sikarwar Shweta Jaswal Rajeev Sharma Ganesan G Ranjan Ganguli Mukesh Kumar Mahawar Dept. of Computer Science PGV College Jiwaji University Gwalior India Dept. of Pharmacy Lingayas Vidyapeeth University Faridabad India Dept. of BMC Mangalayatan University Aligarh India Dept. of AI and Data Science Dhanalakshmi Srinivasan University Coimbatore India Dept. of Computing and IT Usha Martin University Ranchi India Dept. of Commerce & Management Madhav University Sirohi India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Stock markets are known for their volatility, as prices can quickly change due to various factors like company performance and geopolitical events. Predictive models face challenges in capturing the complex patterns driving these fluctuations. This paper introduces a new transformer-based approach to stock price prediction, utilizing self-attention mechanisms to understand long-term dependencies and intricate patterns in financial data. The model is specifically designed to handle the rapid changes and volatility seen in stock markets, making it suitable for real-time prediction tasks. With a multi-head attention architecture, the transformer efficiently focuses on different parts of the input sequence, prioritizing the most relevant factors influencing stock prices.

关键词： Uncertainty Systematics Neural networks Predictive models Transformers data models Timing Stock markets Tuning Investment

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：