检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Yan, Zhiying Liang, Yiyuan Cai, Shilv Zhang, Tao Zhong, Sheng Yan, Luxin Zou, Xu Huazhong University of Science and Technology China National Key Laboratory of Multispectral Information Intelligent Processing Technology China Nanyang Technological University Singapore

Semantic 4D Gaussians can be used for reconstructing and understanding dynamic scenes, with temporal variations than static scenes. Directly applying static methods to understand dynamic scenes will fail to capture the temporal features. Few works focus on dynamic scene understanding based on Gaussian Splatting, since once the same update strategy is employed for both dynamic and static parts, regardless of the distinction and interaction between Gaussians, significant artifacts and noise appear. We propose Dual-Hierarchical Optimization (DHO), which consists of Hierarchical Gaussian Flow and Hierarchical Gaussian Guidance in a divide-and-conquer manner. The former implements effective division of static and dynamic rendering and features. The latter helps to mitigate the issue of dynamic foreground rendering distortion in textured complex scenes. Extensive experiments show that our method consistently outperforms the baselines on both synthetic and real-world datasets, and supports various downstream tasks. Project Page: https://***/DHO. Copyright © 2025, The Authors. All rights reserved.

关键词： 3D reconstruction

来源：评论

学校读者我要写书评

暂无评论

Reform of Blended-Teaching Mode for Discipline English Based on Mobile Terminal 17th

Reform of Blended-Teaching Mode for Discipline English Based...

引用

17th International Conference on Computer Science and Education, ICCSE 2022

作者： Yu, Niefang Peng, Xiaoning Li, Xiaomei Lu, Youmin School of Computer and Artificial Intelligence Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9789819924455

In the context of the era of big data, the rise of using mobile terminal is also putting forward new requirements for the teaching of Discipline English. Based on some general problems in the teaching practice of this course, we discuss whether adopting the blended-teaching mode can expand the learning space and time, enrich the learning interaction, and improve the learning efficiency in the context of big data based on mobile terminals. And then improve the quality of teaching and enhance application ability for the Discipline English. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Molecular origin for toughness of hydrogel artificial spider silk for surgical sutures

引用

Science China Chemistry 2025年

作者： Jiatian Li Abdul Qadeer Khan Weiqiang Zhao Zhipeng Lai Qiankun Bao Muhammad Rafique Fazhi Ye Jie Bai Qiang Zhou Liqiang Mai Zongqian Wang Enzhao Liu Xiang Zhou Zunfeng Liu Chemical Engineering College Inner Mongolia University of Technology Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease Department of Cardiology Tianjin Institute of CardiologyThe Second Hospital of Tianjin Medical University State Key Laboratory of Medicinal Chemical Biology Key Laboratory of Functional Polymer Materials Tianjin Key Laboratory of Functional Polymer Materials College of Chemistry Nankai University School of Biomedical Engineering and Med-X Research Institute Shanghai Jiao Tong University State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology Tianjin First Central Hospital School of Textile and Garment Anhui Polytechnic University Department of Science School of Traditional Chinese Pharmacy China Pharmaceutical University

The pursuit of biomimetic fibers with simultaneous high toughness and strength persists, despite their inherent ***, for artificial spider silk based on gel fiber, it is still unclear for the molecular chain attributes related to the improvement of the strength and toughness. Here, a hydrogel fiber was prepared by mimicking the molecular structure of natural spider silk, and we delved into the molecular chain structure characteristics related to the strength, toughness and damping capacity of gel fiber, such as crosslinking density, molecular chain orientation and hydrogen bond interaction. The results indicate that a certain increase in crosslinking density and molecular chain orientation contributes to the enhancement of tensile strength, while the toughness and damping remain essentially unaltered. The thermal dissociation of hydrogen bond could enhance the toughness in a specific range, while the humidity destruction of hydrogen bond would reduce the *** well-regulation control of the weight ratio of polyacrylamide （PAM） to poly（acrylic acid）(PAA), the PAM@PAA gel fibers could reach maximum breaking strength of 1.02 GPa, maximum toughness of 149 MJ m-3, and damping capacity of 95%.PAM@PAA gel fiber has demonstrated excellent wound healing performance and biocompatibility in vivo evaluation as a surgical suture, which indicates its potential in biomedical applications.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Course Homework Reform in Universities Based on Extended Reality 17th

Course Homework Reform in Universities Based on Extended Rea...

引用

17th International Conference on Computer Science and Education, ICCSE 2022

作者： Fu, Jinrong Liu, Yiwen Mi, Chunqiao Peng, Xiaoning Xiao, Jianhua School of Computer and Artificial Intelligence Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9789819924486

In view of the serious issues commonly existing with coursework in Chinese universities at present, such as its original function weakening or being suppressed, its form being too abstract and lack of elaborate design. In order to resolve these problems observed in usual homework, this paper proposes a reform scheme for cloud coursework based on Extended Reality and Bloom model. It mainly includes five key parts, such as interaction, scene, comprehensive evaluation, digital twin and data analysis. In addition, the reform’s effectiveness is illustrated through amassing and comparing the feedback of college students for teaching. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Augmented reality

来源：评论

学校读者我要写书评

暂无评论

MFGB: Molecular Properties Prediction leveraging Self-supervised Morgan Fingerprint Representation Learning

MFGB: Molecular Properties Prediction leveraging Self-superv...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Fang, Shuyan Liu, Yu Liu, Song Wuhan University of Science and Technology School of Computer Science and Technology Wuhan China Wuhan University of Science and Technology Hubei Prov. Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China Wuhan University of Science and Technology Hubei Provincial Key Laboratory of Occupational Hazard Identification and Control Hubei Province Wuhan China

ISBN: (纸本)9798350337488

Simple molecular graphs or molecular line notations are insufficient for molecular representation learning models that automatically learn molecule representations to acquire deep semantic features about chemistry. Therefore, we proposed the Morgan Fingerprint Graph, a novel molecular graph model constructed with the nodes of the Morgan Fingerprint Identifier, whose nodes incorporate various properties of the atom neighborhood. Based on our Morgan Fingerprint Graph, we trained a BERT-based molecular representation model MFGB which employs a local rather than global attention module to adapt the model to the Morgan Fingerprint Graph. The attention module of MFGB regulates the exchange of information in the attention layer based on the connection information of the atoms in the molecules. To verify the effectiveness of MFGB, we selected several mainstream datasets for evaluation. The results demonstrated that our method improve the performance of molecular properties prediction. In addition, we analyzed MFGB from both the over-smoothing and the attention perspectives. The analysis indicates that the node vector representations learned from the Morgan Fingerprint Graph are generally less smoothing, and the attention distributions of MFGB match the partial charge density distribution of the molecules. © 2023 IEEE.

关键词： Molecular property prediction Molecular representation learning Morgan Fingerprint Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Speculative decoding with CTC-based draft model for LLM inference acceleration 24

Speculative decoding with CTC-based draft model for LLM infe...

引用

Proceedings of the 38th International Conference on Neural Information processing Systems

作者： Zhuofan Wen Shangtong Gui Yang Feng Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences and University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences and State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences and University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences and Key Laboratory of Al Safety Chinese Academy of Sciences and University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798331314385

Inference acceleration of large language models (LLMs) has been put forward in many application scenarios and speculative decoding has shown its advantage in addressing inference acceleration. Speculative decoding usually introduces a draft model to assist the base LLM where the draft model produces drafts and the base LLM verifies the draft for acceptance or rejection. In this framework, the final inference speed is decided by the decoding speed of the draft model and the acceptance rate of the draft provided by the draft model. Currently the widely used draft models usually generate draft tokens for the next several positions in a non-autoregressive way without considering the correlations between draft tokens. Therefore, it has a high decoding speed but an unsatisfactory acceptance rate. In this paper, we focus on how to improve the performance of the draft model and aim to accelerate inference via a high acceptance rate. To this end, we propose a CTC-based draft model which strengthens the correlations between draft tokens during the draft phase, thereby generating higher-quality draft candidate sequences. Experiment results show that compared to strong baselines, the proposed method can achieve a higher acceptance rate and hence a faster inference speed.

关键词：

来源：评论

学校读者我要写书评

暂无评论

TransCGAN-based Parameter Extraction Framework for SAR Image Simulation

TransCGAN-based Parameter Extraction Framework for SAR Image...

引用

Signal, Information and Data processing (ICSIDP), IEEE International Conference on

作者： Sidan Deng Jingfei He Yongfei Mao Liangbo Zhao Liang Chen Hao Shi National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing & Chongqing Innovation Center Beijing Institute of Technology Chongqing China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing China Institute of Remote Sensing Satellite China Academy of Space Technology Beijing China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing & Chongqing Innovation Center Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Synthetic Aperture Radar (SAR) images have a wide range of applications due to their all-weather and all-day working conditions. However, SAR images with different scenarios and imaging conditions are insufficient or even rare, which is required in specific SAR image tasks. Fortunately, SAR image simulation technology can provide ample simulated images under these conditions at a low cost, addressing the scarcity of specific real SAR data. Accurate parameters are crucial for obtaining high-quality simulated images. However, it is time-consuming and labour-intensive to adjust parameters manually, and it often fails to achieve accurate simulation parameters. To tackle this problem, this paper proposes a TransCGAN-based SAR image simulation method that combines deep learning with traditional methods. By training TransCGAN, a conditional generative adversarial network (CGAN) integrated with Transformer architecture, a mapping between SAR images and simulation parameters is established. This enables the extraction of simulation parameters directly from the real SAR image, guided by the corresponding real SAR image. Ultimately, the parameters are subsequently converted into simulated SAR images via simulation software. Experimental results demonstrate that our TransCGAN-based method can effectively extract accurate simulation parameters from real SAR images, resulting in simulated images holding high similarity to real images.

关键词： Training Accuracy Imaging Radar imaging Transformers Generative adversarial networks Radar polarimetry Software Synthetic aperture radar Parameter extraction

来源：评论

学校读者我要写书评

暂无评论

TF-DTA: A Deep Learning Approach Using Transformer Encoder to Predict Drug-Target Binding Affinity

TF-DTA: A Deep Learning Approach Using Transformer Encoder t...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Li, Wenjun Zhou, Yiqiang Tang, Xiwei Changsha University of Science and Technology Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Hunan Changsha China Hunan First Normal University School of Computer Science Hunan Changsha China

ISBN: (纸本)9798350337488

Bioinformatics is a rapidly growing field that involves the application of computational methods to analyze and interpret biological data. One important task in bioinformatics is predicting the drug-target affinity (DTA), which plays a significant role in drug discovery through virtual screening. Effectively predicting the association between drug molecules and target molecules can speed up the drug discovery process. The DTA can be quantitatively measured. This quantifiable affinity is more precise than a simple binary relationship. In this study, we propose a deep learning model for DTA prediction that utilizes the encoder module of the Transformer architecture. Our proposed model utilizes Convolutional Neural Networks (CNNs) and the encoder module of Transformer to characterize protein and drug sequences. The model outperforms some methods such as KronRLS, SimBoost and DeepDTA as evidenced by superior evaluation metrics such as Mean Squared Error (MSE), Concordance Index (CI), and Regression toward the Mean Index (rm2). These results demonstrate the effectiveness of the Transformer's encoder in extracting meaningful representations from sequences, thereby improving the accuracy of DTA prediction. Deep learning models for DTA prediction can accelerate drug discovery by identifying drug candidates with high binding affinity to specific targets. Compared to traditional methods, the use of machine learning technology enables more effective and efficient drug discovery. © 2023 IEEE.

关键词： deep learning drug-target affinity multi-head attention mechanism

来源：评论

学校读者我要写书评

暂无评论

Enhanced fireworks algorithm based on particle swarm optimization and reverse learning of small-hole imaging experiment

Enhanced fireworks algorithm based on particle swarm optimiz...

引用

2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Jiao, Jiao Li, Jun Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System College of Computer Science and Technology Wuhan China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Since the traditional fireworks algorithm (FWA) is easy to fall into local optimization and has insufficient global optimization capability and low optimization accuracy, this paper proposes the enhanced fireworks algorithm (EFWA-SI) based on particle swarm optimization and small-hole imaging reverse learning. First, a dynamic explosion amplitude mechanism based on the evolution speed of the population is designed. The evolution speed of the population is dynamically adjusted by evaluating the evolution speed of the fitness in each iteration process, and the global and local search information is controlled. Secondly, the small-hole imaging reverse learning strategy combined with the idea of optimal-worst reverse learning is proposed. This strategy increases the diversity of the optimal-seeking positions and improves the ability of the algorithm to jump out of the local optimum;Finally, the current position is updated according to the position and speed of each firework, and the globally optimal fireworks and personally optimal fireworks are updated simultaneously to improve the individual heuristic learning ability. Through the simulation experiments on the CEC2015 test function and comparison with related algorithms, the results show that the algorithm performs better in convergence speed, computational accuracy, and stability. © 2022 IEEE.

关键词： Global optimization

来源：评论

学校读者我要写书评

暂无评论

A rapid method for extraction,purification and structure analysis of chondroitin sulfate from six marine tissues

引用

Food Quality and Safety 2023年第1期7卷 20-29页

作者： Yuying Wang Shokouh Ahmadi Chengxiao Yu Laiming Zhang Xinxin Hu Xingqian Ye Shiguo Chen College of Biosystems Engineering and Food Science National-Local Joint Engineering Laboratory of Intelligent Food Technology and EquipmentZhejiang Key Laboratory for Agro-Food ProcessingZhejiang Engineering Laboratory of Food Technology and EquipmentZhejiang UniversityHangzhouChina Zhejiang University Zhongyuan Institute ZhengzhouChina Shandong(Linyi)Institute of Modern Agriculture Zhejiang UniversityLinliChina Fuli Institute of Food Science Zhejiang UniversityHangzhouChina Ningbo Research Institute Zhejiang UniversityHangzhouChina

This study was designed to establish a strategy for the extraction,purification,and structure analysis of chondroitin sulfate(cs)in milligram *** acidic polysaccharides were extracted from six kinds of marine animals by enzymatic hydrolysis and hexadecylpyridinium chloride precipitation and purified by Q Sepharose Fast Flow strong anion exchange *** purification of each crude polysaccharide was completed within 1 *** structure of the polysaccharides,*** chemical characterization,functional group,molecular weight and monosaccharide composition,were analyzed by colorimetry,nuclear magnetic resonace and high-performance liquid chromatogrpahy,*** polysaccharides were identified as *** oligosaccharide profile produced by enzyme hydrolysis of polysaccharides was determined by strong anion-exchange high-performance liquid *** method can be widely applied to the extraction and characterization of chondroitin sulfate from unknown raw materials,screening high-quality sources of functional polysaccharides,and laying a good foundation for thefollowing studyof the structure-function relationshipof polysaccharides.

关键词： Chondroitin sulfate marinepolysaccharide disaccharide analysis

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：