检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Wang, Chengyuan Wu, Yuheng Liu, Yuan Wang, Enheng Mao, Xuegang Niu, Qian Feng, Tiantian Yang, Liwei Liu, Hongpeng Lu, Wangyang Yao, Yao Sun, Xiaofan School of Forestry Northeast Forestry University Harbin150040 China Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education Northeast Forestry University Harbin150040 China Research and Development Center of Big Data for Ecosystem Northeast Forestry University Harbin150040 China Department of Teaching and Research Management Maoershan Teaching Area Northeast Forestry University Harbin150611 China State Key Laboratory of Environmental Criteria and Risk Assessment Chinese Research Academy of Environmental Sciences Beijing100012 China State Environmental Protection Key Laboratory of Simulation and Control of Groundwater Pollution Chinese Research Academy of Environmental Sciences Beijing100012 China

Nighttime lighting (NTL) remote sensing data provides globally consistent, long-term observations of surface nighttime light and is widely used to assess regional economic and social development. However, it remains essential to monitor the Sustainable Development Goals (SDGs) process from various perspectives, such as NTL landscape patterns, light equality, and light intensity, to enhance the fairness and consistency of SDGs assessments through geography information and remote sensing. By comparing various NTL indices, this research tracks the progress of achieving the 17 SDGs in China, uncovering potential applications of NTL in sustainability science. Results from 2000 to 2015 indicate that 84.2% of NTLs in China exhibited an increasing trend, primarily in urban fringe areas, while urban centers showed a declining trend. The Nighttime Lighting Landscape Index (NTLI), integrating landscape hierarchy, lighting equality, and light intensity, proved optimal for monitoring SDGs, effectively tracking the development of SDG1, SDG3, SDG4, sdg8, SDG9, and SDG11. NTLI generally synergizes with most SDGs, though trade-offs are evident with SDG5, SDG6, and SDG12. The NTLI and SDG score have shown an increasing trend towards coordination over time, with more subnational regions exhibiting coupling phenomena. NTL is more suitable for monitoring the sustainable development process in the eastern coastal region of China. This study underscores the sensitivity and relevance of NTL instruments in monitoring sustainable development, proving the crucial role of landscape patterns and development equity indicators in the SDGs assessment system through NTL. It will facilitate constructing an equitable and non-discriminatory SDGs tracking process framework with a consistent observational baseline for large-scale regions using geo-information data. © 2024, The Authors. All rights reserved.

关键词： Remote sensing

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SurgicalGaussian: Deformable 3D Gaussians for High-Fidelity Surgical Scene Reconstruction

arXiv

引用

arXiv 2024年

作者： Xie, Weixing Yao, Junfeng Cao, Xianpeng Lin, Qiqin Tang, Zerui Dong, Xiao Guo, Xiaohu Center for Digital Media Computing School of Film School of Informatics Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism China Guangdong Provincial Key Laboratory IRADS Department of Computer Science BNU-HKBU United International College Zhuhai China Department of Computer Science The University of Texas at Dallas Dallas United States

Dynamic reconstruction of deformable tissues in endoscopic video is a key technology for robot-assisted surgery. Recent reconstruction methods based on neural radiance fields (NeRFs) have achieved remarkable results in the reconstruction of surgical scenes. However, based on implicit representation, NeRFs struggle to capture the intricate details of objects in the scene and cannot achieve real-time rendering. In addition, restricted single view perception and occluded instruments also propose special challenges in surgical scene reconstruction. To address these issues, we develop SurgicalGaussian, a deformable 3D Gaussian Splatting method to model dynamic surgical scenes. Our approach models the spatio-temporal features of soft tissues at each time stamp via a forward-mapping deformation MLP and regularization to constrain local 3D Gaussians to comply with consistent movement. With the depth initialization strategy and tool mask-guided training, our method can remove surgical instruments and reconstruct high-fidelity surgical scenes. Through experiments on various surgical videos, our network outperforms existing method on many aspects, including rendering quality, rendering speed and GPU usage. The project page can be found at https://***. © 2024, CC BY-SA.

关键词： Tissue

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BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations

arXiv

引用

arXiv 2023年

作者： Pei, Qizhi Zhang, Wei Zhu, Jinhua Wu, Kehan Gao, Kaiyuan Wu, Lijun Xia, Yingce Yan, Rui Gaoling School of Artificial Intelligence Renmin University of China China University of Science and Technology of China China Huazhong University of Science and Technology China Microsoft Research United States Engineering Research Center of Next-Generation Intelligent Search and Recommendation Ministry of Education China Beijing Key Laboratory of Big Data Management and Analysis Methods China

Recent advancements in biological research leverage the integration of molecules, proteins, and natural language to enhance drug discovery. However, current models exhibit several limitations, such as the generation of invalid molecular SMILES, underutilization of contextual information, and equal treatment of structured and unstructured knowledge. To address these issues, we propose BioT5, a comprehensive pretraining framework that enriches cross-modal integration in biology with chemical knowledge and natural language associations. BioT5 utilizes SELFIES for 100% robust molecular representations and extracts knowledge from the surrounding context of bio-entities in unstructured biological literature. Furthermore, BioT5 distinguishes between structured and unstructured knowledge, leading to more effective utilization of information. After fine-tuning, BioT5 shows superior performance across a wide range of tasks, demonstrating its strong capability of capturing underlying relations and properties of bio-entities. Our code is available at https://***/QizhiPei/BioT5. Copyright © 2023, The Authors. All rights reserved.

关键词： Integration

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Depth selection for deep ReLU nets in feature extraction and generalization

arXiv

引用

arXiv 2020年

作者： Han, Zhi Yu, Siquan Lin, Shao-Bo Zhou, Ding-Xuan State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China School of Information Science and Engineering Northeastern University Shenyang China Center of Intelligent Decision-Making and Machine Learning School of Management Xi'an Jiaotong University Xi'an China School of Data Science Department of Mathematics City University of Hong Kong Hong Kong

Deep learning is recognized to be capable of discovering deep features for representation learning and pattern recognition without requiring elegant feature engineering techniques by taking advantage of human ingenuity and prior knowledge. Thus it has triggered enormous research activities in machine learning and pattern recognition. One of the most important challenge of deep learning is to figure out relations between a feature and the depth of deep neural networks (deep nets for short) to reflect the necessity of depth. Our purpose is to quantify this feature-depth correspondence in feature extraction and generalization. We present the adaptivity of features to depths and vice-verse via showing a depth-parameter trade-off in extracting both single feature and composite features. Based on these results, we prove that implementing the classical empirical risk minimization on deep nets can achieve the optimal generalization performance for numerous learning tasks. Our theoretical results are verified by a series of numerical experiments including toy simulations and a real application of earthquake seismic intensity prediction. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction

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Automatically Labeling Aging Scenarios with a Machine Learning Approach 1

引用

24th International Conference on Human-Computer Interaction, HCII 2022

作者： An, Ning Xu, Yang Gao, Qinglin Zhu, Wenjie Wu, Aoran Chen, Honglin School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Shanghai Tianyu Senior Living Service Co. Ltd. Shanghai China School of Basic Courses Bengbu Medical College Bengbu China Management and Business Department Skidmore College Saratoga SpringsNY United States Department of Social Sciences University of Eastern Finland Kuopio70150 Finland

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

Older adults have diversified needs often associated with particular aging scenarios. People started to use aging scenarios to provide better Smart Eldercare services and develop innovative solutions. The problem is how to manage these scenarios effectively. To our best knowledge, this study is the first to develop a model to provide a structured framework of aging scenarios for research and teaching of eldercare services. Labeling aging scenarios can facilitate their collection, classification, and consolidation within this model. This study uses the automatic labeling approach to manage and utilize aging scenarios. Under each class, there are labels with different numbers and smaller granularity to reflect the requirements in each scenario case. Since a scenario case often involves more than one label, properly labeling it becomes a multi-label text classification problem. We extend the multi-label text classification model LSAN for labeling aging scenario cases. For evaluation purposes, we collected 938 scenario cases as the dataset. Experimental results show that our proposed method can achieve an average accuracy of 61.66%, better than other classical methods. © 2022, Springer Nature Switzerland AG.

关键词： Classification (of information)

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Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features

arXiv

引用

arXiv 2024年

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China School of Computer Science and Tech. University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management CAS China

Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations. However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations, such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated. Considering the significant increase in activations, a full-scale quantitative comparison is no longer operational. Instead, we seek to understand the properties of these activations, such that the activations that are clearly inferior can be filtered out in advance via simple qualitative evaluation. After careful analysis, we discover three properties universal among diffusion models, enabling this study to go beyond specific models. On top of this, we present effective feature selection solutions for several popular diffusion models. Finally, the experiments across multiple discriminative tasks validate the superiority of our method over the SOTA competitors. Our code is available at this url. © 2024, CC BY.

关键词： Semantics

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Suppress Content Shift: Better Diffusion Features via Off-the-Shelf Generation Techniques

arXiv

引用

arXiv 2024年

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Yang, Zhiyong Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Computer Science and Tech. University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management CAS China School of Cyber Science and Tech. Sun Yat-sen University Shenzhen Campus China

Diffusion models are powerful generative models, and this capability can also be applied to discrimination. The inner activations of a pre-trained diffusion model can serve as features for discriminative tasks, namely, diffusion feature. We discover that diffusion feature has been hindered by a hidden yet universal phenomenon that we call content shift. To be specific, there are content differences between features and the input image, such as the exact shape of a certain object. We locate the cause of content shift as one inherent characteristic of diffusion models, which suggests the broad existence of this phenomenon in diffusion feature. Further empirical study also indicates that its negative impact is not negligible even when content shift is not visually perceivable. Hence, we propose to suppress content shift to enhance the overall quality of diffusion features. Specifically, content shift is related to the information drift during the process of recovering an image from the noisy input, pointing out the possibility of turning off-the-shelf generation techniques into tools for content shift suppression. We further propose a practical guideline named GATE to efficiently evaluate the potential benefit of a technique and provide an implementation of our methodology. Despite the simplicity, the proposed approach has achieved superior results on various tasks and datasets, validating its potential as a generic booster for diffusion features. Our code is available at this url. © 2024, CC BY.

关键词：

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data-driven optimization algorithm for economic operation of microgrid based on dynamic Kriging model and two-stage search strategy

Research Square

引用

Research Square 2021年

作者： Fan, Chaodong Hou, Bo Qu, Xilong Xiao, Leyi Cheng, Fanyong Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology School of Information Technology and Management Hunan University of Finance and Economics Changsha410205 China Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment Ministry of Education Anhui Polytechnic University Wuhu241000 China Foshan Green Intelligent Manufacturing Research Institute Xiangtan University Guangdong 528311 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore School of Automation and Electronic Information Xiangtan University Xiangtan411105 China

The economic operation optimization of microgrid is an important research topic in the power system. Therefore, this paper proposes a surrogate model particle swarm optimization algorithm based on the global-local search mechanism. Firstly, aiming at the problem that the statistical information of Kriging model is difficult to guarantee the prediction accuracy, the dynamic transformation is carried out to enhance the robustness of the model;secondly, the global-local search mechanism is introduced to make the algorithm fully explore the fitness landscape near the Kriging model after quickly locating the current optimal particle position, so as to achieve the balance of convergence quality and convergence efficiency. The proposed method has been tested on numerous benchmark test functions from two test suites, and the results show that the proposed algorithm has more advantages than other comparison algorithms in optimization accuracy. Finally, simulations are carried out in two operating modes of microgrid islanding and grid-connected, which has verified the effectiveness of the proposed method. © 2021, CC BY.

关键词： Particle swarm optimization (PSO)

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DRSM: EFFICIENT NEURAL 4D DECOMPOSITION FOR DYNAMIC RECONSTRUCTION IN STATIONARY MONOCULAR CAMERAS

arXiv

引用

arXiv 2024年

作者： Xie, Weixing Dong, Xiao Yang, Yong Lin, Qiqin Chen, Jingze Yao, Junfeng Guo, Xiaohu Center for Digital Media Computing School of Film School of Informatics Xiamen University China National Institute for Data Science in Health and Medicine Xiamen University China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Department of Computer Science BNU-HKBU United International College China Department of Computer Science The University of Texas Dallas United States

With the popularity of monocular videos generated by video sharing and live broadcasting applications, reconstructing and editing dynamic scenes in stationary monocular cameras has become a special but anticipated technology. In contrast to scene reconstructions that exploit multi-view observations, the problem of modeling a dynamic scene from a single view is significantly more under-constrained and ill-posed. Inspired by recent progress in neural rendering, we present a novel framework to tackle 4D decomposition problem for dynamic scenes in monocular cameras. Our framework utilizes decomposed static and dynamic feature planes to represent 4D scenes and emphasizes the learning of dynamic regions through dense ray casting. Inadequate 3D clues from a single-view and occlusion are also particular challenges in scene reconstruction. To overcome these difficulties, we propose deep supervised optimization and ray casting strategies. With experiments on various videos, our method generates higher-fidelity results than existing methods for single-view dynamic scene representation. © 2024, CC BY.

关键词： Cameras

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An Approach for Estimating Microbial Growth and Chlorinated Ethenes Biotransformation in Groundwater Based on Discrete Observations

SSRN

引用

SSRN 2024年

作者： Luo, Moye Cao, Shaohua Xu, Chenghua Zhang, Xiaodong Zhu, Xin Zhan, Manjun Yu, Dandan Long, Tao Yu, Ran Department of Environmental Science and Engineering School of Energy and Environment Southeast University Jiangsu Nanjing210096 China State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control Nanjing Institute of Environmental Sciences Ministry of Ecology and Environment of China Jiangsu Nanjing210042 China Jiangsu geological engineering environment Intelligent Monitoring Engineering Research Center The First Geological Brigade of Jiangsu Provincial Bureau of Geology and Mineral Resources Jiangsu Nanjing210041 China Nanjing Research Institute of Environmental Protection Nanjing Environmental Protection Bureau Jiangsu Nanjing210013 China

The bioremediation of chlorinated ethenes (CEs) contaminated groundwater is attracting increasingly attention in practical remediation projects. However, modelling of microbial metabolic processes under the constraints of substrate and environmental factors is inadequate. This study developed a new kinetic model, which incorporated the logistic model and Dual-Monod kinetic to represent the interaction between the controlled microbial growth and the bioavailable substrates in CE-contaminated groundwater. The proposed model was based on discrete observations to simulate microbial growth under the constraints of substrate and environmental conditions, reducing the amount of observational data required for the model. Meanwhile, the proposed model introduced two new kinetic parameters, the effective specific growth rate [[EQUATION]] and the real self-limiting coefficient of microbial growth [[EQUATION]], to simplified the number of independent parameters. A parameter estimation method based on the proposed Newton's algorithm for the proposed model was also developed. The model was validated based on the hypothetical data, experimental results, and a published dataset, demonstrated the successful simulation of microbial growth and the sequential biodegradation of PCE in groundwater systems (*E © 2024, The Authors. All rights reserved.

关键词： Biodegradation

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