检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Gao, Xing Guo, Li Han, Zongjian Zhang, Yi School of Mathematics and Statistics Lanzhou University Lanzhou730000 China Gansu Provincial Research Center for Basic Disciplines of Mathematics and Statistics Lanzhou730070 China Department of Mathematics and Computer Science Rutgers University NewarkNJ07102 United States School of Mathematical Sciences Tonji University Shanghai200092 China School of Mathematics and Statistics Center for Applied Mathematics of Jiangsu Province Jiangsu International Joint Laboratory on System Modeling and Data Analysis NUIST Jiangsu Nanjing210044 China

Rota-Baxter operators on various structures have found important applications in diverse areas, from renormalization of quantum field theory to Yang-Baxter equations. Relative Rota-Baxter operators on Lie algebras are closely related to pre-Lie algebras and post-Lie algebras. Some of their group counterparts have been introduced to study post-groups, skew left braces and set-theoretic solutions of Yang-Baxter equations, but searching suitable notions of relative Rota-Baxter operators on groups with weight zero and pre-groups has been challenging and has been the focus of recent studies, by provisionally imposing an abelian condition. Arising from the works of Balinsky-Novikov and Gelfand-Dorfman, Novikov algebras and their constructions from differential commutative algebras have led to broad applications. Finding their suitable counterparts for groups and Lie algebras has also attracted quite much recent attention. This paper uses one-sided-inverse pairs of maps to give a perturbative approach to a general notion of relative Rota-Baxter operators and differential operators on a group and a Lie algebra with limit-weight. With the extra condition of limit-abelianess on the group or Lie algebra, we give an interpretation of relative Rota-Baxter and differential operators with weight zero. These operators motivate us to define pre-groups and Novikov groups respectively as the induced structures. The tangent maps of these operators are shown to give Rota-Baxter and differential operators with weight zero on Lie algebras. The tangent spaces of the pre-Lie and Novikov Lie groups are pre-Lie algebras and Novikov Lie algebras, fulfilling the expected property. Furthermore, limit-weight relative Rota-Baxter operators on groups give rise to skew left braces and then set-theoretic solutions of the Yang-Baxter *** Codes 22E60, 17B38, 17B40, 16W99, 45N05 Copyright © 2024, The Authors. All rights reserved.

关键词： Lie groups

来源：评论

学校读者我要写书评

暂无评论

When to Update Your Model: Constrained Model-based Reinforcement Learning

arXiv

引用

arXiv 2022年

作者： Ji, Tianying Luo, Yu Sun, Fuchun Jing, Mingxuan He, Fengxiang Huang, Wenbing Department of Computer Science and Technology Tsinghua University China Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences China JD Explore Academy *** Inc China Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

Designing and analyzing model-based RL (MBRL) algorithms with guaranteed monotonic improvement has been challenging, mainly due to the interdependence between policy optimization and model learning. Existing discrepancy bounds generally ignore the impacts of model shifts, and their corresponding algorithms are prone to degrade performance by drastic model updating. In this work, we first propose a novel and general theoretical scheme for a non-decreasing performance guarantee of MBRL. Our follow-up derived bounds reveal the relationship between model shifts and performance improvement. These discoveries encourage us to formulate a constrained lower-bound optimization problem to permit the monotonicity of MBRL. A further example demonstrates that learning models from a dynamically-varying number of explorations benefit the eventual returns. Motivated by these analyses, we design a simple but effective algorithm CMLO1 (Constrained Model-shift Lower-bound Optimization), by introducing an event-triggered mechanism that flexibly determines when to update the model. Experiments show that CMLO surpasses other state-of-the-art methods and produces a boost when various policy optimization methods are employed. © 2022, CC BY-NC-SA.

关键词： Constrained optimization

来源：评论

学校读者我要写书评

暂无评论

Efficient Visual Perception of Human-Robot Walking Environments Using Semi-Supervised Learning

Efficient Visual Perception of Human-Robot Walking Environme...

引用

IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Dmytro Kuzmenko Oleksii Tsepa Andrew Garrett Kurbis Alex Mihailidis Brokoslaw Laschowski Department of Mathematics National University of Kyiv-Mohyla Academy Kyiv Ukraine Department of Mathematical Methods of System Analysis Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine Department of Computer Science University of Toronto Toronto Canada Department of Systems Design Engineering University of Waterloo Waterloo Canada Temerty Faculty of Medicine University of Toronto Toronto Canada Institute of Biomedical Engineering University of Toronto Toronto Canada Toronto Rehabilitation Institute Toronto Canada Department of Mechanical and Industrial Engineering Robotics Institute University of Toronto Toronto Canada

Convolutional neural networks trained using supervised learning can improve visual perception for human-robot walking. These advances have been possible due to largescale datasets like ExoNet and StairNet - the largest open-source image datasets of real-world walking environments. However, these datasets require vast amounts of manually annotated data, the development of which is time consuming and labor intensive. Here we present a novel semi-supervised learning system (ExoNet-SSL) that uses over 1.2 million unlabelled images from ExoNet to improve training efficiency. We developed a deep learning model based on mobile vision transformers and trained the model using semi-supervised learning for image classification. Compared to standard supervised learning (98.4%), our ExoNet-SSL system was able to maintain high prediction accuracy (98.8%) when tested on previously unseen environments, while requiring 35% fewer labelled images during training. These results show that semi-supervised learning can improve training efficiency by leveraging large amounts of unlabelled data and minimize the size requirements for manually annotated images. Future research $\text{will}$ focus on model deployment for onboard real-time inference and control of human-robot walking.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Association of PM_(2.5)Reduction with Improved Kidney Function:A Nationwide Quasiexperiment among Chinese Adults

引用

Health Data science 2022年第1期2022卷 115-123页

作者： Yiqun Han Tao Xue Frank J.Kelly Yixuan Zheng Yao Yao Jiajianghui Li Jiwei Li Chun Fan Pengfei Li Tong Zhu Environmental Research Group MRC Centre for Environment and HealthImperial College LondonLondonUK BIC-ESAT and SKL-ESPC College of Environmental Sciences and EngineeringCenter for Environment and HealthPeking UniversityBeijing 100871China Institute of Reproductive and Child Health/Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics School of Public HealthPeking UniversityBeijingChina Center of Air Quality Simulation and System Analysis Chinese Academy of Environmental Planning100012 BeijingChina China Center for Health Development Studies Peking UniversityBeijingChina College of Computer Science and Technology Zhejiang UniversityHangzhouChina Computer Center Peking University and Peng Cheng LaboratoryChina Advanced Institute of Information Technology Peking UniversityChina

*** evidence from human studies has revealed the adverse impact of ambient fine particles(PM_(2.5))on health outcomes related to metabolic disorders and distant *** exposure to ambient PM_(2.5)leads to kidney impairment remains *** rapid air quality improvement driven by the clean air actions in China since 2013 provides an opportunity for a quasiexperiment to investigate the beneficial effect of PM_(2.5)reduction on kidney *** on two repeated nationwide surveys of the same population of 5115 adults in 2011 and 2015,we conducted a difference-indifference *** in long-term exposure to ambient PM_(2.5)were associated with changes in kidney function biomarkers,including estimated glomerular filtration rate by serum creatinine(GFRscr)or cystatin C(GFRcys),blood urea nitrogen(BUN),and uric acid(UA).*** a 10μg/m^(3)reduction in PM_(2.5),a significant improvement was observed for multiple kidney functional biomarkers,including GFRscr,BUN and UA,with a change of 0.42(95%confidence interval[CI]:0.06,0.78)mL/min/1.73m^(2),-0.38(-0.64,-0.12)mg/dL,and-0.06(-0.12,-0.00)mg/dL,respectively.A lower socioeconomic status,indicated by rural residence or low educational level,enhanced the adverse effect of PM_(2.5)on kidney *** results support a significant nephrotoxicity of PM_(2.5)based on multiple serum biomarkers and indicate a beneficial effect of improved air quality on kidney function.

关键词： Kidney organs ambient

来源：评论

学校读者我要写书评

暂无评论

Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions

arXiv

引用

arXiv 2022年

作者： Boussange, Victor Becker, Sebastian Jentzen, Arnulf Kuckuck, Benno Pellissier, Loïc Switzerland Landscape Ecology Institute of Terrestrial Ecosystems Department of Environmental Systems Science ETH Zürich Switzerland Risklab Department of Mathematics ETH Zürich Switzerland School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China Applied Mathematics Institute for Analysis and Numerics Faculty of Mathematics and Computer Science University of Münster Germany Landscape Ecology Institute of Terrestrial Ecosystems Department of Environmental System Science ETH Zürich Switzerland

Nonlinear partial differential equations (PDEs) are used to model dynamical processes in a large number of scientific fields, ranging from finance to biology. In many applications standard local models are not sufficient to accurately account for certain non-local phenomena such as, e.g., interactions at a distance. In order to properly capture these phenomena non-local nonlinear PDE models are frequently employed in the literature. In this article we propose two numerical methods based on machine learning and on Picard iterations, respectively, to approximately solve non-local nonlinear PDEs. The proposed machine learning-based method is an extended variant of a deep learning-based splitting-up type approximation method previously introduced in the literature and utilizes neural networks to provide approximate solutions on a subset of the spatial domain of the solution. The Picard iterations-based method is an extended variant of the so-called full history recursive multilevel Picard approximation scheme previously introduced in the literature and provides an approximate solution for a single point of the domain. Both methods are mesh-free and allow non-local nonlinear PDEs with Neumann boundary conditions to be solved in high dimensions. In the two methods, the numerical difficulties arising due to the dimensionality of the PDEs are avoided by (i) using the correspondence between the expected trajectory of reflected stochastic processes and the solution of PDEs (given by the Feynman-Kac formula) and by (ii) using a plain vanilla Monte Carlo integration to handle the nonlocal term. We evaluate the performance of the two methods on five different PDEs arising in physics and biology. In all cases, the methods yield good results in up to 10 dimensions with short run times. Our work extends recently developed methods to overcome the curse of dimensionality in solving *** Codes 35R09 (Primary) 65M75, 45K05, 35K20, 65C05, 65M22, 68T07 (Secondary). Copyright © 2022,

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Quantification and comparison of prediction uncertainty associated with different practices of crop modeling

引用

Agricultural and Forest Meteorology 2025年 371卷

作者： Gao, Yujing Wallach, Daniel Gu, Baojing Tang, Liang Lin, Tao Chang, Xini Hasegawa, Toshi Asseng, Senthold Kahveci, Tamer Hoogenboom, Gerrit College of Environmental and Resource Sciences Zhejiang University Hangzhou China Agricultural & Biological Engineering Department University of Florida Gainesville United States College of Biosystems Engineering and Food Science Zhejiang University Hangzhou China Institute of Crop Science and Resource Conservation University of Bonn Germany National Engineering and Technology Center for Information Agriculture Key Laboratory for Crop System Analysis and Decision Making Ministry of Agriculture Jiangsu Key Laboratory for Information Agriculture Jiangsu Collaborative Innovation Center for Modern Crop Production Nanjing Agricultural University Jiangsu Nanjing China National Agriculture and Food Research Organization Ibaraki Tsukuba Japan Technical University of Munich Department of Life Science Engineering Freising Germany Department of Computer and Information Science and Engineering University of Florida Gainesville United States

Crop models are widely used as decision support tools in agriculture and natural resource management. However, the current practices in crop modeling and evaluation vary significantly, making it challenging to compare uncertainties across different models and studies. This study aims to quantify and compare the uncertainties associated with four different crop modeling practices using a standardized evaluation framework. The four modeling practices are: 1) using a single model, considering only model bias for uncertainty;2) using a single model, accounting for both model bias and parameter uncertainty;3) employing a multi-model ensemble to account for model bias, parameter uncertainty, and structural uncertainties;and 4) using a multi-model ensemble to consider uncertainties induced by model bias, parameters, structures, and inputs. We developed a framework that integrates Markov Chain Monte Carlo (MCMC) and Bayesian Model Averaging (BMA) to consistently quantify prediction uncertainty across these practices. The framework was applied to an Asian rice dataset. The results revealed that common model evaluation approach (Practice 1) tends to underestimate the uncertainty of model predictions. Relying on a single process-based model presents a substantial risk in critical decision-making situations. In contrast, the BMA ensemble predictor (e-BMA) demonstrated higher reliability, making it a preferable choice for future decision support. Our Bayesian framework provides a more robust and adaptable approach for project-specific decision-making, with promising applications in digital agriculture. © 2025 Elsevier B.V.

关键词： Bayesian model averaging Crop model Model structure uncertainty Multi-model ensemble Parameter uncertainty Uncertainty quantification

来源：评论

学校读者我要写书评

暂无评论

MetAug: Contrastive Learning via Meta Feature Augmentation

arXiv

引用

arXiv 2022年

作者： Li, Jiangmeng Qiang, Wenwen Zheng, Changwen Su, Bing Xiong, Hui Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Guangdong China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Guangzhou China Department of Computer Science Engineering The Hong Kong University of Science and Technology Hong Kong

What matters for contrastive learning? We argue that contrastive learning heavily relies on informative features, or "hard" (positive or negative) features. Early works include more informative features by applying complex data augmentations and large batch size or memory bank, and recent works design elaborate sampling approaches to explore informative features. The key challenge toward exploring such features is that the source multi-view data is generated by applying random data augmentations, making it infeasible to always add useful information in the augmented data. Consequently, the informativeness of features learned from such augmented data is limited. In response, we propose to directly augment the features in latent space, thereby learning discriminative representations without a large amount of input data. We perform a meta learning technique to build the augmentation generator that updates its network parameters by considering the performance of the encoder. However, insufficient input data may lead the encoder to learn collapsed features and therefore malfunction the augmentation generator. A new margin-injected regularization is further added in the objective function to avoid the encoder learning a degenerate mapping. To contrast all features in one gradient back-propagation step, we adopt the proposed optimization-driven unified contrastive loss instead of the conventional contrastive loss. Empirically, our method achieves state-of-the-art results on several benchmark datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Backpropagation

来源：评论

学校读者我要写书评

暂无评论

Interventional Contrastive Learning with Meta Semantic Regularizer

arXiv

引用

arXiv 2022年

作者： Qiang, Wenwen Li, Jiangmeng Zheng, Changwen Su, Bing Xiong, Hui Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Guangdong China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Guangzhou China Department of Computer Science & Engineering The Hong Kong University of Science and Technology Hong Kong

Contrastive learning (CL)-based self-supervised learning models learn visual representations in a pairwise manner. Although the prevailing CL model has achieved great progress, in this paper, we uncover an ever-overlooked phenomenon: When the CL model is trained with full images, the performance tested in full images is better than that in foreground areas;when the CL model is trained with foreground areas, the performance tested in full images is worse than that in foreground areas. This observation reveals that backgrounds in images may interfere with the model learning semantic information and their influence has not been fully eliminated. To tackle this issue, we build a Structural Causal Model (SCM) to model the background as a confounder. We propose a backdoor adjustment-based regularization method, namely Interventional Contrastive Learning with Meta Semantic Regularizer (ICL-MSR), to perform causal intervention towards the proposed SCM. ICL-MSR can be incorporated into any existing CL methods to alleviate background distractions from representation learning. Theoretically, we prove that ICL-MSR achieves a tighter error bound. Empirically, our experiments on multiple benchmark datasets demonstrate that ICL-MSR is able to improve the performances of different state-of-the-art CL methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

ImplantFormer: Vision Transformer based Implant Position Regression Using Dental CBCT Data

arXiv

引用

arXiv 2022年

作者： Yang, Xinquan Li, Xuguang Li, Xuechen Wu, Peixi Shen, Linlin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China The School of Dentistry Shenzhen University Shenzhen China

Implant prosthesis is the most appropriate treatment for dentition defect or dentition loss, which usually involves a surgical guide design process to decide the implant position. However, such design heavily relies on the subjective experiences of dentists. In this paper, a transformer-based Implant Position Regression Network, ImplantFormer, is proposed to automatically predict the implant position based on the oral CBCT data. We creatively propose to predict the implant position using the 2D axial view of the tooth crown area and fit a centerline of the implant to obtain the actual implant position at the tooth root. Convolutional stem and decoder are designed to coarsely extract image features before the operation of patch embedding and integrate multi-level feature maps for robust prediction, respectively. As both long-range relationship and local features are involved, our approach can better represent global information and achieves better location performance. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed ImplantFormer achieves superior performance than existing methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Modeling Multiple Views via Implicitly Preserving Global Consistency and Local Complementarity

arXiv

引用

arXiv 2022年

作者： Li, Jiangmeng Qiang, Wenwen Zheng, Changwen Su, Bing Razzak, Farid Wen, Ji-Rong Xiong, Hui The University of Chinese Academy of Sciences Beijing China The Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China The New York University Columbia University New York United States The Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China Guangzhou China Department of Computer Science & Engineering The Hong Kong University of Science and Technology Hong Kong

While self-supervised learning techniques are often used to mine hidden knowledge from unlabeled data via modeling multiple views, it is unclear how to perform effective representation learning in a complex and inconsistent context. To this end, we propose a new multi-view self-supervised learning method, namely consistency and complementarity network (CoCoNet), to comprehensively learn global inter-view consistent and local cross-view complementarity-preserving representations from multiple views. To capture crucial common knowledge which is implicitly shared among views, CoCoNet employs a global consistency module that aligns the probabilistic distribution of views by utilizing an efficient discrepancy metric based on the generalized sliced Wasserstein distance. To incorporate cross-view complementary information, CoCoNet proposes a heuristic complementarity-aware contrastive learning approach, which extracts a complementarity-factor jointing cross-view discriminative knowledge and uses it as the contrast to guide the learning of view-specific encoders. Theoretically, the superiority of CoCoNet is verified by our information-theoretical-based analyses. Empirically, our thorough experimental results show that CoCoNet outperforms the state-of-the-art self-supervised methods by a significant margin, for instance, CoCoNet beats the best benchmark method by an average margin of 1.1% on ImageNet. Copyright © 2022, The Authors. All rights reserved.

关键词： Supervised learning

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：