检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Hille, Tobias Stubbemann, Maximilian Hanika, Tom Knowledge & Data Engineering Group University of Kassel Kassel Germany Interdisciplinary Research Center for Information System Design University of Kassel Kassel Germany Information Systems and Machine Learning Lab University of Hildesheim Hildesheim Germany Institute of Computer Science University of Hildesheim Hildesheim Germany

Difficulties in replication and reproducibility of empirical evidences in machine learning research have become a prominent topic in recent years. Ensuring that machine learning research results are sound and reliable requires reproducibility, which verifies the reliability of research findings using the same code and data. This promotes open and accessible research, robust experimental workflows, and the rapid integration of new findings. Evaluating the degree to which research publications support these different aspects of reproducibility is one goal of the present work. For this we introduce an ontology of reproducibility in machine learning and apply it to methods for graph neural networks. Building on these efforts we turn towards another critical challenge in machine learning, namely the curse of dimensionality, which poses challenges in data collection, representation, and analysis, making it harder to find representative data and impeding the training and inference processes. Using the closely linked concept of geometric intrinsic dimension we investigate to which extend the used machine learning models are influenced by the intrinsic dimension of the data sets they are trained *** Codes 68T01 68T07 68T09 51F99 Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Transferring Traffic Predictions to Urban Regions Without Target data

Transferring Traffic Predictions to Urban Regions Without Ta...

引用

International Conference on Intelligent Transportation

作者： Stefan Schestakov Simon Gottschalk Nicolas Tempelmeier Thorben Funke Elena Demidova L3S Research Center Leibniz University Hannover Hannover Germany Volkswagen AG Commercial Vehicles Hannover Germany Data Science & Intelligent Systems (DSIS) Research Group University of Bonn and Lamarr Institute for Machine Learning and Artificial Intelligence Bonn Germany

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

The scarcity of spatiotemporal traffic data for many urban regions significantly limits the availability of location-specific predictive models for traffic management, mobility services, and road safety. For example, whereas some cities release taxi data for several years, for many other cities traffic data is not available. Although existing transfer learning approaches aim to alleviate this problem, they require at least some data to be available in the target urban region. Traffic prediction in the regions without data remains a critical and unsolved task. To this extent, we propose Zero-ST, a novel transfer learning approach that leverages inductive road features and road topology to learn latent road representations encoding traffic pattern similarity. Our experimental evaluation on real-world data demonstrates the effectiveness of Zero-ST for the traffic speed prediction task in several urban regions. On this task, Zero-ST achieves an error reduction of at least 4.77% concerning MAE compared to the baselines.

关键词： Roads Transfer learning Urban areas Traffic control Predictive models Road safety Spatiotemporal phenomena Topology Public transportation Knowledge transfer

来源：评论

学校读者我要写书评

暂无评论

Quantization of Bandlimited Graph Signals

Quantization of Bandlimited Graph Signals

引用

International Conference on Sampling Theory and Applications (SampTA)

作者： Felix Krahmer He Lyu Rayan Saab Anna Veselovska Rongrong Wang Department of Mathematics & Munich Data Science Institute Technical University of Munich and Munich Center for Machine Learning Garching/Munich Germany Department of Mathematics & Halicioglu Data Science Institute University of California San Diego San Diego USA Department of Computational Mathematics Science and Engineering & Department of Mathematics Michigan State University East Lansing USA

Graph models and graph-based signals are becoming increasingly important in machine learning, natural sciences, and modern signal processing. In this paper, we address the problem of quantizing bandlimited graph signals. We introduce two classes of noise-shaping algorithms for graph signals that differ in their sampling methodologies. We demonstrate that these algorithms can be efficiently used to construct quantized representatives of bandlimited graph-based signals with bounded amplitude. Moreover, for one of the algorithms, we provide theoretical guarantees on the relative error between the quantized representative and the true signal.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Investor Risk Profile Determination Model

Investor Risk Profile Determination Model

引用

International Conference on Management of Large-Scale System Development (MLSD)

作者： Victor Gorelik Tatiana Zolotova Department of Simulation Systems and Operations Research Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Russia Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia

An assessment of the investor’s risk profile is proposed as a risk coefficient in a model with a linear convolution of expected return and variance. The value of the risk coefficient is found from solving the optimization problem of the model with a limitation on profitability. The proposed method is implemented in the form of a software package using the example of the stock market.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An iterative-based difference scheme for nonlinear fractional integro-differential equations of Volterra type

引用

Partial Differential Equations in Applied Mathematics 2025年 13卷

作者： Saini, Gaurav Ghosh, Bappa Chand, Sunita Mohapatra, Jugal Center for Data Science Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) India Center for Artificial Intelligence and Machine Learning Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) India Department of Mathematics Siksha ‘O’ Anusandhan (Deemed to be University) India Department of Mathematics National Institute of Technology Rourkela India

This paper presents an iterative difference scheme for solving nonlinear fractional integro-differential equations of Volterra type, which are widely used in modeling memory-dependent phenomena in various scientific and engineering applications. The scheme employs Caputo's fractional derivative to handle the non-local nature of the fractional derivative and introduces an iterative approach to enhance accuracy and stability. By discretizing the domain of interest on a uniform mesh and the present non-local differential operator using a well-established L1 technique, we derive a nonlinear difference equation that captures the essential dynamics of the underlying continuous problem. The corresponding nonlinear equation is solved by employing the Daftardar-Gejji and Jafari (DGJ) method. The convergence and stability of the proposed scheme are rigorously analyzed under sufficiently smooth initial data, and the method produces reliable numerical solutions. Numerical experiments are conducted to validate the effectiveness of the scheme, demonstrating its efficiency. © 2025 The Authors

关键词： Composite trapezoidal formula Error estimation L1 technique Nonlinear integro-differential equations

来源：评论

学校读者我要写书评

暂无评论

Domain Adaptation-Based Deep learning Models for Forecasting and Diagnosis of Glaucoma Disease

TechRxiv

引用

TechRxiv 2022年

作者： Madadi, Yeganeh Abu-Serhan, Hashem Yousefi, Siamak The Data Mining and Machine Learning Laboratory Department of Ophthalmology University of Tennessee Health Science Center TN United States The Hamad Medical Corporation QA Doha Qatar

Domain adaptation methods are designed to extract shared domain-invariant features by projecting data on a common subspace in order to align their domain distributions. However, these methods do not usually consider domain-specific features, and therefore their distributions may not be well aligned. To address this problem, we introduce a novel model that learns domain-invariant and domain-specific representations to extract both their general and specific features. We also propose progressive weighting to accurately transfer the source domain knowledge and to mitigate negative knowledge transfer from the source to the target domain and to employ low-rank coding for aligning the source and target distributions. We evaluated the model based on several real-world datasets and showed that our method significantly improved the accuracy for forecasting and diagnosis of glaucoma disease from fundus photographs. © 2022-10-25, CC BY.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Marine Predators Algorithm for Energy Scheduling Problem Using Renewable Energy

Marine Predators Algorithm for Energy Scheduling Problem Usi...

引用

Cyber Resilience (ICCR), International Conference on

作者： Sharif Naser Makhadmeh Ammar Kamal Abasi Mohammed Azmi Al-Betar Department of Data Science and Artificial Intelligence University of Petra Amman Jordan Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi United Arab Emirates Artificial Intelligence Research Center (AIRC) Ajman University Ajman United Arab Emirates

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

The Energy Scheduling Problem (ESP) involves scheduling smart home appliances based on electricity pricing schemes. This entails adjusting the timing of operations for these appliances across different periods. The primary aim of this scheduling approach is to reduce cost and the Peak-to-Average Ratio (PAR), while enhancing user comfort. This study employs the Marine Predators Algorithm (MPA) to address ESP and derive an optimal schedule for smart home appliances. Additionally, a renewable energy source (RES) on the basis of a PV system is introduced to further optimize appliance schedules by providing a smart home with energy, particularly during peak periods. The obtained outcomes demonstrate the effectiveness of the introduced RES in reducing costs of electricity and enhancing user satisfaction. Furthermore, the performance of MPA is compared with that of the wind-driven optimization algorithm, with MPA proving superior in achieving ESP objectives.

关键词： Home appliances Optimization Pricing Heuristic algorithms Optimal scheduling Electricity Dynamic scheduling

来源：评论

学校读者我要写书评

暂无评论

machine learning for Methane Detection and Quantification from Space - A survey

arXiv

引用

arXiv 2024年

作者： Tiemann, Enno Zhou, Shanyu Kläser, Alexander Heidler, Konrad Schneider, Rochelle Zhu, Xiao Xiang Data Science in Earth Observation Technical University of Munich Arcisstraße 21 Munich80333 Germany OHB Digital Connect GmbH Bremen Germany University of Valencia Valencia Spain Φ-Lab European Space Agency Frascati Italy Munich Center for Machine Learning Munich Germany

Methane (CH4) is a potent anthropogenic greenhouse gas, contributing 86 times more to global warming than Carbon Dioxide (CO2) over 20 years, and it also acts as an air pollutant. Given its high radiative forcing potential and relatively short atmospheric lifetime (9±1 years), methane has important implications for climate change, therefore, cutting methane emissions is crucial for effective climate change mitigation. This work expands existing information on operational methane point source detection sensors in the Short-Wave Infrared (SWIR) bands. It reviews the state-of-the-art for traditional as well as machine learning (ML) approaches. The architecture and data used in such ML models will be discussed separately for methane plume segmentation and emission rate estimation. Traditionally, experts rely on labor-intensive manually adjusted methods for methane detection. However, ML approaches offer greater scalability. Our analysis reveals that ML models outperform traditional methods, particularly those based on convolutional neural networks (CNN), which are based on the U-net and transformer architectures. These ML models extract valuable information from methane-sensitive spectral data, enabling a more accurate detection. Challenges arise when comparing these methods due to variations in data, sensor specifications, and evaluation metrics. To address this, we discuss existing datasets and metrics, providing an overview of available resources and identifying open research problems. Finally, we explore potential future advances in ML, emphasizing approaches for model comparability, large dataset creation, and the European Union's forthcoming methane strategy. © 2024, CC BY.

关键词： Greenhouse gas emissions

来源：评论

学校读者我要写书评

暂无评论

Can Multiple Phylogenetic Trees Be Displayed in a Tree-Child Network Simultaneously?

arXiv

引用

arXiv 2022年

作者： Wu, Yufeng Zhang, Louxin Department of Computer Science and Engineering University of Connecticut Storrs CT06269 United States Department of Mathematics Center for Data Science and Machine Learning National University of Singapore Singapore119076 Singapore

A binary phylogenetic network on a taxon set X is a rooted acyclic digraph in which the degree of each nonleaf node is three and its leaves (i.e. degree-one nodes) are uniquely labeled with the taxa of X. It is tree-child if each nonleaf node has at least one child of indegree one. A set of binary phylogenetic trees may or may not be simultaneously displayed in a binary tree-child network. Necessary conditions for multiple phylogenetic trees being simultaneously displayed in a tree-child network are given here. In particular, it is proved that any two phylogenetic trees can always simultaneously be displayed in some tree-child network on the same taxa set. It is also proved that any set of multiple binary phylogenetic trees can always simultaneously be displayed in some non-binary tree-child network on the same taxa set, where each nonleaf node is of either indegree one and outdegree two or indegree at least two and outdegree *** Codes 05C30 © 2022, CC BY.

关键词： Binary trees

来源：评论

学校读者我要写书评

暂无评论

Ai-Driven Automated Tool for Abdominal CT Body Composition Analysis in Gastrointestinal Cancer Management

Ai-Driven Automated Tool for Abdominal CT Body Composition A...

引用

IEEE International Symposium on Biomedical Imaging

作者： Xinyu Nan Meng He Zifan Chen Bin Dong Lei Tang Li Zhang Center for Data Science Peking University China Department of Radiology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education) Peking University Cancer Hospital and Institute Beijing China Beijing International Center for Mathematical Research (BICMR) Peking University Beijing China Center for Machine Learning Research Peking University Beijing China National Biomedical Imaging Center Peking University Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

The incidence of gastrointestinal cancers remains significantly high, particularly in China, emphasizing the importance of accurate prognostic assessments and effective treatment strategies. Research shows a strong correlation between abdominal muscle and fat tissue composition and patient outcomes. However, existing manual methods for analyzing abdominal tissue composition are time-consuming and costly, limiting clinical research scalability. To address these challenges, we developed an AI-driven tool for automated analysis of abdominal CT scans to effectively identify and segment muscle, subcutaneous fat, and visceral fat. Our tool integrates a multi-view localization model and a high-precision 2D nnUNet-based segmentation model, demonstrating a localization accuracy of 90% and a Dice Score Coefficient of 0.967 for segmentation. Furthermore, it features an interactive interface that allows clinicians to refine the segmentation results, ensuring high-quality outcomes effectively. Our tool offers a standardized method for effectively extracting critical abdominal tissues, potentially enhancing the management and treatment for gastrointestinal cancers. The code is available at https://***/NanXinyu/***.

关键词： Location awareness Analytical models Accuracy Computed tomography Scalability Manuals Muscles Gastrointestinal tract Fats Cancer

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：