检索结果-内蒙古大学图书馆

data quantity governance for machine learning in materials science

National Science Review 2023年第7期10卷 234-250页

作者： Yue Liu Zhengwei Yang Xinxin Zou Shuchang Ma Dahui Liu Maxim Avdeev Siqi Shi School of Computer Engineering and Science Shanghai University Shanghai Engineering Research Center of Intelligent Computing System Australian Nuclear Science and Technology Organisation School of Chemistry The University of Sydney State Key Laboratory of Advanced Special Steel School of Materials Science and Engineering Shanghai University Materials Genome Institute Shanghai University

data-driven machine learning(ML) is widely employed in the analysis of materials structure-activity relationships,performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate ***,because of the laborious process of materials data acquisition,ML models encounter the issue of the mismatch between a high dimension of feature space and a small sample size(for traditional ML models) or the mismatch between model parameters and sample size(for deep-learning models),usually resulting in terrible ***,we review the efforts for tackling this issue via feature reduction,sample augmentation and specific ML approaches,and show that the balance between the number of samples and features or model parameters should attract great attention during data quantity *** this,we propose a synergistic data quantity governance flow with the incorporation of materials domain *** summarizing the approaches to incorporating materials domain knowledge into the process of ML,we provide examples of incorporating domain knowledge into governance schemes to demonstrate the advantages of the approach and *** work paves the way for obtaining the required high-quality data to accelerate materials design and discovery based on ML.

关键词： machine learning data governance data quantity materials science

来源：评论

学校读者我要写书评

暂无评论

On the Interplay Between Ocean Color data Quality and data quantity: Impacts of Quality Control Flags

引用

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2020年第5期17卷 745-749页

作者： Hu, Chuanmin Barnes, Brian B. Feng, Lian Wang, Menghua Jiang, Lide Univ S Florida Coll Marine Sci St Petersburg FL 33701 USA South Univ Sci & Technol Sch Environm Sci & Engn Shenzhen 430079 Peoples R China NOAA NESDIS Ctr Satellite Applicat & Res College Pk MD 20740 USA

Nearly all calibration/validation activities for the satellite ocean color missions have focused on data quality to produce data products of the highest quality (i.e., science quality) for climate-related research. Little attention, however, has been paid to data quantity, particularly on how data quality control during data processing impacts downstream data quality and data quantity. In this letter, we attempt to fill this knowledge gap using measurements from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP). For this sensor, the same level-1B data are processed independently using different quality control methods by NASA and NOAA, respectively, allowing for an in-depth evaluation of the interplay between data quantity and quality. The results indicate that the methods to identify stray light and sun glint are the two primary quality control procedures affecting data quantity, where the criteria for flagging pixels "contaminated" by stray light and sun glint may be relaxed in the NASA ocean color data processing to increase data quantity without compromising data quality.

关键词： Oceans Image color analysis data integrity Quality control Sun Sea measurements NASA Calibration data quality data quantity level-2 flags stray light ocean color quality control remote sensing sun glint uncertainty validation

来源：评论

学校读者我要写书评

暂无评论

A convergence-speed-dependent data quantity definition and its effect on risk estimation

引用

JOURNAL OF ASSET MANAGEMENT 2019年第6期20卷 469-475页

作者： Krause, Jakob Martin Luther Univ Halle Wittenberg Sch Econ & Business Grosse Steinstr 73 D-06108 Halle Germany European Commod Clearing Augustuspl 9 D-04109 Leipzig Germany

data quantity plays a crucial role in the estimation of risk measures since 'more data' lead to a better estimator convergence and consequently to a better risk assessment. The objective of this paper is to use this relationship between data quantity and estimator convergence to formally derive a measure of data quantity for estimators based on weighted observations. For the case of a variance estimation and using exponentially weighted observations, this procedure leads to analytical formulas for the implied measure of data quantity. As such, this paper specifies the theoretical underpinnings of measures of data quantity which have been present in the literature (effective number of scenarios) and, as an application, demonstrates the effect of the specific measure of data quantity on risk assessment.

关键词： data quantity EWMA Weighted estimation

来源：评论

学校读者我要写书评

暂无评论

"She is in a Bad Mood Now": Leveraging Peers to Increase data quantity via a Chatbot-Based ESM 19

"She is in a Bad Mood Now": Leveraging Peers to Increase Dat...

引用

21st International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI)

作者： Chang, Yu-Lin Chang, Yung-Ju Shen, Chih-Ya Natl Tsing Hua Univ Dept Comp Sci Hsinchu Taiwan Natl Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan

ISBN: (纸本)9781450368254

The experience sampling method (ESM) is widely used for collecting in situ experiences in various domains. One known limitation, however, is its reliance on participants being receptive to ESM questionnaires at the sampled moments. At moments when participants cannot notice or respond to an ESM questionnaire, researchers cannot obtain a response. In this research, we explored the feasibility of inviting peers to provide information about participants in an ESM study. Results from a two-week experiment with a total of 27 participants and 82 peers showed that including peers' ESM responses increased ESM data quantity. Furthermore, the agreement between the peers' and the participants responses could be maintained by asking peers' confidence. Even considering only data with high confidence could increase data quantity. Moreover, inviting peers had a positive impact on the participant's compliance to respond. These results suggest that using peer ESM to obtain more in-situ data about participants is promising.

关键词： Experience Sampling Method ESM Peer Assessment Peer-ESM Chatbot data quantity data Quality

来源：评论

学校读者我要写书评

暂无评论

Sensitivity analysis of microbial growth parameter distributions with respect to data quality and quantity by using Monte Carlo analysis

引用

MATHEMATICS AND COMPUTERS IN SIMULATION 2004年第3期65卷 231-243页

作者： Poschet, F Bernaerts, K Geeraerd, AH Scheerlinck, N Nicolaï, BM Van Impe, JF Katholieke Univ Leuven BioTec Dept Chem Engn B-3001 Heverlee Belgium Katholieke Univ Leuven Lab Postharvest Technol Dept Agro Engn & Econ B-3001 Heverlee Belgium

Nowadays, most of the mathematical models used in predictive microbiology are deterministic, i.e. their model output is only one single value for the microbial load at a certain time instant. For more advanced exploitation of predictive microbiology in the context of hazard analysis and critical control points (HACCP) and risk analysis studies, stochastic models should be developed. Such models predict a probability mass function for the microbial load at a certain time instant. An excellent method to deal with stochastic variables is Monte Carlo analysis. In this research, the sensitivity of microbial growth model parameter distributions with respect to data quality and quantity is investigated using Monte Carlo analysis. The proposed approach is illustrated with experimental growth data. There appears to be a linear relation between data quality (expressed by means of the standard deviation of the normal distribution assumed on experimental data) and model parameter uncertainty (expressed by means of the standard deviation of the model parameter distribution). The quantity of data (expressed by means of the number of experimental data points) as well as the positioning of these data in time have a substantial influence on model parameter uncertainty. This has implications for optimal experiment design. (C) 2004 IMACS. Published by Elsevier B.V. All rights reserved.

关键词： Monte Carlo analysis non-linear predictive growth model parameter uncertainty data quality data quantity

来源：评论

学校读者我要写书评

暂无评论

Cool data: quantity AND quality

引用

ACTA CRYSTALLOGRAPHICA SECTION D-STRUCTURAL BIOLOGY 1999年第10期55卷 1641-1653页

作者： Garman, E Univ Oxford Dept Biochem Lab Mol Biophys Oxford OX1 3QU England

The use of cryo-techniques in macromolecular crystallography has increased enormously over the last eight years and has become a vital part of modern X-ray data-collection methods. This paper presents some reasons fbr the rise in popularity of cryo-techniques and a brief outline of the basic methods, followed by a detailed discussion of factors to be considered when trying to optimize both the quantity and quality of the data collected. As more experimenters at synchrotrons observe significant radiation damage to crystals held near 100 K, the available options for further prolonging crystal lifetime and extending the techniques become worth investigating. Some possibilities and parameters to be considered are presented, although these must remain speculative until more experimental data are available.

关键词： cryo-techniques data quality data quantity radiation damage.

来源：评论

学校读者我要写书评

暂无评论

Effects of data quality and quantity on deep learning for protein-ligand binding affinity prediction

引用

BIOORGANIC & MEDICINAL CHEMISTRY 2022年 72卷 117003页

作者： Fan, Frankie J. Shi, Yun Cent Queensland Univ Sch Hlth Med & Appl Sci Bundaberg Qld 4670 Australia Griffith Univ Inst Glyc Southport Qld 4222 Australia Canva Surry Hills NSW 2010 Australia

Prediction of protein-ligand binding affinities is crucial for computational drug discovery. A number of deep learning approaches have been developed in recent years to improve the accuracy of such affinity prediction. While the predicting power of these systems have advanced to some degrees depending on the dataset used for model training and testing, the effects of the quality and quantity of the underlying data have not been thor-oughly examined. In this study, we employed erroneous datasets and data subsets of different sizes, created from one of the largest databases of experimental binding affinities, to train and evaluate a deep learning system based on convolutional neural networks. Our results show that data quality and quantity do have significant impacts on the prediction performance of trained models. Depending on the variations in data quality and quantity, the performance discrepancies could be comparable to or even larger than those observed among different deep learning approaches. In particular, the presence of proteins in the training data leads to a dramatic increase in prediction accuracy. This implies that continued accumulation of high-quality affinity data, especially for new protein targets, is indispensable for improving deep learning models to better predict protein-ligand binding affinities.

关键词： Binding affinity prediction Machine learning data quality data quantity Deep learning

来源：评论

学校读者我要写书评

暂无评论

Regional variation in the impacts of the COVID-19 pandemic on the quantity and quality of data collected by the project eBird

引用

BIOLOGICAL CONSERVATION 2021年 254卷 108974页

作者： Hochachka, Wesley M. Alonso, Hany Gutierrez-Exposito, Carlos Miller, Eliot Johnston, Alison Cornell Univ Ornithol Lab Ithaca NY 14853 USA Portuguese Soc Study Birds SPEA Lisbon Portugal CSIC Conservat Biol Dept Estn Biol Donana Seville Spain

The COVID-19 pandemic has likely affected natural systems around the world;the curtailment of human activity has also affected the collection of data needed to identify the indirect effects of this pandemic on natural systems. We describe how the outbreak of COVID-19 disease, and associated stay-at-home orders in four political regions, have affected the quantity and quality of data collected by participants in one volunteer-based bird monitoring project, eBird. The four regions were selected both for their early and prolonged periods of mandated changes to human activity, and because of the high densities of observations collected. We compared the months of April 2020 with April in previous years. The most notable change was in the landscapes in which observations were made: in all but one region human-dominated landscapes were proportionally more common in the data in April 2020, and observations made near the rarer wetland habitat were less prevalent. We also found subtler changes in quantity of data collected, as well as in observer effort within observation periods. Finally, we found that these effects of COVID-19 disease varied across the political units, and thus we conclude that any analyses of eBird data will require region-specific examination of whether there have been any changes to the data collection process during the COVID-19 pandemic that would need to be taken into account.

关键词： Citizen science community science COVID-19 data quality data quantity eBird Observer behaviour

来源：评论

学校读者我要写书评

暂无评论

Atom-based-segmented MP compression algorithm for seismic data

引用

IET SIGNAL PROCESSING 2018年第3期12卷 284-293页

作者： Yin, Zhiyuan Zhou, Yan Li, Yongxin Nanjing Univ Sci & Technol Sch Mech Engn Dept Instrumentat Sci & Technol Xiaolingwei 200 Nanjing Jiangsu Peoples R China

data compression is an effective way to improve the seismic data transmission efficiency. The features of seismic exploration are long sampling time and large data quantity, so the compression algorithm should achieve high-fidelity, high-compression ratio (CR) and low-compression time. Since the existing compression algorithms cannot meet the requirements of site-collected seismic data compression, the segmented matching pursuit (SMP) compression algorithm based on new atom dictionary is proposed. This method is based on the principle of MP. A novel-segmented compression structure is adopted. The modified Morlet wave atom dictionary is designed to replace the previous dictionaries. The results of comparative experiments show that the proposed algorithm makes improvements in CR and compression time with the same fidelity requirement.

关键词： data compression seismology geophysical signal processing atom-based-segmented MP compression algorithm data compression seismic data transmission efficiency seismic exploration sampling time data quantity high-fidelity high-compression ratio low-compression time site-collected seismic data compression matching pursuit modified Morlet wave atom dictionary fidelity requirement

来源：评论

学校读者我要写书评

暂无评论

Age of Information and Performance Analysis for UAV-Aided IoT Systems

引用

IEEE INTERNET OF THINGS JOURNAL 2021年第19期8卷 14447-14457页

作者： Han, Rui Wang, Jiaxing Bai, Lin Liu, Jianwei Choi, Jinho Beihang Univ Sch Cyber Sci & Technol Beijing 100191 Peoples R China Beihang Univ Sch Cyber Sci & Technol & Beijing Lab Beijing 100191 Peoples R China Deakin Univ Sch Informat Technol Geelong Vic 3220 Australia

In the Internet of Things (IoT), numerous IoT devices are deployed for environment sensing, information collecting, and data transmitting to enable different operations, including patrol monitor, industrial automation, and system control. Considering the limited power and computation capability of IoT devices, mobile-edge computing (MEC) is applied to enhance the usage of IoT. Since unmanned aerial vehicles (UAVs) can be used as MEC servers, they become an efficient means to collect data packets and assist computation. In this article, we develop UAV-aided IoT systems, where the performance of data collection is analyzed in terms of packet loss rate and data quantity using a Markov chain. Then, in order to meet the diverse service requirements, the computation frequency of UAV is designed according to the preference coefficients of the cost on energy and time consumption. Finally, the system Age of Information (AoI) is considered to define the freshness of data packets, where the models of single-IoT device and multi-IoT devices with first-come-first-served (FCFS) principle and M/M/1 queuing are analyzed. The simulation results show that the proposed system is able to provide robust data collection and efficient computation for IoT devices.

关键词： Internet of Things data collection data models Packet loss Computational modeling Servers Markov processes Age of Information (AoI) data quantity Internet of Things (IoT) mobile-edge computing (MEC) unmanned aerial vehicle (UAV)

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：