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9th International Conference on Smart City Applications, SCA 2024

作者： Ed-Daoudi, Rachid Ettaki, Badia Zerouaoui, Jamal LyRICA: Research Laboratory in Computer Science Data Sciences and Artificial Intelligence School of Information Sciences B.P: 6204 Rabat-Instituts 10100 Rabat Morocco Laboratory of Engineering Sciences and Modeling Faculty of Sciences Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco

ISBN: (纸本)9783031886522

The increasing population and water demand in North Africa, particularly in semi-arid and arid regions like Morocco, necessitate efficient water resource management. Morocco experiences significant water scarcity due to persistent droughts and rising agricultural water demand. Accurate estimation of crop water requirements, particularly crop evapotranspiration, influenced by climatic and crop-specific conditions, is vital for improving water use efficiency. This study focuses on wheat, a staple crop in Morocco's Draa-tafilalet region, encompassing Ouarzazate, Zagora, and Tinghir. Traditional methods for estimating the crop coefficient require comprehensive climatic datasets, often unavailable. Therefore, this study aims to develop and validate artificial neural network models to predict monthly Kc values for wheat using limited climatic data. The results showed that the optimal model for predicting crop coefficients used a minimum and maximum temperatures, along with solar radiation, applying different configurations of hidden neuron layers for each region: (7, 5) for Ouarzazate, (8, 6) for Zagora, and (9, 6) for Tinghir. The best model's accuracy was close to 1, showing a statistically significant accordance between observed and predicted values. This study highlights the model's high accuracy and recommends its use for predicting crop coefficients values with limited climatic factors. It also supports water users in developing and updating Kc databases for each region based on climatic conditions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Resource allocation

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Iteration problem for several chaos in non-autonomous discrete system

arXiv

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arXiv 2024年

作者： Zeng, Hongbo Huang, Chuangxia Liu, Bingwen School of Mathematics and Statistics Changsha University of Science and Technology Hunan Provincial Key Laboratory of Mathematical Modeling and Analysis in Engineering Hunan Changsha410114 China College of Data Science Jiaxing University Zhejiang Jiaxing314001 China

In this paper we investigate the iteration problem for several chaos in non-autonomous discrete system (X, f1,∞). Firstly, we prove that the Li-Yorke chaos of a non-autonomous discrete dynamical system is preserved under iterations when f1,∞ converges to f, which weakens the condition in the literature that f1,∞ uniformly converges to f . Besides, we prove that both DC2’ and Kato’s chaos of a non-autonomous discrete dynamical system are iteration invariants. Additionally, we give a sufficient condition for non-autonomous discrete dynamical system to be Li-Yorke chaos. Finally, we give an example to show that the DC3 of a non-autonomous discrete dynamical system is not inherited under iterations, which partly answers an open question proposed by Wu and Zhu(Chaos in a class of non-autonomous discrete systems, ***. 2013,26:431-436). Copyright © 2024, The Authors. All rights reserved.

关键词： Dynamical systems

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Balanced truncation with conformal maps

arXiv

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arXiv 2024年

作者： Borghi, Alessandro Breiten, Tobias Gugercin, Serkan Technical University of Berlin Mathematics Department Straße des 17. Juni 136 Berlin10623 Germany Department of Mathematics Division of Computational Modeling and Data Analytics Academy of Data Science Virginia Tech BlacksburgVA24061 United States

We consider the problem of constructing reduced models for large scale systems with poles in general domains in the complex plane (as opposed to, e.g., the open left-half plane or the open unit disk). Our goal is to design a model reduction scheme, building upon theoretically established methodologies, yet encompassing this new class of models. To this aim, we develop a balanced truncation framework through conformal maps to handle poles in general domains. The major difference from classical balanced truncation resides in the formulation of the Gramians. We show that these new Gramians can still be computed by solving modified Lyapunov equations for specific conformal maps. A numerical algorithm to perform balanced truncation with conformal maps is developed and is tested on three numerical examples, namely a heat model, the Schrödinger equation, and the undamped linear wave equation, the latter two having spectra on the imaginary axis. Copyright © 2024, The Authors. All rights reserved.

关键词： Poles

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Global High Categorical Resolution Land Cover Mapping via Weak Supervision

arXiv

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arXiv 2024年

作者： Tong, Xin-Yi Dong, Runmin Zhu, Xiao Xiang Chair of Data Science in Earth Observation Technical University of Munich Germany Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System Science Tsinghua University China Munich Center for Machine Learning Germany

Land cover information is indispensable for advancing the United Nations’ sustainable development goals, and land cover mapping under a more detailed category system would significantly contribute to economic livelihood tracking and environmental degradation measurement. However, the substantial difficulty in acquiring fine-grained training data makes the implementation of this task particularly challenging. Here, we propose to combine fully labeled source domain and weakly labeled target domain for weakly supervised domain adaptation (WSDA). This is beneficial as the utilization of sparse and coarse weak labels can considerably alleviate the labor required for precise and detailed land cover annotation. Specifically, we introduce the Prototype-based pseudo-label Rectification and Expansion (PRE) approach, which leverages the prototypes (i.e., the class-wise feature centroids) as the bridge to connect sparse labels and global feature distributions. According to the feature distances to the prototypes, the confidence of pseudo-labels predicted in the unlabeled regions of the target domain is assessed. This confidence is then utilized to guide the dynamic expansion and rectification of pseudo-labels. Based on PRE, we carry out high categorical resolution land cover mapping for 10 cities in different regions around the world, severally using PlanetScope, Gaofen-1, and Sentinel-2 satellite images. In the study areas, we achieve cross-sensor, cross-category, and cross-continent WSDA, with the overall accuracy exceeding 80%. The promising results indicate that PRE is capable of reducing the dependency of land cover classification on high-quality annotations, thereby improving label efficiency. We expect our work to enable global fine-grained land cover mapping, which in turn promote Earth observation to provide more precise and thorough information for environmental monitoring. Copyright © 2024, The Authors. All rights reserved.

关键词： Expansion

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Pre-trained language models in Spanish for health insurance coverage 5

Pre-trained language models in Spanish for health insurance ...

引用

5th Workshop on Clinical Natural Language Processing, ClinicalNLP 2023. held at ACL 2023

作者： Aracena, Claudio Rodríguez, Nicolás Rocco, Victor Dunstan, Jocelyn Faculty of Physical and Mathematical Sciences University of Chile Chile Millennium Institute Foundational Research on Data Chile Chilean Safety Association Chile Department of Computer Science Catholic University of Chile Chile Institute for Mathematical and Computational Engineering Catholic University of Chile Chile Center for Mathematical Modeling University of Chile Chile

ISBN: (纸本)9781959429883

The field of clinical natural language processing (NLP) can extract useful information from clinical text. Since 2017, the NLP field has shifted towards using pre-trained language models (PLMs), improving performance in several tasks. Most of the research in this field has focused on English text, but there are some available PLMs in Spanish. In this work, we use clinical PLMs to analyze text from admission and medical reports in Spanish for an insurance and health provider to give a probability of no coverage in a labor insurance process. Our results show that fine-tuning a PLM pretrained with the provider's data leads to better results, but this process is time-consuming and computationally expensive. At least for this task, fine-tuning publicly available clinical PLM leads to comparable results to a custom PLM, but in less time and with fewer resources. Analyzing large volumes of insurance requests is burdensome for employers, and models can ease this task by pre-classifying reports that are likely not to have coverage. Our approach of entirely using clinical-related text improves the current models while reinforcing the idea of clinical support systems that simplify human labor but do not replace it. To our knowledge, the clinical corpus collected for this study is the largest one reported for the Spanish language. © 2023 Association for Computational Linguistics.

关键词： Health insurance

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GroupEnc: encoder with group loss for global structure preservation

arXiv

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arXiv 2023年

作者： Novak, David Van Gassen, Sofie Saeys, Yvan Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium Data Mining and Modeling for Biomedicine Center for Inflammation Research VIB-UGent Belgium

Recent advances in dimensionality reduction have achieved more accurate lower-dimensional embeddings of high-dimensional data. In addition to visualisation purposes, these embeddings can be used for downstream processing, including batch effect normalisation, clustering, community detection or trajectory inference. We use the notion of structure preservation at both local and global levels to create a deep learning model, based on a variational autoencoder (VAE) and the stochastic quartet loss from the SQuadMDS algorithm. Our encoder model, called GroupEnc, uses a 'group loss' function to create embeddings with less global structure distortion than VAEs do, while keeping the model parametric and the architecture flexible. We validate our approach using publicly available biological single-cell transcriptomic datasets, employing RNX curves for evaluation. © 2023, CC BY.

关键词： Clustering algorithms

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FREQUENCY-BASED REDUCED MODELS FROM PURELY TIME-DOMAIN data VIA data INFORMATIVITY

arXiv

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arXiv 2023年

作者： Ackermann, Michael S. Gugercin, Serkan Department of Mathematics Virginia Tech BlacksburgVA24061 United States Department of Mathematics Division of Computational Modeling and Data Analytics Academy of Data Science Virginia Tech BlacksburgVA24061 United States

Frequency-based methods have been successfully employed in creating high-fidelity data-driven reduced order models (DDROMs) for linear dynamical systems. These methods require access to values (and sometimes derivatives) of the frequency-response function (transfer function) in the complex plane. These frequency domain values can at times be costly or difficult to obtain (especially if the method of choice requires resampling);instead one may have access to only time-domain input-output data. The data informativity approach to moment matching provides a powerful new framework for recovering the required frequency data from a single time-domain trajectory. In this work, we analyze and extend upon this framework, resulting in significantly improved conditioning of the associated linear systems, an error indicator, and the removal of an assumption that the system order is known. This analysis leads to a robust algorithm for recovering frequency information from time-domain data, suitable for large-scale systems. We demonstrate the effectiveness of our algorithm by forming frequency-based DDROMs from time-domain data of several dynamical systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Transfer functions

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Cross-sectional rainfall observation on the central-western Tibetan Plateau in the warm season:System design and preliminary results

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science China Earth sciences 2023年第5期66卷 1015-1030页

作者： Kun YANG Yingying CHEN Lazhu Changhui ZHAN Xiaoyan LING Xu ZHOU Yaozhi JIANG Xiangnan YAO Hui LU Xiaogang MA Lin OUYANG Weihao PAN Yanghang REN Changkun SHAO Jiaxin TIAN Yan WANG Hua YANG Siyu YUE Ke ZHANG Dingchi ZHAO Long ZHAO Jianhong ZHOU Mijun ZOU Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceInstitute for Global Change StudiesTsinghua UniversityBeijing100084China National Tibetan Plateau Data Center State Key Laboratory of Tibetan Plateau Earth System and Resource EnvironmentInstitute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing100101China Research Center for Ecology College of ScienceTibet UniversityLhasa850011China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Science and Natural Resources ResearchChinese Academy of SciencesBeijing100101China Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station School of Geographical SciencesSouthwest UniversityChongqing400715China

The central and western Tibetan Plateau(CWTP)is characterized by harsh environment and strong interactions among the spheres of earth as well as significant changes in climate and water cycles over the past four *** lack of precipitation observations is a bottleneck for the study of land surface processes in this *** the past six years,we have designed and established two observation transects across the south-north and the west-east in this region to obtain hourly rainfall data during the warm season(May-September).The south-north transect extends from Yadong Valley on the southern slope of the Himalayas to Shuanghu County in the hinterland of the plateau,with a total of 31stations;the west-east transect extends from Shiquanhe in the west to Naqu in the central TP,with a total of 22 *** observation dataset has been applied to clarify the spatiotemporal characteristics of precipitation in the CWTP,to evaluate the quality of typical gridded precipitation products,to support the development of regional climate models,and to reveal the processes of summertime lake-air *** observation dataset has been released in the National Tibetan Plateau data Center.

关键词： Central and western Tibetan Plateau Rainfall observation transects Observation uncertainty Spatiotemporal characteristics of precipitation Precipitation data evaluation

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Unveling the Precision of Deep Learning Models for Stock Price Prediction: A Comparative Analysis of Bi-LSTM, LSTM, and GRU

Unveling the Precision of Deep Learning Models for Stock Pri...

引用

Converging Technology in Electrical and Information Engineering (ICCTEIE), International Conference on

作者： Thirza Baihaqi Matthew Aaron Sugiyarto Rayhan Prawira Daksa Felix Indra Kurniadi Muhammad Fakhruddin Hasitha Erandi Data Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Research & Development Centre for Mathematical Modeling Department of Mathematics University of Colombo Colombo Sri Lanka

Due to the unpredictability of the stock market, accurate prognostic models are necessary for investing. In recent years, machine learning techniques, specifically deep learning algorithms, have grown in popularity for predicting stock prices. This paper seeks to compare the stock-price forecasting abilities of several deep learning models, including LSTM, Bi-LSTM, and GRU. The algorithms make use of the capabilities of Recurrent Neural Networks (RNNs), with a particular emphasis on the Long-Short Term Memory (LSTM) model. The primary objective is to evaluate the accuracy of these machine learning algorithms at predicting stock market values and to determine how the number of training epochs affects model performance. Through comparative analysis, we intend to identify the most accurate model for predicting stock prices. Using historical stock market data, the research involves training and evaluating the various models. Common evaluation metrics, such as Root Mean Square Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE), are used to evaluate the performance of each model. In terms of RMSE, MSE, and MAE, the bi-LSTM model outperforms the other models, obtaining values of 0.00029, 0.00029, and 0.01 respectively.

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Using Machine Learning with Pyspark for Solving a Big data Problem: Searching for Particles in High Energy Physics with Big Mass (HEPMASS) 7th

Using Machine Learning with Pyspark for Solving a Big Dat...

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7th International Conference on Smart City Applications, SCA 2022

作者： Azhari, Mourad Ahaji, Khalid Abarda, Abdallah Ettaki, Badia Zerouaoui, Jamal Center of Guidance and Planning Rabat Morocco Laboratoire de Modélisation Mathématiques et de Calculs Economiques FSJES Université Hassan 1er Settat Morocco Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering Department of Data Content and Knowledge Engineering School of Information Sciences Rabat Morocco Laboratory of Engineering Sciences and Modeling Faculty of Sciences Ibn Tofail University Kenitra Morocco

ISBN: (纸本)9783031268519

The search for exotic particles is one of the most challenging topics for physicists. This work aims to resolve the Big data problem in the exotic particle area using the Apache Spark environment with the MLlib library. For this purpose, we analyze and discuss the performance of the following methods: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Tree (GBT). The results are very encouraging and prove that these proposed methods run efficiently with an Accuracy exceeding (83%). While the Gradient Boosted Tree (GBT) reaches a high AUC score 85%, better than Decision Tree (DT) and Random Forest. In this manuscript, we attempt to solve a big data problem using the "HEPMASS" dataset. Our data includes 10.5 million examples collected from the UCI website for the experimental phase. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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