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A tree-based algorithm for the integration of monomials in the Chow ring of the moduli space of stable marked curves of genus zero

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JOURNAL OF SYMBOLIC COMPUTATION 2024年 122卷

作者： Qi, Jiayue Johannes Kepler Univ Linz Res Inst Symbol Computat Linz Austria

The Chow ring of the moduli space of marked rational curves is generated by Keel's divisor classes. The top graded part of this Chow ring is isomorphic to the integers, generated by the class of a single point. In this paper, we give an equivalent graphical characterization on the monomials in this Chow ring, as well as the characterization on the algebraic reduction on such monomials. Moreover, we provide an algorithm for computing the intersection degree of tuples of Keel's divisor classes - we call it the forest algorithm;the complexity of which is O(n3) in the worst case, where n refers to the number of marks in the ambient moduli space. Last but not least, we introduce three identities on multinomial coefficients which naturally came into play, showing that they are all equivalent to the correctness of the base case of the forest algorithm. (c) 2023 Elsevier Ltd. All rights reserved.

关键词： Integral value of monomials in the Chow ring of the moduli space of stable marked curves of genus zero Graphical characterization of monomials Graphical equivalent characterization of algebraic reductions on monomials tree-based algorithm The algorithm on a forest with linear complexity Identities on multinomial coefficients

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Anomaly detection on displacement rates and deformation pattern features using tree-based algorithm in Japan and Indonesia

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Geodesy and Geodynamics 2023年第2期14卷 150-162页

作者： Adi Wibowo Satriawan Rasyid Purnama Cecep Pratama Leni Sophia Heliani David P.Sahara Sidik Tri Wibowo Department of Informatics Universitas Diponeegoro UniversitySemarangIndonesia Department of Geodetic Engineering Universitas Gadjah MadaYogyakartaIndonesia Global Geophysics Research Group Faculty of Mining and Petroleum EngineeringInstitute of Technology BandungBandungIndonesia Geospatial Information Agency Indonesia

Research on strain anomalies and large earthquakes based on temporal and spatial crustal activities has been rapidly growing due to data availability, especially in Japan and Indonesia. However, many research works used local-scale case studies that focused on a specific earthquake characteristic using knowledgedriven techniques, such as crustal deformation analysis. In this study, a data-driven-based analysis is used to detect anomalies using displacement rates and deformation pattern features extracted from daily global navigation satellite system(GNSS) data using a machine learning algorithm. The GNSS data with188 and 1181 continuously operating reference stations from Indonesia and Japan, respectively, are used to identify the anomaly of recent major earthquakes in the last two decades. Feature displacement rates and deformation patterns are processed in several window times with 2560 experiment scenarios to produce the best detection using tree-based algorithms. tree-based algorithms with a single estimator(decision tree), ensemble bagging(bagging, random forest and Extra trees), and ensemble boosting(AdaBoost, gradient boosting, LGBM, and XGB) are applied in the study. The experiment test using realtime scenario GNSSdailydatareveals high F1-scores and accuracy for anomaly detection using slope windowing 365 and 730 days of 91-day displacement rates and then 7-day deformation pattern features in tree-based algorithms. The results show the potential for medium-term anomaly detection using GNSS data without the need for multiple vulnerability assessments.

关键词： Anomaly GNSS Displacement rates Deformation pattern tree-based algorithm

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A novel tree-based algorithm for real-time prediction of rockburst risk using field microseismic monitoring

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ENVIRONMENTAL EARTH SCIENCES 2021年第16期80卷 504-504页

作者： Yin, Xin Liu, Quansheng Pan, Yucong Huang, Xing Wuhan Univ Sch Civil Engn Key Lab Geotech & Struct Engn Safety Hubei Prov Wuhan 430072 Peoples R China Wuhan Univ State Key Lab Water Resources & Hydropower Engn S Wuhan 430072 Peoples R China Chinese Acad Sci Inst Rock & Soil Mech State Key Lab Geomech & Geotech Engn Wuhan 430071 Peoples R China

Rockburst is a kind of complex and catastrophic dynamic geological disaster in the development and utilization of underground space, which seriously threatens the safety of personnel and environment. Due to the suddenness in time and randomness in space, the prediction of rockburst becomes a great challenge. Microseismic monitoring is capable to continuously capture rock microfracture signals in real time, which offers an effective means for rockburst prediction. With the explosive growth of monitoring data, the conventional manual forecasting methods are laborious and time-consuming. Therefore, artificial intelligence was introduced to improve prediction efficiency. A novel tree-based algorithm was proposed. Its basic idea was to automatically recognize precursory microseismic sequences for the real-time prediction of rockburst intensity. The database consisting of 1500 microseismic events was analyzed. To establish precursory microseismic sequences, dimensionality reduction of the database was first implemented by t-SNE algorithm. Then, k-means clustering algorithm was employed for labelling 1500 microseismic events. Before that, canopy algorithm was adopted to determine the number of clusters. Finally, 300 precursory microseismic sequences were formed by the grouping rule. They were further partitioned into two parts through stratified sampling: 70% for training and 30% for validation. The validation results indicated that the precursor tree with pruning achieved a higher prediction accuracy of 98.9% than one without pruning on the validation set. And the increase was separately 12.2%, 9.2% and 28.6% on the whole validation set and each classes (low/moderate rockburst). In comparison with low rockburst, moderate rockburst was minority class. The improved accuracy on moderate rockburst suggested that pruning can enhance the recognition ability of precursor tree for the minority class. Additionally, two extra rockburst cases were collected from a diversion tunnel i

关键词： Rockburst Intensity prediction tree-based algorithm Microseismic monitoring Precursory microseismic sequence

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A fast tree-based algorithm for Compressed Sensing with sparse-tree prior

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SIGNAL PROCESSING 2015年 108卷 628-641页

作者： Bui, H. Q. La, C. N. H. Da, M. N. Univ Illinois Urbana IL 61801 USA

In Compressed Sensing, the sparse representation property of an unknown signal in a certain basis has been used as the only prior knowledge for signal reconstruction from a limited number of measurements. Recently, more and more research has focused on model-based recovery algorithms, in which special structures of the unknown signal are exploited in addition to the sparse prior. A popular structure is the sparse-tree structure exhibited in the wavelet transform of piecewise smooth signals and in many practical models. In this paper, a reconstruction algorithm that exploits this sparse-tree prior, the tree-based Orthogonal Matching Pursuit (TOMP) algorithm, is proposed and studied in detail. Theoretical analyses and empirical experiments show that the proposed algorithm gives reconstruction quality comparable with more sophisticated algorithms, while being much simpler. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Compressed Sensing Sparse-tree prior tree structure tree-based algorithm Greedy Orthogonal Matching Pursuit

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Exact and efficient search-based wall distance algorithm for partitioned unstructured grids

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COMPUTERS & FLUIDS 2025年 288卷

作者： Oh, Jun Seok Kung, Tae Yoon Kim, Kyu Hong Seoul Natl Univ Dept Aerosp Engn 105-401 Gwanak Ro Seoul 08826 South Korea Seoul Natl Univ Inst Adv Aerosp Technol Seoul 08826 South Korea

In this paper, we develop a novel search-based wall distance calculation algorithm. The algorithm is highly efficient and satisfies the crucial requirement of exactness in wall distance calculations, taking into account the full geometry of the discretized surface. Unlike conventional search-based algorithms that use element-wise bounding boxes or auxiliary grids, the developed algorithm employs only a set of zero-dimensional reference points representing the elements of the discretized surface. Reference points can be chosen as the centers of faces, the centers of edges, or the vertices. The conservative relation between the approximate distance using one of these references and the exact distance is established, allowing for the efficient extraction of element candidates using only low-level information. The algorithm does not require complex pre-processing of the surface or any information about the query points, ensuring high software modularity. An intuitive load balancing procedure is also proposed to address the load imbalance arising from partitioning-based parallelization. Numerical test demonstrates that the developed algorithm shows three orders of magnitude speed-up compared to exhaustive search and one to two orders of magnitude speed-up compared to other search-based algorithms. It also shows high parallel scalability on partitioned meshes, indicating its feasibility for large-scale problems.

关键词： Geometric algorithm Minimum wall distance tree-based algorithm Computational fluid dynamics Unstructured grids

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Efficient Motif Discovery in Protein Sequences Using a Branch and Bound algorithm

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2024年第4期28卷 2437-2444页

作者： Mohammadi, Rahele Neamatollahi, Peyman Moradi, Morteza Naghibzadeh, Mahmoud Savadi, Abdorreza Ferdowsi Univ Mashhad Comp Engn Dept Mashhad *** Iran Hakim Sabzevari Univ Comp Engn Dept Sabzevar *** Iran

Identifying motifs within sets of protein sequences constitutes a pivotal challenge in proteomics, imparting insights into protein evolution, function prediction, and structural attributes. Motifs hold the potential to unveil crucial protein aspects like transcription factor binding sites and protein-protein interaction regions. However, prevailing techniques for identifying motif sequences in extensive protein collections often entail significant time investments. Furthermore, ensuring the accuracy of obtained results remains a persistent motif discovery challenge. This paper introduces an innovative approach-a branch and bound algorithm-for exact motif identification across diverse lengths. This algorithm exhibits superior performance in terms of reduced runtime and enhanced result accuracy, as compared to existing methods. To achieve this objective, the study constructs a comprehensive tree structure encompassing potential motif evolution pathways. Subsequently, the tree is pruned based on motif length and targeted similarity thresholds. The proposed algorithm efficiently identifies all potential motif subsequences, characterized by maximal similarity, within expansive protein sequence datasets. Experimental findings affirm the algorithm's efficacy, highlighting its superior performance in terms of runtime, motif count, and accuracy, in comparison to prevalent practical techniques.

关键词： Proteins Protein engineering Biology Bioinformatics Search methods Amino acids Runtime branch and bound approach motif discovery protein sequences tree-based algorithm

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Peak-based Machine Learning for Plastic Type Classification in Time-of-Flight Secondary Ion Mass Spectrometry

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JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024年第12期35卷 3107-3115页

作者： Son, Jin Gyeong Shon, Hyun Kyong Kim, Ji-Eun Lee, In-Ho Lee, Tae Geol Korea Res Inst Stand & Sci Nanobio Measurement Grp Daejeon 34113 South Korea AIRISS Co Ltd Res Inst Daejeon 34037 South Korea Korea Res Inst Stand & Sci Mat Property Metrol Grp Daejeon 34113 South Korea

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) measurement data and machine learning were used in this work to classify six different types of plastics. In order to take into account the characteristics of the measurement data, the local maxima of the measurement data were first examined in a preprocessing step. Several machine learning methods were then implemented to create a model that could successfully classify the plastics. To visualize the data distribution, we applied a dimensionality reduction method, namely, principal component analysis. Finally, to distinguish between the six types of plastics, we conducted an ensemble analysis using four tree-based algorithms: decision tree, random forest, gradient boosting, and LIGHTGBM. This approach can identify the feature importance of plastic samples and allow the inference of the chemical properties of each plastic type. In this way, ToF-SIMS data could be utilized to successfully classify plastics and enhance explainability.

关键词： machine learning mass spectra preprocessing ToF-SIMS PCA tree-based algorithm featureimportance

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Estimating reservoir evaporation rate: fusing remotely sensed solar radiation and temperature features with machine learning algorithms

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THEORETICAL AND APPLIED CLIMATOLOGY 2025年第3期156卷 1-18页

作者： Eshetu, Kidist Demessie Seifu, Tesema Kebede Alamirew, Tena Woldesenbet, Tekalegn Ayele Haramaya Univ Haramaya Inst Technol POB 138 Diredawa Ethiopia Addis Ababa Univ Ethiopian Inst Water Resources POB 1176 Addis Ababa Ethiopia Addis Ababa Univ Water & Land Resources Ctr WLRC POB 1176 Addis Ababa Ethiopia

Remotely sensed data fused with data-driven technologies prove crucial for determining lake evaporation and effectively regulating reservoirs in areas with inadequate information. This study evaluates how well solar radiation (Ra) and temperature data from reanalysis/ satellite measurements perform to anticipate lake evaporation on a daily and seasonal basis. The approach evaluates the effectiveness of five Machine Learning algorithms under three input scenarios at three meteorological stations in the Awash basin, Ethiopia: Gradient Boosting (GB), Random forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and multilayer perceptron (MLP). XGBoost performs exceptionally well on the training set in each of the three scenarios. GB and MLP performs well throughout the test set for all three input scenarios. Subsequently, when employing models in the Metehara, Melkasa, and Dubti stations, IS1 is the best scenario for RMSE, NSE, and KGE. Consequently, the model predicts encouraging outcomes for each location over the Tseday and Kiremit seasons. The outcomes of the study suggest that identifying the best representative satellite/reanalysis data of temperature and solar radiation for particular areas can lead to effective performance. This investigation provides insight for simulating a reservoir's existing operating system.

关键词： Machine learning algorithm Lake evaporation reanalysis/satellite data tree-based algorithm MLP SVR

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TMIA: A tree-based Multi-Reader Interactive Anti-Collision algorithm for RFID Tag Identification

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IEEE ACCESS 2020年 8卷 81594-81605页

作者： Song, Jianhua He, Shuai Yao, Hui Hubei Univ Sch Comp Sci & Informat Engn Wuhan 430062 Peoples R China Engn & Tech Res Ctr Hubei Prov Educ Informatizat Wuhan 430062 Peoples R China Engn & Tech Res Ctr Hubei Prov Software Engn Wuhan 430062 Peoples R China Engn Res Ctr Hubei Prov Intelligent Govt Affairs Wuhan 430062 Peoples R China

In the RFID system, the tag collision problem seriously affects the efficiency of tag identification. Among the anti-collision algorithms, tree-based anti-collision algorithms are popular for the reason that they can ensure that the tags can theoretically 100% be identified by readers. As multi-reader scenarios are more and more widely used in complex internet of things environments, there are more collision slots and idle slots in traditional anti-collision algorithms, which affects the efficiency of the algorithm. We propose a tree-based multi-reader interactive anti-collision algorithm (TMIA) for multi-reader tag identification scenarios to solve the problems above. Readers optimize the broadcast prefix sequence by sharing the broadcast results. The proposed algorithm works in two phases: In the first phase, the reader selects a suitable broadcast prefix by the multi-reader inverse probability function (MIPF) to reduce the initial redundant collision in tag identification. In the second phase, the priority of the broadcast prefix is adjusted through the information exchange between readers, the readers thus avoid broadcasting a large number of invalid prefixes. Theoretical analysis and simulation results show that TMIA has a lower total number of slots and better system efficiency than existing tree-based algorithms. TMIA also greatly reduce the number of collision slots.

关键词： RFID tag recognition multi-reader tree-based algorithm tag anti-collision

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Advanced machine learning approaches for predicting permeability in reservoir pay zones based on core analyses

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HELIYON 2024年第12期10卷 e32666页

作者： Hussen, Amad Munshi, Tanveer Alam Jahan, Labiba Nusrat Hashan, Mahamudul Shahjalal Univ Sci & Technol Dept Petr & Min Engn Sylhet 3114 Bangladesh

Permeability is the most important petrophysical characteristic for determining how fluids pass through reservoir rocks. This study aims to develop and assess intelligent computer-based models for predicting permeability. The research focuses on three novel models -Decision tree, Bagging tree, and Extra trees -while also investigating previously applied techniques such as random forest, support vector regressor (SVR), and multiple variable regression (MVR). The primary dataset consists of 197 data points from a heterogeneous petroleum reservoir in the Jeanne d 'Arc Basin, including laboratory-derived permeability ( K ), oil saturation ( S O ), water saturation ( S W ), grain density ( rho gr ), porosity ( phi), and depth. The most effective machine learning models are identified by a thorough analysis that makes use of a variety of statistical metrics, such as the coefficient of the determinant (R 2 ), mean squared error (MSE), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), maximum error (maxE), and minimum error (minE). Additionally, core features are ranked based on their importance in permeability modeling. This study deviates from conventional approaches by proposing an efficient means of forecasting permeability, reducing reliance on labor-intensive and time-consuming laboratory work. The findings reveal that MVR is unsuitable for permeability prediction, with all developed models outperforming it. Extra trees emerges as the most accurate model, with an R 2 of 0.976, while random forest and bagging tree exhibit slightly lower R 2 values of 0.961 and 0.964, respectively. The ranking of these algorithms based on performance criteria is as follows: extra trees, bagging tree, random forest, SVR, decision tree, and MVR. The study also presents a detailed analysis of the impact of input parameters, highlighting porosity ( phi) and water saturation ( S W ) as the most influential, while grain density ( rho gr ), oil sa

关键词： Reservoir characterization Machine learning tree-based algorithm Core data Permeability modeling Porosity Oil saturation Core features ranking

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