Tensor tree learns hidden relational structures in data to construct generative models by Kenji Harada, Tsuyoshi Okubo, Naoki Kawashima

“…The proposed method offers enhanced performance and uncovers hidden relational structures in the target data. We illustrate potential practical applications with four examples: (i) random patterns, (ii) QMNIST handwritten digits, (iii) Bayesian networks, and (iv) the pattern of stock price fluctuation pattern in S&P500. …”
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Offline System for 2D Indoor Navigation Utilizing Advanced Data Structures by Jorge Luis Veloz, Leo Sebastián Intriago, Jean Carlos Palma, Andrea Katherine Alcívar-Cedeño, Álvaro Antón-Sacho, Pablo Fernández-Arias, Edwan Anderson Ariza, Diego Vergara

“…The system leverages advanced spatial modeling techniques to optimize pathfinding and resource efficiency. Utilizing a structured development process, the proposed solution integrates lightweight data structures and modular components to minimize computational load and enhance scalability. …”
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Model reduction of structural mechanical response in the time domain by Xin Yan, Xinyu Guo, Ningya He, Jinglong Shi, Daquan Zhao

“…Abstract A novel model reduction approach for analyzing structural mechanical response states in automotive systems is introduced, leveraging time-domain signal data from road testing. …”
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Enhancing Anomaly Detection in Structured Data Using Siamese Neural Networks as a Feature Extractor by Elizabeth P. Chou, Bo-Cheng Hsieh

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Beyond contrastive learning: adaptive graph representations with mutual information maximization for blockchain and structured data by Yifeng Zhang, Qianqian Ren, Yourong Chen, Meng Han

“…Abstract Graph representation learning has emerged as a powerful approach for modeling structured data across diverse domains, including social networks, biochemical interactions, and financial transaction systems. …”
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Identifying the diagnostic gap of tardive dyskinesia: an analysis of semi-structured electronic health record data by Kira Griffiths, Yida Won, Zachery Lee, Lu Wang, Christoph U. Correll, Rashmi Patel

“…Results Altogether, 1,301 (4.0%) patients had either description of abnormal movements associated with TD (n=691) or documented TD (n=610) within semi-structured EHR data. Of those patients, only 64 (4.9%) had an ICD-TD diagnosis in structured EHR data. …”
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Federated Learning in Healthcare: A Benchmark Comparison of Engineering and Statistical Approaches for Structured Data Analysis by Siqi Li, Di Miao, Qiming Wu, Chuan Hong, Danny D’Agostino, Xin Li, Yilin Ning, Yuqing Shang, Ziwen Wang, Molei Liu, Huazhu Fu, Marcus Eng Hock Ong, Hamed Haddadi, Nan Liu

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Time Series Forecasting for Air Quality with Structured and Unstructured Data Using Artificial Neural Networks by Kenneth Chan, Paul Matthews, Kamran Munir

“…CORD is a model that allows for dual input types: structured data from air quality data collected with meteorological cameras and images (unstructured data) from monitoring stations. …”
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Semi-automatic staging area for high-quality structured data extraction from scientific literature by Luca Foppiano, Tomoya Mato, Kensei Terashima, Pedro Ortiz Suarez, Taku Tou, Chikako Sakai, Wei-Sheng Wang, Toshiyuki Amagasa, Yoshihiko Takano, Masashi Ishii

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A Structured Data Model for Asset Health Index Integration in Digital Twins of Energy Converters by Juan F. Gómez Fernández, Eduardo Candón Fernández, Adolfo Crespo Márquez

“…First, the proposed AHI methodology is introduced, and after a structured data model specifically designed, orientated to a basic and economic cloud implementation architecture. …”
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Board structure and bank performance: Evidence from Ethiopia by Alem Gebremedhin Berhe

Subjects: “…board structure…”
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Implementation of flexible DEA structure in the management of business processes by Šegrt Slobodan, Tomić Radoljub, Anđelković Maja

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Guided Regularizers for Structured Reduction of Neural Networks by Ali Haisam Muhammad Rafid, Adrian Sandu

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Digiral marketplaces and their effect to corporates’ business models and structures by Sergey N. Lavrov, Sergey V. Neklyudov

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Structure and nodal points of Smart City infrastructure by Magdalena Grochulska-Salak, Tomasz Wężyk

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Structuring a textile knitting dataset for machine learning and data mining applicationsMendeley Data by Toufique Ahmed, Abu Saleh Muhammad Junayed

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Data of the crystal structure of xylose isomerase from Streptomyces avermitilisProtein Data BankZENODO by Ki Hyun Nam

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Cognitive Structure of Randomness by G. I. Berestnev

“…The goal, first outlined in linguistics, the identification of deep cognitive characteristics in the structure of thought about randomness in the Russian and Indo-European linguistic mentality with the involvement of typological data is being realized. …”
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OCCUPATIONAL STRUCTURE OF POVERTY by Tünde-Ilona KELE

“…The main objective of this paper is to present the relationship between poverty, unemployment and education reaching the occupational structure of poverty. In order to understand the interdependence of the three concepts, I have started with the review of the definitions given by renowned specialists in the study of poverty. …”
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SemantiPack: An Efficient Real-World Data Compressor Using Structural and Semantic Metadata by Yoshiteru Nagata, Daiki Kohama, Yoshiki Watanabe, Shin Katayama, Kenta Urano, Takuro Yonezawa, Nobuo Kawaguchi

“…The exponential growth of Real-World Data (RWD), primarily collected from IoT sensors and spanning domains such as mobility, environment, and energy consumption, presents critical challenges due to its scale, heterogeneity, and structural variability. …”
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