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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THE NEOTECTONIC STRUCTURE OF INTERMOUNTAIN BASINS OF MOUNTAINOUS ALTAI ACCORDING TO ELECTROMAGNETIC AND GEOLOGICAL DATA by N. N. Nevedrova, A. M. Sanchaa, E. V. Deev, S. M. Babushkin

“…The article presents new data on the geoelectrical structure of Chuya, Kuray and Uymon intermountain basins, which are the three largest ones in Mountainous Altai, Russia. …”
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Information hiding algorithm based on mapping and structure data of 3D model by Shuai REN, Zhen WANG, Dongxu SU, Tao ZHANG, Dejun MU

“…The existing 3D information hiding schemes are not robust enough against the joint attacks,as a result the secret information will be vulnerable and cannot be extracted correctly.In order to solve the above problem,an information hiding algorithm based on mapping and structure data of 3D models was proposed.First,several texture maps of the original 3D models in .stl format were picked from the standard model library,so the backup secret data after twice two-dimension discrete Daubechies transform can be embedded using dbl function just as the watermark.Secondly,the original 3D model in .stl format was operated by frame sampling in wavelet domain to obtain the coefficient in transform domain,thus the secret data was embedded into the corresponding transform coefficient.Finally,the .obj documents with the secret information were generated by multiplying the 2D texture map data and the 3D .stl data matrix based on orthogonal projection.Texture maps and coordinate space of 3D model were both used to embed the secret information repeatedly in order to enhance the robustness.The experiment analysis indicated that the imperceptibility,robustness and resistance against analysis are improved and information transmission safety in complex environment can be achieved based on the redundancy space of multi-type carriers.…”
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MRS-BIDS, an extension to the Brain Imaging Data Structure for magnetic resonance spectroscopy by Amy E. Bouchard, Dickson Wong, Wolfgang Bogner, Rémi Gau, Yaroslav O. Halchenko, Damon G. Lamb, Christopher J. Markiewicz, Paul G. Mullins, Guiomar Niso, Georg Oeltzschner, BIDS Maintainers, William T. Clarke, Martin Wilson, Mark Mikkelsen

“…The Brain Imaging Data Structure (BIDS) is an increasingly adopted standard for organizing scientific data and metadata. …”
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Combining Several Substitution Cipher Algorithms using Circular Queue Data Structure by Noor Ibraheem, Mokhtar Hasan

“…Lots of algorithms and techniques are available for data security.  This paper presents a cryptosystem that combines several Substitution Cipher Algorithms along with the Circular queue data structure. …”
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Fast and accurate short-read alignment with hybrid hash-tree data structure by Junichiro Makino, Toshikazu Ebisuzaki, Ryutaro Himeno, Yoshihide Hayashizaki

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A CDE-based data structure for radiotherapeutic decision-making in breast cancer by Fabio Dennstädt, Maximilian Schmalfuss, Johannes Zink, Janna Hastings, Roberto Gaio, Max Schmerder, Nikola Cihoric, Paul Martin Putora

“…Abstract Background The growing complexity of oncology and radiation therapy demands structured and precise data management strategies. The National Institutes of Health (NIH) have introduced Common Data Elements (CDEs) as a uniform approach to facilitate consistent data collection. …”
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Determination of Spatial-Temporal Correlation Structure of Troposphere Ozone Data in Tehran City by S.S. Mousavi, M. Mohammadzadeh

“…For eliminating the trend of data, a dynamic linear model is used, then some features of correlation structure of de-trended data, such as stationarity, symmetry and separability are considered. …”
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Research on Smart Scaling Mechanism and Structure for Big Data Network Server Groups by Hongyao Ju

“…The principle and construction of the structure of smart telescopic on big data network server groups were elaborated.The key technologies involved in the smart scaling of big data network server groups were discussed.After investigating key technologies,including the monitoring of the workload of the server group,the smart control of the number of servers,and the scheduling of access loads,the principle and realization method for the smart scaling of big data network server groups were proposed and technical supports were provided for the smart scaling and effective energy conservation of big data network server groups.According to the proposed construction method and key technologies,a smart scaling model for big data network server groups was provided as well.…”
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New data on the geological structure and zonality of the Vorontsovka gold field in the Northern Urals by Vikentyev I. V., Vikentieva O. V., Blokov I. V., Vykhristenko R. I., Yushko N. A., Krinochkin L. A.

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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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Information hiding algorithm based on mapping and structure data of 3D model by Shuai REN, Zhen WANG, Dongxu SU, Tao ZHANG, Dejun MU

“…The existing 3D information hiding schemes are not robust enough against the joint attacks,as a result the secret information will be vulnerable and cannot be extracted correctly.In order to solve the above problem,an information hiding algorithm based on mapping and structure data of 3D models was proposed.First,several texture maps of the original 3D models in .stl format were picked from the standard model library,so the backup secret data after twice two-dimension discrete Daubechies transform can be embedded using dbl function just as the watermark.Secondly,the original 3D model in .stl format was operated by frame sampling in wavelet domain to obtain the coefficient in transform domain,thus the secret data was embedded into the corresponding transform coefficient.Finally,the .obj documents with the secret information were generated by multiplying the 2D texture map data and the 3D .stl data matrix based on orthogonal projection.Texture maps and coordinate space of 3D model were both used to embed the secret information repeatedly in order to enhance the robustness.The experiment analysis indicated that the imperceptibility,robustness and resistance against analysis are improved and information transmission safety in complex environment can be achieved based on the redundancy space of multi-type carriers.…”
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Damage Indicators for Structural Monitoring of Fiber-Reinforced Polymer-Strengthened Concrete Structures Based on Manifold Invariance Defined on Latent Space of Deep Autoencoders by Javier Montes, Juan Pérez, Ricardo Perera

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Large Language Model-Driven Structured Output: A Comprehensive Benchmark and Spatial Data Generation Framework by Diya Li, Yue Zhao, Zhifang Wang, Calvin Jung, Zhe Zhang

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Constructing Rasterized Covariates from LiDAR Point Cloud Data via Structured Query Language by Rory Pittman, Baoxin Hu

Subjects: “…structured query language (SQL)…”
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Enhancing structured data generation with GPT-4o evaluating prompt efficiency across prompt styles by Ashraf Elnashar, Jules White, Douglas C. Schmidt

Subjects: “…structured data generation…”
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UPN-GCN: Update Positive-Negative Graph Convolution Neural Network in Non-Euclidean Structured Data by Lijun Fan, Shichao Yi, Wenrui Guan, Pingxin Wang

“…Graph Convolutional neural Networks (GCNs) demonstrate exceptional effectiveness when working with data that have non-Euclidean structures. In recent years, numerous researchers have utilized GCNs for data with Euclidean structures. such as image data and language data, and achieved good results, expanding the application scope of GCN. …”
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T2F: a domain-agnostic multi-agent framework for unstructured text to factuality evaluation items generation by Xin Tong, Jingya Wang, Yasen Aizezi, Hanming Zhai, Bo Jin

“…The generated data effectively assess LLMs’ factuality accuracy, highlighting T2F’s capability as a scalable and domain-agnostic factuality evaluation framework.…”
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Quantifying Tree Structural Change in an African Savanna by Utilizing Multi-Temporal TLS Data by Tasiyiwa Priscilla Muumbe, Jussi Baade, Pasi Raumonen, Corli Coetsee, Jenia Singh, Christiane Schmullius

“…Structural changes in savanna trees vary spatially and temporally because of both biotic and abiotic drivers, as well as the complex interactions between them. …”
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Natural Language Processing framework for identifying abdominal aortic aneurysm repairs using unstructured electronic health records by Daniel C. Thompson, Reza Mofidi

“…Abstract Patient identification for national registries often relies upon clinician recognition of cases or retrospective searches using potentially inaccurate clinical codes, leading to incomplete data capture and inefficiencies. Natural Language Processing (NLP) offers a promising solution by automating analysis of electronic health records (EHRs). …”
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