Search Results - "sparse data"

Chromatin 3D modelling from sparse 3C-based datasets

Authors: Mendieta Esteban, Julen

Contributors: University/Department: Universitat Pompeu Fabra. Departament de Ciències Experimentals i de la Salut

Thesis Advisors: Martí Renom, Marc A., Farabella, Irene

Source: TDX (Tesis Doctorals en Xarxa)

Subject Terms: Genome, Structure, pcHi-C, 3D Modelling, Chromatin, Estructura genoma, Modelado 3D, Cromatina, Datos, Sparse datA

File Description: application/pdf

Access URL: http://hdl.handle.net/10803/670311

Signal Sparsity Considerations forUsing VAE withNon-visual Data: Case Study ofProximity Sensors onaMobile Robot

Authors: Hagen, Oksana Gaudl, Swen

Source: Communications in Computer and Information Science. :73-82

Subject Terms: Computer Sciences, Datavetenskap (datalogi), disentangled representations, mobile robot, proximity sensors, sparse data, VAE

Access URL: https://gup.ub.gu.se/publication/350655

Enhancing collaborative animation design recommendations with social relationship-aware deepfake techniques

Authors: Xiaoxia Yang Edris Khezri

Source: Complex & Intelligent Systems, Vol 11, Iss 12, Pp 1-12 (2025)

Subject Terms: Social relationship, Sparse data, Deepfake, Collaborative filtering, Animation recommendation, Electronic computers. Computer science, QA75.5-76.95, Information technology, T58.5-58.64

File Description: electronic resource

Relation: https://doaj.org/toc/2199-4536; https://doaj.org/toc/2198-6053

Access URL: https://doaj.org/article/a4b07ff91d79407cb676ab96f9b20267

A deep learning framework with hybrid stacked sparse autoencoder for type 2 diabetes prediction

Authors: Abdussamad Hanita Daud Rajalingam Sokkalingam et al.

Source: Scientific Reports, Vol 15, Iss 1, Pp 1-22 (2025)

Subject Terms: Autoencoder, Deep learning, Diabetes prediction, Feature selection, Machine learning, Sparse data, Medicine, Science

File Description: electronic resource

Relation: https://doaj.org/toc/2045-2322

Access URL: https://doaj.org/article/2fea4da469ce4f14bd82089d355a10f4

A data efficient framework for analyzing structural transformation in low and middle income economies

Authors: Ronald Katende

Source: Scientific Reports, Vol 15, Iss 1, Pp 1-24 (2025)

Subject Terms: Structural transformation, Bayesian hierarchical modeling, Machine learning imputation, Sparse data, Low- and middle-income countries, Economic forecasting, Medicine, Science

File Description: electronic resource

Relation: https://doaj.org/toc/2045-2322

Access URL: https://doaj.org/article/56f762232e72467e83c03d66b22d9770

Memory in Motion: Exploring Leaky Integration of Time Surfaces for Event-based Eye-tracking

Authors: Boretti, Chiara Bich, Philippe Prono, Luciano et al.

Source: 2024 IEEE Biomedical Circuits and Systems Conference (BioCAS). :1-5

Subject Terms: Augmented Reality (AR), Virtual Reality (VR), Eye tracking, Healthcare training, Low-latency, Low-power, Event-based cameras, High temporal resolution, Sparse data format, Data preprocessing, Deep Neural Networks (DNNs), Pupil position estimation, Time surfaces, Leakage factor, Memory channels, Forgetting rates, Non-recurrent neural estimators, MobileNet-V3L, DVS recordings, P10 accuracy, Euclidean error, Resource-constrained devices, Real-time scenarios, Energy-efficient

File Description: application/pdf

Access URL: https://ieeexplore.ieee.org/document/10798345
https://doi.org/10.1109/biocas61083.2024.10798345
https://hdl.handle.net/11583/2996076

Parameter identification for PDEs using sparse interior data and a recurrent neural network

Authors: Jie Long Abdul Khaliq Khaled M. Furati

Source: Scientific Reports, Vol 15, Iss 1, Pp 1-13 (2025)

Subject Terms: Hybrid model, Time-dependent PDEs, Inverse problem, Sparse data, Deep learning, Medicine, Science

File Description: electronic resource

Relation: https://doaj.org/toc/2045-2322

Access URL: https://doaj.org/article/70ab8a77ad0546f48828416a8ca574f1

Quantifying model output uncertainty from sparse input data: a case study in the Mississippi sound and mobile bay

Authors: Meena Raju Anna Linhoss Raúl J. Osorio

Source: Discover Applied Sciences, Vol 7, Iss 8, Pp 1-21 (2025)

Subject Terms: Sparse data, Interpolation, EFDC+, Mississippi sound, Mobile Bay, Hydrodynamics, Science (General), Q1-390

File Description: electronic resource

Relation: https://doaj.org/toc/3004-9261

Access URL: https://doaj.org/article/58f47bdad8e148ffbb0cc7b80b12486b

Symbolic Compression Algorithms based on Prime Entropy Windows

Authors: Rezapour, Majid Rezapour, Ramin

Subject Terms: Biological data, Sparse data compression, Symbolic compression, Context-aware encoding, Information theory, Data compression, Prime numbers, Lossless compression, Prime entropy windows, Entropy modulation

Optimizing Graph Learning using Hierarchical Graph Adjacency Matrix (HGAM)

Authors: Robert Benke Emmanouil-Ioannis Farsarakis Michal Szarmach et al.

Source: TASK Quarterly, Vol 28, Iss 3 (2025)

Subject Terms: Data structure optimization, graph neural networks, graph learning, sparse data, Information technology, T58.5-58.64

File Description: electronic resource

Relation: https://journal.mostwiedzy.pl/TASKQuarterly/article/view/3535; https://doaj.org/toc/1428-6394

Access URL: https://doaj.org/article/eb44173791084f54830e88c7b748f479

Enhancing enterprise knowledge retrieval via cross-domain deep recommendation: a sparse data approach

Authors: Ting Li

Source: Scientific Reports, Vol 15, Iss 1, Pp 1-15 (2025)

Subject Terms: Enterprise knowledge retrieval, Cross-domain recommendation, Sparse data scenarios, Deep generative model, CDR-VAE, Medicine, Science

File Description: electronic resource

Relation: https://doaj.org/toc/2045-2322

Access URL: https://doaj.org/article/3c8cb7ae5e904734997579fded045dba

Data-Driven Method of Modeling Sparse Flow Field Data

Authors: WANG Hongxin, XU Degang, ZHOU Kaiwen, LI Linwen, WEN Xin

Source: Shanghai Jiaotong Daxue xuebao, Vol 59, Iss 5, Pp 684-690 (2025)

Subject Terms: data-driven, reduced order model, dynamic mode decomposition, location optimization, sparse data, flow field reconstruction, Engineering (General). Civil engineering (General), TA1-2040, Chemical engineering, TP155-156, Naval architecture. Shipbuilding. Marine engineering, VM1-989

File Description: electronic resource

Relation: https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-5-684.shtml; https://doaj.org/toc/1006-2467

Access URL: https://doaj.org/article/324f368d57cd4a5e8e0e8aebf935d163

Fusion of sparse non-co-located measurements from multiple sources for geotechnical site investigation

Authors: Zheng Guan Yu Wang Kok-Kwang Phoon

Source: Canadian Geotechnical Journal. 61:1574-1592

Subject Terms: Joint representation, Compressive sampling, 0211 other engineering and technologies, Sparse data, 02 engineering and technology, Data fusion, Geotechnical site characterization

Climatrix: Xarray accessor for climate data sampling and reconstruction

Authors: Jakub Walczak Wojciech Żyndul

Source: SoftwareX, Vol 31, Iss , Pp 102263- (2025)

Subject Terms: Climate modeling, Sparse data reconstruction, Point cloud, Xarray accessor, Deep learning, Computer software, QA76.75-76.765

File Description: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2352711025002304; https://doaj.org/toc/2352-7110

Access URL: https://doaj.org/article/a85a87dd4a7f4de69e9b4bbde3f46f32

Latent Feature‐Based Type 2 Diabetes Prediction Using a Hybrid Stacked Sparse Autoencoder and Machine Learning Models

Authors: Abdussamad Hanita Daud Rajalingam Sokkalingam et al.

Source: Engineering Reports, Vol 7, Iss 9, Pp n/a-n/a (2025)

Subject Terms: autoencoder, hybrid model, machine learning models, sparse data, Type 2 diabetes prediction, Engineering (General). Civil engineering (General), TA1-2040, Electronic computers. Computer science, QA75.5-76.95

File Description: electronic resource

Relation: https://doaj.org/toc/2577-8196

Access URL: https://doaj.org/article/ce842a66c11744049ad6331118fec323

LorDist: a novel method for calculating the distance based on functional data analysis with application to longitudinal microbial data

Authors: Xinhe Qi Menghan Zhang Tongqing Wei et al.

Source: Microbiology Spectrum, Vol 13, Iss 8 (2025)

Subject Terms: longitudinal microbial data, functional data analysis, sparse data, Microbiology, QR1-502

File Description: electronic resource

Relation: https://doaj.org/toc/2165-0497

Access URL: https://doaj.org/article/9f3e2dd2292e42b4994c5fa39824ddd6

Fairness modeling for topics with different scales in short texts

Authors: Chuangying Zhu Yongyu Liang Xinyuan Liang et al.

Source: PeerJ Computer Science, Vol 11, p e2936 (2025)

Subject Terms: Topic model, Sparse data, Short text, Data dependency, Social media, Electronic computers. Computer science, QA75.5-76.95

File Description: electronic resource

Relation: https://peerj.com/articles/cs-2936.pdf; https://peerj.com/articles/cs-2936/; https://doaj.org/toc/2376-5992

Access URL: https://doaj.org/article/2ab33c8f0d4743d28890b8a0bdb3ae65

Multivariate random-effects meta-analysis for sparse data using smvmeta

Authors: Rose, Christopher James

Source: The Stata Journal: Promoting communications on statistics and Stata. 24:301-328

Subject Terms: sparse data, 03 medical and health sciences, 0302 clinical medicine, Risk and Uncertainty, smvmeta, risk factors, penalized maximum likelihood, Teaching/Communication/Extension/Profession, multivariate meta-analysis, st0749, 0101 mathematics, 01 natural sciences, dimensionality reduction

Bayesian ensemble methods for predicting ground deformation due to tunnelling with sparse monitoring data

Authors: Zhang, Zilong Zhang, Tingting Li, Xiaozhou et al.

Contributors: Zhang, Zilong Zhang, Tingting Li, Xiaozhou et al.

Source: Underground Space, Vol 16, Iss, Pp 79-93 (2024)

Subject Terms: [SPI.GCIV]Engineering Sciences [physics]/Civil Engineering, [SPI]Engineering Sciences [physics], Tunnelling-induced ground deformations, Bayesian model averaging, 0211 other engineering and technologies, Sparse data, TA703-712, Engineering geology. Rock mechanics. Soil mechanics. Underground construction, 02 engineering and technology, Model uncertainties

Access URL: https://doaj.org/article/4ceb0bc4398042629b0b6d5a9af3992d

Detection of dynamic communities in temporal networks with sparse data

Authors: Nataša Djurdjevac Conrad Elisa Tonello Johannes Zonker et al.

Source: Applied Network Science, Vol 10, Iss 1, Pp 1-29 (2025)

Subject Terms: Temporal networks, Sparse data, Dynamic communities, Temporal resolution, Temporal clustering, Applied mathematics. Quantitative methods, T57-57.97

File Description: electronic resource

Relation: https://doaj.org/toc/2364-8228

Access URL: https://doaj.org/article/245353405f9f4444a35af3512a7cc437