Ventricle surface reconstruction from cardiac MR slices using deep learning by Xu, H, Zacur, E, Schneider, J, Grau, V

“…Reconstructing 3D ventricular surfaces from 2D cardiac MR data is challenging due to the sparsity of the input data and the presence of interslice misalignment. …”

AgeTrust: a new temporal trust-based collaborative filtering approach by Moghaddam, Morteza Ghorbani, Mustapha, Norwati, Mustapha, Aida, Mohd Sharef, Nurfadhlina, Elahian, Anousheh

“…This approach focuses on previous indicate preferences which is known for its traditional problems such as cold-start, sparsity and hacking. For solving the problem of hacking and improving the accuracy, trust-based CF methods have been proposed previously. …”

An efficient traffic state estimation model based on fuzzy C-mean clustering and MDL using FCD by Ahanin, Fatemeh, Mustapha, Norwati, Sulaiman, Nasir, Zolkepli, Maslina

“…However, FCD suffers from data sparseness and many researches have been done to improve traffic estimation accuracy with respect to data sparsity. In this paper, a new model based on Fuzzy C-Mean (FCM) clustering and Minimum Description Length (MDL) is proposed to estimate the missing traffic state using FCD. …”
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Automated interestingness measure selection for exhibition recommender systems by Bong, Kok Keong, Joest, Matthias, Quix, Christoph, Anwar, Toni

“…The recommendations are ranked based on various Objective Interestingness Measures (OIMs) that quantify the interestingness of an association. Due to data sparsity, some OIMs cannot provide distinct values for different rules and hamper the ranking process. …”

Power Boosting for ordered multiple hypotheses with application to Genome-Wide Association Studies by Ramos, Mark Louie Frac, Park, DoHwan

“…It is shown that this method is able to control family-wise error rate in the weak sense and numerical evidence shows that this method controls false discovery rate in the strong sense under sparsity. The method is applied to some genome- wide association studies data with asthma and it is argued that this Power Boosting method may be combined with existing error- rate controlling methods in order to improve true positive rates at controllable and possibly negligible cost to the nominal level of error- rate control.…”
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Polynomial systems : graphical structure, geometry, and applications by Cifuentes Pardo, Diego Fernando

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Theoretical guarantees and complexity reduction in information planning by Papachristoudis, Georgios

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A hybrid anomaly detection method for high dimensional data by Xin Zhang, Pingping Wei, Qingling Wang

“…Anomaly detection of high-dimensional data is a challenge because the sparsity of the data distribution caused by high dimensionality hardly provides rich information distinguishing anomalous instances from normal instances. …”
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scAEGAN: Unification of single-cell genomics data by adversarial learning of latent space correspondences. by Sumeer Ahmad Khan, Robert Lehmann, Xabier Martinez-de-Morentin, Alberto Maillo, Vincenzo Lagani, Narsis A Kiani, David Gomez-Cabrero, Jesper Tegner

“…The scAEGAN outperforms Seurat3 in library integration, is more robust against data sparsity, and beats Seurat 4 in integrating paired data from the same cell. …”
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Sparse Bayesian Learning for DOA Estimation with Mutual Coupling by Jisheng Dai, Nan Hu, Weichao Xu, Chunqi Chang

“…Unlike the existing method that only uses stationary priors, our new approach utilizes a hierarchical form of the Student t prior to enforce the sparsity of the unknown signal more heavily. We also provide a distinct Bayesian inference for the expectation-maximization (EM) algorithm, which can update the mutual coupling coefficients more efficiently. …”
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Survey of Research on Personalized News Recommendation Approaches by MENG Xiangfu, HUO Hongjin, ZHANG Xiaoyan, WANG Wanchun, ZHU Jinxia

“…Finally, it analyzes the challenges of the current personalized news recommendation, discusses how to solve the problems of data sparsity, model interpretability, diversity of recommendation results and news privacy protection in personalized news recommendation system, and puts forward more specific and operable research ideas in the future research direction.…”
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Enhancing scientific discoveries in molecular biology with deep generative models by Romain Lopez, Adam Gayoso, Nir Yosef

“…Abstract Generative models provide a well‐established statistical framework for evaluating uncertainty and deriving conclusions from large data sets especially in the presence of noise, sparsity, and bias. Initially developed for computer vision and natural language processing, these models have been shown to effectively summarize the complexity that underlies many types of data and enable a range of applications including supervised learning tasks, such as assigning labels to images; unsupervised learning tasks, such as dimensionality reduction; and out‐of‐sample generation, such as de novo image synthesis. …”
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A Biterm Topic Model for Sparse Mutation Data by Itay Sason, Yuexi Chen, Mark D. M. Leiserson, Roded Sharan

“…In particular, we previously developed the Mix model that clusters samples to handle data sparsity. However, the Mix model had two hyper-parameters, including the number of signatures and the number of clusters, that were very costly to learn. …”
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A Proportionate Normalized Maximum Correntropy Criterion Algorithm with Correntropy Induced Metric Constraint for Identifying Sparse Systems by Yingsong Li, Yanyan Wang, Laijun Sun

“…The CIM scheme is incorporated into the basic MCC to further utilize the sparsity of inherent sparse systems, resulting in the name of the CIM-PNMCC algorithm. …”
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Computation of Graph Fourier Transform Centrality Using Graph Filter by Chien-Cheng Tseng, Su-Ling Lee

“…The computational complexity comparison and experimental results show that the proposed graph filter method is more computationally efficient than conventional GFT method because the sparsity of Laplacian matrix is used in the implementation structure. …”
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A measurement method of batch solution concentration based on normalized compressed sensing by Wei He, Songsheng Zhu, Wei Wang

“…The method is based on the sparsity of natural signals and can reconstruct the original batch concentration signals with a high level of accuracy while taking fewer measurements. …”
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A Semi-Supervised Model for Top-N Recommendation by Shulong Chen, Yuxing Peng

“…Top-N recommendation is an important recommendation technique that delivers a ranked top-N item list to each user. Data sparsity is a great challenge for top-N recommendation. …”
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Posterior contraction rate of sparse latent feature models with application to proteomics by Tong Li, Tianjian Zhou, Kam-Wah Tsui, Lin Wei, Yuan Ji

“…In this paper, we show that under mild sparsity condition, the posterior distribution of the latent feature matrix, generated via IBP or pIBP priors, converges to the true latent feature matrix asymptotically. …”
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UN LIENZO DEL PINTOR RIOJANO DIEGO DE LEYVA EN CASTAGENA, Y SU FUENTE DE INSPIRACIÓN GRABADA by José Carlos Agüera Ros

“…This surname is identifiable With such of 'Diego de Leyva,. an artist from La Rioja, working at Burgos between 1628 to 1637, and who finished his life as a monk, joined to the Charterhouse of Miraflores. his personality is until today, one of others among the pictoric background of the Spanish 1600th, and needs to be actualized because the few information available about this artist, shows a great sparsity. The picture we are hereby presenting, has been collected from the artistical repertories. as from Ponz, and it is in perfect concordance with the characteristics of his production, which were obtained from the above sources. …”
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HDS-Net: Achieving fine-grained skin lesion segmentation using hybrid encoding and dynamic sparse attention. by You Xue, Xinya Chen, Pei Liu, Xiaoyi Lv

“…Additionally, a dynamic sparse attention mechanism is introduced, mitigating the impact of irrelevant redundancies on segmentation performance by precisely controlling the sparsity ratio. Experimental results on multiple public datasets demonstrate a significant improvement in Dice coefficients, reaching 0.914, 0.857, and 0.898, respectively.…”
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