Acute Changes in the Resting Brain Networks in Concussion Patients: Small-World Topology Perspective by Hong-mei Kuang, Yan Chen, Ji-lan Huang, Jian Li, Ning Zhang, Hong-hui Ai, Guo-jin Xia

“…A complex network analysis method based on graph theory was used to calculate the parameters of small-world networks under different degrees of network sparsity. All subjects were evaluated using the Glasgow Coma Scale and Rivermead Postconcussion Symptom Questionnaire. …”
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Sparse Parabolic Radon Transform with Nonconvex Mixed Regularization for Multiple Attenuation by Qiuying Wu, Bin Hu, Cai Liu, Junming Zhang

“…We demonstrate that it improves the sparsity and resolution of the Radon domain data, and better results are obtained.…”
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Collaborative Filtering Recommender System: Overview and Challenges by Al-Bashiri, Hael, Abdulgabber, Mansoor Abdullateef, Awanis, Romli, Hujainah, Fadhl

“…The major finding of this paper is the CF main problems: Data sparsity, Cold-star, and Scalability. By presenting of these challenges the quality of recommendations can be improved by proposing new methods. …”
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Heterogeneous Graph Neural Network for Short Text Classification by Bingjie Zhang, Qing He, Damin Zhang

“…Aiming at the sparsity of short text features, lack of context, and the inability of word embedding and external knowledge bases to supplement short text information, this paper proposes a text, word and POS tag-based graph convolutional network (TWPGCN) performs short text classification. …”
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Recent Advances in Positive-Instance Driven Graph Searching by Max Bannach, Sebastian Berndt

“…It was popularized through the graph sparsity project and is theoretically well understood—but the first practical algorithm was discovered only recently by Trimble (IPEC 2020) and is based on the same paradigm. …”
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Underwater Acoustic Matched Field Imaging Based on Compressed Sensing by Huichen Yan, Jia Xu, Teng Long, Xudong Zhang

“…Matched field processing (MFP) is an effective method for underwater target imaging and localizing, but its performance is not guaranteed due to the nonuniqueness and instability problems caused by the underdetermined essence of MFP. By exploiting the sparsity of the targets in an imaging area, this paper proposes a compressive sensing MFP (CS-MFP) model from wave propagation theory by using randomly deployed sensors. …”
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Reduced‐rank space‐time adaptive processing algorithm based on multistage selections of angle‐Doppler filters by Zhaocheng Yang, Xiaoye Wang

“…Numerical examples are provided, and they demonstrate that the proposed algorithm is able to offer better performance than the existing sparsity‐based and the conventional reduced‐dimension algorithms under the intrinsic clutter motion and array gain and phase errors.…”
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A Fault Diagnosis Method for Rolling Bearings Based on Parameter Transfer Learning under Imbalance Data Sets by Cheng Peng, Lingling Li, Qing Chen, Zhaohui Tang, Weihua Gui, Jing He

“…Firstly, the discriminator of the generative adversarial network (GAN) is improved by enhancing its sparsity, and then adopts the adversarial mechanism to continuously optimize the recognition ability of the discriminator; finally, the parameter transfer learning (PTL) method is applied to transfer the trained discriminator to target domain to solve the fault diagnosis problem with only a small number of label samples. …”
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Quality of child development scales. A systematic review by Araceli Sánchez Raya, Sara Maria Luque de Dios, Juan Antonio Moriana Elvira

“…We conclude that the quality of the scale metrics and other common aspects of these tests need to be improved, particularly sample sparsity and heterogeneity, as well as cultural biases. …”
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Designing sparse sensing matrix for compressive sensing to reconstruct high resolution medical images by Vibha Tiwari, P.P. Bansod, Abhay Kumar

“…The role played by sensing matrix $ \Phi $ and sparsity matrix $ \Psi $ is vital in faithful reconstruction. …”
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Dependency graph for short text extraction and summarization by Nigel Franciscus, Xuguang Ren, Bela Stantic

“…However, most of these textual data are in the form of short and fragmented texts which are difficult to visually extract due to the sparsity issue and the context in the content is often unknown. …”
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Normalization and de-noising of single-cell Hi-C data with BandNorm and scVI-3D by Ye Zheng, Siqi Shen, Sündüz Keleş

“…In benchmarking experiments, BandNorm yields leading performances in a time and memory efficient manner for cell-type separation, identification of interacting loci, and recovery of cell-type relationships, while scVI-3D exhibits advantages for rare cell types and under high sparsity scenarios. Application of BandNorm coupled with gene-associating domain analysis reveals scRNA-seq validated sub-cell type identification.…”
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KADER KESEHATAN MASYARAKAT SEBAGAI SALAH SATU BAGIAN DARI UPAYA PENGEMBANGAN MASYARAKAT by Peter Andreas

“…Using the community members as part of its health activity will stimulate the optimum usage the community potential to cope with the problem of sparsity in health care. Community health workers will bridge out the health gap disparsities between those who can afford  health care and those who cannot. …”
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Fast dictionary learning from incomplete data by Valeriya Naumova, Karin Schnass

“…To further confirm the appropriateness of the learned dictionaries, we explore an application to sparsity-based image inpainting. There the ITKrMM dictionaries show a similar performance to other learned dictionaries like wKSVD and BPFA and a superior performance to other algorithms based on pre-defined/analytic dictionaries.…”
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Hybrid Recommendation Using Temporal Data for Accuracy Improvement in Item Recommendation by Desabandhu Parasuraman, Sathiyamoorthy Elumalai

“…But, both the techniques have some limitations like data sparsity, cold start, gray sheep and scalability issues. …”
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Factorized discriminant analysis for genetic signatures of neuronal phenotypes by Mu Qiao

“…To augment this method, we integrate it with a sparsity-based regularization algorithm. This integration is crucial as it selects a subset of genes pivotal to a specific phenotypic feature or a combination thereof. …”
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Distributed Field Estimation Using Sensor Networks Based on H∞ Consensus Filtering by Haiyang Yu, Rubo Zhang, Junwei Wu, Xiuwen Li

“…The finite element method is employed to discretize the infinite dimensional process, which is described by a partial differential equation, and an approximate finite dimensional linear system is established. Due to the sparsity on the spatial distribution of the source function, the ℓ 1 -regularized H ∞ filtering is introduced to solve the estimation problem, which attempts to provide better performance than the classical centralized Kalman filtering. …”
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Training Spiking Neural Networks for Reinforcement Learning Tasks With Temporal Coding Method by Guanlin Wu, Dongchen Liang, Shaotong Luan, Ji Wang

“…To tackle the problem of high sparsity of spikes, we introduce a self-incremental variable to push each spiking neuron to fire, which makes SNNs fully differentiable. …”
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Imaging Incoherent Target Using Hadamard Basis Patterns by Tanushree Karmakar, Rajeev Singh, Rakesh Kumar Singh

“…The Hadamard basis, which has the characteristics of a two-bit value {−1, 1} and sparsity in its transformed domain, has been used in the illumination patterns and successfully utilized for imaging the incoherent target. …”
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Topic modeling for conversations for mental health helplines with utterance embedding by Salim Salmi, Rob van der Mei, Saskia Mérelle, Sandjai Bhulai

“…However, with increased data sparsity, different methods need to be considered. …”
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