Building a Compact Convolutional Neural Network for Embedded Intelligent Sensor Systems Using Group Sparsity and Knowledge Distillation by Jungchan Cho, Minsik Lee

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Denoising on Textured Image Using Total Generalized Variation With Overlapping Group Sparsity Based on Fast Split Bregman Method by Cheng Zhang, Kin Sam Yen

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Feature Enhancement of Interferometric Synthetic Aperture Radar Image Formation Using Sparse Bayesian Learning in Joint Sparsity Approach by Hou Yuxing, Xu Gang

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Shadow, quasinormal modes, greybody bounds, and Hawking sparsity of loop quantum gravity motivated non-rotating black hole by Sohan Kumar Jha

“…We also study greybody bounds, power spectrum, and sparsity of Hawking radiation. Greybody bounds for electromagnetic perturbations do not depend on $$\alpha $$ α . …”
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Bayesian 3D X-ray Computed Tomography with a Hierarchical Prior Model for Sparsity in Haar Transform Domain by Li Wang, Ali Mohammad-Djafari, Nicolas Gac, Mircea Dumitru

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Commentary: Data Processing Thresholds for Abundance and Sparsity and Missed Biological Insights in an Untargeted Chemical Analysis of Blood Specimens for Exposomics by Pekka Keski-Rahkonen, Oliver Robinson, Rossella Alfano, Rossella Alfano, Michelle Plusquin, Augustin Scalbert

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Array-based underwater acoustic target classification with spectrum reconstruction based on joint sparsity and frequency shift invariant feature by Lu, Chenxiang, Zeng, Xiangyang, Wang, Qiang, Wang, Lu, Jin, Anqi

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scCGImpute: An Imputation Method for Single-Cell RNA Sequencing Data Based on Similarities between Cells and Relationships among Genes by Tiantian Liu, Yuanyuan Li

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IMPACT OF IMPUTATION ON CLUSTER-BASED COLLABORATIVE FILTERING APPROACH FOR RECOMMENDATION SYSTEM by Noor Ifada, Susi Susanti, Mulaab

Subjects: “…clustering, collaborative filtering, imputation, sparsity…”
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Disentangled variational auto-encoder enhanced by counterfactual data for debiasing recommendation by Yupu Guo, Fei Cai, Jianming Zheng, Xin Zhang, Honghui Chen

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Radio Frequency Interference Mitigation for Synthetic Aperture Radar Based on the Time-Frequency Constraint Joint Low-Rank and Sparsity Properties by Yi Ding, Weiwei Fan, Zijing Zhang, Feng Zhou, Bingbing Lu

“…The TF constraint concept, in lieu of the special sparsity, is introduced in this algorithm to describe the structural distribution of RFI because of its aggregation characteristic in the TF spectrogram. …”
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A sparsity preserving genetic algorithm for extracting diverse functional 3D designs from deep generative neural networks by James D. Cunningham, Dule Shu, Timothy W. Simpson, Conrad S. Tucker

“…This work bridges this gap by proposing a method to extract a set of functional designs from the latent space of a point cloud generating GNN, without sacrificing the aforementioned aspects of a GNN that are appealing for design exploration. We introduce a sparsity preserving cost function and initialization strategy for a genetic algorithm (GA) to optimize over the latent space of a point cloud generating autoencoder GNN. …”
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Multi objective task resource allocation method based on hierarchical Bayesian adaptive sparsity for edge computing in low voltage stations by Yupeng Liu, Bofeng Yan, Jia Yu

“…Abstract In order to achieve more efficient and optimised resource scheduling, this research carried out a multi‐objective task resource allocation method for low‐voltage station edge computing based on hierarchical Bayesian adaptive sparsity. Based on hierarchical Bayesian adaptive sparsity, the multi‐objective task resource allocation technical framework for edge computing in low‐voltage stations is established, which is composed of end pipe edge cloud; After collecting real‐time operation data of power distribution equipment, substation terminals, transmission terminals, etc. in the architecture end, it is transmitted to the data middle platform and service middle platform of the Internet of Things management platform in the cloud through the edge Internet of Things agent; Set and solve the constraint conditions, and build a multi type flexible load hierarchical optimal allocation model; The abnormal area topology identification sub module of multi‐objective task resource of low‐voltage station area edge computing is used to identify the abnormal area topology of the current low‐voltage station area; Taking it as input, the multi‐objective task resources of edge computing are allocated, and the multi‐objective task resources allocation method of edge computing in low pressure platform area is realized under the differential evolution algorithm. …”
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Blind Unmixing of Hyperspectral Remote Sensing Data: A New Geometrical Method Based on a Two-Source Sparsity Constraint by Djaouad Benachir, Yannick Deville, Shahram Hosseini, Moussa Sofiane Karoui

“…The case when, for each pure material, the image includes at least one pixel or zone which only contains that material yielded attractive unmixing methods, but corresponds to a stringent sparsity condition. We here aim at relaxing that condition, by only requesting a few tiny pixel zones to contain two pure materials. …”
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High-Quality Multispectral Image Reconstruction for the Spectral Camera Based on Ghost Imaging via Sparsity Constraints Using CoT-Unet by Tao Hu, Jianxia Chen, Shu Wang, Jianrong Wu, Ziyan Chen, Zhifu Tian, Ruipeng Ma, Di Wu

“…To solve the problem of poor quality in ghost imaging via sparsity constraints (GISC) multispectral image reconstruction with correlation operations and compressed sensing algorithms under low sampling rate detection conditions, we propose an end-to-end deep-learning-based method. …”
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Sparsity-Aware 25-Gb/s Memory Link With 0.0375-pJ/bit Signaling Efficiency for Machine Learning Hardware by Shovon Dey, Can Ni, Alberto Leon Cevallos, Raju Machupalli, Mrinal Mandal, Masum Hossain

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Underwater Acoustic Target Feature Fusion Method Based on Multi-Kernel Sparsity Preserve Multi-Set Canonical Correlation Analysis

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Hyperspectral and Multispectral Image Fusion with Automated Extraction of Image-Based Endmember Bundles and Sparsity-Based Unmixing to Deal with Spectral Variability by Salah Eddine Brezini, Yannick Deville

“…In this paper, a new hypersharpening method addressing spectral variability by considering the spectra bundles-based method, namely the <i>Automated Extraction of Endmember Bundles</i> (AEEB), and the sparsity-based method called <i>Sparse Unmixing by Variable Splitting and Augmented Lagrangian</i> (SUnSAL), is introduced. …”
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Least Mean p-Power-Based Sparsity-Driven Adaptive Line Enhancer for Passive Sonars Amid Under-Ice Noise by Yujiao Lv, Cheng Chi, Haining Huang, Shenglong Jin

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Joint Sparsity for TomoSAR Imaging in Urban Areas Using Building POI and TerraSAR-X Staring Spotlight Data by Lei Pang, Yanfeng Gai, Tian Zhang

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