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Scalable and Privacy-Conscious End-to-End Processing of Large-Scale Clinical Data for Precision Medicine: Empirical Evaluation Study.

JMIR Med Inform
Lee J, Hwang S, Lee KH.
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Proof-of-concept study of sparse processing particle image velocimetry for real time flow observation

Experiments in Fluids, 2022
In this paper, we overview, evaluate, and demonstrate the sparse processing particle image velocimetry (SPPIV) as a real-time flow field estimation method using the particle image velocimetry (PIV), whereas SPPIV was previously proposed with its ...
Naoki Kanda, Chihaya Abe, Shintaro Goto, Keigo Yamada, Kumi Nakai, Y. Saito, Keisuke Asai, T. Nonomura   +7 more
semanticscholar   +1 more source

Low-Rank and Sparse Representation for Hyperspectral Image Processing: A review

IEEE Geoscience and Remote Sensing Magazine, 2022
Combining rich spectral and spatial information, a hyperspectral image (HSI) can provide a more comprehensive characterization of the Earth’s surface. To better exploit HSIs, a large number of algorithms have been developed during the past few decades ...
Jiangtao Peng, Weiwei Sun, Hengchao Li, Wei Li, Xiangchao Meng, Chiru Ge, Qian Du   +6 more
semanticscholar   +1 more source

Towards Efficient Sparse Matrix Vector Multiplication on Real Processing-In-Memory Architectures

Measurement and Modeling of Computer Systems, 2022
Several manufacturers have already started to commercialize near-bank Processing-In-Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures place simple cores close to DRAM banks.
Christina Giannoula, Ivan Fernandez, Juan Gómez-Luna, N. Koziris, G. Goumas, O. Mutlu   +5 more
semanticscholar   +1 more source

SpaceA: Sparse Matrix Vector Multiplication on Processing-in-Memory Accelerator

International Symposium on High-Performance Computer Architecture, 2021
Sparse matrix-vector multiplication (SpMV) is an important primitive across a wide range of application domains such as scientific computing and graph analytics.
Xinfeng Xie, Zheng Liang, P. Gu, Abanti Basak, Lei Deng, Ling Liang, Xing Hu, Yuan Xie   +7 more
semanticscholar   +1 more source

Multiview Variational Sparse Gaussian Processes

IEEE Transactions on Neural Networks and Learning Systems, 2021
Gaussian process (GP) models are flexible nonparametric models widely used in a variety of tasks. Variational sparse GP (VSGP) scales GP models to large data sets by summarizing the posterior process with a set of inducing points. In this article, we extend VSGP to handle multiview data.
Liang Mao, Shiliang Sun
openaire   +2 more sources