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Image Completion in Embedded Space Using Multistage Tensor Ring Decomposition [PDF]

Frontiers in Artificial Intelligence, 2021
Tensor Completion is an important problem in big data processing. Usually, data acquired from different aspects of a multimodal phenomenon or different sensors are incomplete due to different reasons such as noise, low sampling rate or human mistake.
Farnaz Sedighin, Farnaz Sedighin, Andrzej Cichocki, Andrzej Cichocki   +3 more
doaj   +2 more sources

Moving objects detection based on tensor ring low rank decomposition [PDF]

Scientific Reports
The advancement of high-quality camera technology has increased the demand for efficient video analysis methods. Current methods mostly rely on matrix-based approaches, which break data structures and lose some spatial information.
Ruixuan Chen, Xusheng Li, Chenda Chen, Shiyu Zhu, Qipeng Chen, Jianting Cao   +5 more
doaj   +2 more sources

A Novel Tensor Ring Sparsity Measurement for Image Completion [PDF]

Entropy
As a promising data analysis technique, sparse modeling has gained widespread traction in the field of image processing, particularly for image recovery.
Junhua Zeng, Yuning Qiu, Yumeng Ma, Andong Wang, Qibin Zhao   +4 more
doaj   +2 more sources

TR-SNN: a lightweight spiking neural network based on tensor ring decomposition

Brain-Apparatus Communication
Aim Spiking neural networks (SNNs), inspired by biological neural mechanisms, have demonstrated remarkable capabilities in various intelligent tasks.
Shifeng Mao, Baoxin Yang, Hongze Sun, Wuque Cai, Daqing Guo   +4 more
doaj   +2 more sources

Low rank based FAZ segmentation in OCTA images [PDF]

Scientific Reports
Optical Coherence Tomography Angiography (OCTA) is a non-invasive method for vascular imaging of different tissues. Several diseases can disturb the blood supplying of retinal tissues which leads to loss of blood vessels and a reduction in the vascular ...
Farnaz Sedighin, Fatemeh Rezaei, Maryam Monemian   +2 more
doaj   +2 more sources

Joint-Way Compression for LDPC Neural Decoding Algorithm With Tensor-Ring Decomposition

IEEE Access, 2023
In this paper, we propose low complexity joint-way compression algorithms with Tensor-Ring (TR) decomposition and weight sharing to further lower the storage and computational complexity requirements for low density parity check (LDPC) neural decoding ...
Yuanhui Liang, Chan-Tong Lam, Benjamin K. Ng   +2 more
doaj   +1 more source

Spatiotemporal traffic data imputation by synergizing low tensor ring rank and nonlocal subspace regularization

IET Intelligent Transport Systems, 2023
Spatiotemporal traffic data usually suffers from missing entries in the data acquisition and transmission process. Existing imputation methods only consider the global/local structure of spatiotemporal traffic data, resulting in insufficient estimation ...
Peng‐Ling Wu, Meng Ding, Yu‐Bang Zheng   +2 more
doaj   +1 more source

Operator bases, $S$-matrices, and their partition functions [PDF]

, 2017
Relativistic quantum systems that admit scattering experiments are quantitatively described by effective field theories, where $S$-matrix kinematics and symmetry considerations are encoded in the operator spectrum of the EFT. In this paper we use the $S$-
Henning, Brian, Lu, Xiaochuan, Melia, Tom, Murayama, Hitoshi   +3 more
core   +2 more sources

Classification of Matrix-Product Unitaries with Symmetries [PDF]

, 2019
We prove that matrix-product unitaries (MPUs) with on-site unitary symmetries are completely classified by the (chiral) index and the cohomology class of the symmetry group $G$, provided that we can add trivial and symmetric ancillas with arbitrary on ...
Cirac, J. Ignacio, Gong, Zongping, Schuch, Norbert, Sünderhauf, Christoph   +3 more
core   +3 more sources

Spectral Quadratic Variation Regularized Autoweighted Tensor Ring Decomposition for Hyperspectral Image Reconstruction

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
The structure information of hyperspectral image (HSI) is well-characterized by tensors, surpassing the capabilities of traditional compressive sensing reconstruction models based on vectors and matrices.
Xinwei Wan, Dan Li, Fanqiang Kong, Yanyan Lv, Qiang Wang   +4 more
doaj   +1 more source