A5010873564- Complex Network Analysis Techniques
- Advanced Graph Neural Networks
- Face and Expression Recognition
- Text and Document Classification Technologies
- Recommender Systems and Techniques
- Matrix Theory and Algorithms
- Opinion Dynamics and Social Influence
- Blind Source Separation Techniques
- Adaptive optics and wavefront sensing
- Advanced Computing and Algorithms
- Nonlinear Dynamics and Pattern Formation
- Error Correcting Code Techniques
- Optical Systems and Laser Technology
- Mathematical and Theoretical Epidemiology and Ecology Models
- Geological and Geochemical Analysis
- Gene expression and cancer classification
- Astronomical Observations and Instrumentation
- Astronomy and Astrophysical Research
- Differential Equations and Numerical Methods
- Nonlinear Differential Equations Analysis
- Neural Networks and Applications
- Geochemistry and Geologic Mapping
- Advanced Fiber Laser Technologies
- Image Retrieval and Classification Techniques
- Parallel Computing and Optimization Techniques
Dongguan University of Technology
2023-2024
University of Science and Technology of China
2022-2024
Chongqing Institute of Green and Intelligent Technology
2018-2023
Chongqing University of Posts and Telecommunications
2020-2023
Southwest University
2023
University of Electronic Science and Technology of China
2023
Institute of Computing Technology
2022
Chinese Academy of Sciences
2007-2022
University of Chinese Academy of Sciences
2021-2022
Shanghai Institute of Optics and Fine Mechanics
2007-2022
Non-negative latent factor (NLF) models can efficiently acquire useful knowledge from high-dimensional and sparse (HiDS) matrices filled with non-negative data. Single factor-dependent, multiplicative update (SLF-NMU) is an efficient algorithm for building NLF model on HiDS matrix, yet it suffers slow convergence. A momentum method frequently adopted to accelerate a learning algorithm, but incompatible those implicitly adopting gradients like SLF-NMU. To build fast (FNLF) model, we propose...
Community detection is a popular yet thorny issue in social network analysis. A symmetric and nonnegative matrix factorization (SNMF) model based on multiplicative update (NMU) scheme frequently adopted to address it. Current research mainly focuses integrating additional information into it without considering the effects of learning scheme. This study aims implement highly accurate community detectors via connections between an SNMF-based detector's accuracy NMU scheme's scaling factor....
Non-negativity is vital for a latent factor (LF)-based model to preserve the important feature of high-dimensional and sparse (HiDS) matrix in recommender systems, i.e., none its entries negative. Current non-negative models rely on constraints-combined training schemes. However, they lack flexibility, scalability, or compatibility with general This work aims perform unconstrained analysis (UNLFA) HiDS matrices. To do so, we innovatively transfer non-negativity constraints from decision...
Latent factor (LF) models are highly effective in extracting useful knowledge from High-Dimensional and Sparse (HiDS) matrices which commonly seen various industrial applications. An LF model usually adopts iterative optimizers, may consume many iterations to achieve a local optima, resulting considerable time cost. Hence, determining how accelerate the training process for has become significant issue. To address this, this work proposes randomized latent (RLF) model. It incorporates...
Community detection, aiming at determining correct affiliation of each node in a network, is critical task complex network analysis. Owing to its high efficiency, Symmetric and Non-negative Matrix Factorization (SNMF) frequently adopted handle this task. However, existing SNMF models mostly focus on network's first-order topological information described by adjacency matrix without considering the implicit associations among involved nodes. To address issue, study proposes Pointwise mutual...
A fast non-negative latent factor (FNLF) model for a high-dimensional and sparse (HiDS) matrix adopts Single Latent Factor-dependent, Non-negative, Multiplicative Momentum-incorporated Update (SLF-NM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> U) algorithm, which enables its convergence. It is crucial to achieve rigorously theoretical proof regarding convergence, has not been provided in prior research. Aiming at addressing this...
A single latent factor (LF)-dependent, nonnegative, and multiplicative update (SLF-NMU) learning algorithm is highly efficient in building a nonnegative LF (NLF) model defined on high-dimensional sparse (HiDS) matrix. However, convergence characteristics of such NLF models are never justified theory. To address this issue, study conducts rigorous analysis for an SLF-NMU-based model. The main idea twofold: 1) proving that its objective keeps nonincreasing with rules via constructing specific...
A non-negative latent factor (NLF) model with a single factor-dependent, and multiplicative update (SLF-NMU) algorithm is frequently adopted to extract useful knowledge from data represented by high-dimensional sparse (HiDS) matrices arising various service-oriented applications. However, its convergence rate slow. To address this issue, study proposes <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</u> eneralized Nesterov's...
Non-negative latent factor (NLF) models well represent high-dimensional and sparse (HiDS) matrices filled with non-negative data, which are frequently encountered in industrial applications like recommender systems. However, current NLF mostly adopt Euclidean distance their objective function, represents a special case of β-divergence function. Hence, it is highly desired to design β-divergence-based ( β-NLF) model that uses investigate its performance systems as β varies. To do so, we first...
A nonnegative latent factorization of tensors (NLFT) model precisely represents the temporal patterns hidden in multichannel data emerging from various applications. It often adopts a single factor-dependent, and multiplicative update on tensor (SLF-NMUT) algorithm. However, learning depth this algorithm is not adjustable, resulting frequent training fluctuation or poor convergence caused by overshooting. To address issue, study carefully investigates connections between performance an NLFT...
While research has shown that the agile chip design methodology is promising to sustain scaling of computing performance in a more efficient way, it still limited usage actual applications due two major obstacles: 1) Lack tool-chain and developing framework supporting design, especially for large-scale modern processors. 2) The conventional verification methods are less become bottleneck entire process. To tackle both issues, we propose MINJIE, an open-source platform processor development...
Community is a fundamental and highly desired pattern in Large-scale Undirected Network (LUN). detection vital issue when LUN representation learning performed. Owing to its good scalability interpretability, Symmetric Non-negative Matrix Factorization model frequently utilized tackle this issue. It adopts unique Latent Factor (LF) matrix for precisely representing LUN's symmetry, which, unfortunately, leads reduced LF space that decreases ability target LUN. Motivated by discovery, study...
Community describes the functional mechanism of an undirected network, making community detection a fundamental tool for graph representation learning-related applications like social circle discovery. To date, Symmetric and Nonnegative Matrix Factorization (SNMF) model has been frequently adopted to address this issue owing its high interpretability scalability. However, most existing SNMF-based detectors neglect high-order proximity in thus suffering from accuracy loss caused by incomplete...
Dear Editor, This letter presents a novel symmetry and nonnegativity-constrained matrix factorization (SNCMF)-based community detection model on undirected networks such as social network. Community is fundamental characteristic of network, making vital yet thorny issue in network representation. Owing to its high interpretability scalability, symmetric nonnegative (SNMF) frequently adopted address this issue. However, it adopts unique latent factor (LF) for representing an network's...
A Symmetric Non-negative Matrix Factorization (SNMF)-based network embedding model adopts a unique Latent Factor (LF) matrix for describing the symmetry of an undirected network, which reduces its representation ability to target and thus resulting in accuracy loss when performing community detection. To address this issue, paper proposes new model, i.e., Alternating Direction Method Multipliers ( <underline xmlns:mml="http://www.w3.org/1998/Math/MathML"...
Get PDF Email Share with Facebook Tweet This Post on reddit LinkedIn Add to CiteULike Mendeley BibSonomy Citation Copy Text J. Zhu, X. Xie, Q. Yang, Kang, H. A. Guo, P. Gao, Z. Liu, Fan, D. Oyang, Wei, and Wang, "Introduction SG-II 5 PW Laser Facility," in Conference Lasers Electro-Optics, OSA Technical Digest (2016) (Optica Publishing Group, 2016), paper SM1M.7. Export BibTex Endnote (RIS) HTML Plain alert Save article
(Abridged) This is the Maunakea Spectroscopic Explorer 2018 book. It intended as a concise reference guide to all aspects of scientific and technical design MSE, for international astronomy engineering communities, related agencies. The current version status report MSE's science goals their practical implementation, following System Conceptual Design Review, held in January 2018. MSE planned 10-m class, wide-field, optical near-infrared facility, designed enable transformative science,...
In this paper, we study the (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mn>3</mml:mn><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:math>)-dimensional time-fractional Camassa-Holm-Kadomtsev-Petviashvili equation with a conformable fractional derivative. By complex transform and bifurcation method for dynamical systems, investigate behavior of solutions traveling wave system seek all possible exact equation. Furthermore, phase portraits remarkable features are...
Non-negative latent factor (NLF) models can efficiently acquire useful knowledge from high-dimensional and sparse (HiDS) matrices filled with non-negative data. Single factor-dependent, multiplicative update (SLF-NMU) is an efficient algorithm for building NLF model on HiDS matrix, yet it suffers slow convergence. On the other hand, a momentum method frequently adopted to accelerate learning explicitly depending gradients, incompatible algorithms implicitly like SLF-NMU. To build fast model,...