Polynomial filtering can provide a highly effective means of computing all eigenvalues real symmetric (or complex Hermitian) matrix that are located in given interval, anywhere the spectrum. This paper describes technique for tackling this problem by combining thick-restart version Lanczos algorithm with deflation (``locking'') and new type polynomial filter obtained from least-squares technique. The resulting be utilized "spectrum-slicing" approach whereby very large number associated...

Nowadays, the large online systems are constructed on basis of microservice architecture. A failure in this architecture may cause a series failures due to fault propagation. Thus, need be monitored comprehensively ensure service quality. Even though many anomaly detection techniques have been proposed, few them can directly applied given or cloud server industrial environment. To settle these challenges, paper presents SLA-VAE, semi-supervised learning based active framework using...

This paper presents a parallel preconditioning approach based on incomplete LU (ILU) factorizations in the framework of Domain Decomposition (DD) for general sparse linear systems. We focus distributed memory architectures, specifically, those that are equipped with graphic processing units (GPUs). In addition to block-Jacobi, we present purpose two-level ILU Schur complement-based approaches, where different strategies presented solve coarse-level reduced system. These combined modified...

The Returning Farmland to Forest Program (RFFP) is widely known as one of China’s largest and most successful payment schemes for ecosystem service projects the achievement both environmental economic sustainability. By sponsoring afforestation activities compensating farmers converting cropland forest, project was designed achieve multiple goals. Ecologically, program aims expand forest cover reduce flood soil erosion. Economically, it alleviate poverty improve rural livelihoods. Although...

This paper describes a software package called EVSL (for eigenvalues slicing library) for solving large sparse real symmetric standard and generalized eigenvalue problems. As its name indicates, the exploits spectrum slicing, strategy that consists of dividing into number subintervals extracting eigenpairs from each subinterval independently. In order to enable such strategy, methods in utilize quick calculation spectral density given matrix (or pair). What distinguishes other available...

This study presents a novel continuous learning framework tailored for brain tumour segmentation, addressing critical step in both diagnosis and treatment planning. addresses common challenges such as computational complexity, limited generalisability, the extensive need manual annotation.

This paper describes a multilevel preconditioning technique for solving sparse symmetric linear systems of equations. “Multilevel Schur Low-Rank” (MSLR) preconditioner first builds tree structure $\mathcal{T}$ based on hierarchical decomposition the matrix and then computes an approximate inverse original level by level. Unlike classical direct solvers, construction MSLR follows top-down traversal exploits low-rank property that is satisfied difference between inverses local complements...

Multigrid methods are one of the most efficient techniques for solving large sparse linear systems arising from partial differential equations (PDEs) and graph Laplacians machine learning applications. One key components multigrid is smoothing, which aims at reducing high-frequency errors on each grid level. However, finding optimal smoothing algorithms problem-dependent can impose challenges many problems. In this paper, we propose an adaptive framework optimized smoothers operator stencils...

This paper presents a preconditioning method based on an approximate inverse of the original matrix, computed recursively from multilevel low-rank (MLR) expansion approach. The basic idea is to divide problem in two and apply approximation matrix obtained Sherman--Morrison formula. by few steps Lanczos bidiagonalization procedure. MLR preconditioner has been motivated its potential for exploiting different levels parallelism modern high-performance platforms, though this feature not yet...

Abstract Glioblastoma multiforme (GBM) is the most common and deadly primary malignant brain tumor. As GBM tumor aggressive shows high biological heterogeneity, overall survival (OS) time extremely low even with treatment. If OS can be predicted before surgery, developing personalized treatment plans for patients will beneficial. Magnetic resonance imaging (MRI) a commonly used diagnostic tool tumors high‐resolution sound effects. However, in clinical practice, doctors mainly rely on...

Introduction Glioma segmentation is vital for diagnostic decision-making, monitoring disease progression, and surgical planning. However, this task hindered by substantial heterogeneity within gliomas imbalanced region distributions, posing challenges to existing methods. Methods To address these challenges, we propose the DeepGlioSeg network, a U-shaped architecture with skip connections continuous contextual feature integration. The model includes two primary components. First, CTPC...

Summary This paper introduces a robust preconditioner for general sparse matrices based on low‐rank approximations of the Schur complement in Domain Decomposition framework. In this ‘Schur Low Rank’ preconditioning approach, coefficient matrix is first decoupled by graph partitioner, and then correction exploited to compute an approximate inverse associated with interface unknowns. The method avoids explicit formation complement. We show feasibility strategy model problem conduct detailed...

This paper presents a parallel preconditioning method for distributed sparse linear systems, based on an approximate inverse of the original matrix, that adopts general framework matrices and exploits domain decomposition (DD) low-rank corrections. The DD approach decouples matrix and, once inverted, approximation is applied by exploiting Sherman--Morrison--Woodbury formula, which yields two variants methods. expansion computed Lanczos procedure with reorthogonalizations. Numerical...

Utilizing statistical models of binary images is a common and effective means to steganalyze images, the design model essential performance steganalysis. In this paper, we propose new based on histogram pixel structuring elements (SEs), which suitable representation image for task The texture property dependency among pixels are considered inside SEs. SEs with different patterns will be evaluated comprehensively according criterion, some them selected construct feature set training...

In this paper, we address the problem of cooperative mapping (CM) using datasets collected by multiple users at different times, when transformation between users' starting poses is unknown. Specifically, formulate CM as a constrained optimization problem, in which each user's independently estimated trajectory and map are merged together imposing geometric constraints commonly observed point line features. Additionally, provide an algorithm for efficiently solving taking advantage its...

Incomplete LU factorization (ILU) techniques are a well-known class of preconditioners, often used in conjunction with Krylov accelerators for the iterative solution linear systems equations. However, certain problems, ILU factorizations can yield factors that unstable and some cases quite dense. Reordering based on permuting matrix prior to performing have been shown improve quality factorization, resulting preconditioner. In this paper, we examine effect reordering multilevel graph...

A highly parallel algorithm has been developed and exploited to compute the planetary normal modes of elastic-gravitational system, which is approximated via mixed finite element method on unstructured tetrahedral meshes. The eigenmodes relevant generalized eigenvalue problem were extracted by a Lanczos approach combined with polynomial filtering. In contrast standard shift-and-invert full-mode coupling algorithms, filtering technique ideally suited for solving large-scale 3-D interior...