5,928 research outputs found
Ultra-Scalable Spectral Clustering and Ensemble Clustering
This paper focuses on scalability and robustness of spectral clustering for
extremely large-scale datasets with limited resources. Two novel algorithms are
proposed, namely, ultra-scalable spectral clustering (U-SPEC) and
ultra-scalable ensemble clustering (U-SENC). In U-SPEC, a hybrid representative
selection strategy and a fast approximation method for K-nearest
representatives are proposed for the construction of a sparse affinity
sub-matrix. By interpreting the sparse sub-matrix as a bipartite graph, the
transfer cut is then utilized to efficiently partition the graph and obtain the
clustering result. In U-SENC, multiple U-SPEC clusterers are further integrated
into an ensemble clustering framework to enhance the robustness of U-SPEC while
maintaining high efficiency. Based on the ensemble generation via multiple
U-SEPC's, a new bipartite graph is constructed between objects and base
clusters and then efficiently partitioned to achieve the consensus clustering
result. It is noteworthy that both U-SPEC and U-SENC have nearly linear time
and space complexity, and are capable of robustly and efficiently partitioning
ten-million-level nonlinearly-separable datasets on a PC with 64GB memory.
Experiments on various large-scale datasets have demonstrated the scalability
and robustness of our algorithms. The MATLAB code and experimental data are
available at https://www.researchgate.net/publication/330760669.Comment: To appear in IEEE Transactions on Knowledge and Data Engineering,
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ALL-E: Aesthetics-guided Low-light Image Enhancement
Evaluating the performance of low-light image enhancement (LLE) is highly
subjective, thus making integrating human preferences into image enhancement a
necessity. Existing methods fail to consider this and present a series of
potentially valid heuristic criteria for training enhancement models. In this
paper, we propose a new paradigm, i.e., aesthetics-guided low-light image
enhancement (ALL-E), which introduces aesthetic preferences to LLE and
motivates training in a reinforcement learning framework with an aesthetic
reward. Each pixel, functioning as an agent, refines itself by recursive
actions, i.e., its corresponding adjustment curve is estimated sequentially.
Extensive experiments show that integrating aesthetic assessment improves both
subjective experience and objective evaluation. Our results on various
benchmarks demonstrate the superiority of ALL-E over state-of-the-art methods.
Source code and models are in the project page
Uniaxial Tension Simulation Using Real Microstructure-based Representative Volume Elements Model of Dual Phase Steel Plate
AbstractDual-phase steels have become a favored material for car bodies. In this study, the deformation behavior of dual-phase steels under uniaxial tension is investigated by means of 2D Representative Volume Elements (RVE) model. The real metallographic graphs including particle geometry, distribution and morphology are considered in this RVE model. Stress and strain distributions between martensite and ferrite are analyzed. The results show that martensite undertakes most stress without significant strain while ferrite shares the most strain. The tensile failure is the result of the deforming inhomogeneity between martensite phase and ferrite phase, which is the key factor triggering the plastic strain localization on specimen section during the tensile test
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