6,313 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,
201
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
Superconducting fluctuations and charge-4 plaquette state at strong coupling
Recent experiments indicate that superconducting fluctuations also play an
important role in overdoped cuprates. Here we apply the static auxiliary field
Monte Carlo approach to study phase correlations of the pairing fields in a
microscopic model with spin-singlet pairing interaction. We find that the
short- and long-range phase correlations are well captured by the phase mutual
information, which allows us to construct a theoretical phase diagram
containing the uniform -wave superconducting region, the phase fluctuating
region, the local pairing region, and the disordered region. We show that the
gradual development of phase coherence has a number of consequences on
spectroscopic measurements, such as the development of the Fermi arc and the
anisotropy in the angle-resolved spectra, scattering rate, entropy, specific
heat, and quasiparticle dispersion, in good agreement with experimental
observations. For strong coupling, our Monte Carlo simulation reveals an
unexpected charge-4 plaquette state with -wave bonds, which competes with
the uniform -wave superconductivity and exhibits a U-shaped density of
states
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