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The British landscape painter John Constable is considered foundational for the Realist movement in 19th-century European painting. Constable’s painted skies, in particular, were seen as remarkably accurate by his contemporaries, an impression shared by many viewers today. Yet, assessing the accuracy of realist paintings like Constable’s is subjective or intuitive, even for professional art historians, making it difficult to say with certainty what set Constable’s skies apart from those of his contemporaries. Our goal is to contribute to a more objective understanding of Constable’s realism. We propose a new machine-learning-based paradigm for studying pictorial realism in an explainable way. Our framework assesses realism by measuring the similarity between clouds painted by artists noted for their skies, like Constable, and photographs of clouds. The experimental results of cloud classification show that Constable approximates more consistently than his contemporaries the formal features of actual clouds in his paintings. The study, as a novel interdisciplinary approach that combines computer vision and machine learning, meteorology, and art history, is a springboard for broader and deeper analyses of pictorial realism. 

Multi-view data can be generated from diverse sources, by different technologies, and in multiple modalities. In various fields, integrating information from multi-view data has pushed the frontier of discovery. In this paper, we develop a new approach for multi-view clustering, which overcomes the limitations of existing methods such as the need of pooling data across views, restrictions on the clustering algorithms allowed within each view, and the disregard for complementary information between views. Our new method, called CPS-merge analysis , merges clusters formed by the Cartesian product of single-view cluster labels, guided by the principle of maximizing clustering stability as evaluated by CPS analysis. In addition, we introduce measures to quantify the contribution of each view to the formation of any cluster. CPS-merge analysis can be easily incorporated into an existing clustering pipeline because it only requires single-view cluster labels instead of the original data. We can thus readily apply advanced single-view clustering algorithms. Importantly, our approach accounts for both consensus and complementary effects between different views, whereas existing ensemble methods focus on finding a consensus for multiple clustering results, implying that results from different views are variations of one clustering structure. Through experiments on single-cell datasets, we demonstrate that our approach frequently outperforms other state-of-the-art methods. 

We report the first study on a GaAs/GaAsSb core−shell (CS)-configured nanowire (NW)-based separate absorption, charge control, and multiplication region avalanche photodiode (APD) operating in the near-infrared (NIR) region. Heterostructure NWs consisted of GaAs and tunable band gap GaAs1−xSbx serving as the multiplication and absorption layers, respectively. A doping compensation of absorber material to boost material absorption, segment-wise annealing to suppress trap-assisted tunneling, and an intrinsic i-type and n-type combination of the hybrid axial core to suppress axial electric field are successfully adopted in this work to realize a room-temperature (RT) avalanche photodetection extending up to 1.3 μm. In an APD device operating at RT with a unity-gain responsivity of 0.2−0.25 A/W at ∼5 V, the peak gain of 160 @ 1064 nm and 18 V reverse bias, gain >50 @ 1.3 μm, are demonstrated. Thus, this work provides a foundation and prospects for exploiting greater freedom in NW photodiode design using hybrid axial and CS heterostructures. 

Neutron stars may experience differential rotation on short, dynamical timescales following extreme astrophysical events like binary neutron star mergers. In this work, the masses and radii of differentially rotating neutron star models are computed. We employ a set of equations of states for dense hypernuclear and ‐admixed‐hypernuclear matter obtained within the framework of CDF theory in the relativistic Hartree‐Fock (RHF) approximation. Results are shown for varying meson‐ couplings, or equivalently the ‐potential in nuclear matter. A comparison of our results with those obtained for nonrotating stars shows that the maximum mass difference between differentially rotating and static stars is independent of the underlying particle composition of the star. We further find that the decrease in the radii and increase in the maximum masses of stellar models when ‐isobars are added to hyperonuclear matter (as initially observed for static and uniformly rotating stars) persist also in the case of differentially rotating neutron stars.