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Superconductivity in strongly correlated electron systems frequently exhibits broken rotational symmetry, raising fundamental questions about the underlying order parameter symmetry. In this work, we demonstrate that electronic nematicity--driven by Coulomb-mediated rotational symmetry breaking--serves as a crucial link to understanding the nature of superconductivity. Utilizing a novel framework of angle-resolved measurement, we reveal an interring angular interplay among nematicity, superconductivity, and strange metallicity in magic-angle twisted trilayer graphene. By establishing a direct correlation between the preferred superconducting transport direction and the principal axis of the metallic phase, our findings place strong constrains on the symmetry of the superconducting order parameter. This work introduces a new paradigm for probing the microscopic mechanisms governing superconductivity in strongly interacting two-dimensional systems. 

ABSTRACT The rapid emergence of single‐cell data has facilitated the study of many different biological conditions at the cellular level. Cluster analysis has been widely applied to identify cell types, capturing the essential patterns of the original data in a much more concise form. One challenge in the cluster analysis of cells is matching clusters extracted from datasets of different origins or conditions. Many existing algorithms cannot recognize new cell types present in only one of the two samples when establishing a correspondence between clusters obtained from two samples. Additionally, when there are more than two samples, it is advantageous to align clusters across all samples simultaneously rather than performing pairwise alignment. Our approach aims to construct a taxonomy for cell clusters across all samples to better annotate these clusters and effectively extract features for downstream analysis. A new system for constructing cell‐type taxonomy has been developed by combining the technique of optimal transport with relaxed marginal constraints (OT‐RMC) and the simultaneous alignment of clusters across multiple samples. OT‐RMC allows us to address challenges that arise when the proportions of clusters vary substantially between samples or when some clusters do not appear in all the samples. Experiments on more than 20 datasets demonstrate that the taxonomy constructed by this new system can yield highly accurate annotation of cell types. Additionally, sample‐level features extracted based on the taxonomy result in accurate classification of samples. 

Claude Monet's late paintings of Water Lilies exhibit stylistic transformations that are often characterized by art historians as increasingly abstract and gesturally expressive. However, it remains challenging to define and systematically identify this stylistic shift. Here, we introduce a machine learning framework for analyzing Monet's evolving brushwork using streamline curves: computational representations that capture the dynamic movement patterns inherent in brushstrokes. From 554 image patches sampled from 47 paintings spanning early (pre-1913) and later (post-1913) periods of Monet's output, we extract streamlines and compute geometric features for each, including smoothness of curvature and directional variability. Each image is represented as a set of streamline feature vectors, a data type referred to as distributional. A new deep neural network architecture named Composition to Attribute (C2A) is designed for classifying distributional data. We hypothesize that Monet's so-called 'abstract' style does not uniformly characterize all late period Water Lilies, and that non-abstract flowers, regardless of period, share similar brushwork qualities. Under these assumptions, building on C2A, weproposeanovel learning paradigm namedDiscover Embedded Group with Asymmetry (DEGA) which enforces a shared distribution of DNN-extracted features for non-abstract flower patches across both pe riods while distinguishing the abstract ones. DEGA reveals a meaningful two-dimensional feature space, where one dimension differentiates abstract from mimetic Water Lilies, while the other separates abstract f lowers from close-up flowers of the early period. Our findings suggest that the so-called 'abstract' qualities of Monet's late style retain certain visual affinities with his earlier approach to depicting close-up floral motifs. When this brushwork is used in more expansive scenes, the depiction of flowers shifts away from realistic renderings of individual petals toward a looser, more allusive expression, conveying a sense of floral presence rather than botanical detail. This study highlights the value of computational analysis for a more accurate understanding of an artist's stylistic development. 

In this paper we propose a method for the optimal allocation of observations between an intrinsically explainable glass box model and a black box model. An optimal allocation being defined as one which, for any given explainability level (i.e. the proportion of observations for which the explainable model is the prediction function), maximizes the performance of the ensemble on the underlying task, and maximizes performance of the explainable model on the observations allocated to it, subject to the maximal ensemble performance condition. The proposed method is shown to produce such explainability optimal allocations on a benchmark suite of tabular datasets across a variety of explainable and black box model types. These learned allocations are found to consistently maintain ensemble performance at very high explainability levels (explaining 74% of observations on average), and in some cases even outperform both the component explainable and black box models while improving explainability.