@article{9679,
  abstract     = {The relative motion of three impenetrable particles on a ring, in our case two identical fermions and one impurity, is isomorphic to a triangular quantum billiard. Depending on the ratio κ of the impurity and fermion masses, the billiards can be integrable or non-integrable (also referred to in the main text as chaotic). To set the stage, we first investigate the energy level distributions of the billiards as a function of 1/κ ∈ [0, 1] and find no evidence of integrable cases beyond the limiting values 1/κ = 1 and 1/κ = 0. Then, we use machine learning tools to analyze properties of probability distributions of individual quantum states. We find that convolutional neural networks can correctly classify integrable and non-integrable states. The decisive features of the wave functions are the normalization and a large number of zero elements, corresponding to the existence of a nodal line. The network achieves typical accuracies of 97%, suggesting that machine learning tools can be used to analyze and classify the morphology of probability densities obtained in theory or experiment.},
  author       = {Huber, David and Marchukov, Oleksandr V. and Hammer, Hans Werner and Volosniev, Artem},
  issn         = {13672630},
  journal      = {New Journal of Physics},
  number       = {6},
  publisher    = {IOP Publishing},
  title        = {{Morphology of three-body quantum states from machine learning}},
  doi          = {10.1088/1367-2630/ac0576},
  volume       = {23},
  year         = {2021},
}

@article{10178,
  abstract     = {In dense biological tissues, cell types performing different roles remain segregated by maintaining sharp interfaces. To better understand the mechanisms for such sharp compartmentalization, we study the effect of an imposed heterotypic tension at the interface between two distinct cell types in a fully 3D Voronoi model for confluent tissues. We find that cells rapidly sort and self-organize to generate a tissue-scale interface between cell types, and cells adjacent to this interface exhibit signature geometric features including nematic-like ordering, bimodal facet areas, and registration, or alignment, of cell centers on either side of the two-tissue interface. The magnitude of these features scales directly with the magnitude of the imposed tension, suggesting that biologists can estimate the magnitude of tissue surface tension between two tissue types simply by segmenting a 3D tissue. To uncover the underlying physical mechanisms driving these geometric features, we develop two minimal, ordered models using two different underlying lattices that identify an energetic competition between bulk cell shapes and tissue interface area. When the interface area dominates, changes to neighbor topology are costly and occur less frequently, which generates the observed geometric features.},
  author       = {Sahu, Preeti and Schwarz, J. M. and Manning, M. Lisa},
  issn         = {13672630},
  journal      = {New Journal of Physics},
  number       = {9},
  publisher    = {IOP Publishing},
  title        = {{Geometric signatures of tissue surface tension in a three-dimensional model of confluent tissue}},
  doi          = {10.1088/1367-2630/ac23f1},
  volume       = {23},
  year         = {2021},
}