Component trees are region-based representations that encode the inclusion relationship of the threshold sets of an image. These representations are one of the most promising strategies for the analysis and the interpretation of spatial information of complex scenes as they allow the simple and efficient implementation of connected filters. This work proposes a new efficient hybrid algorithm for the parallel computation of two particular component trees-the max-and min-tree-in shared and distributed memory environments. For the node-local computation a modified version of the flooding-based algorithm of Salembier is employed. A novel tuple-based merging scheme allows to merge the acquired partial images into a globally correct view. Using the proposed approach a speed-up of up to 44.88 using 128 processing cores on eight-bit gray-scale images could be achieved. This is more than a five-fold increase over the state-of-the-art shared-memory algorithm, while also requiring only one-thirty-second of the memory.

technology demonstrates outstanding power conversion efficiencies (PCEs), exceeding 25%. [3] Despite numerous favorable optoelectronic properties of perovskite semiconductors, four key challenges remain and delay the successful commercialization of perovskite solar cells (PSCs): 1) the long-term stability, 2) the toxicity of the contained lead, 3) upscaling to large-areas, and 4) unlocking cost-effective, reliable large-scale production (high throughput and high yield). [2,4] Traditional efforts in material science and device engineering in the field are based on countless trialand-error experiments. However, these approaches for material discovery, process development, characterization, full device evaluation, and stability testing are often complicated, expensive, laborious, and time-consuming given the large experimental parameter space. [5] These drawbacks motivate the implementation of autonomous experimentation methods and data-driven techniques like machine learning (ML). [6,7] In an increasing number of research fields, ML methods are employed to identify yet undiscovered correlations and to provide insights into fundamental working principles. Besides pattern extraction, ML can be utilized to make classifications or predictions and to uncover new insights into the studied data. For this reason, ML algorithms are successfully adopted to an increasing number of applications in materials science, [8][9][10][11] encompassing,

Molecular simulations are a powerful tool to complement and interpret ambiguous experimental data on biomolecules to obtain structural models. Such data-assisted simulations often rely on parameters, the choice of which is highly non-trivial and crucial to performance. The key challenge is weighting experimental information with respect to the underlying physical model. We introduce FLAPS, a self-adapting variant of dynamic particle swarm optimization, to overcome this parameter selection problem. FLAPS is suited for the optimization of composite objective functions that depend on both the optimization parameters and additional, a priori unknown weighting parameters, which substantially influence the search-space topology. These weighting parameters are learned at runtime, yielding a dynamically evolving and iteratively refined search-space topology. As a practical example, we show how FLAPS can be used to find functional parameters for small-angle X-ray scattering-guided protein simulations.

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In this work, we present a neural approach to reconstructing rooted tree graphs describing hierarchical interactions, using a novel representation we term the Lowest Common Ancestor Generations (LCAG) matrix. This compact formulation is equivalent to the adjacency matrix, but enables learning a tree’s structure from its leaves alone without the prior assumptions required if using the adjacency matrix directly. Employing the LCAG therefore enables the first end-to-end trainable solution which learns the hierarchical structure of varying tree sizes directly, using only the terminal tree leaves to do so. In the case of high-energy particle physics, a particle decay forms a hierarchical tree structure of which only the final products can be observed experimentally, and the large combinatorial space of possible trees makes an analytic solution intractable. We demonstrate the use of the LCAG as a target in the task of predicting simulated particle physics decay structures using both a Transformer encoder and a Neural Relational Inference encoder Graph Neural Network. With this approach, we are able to correctly predict the LCAG purely from leaf features for a maximum tree-depth of 8 in 92.5% of cases for trees up to 6 leaves (including) and 59.7% for trees up to 10 in our simulated dataset.