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In the so-called kissing number problem, the question of the maximum number of spheres of the same size that can touch a sphere in their midst without overlaps is investigated. While in one and two dimensions the kissing numbers can be easily determined as 2 and 6, respectively, the dispute between Isaac Newton and David Gregory from 1694 whether the kissing number in three dimensions is 12 or 13 could only be resolved in favor of Newton in 1953. Only for a few higher dimensions exact kissing numbers are known. We consider a bidisperse extension of the kissing number problem in three dimensions, where the sphere in the center has a larger radius than the surrounding spheres, and again pose the question of the maximum number of surrounding spheres that can touch the sphere in their midst without overlap. To determine this maximum number for various ratios between the radii of the center sphere and the surrounding spheres, we develop a heuristic optimization algorithm based on Simulated Annealing and its deterministic variant Threshold Accepting, which have been used to achieve excellent results in other sphere packing problems. This maximum number also serves as an upper bound for the number of contacts between spheres of different sizes in polydisperse systems with a given ratio between the radii of the largest and the smallest sphere.

Experimentally, periodically released droplets in systems of widening pipes show clustering. This is surprising, as purely hydrodynamic interactions are repulsive so that agglomeration should be prevented. In the main part of this paper, we investigate the clustering of droplets under the influence of phenomenological hydrostatic forces and some hypothetical attraction. In two appendices, we explain why a direct numerical simulation for this system is rather more difficult (and probably not possible with current methods) than the “simple” geometry would suggest.

Within the scope of the European Horizon 2020 project ACDC – Artificial Cells with Distributed Cores to Decipher Protein Function, we aim at the development of a chemical compiler governing the three-dimensional arrangement of droplets, which are filled with various chemicals. Neighboring droplets form bilayers containing pores through which chemicals can move from one droplet to its neighbors. When achieving a desired three-dimensional configuration of droplets, we can thus enable gradual biochemical reaction schemes for various purposes, e.g., for the production of some desired macromolecules for pharmaceutical purposes. In this paper, we focus on geometric restrictions to possible arrangements of droplets. We present analytic results for the buttercup problem and a heuristic optimization method for the kissing number problem, which we then apply to find (quasi) optimum values for a bidisperse kissing number problem, in which the center sphere exhibits a larger radius.

We aim at planning and creating specific agglomerations of droplets to study synergic communication using these as programmable units. In this paper, we give an overview of preliminary obstacles for the various research issues, namely of how to create droplets, how to set up droplet agglomerations using DNA technology, how to prepare them for confocal microscopy, how to make a computer see droplets on photos, how to analyze networks of droplets, how to perform simulations mimicking experiments, and how to plan specific agglomerations of droplets.

Background: Cone beam computed tomography (CBCT) is often employed on radiation therapy treatment devices (linear accelerators) used in image-guided radiation therapy (IGRT). For each treatment session, it is necessary to obtain the image of the day in order to accurately position the patient, and to enable adaptive treatment capabilities including auto-segmentation and dose calculation. Reconstructed CBCT images often suffer from artifacts, in particular those induced by patient motion. Deep-learning based approaches promise ways to mitigate such artifacts. Purpose: We propose a novel deep-learning based approach with the goal to reduce motion induced artifacts in CBCT images and improve image quality. It is based on supervised learning and includes neural network architectures employed as pre- and/or post-processing steps during CBCT reconstruction. Methods: Our approach is based on deep convolutional neural networks which complement the standard CBCT reconstruction, which is performed either with the analytical Feldkamp-Davis-Kress (FDK) method, or with an iterative algebraic reconstruction technique (SART-TV). The neural networks, which are based on refined U-net architectures, are trained end-to-end in a supervised learning setup. Labeled training data are obtained by means of a motion simulation, which uses the two extreme phases of 4D CT scans, their deformation vector fields, as well as time-dependent amplitude signals as input. The trained networks are validated against ground truth using quantitative metrics, as well as by using real patient CBCT scans for a qualitative evaluation by clinical experts. Results: The presented novel approach is able to generalize to unseen data and yields significant reductions in motion induced artifacts as well as improvements in image quality compared with existing state-of-the-art CBCT reconstruction algorithms (up to +6.3 dB and +0.19 improvements in peak signal-to-noise ratio, PSNR, and structural similarity index measu

Purpose: Respiratory gated 4D-CBCT suffers from sparseness artefacts caused by the limited number of projections available for each respiratory phase/amplitude. These artefacts severely impact deformable image registration methods used to extract motion information. We use deep learning-based methods to predict displacement vector-fields (DVF) from sparse 4D-CBCT images to alleviate the impacts of sparseness artefacts. Methods: We trained U-Net-type convolutional neural network models to predict multiple (10) DVFs in a single forward pass given multiple sparse, gated CBCT and an optional artefact-free reference image as inputs. The predicted DVFs are used to warp the reference image to the different motion states, resulting in an artefact-free image for each state. The supervised training uses data generated by a motion simulation framework. The training dataset consists of 560 simulated 4D-CBCT images of 56 different patients; the generated data include fully sampled ground-truth images that are used to train the network. We compare the results of our method to pairwise image registration (reference image to single sparse image) using a) the deeds algorithm and b) VoxelMorph with image pair inputs. Results: We show that our method clearly outperforms pairwise registration using the deeds algorithm alone. PSNR improved from 25.8 to 46.4, SSIM from 0.9296 to 0.9999. In addition, the runtime of our learning-based method is orders of magnitude shorter (2 seconds instead of 10 minutes). Our results also indicate slightly improved performance compared to pairwise registration (delta-PSNR=1.2). We also trained a model that does not require the artefact-free reference image (which is usually not available) during inference demonstrating only marginally compromised results (delta-PSNR=-0.8). Conclusion: To the best of our knowledge, this is the first time CNNs are used to predict multi-phase DVFs in a single forward pass. This enables novel applications such as 4D-auto-segm

We simulate the movement and agglomeration of oil droplets in water under constraints, like confinement, using a simplified stochastic-hydrodynamic model. In the analysis of the network created by the droplets in the agglomeration, we focus on the paths between pairs of droplets and compare the computational results for various system sizes.

The morphological evolution of bivalves is documented by a rich fossil record. It is believed that the shell shape and surface sculpture play an important role for the burrowing performance of endobenthic species. While detailed morphometric studies of bivalve shells have been done, there are almost no studies experimentally testing their dynamic properties. To investigate the functional morphology of the bivalve shell, we employed a synthetic methodology and built an experimental setup to simulate the burrowing process. Using an evolutionary algorithm and a printer that prints three dimensional (3D) objects, the first ever artificial evolution of a physical bivalve shell was performed. The result was a vertically flattened shell occupying only the top sediment layers. Insufficient control of the sediment was the major limitation of the setup and restricted the significance of the results. Nevertheless, it is demonstrated that systematic palaeontological research may substantially profit from synthetic methods. We suggest investigating functional morphologies not only by emulating the dynamical processes but also evolutionary pressure using evolutionary algorithms., + ID der Publikation: unilu_8837 + Sprache: Englisch + Letzte Aktualisierung: 2018-06-06 13:18:10

Biomimetics is a fruitful combination of biology and engineering, leading not only to technological innovations but also new insights into biological questions. In this ongoing project, embodied artificial intelligence (embodied AI), artificial evolution and palaeontology are combined to investigate the functional morphology of bivalves. This cross-fertilization allows to expand biomimetics from current biological systems to the whole evolutionary history and to apply the synthetic approach common in embodied AI as a method to tackle open palaeontological questions. So far, a robotic platform has been built to mimic the burrowing technique applied by bivalves. First results show interesting insights into underwater burrowing. We plan to build a more complex version of the system and to perform evolutionary robotics experiments., + ID der Publikation: unilu_22703 + Sprache: Englisch + Letzte Aktualisierung: 2018-04-11 15:46:14 + Veranstaltung: Proceedings of the 2nd European Future Technologies Conference and Exhibition 2011 (FET 11)

Living Technology is researching novel IT making strong use of programmable chemical systems. These chemical systems shall finally converge to artificial cells resulting in evolvable complex information systems. We focus on procedural manageability and information processing capabilities of such information systems. Here, we present a novel resource-saving formation, processing, and examination procedure to generate and handle single compartments representing preliminary stages of artificial cells. Its potential is exemplified by testing the influence of different glycerophospholipids on the stability of the compartments. We discuss how the procedure could be used both in evolutionary optimization of self-assembling amphiphilic systems and in engineering tailored communication networks enabling life-like information processing in multicompartment aggregates of programmable composition and spatial configuration.

Within the scope of the European Horizon 2020 project ACDC – Artificial Cells with Distributed Cores to Decipher Protein Function, we aim at the development of a chemical compiler governing the three-dimensional arrangement of droplets, which are filled with various chemicals. Neighboring droplets form bilayers with pores which allow chemicals to move from one droplet to its neighbors. With an appropriate three-dimensional configuration of droplets, we can thus enable gradual biochemical reaction schemes for various purposes, e.g., for the production of macromolecules for pharmaceutical purposes. In this paper, we demonstrate with artificial chemistry simulations that the ACDC technology is excellently suitable to maximize the yield of desired reaction products or to minimize the relative output of unwanted side products.

We aim at planning and creating specific agglomerations of droplets to study synergic communication using these as programmable units. In this paper, we give an overview of preliminary obstacles for the various research issues, namely of how to create droplets, how to set up droplet agglomerations using DNA technology, how to prepare them for confocal microscopy, how to make a computer see droplets on photos, how to analyze networks of droplets, how to perform simulations mimicking experiments, and how to plan specific agglomerations of droplets.

Within the scope of the European Horizon 2020 project ACDC – Artificial Cells with Distributed Cores to Decipher Protein Function, we aim at the development of a chemical compiler governing the three-dimensional arrangement of droplets, which are filled with various chemicals. Neighboring droplets form bilayers containing pores through which chemicals can move from one droplet to its neighbors. When achieving a desired three-dimensional configuration of droplets, we can thus enable gradual biochemical reaction schemes for various purposes, e.g., for the production of some desired macromolecules for pharmaceutical purposes. In this paper, we focus on geometric restrictions to possible arrangements of droplets. We present analytic results for the buttercup problem and a heuristic optimization method for the kissing number problem, which we then apply to find (quasi) optimum values for a bidisperse kissing number problem, in which the center sphere exhibits a larger radius.