Pattern Avoidability with Involution

An infinte word w avoids a pattern p with the involution t if there is no substitution for the variables in p and no involution t such that the resulting word is a factor of w. We investigate the avoidance of patterns with respect to the size of the alphabet. For example, it is shown that the pattern a t(a) a can be avoided over three letters but not two letters, whereas it is well known that a a a is avoidable over two letters.Comment: In Proceedings WORDS 2011, arXiv:1108.341

Pseudomonas aeruginosa Triggered Exosomal Release of ADAM10 Mediates Proteolytic Cleavage in Trans

Pneumonia is a life-threatening disease often caused by infection with Streptococcus pneumo niae and Pseudomonas aeruginosa. Many of the mediators (e.g., TNF, IL-6R) and junction molecules (e.g., E-cadherin) orchestrating inflammatory cell recruitment and loss of barrier integrity are prote olytically cleaved through a disintegrin and metalloproteinases (ADAMs). We could show by Western blot, surface expression analysis and measurement of proteolytic activity in cell-based assays, that ADAM10 in epithelial cells is upregulated and activated upon infection with Pseudomonas aeruginosa and Exotoxin A (ExoA), but not upon infection with Streptococcus pneumoniae. Targeting ADAM10 by pharmacological inhibition or gene silencing, we demonstrated that this activation was critical for cleavage of E-cadherin and modulated permeability and epithelial integrity. Stimulation with heat-inactivated bacteria revealed that the activation was based on the toxin repertoire rather than the interaction with the bacterial particle itself. Furthermore, calcium imaging experiments showed that the ExoA action was based on the induction of calcium influx. Investigating the extracellular vesicles and their proteolytic activity, we could show that Pseudomonas aeruginosa triggered exosomal release of ADAM10 and proteolytic cleavage in trans. This newly described mechanism could consti tute an essential mechanism causing systemic inflammation in patients suffering from Pseudomonas aeruginosa-induced pneumonia stimulating future translational studies

Intrinsically Selective Mass Scaling with Hierarchic Structural Element Formulations

[EN] Hierarchic shear deformable structural element formulations possess the advantage of being intrinsically free from transverse shear locking, that is they avoid transverse shear locking a priori through reparametrization of the kinematic variables. This reparametrization results in shear deformable beam, plate and shell formulations with distinct transverse shear degrees of freedom. The basic idea of selective mass scaling within explicit dynamic analyses is to scale down the highest frequencies in order to increase the critical time step size, while keeping the low frequency modes mostly unaffected. In most concepts, this comes at the cost of nondiagonal mass matrices. In this contribution, we present first investigations on selective mass scaling for hierarchic formulations. Since hierarchic structural formulations possess distinct transverse shear degrees of freedom, they offer the intrinsic ability for selective scaling of the high frequency shear modes, while keeping the bending dominated low frequency modes mostly unaffected. The proposed instrinsically selective mass scaling concept achieves high accuracy, which is typical for selective mass scaling schemes, but in contrast to existing concepts it retains the simplicity of a conventianl mass scaling method and preserves the diagonal structure of a lumped mass matrix. As model problem, we study frequency spectra of different isogeometric Timoshenko beam formulations for a simply supported beam. We discuss the effects of transverse shear parametrization, locking and mass lumping on the accuracy of results.This work has been partially supported by the Deutsche Forschungsgemeinschaft (DFG) under grant OE 728/1-1. This support is gratefully acknowledged.Oesterle, B.; Trippmacher, J.; Tkachuk, A.; Bischoff, M. (2022). Intrinsically Selective Mass Scaling with Hierarchic Structural Element Formulations. En Proceedings of the YIC 2021 - VI ECCOMAS Young Investigators Conference. Editorial Universitat Politècnica de València. 99-108. https://doi.org/10.4995/YIC2021.2021.12418OCS9910

Improving efficiency and robustness of enhanced assumed strain elements for nonlinear problems

The enhanced assumed strain (EAS) method is one of the most frequently used methods to avoid locking in solid and structural finite elements. One issue of EAS elements in the context of geometrically nonlinear analyses is their lack of robustness in the Newton–Raphson scheme, which is characterized by the necessity of small load increments and large number of iterations. In the present work we extend the recently proposed mixed integration point (MIP) method to EAS elements in order to overcome this drawback in numerous applications. Furthermore, the MIP method is generalized to generic material models, which makes this simple method easily applicable for a broad class of problems. In the numerical simulations in this work, we compare standard strain‐based EAS elements and their MIP improved versions to elements based on the assumed stress method in order to explain when and why the MIP method allows to improve robustness. A further novelty in the present work is an inverse stress‐strain relation for a Neo‐Hookean material model

Improved Multi-Scale Grid Rendering of Point Clouds for Radar Object Detection Networks

Architectures that first convert point clouds to a grid representation and then apply convolutional neural networks achieve good performance for radar-based object detection. However, the transfer from irregular point cloud data to a dense grid structure is often associated with a loss of information, due to the discretization and aggregation of points. In this paper, we propose a novel architecture, multi-scale KPPillarsBEV, that aims to mitigate the negative effects of grid rendering. Specifically, we propose a novel grid rendering method, KPBEV, which leverages the descriptive power of kernel point convolutions to improve the encoding of local point cloud contexts during grid rendering. In addition, we propose a general multi-scale grid rendering formulation to incorporate multi-scale feature maps into convolutional backbones of detection networks with arbitrary grid rendering methods. We perform extensive experiments on the nuScenes dataset and evaluate the methods in terms of detection performance and computational complexity. The proposed multi-scale KPPillarsBEV architecture outperforms the baseline by 5.37% and the previous state of the art by 2.88% in Car AP4.0 (average precision for a matching threshold of 4 meters) on the nuScenes validation set. Moreover, the proposed single-scale KPBEV grid rendering improves the Car AP4.0 by 2.90% over the baseline while maintaining the same inference speed.Comment: (c) 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Exploiting Sparsity in Automotive Radar Object Detection Networks

Having precise perception of the environment is crucial for ensuring the secure and reliable functioning of autonomous driving systems. Radar object detection networks are one fundamental part of such systems. CNN-based object detectors showed good performance in this context, but they require large compute resources. This paper investigates sparse convolutional object detection networks, which combine powerful grid-based detection with low compute resources. We investigate radar specific challenges and propose sparse kernel point pillars (SKPP) and dual voxel point convolutions (DVPC) as remedies for the grid rendering and sparse backbone architectures. We evaluate our SKPP-DPVCN architecture on nuScenes, which outperforms the baseline by 5.89% and the previous state of the art by 4.19% in Car AP4.0. Moreover, SKPP-DPVCN reduces the average scale error (ASE) by 21.41% over the baseline

Sub-mm/mm optical properties of real protoplanetary matter derived from Rosetta/MIRO observations of comet 67P

Optical properties are required for the correct understanding and modelling of protoplanetary and debris discs. By assuming that comets are the most pristine bodies in the solar system, our goal is to derive optical constants of real protoplanetary material. We determine the complex index of refraction of the near-surface material of comet 67P/Churyumov-Gerasimenko by fitting the sub-millimetre/millimetre observations of the thermal emission of the comet's sub-surface made by the Microwave Instrument for the Rosetta Orbiter (MIRO) with synthetic temperatures derived from a thermophysical model and radiative-transfer models. According to the two major formation scenarios of comets, we model the sub-surface layers to consist of pebbles as well as of homogeneously packed dust grains. In the case of a homogeneous dusty surface material, we find a solution for the length-absorption coefficient of $\alpha \approx 0.22~\mathrm{cm^{-1}}$ for a wavelength of 1.594 mm and $\alpha \geq 3.84~\mathrm{cm^{-1}}$ for a wavelength of 0.533 mm and a constant thermal conductivity of $0.006~\mathrm{Wm^{-1}K^{-1}}$. For the pebble scenario, we find for the pebbles and a wavelength of 1.594 mm a complex refractive index of $n = (1.074 - 1.256) + \mathrm{i} \, (2.580 - 7.431)\cdot 10^{-3}$ for pebble radii between 1 mm and 6 mm. Taking into account other constraints, our results point towards a pebble makeup of the cometary sub-surface with pebble radii between 3 mm and 6 mm. The derived real part of the refractive index is used to constrain the composition of the pebbles and their volume filling factor. The optical and physical properties are discussed in the context of protoplanetary and debris disc observations.Comment: Accepted for publication in MNRA

Learning Throttle Valve Control Using Policy Search

Abstract. The throttle valve is a technical device used for regulating a fluid or a gas flow. Throttle valve control is a challenging task, due to its complex dynamics and demanding constraints for the controller. Using state-of-the-art throttle valve control, such as model-free PID controllers, time-consuming and manual adjusting of the controller is necessary. In this paper, we investigate how reinforcement learning (RL) can help to alleviate the effort of manual controller design by automatically learning a control policy from experiences. In order to obtain a valid control policy for the throttle valve, several constraints need to be addressed, such as no-overshoot. Furthermore, the learned controller must be able to follow given desired trajectories, while moving the valve from any start to any goal position and, thus, multi-targets policy learning needs to be considered for RL. In this study, we employ a policy search RL approach, Pilco [2], to learn a throttle valve control policy. We adapt the Pilco algorithm, while taking into account the practical requirements and constraints for the controller. For evaluation, we employ the resulting algorithm to solve several control tasks in simulation, as well as on a physical throttle valve system. The results show that policy search RL is able to learn a consistent control policy for complex, real-world systems.