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Three-Dimensional Polarized Light Imaging (3D-PLI) and Computational Scattered Light Imaging (ComSLI) map dense nerve fibers in brain sections with micrometer resolution using visible light. 3D-PLI reconstructs single fiber orientations, while ComSLI captures multiple directions per pixel, offering deep insights into brain tissue structure. Here, we introduce the Scattering Polarimeter, a high-speed correlative microscope to leverage the strengths of both methods. Based on a M\"uller polarimeter, it incorporates variable retarders and a large-area light source for direct and oblique illumination, enabling rapid 3D-PLI and ComSLI measurements as well as measuring the M\"uller matrix per pixel. Applied to human and vervet monkey brain sections, the Scattering Polarimeter generates results comparable to state-of-the-art 3D-PLI and ComSLI setups and creates a multi-modal fiber direction map, integrating the robust fiber orientations obtained from 3D-PLI with fiber crossings from ComSLI. Furthermore, we discuss applications of the Scattering Polarimeter for unprecedented correlative and multi-modal brain imaging., Comment: 29 pages, 8 figures, all study data are available on https://doi.org/10.26165/JUELICH-DATA/CUXQYF

With increasing emphasis on the study of active solids, the features of these classes of nonequilibrium systems and materials beyond their mere existence shift into focus. One concept of active solids addresses them as active, self-propelled units that are elastically linked to each other. The emergence of orientationally ordered, collectively moving states in such systems has been demonstrated. We here analyze the excitability of such collectively moving elastic states. To this end, we determine corresponding fluctuation spectra. They indicate that collectively excitable modes exist in the migrating solid. Differences arise when compared to those of corresponding passive solids. We provide evidence that the modes of excitation associated with the intrinsic fluctuations are related to corresponding modes of entropy production. Overall, we hope to stimulate by our investigation future experimental studies that focus on excitations in active solids.

Bacterial suspensions and other active fluids can develop highly dynamical vortex states called mesoscale turbulence. Here, we reveal the pronounced effect of non-Newtonian rheology of the carrier fluid, concentrating on shear thinning. Remarkably, this leads to hysteretic emergence of the turbulent state as a function of activity. A self-sustained heterogeneous state of coexisting turbulent and quiescent areas is identified. We find the signature of a directed percolation transition. Our results are important, for instance, when addressing active objects in biological media.

Vehicles in public traffic that are equipped with Automated Driving Systems are subject to a number of expectations: Among other aspects, their behavior should be safe, conforming to the rules of the road and provide mobility to their users. This poses challenges for the developers of such systems: Developers are responsible for specifying this behavior, for example, in terms of requirements at system design time. As we will discuss in the article, this specification always involves the need for assumptions and trade-offs. As a result, insufficiencies in such a behavior specification can occur that can potentially lead to unsafe system behavior. In order to support the identification of specification insufficiencies, requirements and respective assumptions need to be made explicit. In this article, we propose the Semantic Norm Behavior Analysis as an ontology-based approach to specify the behavior for an Automated Driving System equipped vehicle. We use ontologies to formally represent specified behavior for a targeted operational environment, and to establish traceability between specified behavior and the addressed stakeholder needs. Furthermore, we illustrate the application of the Semantic Norm Behavior Analysis in a German legal context with two example scenarios and evaluate our results. Our evaluation shows that the explicit documentation of assumptions in the behavior specification supports both the identification of specification insufficiencies and their treatment. Therefore, this article provides requirements, terminology and an according methodology to facilitate ontology-based behavior specifications in automated driving., Comment: 24 pages, 12 figures, submitted for publication

To provide insight into the basic properties of emerging structures when bacteria or other microorganisms conquer surfaces, it is crucial to analyze their growth behavior during the formation of thin films. In this regard, many theoretical studies focus on the behavior of elongating straight objects. They repel each other through volume exclusion and divide into two halves when reaching a certain threshold length. However, in reality, hardly any object of a certain elongation is perfectly straight. Therefore, we here study the consequences of curvature on the growth of colonies and thin active films. A given amount of curvature is prescribed to each growing individual. Particularly, we analyze how this individual curvature affects the size of orientationally ordered domains in the colony and find a significant decrease. Instead, strings of stacked curved cells emerge that show branched structures. Furthermore, we identify a significant spatio-orientational coupling that is not observed in colonies of straight cells. Our results are important for a fundamental understanding of the interaction and spreading of microorganisms on surfaces, with implications for medical applications and bioengineering., Comment: 8+2 pages, 5+2 figures

This paper introduces an innovative graphical analysis tool for investigating the dynamics of Memristor Cellular Nonlinear Networks (M-CNNs) featuring 2nd-order processing elements, known as M-CNN cells. In the era of specialized hardware catering to the demands of intelligent autonomous systems, the integration of memristors within Cellular Nonlinear Networks (CNNs) has emerged as a promising paradigm due to their exceptional characteristics. However, the standard Dynamic Route Map (DRM) analysis, applicable to 1st-order systems, fails to address the intricacies of 2nd-order M-CNN cell dynamics, as well the 2nd-order DRM (DRM2) exhibits limitations on the graphical illustration of local dynamical properties of the M-CNN cells, e.g. state derivative's magnitude. To address this limitation, we propose a novel integration of M-CNN cell vector field into the cell's phase portrait, enhancing the analysis efficacy and enabling efficient M-CNN cell design. A comprehensive exploration of M-CNN cell dynamics is presented, showcasing the utility of the proposed graphical tool for various scenarios, including bistable and monostable behavior, and demonstrating its superior ability to reveal subtle variations in cell behavior. Through this work, we offer a refined perspective on the analysis and design of M-CNNs, paving the way for advanced applications in edge computing and specialized hardware., Comment: Presented at the 18th IEEE International Workshop on Cellular Nanoscale Networks and their Applications (CNNA'23) and the 8th Memristor and Memristive Symposium

Magnetic Resonance Fingerprinting (MRF) is a time-efficient approach to quantitative MRI for multiparametric tissue mapping. The reconstruction of quantitative maps requires tailored algorithms for removing aliasing artefacts from the compressed sampled MRF acquisitions. Within approaches found in the literature, many focus solely on two-dimensional (2D) image reconstruction, neglecting the extension to volumetric (3D) scans despite their higher relevance and clinical value. A reason for this is that transitioning to 3D imaging without appropriate mitigations presents significant challenges, including increased computational cost and storage requirements, and the need for large amount of ground-truth (artefact-free) data for training. To address these issues, we introduce StoDIP, a new algorithm that extends the ground-truth-free Deep Image Prior (DIP) reconstruction to 3D MRF imaging. StoDIP employs memory-efficient stochastic updates across the multicoil MRF data, a carefully selected neural network architecture, as well as faster nonuniform FFT (NUFFT) transformations. This enables a faster convergence compared against a conventional DIP implementation without these features. Tested on a dataset of whole-brain scans from healthy volunteers, StoDIP demonstrated superior performance over the ground-truth-free reconstruction baselines, both quantitatively and qualitatively., Comment: 10 pages, 2 figures, 1 table, 1 algorithm

The estimation of multi-parametric quantitative maps from Magnetic Resonance Fingerprinting (MRF) compressed sampled acquisitions, albeit successful, remains a challenge due to the high underspampling rate and artifacts naturally occuring during image reconstruction. Whilst state-of-the-art DL methods can successfully address the task, to fully exploit their capabilities they often require training on a paired dataset, in an area where ground truth is seldom available. In this work, we propose a method that combines a deep image prior (DIP) module that, without ground truth and in conjunction with a Bloch consistency enforcing autoencoder, can tackle the problem, resulting in a method faster and of equivalent or better accuracy than DIP-MRF., Comment: 4 pages, 3 figures 1 table, presented at ISBI 2024

One of the most promising solutions for uncertainty quantification in high-dimensional statistics is the debiased LASSO that relies on unconstrained $\ell_1$-minimization. The initial works focused on real Gaussian designs as a toy model for this problem. However, in medical imaging applications, such as compressive sensing for MRI, the measurement system is represented by a (subsampled) complex Fourier matrix. The purpose of this work is to extend the method to the MRI case in order to construct confidence intervals for each pixel of an MR image. We show that a sufficient amount of data is $n \gtrsim \max\{ s_0\log^2 s_0\log p, s_0 \log^2 p \}$., Comment: Recognized with Best Student Paper Award at ICASSP 2023. arXiv admin note: substantial text overlap with arXiv:2212.14864

Over the last few years, debiased estimators have been proposed in order to establish rigorous confidence intervals for high-dimensional problems in machine learning and data science. The core argument is that the error of these estimators with respect to the ground truth can be expressed as a Gaussian variable plus a remainder term that vanishes as long as the dimension of the problem is sufficiently high. Thus, uncertainty quantification (UQ) can be performed exploiting the Gaussian model. Empirically, however, the remainder term cannot be neglected in many realistic situations of moderately-sized dimensions, in particular in certain structured measurement scenarios such as Magnetic Resonance Imaging (MRI). This, in turn, can downgrade the advantage of the UQ methods as compared to non-UQ approaches such as the standard LASSO. In this paper, we present a method to improve the debiased estimator by sampling without replacement. Our approach leverages recent results of ours on the structure of the random nature of certain sampling schemes showing how a transition between sampling with and without replacement can lead to a weighted reconstruction scheme with improved performance for the standard LASSO. In this paper, we illustrate how this reweighted sampling idea can also improve the debiased estimator and, consequently, provide a better method for UQ in Fourier imaging., Comment: Accepted at Cosera 2024

Calculating by analytical theory the deformation of finite-sized elastic bodies in response to internally applied forces is a challenge. Here, we derive explicit analytical expressions for the amplitudes of modes of surface deformation of a homogeneous, isotropic, linearly elastic sphere. The modes under consideration correspond to overall changes in volume and to relative uniaxial elongation or contraction. Both situations of an elastic sphere embedded under no-slip conditions in a linearly elastic, homogeneous, isotropic, infinitely extended background medium and of a free-standing elastic sphere are considered. For example, our analytical expressions are important as a basis for computational material optimization in the context of spherical soft actuators., Comment: 29 pages, 0 figures

Dynamics at low Reynolds numbers experiences recent revival in the fields of biophysics and active matter. While in bulk isotropic fluids it is exhaustively studied, this is less so in anisotropic fluids and in confined situations. Here, we combine the latter two by studying the rotation of a disk-like inclusion in a uniaxially anisotropic, globally oriented, incompressible two-dimensional fluid film. In terms of a perturbative expansion in parameters that quantify anisotropies in viscosity and in additional linear friction with a supporting substrate or other type of confinement, we derive analytical expressions for the resulting hydrodynamic flow and pressure fields as well as for the resistance and mobility coefficients of the rotating disk. It turns out that, in contrast to translational motion, the solutions remain well-behaved also in the absence of the additional linear friction. Comparison with results from finite-element simulations show very good agreement with those from our analytical calculations. Besides applications to describe technological systems, for instance, in the area of microfluidics and thin cells of aligned nematic liquid crystals, our solutions are important for quantitative theoretical approaches to fluid membranes and thin films in general featuring a preferred direction., Comment: 17 pages, 5 figures

We demonstrate a novel approach to the reconstruction of scanning probe x-ray diffraction tomography data with anisotropic poly crystalline samples. The method involves reconstructing a voxel map containing an orientation distribution function in each voxel of an extended 3D sample. This method differs from existing approaches by not relying on a peak-finding and is therefore applicable to sample systems consisting of small and highly mosaic crystalline domains that are not handled well by existing methods. Samples of interest include bio-minerals and a range of small-graines microstructures common in engineering metals. By choosing a particular kind of basis functions, we can effectively utilize non-negativity in orientation-space for samples with sparse texture. This enables us to achieve stable solutions at high angular resolutions where the problem would otherwise be under determined. We demonstrate the new approach using data from a shot peened martensite sample where we are able to map the twinning micro structure in the interior of a bulk sample without resolving the individual lattice domains. We also demonstrate the approach on a piece of gastropods shell with a mosaic micro structure.

The rapid research and development of generative artificial intelligence has enabled the generation of high-quality images, text, and 3D models from text prompts. This advancement impels an inquiry into whether these models can be leveraged to create digital artifacts for both creative and engineering applications. Drawing on innovative designs from other domains may be one answer to this question, much like the historical practice of ``bionics", where humans have sought inspiration from nature's exemplary designs. This raises the intriguing possibility of using generative models to simultaneously tackle design tasks across multiple domains, facilitating cross-domain learning and resulting in a series of innovative design solutions. In this paper, we propose LLM2FEA as the first attempt to discover novel designs in generative models by transferring knowledge across multiple domains. By utilizing a multi-factorial evolutionary algorithm (MFEA) to drive a large language model, LLM2FEA integrates knowledge from various fields to generate prompts that guide the generative model in discovering novel and practical objects. Experimental results in the context of 3D aerodynamic design verify the discovery capabilities of the proposed LLM2FEA. The designs generated by LLM2FEA not only satisfy practicality requirements to a certain degree but also feature novel and aesthetically pleasing shapes, demonstrating the potential applications of LLM2FEA in discovery tasks., Comment: This work has been submitted to the IEEE for possible publication

Engineering design optimization requires an efficient combination of a 3D shape representation, an optimization algorithm, and a design performance evaluation method, which is often computationally expensive. We present a prompt evolution design optimization (PEDO) framework contextualized in a vehicle design scenario that leverages a vision-language model for penalizing impractical car designs synthesized by a generative model. The backbone of our framework is an evolutionary strategy coupled with an optimization objective function that comprises a physics-based solver and a vision-language model for practical or functional guidance in the generated car designs. In the prompt evolutionary search, the optimizer iteratively generates a population of text prompts, which embed user specifications on the aerodynamic performance and visual preferences of the 3D car designs. Then, in addition to the computational fluid dynamics simulations, the pre-trained vision-language model is used to penalize impractical designs and, thus, foster the evolutionary algorithm to seek more viable designs. Our investigations on a car design optimization problem show a wide spread of potential car designs generated at the early phase of the search, which indicates a good diversity of designs in the initial populations, and an increase of over 20\% in the probability of generating practical designs compared to a baseline framework without using a vision-language model. Visual inspection of the designs against the performance results demonstrates prompt evolution as a very promising paradigm for finding novel designs with good optimization performance while providing ease of use in specifying design specifications and preferences via a natural language interface., Comment: Accepted and to be published in IEEE Congress on Evolutionary Computation (CEC) 2024. Copyright 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses

In this work, methods are presented to automatically generate a fully atomistic LAMMPS models of arbitrary linear multiblock polyurethane copolymers. The routine detailed here receives as parameters the number of repeat units per hard block, the number of units in a soft block, and the number of soft blocks per chain, as well as chemical formulae of three monomers which will form the hard component, soft component, and chain extender. A routine is detailed for converting the chemical structure of a free monomer to the urethane bonded repeat units in a polymer. The python package RadonPy is leveraged to assemble these units into blocks, and the blocks into copolymers. Care is taken in this work to ensure that plausible atomic charges are assigned to repeat units in different parts of the chain. The static structure factor is calculated for a variety of chemistries, and the results compared with wide angle x-ray scattering data from experiments with corresponding composition. The generated models reproduce the amorphous halo observed in the scattering data as well as some of the finer details. Structure factor calculations are decomposed into the partial structure factors to interrogate the structural properties of the two block types separately. Parametric surveys are carried out of the effects of various parameters, including temperature, soft block length, and block connectivity on the observed structure. The routine detailed here for constructing models is robust enough to be executed automatically in a high throughput workflow for material design and discovery., Comment: 6 figures, 18 pages

Functionalized thin elastic films and membranes frequently feature internal sites of net forces or stresses. These are, for instance, active sites of actuation, or rigid inclusions in a strained membrane that induce counterstress upon externally imposed deformations. We theoretically analyze the geometry of isotropic, flat, thin, linearly elastic films or membranes of finite thickness, laterally extended to infinity. At the mathematical core of such characterizations are the fundamental solutions for localized force and stress singularities associated with corresponding Green's functions. We derive such solutions in three dimensions and place them into the context of previous two-dimensional calculations. To this end, we consider both no-slip and stress-free conditions at the top and/or bottom surfaces. We provide an understanding for why the fully free-standing thin elastic membrane leads to diverging solutions in most geometries and compare these situations to the truly two-dimensional case. A no-slip support of at least one of the surfaces stabilizes the solution, which illustrates that the divergences in the fully free-standing case are connected to global deformations. Within the mentioned framework, our results are important for associated theoretical characterizations of thin elastic films, whether supported or free-standing, and of membranes subject to internal or external forces or stresses., Comment: 13 pages

We present a fast generative modeling approach for resistive memories that reproduces the complex statistical properties of real-world devices. To enable efficient modeling of analog circuits, the model is implemented in Verilog-A. By training on extensive measurement data of integrated 1T1R arrays (6,000 cycles of 512 devices), an autoregressive stochastic process accurately accounts for the cross-correlations between the switching parameters, while non-linear transformations ensure agreement with both cycle-to-cycle (C2C) and device-to-device (D2D) variability. Benchmarks show that this statistically comprehensive model achieves read/write throughputs exceeding those of even highly simplified and deterministic compact models., Comment: This work has been submitted to the IEEE for possible publication. Code is available at https://zenodo.org/doi/10.5281/zenodo.10942560

To make progress towards the development of a theory on the motion of inclusions in thin structured films and membranes, we here consider as an initial step a circular disk in a two-dimensional, uniaxially anisotropic fluid layer. We assume overdamped dynamics, incompressibility of the fluid, and global alignment of the axis of anisotropy. Motion within this layer is affected by additional linear friction with the environment, for instance, a supporting substrate. We investigate the induced flows in the fluid when the disk is translated parallel or perpendicular to the direction of anisotropy. Moreover, expressions for corresponding mobilities and resistance coefficients of the disk are derived. Our results are obtained within the framework of a perturbative expansion in the parameters that quantify the anisotropy of the fluid. Good agreement is found for moderate anisotropy when compared to associated results from finite-element simulations. At pronounced anisotropy, the induced flow fields are still predicted qualitatively correctly by the perturbative theory, although quantitative deviations arise. We hope to stimulate with our investigations corresponding experimental analyses, for example, concerning fluid flows in anisotropic thin films on uniaxially rubbed supporting substrates., Comment: 14 pages, 5 figures

The need for structuring on micrometer scales is abundant, for example, in view of phononic applications. We here outline a novel approach based on the phenomenon of active turbulence on the mesoscale. As we demonstrate, a shear-thickening carrier fluid of active microswimmers intrinsically stabilizes regular vortex patterns of otherwise turbulent active suspensions. The fluid self-organizes into a periodically structured nonequilibrium state. Introducing additional passive particles of intermediate size leads to regular spatial organization of these objects. Our approach opens a new path towards functionalization through patterning of thin films and membranes.

Analogue test structures were fabricated using the Tower Partners Semiconductor Co. CMOS 65 nm ISC process. The purpose was to characterise and qualify this process and to optimise the sensor for the next generation of Monolithic Active Pixels Sensors for high-energy physics. The technology was explored in several variants which differed by: doping levels, pixel geometries and pixel pitches (10-25 $\mu$m). These variants have been tested following exposure to varying levels of irradiation up to 3 MGy and $10^{16}$ 1 MeV n$_\text{eq}$ cm$^{-2}$. Here the results from prototypes that feature direct analogue output of a 4$\times$4 pixel matrix are reported, allowing the systematic and detailed study of charge collection properties. Measurements were taken both using $^{55}$Fe X-ray sources and in beam tests using minimum ionizing particles. The results not only demonstrate the feasibility of using this technology for particle detection but also serve as a reference for future applications and optimisations.

Simulation frameworks such MemTorch, DNN+NeuroSim, and aihwkit are commonly used to facilitate the end-to-end co-design of memristive machine learning (ML) accelerators. These simulators can take device nonidealities into account and are integrated with modern ML frameworks. However, memristors in these simulators are modeled with either lookup tables or simple analytic models with basic nonlinearities. These simple models are unable to capture certain performance-critical aspects of device nonidealities. For example, they ignore the physical cause of switching, which induces errors in switching timings and thus incorrect estimations of conductance states. This work aims at bringing physical dynamics into consideration to model nonidealities while being compatible with GPU accelerators. We focus on Valence Change Memory (VCM) cells, where the switching nonlinearity and SET/RESET asymmetry relate tightly with the thermal resistance, ion mobility, Schottky barrier height, parasitic resistance, and other effects. The resulting dynamics require solving an ODE that captures changes in oxygen vacancies. We modified a physics-derived SPICE-level VCM model, integrated it with the aihwkit simulator and tested the performance with the MNIST dataset. Results show that noise that disrupts the SET/RESET matching affects network performance the most. This work serves as a tool for evaluating how physical dynamics in memristive devices affect neural network accuracy and can be used to guide the development of future integrated devices., Comment: This work is published at the 2022 56th Asilomar Conference

Enhancing the switching speed of oxide-based memristive devices at a low voltage level is crucial for their use as non-volatile memory and their integration into emerging computing paradigms such as neuromorphic computing. Efforts to accelerate the switching speed often result in an energy tradeoff, leading to an increase of the minimum working voltage. In our study, we present an innovative solution: the introduction of a low thermal conductivity layer placed within the active electrode, which impedes the dissipation of heat generated during the switching process. The result is a notable acceleration in the switching speed of the memristive model system SrTiO$_{3}$ by a remarkable factor of 10$^{3}$, while preserving the integrity of the switching layer and the interfaces with the electrodes, rendering it adaptable to various filamentary memristive systems. The incorporation of HfO$_{2}$ or TaO$_{x}$ as heat-blocking layers not only streamlines the fabrication process, but also ensures compatibility with complementary metal-oxide-semiconductor technology., Comment: 9 pages, 3 figures

We study stochastic motion under a nonlinear frictional force that levels off with increasing velocity. Specifically, our frictional force is of the so-called Coulomb-tanh type. At small speed, it increases approximately linearly with velocity, while at large speed it approaches a constant magnitude, similarly to solid (dry, Coulomb) friction. In one spatial dimension, a formal analogy between the associated Fokker-Planck equation and the Schr\"odinger equation for a quantum-mechanical oscillator in a nonharmonic P\"oschl-Teller potential is revealed. Then, the stationary velocity statistics can be treated analytically. From such analytical considerations, we determine associated diffusion coefficients, which we confirm by agent-based simulations. Moreover, from such simulations and from numerically solving the associated Fokker-Planck equation, we find that the spatial distribution function, starting from an initial Gaussian shape, develops tails that appear exponential at intermediate time scales. At small magnitudes of stochastic driving, the velocity distribution resembles the case of linear friction, while at large magnitudes it rather approaches the case of solid (dry, Coulomb) friction. The same is true for diffusion coefficients. In a certain sense, thus interpolating between the two extreme cases of linear friction and solid (dry, Coulomb) friction, our approach should be useful to describe several cases of practical relevance. For instance, a reduced increase in friction with increasing relative speed is typical of shear-thinning behavior. Therefore, driven motion in shear-thinning environments is one specific example to which our description may be applied., Comment: 12 pages, 7 figures, to be published in Physical Review E

Amongst the various fascinating types of material behavior featured by magnetic gels and elastomers are magnetostrictive effects. That is, deformations in shape or changes in volume are induced from outside by external magnetic fields. Application of the materials as soft actuators is therefore conceivable. Mostly, straight contraction or extension of the materials along a certain direction is discussed and investigated in this context. Here, we demonstrate that various further, different, higher modes of deformation can be excited. To this end, different spatial arrangements of the magnetizable particles enclosed by the soft elastic matrix, which constitute the materials, need to be controlled and realized. We address various different types of spatial configurations of the particles and evaluate resulting types of deformation using theoretical tools developed for this purpose. Examples are sheet-like arrangements of particles, circular or star-shaped arrangements of chain-like aggregates, or actual three-dimensional star-like particle configurations. We hope to stimulate with our work the development of experimental design and engineering methods so that selected spatial particle arrangements in magnetic gels and elastomers can be put to reality. Overall, we in this way wish to promote the transfer of these promising class of materials to real-world applications., Comment: 17 pages, 19 figures