86 research outputs found

    Well-definedness of Physical Law Learning: The Uniqueness Problem

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    Physical law learning is the ambiguous attempt at automating the derivation of governing equations with the use of machine learning techniques. The current literature focuses however solely on the development of methods to achieve this goal, and a theoretical foundation is at present missing. This paper shall thus serve as a first step to build a comprehensive theoretical framework for learning physical laws, aiming to provide reliability to according algorithms. One key problem consists in the fact that the governing equations might not be uniquely determined by the given data. We will study this problem in the common situation that a physical law is described by an ordinary or partial differential equation. For various different classes of differential equations, we provide both necessary and sufficient conditions for a function to uniquely determine the differential equation which is governing the phenomenon. We then use our results to devise numerical algorithms to determine whether a function solves a differential equation uniquely. Finally, we provide extensive numerical experiments showing that our algorithms in combination with common approaches for learning physical laws indeed allow to guarantee that a unique governing differential equation is learnt, without assuming any knowledge about the function, thereby ensuring reliability

    ParFam -- Symbolic Regression Based on Continuous Global Optimization

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    The problem of symbolic regression (SR) arises in many different applications, such as identifying physical laws or deriving mathematical equations describing the behavior of financial markets from given data. Various methods exist to address the problem of SR, often based on genetic programming. However, these methods are usually quite complicated and require a lot of hyperparameter tuning and computational resources. In this paper, we present our new method ParFam that utilizes parametric families of suitable symbolic functions to translate the discrete symbolic regression problem into a continuous one, resulting in a more straightforward setup compared to current state-of-the-art methods. In combination with a powerful global optimizer, this approach results in an effective method to tackle the problem of SR. Furthermore, it can be easily extended to more advanced algorithms, e.g., by adding a deep neural network to find good-fitting parametric families. We prove the performance of ParFam with extensive numerical experiments based on the common SR benchmark suit SRBench, showing that we achieve state-of-the-art results. Our code and results can be found at https://github.com/Philipp238/parfam .Comment: Code: https://github.com/Philipp238/parfa

    Connecting Wireless Sensor Networks to the Robot Operating System

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    AbstractRobot systems largely depend on embedded systems to operate. The interfaces of those embedded systems, e.g. motor controllers or laser scanners, are often vendor-specific and therefore require a component that translates from/to the Robot Operating System (ROS) Middleware interface. In this work we present an implementation and evaluation of a ROS Middleware client based on the Contiki operating systems, which is suitable for constrained embedded devices, like wireless sensor nodes. We show that in-buffer processing of ROS messages without relying on dynamic memory allocation is possible. That message contents can be accessed conveniently via well-known concepts of the C language (structs) with negligible processing overhead compared to a C++-based client. And that the message-passing middle- ware concept of ROS fits nicely in Contiki's event-based nature. Furthermore, in order for an environment enriched with wireless sensor network to help robots in navigating, understanding and manipulating environments a direct integration is mandatory

    Wire arc additive manufacturing (WAAM) of aluminium alloy AlMg5Mn with energy-reduced gas metal arc welding (GMAW)

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    Large-scale aluminum parts are used in aerospace and automotive industries, due to excellent strength, light weight, and the good corrosion resistance of the material. Additive manufacturing processes enable both cost and time savings in the context of component manufacturing. Thereby, wire arc additive manufacturing (WAAM) is particularly suitable for the production of large volume parts due to deposition rates in the range of kilograms per hour. Challenges during the manufacturing process of aluminum alloys, such as porosity or poor mechanical properties, can be overcome by using arc technologies with adaptable energy input. In this study, WAAM of AlMg5Mn alloy was systematically investigated by using the gas metal arc welding (GMAW) process. Herein, correlations between the energy input and the resulting temperature–time-regimes show the effect on resulting microstructure, weld seam irregularities and the mechanical properties of additively manufactured aluminum parts. Therefore, multilayer walls were built layer wise using the cold metal transfer (CMT) process including conventional CMT, CMT advanced and CMT pulse advanced arc modes. These processing strategies were analyzed by means of energy input, whereby the geometrical features of the layers could be controlled as well as the porosity to area portion to below 1% in the WAAM parts. Furthermore, the investigations show the that mechanical properties like tensile strength and material hardness can be adapted throughout the energy input per unit length significantl

    Learning-based adaption of robotic friction models

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    In the Fourth Industrial Revolution, wherein artificial intelligence and the automation of machines occupy a central role, the deployment of robots is indispensable. However, the manufacturing process using robots, especially in collaboration with humans, is highly intricate. In particular, modeling the friction torque in robotic joints is a longstanding problem due to the lack of a good mathematical description. This motivates the usage of data-driven methods in recent works. However, model-based and data-driven models often exhibit limitations in their ability to generalize beyond the specific dynamics they were trained on, as we demonstrate in this paper. To address this challenge, we introduce a novel approach based on residual learning, which aims to adapt an existing friction model to new dynamics using as little data as possible. We validate our approach by training a base neural network on a symmetric friction data set to learn an accurate relation between the velocity and the friction torque. Subsequently, to adapt to more complex asymmetric settings, we train a second network on a small dataset, focusing on predicting the residual of the initial network's output. By combining the output of both networks in a suitable manner, our proposed estimator outperforms the conventional model-based approach and the base neural network significantly. Furthermore, we evaluate our method on trajectories involving external loads and still observe a substantial improvement, approximately 60-70\%, over the conventional approach. Our method does not rely on data with external load during training, eliminating the need for external torque sensors. This demonstrates the generalization capability of our approach, even with a small amount of data-only 43 seconds of a robot movement-enabling adaptation to diverse scenarios based on prior knowledge about friction in different settings

    Cynomolgus monkey's choroid reference database derived from hybrid deep learning optical coherence tomography segmentation.

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    Cynomolgus monkeys exhibit human-like features, such as a fovea, so they are often used in non-clinical research. Nevertheless, little is known about the natural variation of the choroidal thickness in relation to origin and sex. A combination of deep learning and a deterministic computer vision algorithm was applied for automatic segmentation of foveolar optical coherence tomography images in cynomolgus monkeys. The main evaluation parameters were choroidal thickness and surface area directed from the deepest point on OCT images within the fovea, marked as the nulla with regard to sex and origin. Reference choroid landmarks were set underneath the nulla and at 500 µm intervals laterally up to a distance of 2000 µm nasally and temporally, complemented by a sub-analysis of the central bouquet of cones. 203 animals contributed 374 eyes for a reference choroid database. The overall average central choroidal thickness was 193 µm with a coefficient of variation of 7.8%, and the overall mean surface area of the central bouquet temporally was 19,335 µm2 and nasally was 19,283 µm2. The choroidal thickness of the fovea appears relatively homogeneous between the sexes and the studied origins. However, considerable natural variation has been observed, which needs to be appreciated

    Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence

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    Machine learning has greatly facilitated the analysis of medical data, while the internal operations usually remain intransparent. To better comprehend these opaque procedures, a convolutional neural network for optical coherence tomography image segmentation was enhanced with a Traceable Relevance Explainability (T-REX) technique. The proposed application was based on three components: ground truth generation by multiple graders, calculation of Hamming distances among graders and the machine learning algorithm, as well as a smart data visualization ('neural recording'). An overall average variability of 1.75% between the human graders and the algorithm was found, slightly minor to 2.02% among human graders. The ambiguity in ground truth had noteworthy impact on machine learning results, which could be visualized. The convolutional neural network balanced between graders and allowed for modifiable predictions dependent on the compartment. Using the proposed T-REX setup, machine learning processes could be rendered more transparent and understandable, possibly leading to optimized applications

    Immunomodulatory effects of tick saliva on dermal cells exposed to \u3cem\u3eBorrelia burgdorferi\u3c/em\u3e, the agent of Lyme disease

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    Background: The prolonged feeding process of ixodid ticks, in combination with bacterial transmission, should lead to a robust inflammatory response at the blood-feeding site. Yet, factors present in tick saliva may down-regulate such responses, which may be beneficial to spirochete transmission. The primary goal of this study was to test the hypothesis that tick saliva, in the context of Borrelia burgdorferi, can have widespread effects on the production of immune mediators in skin. Methods: A cross-section of tick feeding on skin was examined histologically. Human THP-1 cells stimulated with B. burgdorferi and grown in the presence or absence of tick saliva were examined by human DNA microarray, cytokine bead array, sandwich ELISA, and qRT-PCR. Similar experiments were also conducted using dermal fibroblasts. Results: Tick feeding on skin showed dermal infiltration of histiocytes and granulocytes at the bite location. Changes in monocytic transcript levels during co-culture with B. burgdorferi and saliva indicated that tick saliva had a suppressive effect on the expression of certain pro-inflammatory mediators, such as IL-8 (CXCL8) and TLR2, but had a stimulatory effect on specific molecules such as the Interleukin 10 receptor, alpha subunit (IL-10RA), a known mediator of the immunosuppressive signal of IL-10. Stimulated cell culture supernatants were analyzed via antigen-capture ELISA and cytokine bead array for inflammatory mediator production. Treatment of monocytes with saliva significantly reduced the expression of several key mediators including IL-6, IL-8 and TNF-alpha. Tick saliva had an opposite effect on dermal fibroblasts. Rather than inhibiting, saliva enhanced production of pro-inflammatory mediators, including IL-8 and IL-6 from these sentinel skin cells. Conclusions: The effects of ixodid tick saliva on resident skin cells is cell type-dependent. The response to both tick and pathogen at the site of feeding favors pathogen transmission, but may not be wholly suppressed by tick saliva
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