arXiv subjects

Andreas Müller

Publications and source records attributed to Andreas Müller.

At least 19 recordsLinked to original sources

arXiv2026

Transient multimode heat transfer of an industrial automated tape laying process under rapidly changing conditions

This work presents a transient heat-transfer model of an industrial automated tape laying (ATL) process designed to overcome the limitations of conventional thermal models in composite manufacturing. The model solves the heat-conduction equation with coupled advection, conduction, convection, and radiation. A key innovation is the implementation of an analytical view factor approach that accounts for finite emitter and tape widths, thereby correcting systematic overestimations of radiative heat flux inherent in 1.5D simplifications. Furthermore, a local convection assessment incorporates mixed convection effects characterized by the Richardson number, ensuring accuracy across a wide range of process speeds. The ATL system is represented by two interacting subsystems: the moving tape substrate and the infrared heat sources. The tape is discretized using a two-node model that resolves the physical phase shift between the heated and monitored surfaces. Numerical stability under high dynamics is ensured by a monolithic solution strategy using a high-order implicit integration scheme. Model predictions were validated on an industrial ATL line, demonstrating an overall deviation of only 1.08% (NRMSE) under rapid velocity and current modulations. This framework provides a high-fidelity, physics-based foundation for thermal state estimation, supporting consistent in-situ consolidation and improved part quality.

cs.RO↗

arXiv2026

Agentic-J: An AI Agent for Biological Microscopy Image Analysis

Biological image analysis increasingly demands integration across heterogeneous tools, programming environments, and domain knowledge that few researchers can command simultaneously. We present Agentic-J, a containerised, multi-agent AI assistant, primarily for ImageJ/Fiji that enables biologists to specify analysis tasks in natural language, from nuclei segmentation and cell tracking to multi-condition quantification. The agent generates executable scripts organised into a documented project structure, so every analysis decision is traceable and the workflow can be reproduced or shared. The specialised sub-agents handle plugin management, code generation, debugging, quality assurance, and statistical reporting. In this paper we introduce the system's design, demonstrate real biological microscopy image analysis workflows, and detailed the technical implementation.

cs.MA↗

arXiv2026

On the Robustness of Watermarking for Autoregressive Image Generation

The proliferation of autoregressive (AR) image generators demands reliable detection and attribution of their outputs to mitigate misinformation, and to filter synthetic images from training data to prevent model collapse. To address this need, watermarking techniques, specifically designed for AR models, embed a subtle signal at generation time, enabling downstream verification through a corresponding watermark detector. In this work, we study these schemes and demonstrate their vulnerability to both watermark removal and forgery attacks. We assess existing attacks and further introduce three new attacks: (i) a vector-quantized regeneration removal attack, (ii) adversarial optimization-based attack, and (iii) a frequency injection attack. Our evaluation reveals that removal and forgery attacks can be effective with access to a single watermarked reference image and without access to original model parameters or watermarking secrets. Our findings indicate that existing watermarking schemes for AR image generation do not reliably support synthetic content detection for dataset filtering. Moreover, they enable Watermark Mimicry, whereby authentic images can be manipulated to imitate a generator's watermark and trigger false detection to prevent their inclusion in future model training.

cs.CV↗

arXiv2026

ClusterMark: Towards Robust Watermarking for Autoregressive Image Generators with Visual Token Clustering

In-generation watermarking for latent diffusion models has recently shown high robustness in marking generated images for easier detection and attribution. However, its application to autoregressive (AR) image models is underexplored. Autoregressive models generate images by autoregressively predicting a sequence of visual tokens that are then decoded into pixels using a VQ-VAE decoder. Inspired by KGW watermarking for large language models, we examine token-level watermarking schemes that bias the next-token prediction based on prior tokens. We find that a direct transfer of these schemes works in principle, but the detectability of the watermarks decreases considerably under common image perturbations. As a remedy, we propose a watermarking approach based on visual token clustering, which assigns similar tokens to the same set (red or green). We investigate token clustering in a training-free setting, as well as in combination with a more accurate fine-tuned token or cluster predictor. Overall, our experiments show that cluster-based watermarks greatly improve robustness against perturbations and regeneration attacks while preserving image quality, outperforming a set of baselines and concurrent works. Moreover, our methods offer fast verification runtime, comparable to lightweight post-hoc watermarking techniques.

cs.CV↗

arXiv2026

Using tablets and smartphones as experimental tools in the physics classroom: effects on learning and motivation

According to the literature, mobile devices as experimental tools (MDET) can offer educational benefits by creating authentic, real-life contexts for physics learning, enhancing student motivation through the use of familiar technology, and supporting cognitive processes by providing multiple representations of phenomena. However, concerns have been raised about potential distractions and cognitive overload. Regarding these conflicting perspectives, few empirical studies on the impact of MDET in real classroom settings of regular, full-length physics courses are available, focusing on a non-specialized high-school target group. We present a study of a mechanics course in such a new setting, addressing the tight curricular, material, and practical constraints inherent to it. A quasi experimental pre post design comparing a treatment group using MDET and a control group without (same content, lesson plan, and teachers) was used. The 19-week teaching sequence focused on conceptual learning and motivational outcomes, controlled by several predictor variables. Findings reveal substantial pre post learning gains for both groups (Cohen d = 0.9) and small gains for perceived relation to reality (d = 0.29). But no significant differences between treatments were found, indicating that MDET do not outperform conventional teaching under the given constraints. Moreover, no evidence of negative effects such as distraction or cognitive overload was observed, and little to no interactions with predictors such as gender or prior knowledge were found. In conclusion, MDET show considerable potential as an effective option for integrating technology into teaching, offering learning outcomes comparable to those of successful conventional teaching, but not better.

physics.ed-ph↗

arXiv2026

Performance and Experimental Analysis of Strain-based Models for Continuum Robots

Although strain-based models have been widely adopted in robotics, no comparison beyond the uniform bending test is commonly recognized to assess their performance. In addition, the increasing effort in prototyping continuum robots highlights the need to assess the applicability of these models and the necessity of comprehensive performance evaluation. To address this gap, this work investigates the shape reconstruction abilities of a third-order strain interpolation method, examining its ability to capture both individual and combined deformation effects. These results are compared and discussed against the Geometric-Variable Strain approach. Subsequently, simulation results are experimentally verified by reshaping a slender rod while recording the resulting configurations using cameras. The rod configuration is imposed using a manipulator displacing one of its tips and extracted through reflective markers, without the aid of any other external sensor -- i.e. strain gauges or wrench sensors placed along the rod. The experiments demonstrate good agreement between the model predictions and observed shapes, with average error of 0.58% of the rod length and average computational time of 0.32s per configuration, outperforming existing models.

cs.RO↗

arXiv2025

Chasing Shadows: Pitfalls in LLM Security Research

Large language models (LLMs) are increasingly prevalent in security research. Their unique characteristics, however, introduce challenges that undermine established paradigms of reproducibility, rigor, and evaluation. Prior work has identified common pitfalls in traditional machine learning research, but these studies predate the advent of LLMs. In this paper, we identify nine common pitfalls that have become (more) relevant with the emergence of LLMs and that can compromise the validity of research involving them. These pitfalls span the entire computation process, from data collection, pre-training, and fine-tuning to prompting and evaluation. We assess the prevalence of these pitfalls across all 72 peer-reviewed papers published at leading Security and Software Engineering venues between 2023 and 2024. We find that every paper contains at least one pitfall, and each pitfall appears in multiple papers. Yet only 15.7% of the present pitfalls were explicitly discussed, suggesting that the majority remain unrecognized. To understand their practical impact, we conduct four empirical case studies showing how individual pitfalls can mislead evaluation, inflate performance, or impair reproducibility. Based on our findings, we offer actionable guidelines to support the community in future work.

cs.CR↗

arXiv2025

Learning Dynamics in Memristor-Based Equilibrium Propagation

Memristor-based in-memory computing has emerged as a promising paradigm to overcome the constraints of the von Neumann bottleneck and the memory wall by enabling fully parallelisable and energy-efficient vector-matrix multiplications. We investigate the effect of nonlinear, memristor-driven weight updates on the convergence behaviour of neural networks trained with equilibrium propagation (EqProp). Six memristor models were characterised by their voltage-current hysteresis and integrated into the EBANA framework for evaluation on two benchmark classification tasks. EqProp can achieve robust convergence under nonlinear weight updates, provided that memristors exhibit a sufficiently wide resistance range of at least an order of magnitude.

cs.LG↗

arXiv2025

A Graph-Based Laser Path Solver Algorithm for Virtual Reality Laboratory Simulations

femtoPro is an interactive virtual reality (VR) laser laboratory balancing the contrasting challenges of accuracy and computational efficiency in optics simulations. It can simulate linear and nonlinear optical phenomena in real time, a task that pushes the boundaries of current consumer hardware. This paper details the concept, implementation, and evaluation of a dynamic graph-based solution tailored to the specific requirements and challenges of the simulation. Resource usage is optimized through a selective updating strategy that identifies and preserves laser paths unchanged between simulation frames, eliminating the need for unnecessary recalculations. Benchmarking of real-world scenarios confirms that our approach delivers a smooth user experience, even on mobile VR platforms with limited computing power. The methodologies, solutions and insights outlined in this paper may be applicable to other interactive, dynamic graph-based real-time simulations.

physics.optics↗

arXiv2025

Paper Science

Paper has a lot of interesting properties with which quite a lot of standard topics of science education can be turned into hands-on activities. Among others, experiments are presented on elasticity, capillarity, feedback oscillations, flight, friction, perception and thermal expansion.

physics.ed-ph↗

arXiv2025

Black-Box Forgery Attacks on Semantic Watermarks for Diffusion Models

Integrating watermarking into the generation process of latent diffusion models (LDMs) simplifies detection and attribution of generated content. Semantic watermarks, such as Tree-Rings and Gaussian Shading, represent a novel class of watermarking techniques that are easy to implement and highly robust against various perturbations. However, our work demonstrates a fundamental security vulnerability of semantic watermarks. We show that attackers can leverage unrelated models, even with different latent spaces and architectures (UNet vs DiT), to perform powerful and realistic forgery attacks. Specifically, we design two watermark forgery attacks. The first imprints a targeted watermark into real images by manipulating the latent representation of an arbitrary image in an unrelated LDM to get closer to the latent representation of a watermarked image. We also show that this technique can be used for watermark removal. The second attack generates new images with the target watermark by inverting a watermarked image and re-generating it with an arbitrary prompt. Both attacks just need a single reference image with the target watermark. Overall, our findings question the applicability of semantic watermarks by revealing that attackers can easily forge or remove these watermarks under realistic conditions.

cs.CR↗

arXiv2025

femtoPro: Real-time linear and nonlinear optics simulations

Real-time optics and spectroscopy simulations ideally provide results at update rates of 120 Hz or more without any noticeable delay between changing input parameters and the calculated results. Such calculations require models of sufficient speed yet adequate level of detail in the physical approximations to contain the essential features of the simulated phenomena. We discuss a representation of femtosecond laser pulses in which fast phase oscillations due to carrier frequency and due to spatial propagation are separated out and amplitude modulations due to Gaussian beam propagation are also separated and treated explicitly. We derive simplified expressions for the spatial modulations of laser beams. Further, we derive visibility and beam-overlap factors describing multi-pulse interference. We obtain simplified expressions for radius and curvature of nonlinear signal beams in the case of fundamental beams with different convergence, different beam waist, and imperfect mutual overlap. The described model is implemented in the virtual-reality laser laboratory simulation "femtoPro," but the derived equations can be used independently for other applications.

physics.optics↗

arXiv2025

Recitation tasks revamped? Students' perceptions of smartphone-based experimental and programming tasks in introductory mechanics

This exploratory field study investigates the integration of innovative forms of recitation tasks in a first-year introductory mechanics course, focusing on smartphone-based experimental tasks alongside programming and standard recitation tasks. Smartphones, combined with external sensor modules, serve as a gateway enabling students to conduct various low-cost and authentic physics experiments with first-hand data collection outside traditional lab settings. These tasks aim to enhance students' agency in independent physics experimentation and enrich homework assignments by dissolving boundaries between lectures, recitation sessions, and traditional labs, and thereby linking theoretical and experimental aspects of undergraduate physics education. To explore this potential, we implemented and evaluated a sample set of nine smartphone-based experimental tasks and, for comparison, three programming tasks as weekly exercises in a first-year physics course at RWTH Aachen University. We investigated students' perceptions of learning with these new tasks through twelve short surveys involving up to 188 participants. In two additional surveys with 108 and 78 participants, students assessed affective responses to the smartphone-based experimental tasks relative to the programming and standard recitation tasks. Our findings indicate that the smartphone-based experimental tasks were generally well-suited to the students and tended to outperform the programming tasks in terms of perceptions of learning with the tasks and affective responses. Overall, students responded positively to the new experimental tasks, with perceptions comparable to, or only partly below, those of long-established standard recitation tasks. These results suggest that smartphone-based experimental tasks can be successfully integrated into teaching and contribute to refining traditional recitation tasks.

physics.ed-ph↗

arXiv2025

MotherNet: Fast Training and Inference via Hyper-Network Transformers

Foundation models are transforming machine learning across many modalities, with in-context learning replacing classical model training. Recent work on tabular data hints at a similar opportunity to build foundation models for classification for numerical data. However, existing meta-learning approaches can not compete with tree-based methods in terms of inference time. In this paper, we propose MotherNet, a hypernetwork architecture trained on synthetic classification tasks that, once prompted with a never-seen-before training set generates the weights of a trained ``child'' neural-network by in-context learning using a single forward pass. In contrast to most existing hypernetworks that are usually trained for relatively constrained multi-task settings, MotherNet can create models for multiclass classification on arbitrary tabular datasets without any dataset specific gradient descent. The child network generated by MotherNet outperforms neural networks trained using gradient descent on small datasets, and is comparable to predictions by TabPFN and standard ML methods like Gradient Boosting. Unlike a direct application of TabPFN, MotherNet generated networks are highly efficient at inference time. We also demonstrate that HyperFast is unable to perform effective in-context learning on small datasets, and heavily relies on dataset specific fine-tuning and hyper-parameter tuning, while MotherNet requires no fine-tuning or per-dataset hyper-parameters.

cs.LG↗

arXiv2025

Towards A Correct Usage of Cryptography in Semantic Watermarks for Diffusion Models

Semantic watermarking methods enable the direct integration of watermarks into the generation process of latent diffusion models by only modifying the initial latent noise. One line of approaches building on Gaussian Shading relies on cryptographic primitives to steer the sampling process of the latent noise. However, we identify several issues in the usage of cryptographic techniques in Gaussian Shading, particularly in its proof of lossless performance and key management, causing ambiguity in follow-up works, too. In this work, we therefore revisit the cryptographic primitives for semantic watermarking. We introduce a novel, general proof of lossless performance based on IND\$-CPA security for semantic watermarks. We then discuss the configuration of the cryptographic primitives in semantic watermarks with respect to security, efficiency, and generation quality.

cs.CR↗

arXiv2024

Evaluating Gender Bias in Large Language Models

Gender bias in artificial intelligence has become an important issue, particularly in the context of language models used in communication-oriented applications. This study examines the extent to which Large Language Models (LLMs) exhibit gender bias in pronoun selection in occupational contexts. The analysis evaluates the models GPT-4, GPT-4o, PaLM 2 Text Bison and Gemini 1.0 Pro using a self-generated dataset. The jobs considered include a range of occupations, from those with a significant male presence to those with a notable female concentration, as well as jobs with a relatively equal gender distribution. Three different sentence processing methods were used to assess potential gender bias: masked tokens, unmasked sentences, and sentence completion. In addition, the LLMs suggested names of individuals in specific occupations, which were then examined for gender distribution. The results show a positive correlation between the models' pronoun choices and the gender distribution present in U.S. labor force data. Female pronouns were more often associated with female-dominated occupations, while male pronouns were more often associated with male-dominated occupations. Sentence completion showed the strongest correlation with actual gender distribution, while name generation resulted in a more balanced 'politically correct' gender distribution, albeit with notable variations in predominantly male or female occupations. Overall, the prompting method had a greater impact on gender distribution than the model selection itself, highlighting the complexity of addressing gender bias in LLMs. The findings highlight the importance of prompting in gender mapping.

cs.CL↗

arXiv2024

Conceptualization and Quantitative study of Aesthetic and Affective Perception of Pictures in Physics Education

Pictures in physics education go beyond instructional functions and serve affective roles, such as attracting attention, creating fascination, and fostering engagement with the depicted content. Recognizing the importance of these affective functions highlights the need to understand and utilize aesthetic pictures in a research-based educational environment. Prior research suggests that aesthetic and affective attractiveness in pictures enhances enjoyment and engagement with the physics content. This paper offers three main contributions: Firstly, it conceptualizes and presents research-based criteria for selecting pictures perceived as aesthetically pleasing, drawing on insights from psychology and physics education research. Following these criteria, aesthetic pictures related to a given curricular content can be selected. Secondly, the paper applies these criteria to selecting pictures showing geometrical optics. It then delves into an evaluation of students' aesthetic and affective perception of the selected pictures. A validated instrument measured these responses, showing strong reliability (aesthetic perception: $α_C$ = 0.87 [0.85, 0.89]; affective perception: $α_C$ = 0.82 [0.80, 0.85]). Thirdly, it combines decorative and instructional functions in tasks and compares students' perceptions of aesthetic pictures (AP) and classroom experiment pictures (CEP) in junior high school ($N$ = 118), using a crossover design. Results indicated significantly better aesthetic and affective evaluations for APs, with large effect sizes (AP vs. CEP, aesthetic and affective perception: $d$ = 1.05 - 1.56 and 0.85 - 1.48, respectively). We conclude that the here developed and investigated criteria are useful for selecting aesthetic and affective pictures. This provides a basis for further leveraging their educational potential to create fascination and engagement in science education.

physics.ed-ph↗

arXiv2024

A tour d'horizon of de Casteljau's work

Whilst Paul de Casteljau is now famous for his fundamental algorithm of curve and surface approximation, little is known about his other findings. This article offers an insight into his results in geometry, algebra and number theory. Related to geometry, his classical algorithm is reviewed as an index reduction of a polar form. This idea is used to show de Casteljau's algebraic way of smoothing, which long went unnoticed. We will also see an analytic polar form and its use in finding the intersection of two curves. The article summarises unpublished material on metric geometry. It includes theoretical advances, e.g., the 14-point strophoid or a way to link Apollonian circles with confocal conics, and also practical applications such as a recurrence for conjugate mirrors in geometric optics. A view on regular polygons leads to an approximation of their diagonals by golden matrices, a generalisation of the golden ratio. Relevant algebraic findings include matrix quaternions (and anti-quaternions) and their link with Lorentz' equations. De Casteljau generalised the Euclidean algorithm and developed an automated method for approximating the roots of a class of polynomial equations. His contributions to number theory not only include aspects on the sum of four squares as in quaternions, but also a view on a particular sum of three cubes. After a review of a complete quadrilateral in a heptagon and its angles, the paper concludes with a summary of de Casteljau's key achievements. The article contains a comprehensive bibliography of de Casteljau's works, including previously unpublished material.

math.HO↗