arXiv subjects

Alexander Müller

Publications and source records attributed to Alexander Müller.

12 recordsLinked to original sources

arXiv2026

Effects of Tool Wear on the Surface Texture in Turning: A Feature Characterization Approach Based on ISO 21920-2

The surface texture of a turned component acts as a fingerprint of both the process parameters and the tool wear condition, imaging the cutting edge. Classical surface parameters such as $R_\mathrm{a}$ or $R_\mathrm{q}$ describe the topography only globally and allow no spatially resolved evaluation of the process-induced deterministic structures. This work investigates how far the feature characterization standardized in ISO 21920-2 makes this wear information accessible and physically interpretable. The database comprises roughness profiles of twelve AlTiN-coated carbide inserts (CNMG120408) machining normalized AISI 1045 steel, measured at nine wear states over the entire tool life, with three replicate profiles per state. The correlation of standardized field and feature parameters with crater wear, flank wear, and cutting time is first examined. Watershed segmentation is then adapted to extract the rotational tool grooves and evaluate their geometry statistically. A newly developed mean-feature approach decomposes the profile into a deterministic and a stochastic component. Wear-induced changes are almost entirely carried by the deterministic component, and within it by the trailing flank of the cutting groove. A comparison with confocal measurements confirms that the mean feature reconstructs the engaged cutting edge geometry, with the trailing-flank steepening attributable to notch wear on the secondary cutting edge. An exhaustive evaluation of more than 920,000 feature characterization combinations and multivariate models reveals that the groove-level mean maximum absolute gradient $\overline{R_\mathrm{dt}}_\mathrm{groove}$ alone explains 83-90% of the variance of the wear indicators, so that a single, physically motivated parameter suffices for robust wear estimation. A follow-up study will investigate inline monitoring using scattered light sensors.

eess.SP↗

arXiv2026

Parametric Open Source Games

Open-source game theory studies agents whose behavior may depend on one another's decision procedures, but most existing models use discrete or symbolic programs. We introduce parametric open-source games, a continuous analogue of program equilibria in which players choose parameter vectors and semantics maps convert the full parameter profile into mixed actions in an underlying finite game. We establish equilibrium existence results, derive an exact coupling threshold at which selfish gradient ascent in symmetric $2\times2$ games switches from defection toward cooperation, and give a one-dimensional boundary test for parametric program Nash equilibria. We further extend the framework to a neural semantics class whose first-order cooperation condition is governed by the ratio of cross-player to self-player sensitivity. Across canonical games, the framework shows how access to internal parameterizations can qualitatively reshape learning dynamics and equilibrium structure, and how sufficiently strong open-source coupling can steer selfish optimization toward cooperative outcomes.

cs.GT↗

arXiv2026

Cross-Modal Contrastive Learning of ECG and Angiography Representations for Severe Stenosis Classification

Coronary artery stenosis is a common cardiovascular disease, with severe, untreated cases posing significant risks of heart attack. Although coronary (X-ray) angiograms remain the standard for stenosis diagnosis, they are invasive, time- and resource-intensive, and therefore only performed on patients with a high probability of disease based on symptoms and prior clinical tests. However, a subset of patients, especially those without symptoms, may remain undiagnosed. Detecting indications of stenosis from ECGs, which are fast, cheap, non-invasive, and thus routinely acquired even in asymptomatic patients, would support early diagnosis. However, as no reliable stenosis-specific signal has been identified in ECGs, they can not currently be used for stenosis risk stratification. To address this, we introduce StenCE, a pretraining framework, allowing stratification of patients based on features derived directly from ECGs. Evaluations across varying stenosis severity thresholds and additional ECG disease classification tasks demonstrate consistent performance improvements across different ECG encoders, outperforming previous work. The obtained models successfully detect signals for stenosis diagnosis in ECGs and are the first to achieve high performance in severe stenosis classification. The source code is available at https://github.com/NikolaCenic/ecg-stenosis-cls.

cs.LG↗

arXiv2026

Market-Analysis-Driven Methodology for Assessing Charging Station Cybersecurity

Modern charging communication standards for electric vehicles include optional security controls such as TLS-based authentication and encryption. However, with tens of thousands of fast charging points deployed in any given country, individually testing each one for security control support is infeasible. This paper proposes a scalable, extrapolation-based methodology for assessing charging station cybersecurity at a national level. A market analysis identifies operator-manufacturer pairs, enabling the targeted selection of charging stations for field testing, whose results can then be extrapolated to all stations sharing the same combination. We demonstrate this methodology for Germany, covering over 40000 CCS charging points as of December 2025. With a manageable number of field tests, our extrapolated data examines 51.9\% of german CCS charging stations. It shows that only 27.4\% of charging stations in our scope provide TLS-protected communication, despite widespread theoretical support.

cs.CR↗

arXiv2026

Echo2ECG: Enhancing ECG Representations with Cardiac Morphology from Multi-View Echos

Electrocardiography (ECG) is a low-cost, widely used modality for diagnosing electrical abnormalities like atrial fibrillation by capturing the heart's electrical activity. However, it cannot directly measure cardiac morphological phenotypes, such as left ventricular ejection fraction (LVEF), which typically require echocardiography (Echo). Predicting these phenotypes from ECG would enable early, accessible health screening. Existing self-supervised methods suffer from a representational mismatch by aligning ECGs to single-view Echos, which only capture local, spatially restricted anatomical snapshots. To address this, we propose Echo2ECG, a multimodal self-supervised learning framework that enriches ECG representations with the heart's morphological structure captured in multi-view Echos. We evaluate Echo2ECG as an ECG feature extractor on two clinically relevant tasks that fundamentally require morphological information: (1) classification of structural cardiac phenotypes across three datasets, and (2) retrieval of Echo studies with similar morphological characteristics using ECG queries. Our extracted ECG representations consistently outperform those of state-of-the-art unimodal and multimodal baselines across both tasks, despite being 18x smaller than the largest baseline. These results demonstrate that Echo2ECG is a robust, powerful ECG feature extractor. Our code is accessible at https://github.com/michelleespranita/Echo2ECG.

cs.LG↗

arXiv2026

Multi-View Stenosis Classification Leveraging Transformer-Based Multiple-Instance Learning Using Real-World Clinical Data

Coronary artery stenosis is a leading cause of cardiovascular disease, diagnosed by analyzing the coronary arteries from multiple angiography views. Although numerous deep-learning models have been proposed for stenosis detection from a single angiography view, their performance heavily relies on expensive view-level annotations, which are often not readily available in hospital systems. Moreover, these models fail to capture the temporal dynamics and dependencies among multiple views, which are crucial for clinical diagnosis. To address this, we propose SegmentMIL, a transformer-based multi-view multiple-instance learning framework for patient-level stenosis classification. Trained on a real-world clinical dataset, using patient-level supervision and without any view-level annotations, SegmentMIL jointly predicts the presence of stenosis and localizes the affected anatomical region, distinguishing between the right and left coronary arteries and their respective segments. SegmentMIL obtains high performance on internal and external evaluations and outperforms both view-level models and classical MIL baselines, underscoring its potential as a clinically viable and scalable solution for coronary stenosis diagnosis. Our code is available at https://github.com/NikolaCenic/mil-stenosis.

cs.CV↗

arXiv2025

EU-Agent-Bench: Measuring Illegal Behavior of LLM Agents Under EU Law

Large language models (LLMs) are increasingly deployed as agents in various contexts by providing tools at their disposal. However, LLM agents can exhibit unpredictable behaviors, including taking undesirable and/or unsafe actions. In order to measure the latent propensity of LLM agents for taking illegal actions under an EU legislative context, we introduce EU-Agent-Bench, a verifiable human-curated benchmark that evaluates an agent's alignment with EU legal norms in situations where benign user inputs could lead to unlawful actions. Our benchmark spans scenarios across several categories, including data protection, bias/discrimination, and scientific integrity, with each user request allowing for both compliant and non-compliant execution of the requested actions. Comparing the model's function calls against a rubric exhaustively supported by citations of the relevant legislature, we evaluate the legal compliance of frontier LLMs, and furthermore investigate the compliance effect of providing the relevant legislative excerpts in the agent's system prompt along with explicit instructions to comply. We release a public preview set for the research community, while holding out a private test set to prevent data contamination in evaluating upcoming models. We encourage future work extending agentic safety benchmarks to different legal jurisdictions and to multi-turn and multilingual interactions. We release our code on \href{https://github.com/ilijalichkovski/eu-agent-bench}{this URL}.

cs.AI↗

arXiv2024

Feature Characterization for Profile Surface Texture

Conventional field parameters for surface measurement use all data points, while feature characterization focuses on subsets extracted by watershed segmentation. This approach enables the extraction of specific features that are potentially responsible for the function of the surface or are a direct reflection of the manufacturing process, allowing for a more accurate assessment of both aspects. Feature characterization with the underlying watershed segmentation for areal surface topographies has been standardized for over a decade and is well established in industry and research. In contrast, feature characterization for surface profiles has been standardized recently, and the corresponding standard for watershed segmentation is planned to be published in the near future. Since the standards do not provide guidelines for implementation, this paper presents an unambiguous algorithm of the watershed segmentation and the feature characterization for surface profiles. This framework provides the basis for future work, mainly investigating the relationship between feature parameters based on feature characterization and the function of the surface or manufacturing process. For this purpose, recommendations for the configuration and extensions of the toolbox can also be developed, which could find their way into the ISO standards.

eess.SP↗

arXiv2023

Thin cylindrical magnetic nanodots revisited: variational formulation, accurate solution and phase diagram

We investigate the variational formulation and corresponding minimizing energies for the detection of energetically favorable magnetization states of thin cylindrical magnetic nanodots. Opposed to frequently used heuristic procedures found in the literature, we revisit the underlying governing equations and construct a rigorous variational approach that takes both exchange and demagnetization energy into account. Based on a combination of Ritz's method and a Fourier series expansion of the solution field, we are able to pinpoint the precision of solutions, which are given by vortex modes or single-domain states, down to an arbitrary degree of precision. Furthermore, our model allows to derive an expression for the demagnetization energy in closed form for the in-plane single-domain state, which we compare to results from the literature. A key outcome of the present investigation is an accurate phase diagram, which we obtain by comparing the vortex mode's energy minimizers with those of the single-domain states. This phase diagram is validated with data of two- and three-dimensional models from literature. By means of the phase diagram, we particularly find the critical radius at which the vortex mode becomes unfavorable with machine precision. All relevant data and codes related to the present contribution are available at http://dx.doi.org/10.18419/darus-3103.

cond-mat.mes-hall↗

arXiv2021

Prenatal stress perturbs fetal iron homeostasis in a sex-specific manner

What is the influence of chronic maternal prenatal stress (PS) on fetal iron homeostasis? In a prospective case-control study in 164 pregnant women, we show that cord blood transferrin saturation is lower in male stressed neonates. The total effect of PS exposure on fetal ferritin revealed a decrease of 15.4% compared with controls. Electrocardiogram-based Fetal Stress Index (FSI) identified affected fetuses non-invasively during the third trimester of gestation. FSI-based timely detection of fetuses affected by PS can support early individualized iron supplementation and neurodevelopmental follow-up to prevent long-term sequelae due to PS-exacerbated impairment of the iron homeostasis.

q-bio.QM↗

arXiv2019

Patterned Probes for High Precision 4D-STEM Bragg Measurements

Nanoscale strain mapping by four-dimensional scanning transmission electron microscopy (4D-STEM) relies on determining the precise locations of Bragg-scattered electrons in a sequence of diffraction patterns, a task which is complicated by dynamical scattering, inelastic scattering, and shot noise. These features hinder accurate automated computational detection and position measurement of the diffracted disks, limiting the precision of measurements of local deformation. Here, we investigate the use of patterned probes to improve the precision of strain mapping. We imprint a "bullseye" pattern onto the probe, by using a binary mask in the probe-forming aperture, to improve the robustness of the peak finding algorithm to intensity modulations inside the diffracted disks. We show that this imprinting leads to substantially improved strain-mapping precision at the expense of a slight decrease in spatial resolution. In experiments on an unstrained silicon reference sample, we observe an improvement in strain measurement precision from 2.7% of the reciprocal lattice vectors with standard probes to 0.3% using bullseye probes for a thin sample, and an improvement from 4.7% to 0.8% for a thick sample. We also use multislice simulations to explore how sample thickness and electron dose limit the attainable accuracy and precision for 4D-STEM strain measurements.

physics.app-ph↗

arXiv2016

Resistive Switching and Voltage Induced Modulation of Tunneling Magnetoresistance in Nanosized Perpendicular Organic Spin Valves

Nanoscale multifunctional perpendicular organic spin valves have been fabricated. The devices based on an La$_{0.7}$Sr$_{0.3}$MnO$_3$/Alq$_3$/Co trilayer show resistive switching of up to 4-5 orders of magnitude and magnetoresistance as high as -70% the latter even changing sign when voltage pulses are applied. This combination of phenomena is typically observed in multiferroic tunnel junctions where it is attributed to magnetoelectric coupling between a ferromagnet and a ferroelectric material. Modeling indicates that here the switching originates from a modification of the La$_{0.7}$Sr$_{0.3}$MnO$_3$ surface. This modification influences the tunneling of charge carriers and thus both the electrical resistance and the tunneling magnetoresistance which occurs at pinholes in the organic layer.

cond-mat.mes-hall↗