arXiv subjects

Michael Schulze

Publications and source records attributed to Michael Schulze.

13 recordsLinked to original sources

arXiv2026

Electrical observation via spin Seebeck effect of fractionalized excitations in a magnetic insulator

Fractionalized excitations are among the most striking signatures of emergence in quantum matter. While widely sought in frustrated magnets, their detection and characterization remain challenging, motivating the exploration of new probes. Meanwhile, Spintronics offers versatile tools for probing spin-related phenomena. In particular, the spin Seebeck effect (SSE) converts thermally driven magnetic excitations into a voltage in an adjacent metal, providing electrical access to the underlying dynamics and transport properties. Here we employ the SSE to probe emergent magnetic monopoles in the non-collinear Ising magnet Dy$_2$Ti$_2$O$_7$, a rare instance of a three-dimensional fractionalized magnet. We observe an SSE signal featuring a pronounced peak at monopole proliferation, accompanied by characteristic frequency and angular dependence. Our results broaden the scope of spintronic methods for detecting exotic excitations, provide new insights into magnetic insulators generally and monopole physics specifically, and suggest the potential of quantum materials as functional interfaces.

cond-mat.str-el↗

arXiv2026

The Evolution of Magnetism in a Thin Film Pyrochlore Ferromagnetic Insulator

The pyrochlore vanadates are compelling candidates for next-generation dissipationless devices. Lu2V2O7 and Y2V2O7 are ferromagnetic insulators (Tc ~ 70 K) that are believed to exhibit the magnon Hall effect and are expected to host topological magnons. Their completely dissipationless magnon edge states could be harnessed to realize low-power information transport in spintronic or magnonic devices. As a crucial step in the realization of devices, we synthesize the first thin films of pyrochlore Y2V2O7 on isostructural Y2Ti2O7 substrates and explore the evolution of their magnetic properties down to the ultrathin limit. All films are insulating ferromagnets with transition temperatures of up to the bulk value (Tc ~ 68 K) that decrease with thickness according to finite-size effects. Our films also exhibit a change in anisotropy from in-plane to out-of-plane easy axis coincident with the development of partial strain relaxation and nonzero magnetic hysteresis in an applied field. This evolution demonstrates the impact of strain on magnetic anisotropy and paves the way to tunable magnon topology.

cond-mat.mtrl-sci↗

arXiv2025

Floating zone growth of high-purity MgO substrate single crystals

MgO single crystals with diameters between 3.5 and 5 mm and lengths up to 40 mm were grown by the optical floating zone technique (OFZ). Despite challenging material properties such as the high melting point of 2825 °C, very high evaporation rate and perfect {100} cleaving characteristics, crack-free crystals were grown at high growth rates exceeding 40 mm/h and at high thermal gradients. Chemical investigations revealed that the OFZ technique is suitable for the preparation of substrate crystals with a purity of 5N to facilitate the development of novel demonstrator devices based on epitaxially grown thin films. The achieved purity level is improved by more than one order of magnitude if compared to commercial MgO substrate single crystals graded as high purity.

cond-mat.mtrl-sci↗

arXiv2025

Observation via spin Seebeck effect of macroscopic magnetic transport from emergent magnetic monopoles

Magnetic monopoles, elusive in high-energy physics, have been realised as emergent quasiparticles in solid-state systems, where their unique properties hold promise for novel spintronic applications. Magnetic monopoles have been invoked in diverse platforms, including skyrmion lattices, chiral magnets, soft ferromagnets, aritifical nanomagnets. Yet, a demonstration of their role in magnetic transport has remained elusive. Here, we report such an observation via the spin Seebeck effect in the bulk insulating pyrochlore oxide, spin ice $\mathrm{Dy_2Ti_2O_7}$. By applying a thermal gradient perpendicular to a $[111]$-oriented magnetic field, we detect a transverse spin Seebeck voltage marked by a dominant peak at the onset of monopole proliferation, accompanied by a secondary feature and frequency-dependent behavior. Our findings establish a direct link between monopole dynamics and magnetic transport in an insulating medium, establishing a new pathway for probing fractionalized excitations and advancing towards novel spintronic applications.

cond-mat.str-el↗

arXiv2025

Classifier Ensemble for Efficient Uncertainty Calibration of Deep Neural Networks for Image Classification

This paper investigates novel classifier ensemble techniques for uncertainty calibration applied to various deep neural networks for image classification. We evaluate both accuracy and calibration metrics, focusing on Expected Calibration Error (ECE) and Maximum Calibration Error (MCE). Our work compares different methods for building simple yet efficient classifier ensembles, including majority voting and several metamodel-based approaches. Our evaluation reveals that while state-of-the-art deep neural networks for image classification achieve high accuracy on standard datasets, they frequently suffer from significant calibration errors. Basic ensemble techniques like majority voting provide modest improvements, while metamodel-based ensembles consistently reduce ECE and MCE across all architectures. Notably, the largest of our compared metamodels demonstrate the most substantial calibration improvements, with minimal impact on accuracy. Moreover, classifier ensembles with metamodels outperform traditional model ensembles in calibration performance, while requiring significantly fewer parameters. In comparison to traditional post-hoc calibration methods, our approach removes the need for a separate calibration dataset. These findings underscore the potential of our proposed metamodel-based classifier ensembles as an efficient and effective approach to improving model calibration, thereby contributing to more reliable deep learning systems.

cs.CV↗

arXiv2024

RadarPillars: Efficient Object Detection from 4D Radar Point Clouds

Automotive radar systems have evolved to provide not only range, azimuth and Doppler velocity, but also elevation data. This additional dimension allows for the representation of 4D radar as a 3D point cloud. As a result, existing deep learning methods for 3D object detection, which were initially developed for LiDAR data, are often applied to these radar point clouds. However, this neglects the special characteristics of 4D radar data, such as the extreme sparsity and the optimal utilization of velocity information. To address these gaps in the state-of-the-art, we present RadarPillars, a pillar-based object detection network. By decomposing radial velocity data, introducing PillarAttention for efficient feature extraction, and studying layer scaling to accommodate radar sparsity, RadarPillars significantly outperform state-of-the-art detection results on the View-of-Delft dataset. Importantly, this comes at a significantly reduced parameter count, surpassing existing methods in terms of efficiency and enabling real-time performance on edge devices.

cs.CV↗

arXiv2021

Interactively Constructing Knowledge Graphs from Messy User-Generated Spreadsheets

When spreadsheets are filled freely by knowledge workers, they can contain rather unstructured content. For humans and especially machines it becomes difficult to interpret such data properly. Therefore, spreadsheets are often converted to a more explicit, formal and structured form, for example, to a knowledge graph. However, if a data maintenance strategy has been missing and user-generated data becomes "messy", the construction of knowledge graphs will be a challenging task. In this paper, we catalog several of those challenges and propose an interactive approach to solve them. Our approach includes a graphical user interface which enables knowledge engineers to bulk-annotate spreadsheet cells with extracted information. Based on the cells' annotations a knowledge graph is ultimately formed. Using five spreadsheets from an industrial scenario, we built a 25k-triple graph during our evaluation. We compared our method with the state-of-the-art RDF Mapping Language (RML) attempt. The comparison highlights contributions of our approach.

cs.DB↗

arXiv2020

Bridging the Technology Gap Between Industry and Semantic Web: Generating Databases and Server Code From RDF

Despite great advances in the area of Semantic Web, industry rather seldom adopts Semantic Web technologies and their storage and query concepts. Instead, relational databases (RDB) are often deployed to store business-critical data, which are accessed via REST interfaces. Yet, some enterprises would greatly benefit from Semantic Web related datasets which are usually represented with the Resource Description Framework (RDF). To bridge this technology gap, we propose a fully automatic approach that generates suitable RDB models with REST APIs to access them. In our evaluation, generated databases from different RDF datasets are examined and compared. Our findings show that the databases sufficiently reflect their counterparts while the API is able to reproduce rather simple SPARQL queries. Potentials for improvements are identified, for example, the reduction of data redundancies in generated databases.

cs.DB↗

arXiv2020

The Person Index Challenge: Extraction of Persons from Messy, Short Texts

When persons are mentioned in texts with their first name, last name and/or middle names, there can be a high variation which of their names are used, how their names are ordered and if their names are abbreviated. If multiple persons are mentioned consecutively in very different ways, especially short texts can be perceived as "messy". Once ambiguous names occur, associations to persons may not be inferred correctly. Despite these eventualities, in this paper we ask how well an unsupervised algorithm can build a person index from short texts. We define a person index as a structured table that distinctly catalogs individuals by their names. First, we give a formal definition of the problem and describe a procedure to generate ground truth data for future evaluations. To give a first solution to this challenge, a baseline approach is implemented. By using our proposed evaluation strategy, we test the performance of the baseline and suggest further improvements. For future research the source code is publicly available.

cs.CL↗

arXiv2015

Interface control by homoepitaxial growth in pulsed laser deposited iron chalcogenide thin ilms

Thin film growth of iron chalcogenides by pulsed laser deposition (PLD) is still a delicate issue in terms of simultaneous control of stoichiometry, texture, substrate/film interface properties, and superconducting properties. The high volatility of the constituents sharply limits optimal deposition temperatures to a narrow window and mainly challenges reproducibility for vacuum based methods. In this work we demonstrate the beneficial introduction of a semiconducting FeSe$_{1\text{--}x}$Te$_x$ seed layer for subsequent homoepitaxial growth of superconducting FeSe$_{1\text{--}x}$Te$_x$ thin film on MgO substrates. MgO is one of the most favorable substrates used in superconducting thin film applications, but the controlled growth of iron chalcogenide thin films on MgO has not yet been optimized and is the least understood. The large mismatch between the lattice constants of MgO and FeSe$_{1\text{--}x}$Te$_x$ of about 11% results in thin films with a mixed texture, that prevents further accurate investigations of a correlation between structural and electrical properties of FeSe$_{1\text{--}x}$Te$_x$. Here we present an effective way to significantly improve epitaxial growth of superconducting FeSe$_{1\text{--}x}$Te$_x$ thin films with reproducible high critical temperatures ($\geq$ 17 K) at reduced deposition temperatures (200 °C - 320 °C) on MgO using PLD. This offers a broad scope of various applications.

cond-mat.supr-con↗

arXiv2013

Intrinsic pinning and the critical current scaling of clean epitaxial Fe(Se,Te) thin films

We report on the transport properties of clean, epitaxial Fe(Se,Te) thin films prepared on Fe-buffered MgO (001) single crystalline substrates by pulsed laser deposition. Near Tc a steep slope of the upper critical field for H||ab was observed (74.1 T/K), leading to a very short out-of-plane coherence length, ξc, of 0.2 nm, yielding 2ξc(0) approximately 0.4 nm. This value is shorter than the interlayer distance (0.605 nm) between Fe-Se(Te) planes, indicative of modulation of the superconducting order parameter along the c-axis. An inverse correlation between the power law exponent N of the electric field-current density (E-J) curve and the critical current density, Jc, has been observed at 4 K, when the orientation of H was close to the ab-plane. These results prove the presence of intrinsic pinning in Fe(Se,Te). A successful scaling of the angular dependent Jc and the corresponding exponent N can be realized by the anisotropic Ginzburg Landau approach with appropriate Γvalues 2~3.5. The temperature dependence of Γbehaves almost identically to that of the penetration depth anisotropy.

cond-mat.supr-con↗

arXiv2011

Generic Fe buffer layers for Fe-based superconductors: Epitaxial FeSe1-xTex thin films

Biaxially textured FeSe1-xTex films have been realized on Fe-buffered MgO substrates by pulsed laser deposition. Similar to the Fe/BaFe2As2 bilayers, the crystalline quality of FeSe1-xTex films exhibit a sharp out-of-plane and in-plane texture less than 0.9°. The Fe/FeSe1-xTex bilayers showed high superconducting transition temperatures of over 17 K. The angular-dependent critical current densities exhibit peaks positioned at H // ab similar to other pnictide thin films. The volume pinning force of FeSe1-xTex in this direction is very strong compared with that of Co-doped BaFe2As2, due to a good matching between the interlayer distance in the c direction and the out-of-plane coherence length.

cond-mat.supr-con↗

arXiv2004

On the resolvent of the Laplacian on functions for degenerating surfaces of finite geometry

We consider families of degenerating hyperbolic surfaces. The surfaces are geometrically finite of fixed topological type. Let Z(s) be the Selberg Zeta function of a surface, and let Z_d(s) be the contribution of the pinched geodesics to the Zeta function. Extending a result of Hejhal and Wolpert, we prove that the quotient of these two terms converges to the Zeta function of the limit surface for all arguments s with re(s)>1/2. The technique is an examination of resolvent of the Laplacian, which is composed from that for elementary surfaces via meromorphic Fredholm theory. The resolvent is shown to converge on the complement of the essential spectrum of the limit surface. We also use this property to define approximate Eisenstein functions and scattering matrices.

math.DG↗