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We develop a general framework (multidimensional asymptotic classes, or m.a.c.s) for handling classes of finite first order structures with a strong uniformity condition on cardinalities of definable sets: The condition asserts that definable families given by a formula \phi(x,y) should take on a fixed number n_\phi of approximate sizes in any M in the class, with those sizes varying with M. The prototype is the class of all finite fields, where the uniformity is given by a theorem of Chatzidakis, van den Dries and Macintyre. It inspired the development of asymptotic classes of finite structures, which this new framework extends. The underlying theory of m.a.c.s is developed, including preservation under bi-interpretability, and a proof that for the m.a.c. condition to hold it suffices to consider formulas \phi(x,y) with x a single variable. Many examples of m.a.c.s are given, including 2-sorted structures (F,V) where V is a vector space over a finite field F possibly equipped with a bilinear form, and an example arising from representations of quivers of finite representation type. We also give examples and structural results for multidimensional exact classes (m.e.c.s), where the definable sets take a fixed number of precisely specified cardinalities, which again vary with M. We also develop a notion of infinite generalised measurable structure, whereby definable sets are assigned values in an ordered semiring. We show that any infinite ultraproduct of a m.a.c. is generalised measurable, that values can be taken in an ordered ring if the m.a.c. is a m.e.c., and explore model-theoretic consequences of generalised measurability. Such a structure cannot have the strict order property, and stability-theoretic properties can be read off from the measures in the semiring., Comment: 76 pages

Inherent properties of superconducting Bi2Sr2CaCu2O8+x films, such as the high superconducting transition temperature Tc, efficient Josephson coupling between neighboring CuO layers, and fast quasiparticle relaxation dynamics, make them a promising platform for advances in quantum computing and communication technologies. However, preserving two-dimensional superconductivity during device fabrication is an outstanding experimental challenge because of the fast degradation of the superconducting properties of two-dimensional flakes when they are exposed to moisture, organic sol vents, and heat. Here, to realize superconducting devices utilizing two-dimensional superconducting films, we develop a novel fabrication technique relying on the cryogenic dry transfer of printable circuits embedded into a silicon nitride membrane. This approach separates the circuit fabrication stage requiring chemically reactive substances and ionizing physical processes from the creation of the thin superconducting structures. Apart from providing electrical contacts in a single transfer step, the membrane encapsulates the surface of the crystal shielding it from the environment. The fabricated atomically thin Bi2Sr2CaCu2O8+x-based devices show high superconducting transition temperature Tc ~ 91 K close to that of the bulk crystal and demonstrate stable super conducting properties, Comment: 21 pages, 5 figures

Deep learning in medical imaging has the potential to minimize the risk of diagnostic errors, reduce radiologist workload, and accelerate diagnosis. Training such deep learning models requires large and accurate datasets, with annotations for all training samples. However, in the medical imaging domain, annotated datasets for specific tasks are often small due to the high complexity of annotations, limited access, or the rarity of diseases. To address this challenge, deep learning models can be pre-trained on large image datasets without annotations using methods from the field of self-supervised learning. After pre-training, small annotated datasets are sufficient to fine-tune the models for a specific task. The most popular self-supervised pre-training approaches in medical imaging are based on contrastive learning. However, recent studies in natural image processing indicate a strong potential for masked autoencoder approaches. Our work compares state-of-the-art contrastive learning methods with the recently introduced masked autoencoder approach "SparK" for convolutional neural networks (CNNs) on medical images. Therefore we pre-train on a large unannotated CT image dataset and fine-tune on several CT classification tasks. Due to the challenge of obtaining sufficient annotated training data in medical imaging, it is of particular interest to evaluate how the self-supervised pre-training methods perform when fine-tuning on small datasets. By experimenting with gradually reducing the training dataset size for fine-tuning, we find that the reduction has different effects depending on the type of pre-training chosen. The SparK pre-training method is more robust to the training dataset size than the contrastive methods. Based on our results, we propose the SparK pre-training for medical imaging tasks with only small annotated datasets., Comment: Accepted in Nature Scientific Reports

Twisted van der Waals (vdW) heterostructures offer a unique platform for engineering the efficient Josephson coupling between cuprate thin crystals harboring the nodal superconducting order parameter. Preparing vdW heterostructures-based Josephson junction comprising stacked cuprates requires maintaining an ordered interface with preserved surface superconductivity. Here, we report the preparation of the Josephson junction out of the stacked Bi2Sr2CuCa2O8+d crystals using the cryogenic dry transfer technique and encapsulating the junction with an insulating layer, that protects the interface during the electrical contacts evaporation at the 10-6 mbar base pressure. We find that the Josephson critical current Ic has a maximum at low twist angles, comparable to that of the bulk intrinsic Josephson junctions, and is reduced by two orders of magnitude at twist angles close to 45{\deg}. The reduction of Ic occurs due to a mismatch between superconducting d-wave order parameters, which suppresses the direct Cooper pair tunneling.

Antiferromagnets have recently been propelled to the forefront of spintronics by their high potential for revolutionizing memory technologies. For this, understanding the formation and driving mechanisms of the domain structure is paramount. In this work, we investigate the domain structure in a thin-film canted antiferromagnet $\alpha$-Fe$_2$O$_3$. We find that the internal destressing fields driving the formation of domains do not follow the crystal symmetry of $\alpha$-Fe$_2$O$_3$, but fluctuate due to substrate clamping. This leads to an overall isotropic distribution of the N\'eel order with locally varying effective anisotropy in antiferromagnetic thin films. Furthermore, we show that the weak ferromagnetic nature of $\alpha$-Fe$_2$O$_3$ leads to a qualitatively different dependence on magnetic field compared to collinear antiferromagnets such as NiO. The insights gained from our work serve as a foundation for further studies of electrical and optical manipulation of the domain structure of antiferromagnetic thin films., Comment: 9 pages, 5 figures

Linear magnetoresistance (LMR) is of particular interest for memory, electronics, and sensing applications, especially when it does not saturate over a wide range of magnetic fields. One of its principal origins is local mobility or density inhomogeneities, often structural, which in the Parish-Littlewood theory leads to an unsaturating LMR proportional to mobility. Structural disorder, however, also tends to limit the mobility and hence the overall LMR amplitude. An alternative route to achieve large LMR is via non-structural inhomogeneities which do not affect the zero field mobility, like magnetic domains. Here, linear positive magnetoresistance caused by magnetic texture is reported in \ch{LaTiO3}/\ch{SrTiO3} heterostructures. The LMR amplitude reaches up to 6500\% at 9T. This colossal value is understood by the unusual combination of a very high thin film mobility, up to 40 000 cm$^2$/V.s, and a very large coverage of low-mobility regions. These regions correlate with a striped magnetic structure, compatible with a spiral magnetic texture in the \ch{LaTiO3} film, revealed by low temperature Lorentz transmission electron microscopy. These results provide a novel route for the engineering of large-LMR devices.

Here, by making use of medium and high resolution autocorrected off-axis electron holography, we directly probe the electrostatic potential as well as in-plane and out-of-plane charge delocalization at edges and steps in multilayer hexagonal boron nitride. In combination with ab-initio calculations, the data allows to directly reveal the formation of out-of-plane covalent bonds at folded zig-zag edges and steps comprising two monolayers and the absence of which at monolayer steps. The technique paves the way for studying other charge (de)localization phenomena in 2D materials, e.g., at polar edges, topological edge states and defects.

High-temperature cuprate superconductors-based van der Waals (vdW) heterostructures hold high technological promise. One of the obstacles hindering progress is the detrimental effect of disorder on the properties of Josephson junctions (JJ) realized by the vdW devices. Here we report the new method of fabricating twisted vdW heterostructures made of Bi2Sr2CuCa2O8+d, crucially improving the JJ characteristics, pushing them up to those of the intrinsic JJs in bulk samples. The method combines a cryogenic stacking using a solvent-free stencil mask technique and covering interface by the insulating hexagonal boron nitride crystals. Despite the high-vacuum condition down to 10-6 mbar in the evaporation chamber, the interface appears to be protected from water molecules during the in-situ metal deposition only when fully encapsulated. Comparing the current-voltage curves of encapsulated and unencapsulated interfaces, we reveal that the encapsulated interfaces' characteristics are crucially improved so that the corresponding JJs demonstrate high critical currents and sharpness of the superconducting transition comparable to those of the intrinsic JJs. Finally, we show that the encapsulated heterostructures are more stable in time.

We investigate a tapered, hybrid plasmonic waveguide which has previously been proposed as an optically efficient near-field transducer (NFT), or component thereof, in several devices which aim to exploit nanofocused light. We numerically analyze how light is transported through the waveguide and ultimately focused via effective-mode coupling and taper optimization. Crucial dimensional parameters in this optimization process are identified that are not only necessary to achieve maximum optical throughput, but also optimum thermal performance with specific application towards heat-assisted magnetic recording (HAMR). It is shown that existing devices constructed on similar waveguides may benefit from a heat spreader to avoid deformation of the plasmonic element which we achieve with no cost to the optical efficiency. For HAMR, our design is able to surpass many industry requirements in regard to both optical and thermal efficiency using pertinent figure of merits like 8.5% optical efficiency., Comment: 14 pages, 7 figures, and 3 tables. Published version: see https://doi.org/10.1364/OE.26.001752. Related works: see https://doi.org/10.1364/oe.22.011236, https://doi.org/10.1364/oe.26.030292, and https://doi.org/10.1063/5.0044490. Keywords: Integrated Optics; Components; Integrated Optics Devices; Surface Plasmons; Plasmonics