arXiv subjects

Christian Baumgartner

Publications and source records attributed to Christian Baumgartner.

17 recordsLinked to original sources

arXiv2025

CUTE-MRI: Conformalized Uncertainty-based framework for Time-adaptivE MRI

Magnetic Resonance Imaging (MRI) offers unparalleled soft-tissue contrast but is fundamentally limited by long acquisition times. While deep learning-based accelerated MRI can dramatically shorten scan times, the reconstruction from undersampled data introduces ambiguity resulting from an ill-posed problem with infinitely many possible solutions that propagates to downstream clinical tasks. This uncertainty is usually ignored during the acquisition process as acceleration factors are often fixed a priori, resulting in scans that are either unnecessarily long or of insufficient quality for a given clinical endpoint. This work introduces a dynamic, uncertainty-aware acquisition framework that adjusts scan time on a per-subject basis. Our method leverages a probabilistic reconstruction model to estimate image uncertainty, which is then propagated through a full analysis pipeline to a quantitative metric of interest (e.g., patellar cartilage volume or cardiac ejection fraction). We use conformal prediction to transform this uncertainty into a rigorous, calibrated confidence interval for the metric. During acquisition, the system iteratively samples k-space, updates the reconstruction, and evaluates the confidence interval. The scan terminates automatically once the uncertainty meets a user-predefined precision target. We validate our framework on both knee and cardiac MRI datasets. Our results demonstrate that this adaptive approach reduces scan times compared to fixed protocols while providing formal statistical guarantees on the precision of the final image. This framework moves beyond fixed acceleration factors, enabling patient-specific acquisitions that balance scan efficiency with diagnostic confidence, a critical step towards personalized and resource-efficient MRI.

eess.IV↗

arXiv2024

Spontaneous supercurrents and vortex depinning in two-dimensional arrays of $\varphi_0$-junctions

Two-dimensional arrays of ballistic Josephson junctions are important as model systems for synthetic quantum materials. Here, we investigate arrays of multiterminal junctions which exhibit a phase difference $\varphi_0$ at zero current. When applying an in-plane magnetic field we observe nonreciprocal vortex depinning currents. We explain this effect in terms of a ratchet-like pinning potential, which is induced by spontaneous supercurrent loops. Supercurrent loops arise in multiterminal $\varphi_0$-junction arrays as a consequence of next-nearest neighbor Josephson coupling. Tuning the density of vortices to commensurate values of the frustration parameter results in an enhancement of the ratchet effect. In addition, we find a surprising sign reversal of the ratchet effect near frustration 1/3. Our work calls for the search for novel magnetic structures in artificial crystals in the absence of time-reversal symmetry.

cond-mat.supr-con↗

arXiv2022

Sign reversal of the AC and DC supercurrent diode effect and 0-$π$-like transitions in ballistic Josephson junctions

The recent discovery of intrinsic supercurrent diode effect, and its prompt observation in a rich variety of systems, has shown that nonreciprocal supercurrents naturally emerge when both space- and time-inversion symmetries are broken. In Josephson junctions, nonreciprocal supercurrent can be conveniently described in terms of spin-split Andreev states. Here, we demonstrate a sign reversal of the supercurrent diode effect, in both its AC and DC manifestations. In particular, the AC diode effect -- i.e., the asymmetry of the Josephson inductance as a function of the supercurrent -- allows us to probe the current-phase relation near equilibrium. Using a minimal theoretical model, we can then link the sign reversal of the AC diode effect to the so-called 0-$π$-like transition, a predicted, but still elusive feature of multi-channel junctions. Our results demonstrate the potential of inductance measurements as sensitive probes of the fundamental properties of unconventional Josephson junctions.

cond-mat.supr-con↗

arXiv2022

Anisotropic vortex squeezing in synthetic Rashba superconductors: a manifestation of Lifshitz invariants

Most of 2D superconductors are of type II, i.e., they are penetrated by quantized vortices when exposed to out-of-plane magnetic fields. In presence of a supercurrent, a Lorentz-like force acts on the vortices, leading to drift and dissipation. The current-induced vortex motion is impeded by pinning at defects, enabling the use of superconductors to generate high magnetic fields without dissipation. Usually, the pinning strength decreases upon any type of pair-breaking. Here we show that in Rashba superconductors the application of an in-plane field leads, instead, to an unexpected enhancement of pinning. When rotating the in-plane component of the field with respect to the current direction, the vortex inductance turns out to be highly anisotropic. We explain this phenomenon as a manifestation of Lifshitz invariant terms in the Ginzburg-Landau free energy, which are enabled by inversion and time-reversal symmetry breaking and lead to an elliptic squeezing of vortex cores. Our experiment provides access to a fundamental property of Rashba superconductors and offers an entirely new approach to vortex manipulation.

cond-mat.supr-con↗

arXiv2022

Supercurrent diode effect and magnetochiral anisotropy in few-layer NbSe$_2$

Nonreciprocal transport refers to charge transfer processes that are sensitive to the bias polarity. Until recently, nonreciprocal transport was studied only in dissipative systems, where the nonreciprocal quantity is the resistance. Recent experiments have, however, demonstrated nonreciprocal supercurrent leading to the observation of a supercurrent diode effect in Rashba superconductors, opening the vision of dissipationless electronics. Here we report on a supercurrent diode effect in NbSe$_2$ constrictions obtained by patterning NbSe$_2$ flakes with both even and odd layer number. The observed rectification is driven by valley-Zeeman spin-orbit interaction. We demonstrate a rectification efficiency as large as 60%, considerably larger than the efficiency of devices based on Rashba superconductors. In agreement with recent theory for superconducting transition metal dichalcogenides, we show that the effect is driven by an out-of-plane magnetic field component. Remarkably, we find that the effect becomes field-asymmetric in the presence of an additional in-plane field component transverse to the current direction. Supercurrent diodes offer a further degree of freedom in designing superconducting quantum electronics with the high degree of integrability offered by van der Waals materials.

cond-mat.supr-con↗

arXiv2022

A Human-Centered Machine-Learning Approach for Muscle-Tendon Junction Tracking in Ultrasound Images

Biomechanical and clinical gait research observes muscles and tendons in limbs to study their functions and behaviour. Therefore, movements of distinct anatomical landmarks, such as muscle-tendon junctions, are frequently measured. We propose a reliable and time efficient machine-learning approach to track these junctions in ultrasound videos and support clinical biomechanists in gait analysis. In order to facilitate this process, a method based on deep-learning was introduced. We gathered an extensive dataset, covering 3 functional movements, 2 muscles, collected on 123 healthy and 38 impaired subjects with 3 different ultrasound systems, and providing a total of 66864 annotated ultrasound images in our network training. Furthermore, we used data collected across independent laboratories and curated by researchers with varying levels of experience. For the evaluation of our method a diverse test-set was selected that is independently verified by four specialists. We show that our model achieves similar performance scores to the four human specialists in identifying the muscle-tendon junction position. Our method provides time-efficient tracking of muscle-tendon junctions, with prediction times of up to 0.078 seconds per frame (approx. 100 times faster than manual labeling). All our codes, trained models and test-set were made publicly available and our model is provided as a free-to-use online service on https://deepmtj.org/.

cs.CV↗

arXiv2022

Effect of Rashba and Dresselhaus spin-orbit coupling on supercurrent rectification and magnetochiral anisotropy of ballistic Josephson junctions

Simultaneous breaking of inversion- and time-reversal symmetry in Josephson junction leads to a possible violation of the $I(φ)=-I(-φ)$ equality for the current-phase relation. This is known as anomalous Josephson effect and it produces a phase shift $φ_0$ in sinusoidal current-phase relations. In ballistic Josephson junctions with non-sinusoidal current phase relation the observed phenomenology is much richer, including the supercurrent diode effect and the magnetochiral anisotropy of Josephson inductance. In this work, we present measurements of both effects on arrays of Josephson junctions defined on epitaxial Al/InAs heterostructures. We show that the orientation of the current with respect to the lattice affects the magnetochiral anisotropy, possibly as the result of a finite Dresselhaus component. In addition, we show that the two-fold symmetry of the Josephson inductance reflects in the activation energy for phase slips.

cond-mat.supr-con↗

arXiv2021

A Josephson junction supercurrent diode

Transport is called nonreciprocal when not only the sign, but also the absolute value of the current, depends on the polarity of the applied voltage. It requires simultaneously broken inversion and time-reversal symmetries, e.g., by the interplay of spin-orbit coupling and magnetic field. So far, observation of nonreciprocity was always tied to resistivity, and dissipationless nonreciprocal circuit elements were elusive. Here, we engineer fully superconducting nonreciprocal devices based on highly-transparent Josephson junctions fabricated on InAs quantum wells. We demonstrate supercurrent rectification far below the transition temperature. By measuring Josephson inductance, we can link nonreciprocal supercurrent to the asymmetry of the current-phase relation, and directly derive the supercurrent magnetochiral anisotropy coefficient for the first time. A semi-quantitative model well explains the main features of our experimental data. Nonreciprocal Josephson junctions have the potential to become for superconducting circuits what $pn$-junctions are for traditional electronics, opening the way to novel nondissipative circuit elements.

cond-mat.supr-con↗

arXiv2021

Josephson inductance as a probe for highly ballistic semiconductor-superconductor weak links

We present simultaneous measurements of Josephson inductance and DC transport characteristics of ballistic Josephson junctions based upon an epitaxial Al-InAs heterostructure. The Josephson inductance at finite current bias directly reveals the current-phase relation. The proximity-induced gap, the critical current and the average value of the transparency $\barτ$ are extracted without need for phase bias, demonstrating, e.g.,~a near-unity value of $\barτ=0.94$. Our method allows us to probe the devices deeply in the non-dissipative regime, where ordinary transport measurements are featureless. In perpendicular magnetic field the junctions show a nearly perfect Fraunhofer pattern of the critical current, which is insensitive to the value of $\barτ$. In contrast, the signature of supercurrent interference in the inductance turns out to be extremely sensitive to $\barτ$.

cond-mat.supr-con↗

arXiv2020

Average motion of emerging solar active region polarities II: Joy's law

The tilt of solar active regions described by Joy's law is essential for converting a toroidal field to a poloidal field in Babcock-Leighton dynamo models. In thin flux tube models the Coriolis force causes Joy's law, acting on east-west flows as they rise towards the surface. Our goal is to measure the evolution of the average tilt angle of hundreds of active regions as they emerge, so that we can constrain the origins of Joy's law. We measured the tilt angle of the primary bipoles in 153 emerging active regions in the Solar Dynamics Observatory Helioseismic Emerging Active Region survey. We used line-of-sight magnetic field measurements averaged over 6 hours to define the polarities and measure the tilt angle up to four days after emergence. We find that at the time of emergence the polarities are on average aligned east-west, and that neither the separation nor the tilt depends on latitude. We do find, however, that ARs at higher latitudes have a faster north-south separation speed than those closer to the equator at the emergence time. After emergence, the tilt angle increases and Joy's law is evident about two days later. The scatter in the tilt angle is independent of flux until about one day after emergence, when higher-flux regions have a smaller scatter in tilt angle than lower-flux regions. Our finding that active regions emerge with an east-west alignment is surprising since thin flux tube models predict that tilt angles of rising flux tubes are generated below the surface. Previously reported tilt angle relaxation of deeply anchored flux tubes can be largely explained by the change in east-west separation. We conclude that Joy's law is caused by an inherent north-south separation speed present when the flux first reaches the surface, and that the scatter in the tilt angle is consistent with buffeting of the polarities by supergranulation.

astro-ph.SR↗

arXiv2020

Automatic Tracking of the Muscle Tendon Junction in Healthy and Impaired Subjects using Deep Learning

Recording muscle tendon junction displacements during movement, allows separate investigation of the muscle and tendon behaviour, respectively. In order to provide a fully-automatic tracking method, we employ a novel deep learning approach to detect the position of the muscle tendon junction in ultrasound images. We utilize the attention mechanism to enable the network to focus on relevant regions and to obtain a better interpretation of the results. Our data set consists of a large cohort of 79 healthy subjects and 28 subjects with movement limitations performing passive full range of motion and maximum contraction movements. Our trained network shows robust detection of the muscle tendon junction on a diverse data set of varying quality with a mean absolute error of 2.55$\pm$1 mm. We show that our approach can be applied for various subjects and can be operated in real-time. The complete software package is available for open-source use via: https://github.com/luuleitner/deepMTJ

q-bio.QM↗

arXiv2020

Ultrasound in Locomotion Research -- The Quest for Wider Views

In a systematic review, we investigate current applications of ultrasound in locomotion research. Shortcomings in the range of view of ultrasound systems affect the direct validation of musculoskeletal simulations as inverse approaches have to be applied. We present currently used methods to estimate muscle and tendon length in human plantarflexors.

q-bio.QM↗

arXiv2019

Semi-Supervised and Task-Driven Data Augmentation

Supervised deep learning methods for segmentation require large amounts of labelled training data, without which they are prone to overfitting, not generalizing well to unseen images. In practice, obtaining a large number of annotations from clinical experts is expensive and time-consuming. One way to address scarcity of annotated examples is data augmentation using random spatial and intensity transformations. Recently, it has been proposed to use generative models to synthesize realistic training examples, complementing the random augmentation. So far, these methods have yielded limited gains over the random augmentation. However, there is potential to improve the approach by (i) explicitly modeling deformation fields (non-affine spatial transformation) and intensity transformations and (ii) leveraging unlabelled data during the generative process. With this motivation, we propose a novel task-driven data augmentation method where to synthesize new training examples, a generative network explicitly models and applies deformation fields and additive intensity masks on existing labelled data, modeling shape and intensity variations, respectively. Crucially, the generative model is optimized to be conducive to the task, in this case segmentation, and constrained to match the distribution of images observed from labelled and unlabelled samples. Furthermore, explicit modeling of deformation fields allow synthesizing segmentation masks and images in exact correspondence by simply applying the generated transformation to an input image and the corresponding annotation. Our experiments on cardiac magnetic resonance images (MRI) showed that, for the task of segmentation in small training data scenarios, the proposed method substantially outperforms conventional augmentation techniques.

cs.CV↗

arXiv2018

A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols

Convolutional neural networks (CNNs) have shown promising results on several segmentation tasks in magnetic resonance (MR) images. However, the accuracy of CNNs may degrade severely when segmenting images acquired with different scanners and/or protocols as compared to the training data, thus limiting their practical utility. We address this shortcoming in a lifelong multi-domain learning setting by treating images acquired with different scanners or protocols as samples from different, but related domains. Our solution is a single CNN with shared convolutional filters and domain-specific batch normalization layers, which can be tuned to new domains with only a few ($\approx$ 4) labelled images. Importantly, this is achieved while retaining performance on the older domains whose training data may no longer be available. We evaluate the method for brain structure segmentation in MR images. Results demonstrate that the proposed method largely closes the gap to the benchmark, which is training a dedicated CNN for each scanner.

stat.ML↗

arXiv2017

On the factors determining the eruptive character of solar flares

We investigated how the magnetic field in solar active regions (ARs) controls flare activity, i.e., whether a confined or eruptive flare occurs. We analyzed 44 flares of GOES class M5.0 and larger that occurred during 2011--2015. We used 3D potential magnetic field models to study their location (using the flare distance from the flux-weighted AR center $d_{\mathrm{FC}}$) and the strength of the magnetic field in the corona above (via decay index $n$ and flux ratio). We also present a first systematic study of the orientation of the coronal magnetic field, using the orientation $φ$ of the flare-relevant polarity inversion line as a measure. We analyzed all quantities with respect to the size of the underlying dipole field, characterized by the distance between the opposite-polarity centers, $d_{\mathrm{PC}}$. Flares originating from underneath the AR dipole $(d_{\mathrm{FC}}/d_{\mathrm{PC}}<0.5$) tend to be eruptive if launched from compact ARs ($d_{\mathrm{PC}}\leq60$ Mm) and confined if launched from extended ARs. Flares ejected from the periphery of ARs ($d_{\mathrm{FC}}/d_{\mathrm{PC}}>0.5$) are predominantly eruptive. In confined events the flare-relevant field adjusts its orientation quickly to that of the underlying dipole with height ($Δφ\gtrsim40^\circ$ until the apex of the dipole field), in contrast to eruptive events where it changes more slowly with height. The critical height for torus instability, $h_{\mathrm{crit}}=h(n=1.5)$, discriminates best between confined ($h_{\mathrm{crit}}\gtrsim40$ Mm) and eruptive flares ($h_{\mathrm{crit}}\lesssim40$ Mm). It discriminates better than $Δφ$, implying that the decay of the confining field plays a stronger role than its orientation at different heights.

astro-ph.SR↗

arXiv2017

Few-layer graphene patterned bottom gates for van der Waals heterostructures

We introduce a method of local gating for van der Waals heterostructures, employing a few-layer graphene patterned bottom gate. Being a member of the 2D material family, few-layer graphene adapts perfectly to the commonly used stacking method. Its versatility regarding patterning as well as its flatness make it an ideal candidate for experiments on locally gated 2D materials. Moreover, in combination with ultra-thin hexagonal boron nitride as an insulating layer, sharp potential steps can be created and the quality of the investigated 2D material can be sustained. To underline the good feasibility and performance, we show results on transport experiments in periodically modulated graphene- boron nitride heterostructures, where the charge carrier density is tuned via locally acting patterned few layer graphene bottom gates and a global back gate.

cond-mat.mes-hall↗

arXiv2016

Unsupervised domain adaptation in brain lesion segmentation with adversarial networks

Significant advances have been made towards building accurate automatic segmentation systems for a variety of biomedical applications using machine learning. However, the performance of these systems often degrades when they are applied on new data that differ from the training data, for example, due to variations in imaging protocols. Manually annotating new data for each test domain is not a feasible solution. In this work we investigate unsupervised domain adaptation using adversarial neural networks to train a segmentation method which is more invariant to differences in the input data, and which does not require any annotations on the test domain. Specifically, we learn domain-invariant features by learning to counter an adversarial network, which attempts to classify the domain of the input data by observing the activations of the segmentation network. Furthermore, we propose a multi-connected domain discriminator for improved adversarial training. Our system is evaluated using two MR databases of subjects with traumatic brain injuries, acquired using different scanners and imaging protocols. Using our unsupervised approach, we obtain segmentation accuracies which are close to the upper bound of supervised domain adaptation.

cs.CV↗