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Numerous studies have highlighted that atypical brain development, particularly during infancy and toddlerhood, is linked to an increased likelihood of being diagnosed with a neurodevelopmental condition, such as autism. Accurate brain tissue segmentations for morphological analysis are essential in numerous infant studies. However, due to ongoing white matter (WM) myelination changing tissue contrast in T1- and T2-weighted images, automatic tissue segmentation in 6-month infants is particularly difficult. On the other hand, manual labelling by experts is time-consuming and labor-intensive. In this study, we propose the first 8-tissue segmentation pipeline for six-month-old infant brains. This pipeline utilizes domain adaptation (DA) techniques to leverage our longitudinal data, including neonatal images segmented with the neonatal Developing Human Connectome Project structural pipeline. Our pipeline takes raw 6-month images as inputs and generates the 8-tissue segmentation as outputs, forming an end-to-end segmentation pipeline. The segmented tissues include WM, gray matter (GM), cerebrospinal fluid (CSF), ventricles, cerebellum, basal ganglia, brainstem, and hippocampus/amygdala. Cycle-Consistent Generative Adversarial Network (CycleGAN) and Attention U-Net were employed to achieve the image contrast transformation between neonatal and 6-month images and perform tissue segmentation on the synthesized 6-month images (neonatal images with 6-month intensity contrast), respectively. Moreover, we incorporated the segmentation outputs from Infant Brain Extraction and Analysis Toolbox (iBEAT) and another Attention U-Net to further enhance the performance and construct the end-to-end segmentation pipeline. Our evaluation with real 6-month images achieved a DICE score of 0.92, an HD95 of 1.6, and an ASSD of 0.42., Comment: 11 pages, 4 figures, to be published in MICCAI PIPPI workshop

Clinical adoption of deep learning models has been hindered, in part, because the black-box nature of neural networks leads to concerns regarding their trustworthiness and reliability. These concerns are particularly relevant in the field of neuroimaging due to the complex brain phenotypes and inter-subject heterogeneity often encountered. The challenge can be addressed by interpretable deep learning (iDL) methods that enable the visualisation and interpretation of the inner workings of deep learning models. This study systematically reviewed the literature on neuroimaging applications of iDL methods and critically analysed how iDL explanation properties were evaluated. Seventy-five studies were included, and ten categories of iDL methods were identified. We also reviewed five properties of iDL explanations that were analysed in the included studies: biological validity, robustness, continuity, selectivity, and downstream task performance. We found that the most popular iDL approaches used in the literature may be sub-optimal for neuroimaging data, and we discussed possible future directions for the field.

Money laundering presents a pervasive challenge, burdening society by financing illegal activities. To more effectively combat and detect money laundering, the use of network information is increasingly being explored, exploiting that money laundering necessarily involves interconnected parties. This has lead to a surge in literature on network analytics (NA) for anti-money laundering (AML). The literature, however, is fragmented and a comprehensive overview of existing work is missing. This results in limited understanding of the methods that may be applied and their comparative detection power. Therefore, this paper presents an extensive and systematic review of the literature. We identify and analyse 97 papers in the Web of Science and Scopus databases, resulting in a taxonomy of approaches following the fraud analytics framework of Bockel-Rickermann et al.. Moreover, this paper presents a comprehensive experimental framework to evaluate and compare the performance of prominent NA methods in a uniform setup. The framework is applied on the publicly available Elliptic data set and implements manual feature engineering, random walk-based methods, and deep learning GNNs. We conclude from the results that network analytics increases the predictive power of the AML model with graph neural networks giving the best results. An open source implementation of the experimental framework is provided to facilitate researchers and practitioners to extend upon these results and experiment on proprietary data. As such, we aim to promote a standardised approach towards the analysis and evaluation of network analytics for AML.

Qubits that can be efficiently controlled are pivotal in the development of scalable quantum hardware. Resonant control is commonly embraced to execute high-fidelity quantum gates but demands integration of high-frequency oscillating signals and results in qubit crosstalk and heating. Establishing quantum control based on discrete signals could therefore result in a paradigm shift. This may be accomplished with single-spin semiconductor qubits, if one can engineer hopping spins between quantum dots with site-dependent spin quantization axis. Here, we introduce hopping-based universal quantum logic and obtain single-qubit gate fidelities of 99.97%, coherent shuttling fidelities of 99.992%, and two-qubit gates fidelities of 99.3%, corresponding to error rates that have been predicted to allow for quantum error correction. We demonstrate that hopping spins also constitute an elegant tuning method by statistically mapping the coherence of a 10-quantum dot system. These results motivate dense quantum dot arrays with sparse occupation for efficient and high-connectivity qubit registers., Comment: main text with 18 pages and 3 figures, supplementary materials with 65 pages and 26 figures, in a single file

Pairing of electrons is ubiquitous in electronic systems featuring attractive inter-electron interactions, as exemplified in superconductors. Counter-intuitively, it can also be mediated in certain circumstances by the repulsive Coulomb interaction alone. Quantum Hall (QH) Fabry-P\'erot interferometers (FPIs) tailored in two-dimensional electron gas under a perpendicular magnetic field has been argued to exhibit such unusual electron pairing seemingly without attractive interaction. Here, we show evidence in graphene QH FPIs revealing not only a similar electron pairing at bulk filling factor nu=2 but also an unforeseen emergence of electron tripling characterized by a fractional Aharonov-Bohm flux period h/3e (h is the Planck constant and e the electron charge) at nu=3. Leveraging a novel plunger-gate spectroscopy, we demonstrate that electron pairing (tripling) involves correlated charge transport on two (three) entangled QH edge channels. This spectroscopy indicates a quantum interference flux-periodicity determined by the sum of the phases acquired by the distinct QH edge channels having slightly different interfering areas. While recent theory invokes the dynamical exchange of neutral magnetoplasmons -- dubbed neutralons -- as mediator for electron pairing, our discovery of three entangled QH edge channels with apparent electron tripling defies understanding and introduces a new three-body problem for interacting fermions.

Congenital Heart Disease (CHD) is a group of cardiac malformations present already during fetal life, representing the prevailing category of birth defects globally. Our aim in this study is to aid 3D fetal vessel topology visualisation in aortic arch anomalies, a group which encompasses a range of conditions with significant anatomical heterogeneity. We present a multi-task framework for automated multi-class fetal vessel segmentation from 3D black blood T2w MRI and anomaly classification. Our training data consists of binary manual segmentation masks of the cardiac vessels' region in individual subjects and fully-labelled anomaly-specific population atlases. Our framework combines deep learning label propagation using VoxelMorph with 3D Attention U-Net segmentation and DenseNet121 anomaly classification. We target 11 cardiac vessels and three distinct aortic arch anomalies, including double aortic arch, right aortic arch, and suspected coarctation of the aorta. We incorporate an anomaly classifier into our segmentation pipeline, delivering a multi-task framework with the primary motivation of correcting topological inaccuracies of the segmentation. The hypothesis is that the multi-task approach will encourage the segmenter network to learn anomaly-specific features. As a secondary motivation, an automated diagnosis tool may have the potential to enhance diagnostic confidence in a decision support setting. Our results showcase that our proposed training strategy significantly outperforms label propagation and a network trained exclusively on propagated labels. Our classifier outperforms a classifier trained exclusively on T2w volume images, with an average balanced accuracy of 0.99 (0.01) after joint training. Adding a classifier improves the anatomical and topological accuracy of all correctly classified double aortic arch subjects., Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2023:015

The small footprint of semiconductor qubits is favourable for scalable quantum computing. However, their size also makes them sensitive to their local environment and variations in gate structure. Currently, each device requires tailored gate voltages to confine a single charge per quantum dot, clearly challenging scalability. Here, we tune these gate voltages and equalize them solely through the temporary application of stress voltages. In a double quantum dot, we reach a stable (1,1) charge state at identical and predetermined plunger gate voltage and for various interdot couplings. Applying our findings, we tune a 2$\times$2 quadruple quantum dot such that the (1,1,1,1) charge state is reached when all plunger gates are set to 1 V. The ability to define required gate voltages may relax requirements on control electronics and operations for spin qubit devices, providing means to advance quantum hardware.

The electrical characterisation of classical and quantum devices is a critical step in the development cycle of heterogeneous material stacks for semiconductor spin qubits. In the case of silicon, properties such as disorder and energy separation of conduction band valleys are commonly investigated individually upon modifications in selected parameters of the material stack. However, this reductionist approach fails to consider the interdependence between different structural and electronic properties at the danger of optimising one metric at the expense of the others. Here, we achieve a significant improvement in both disorder and valley splitting by taking a co-design approach to the material stack. We demonstrate isotopically-purified, strained quantum wells with high mobility of 3.14(8)$\times$10$^5$ cm$^2$/Vs and low percolation density of 6.9(1)$\times$10$^{10}$ cm$^{-2}$. These low disorder quantum wells support quantum dots with low charge noise of 0.9(3) $\mu$eV/Hz$^{1/2}$ and large mean valley splitting energy of 0.24(7) meV, measured in qubit devices. By striking the delicate balance between disorder, charge noise, and valley splitting, these findings provide a benchmark for silicon as a host semiconductor for quantum dot qubits. We foresee the application of these heterostructures in larger, high-performance quantum processors.

Fetal Magnetic Resonance Imaging at low field strengths is emerging as an exciting direction in perinatal health. Clinical low field (0.55T) scanners are beneficial for fetal imaging due to their reduced susceptibility-induced artefacts, increased T2* values, and wider bore (widening access for the increasingly obese pregnant population). However, the lack of standard automated image processing tools such as segmentation and reconstruction hampers wider clinical use. In this study, we introduce a semi-automatic pipeline using quantitative MRI for the fetal body at low field strength resulting in fast and detailed quantitative T2* relaxometry analysis of all major fetal body organs. Multi-echo dynamic sequences of the fetal body were acquired and reconstructed into a single high-resolution volume using deformable slice-to-volume reconstruction, generating both structural and quantitative T2* 3D volumes. A neural network trained using a semi-supervised approach was created to automatically segment these fetal body 3D volumes into ten different organs (resulting in dice values > 0.74 for 8 out of 10 organs). The T2* values revealed a strong relationship with GA in the lungs, liver, and kidney parenchyma (R^2>0.5). This pipeline was used successfully for a wide range of GAs (17-40 weeks), and is robust to motion artefacts. Low field fetal MRI can be used to perform advanced MRI analysis, and is a viable option for clinical scanning., Comment: Accepted by MICCAI 2023

Quantum links can interconnect qubit registers and are therefore essential in networked quantum computing. Semiconductor quantum dot qubits have seen significant progress in the high-fidelity operation of small qubit registers but establishing a compelling quantum link remains a challenge. Here, we show that a spin qubit can be shuttled through multiple quantum dots while preserving its quantum information. Remarkably, we achieve these results using hole spin qubits in germanium, despite the presence of strong spin-orbit interaction. We accomplish the shuttling of spin basis states over effective lengths beyond 300 $\mu$m and demonstrate the coherent shuttling of superposition states over effective lengths corresponding to 9 $\mu$m, which we can extend to 49 $\mu$m by incorporating dynamical decoupling. These findings indicate qubit shuttling as an effective approach to route qubits within registers and to establish quantum links between registers., Comment: Main text: 9 pages, 4 figures Supplementary material: 10 pages, 8 figures

Gate-defined quantum dots in silicon-germanium heterostructures have become a compelling platform for quantum computation and simulation. Thus far, developments have been limited to quantum dots defined in a single plane. Here, we propose to advance beyond planar systems by exploiting heterostructures with multiple quantum wells. We demonstrate the operation of a gate-defined vertical double quantum dot in a strained germanium double quantum well. In quantum transport measurements we observe stability diagrams corresponding to a double quantum dot system. We analyze the capacitive coupling to the nearby gates and find two quantum dots accumulated under the central plunger gate. We extract the position and estimated size, from which we conclude that the double quantum dots are vertically stacked in the two quantum wells. We discuss challenges and opportunities and outline potential applications in quantum computing and quantum simulation., Comment: 12 pages including supplementary material

Highly uniform quantum systems are essential for the practical implementation of scalable quantum processors. While quantum dot spin qubits based on semiconductor technology are a promising platform for large-scale quantum computing, their small size makes them particularly sensitive to their local environment. Here, we present a method to electrically obtain a high degree of uniformity in the intrinsic potential landscape using hysteretic shifts of the gate voltage characteristics. We demonstrate the tuning of pinch-off voltages in quantum dot devices over hundreds of millivolts that then remain stable at least for hours. Applying our method, we homogenize the pinch-off voltages of the plunger gates in a linear array for four quantum dots reducing the spread in pinch-off voltage by one order of magnitude. This work provides a new tool for the tuning of quantum dot devices and offers new perspectives for the implementation of scalable spin qubit arrays.

The EFIELD instrument is part of the geophysics and meteorology sensor package DraGMet on the Dragonfly mission, which will explore the surface of Titan in the mid-2030s. EFIELD consists of two electrodes designed to passively record the AC electric field at each landing site. The exploration zone of Dragonfly will mostly consist of dune fields, covered with sand grains. Little is known on the properties of these grains, although Cassini-Huygens observations suggest they are mostly made of organic material produced by Titan's atmospheric photochemistry and evolved at the surface. Little is known also about dune formation and in general about the transport of sediments by winds. The latter much depends on inter-particle forces and therefore on how grains are charged by friction. We demonstrate here that the EFIELD experiment can bring new insights on these questions. We have developed a hydrodynamic-electrostatic model to simulate the trajectory of a wind-blown charged sand grain in the vicinity of an idealized EFIELD probe and to predict how such a grain flying close to the probe would affect its potential. We show that, in some conditions, the resulting perturbation will be strong enough to be detected by the EFIELD probe. More specifically, we find that the detection of typical charged wind-blown grains (200 microns) on Titan requires an instrument standard deviation noise inferior to 1mV, though occasional larger grains flying close to one electrode could be detected with a higher noise level. Furthermore, we propose a method to retrieve information on the charge and velocity of wind-blown charged grains detected by the EFIELD experiment. This method well applies for cases where the particle trajectory can be regarded as quasi-linear. We validate our inversion approach on both synthetic and experimental data obtained with a laboratory prototype of the EFIELD experiment., Comment: Accepted for publication in Icarus (2 Nov. 2022). Supporting Information, datasets and codes available on Zenodo (DOI: 10.5281/zenodo.7274939)

Simulations using highly tunable quantum systems may enable investigations of condensed matter systems beyond the capabilities of classical computers. Quantum dots and donors in semiconductor technology define a natural approach to implement quantum simulation. Several material platforms have been used to study interacting charge states, while gallium arsenide has also been used to investigate spin evolution. However, decoherence remains a key challenge in simulating coherent quantum dynamics. Here, we introduce quantum simulation using hole spins in germanium quantum dots. We demonstrate extensive and coherent control enabling the tuning of multi-spin states in isolated, paired, and fully coupled quantum dots. We then focus on the simulation of resonating valence bonds and measure the evolution between singlet product states which remains coherent over many periods. Finally, we realize four-spin states with $s$-wave and $d$-wave symmetry. These results provide means to perform non-trivial and coherent simulations of correlated electron systems., Comment: Article main text and Supplementary Information Main text: 9 pages, 5 figures Supplementary Information: 15 pages, 9 figures

Besides wide attraction in the industry, IoT is being used to advance STEM and STEAM education across a range of education levels. This work presents a remote sensing platform, named IoT with a Soft Touch, developed to achieve two goals. First, it aims to lower the technicality, stimulating the students to do STE(A)M. Second, the technology is to be used in `softer' applications (e.g., environmental and health care), thereby aiming to attract a more diverse set of student profiles. Students can easily build a wireless sensing device, with a specific application in mind. The modular design of the platform and an intuitive graphical configurator tool allows them to tailor the device's functionality to their needs. The sensor's data is transmitted wirelessly with LoRaWAN. The data can be viewed and analyzed on a dashboard, or the raw data can be extracted for further processing, e.g., as part of the school's STE(A)M curriculum. This work elaborates on the low-power and modular design challenges, and how the platform is used in education.

Magnetic resonance imaging of whole fetal body and placenta is limited by different sources of motion affecting the womb. Usual scanning techniques employ single-shot multi-slice sequences where anatomical information in different slices may be subject to different deformations, contrast variations or artifacts. Volumetric reconstruction formulations have been proposed to correct for these factors, but they must accommodate a non-homogeneous and non-isotropic sampling, so regularization becomes necessary. Thus, in this paper we propose a deep generative prior for robust volumetric reconstructions integrated with a diffeomorphic volume to slice registration method. Experiments are performed to validate our contributions and compare with a state of the art method in a cohort of $72$ fetal datasets in the range of $20-36$ weeks gestational age. Results suggest improved image resolution and more accurate prediction of gestational age at scan when comparing to a state of the art reconstruction method. In addition, gestational age prediction results from our volumetric reconstructions compare favourably with existing brain-based approaches, with boosted accuracy when integrating information of organs other than the brain. Namely, a mean absolute error of $0.618$ weeks ($R^2=0.958$) is achieved when combining fetal brain and trunk information., Comment: 23 pages, 15 figures, 1 table