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Multi-center brain age prediction via dual-modality fusion convolutional network
No full textAccurate prediction of brain age is crucial for identifying deviations between typical individual brain development trajectories and neuropsychiatric disease progression. Although current research has made progress, the effective application of brain age prediction models to multi-center datasets, particularly those with small-sample sizes, remains a significant challenge that is yet to be addressed. To this end, we propose a multi-center data correction method, which employs a domain adaptation correction strategy with Wasserstein distance of optimal transport, along with maximum mean discrepancy to improve the generalizability of brain-age prediction models on small-sample datasets. Additionally, most of the existing brain age models based on neuroimage identify the task of predicting brain age as a regression or classification problem, which may affect the accuracy of the prediction. Therefore, we propose a brain dual-modality fused convolutional neural network model (BrainDCN) for brain age prediction, and optimize this model by introducing a joint loss function of mean absolute error and cross-entropy, which identifies the prediction of brain age as both a regression and classification task. Furthermore, to highlight age-related features, we construct weighting matrices and vectors from a single-center training set and apply them to multi-center datasets to weight important features. We validate the BrainDCN model on the CamCAN dataset and achieve the lowest average absolute error compared to state-of-the-art models, demonstrating its superiority. Notably, the joint loss function and weighted features can further improve the prediction accuracy. More importantly, our proposed multi-center correction method is tested on four neuroimaging datasets and achieves the lowest average absolute error compared to widely used correction methods, highlighting the superior performance of the method in cross-center data integration and analysis. Furthermore, the application to multi-center schizophrenia data shows a mean accelerated aging compared to normal controls. Thus, this research establishes a pivotal methodological foundation for multi-center brain age prediction studies, exhibiting considerable applicability in clinical contexts, which are predominantly characterized by small-sample datasets
Coherent information as a mixed-state topological order parameter of fermions
No full textQuantum error correction protects quantum information against decoherence provided the noise strength remains below a critical threshold. This threshold marks the critical point for the decoding phase transition. Here we connect this transition in the toric code to a topological phase transition in disordered Majorana fermions at high temperatures. A quantum memory in the error correctable phase is captured by the presence of a Majorana zero mode, trapped in vortex defects associated with twisted boundary conditions. These results are established by expressing the coherent information, which measures the amount of recoverable quantum information in a given noisy code, in terms of a mixed-state topological order parameter of fermions. Our work hints at a broader connection of the robustness of quantum information in stabilizer codes and mixed-state topological phase transitions in symmetry protected fermion matter
The XYZ ruby code: Making a case for a three-colored graphical calculus for quantum error correction in spacetime
No full textAnalyzing and developing new quantum error-correcting schemes is one of the most prominent tasks in quantum computing research. In such efforts, introducing time dynamics explicitly in both analysis and design of error-correcting protocols constitutes an important cornerstone. In this work, we present a graphical formalism based on tensor networks to capture the logical action and error-correcting capabilities of any Clifford circuit with Pauli measurements. We showcase the formalism on new Floquet codes derived from topological subsystem codes, which we call XYZ ruby codes. Based on the projective symmetries of the building blocks of the tensor network we develop a framework of Pauli flows. Pauli flows allow for a graphical understanding of all quantities entering an error correction analysis of a circuit, including different types of QEC experiments, such as memory and stability experiments. We lay out how to derive a well-defined decoding problem from the tensor network representation of a protocol and its Pauli flows alone, independent of any stabilizer code or fixed circuit. Importantly, this framework applies to all Clifford protocols and encompasses both measurement- and circuit-based approaches to fault tolerance. We apply our method to our new family of dynamical codes which are in the same topological phase as the 2+1d color code, making them a promising candidate for low-overhead logical gates. In contrast to its static counterpart, the dynamical protocol applies a Z3 automorphism to the logical Pauli group every three timesteps. We highlight some of its topological properties and comment on the anyon physics behind a planar layout. Lastly, we benchmark the performance of the XYZ ruby code on a torus by performing both memory and stability experiments and find competitive circuit-level noise thresholds of 0.18%, comparable with other Floquet codes and 2+1d color codes
Impurities in hydrogen released from perhydro benzyltoluene - Assessment and adsorptive removal
No full textOrigin of Microstructural Degradation in Nickel Oxide–Scandia–Ceria-Stabilized Zirconia during Reduction
No full textRendering the European neutron research landscape
No full textNeutrons, owing to their unique properties, serve as indispensable probes for investigating the structure and dynamics of materials across various length scales. The scientific community utilizing neutron research infrastructures encompasses a diverse range of disciplines, making it challenging to quantify its scientific and societal impact. To address this challenge, we apply Natural Language Processing (NLP) and machine learning techniques to analyze the scientific output of the European neutron science community. Leveraging open-source software toolkits, our method allows for the quantitative assessment of community evolution and research focus. Our analysis reveals consistent growth in the neutron community despite a reduction in sources, underscoring the enduring significance of neutron methods in scientific research. Furthermore, an increase in unique authors and an even distribution of publications across diverse scientific topics highlight the community’s interdisciplinary nature and collaborative spirit. While this study emphasizes neutron scattering, our methodology holds promise for a broad range of scientific communities reliant on Large Research Infrastructures (LRIs), offering opportunities for collaboration, optimization of experimental approaches, and informed decision-making by governmental and funding bodies
Towards a scalable recycling process for ceramics in fuel-electrode-supported solid oxide cells
No full textThe solid oxide cell (SOC) technology relies on high-performance ceramics containing strategically valuable and critical raw materials. This study focuses on the processing of spent cell materials from fuel-electrode-supported SOCs, demonstrating the feasibility of utilizing a significant portion of the ceramic cell in a closed-loop system. More than 85 % of the cell's initial mass was directly incorporated into substrate manufacturing. The air-side perovskites were initially separated using hydrochloric acid treatment, followed by mechanical reprocessing of the remaining half cells. The performance of the resulting full cells containing 50 mass% recycled material in the substrate was evaluated, achieving a current density of up to 1.14 A/cm² at 0.7 V and 750 °C in fuel cell mode, which is comparable to that of non-recycled counterparts. Preliminary experiments for the recovery of leached metal ions from the air electrode were conducted using direct oxalate precipitation while examining pH dependence. Direct oxalate precipitation proved particularly effective in the low pH range for the recovery of a lanthanum oxalate precursor with a purity exceeding 98 %. The results highlight the potential for simple and sustainable practices in SOC technology
Improved Charge Carrier Transport Across Grain Boundaries in N‐type PbSe by Dopant Segregation
No full textAb initio investigation of topological magnetism in two-dimensional van der Waals heterostructures
No full textMagnetism in two-dimensional (2D) van der Walls (vdW) materials is a rapidly evolving field in condensed matter physics and materials science, marked by intriguing discoveries and potential applications. Unlike traditional three-dimensional materials, 2D vdW materials are characterized by their ultra-thin, often single-layer, structure leading to unique magnetic properties triggered by proximity-effects, which are facilitated by the underlying vdW gap. Such properties are not only fundamental for understanding the physics of low-dimensional systems but also hold immense promise for the development of advanced technologies in data storage, spintronics, and quantum computing. Building on the foundational understanding of magnetism in 2D materials, this thesis dives deeper into the specific case of CrTe2 and CrSBr. Based on a multiscale modelling approach that combines first-principles calculations and a Heisenberg model supplied with ab-initio parameters, we report a strong magnetoelastic coupling in a free-standing monolayer of CrTe2. We demonstrate that different crystal structures of a single CrTe2 give rise to non-collinear magnetism through magnetic frustration and the emergence of the Dzyaloshinskii-Moriya interaction (DMI). Utilizing atomistic spin relaxation, we perform a detailed investigation of the complex magnetic properties pertaining to this 2D material impacted by the presence of various types of structural distortions akin to charge density waves. Also, we demonstrate that interfacing a CrTe2 layer with various Te-based layers enables the control of the magnetic exchange and Dzyaloshinskii-Moriya interactions as well as the magnetic anisotropy energy of the whole heterobilayer, and thereby the emergence of topological magnetic phases such as skyrmions and antiferromagnetic N´eel merons. The latter are novel particles in the world of topological magnetism since they arise in a frustrated N´eel magnetic environment and manifest as multiples of intertwined hexamer-textures. Our findings pave a promising road for proximity-induced engineering of both ferromagnetic and long-sought antiferromagnetic chiral objects in the very same 2D material, which is appealing for new information technology devices employing quantum materials. Moreover, we demonstrate the all-electric switching of the topological nature of individual magnetic objects emerging in 2D vdW heterobilayers. We show that an external electric field modifies the vdW gap between CrTe2 and (Rh, Ti)Te2 layers and alters the underlying magnetic interactions. This enables switching between ferromagnetic skyrmions and meron pairs in the CrTe2/RhTe2 heterobilayer while it enhances the stability of frustrated antiferromagnetic merons in the CrTe2/TiTe2 heterobilayer. We envision that the electrical engineering of distinct topological magnetic solitons in a single device could pave the way for novel energy-efficient mechanisms to store and transmit information with applications in spintronics. Finally, via machine learning concepts we integrated linear spin wave theory (LSWT) with activelearning sampling to develop a Kalman Filter Adversarial Bayesian Optimization (KFABO) algorithm. This algorithm excels at managing highly noisy experimental spectra of 2D bulk CrSBr, aiming to map the experimentally extracted magnon spectrum with minimal sampling points and iterations. Additionally, the KFABO algorithm is designed to accurately extract magnetic parameters from inelastic neutron scattering data, significantly enhancing the efficiency and accuracy of experimental measurements