Bilayer topological insulator/ferromagnet (TI/FM) heterostructures are promising for spintronic applications due to their low switching energy and therefore power efficiency. Until recently, the reactivity of TIs with...
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Bilayer topological insulator/ferromagnet (TI/FM) heterostructures are promising for spintronic applications due to their low switching energy and therefore power efficiency. Until recently, the reactivity of TIs with FM films was overlooked in the spin-orbit-torque literature, even though there are reports that it is energetically favorable for TIs to react with transition metals and form interfacial layers. In this study, we fabricated a TI/FM heterostructure comprised of molecular beam epitaxy grown Sb2Te3 and dc sputtered Ni80Fe20. Broadband ferromagnetic resonance revealed spin-pumping evident by the significant enhancement in Gilbert damping, which is likely a signature of the topological surface states or the presence of large spin-orbit coupling in the adjacent Sb2Te3. With low-temperature magnetometry, an exchange bias is observed that indicates an exchange interaction between an antiferromagnet (AFM) and an adjacent FM. Cross-section high-angle annular dark-field scanning transmission electron microscopy characterization of the Sb2Te3−Ni80Fe20 bilayer revealed a complex interface showing diffusion of Fe and Ni into the Sb2Te3 film yielding the formation of a FeTe2 1T−type structural phase. Furthermore, density functional theory calculations revealed that the FeTe2 1T−phase has an AFM ground state. Due to experimental limitations in the electron-energy-loss spectroscopy measurements, the precise chemistry of the interfacial phase could not be determined, therefore it is possible that the FeTe2 1T and/or an intermixed (Fe1–xNix)Te2 1T is the AFM interfacial phase contributing to exchange bias in the system. This work emphasizes the chemical complexity of TI/FM interfaces that host novel, metastable magnetic topological phases and require more in-depth studies of other similar interfaces.
Room-temperature ferromagnetism in graphene is a crucial step toward the practical application of spintronic *** hydrogen adsorption on graphene has been shown to induce magnetic moments,the overall efficiency remains...
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Room-temperature ferromagnetism in graphene is a crucial step toward the practical application of spintronic *** hydrogen adsorption on graphene has been shown to induce magnetic moments,the overall efficiency remains low due to the clustering of hydrogen atoms and weak magnetic *** study demonstrates a highly effective vacancy-assisted hydrogenation method to synthesize hydrogenated graphene(HG)with robust room-temperature *** introduction of vacancies inhibits hydrogen clustering,increases magnetic edge atoms,and enhances the coupling between magnetic *** a result,HG exhibits a Curie temperature of 540 K and a saturation magnetization of 0.69 emu/g at 300 *** findings provide a new approach for the efficient hydrogenation of graphene,paving the way for its applications in spintronic devices.
Polaritons are arousing tremendous interests in physics and material sciences for their unique and amazing properties,especially including the condensation,lasing without inversion and even room-temperature ***,we pro...
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Polaritons are arousing tremendous interests in physics and material sciences for their unique and amazing properties,especially including the condensation,lasing without inversion and even room-temperature ***,we propose a cell vibron polariton(cell-VP):a collectively coherent mode of a photon and all phospholipid molecules in a myelin sheath formed by glial ***-VP can be resonantly self-confined in the myelin sheath under physiological *** observations benefit from the specifically compact,ordered and polar thin-film structure of the sheath,and the relatively strong coupling of the mid-infrared photon with the vibrons of phospholipid tails in the *** underlying physics is revealed to be the collectively coherent superposition of the photon and vibrons,the polariton induced significant enhancement of myelin permittivity,and the resonance of the polariton with the *** captured cell-VPs in myelin sheaths may provide a promising way for super-efficient consumption of extra-weak bioenergy and even directly serve for quantum *** findings further the understanding of nervous system operations at cellular level from the view of quantum mechanics.
The importance of proper data normalization for deep neural networks is well known. However, in continuous-time state-space model estimation, it has been observed that improper normalization of either the hidden state...
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The importance of proper data normalization for deep neural networks is well known. However, in continuous-time state-space model estimation, it has been observed that improper normalization of either the hidden state or hidden state derivative of the model estimate, or even of the time interval can lead to numerical and optimization challenges with deep learning based methods. This results in a reduced model quality. In this contribution, we show that these three normalization tasks are inherently coupled. Due to the existence of this coupling, we propose a solution to all three normalization challenges by introducing a normalization constant at the state derivative level. We show that the appropriate choice of the normalization constant is related to the dynamics of the to-be-identified system and we derive multiple methods of obtaining an effective normalization constant. We compare and discuss all the normalization strategies on a benchmark problem based on experimental data from a cascaded tanks system and compare our results with other methods of the identification literature.
Both passive and active intelligent reflecting surfaces (IRSs) can be deployed in complex environments to enhance wireless network coverage by creating multiple blockage-free cascaded line-of-sight (LoS) links. In thi...
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In healthcare, integrating diverse biomedical data modalities is critical for comprehensive analyses that can lead to better patient outcomes. Graph-based models have emerged as effective tools for handling such compl...
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In healthcare, integrating diverse biomedical data modalities is critical for comprehensive analyses that can lead to better patient outcomes. Graph-based models have emerged as effective tools for handling such complex, non-Euclidean data by leveraging spatial and relational structures. However, model interpretability remains crucial for their successful deployment and for regulatory-approved implementations in clinical settings, as healthcare practitioners need transparent insights to build trust and guide decision-making. In this work, we present a technical and comprehensive review of research on interpretable graph-based models applied to multimodal biomedical data, covering studies from January 2019 to September 2024. Our analysis of published papers reveals a dominant focus on disease classification (particularly cancer) and a heavy reliance on static graph construction methods (e.g., Pearson correlation, Euclidean distance). While some approaches incorporate inherently interpretable designs, most rely on post-hoc techniques originally developed for non-graph data—such as gradient-based saliency or SHAP (Shapley additive explanations)—and only a small fraction utilize graph-specific methods like GNNExplainer. To address these gaps, we categorize existing explainable artificial intelligence (XAI) approaches according to their interpretability strategies and highlight evolving trends, including the graph-in-graph paradigm, the integration of knowledge graphs, and dynamic edge construction. To illuminate their practical implications, we compare and benchmark representative XAI techniques (sensitivity analysis, gradient saliency, SHAP, and graph masking) using a real-world Alzheimer’s disease (AD) dataset. Our findings show that while SHAP and sensitivity analysis reveal a broader spectrum of established disease-related pathways and Gene Ontology terms, gradient saliency and graph masking highlight unique metabolic and transport processes, underscoring the comple
Understanding the mechanism of single photon emission (SPE) in two-dimensional (2D) materials is an unsolved problem important for quantum optical materials and the development of quantum information applications. In ...
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This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized version...
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ISBN:
(纸本)9781665435413
This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which, in turn, aggregates them into a quantized global model and synchronizes the devices. With the goal of jointly determining the set of participating devices in each training iteration and the bitwidths employed at the devices, we pose an optimization problem for minimizing the training loss of quantized FL under a device sampling budget and delay requirement. Our analytical results show that the improvement of FL training loss between two consecutive iterations depends on not only the device selection and quantization scheme, but also on several parameters inherent to the model being learned. As a result, we propose, a model-based reinforcement learning (RL) method to optimize action selection over iterations. Compared to model-free RL, the proposed approach leverages the derived mathematical characterization of the FL training process to discover an effective device selection and quantization scheme without imposing additional device communication overhead. Numerical evaluations show that the proposed FL framework can achieve the same classification performance while reducing the number of training iterations needed for convergence by 20% compared to model-free RL-based FL.
The Internet of Vehicles (IoV) is undergoing a transformative evolution, enabled by advancements in future 6G network technologies, to support intelligent, highly reliable, and low-latency vehicular services. However,...
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The joint hyperspectral image (HSI) and LiDAR data classification aims to interpret ground objects at more detailed and precise level. Although deep learning methods have shown remarkable success in the multisource da...
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