Speech features have been investigated as novel digital biomarkers for many psychiatric and neurocognitive diseases. Microphones are the most used devices for speech recording but inevitably suffering from several dis...
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The quantum anomalous Hall insulator is characterized by a quantized Hall resistance plateau of h/(Ce2) with C being the Chern number. Previously, high-Chern-number insulators with C≥2 that intrinsically suppress the...
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The quantum anomalous Hall insulator is characterized by a quantized Hall resistance plateau of h/(Ce2) with C being the Chern number. Previously, high-Chern-number insulators with C≥2 that intrinsically suppress the energy dissipation in quantized electrical transport have been realized in few-layer MnBi2Te4 nanoflakes under moderate magnetic fields or in Cr-doped topological insulators at extremely low temperatures. It has nevertheless proven elusive to achieve a high-temperature high-Chern-number insulator at zero magnetic field. Here, we demonstrate that the magnetic state in MnBi4Te7 nanoflakes can be consecutively tuned from an antiferromagnetic (AFM) state to an AFM-ferromagnetic (FM) coexistence state, and finally to a robust ferromagnetic (FM) state with large coercivities at temperatures up to 3 K via a protonic gate. This AFM-FM phase transition is well captured by density functional theory simulations that the FM state develops in MnBi4Te7 under hole doping. Notably, we find that the Chern number of FM MnBi4Te7 can be largely tuned not merely by the Zeeman field but also by the sample thickness. Our work demonstrates that gate-tuned MnBi4Te7 nanoflakes hold high potentials for realizing a high-temperature high-Chern-number insulator at zero magnetic field and promising applications in the field of low-energy electronics.
This article introduces the Tenth Dialog System Technology Challenge (DSTC-10). This edition of the DSTC focuses on applying end-to-end dialog technologies for five distinct tasks in dialog systems, namely 1. Incorpor...
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The deployment of various networks (e.g., Internet of Things (IoT) and mobile networks), databases (e.g., nutrition tables and food compositional databases) and social media (e.g., Instagram and Twitter) generates hug...
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Detecting objects in real-world scenes is a complex task due to various challenges, including the vast range of object categories, and potential encounters with previously unknown or unseen objects. The challenges nec...
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The increasing popularity of social media promotes the proliferation of fake news, which has caused significant negative societal effects. Therefore, fake news detection on social media has recently become an emerging...
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Unmanned Aerial Vehicle (UAV) can be used as wireless aerial mobile base station for collecting data from sensors in UAV-based Wireless Sensor Networks (WSNs), which is crucial for providing seamless services and impr...
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Accurate decoding of electroencephalographic (EEG) signals is a crucial foundation for brain-computer interface (BCI) applications. Among various decoding approaches, those that do not require calibration are particul...
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In graph representation learning, the class imbalance problem is a significant challenge that has received much attention from academics. Although current approaches have shown promising results, they have not adequat...
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In graph representation learning, the class imbalance problem is a significant challenge that has received much attention from academics. Although current approaches have shown promising results, they have not adequately addressed the problems of node quantity imbalance and feature space imbalance in datasets. This research presents a node transfer with graph contrastive learning framework (NT-GCL) that aims to improve the representation capabilities of graph neural networks for minority classes nodes by balancing node quantity and feature space distributions. First, the proposed node transfer algorithm redistributes misclassified nodes from majority classes to achieve a balanced distribution of node quantity and feature space. This approach effectively prevents the feature space of minority classes from being compressed by majority classes during information propagation, further mitigating potential imbalance issues. Subsequently, the self-supervised contrastive learning strategy is employed to train the model without relying on labels, reducing the bias introduced by labeled data. Experiments conducted with various encoders on six public datasets demonstrate that NT-GCL exhibits strong competitiveness in class-imbalanced node classification.
This paper proposes for the first time an algorithm PSpan for mining frequent complete subnets from a set of Petri nets. We introduced the concept of complete subnets and the net graph representation. PSpan transforms...
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