Artificial intelligence generated content (AIGC) technologies, with a predominance of large language models (LLMs), have demonstrated remarkable performance improvements in various applications, which have attracted g...
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The functional processes and the upgrading strategies in a company environment depend not only on the firm’s internal and external sourcing strategies but also on international experience and interactions developed o...
A 2-dimensional discrete Fourier transform (2-D DFT) is a required preprocessing step for convolutional neural networks (CNNs) to perform matrix multiplication in convolutional layers. Here we present an ultrasonic wa...
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Several studies investigated the diagnosis of Parkinson’s disease (PD), which utilized machine learning methods such as support vector machine, neural network, Naïve Bayes and K-nearest neighbor. In addition, di...
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As an information-flow privacy property, opacity characterizes whether a malicious external observer (referred to as an intruder) is able to infer the secret behavior of a system. This paper addresses the problem of o...
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With the continuous development of quantum technology, entangled signal has been used in more and more fields. Due to the unique temporal-spatial correlation characteristics of entangled signal, it provides promising ...
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Multi-dimensional classification(MDC) aims to build classification models for multiple heterogenous class spaces simultaneously, where each class space characterizes the semantics of an object w.r.t. one specific dime...
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Multi-dimensional classification(MDC) aims to build classification models for multiple heterogenous class spaces simultaneously, where each class space characterizes the semantics of an object w.r.t. one specific dimension. Modeling dependencies among class spaces plays a key role in solving MDC tasks, where most approaches work by assuming directed acyclic graph(DAG) structure or random chaining structure over class spaces. Different from existing probabilistic strategies, a deterministic strategy named Seem for dependency modeling is proposed in this paper via stacked dependency exploitation. In the first-level, pairwise dependencies are considered which can be modeled more reliably than modeling full dependencies among all class spaces by DAG or chaining structure. In the second-level, the class label of unseen instance *** class space is determined by adaptively stacking predictive outputs from first-level pairwise *** results show that stacked dependency exploitation leads to superior performance against stateof-the-art MDC approaches.
Rotor balancing is the final step in the rotor manufacturing process and may be necessary during rotor refurbishment. The process can be time consuming and costly, especially for supercritical monolithic rotors, impac...
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Smart grids, which integrate modern Internet of Things (IoT) technology and renewable energy sources to boost efficiency, sustainability, and dependability, are poised to bring about a dramatic shift in the future of ...
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Topological photonics enables unprecedented photon manipulation by realizing various topological states, such as corner, edge, and surface states. However, achieving a topological fiber state has remained elusive. Her...
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Topological photonics enables unprecedented photon manipulation by realizing various topological states, such as corner, edge, and surface states. However, achieving a topological fiber state has remained elusive. Here, we demonstrate a topological fiber state in a Weyl gyromagnetic photonic crystal fiber. By applying an in-plane magnetic bias to a gyromagnetic photonic crystal fiber with broken parity-inversion symmetry, we create an asymmetrical Weyl band gap that supports one-way fiber states associated with type-II Weyl points. Dispersion and topological invariant calculations reveal a transition from Weyl surface states to one-way Weyl fiber states. Electromagnetic field simulations confirm the existence of these one-way Weyl fiber states and their robust transport, even in the presence of metallic obstacles along the transport path. Our findings offer an intriguing pathway for exploring topological states and guiding the design of topological fibers.
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