This paper presents a novel biological neural networks based on memristive Tabu learning neuron (MTLN) model influenced by electromagnetic radiation. Despite the model having an unstable equilibrium plane, numerical i...
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Eating monitoring has remained an open challenge in medical research for years due to the lack of non-invasive sensors for continuous monitoring and the reliable methods for automatic behavior detection. In this paper...
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Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as healthcare, science, and engineering. ...
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In the realm of topological quantum physics, it is widely accepted that a global bandgap is necessary to achieve quantized Hall conductance, irrespective of its origin-magnetic field, exchange coupling or other mechan...
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The Area Under the ROC Curve (AUC) is a crucial metric for machine learning, which evaluates the average performance over all possible True Positive Rates (TPRs) and False Positive Rates (FPRs). Based on the knowledge...
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With the augmentation of traffic exponentially, we observe that traffic congestion does not guarantee road safety or enhance the driving experience. In the recent past, Social Internet of Vehicles (SIoV), a social net...
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computer-aided design (CAD) tools are increasingly popular in modern dental practice, particularly for treatment planning or comprehensive prognosis evaluation. In particular, the 2D panoramic X-ray image efficiently ...
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Internet finance fraud is an increasingly serious social and economic problem. Online payment services (OPSs) are the typical models of Internet finance, and the fraudulent transaction in OPSs is also a typical fraud ...
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Internet finance fraud is an increasingly serious social and economic problem. Online payment services (OPSs) are the typical models of Internet finance, and the fraudulent transaction in OPSs is also a typical fraud pattern. The method of identifying fraudulent transactions by constructing a fraud detection model based on machine learning has become a promising idea for online payment anti-fraud. In the process of constructing fraud detection models, the feature engineering is the most critical step. It is also one of the most time-consuming and specialized steps in the relevant area. In the study of feature engineering, the existing online payment fraud detection models are mainly carried out by experts in the form of manual construction based on business knowledge. However, there are many fraud scenarios in OPSs where the process of feature construction is so different. Artificial feature construction methods can no longer meet the increasing demand of anti-fraud. An important way to solve this problem is to automate feature engineering. In the field of Internet financial anti-fraud, the expressibility and interpretability of features play a pivotal role. It is helpful to understand the original source fields and their construction process of important features. This is useful for mining and analyzing the characteristics of fraud methods and follow-up improvement rules engines. These are of great significance for fraud detection models. Therefore, the interpretability of the model method is particularly important. Usually, the optimization of detection accuracy is carried out under the premise of ensuring interpretability. This paper proposed a lightweight, tree-structure, high efficiency and scalable automatic feature engineering method for fraud detection of online payment. The method is as follows: (1) The method has low requirements on the calculation conditions and little dependence on the dataset samples. To realize this advantage, it used the tree structur
Recently, Unmanned Aerial Vehicles (UAVs) present a promising advanced technology that can enhance people life quality and smartness of cities dramatically and increase overall economic efficiency. UAVs have attained ...
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Networking superconducting quantum computers is a longstanding challenge in quantum science. The typical approach has been to cascade transducers: converting to optical frequencies at the transmitter and to microwave ...
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Networking superconducting quantum computers is a longstanding challenge in quantum science. The typical approach has been to cascade transducers: converting to optical frequencies at the transmitter and to microwave frequencies at the receiver. However, the small microwave-optical coupling and added noise have proven formidable obstacles. Instead, we propose optical networking via heralding end-to-end entanglement with one detected photon and teleportation. This new protocol can be implemented on standard transduction hardware while providing significant performance improvements over transduction. In contrast to cascaded direct transduction, our scheme absorbs the low optical-microwave coupling efficiency into the heralding step, thus breaking the rate-fidelity trade-off. Moreover, this technique unifies and simplifies entanglement generation between superconducting devices and other physical modalities in quantum networks.
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