In actual conversational scenarios, we can often determine which parts of the previous dialogue are more critical based on the current inquiry. However, the existing contextual modeling methods often encode the query ...
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The construction of generalized mathematical models is proposed that describe the dynamics of a controlled belt conveyor with a variable angle between the horizontal plane and the plane of the belt. The models under c...
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In the educational field, Key Performance Indicators (KPIs) aim to provide an opportunity to evaluate the performance of employees, teachers’ skills and pedagogy, students’ results, learning objects, educational ins...
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Since myocardial infarction (MI) continues to be a major cause of death worldwide, precise risk prediction is essential. In order to improve MI risk assessment, this work presents an improved predictive model that mak...
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Smart contracts codify real-world transactions and automatically execute the terms of the contract when predefined conditions are met. This paper proposes SmartML, a modeling language for smart contracts tha...
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The article explores a significant scientific challenge related to the development of techniques and tools for constructing discrete models of complex objects using interval difference equations. This approach combine...
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Recommender systems (RS) have become a central tool for providing personalized suggestions, yet the growing complexity of modern methods, such as Graph Neural Networks (GNNs), has introduced new challenges related to ...
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This paper demonstrates the learning of the underlying device physics by mapping device structure images to their corresponding Current-Voltage (IV) characteristics using a novel framework based on variational autoenc...
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ISBN:
(纸本)9784863488038
This paper demonstrates the learning of the underlying device physics by mapping device structure images to their corresponding Current-Voltage (IV) characteristics using a novel framework based on variational autoencoders (VAE). Since VAE is used, domain expertise is not required and the framework can be quickly deployed on any new device and measurement. This is expected to be useful in the compact modeling of novel devices when only device cross-sectional images and electrical characteristics are available (e.g. novel emerging memory). Technology computer-Aided Design (TCAD) generated and hand-drawn Metal-OxideSemiconductor (MOS) device images and noisy drain-currentgate-voltage curves (IDVG) are used for the demonstration. The framework is formed by stacking two VAEs (one for image manifold learning and one for IDVG manifold learning) which communicate with each other through the latent variables. Five independent variables with different strengths are used. It is shown that it can perform inverse design (generate a design structure for a given IDVG) and forward prediction (predict IDVG for a given structure image, which can be used for compact modeling if the image is treated as device parameters) successfully. Since manifold learning is used, the machine is shown to be robust against noise in the inputs (i.e. using hand-drawn images and noisy IDVG curves) and not confused by weak and irrelevant independent variables.
Water Quality Sensors (WQSs) are becoming a promised tool in water quality data assessment and scientific value of aquatic structure. Such sensors are broadly used to produce live results by evaluating major water qua...
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While cycle-accurate simulators are essential tools for architecture research, design, and development, their practicality is limited by an extremely long time-to-solution for realistic applications under investigatio...
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