Lung cancer requires accurate risk assessment and early detection due to its high prevalence and fatality rate. The simultaneous analysis of clinical symptoms and etiological factors using Machine Learning revolutioni...
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Because of their high torque, high power density, and appropriate speed range, brushless DC (BLDC) motors are the top choice for lightweight electric vehicles. The application's goal is to use a Hall position sens...
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Few-shot image classification (FSIC) is a computer vision task from the few-shot learning (FSL) category in which the model learns to classify images using only a few training samples. It has been demonstrated that ev...
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In recent years, novel view synthesis from a monocular image has become a research hot-spot that attracts significant attention. Some recent work identifies latent vectors for high-quality view generation via iterativ...
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In recent years, novel view synthesis from a monocular image has become a research hot-spot that attracts significant attention. Some recent work identifies latent vectors for high-quality view generation via iterative optimisation, which is a time-consuming process. In contrast, some others utilise an encoder learning a mapping function to approximately estimate optimal latent codes, which significantly reduces its processing time but sacrifices reconstruction quality. Consequently, how to balance synthesis quality and its generation efficiency still remains challenging. In this paper, we propose a residual-based encoder to incorporate with a 3D Generative Adversarial Networks (GAN), named ReE3D, for novel view synthesis. It applies an iterative prediction of latent codes to ensure much higher quality of novel view synthesis with an insignificant increase of processing time when compared to existing encoder-based 3D GAN inversion methods. Additionally, we enforce a novel geometric loss constraint on the encoder to predict view-invariant latent codes, thus effectively mitigating the trade-off between geometric and texture quality in 3D GAN inversion. Extensive experimental results demonstrate that our extended encoder-based method has achieved best trade-off performance in terms of novel view synthesis quality and its execution time. Our method has gained comparable synthesis quality with exponentially decreased processing time when compared to iterative optimisation methods, while improved synthesis performance of encoder-based methods significantly. IEEE
Printed electronics (PEs) promises on-demand fabrication, low nonrecurring engineering costs, and subcent fabrication costs. It also allows for high customization that would be infeasible in silicon, and bespoke archi...
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Micro-robotic cell injection is a widely used procedure in cell biology where a small quantity of biological material is inserted into a cell using an automated or semi-automated micro-robotic system. Given its micro-...
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A microcontroller-based ventilator was developed including a health monitoring system featuring a Wi-Fi-based notifier. A pressure-controlled ventilator was included, and an AMBU bag was used for controlling the breat...
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The asymptotic mean squared test error and sensitivity of the Random Features Regression model (RFR) have been recently studied. We build on this work and identify in closed-form the family of Activation Functions (AF...
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Due to the lack of state dimension optimization methods, deep state space models (SSMs) have sacrificed model capacity, training search space, or stability to alleviate computational costs caused by high state dimensi...
In-context learning (ICL) refers to a remarkable capability of pretrained large language models, which can learn a new task given a few examples during inference. However, theoretical understanding of ICL is largely u...
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