In this paper, we propose efficient and practical data-driven methods for weather forecasts. We exploit the information brought by historical weather datasets to build machine-learning-based models. These models are e...
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Spiking Neural Networks (SNNs), are inspired by the biological brain's complicated signaling mechanisms and possess unique characteristics that set them apart from traditional artificial neural networks. This rese...
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ISBN:
(数字)9798350386059
ISBN:
(纸本)9798350386066
Spiking Neural Networks (SNNs), are inspired by the biological brain's complicated signaling mechanisms and possess unique characteristics that set them apart from traditional artificial neural networks. This research study explores the challenging domain of image classification, specifically utilizing the well-known MNIST dataset through the development and thorough evaluation of different neural models for edge computing. However, the primary contribution is the autonomous selection of the best-performing SNN model through various early stopping approaches and validation functions, allowing the models to autonomously adapt during training. In addition, this article presents the standalone AutoML-SNN model, which is the introduction of dynamic elements into selected SNN domains, enhancing their adaptability to complex patterns within the dataset. Furthermore, the early stopping methodologies are used to reduce overfitting hazards, and using the 3000-neuron set, the LIF appeared as the most proficient neural model.
Multilevel qudit systems are increasingly being explored as alternatives to traditional qubit systems due to their denser information storage and processing potential. However, qudits are more susceptible to decoheren...
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Multilevel qudit systems are increasingly being explored as alternatives to traditional qubit systems due to their denser information storage and processing potential. However, qudits are more susceptible to decoherence than qubits due to increased loss channels, noise sensitivity, and crosstalk. To address these challenges, we develop protocols for dynamical decoupling (DD) of qudit systems based on the Heisenberg-Weyl group. We implement and experimentally verify these DD protocols on a superconducting transmon processor that supports qudit operation based on qutrits (d=3) and ququarts (d=4). Specifically, we demonstrate single-qudit DD sequences to decouple qutrits and ququarts from system-bath-induced decoherence. We also introduce two-qudit DD sequences designed to suppress the detrimental cross-Kerr couplings between coupled qudits. This allows us to demonstrate a significant improvement in the fidelity of time-evolved qutrit Bell states. Our results highlight the utility of leveraging DD to enable scalable qudit-based quantum computing.
The advent of Artificial Intelligence (AI) has opened up new possibilities for improving productivity in various industry sectors. In this paper, we propose a novel framework aimed at optimizing systematic literature ...
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The Ensemble Kalman Filters (EnKF) employ a Monte-Carlo approach to represent covariance information, and are affected by sampling errors in operational settings where the number of model realizations is much smaller ...
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In this paper, we propose HiPoNet, an end-to-end differentiable neural network for regression, classification, and representation learning on high-dimensional point clouds. Single-cell data can have high dimensionalit...
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Multi-level qudit systems are increasingly being explored as alternatives to traditional qubit systems due to their denser information storage and processing potential. However, qudits are more susceptible to decohere...
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Generative Adversarial Networks (GANs) typically learn a distribution of images in a large image dataset, and are then able to generate new images from this distribution. However, each natural image has its own intern...
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ISBN:
(数字)9781728148038
ISBN:
(纸本)9781728148045
Generative Adversarial Networks (GANs) typically learn a distribution of images in a large image dataset, and are then able to generate new images from this distribution. However, each natural image has its own internal statistics, captured by its unique distribution of patches. In this paper we propose an "Internal GAN'' (InGAN) - an image-specific GAN - which trains on a single input image and learns its internal distribution of patches. It is then able to synthesize a plethora of new natural images of significantly different sizes, shapes and aspect-ratios - all with the same internal patch-distribution (same "DNA'') as the input image. In particular, despite large changes in global size/shape of the image, all elements inside the image maintain their local size/shape. InGAN is fully unsupervised, requiring no additional data other than the input image itself. Once trained on the input image, it can remap the input to any size or shape in a single feedforward pass, while preserving the same internal patch distribution. InGAN provides a unified framework for a variety of tasks, bridging the gap between textures and natural images.
Generative Adversarial Networks (GANs) typically learn a distribution of images in a large image dataset, and are then able to generate new images from this distribution. However, each natural image has its own intern...
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Context: Software testing is a knowledge intensive process, and therefore can benefit from the use of experience gained from past projects. In this context, principles of Knowledge Management (KM) can be applied to pr...
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