This paper introduces the Localised Adaptive Spatial-Temporal Graph Neural Network model, focusing on the importance of spatial-temporal data modeling in graph structures. The challenges of balancing spatial and temporal dependencies for accurate inference are addressed, along with the use of distri

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Explore the latest innovations in simple multigraph convolution networks presented by Xinjie Shen from South China University of Technology. The research evaluates existing methods, such as PGCN, MGCN, and MIMO-GCN, and introduces novel techniques for building credible graphs through subgraph-level

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Lead and Phase-Lead compensators play a crucial role in improving system stability and response speed. By using the root locus and frequency response methods, these compensators shift the root locus toward the left half-plane, adding positive phase over the frequency range. This leads to increased s

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Our project focuses on enhancing the efficiency of 2D convolutions by implementing parallelization with GPUs. We delve into the significance of convolutions, strategies for parallelization, challenges faced, and the outcomes achieved. Through comparing direct convolution to Fast Fourier Transform (F

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Neural network accelerators are essential due to the computational demands of models like VGG-16, emphasizing the significance of convolution and fully connected layers. Spatial mapping of compute units highlights peak throughput, with memory access often becoming the bottleneck. Addressing over 300

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Autoencoders play a crucial role in supervised and unsupervised learning, with applications ranging from image classification to denoising and watermark removal. They compress input data into a latent space and reconstruct it to produce valuable embeddings. Autoencoders are data-specific, lossy, and

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Super-resolution technology aims to reconstruct high-resolution images from low-resolution inputs, with applications in video surveillance, medical diagnosis, and remote sensing. Various convolutional neural network (CNN) models have been developed, such as SRCNN, VDSR, ESPCN, and FSRCNN, each with

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This content delves into Digital Signal Processing concepts taught in the 4th class of 2020-2021 by Dr. Abbas Hussien and Dr. Ammar Ghalib. It covers topics like Table Lookup Method, Linear Convolution, Circular Convolution, practical examples, and Deconvolution techniques such as Polynomial Approac

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A novel indoor millimeter-wave imaging system based on sparsity estimated compressed sensing and calibrated point spread function is introduced. The system utilizes a unique calibration procedure to process the point spread function acquired from measuring a suspended point scatterer. By estimating

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Explore the concept of matrix factorization for recovering latent factors in a matrix, specifically focusing on user ratings of movies. This technique involves decomposing a matrix into multiple matrices to extract hidden patterns and relationships. The process is crucial for tasks like image denois

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Explore the eigenvalues of sums of non-commuting random symmetric matrices in the context of quantum information. Delve into the complexities of eigenvalue distributions in various scenarios, including random diagonals, orthogonal matrices, and symmetric matrix sums. Gain insights into classical and

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Deep learning models, originally designed for computer vision, have shown remarkable success in various Natural Language Processing (NLP) tasks. This paper presents a simple Convolutional Neural Network (CNN) architecture for sentence classification, utilizing word vectors from an unsupervised neura

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Addressing difficulties in experimental design such as baseline correction, temporally overlapping neural responses, and systematic differences in response timings using a convolution GLM, similar to first-level fMRI analysis. The course focuses on the stop-signal task, EEG correlates of stopping a

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Cutting-edge research on convolution denoising methods for Seebeck calorimeters to reduce noise levels caused by temperature fluctuations. The study explores hardware design, mathematical principles, and examples of denoising applications, aiming to enhance measurement accuracy and stability in larg

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This course delves into utilizing Convolution GLM to address challenges such as baseline correction, overlapping neural responses, and systematic response timing differences in EEG experiments. It focuses on the stop-signal task, EEG correlates of movement stopping, and MEG data analysis. The course

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Utilizing double random fractional Fourier domain encoding for optical security involves encryption and decryption methods based on the fractional Fourier transform of various orders, involving specific mathematical operations and notations. The process includes transforming the input function, encr

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This content delves into the application of deep learning models, such as Sequential Modeler, Feature Extraction, and Discriminator, for processing EEG data from the TUH EEG Corpus. The architecture involves various layers like Convolution, Max Pooling, ReLU activation, and Dropout. It explores temp

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This research paper presents a novel approach, HRL4Ba, for Next-basket Recommendation (NBR) by addressing the challenge of guiding recommendations based on historical baskets that may contain noise products. The proposed Hierarchical Reinforcement Learning framework incorporates dynamic context mode

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This research introduces a Denoising-Guided Deep Reinforcement Learning framework, DRL4So, for enhancing social recommendation systems. By automatically masking noise from social friends to improve recommendation performance, this framework focuses on maximizing the positive utility of social denois

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Delve into the world of Artificial Intelligence (AI) with real industry cases. Learn about Natural Language Processing (NLP) and Computer Vision through examples and practical exercises. Understand NLP's use of probability statistics, intent, utterance, entity, and session elements. Discover how Com

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Explore the concept of Toeplitz Matrix 1x1 Convolution in deep learning for processing arbitrary-sized images. Discover how this technique enables running ConvNets on images of various dimensions efficiently, making use of matrix multiplication with Toeplitz matrices to achieve convolution. Dive int

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Discover new hardware architectures designed for efficient deep neural network processing, including SCNN accelerators for compressed-sparse Convolutional Neural Networks. Learn about convolution operations, memory size versus access energy, dataflow decisions for reuse, and Planar Tiled-Input Stati

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Edge detection is a fundamental operation in image processing, crucial for identifying object boundaries based on rapid changes in brightness. This process involves detecting areas of discontinuity in gray-level values to locate edges, which hold significant information about objects in an image. Co

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CNN, a type of neural network, comprises convolutional, subsampling, and fully connected layers achieving state-of-the-art results in tasks like handwritten digit recognition. CNN is specialized for image input data but can be tricky to train with large-scale datasets due to the complexity of replic

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Cutting-edge methods in astrophotography, such as deconvolution and pixel convolution effects, are explored in this detailed presentation. These techniques offer superior image restoration compared to traditional algorithms, emphasizing the importance of addressing pixelation effects to achieve high

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Examination of Noise2Void, Noise2Noise, and Traditional Denoising Models in image processing, highlighting their unique approaches and challenges. Implementations details, experiments, limitations, and comments are discussed, showcasing the potential and shortcomings of these techniques in denoising

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Rebinning is a data manipulation technique similar to smoothing, where N points are replaced by 1 point using a functional weighting. This process involves resampling data, linear interpolation, boxcar averaging, and convolution with a kernel function. It is essential to consider boundary effects an

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Bloom effects, such as weak scattering and convolution, enhance the visual appeal of games by simulating light scattering. They add realism and customization options to game graphics, improving the overall visual experience. Weak scattering causes subtle yet impactful effects like glare and diffract

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