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iOS 26.2 brings a much needed feature to AirDrop - here's how to use it

iOS 26.2 was full of tweaks and general improvements to the iPhone experience, one of which was a much-needed AirDrop feature. Over the years, Apple has tweaked AirDrop to prevent unknown people from ...
IEEE

Wavelet-Kernel Deformable-Convolution Attention Network for Interpretable Intelligent Diagnosis of Rolling Bearing

Abstract: With the increasing complexity of mechanical equipment, intelligent fault diagnosis (IFD) has become crucial for ensuring production safety and efficiency, but the inherent opacity in ...
Scientific Research Publishing

Dual-Dilated Large Kernel Convolution for Visual Attention Network ()

Visual Attention Networks (VANs) leveraging Large Kernel Attention (LKA) have demonstrated remarkable performance in diverse computer vision tasks, often outperforming Vision Transformers (ViTs) in ...
GitHub

The dimension of the convolution kernel is incorrect

I used the latest Qwen-imageEdit-0925 model file. I have an 8*L40s(40G video memory) machine. Since a single card cannot load 65G of the model and it needs to be loaded separately, I chose to use: ...
pv magazine International

PV module fault diagnosis uses convolutional neural network

Researchers in China have created a dataset of various PV faults and normalized it to accommodate different array sizes and typologies. After testing the new approach in combination with the 1D-CNN ...
IEEE

DDConv: Dynamic Dilated Convolution

Abstract: Dilated convolution is a powerful technique for expanding the receptive field without increasing the convolution kernel size, making it highly valuable in image segmentation tasks. However, ...
GitHub

Inefficient 1D convolution compared to PyTorch

I wrote a small benchmark comparing a 1D depthwise convolution implemented in PyTorch vs. pure JAX on GPU, and found that the JAX version is both substantially slower (≈3×) and draws noticeably more ...
Microsoft

Dynamic Convolution: Attention over Convolution Kernels

Light-weight convolutional neural networks (CNNs) suffer performance degradation as their low computational budgets constrain both the depth (number of convolution layers) and width (number of ...