One‑step process generates high entropy alloy nanoparticles in milliseconds for catalyst creation

A University at Buffalo-led team of researchers has developed a method for producing advanced nanoparticles that could ...
Scientific Research Publishing

On Instability of Standing Waves for the Hartree Equation with a Magnetic Field ()

In this paper, we consider the strong instability of standing waves for the Hartree equation with a constant magnetic field.
IEEE

Intelligent Ghost Wave Suppression Based on Quadratic Convolution Kernel and Attention Mechanism

Abstract: In offshore streamer seismic data processing, ghost wave suppression is a crucial step, as it not only enhances data resolution but also restores the signal’s frequency bandwidth. Recent ...
IEEE

Lenet-5 Convolution Neural Network with Mish Activation Function and Fixed Memory Step Gradient Descent Method

Abstract: Convolutional neural network is the most important algorithm in the field of deep learning. The traditional convolution neural network usually uses Sigmoid or Relu as the activation function ...
GitHub

Comprehensive evaluation of cell-type quantification methods for immuno-oncology

Sturm, G., Finotello, F., Petitprez, F., Zhang, J. D., Baumbach, J., Fridman, W. H., ..., List, M., Aneichyk, T. (2019). Comprehensive evaluation of transcriptome ...
the-decoder

AI-generated CUDA kernels outperform PyTorch in several GPU-heavy machine learning benchmarks

A team at Stanford has shown that large language models can automatically generate highly efficient GPU kernels, sometimes outperforming the standard functions found in the popular machine learning ...
Frontiers

Accurate Tumor Segmentation via Octave Convolution Neural Network

Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many ...
Quanta Magazine

Latest Neural Nets Solve World’s Hardest Equations Faster Than Ever Before

Two new approaches allow deep neural networks to solve entire families of partial differential equations, making it easier to model complicated systems and to do so orders of magnitude faster. In high ...