Synaptic connectivity alone can reveal neuron types

Recent technological advances facilitate the reconstruction of complete brain connectomes in small organisms and partial connectomes in mammals, involving the mapping of the network of neurons and ...
Scientific Research Publishing

A Spatio-Temporal Prediction Model for Wall Turbulence Based on Hybrid Neural Networks ()

The spatio-temporal evolution of wall-bounded turbulence is characterized by high nonlinearity, multi-scale dynamics, and chaotic nature, making its accurate prediction a significant challenge for ...

Open source Mamba 3 arrives to surpass Transformer architecture with nearly 4% improved language modeling, reduced latency

This release is good for developers building long-context applications, real-time reasoning agents, or those seeking to reduce GPU costs in high-volume production environments.
Scientific Research Publishing

UWB NLOS Signal Recognition Based on Deep Learning ()

However, in indoor environments, non-line-of-sight (NLOS) signals significantly degrade the ranging performance of UWB ...
Frontiers

MM-3D Unet: development of a lightweight breast cancer tumor segmentation network utilizing multi-task and depthwise separable convolution

We propose a 3D depth-wise separable convolution, and construct channel expansional convolution (CEC) unit and inverted residual block (IRB) to reduce the parameter count and computational load, ...
IEEE

Research on intelligent identification method of power grid wiring diagram based on deep convolution neural network

Abstract: The continuous evolution of smart grid technology has promoted the intelligent level of power dispatching automation systems to meet more stringent requirements and challenges. In the ...
pv magazine International

Ultra-short-term PV forecasting based on convolutional neural network, long short-term memory

Scientists have created a novel probabilistic model for 5-minutes ahead PV power forecasting. The method combines a convolutional neural network with bidirectional long short-term memory, attention ...
Microsoft

Convolutional neural network transformer (CNNT) for fluorescence microscopy image denoising with improved generalization and fast adaptation

Deep neural networks can improve the quality of fluorescence microscopy images. Previous methods, based on Convolutional Neural Networks (CNNs), require time-consuming training of individual models ...
IEEE

Bimodal Profile Diagrams for Breast Cancer Classification Using Convolution Neural Network

Abstract: This study introduces a bimodal sensing system designed to classify tumorous and nontumorous breast cancer using tactile and multispectral sensors. The proposed approach simplifies the ...