IEEE

Cross-LSTM: Integrating Cross Attention With Long Short-Term Memory Neural Networks in Estimating Joint Moments From Wearable Sensors

Abstract: Precise estimation of joint moments is essential for designing rehabilitation interventions and optimising the control of assistive devices. Current methods, including physics-based ...
IEEE

Low-Resolution Massive MIMO Channel Estimation with LSTM Attention-Based CBDNet

Abstract: Channel estimation of a massive multi-input multi-output (MIMO) system that utilizes a one-bit analog-to-digital converter (ADC) is a foremost challenge. Traditional deep learning (DL) ...
The New Yorker

America’s Betting Craze Has Spread to Its News Networks

Having swallowed sports media, will gambling now devour other kinds of news? Last week, CNN announced a deal with Kalshi, a federally regulated online exchange where Americans can wager on current ...
IEEE

The Research of Wind Farm Power Forecasting Based on VMD-IPSO-CNN-LSTM Hybrid Approach

Abstract: To enhance the accuracy and stability of wind-farm power forecasting, this study proposes a hybrid framework integrating Variational Mode Decomposition (VMD), an improved Particle Swarm ...
The New York Times

Elon Musk Loves What This Guy Has to Say

Mario Nawfal has a knack for getting Mr. Musk’s attention online, and for turning that into big business. Mario Nawfal has a knack for getting Mr. Musk’s attention online, and for turning that into ...
Hosted on MSN

Stop Being A Doormat! Integrate Your Shadow Self - Jordan Peterson Motivation

Stop Being A Doormat! Integrate Your Shadow Self - Jordan Peterson Motivation If you enjoyed this video, please subscribe for more daily content. ===== WisdomTalks Book Recommendations: 1. 12 Rules ...
IEEE

CNN-LSTM-Attention based Predictive Model for Thermal Error in CNC Machine Tool Spindles

Abstract: Aiming at the problem that most thermal error prediction models for machine tools adopt a single neural network architecture, which is difficult to ...
IEEE

Lightweight Real-Time Anomaly Detection and Prediction for Surveillance Videos Using a Self-Supervised Hybrid Autoencoder and Dynamic Graph CNN-LSTM Framework

Abstract: Anomaly detection in surveillance videos is a critical task for ensuring public safety, requiring both high accuracy and real-time efficiency. This study presents a novel lightweight anomaly ...