GitHub

Zero-Training Context Extension for Transformer Encoders via Nonlinear Absolute Positional Embeddings Interpolation

Official implementation of "Zero-Training Context Extension for Transformer Encoders via Nonlinear Absolute Positional Embeddings Interpolation". Paper preprint is coming soon. This implementation ...
KXAN

Ampace and DG Matrix Announce Strategic Partnership to Deliver First UL-Certified Grid-Active BESS Integrated with Multi-Port Solid-State Transformer Architecture for AI Data ...

RALEIGH, N.C., Dec. 16, 2025 /PRNewswire/ -- Ampace, a global leader in advanced lithium-ion energy storage, today announced a strategic collaboration with DG Matrix to deliver the industry's first UL ...
Microsoft

Imaging Transformer for MRI Denoising: a Scalable Model Architecture that enables SNR1 Imaging

Purpose: To propose a flexible and scalable imaging transformer (IT) architecture with three attention modules for multi-dimensional imaging data and apply it to MRI denoising with very low input SNR.
ArchDaily

How 2025 Turned Architectural Visuals Into Disputed Media

For much of modern architectural history, images have functioned as interpretive tools rather than literal records. Renderings, drawings, and competition visuals were traditionally understood as ...
IEEE

Personalized Federated Transformer Architecture with Digital Twin for Enhanced Environmental Perception in Intelligent IoV Systems

Abstract: With 6G-enabled Intelligent Internet of Vehicles (IIoV) generating massive amounts of sensory data, traditional deep learning models struggle to capture long-range relationships across ...
IEEE

Temporal Convolutional and Fusional Transformer Model With Bi-LSTM Encoder-Decoder for Multi-Time-Window Remaining Useful Life Prediction

Health prediction is crucial for ensuring reliability, minimizing downtime, and optimizing maintenance in industrial systems. Remaining Useful Life (RUL) prediction is a key component of this process; ...
GitHub

Variational Autoencoder (VAE) for Image Generation

This project implements a Variational Autoencoder (VAE) for image generation. Unlike standard autoencoders, VAE learns a probabilistic latent space by encoding images to a distribution and sampling ...