Opinion
CIOOpinion

AI isn’t failing, people are failing with AI

AI isn’t the problem — rushing it into the wrong tasks without the right data, expertise or guardrails is what makes projects fall apart.
IEEE

HiTrans-SAM: Hierarchical Transformer Encoder and SAM-Augmented Inputs for Multi-Scale Remote Sensing Image Segmentation

Abstract: Semantic segmentation of remote sensing images is challenging due to complex scenes, substantial variations in object scales, and ambiguous boundaries. In this study, we propose a novel ...
IEEE

Exploring Encoder–Decoder Transformer Structure for Signal Classification

Abstract: Automatic modulation classification (AMC) is one of the fundamental technologies in adaptive communication systems, supporting various tasks such as spectrum surveillance and cognitive radio ...
Business Insider

Rail Vision: Quantum Transportation Delivers First Transformer-Based Neural Decoder for Universal Quantum Error Correction

Achieves superior decoding accuracy and dramatically improved efficiency compared to leading classical algorithms Ra’anana, Israel, Jan. 15, 2026 (GLOBE NEWSWIRE) -- Rail Vision Ltd. (Nasdaq: RVSN) ...
Frontiers

A combined approach to lithology identification using reinforcement learning and transformer algorithms

Lithology identification plays a pivotal role in logging interpretation during drilling operations, directly influencing drilling decisions and efficiency. Conventional lithology identification ...
Scientific Research Publishing

From U-Net to Swin-Unet Transformers: The Next-Generation Advances in Brain Tumor Segmentation with Deep Learning ()

Brain tumor segmentation is a vital step in diagnosis, treatment planning, and prognosis in neuro-oncology. In recent years, deep learning approaches have revolutionized this field, evolving from the ...
GitHub

Understanding Self-Attention(Encoder's Self-Attention and Decoder's Masked Self-Attention) in Transformers

- Driven by the **output**, attending to the **input**. - Each word in the output sequence determines which parts of the input sequence to attend to, forming an **output-oriented attention** mechanism ...