The usage of a transformer based and artificial intelligence driven multidimensional feedback system in english writing instruction - Nature
<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTFAza01HTERMZDhsQWE2UEIxaFRwUXI5TUtubERTbHBQY2J1MHVBQm1vbTBwZ3BpbEMxbXBGYXNRSmNQS0dCWUxESEM4Q3pHcGtfcVEyOXk3TTBfcHF2SUtR?oc=5" target="_blank">The usage of a transformer based and artificial intelligence driven multidimensional feedback system in english writing instruction</a> <font color="#6f6f6f">Nature</font>
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Indian Peak Power demand Forecasting : Transformer Based Implementation of Temporal Architecture
arXiv:2603.29179v1 Announce Type: new Abstract: The long-term forecasting of electricity demand has been a prevalent research topic, primarily because of its economic and strategic relevance. Several machine learning as well as deep learning techniques have been developed in parallel with the growing complexity of the peak demand, planning for generation facilities and transmission augmentation in future. Most of these proposed techniques work on short-term forecasting as long-term forecasting is considerably more challenging due to unpredictable and unforeseeable variables that may arise in the future. This paper proposes a Temporal Fusion Transformer based deep learning approach for long term forecasting of peak power demand. The dataset used in this paper consists of peak power demand i
Convergent Representations of Linguistic Constructions in Human and Artificial Neural Systems
arXiv:2603.29617v1 Announce Type: new Abstract: Understanding how the brain processes linguistic constructions is a central challenge in cognitive neuroscience and linguistics. Recent computational studies show that artificial neural language models spontaneously develop differentiated representations of Argument Structure Constructions (ASCs), generating predictions about when and how construction-level information emerges during processing. The present study tests these predictions in human neural activity using electroencephalography (EEG). Ten native English speakers listened to 200 synthetically generated sentences across four construction types (transitive, ditransitive, caused-motion, resultative) while neural responses were recorded. Analyses using time-frequency methods, feature e
Retinal Malady Classification using AI: A novel ViT-SVM combination architecture
arXiv:2603.29181v1 Announce Type: new Abstract: Macular Holes, Central serous retinopathy and Diabetic Retinopathy are one of the most widespread maladies of the eyes responsible for either partial or complete vision loss, thus making it clear that early detection of the mentioned defects is detrimental for the well-being of the patient. This study intends to introduce the application of Vision Transformer and Support Vector Machine based hybrid architecture (ViT-SVM) and analyse its performance to classify the optical coherence topography (OCT) Scans with the intention to automate the early detection of these retinal defects.
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Mistral AI raises $830M in debt to set up a data center near Paris - TechCrunch
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Anthropic repeats mistake, exposes Claude Code source in latest release - People Matters - HR News
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