Abstract: The traditional Text-Level-GNN effectively captures the structural information of texts but fails to fully extract the semantic information of words during text-level composition. To address ...

Nicola, A.A. (2026) Open Flow Controller Architecture for Seamless Connectivity and Virtualization Technologies.

TL;DR: We propose CUFIT, a robust fine-tuning method for vision foundation models under noisy label conditions, based on the advantages of linear probing and adapters. Download the training data, ...

This study presents SynaptoGen, a differentiable extension of connectome models that links gene expression, protein-protein interaction probabilities, synaptic multiplicity, and synaptic weights, and ...

Williams, A. and Louis, L. (2026) Cumulative Link Modeling of Ordinal Outcomes in the National Health Interview Survey Data: Application to Depressive Symptom Severity. Journal of Data Analysis and ...

This work implements Audio Spectrogram Transformer on ShipsEar Database (A private underwater vessel noise database) which serves as the benchmark for various models on underwater noise classification ...

MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, innovatively launches a quantum-enhanced deep convolutional neural network image 3D reconstruction ...

The average firm operating in Asia-Pacific faces two unworkable options in responsible deployment of AI: rebuilding ...

Metso is launching an innovative, configurable Grinding classification system, setting a new benchmark for the highest efficiency in design, supply and installation of a grinding system while ...

Instagram is introducing a new tool that lets you see and control your algorithm, starting with Reels, the company announced on Wednesday. The new tool, called “Your Algorithm,” lets you view the ...

Background: The performance of a classification algorithm eventually reaches a point of diminishing returns, where the additional sample added does not improve the results. Thus, there is a need to ...

Abstract: To address the problem posed by limited datasets, low classification accuracy, and the predominant reliance on Convolutional Neural Networks in the existing field of brain tumor ...