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Prespecified-time bipartite synchronization of coupled reaction-diffusion memristive neural networks with competitive interactions.

Mathematical biosciences and engineering : MBE
In this paper, we investigate the prespecified-time bipartite synchronization (PTBS) of coupled reaction-diffusion memristive neural networks (CRDMNNs) with both competitive and cooperative interactions. Two types of bipartite synchronization are con...

An octonion-based nonlinear echo state network for speech emotion recognition in Metaverse.

Neural networks : the official journal of the International Neural Network Society
While the Metaverse is becoming a popular trend and drawing much attention from academia, society, and businesses, processing cores used in its infrastructures need to be improved, particularly in terms of signal processing and pattern recognition. A...

Robust Stabilization of Linear Time-Delay Systems under Denial-of-Service Attacks.

Sensors (Basel, Switzerland)
This research examines new methods for stabilizing linear time-delay systems that are subject to denial-of-service (DoS) attacks. The study takes into account the different effects that a DoS attack can have on the system, specifically delay-independ...

Quantifying disorder one atom at a time using an interpretable graph neural network paradigm.

Nature communications
Quantifying the level of atomic disorder within materials is critical to understanding how evolving local structural environments dictate performance and durability. Here, we leverage graph neural networks to define a physically interpretable metric ...

Adaptive control-based synchronization of discrete-time fractional-order fuzzy neural networks with time-varying delays.

Neural networks : the official journal of the International Neural Network Society
This paper is concerned with complete synchronization for discrete-time fractional-order fuzzy neural networks (DFFNNs) with time-varying delays. First, three original equalities and two Caputo σ-difference inequalities are established based on theor...

A survey on few-shot class-incremental learning.

Neural networks : the official journal of the International Neural Network Society
Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples w...

Full robotic cholecystectomy: first worldwide experiences with HUGO RAS surgical platform.

ANZ journal of surgery
BACKGROUND: The Hugo RAS™ system (Medtronic, Minneapolis, MN, USA), approved for gynaecological and urological procedures, has been recently certified for the use in few general surgeries. Only bariatric and colorectal procedures have been described ...

Non-fragile output-feedback control for time-delay neural networks with persistent dwell time switching: A system mode and time scheduler dual-dependent design.

Neural networks : the official journal of the International Neural Network Society
This paper is concerned with non-fragile output-feedback control for time-delay neural networks with persistent dwell time (PDT) switching in a continuous-time setting. The main purpose is to design an output-feedback controller subject to gain fluct...

Interactive segmentation of medical images using deep learning.

Physics in medicine and biology
Medical image segmentation algorithms based on deep learning have achieved good segmentation results in recent years, but they require a large amount of labeled data. When performing pixel-level labeling on medical images, labeling a target requires ...

Enhancing compound confidence in suspect and non-target screening through machine learning-based retention time prediction.

Environmental pollution (Barking, Essex : 1987)
The retention time (RT) of contaminants of emerging concern (CECs) in liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is crucial for database matching in non-targeted screening (NTS) analysis. In this study, we developed a machine l...