AIMC Topic: Time Factors

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Predicting cardiovascular outcomes in Chinese patients with type 2 diabetes by combining risk factor trajectories and machine learning algorithm: a cohort study.

Cardiovascular diabetology
BACKGROUND: Cardiovascular complications are major concerns for Chinese patients with type 2 diabetes. Accurately predicting these risks remains challenging due to limitations in traditional risk models. We aimed to develop a dynamic prediction model...

GARNN: An interpretable graph attentive recurrent neural network for predicting blood glucose levels via multivariate time series.

Neural networks : the official journal of the International Neural Network Society
Accurate prediction of future blood glucose (BG) levels can effectively improve BG management for people living with type 1 or 2 diabetes, thereby reducing complications and improving quality of life. The state of the art of BG prediction has been ac...

Fast In Vivo Two-Photon Fluorescence Imaging via Lateral and Axial Resolution Restoration With Self-Supervised Learning.

Journal of biophotonics
Two-photon fluorescence (TPF) imaging opens a new avenue to achieve high resolution at extended penetration depths. However, it is difficult for conventional TPF imaging systems to simultaneously achieve high resolution and speed. In this work, we de...

Fast finite-time quantized control of multi-layer networks and its applications in secure communication.

Neural networks : the official journal of the International Neural Network Society
This paper introduces a quantized controller to address the challenge of fast finite-time synchronization of multi-layer networks, where each layer represents a distinct type of interaction within complex systems. Firstly, based on the stability theo...

FxTS-Net: Fixed-time stable learning framework for Neural ODEs.

Neural networks : the official journal of the International Neural Network Society
Neural Ordinary Differential Equations (Neural ODEs), as a novel category of modeling big data methods, cleverly link traditional neural networks and dynamical systems. However, it is challenging to ensure the dynamics system reaches a correctly pred...

Temporal pavlovian conditioning of a model spiking neural network for discrimination sequences of short time intervals.

Journal of computational neuroscience
The brain's ability to learn and distinguish rapid sequences of events is essential for timing-dependent tasks, such as those in sports and music. However, the mechanisms underlying this ability remain an active area of research. Here, we present a P...

COSTA: Contrastive Spatial and Temporal Debiasing framework for next POI recommendation.

Neural networks : the official journal of the International Neural Network Society
Current research on next point-of-interest (POI) recommendation focuses on capturing users' behavior patterns residing in their mobility trajectories. However, the learning process will inevitably cause discrepancies between the recommendation and in...

Synergistic modeling of hemorrhagic dengue fever: Passive immunity dynamics and time-delay neural network analysis.

Computational biology and chemistry
Dengue fever poses a formidable epidemiological challenge, particularly for vulnerable groups such as infants. This research paper establishes a mathematical model to describe the dynamics of secondary immunity in infants against dengue hemorrhagic f...

Machine Learning Analysis of Nutrient Associations with Peripheral Arterial Disease: Insights from NHANES 1999-2004.

Annals of vascular surgery
BACKGROUND: Peripheral arterial disease (PAD) is a common manifestation of atherosclerosis, affecting over 200 million people worldwide. The incidence of PAD is increasing due to the aging population. Common risk factors include smoking, diabetes, an...

Optimizing gelation time for cell shape control through active learning.

Soft matter
Hydrogels are popular platforms for cell encapsulation in biomedicine and tissue engineering due to their soft, porous structures, high water content, and excellent tunability. Recent studies highlight that the timing of network formation can be just...