AIMC Topic: Neural Networks, Computer

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An Electrochemical-Electret Coupled Organic Synapse with Single-Polarity Driven Reversible Facilitation-to-Depression Switching.

Advanced materials (Deerfield Beach, Fla.)
Neuromorphic engineering and artificial intelligence demands hardware elements that emulates synapse algorithms. During the last decade electrolyte-gated organic conjugated materials have been explored as a platform for artificial synapses for neurom...

Multiplexed high-throughput immune cell imaging reveals molecular health-associated phenotypes.

Science advances
Phenotypic plasticity is essential to the immune system, yet the factors that shape it are not fully understood. Here, we comprehensively analyze immune cell phenotypes including morphology across human cohorts by single-round multiplexed immunofluor...

Compact Image-Style Transfer: Channel Pruning on the Single Training of a Network.

Sensors (Basel, Switzerland)
Recent image-style transfer methods use the structure of a VGG feature network to encode and decode the feature map of the image. Since the network is designed for the general image-classification task, it has a number of channels and, accordingly, r...

Intrusion Detection in IoT Using Deep Learning.

Sensors (Basel, Switzerland)
Cybersecurity has been widely used in various applications, such as intelligent industrial systems, homes, personal devices, and cars, and has led to innovative developments that continue to face challenges in solving problems related to security met...

Machine Learning Diffusion Monte Carlo Energies.

Journal of chemical theory and computation
We present two machine learning methodologies that are capable of predicting diffusion Monte Carlo (DMC) energies with small data sets (≈60 DMC calculations in total). The first uses voxel deep neural networks (VDNNs) to predict DMC energy densities ...

Explainable Deep-Learning-Assisted Sweat Assessment via a Programmable Colorimetric Chip.

Analytical chemistry
Multianalytes and individual differences of biofluids (such as blood, urine, or sweat) pose enormous complexity and challenges to rapid, facile, high-throughput, and accurate clinical analysis or health assessment. Deep-learning (DL)-assisted image a...

Non-Local Temporal Difference Network for Temporal Action Detection.

Sensors (Basel, Switzerland)
As an important part of video understanding, temporal action detection (TAD) has wide application scenarios. It aims to simultaneously predict the boundary position and class label of every action instance in an untrimmed video. Most of the existing ...

Caps Captioning: A Modern Image Captioning Approach Based on Improved Capsule Network.

Sensors (Basel, Switzerland)
In image captioning models, the main challenge in describing an image is identifying all the objects by precisely considering the relationships between the objects and producing various captions. Over the past few years, many methods have been propos...

Machine learning in medicine: a practical introduction to techniques for data pre-processing, hyperparameter tuning, and model comparison.

BMC medical research methodology
BACKGROUND: There is growing enthusiasm for the application of machine learning (ML) and artificial intelligence (AI) techniques to clinical research and practice. However, instructions on how to develop robust high-quality ML and AI in medicine are ...

The effect of self-organizing map architecture based on the value migration network centrality measures on stock return. Evidence from the US market.

PloS one
Complex financial systems are the subject of current research interest. The notion of complex network is used for understanding the value migration process. Based on the stock data of 498 companies listed in the S&P500, the value migration network ha...