AIMC Topic: Neural Networks, Computer

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Quasi-Volatile MoS Barristor Memory for 1T Compact Neuron by Correlative Charges Trapping and Schottky Barrier Modulation.

ACS applied materials & interfaces
Artificial neurons as the basic units of spiking neural network (SNN) have attracted increasing interest in energy-efficient neuromorphic computing. 2D transition metal dichalcogenide (TMD)-based devices have great potential for high-performance and ...

Modeling CoCu Nanoparticles Using Neural Network-Accelerated Monte Carlo Simulations.

The journal of physical chemistry. A
The correct description of catalytic reactions happening on bimetallic particles is not feasible without proper accounting of the segregation process. In this study, we tried to shed light on the structure of large CoCu particles, for which quite con...

GradFreeBits: Gradient-Free Bit Allocation for Mixed-Precision Neural Networks.

Sensors (Basel, Switzerland)
Quantized neural networks (QNNs) are among the main approaches for deploying deep neural networks on low-resource edge devices. Training QNNs using different levels of precision throughout the network (mixed-precision quantization) typically achieves...

Fast Near-Field Frequency-Diverse Computational Imaging Based on End-to-End Deep-Learning Network.

Sensors (Basel, Switzerland)
The ability to sculpt complex reference waves and probe diverse radiation field patterns have facilitated the rise of metasurface antennas, while there is still a compromise between the required wide operation band and the non-overlapping characteris...

An Adaptive Refinement Scheme for Depth Estimation Networks.

Sensors (Basel, Switzerland)
Deep learning has proved to be a breakthrough in depth generation. However, the generalization ability of deep networks is still limited, and they cannot maintain a satisfactory performance on some inputs. By addressing a similar problem in the segme...

Diagnosis of nasal bone fractures on plain radiographs via convolutional neural networks.

Scientific reports
This study aimed to assess the performance of deep learning (DL) algorithms in the diagnosis of nasal bone fractures on radiographs and compare it with that of experienced radiologists. In this retrospective study, 6713 patients whose nasal radiograp...

Emergent color categorization in a neural network trained for object recognition.

eLife
Color is a prime example of categorical perception, yet it is unclear why and how color categories emerge. On the one hand, prelinguistic infants and several animals treat color categorically. On the other hand, recent modeling endeavors have success...

NetBCE: An Interpretable Deep Neural Network for Accurate Prediction of Linear B-cell Epitopes.

Genomics, proteomics & bioinformatics
Identification of B-cell epitopes (BCEs) plays an essential role in the development of peptide vaccines and immuno-diagnostic reagents, as well as antibody design and production. In this work, we generated a large benchmark dataset comprising 124,879...

Predicting RNA secondary structure by a neural network: what features may be learned?

PeerJ
Deep learning is a class of machine learning techniques capable of creating internal representation of data without explicit preprogramming. Hence, in addition to practical applications, it is of interest to analyze what features of biological data m...

Multi-layer perceptron classification & quantification of neuronal survival in hypoxic-ischemic brain image slices using a novel gradient direction, grey level co-occurrence matrix image training.

PloS one
Hypoxic ischemic encephalopathy (HIE) is a major global cause of neonatal death and lifelong disability. Large animal translational studies of hypoxic ischemic brain injury, such as those conducted in fetal sheep, have and continue to play a key role...