AIMC Topic: Neural Networks, Computer

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qTorch: The quantum tensor contraction handler.

PloS one
Classical simulation of quantum computation is necessary for studying the numerical behavior of quantum algorithms, as there does not yet exist a large viable quantum computer on which to perform numerical tests. Tensor network (TN) contraction is an...

Validating quantum-classical programming models with tensor network simulations.

PloS one
The exploration of hybrid quantum-classical algorithms and programming models on noisy near-term quantum hardware has begun. As hybrid programs scale towards classical intractability, validation and benchmarking are critical to understanding the util...

Proprioceptive Recognition with Artificial Neural Networks Based on Organizations of Spinocerebellar Tract and Cerebellum.

International journal of neural systems
Muscle kinematics and kinetics are nonlinearly encoded by proprioceptors, and the changes in muscle length and velocity are integrated into Ia afferent. Besides, proprioceptive signals from multiple muscles are probably mixed in afferent pathways, wh...

A deep learning framework for unsupervised affine and deformable image registration.

Medical image analysis
Image registration, the process of aligning two or more images, is the core technique of many (semi-)automatic medical image analysis tasks. Recent studies have shown that deep learning methods, notably convolutional neural networks (ConvNets), can b...

Face-from-Depth for Head Pose Estimation on Depth Images.

IEEE transactions on pattern analysis and machine intelligence
Depth cameras allow to set up reliable solutions for people monitoring and behavior understanding, especially when unstable or poor illumination conditions make unusable common RGB sensors. Therefore, we propose a complete framework for the estimatio...

Cell Segmentation Using a Similarity Interface With a Multi-Task Convolutional Neural Network.

IEEE journal of biomedical and health informatics
Even though convolutional neural networks (CNN) have been used for cell segmentation, they require pixel-level ground truth annotations. This paper proposes a multitask learning algorithm for cell detection and segmentation using CNNs. We use dot ann...

Unsupervised Two-Path Neural Network for Cell Event Detection and Classification Using Spatiotemporal Patterns.

IEEE transactions on medical imaging
Automatic event detection in cell videos is essential for monitoring cell populations in biomedicine. Deep learning methods have advantages over traditional approaches for cell event detection due to their ability to capture more discriminative featu...

Technical Note: More accurate and efficient segmentation of organs-at-risk in radiotherapy with convolutional neural networks cascades.

Medical physics
PURPOSE: Manual delineation of organs-at-risk (OARs) in radiotherapy is both time-consuming and subjective. Automated and more accurate segmentation is of the utmost importance in clinical application. The purpose of this study is to further improve ...

Combined molecular dynamics and neural network method for predicting protein antifreeze activity.

Proceedings of the National Academy of Sciences of the United States of America
Antifreeze proteins (AFPs) are a diverse class of proteins that depress the kinetically observable freezing point of water. AFPs have been of scientific interest for decades, but the lack of an accurate model for predicting AFP activity has hindered ...