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Monte Carlo Method

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Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning.

Proceedings of the National Academy of Sciences of the United States of America
Infusing "chemical wisdom" should improve the data-driven approaches that rely exclusively on historical synthetic data for automatic retrosynthesis planning. For this purpose, we designed a chemistry-informed molecular graph (CIMG) to describe chemi...

N⁴ Sim: The First Nervous NaNoNetwork Simulator With Synaptic Molecular Communications.

IEEE transactions on nanobioscience
The unconventional nature of molecular communication necessitates contributions from a host of scientific fields making the simulator design for such systems to be quite challenging. The nervous system is one of the largest and most important nanonet...

Vision-Sensor-Assisted Probabilistic Localization Method for Indoor Environment.

Sensors (Basel, Switzerland)
Among the numerous indoor localization methods, Light-Detection-and-Ranging (LiDAR)-based probabilistic algorithms have been extensively applied to indoor localization due to their real-time performance and high accuracy. Nevertheless, these methods ...

Direct mapping from PET coincidence data to proton-dose and positron activity using a deep learning approach.

Physics in medicine and biology
. Obtaining the intrinsic dose distributions in particle therapy is a challenging problem that needs to be addressed by imaging algorithms to take advantage of secondary particle detectors. In this work, we investigate the utility of deep learning me...

On Sampling Minimum Energy Path.

Journal of chemical theory and computation
Sampling the minimum energy path (MEP) between two minima of a system is often hindered by the presence of an energy barrier separating the two metastable states. As a consequence, direct sampling based on molecular dynamics or Markov Chain Monte Car...

A novel multichannel deep learning model for fast denoising of Monte Carlo dose calculations: preclinical applications.

Physics in medicine and biology
In preclinical radiotherapy with kilovolt (kV) x-ray beams, accurate treatment planning is needed to improve the translation potential to clinical trials. Monte Carlo based radiation transport simulations are the gold standard to calculate the absorb...

Sample-Efficient Neural Architecture Search by Learning Actions for Monte Carlo Tree Search.

IEEE transactions on pattern analysis and machine intelligence
Neural Architecture Search (NAS) has emerged as a promising technique for automatic neural network design. However, existing MCTS based NAS approaches often utilize manually designed action space, which is not directly related to the performance metr...

A deep learning and Monte Carlo based framework for bioluminescence imaging center of mass-guided glioblastoma targeting.

Physics in medicine and biology
Bioluminescence imaging (BLI) is a valuable tool for non-invasive monitoring of glioblastoma multiforme (GBM) tumor-bearing small animals without incurring x-ray radiation burden. However, the use of this imaging modality is limited due to photon sca...

Tutorial: Artificial neural networks to analyze single-case experimental designs.

Psychological methods
Since the start of the 21st century, few advances have had as far-reaching impact in science as the widespread adoption of artificial neural networks in fields as diverse as fundamental physics, clinical medicine, and psychology. In research methods,...

Application of deep neural network and gamma-ray scattering in eccentric scale calculation regardless of the fluids volume fraction inside a pipeline.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
Scale formation is one of the major problems in the oil industry as it can accumulate on the surface of the pipelines, which could even fully block the fluids' passage. It was developed a methodology to detect and quantify the maximum thickness of ec...