TensorBNN: Bayesian Inference for Neural Networks using Tensorflow

TensorBNN is a new package based on TensorFlow that implements Bayesian inference for modern neural network models. The posterior density of neural network model parameters is represented as a point cloud sampled using Hamiltonian Monte Carlo. The TensorBNN package leverages TensorFlow's architecture and training features as well as its ability to use modern graphics processing units (GPU) in both the training and prediction stages

Bayesian Neural Networks for Fast SUSY Predictions

One of the goals of current particle physics research is to obtain evidence for new physics, that is, physics beyond the Standard Model (BSM), at accelerators such as the Large Hadron Collider (LHC) at CERN. The searches for new physics are often guided by BSM theories that depend on many unknown parameters, which, in some cases, makes testing their predictions difficult. In this paper, machine learning is used to model the mapping from the parameter space of the phenomenological Minimal Supersymmetric Standard Model (pMSSM), a BSM theory with 19 free parameters, to some of its predictions. Bayesian neural networks are used to predict cross sections for arbitrary pMSSM parameter points, the mass of the associated lightest neutral Higgs boson, and the theoretical viability of the parameter points. All three quantities are modeled with average percent errors of 3.34% or less and in a time significantly shorter than is possible with the supersymmetry codes from which the results are derived. These results are a further demonstration of the potential for machine learning to model accurately the mapping from the high dimensional spaces of BSM theories to their predictions

VAIM for Solving Inverse Problems

In this work, we propose the Variational Autoencoder Inverse Mapper (VAIM) to solve inverse problems, where there is a demand to accurately restore hidden parameters from indirect observations. VAIM is an autoencoder-based neural network architecture. The encoder and decoder networks approximate the forward and backward mapping, respectively, and a variational latent layer is incorporated into VAIM to learn the posterior parameter distributions with respect to the given observables. VAIM shows promising results on several artificial inverse problems. VAIM further demonstrates preliminary effectiveness in constructing the inverse function mapping quantum correlation functions to observables in a quantum chromodynamics analysis of nucleon structure and hadronization.https://digitalcommons.odu.edu/gradposters2021_sciences/1005/thumbnail.jp

End-to-End Physics Event Generator

We apply generative adversarial network (GAN) technology to build an event generator that simulates particle production in electron-proton scattering that is free of theoretical assumptions about underlying particle dynamics. The difficulty of efficiently training a GAN event simulator lies in learning the complicated pat- terns of the distributions of the particles physical properties. We develop a GAN that selects a set of transformed features from particle momenta that can be generated easily by the generator, and uses these to produce a set of augmented features that improve the sensitivity of the discriminator. The new Feature-Augmented and Transformed GAN (FAT-GAN) is able to faithfully reproduce the distribution of final state electron momenta in inclusive electron scattering, without the need for input derived from domain-based theoretical assumptions. The developed technology can play a significant role in boosting the science of the Jefferson Lab 12 GeV program and the future Electron-Ion Collider.https://digitalcommons.odu.edu/gradposters2021_sciences/1006/thumbnail.jp

Machine Learning in Nuclear Physics

Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate scientific discoveries and societal applications. This Review gives a snapshot of nuclear physics research which has been transformed by machine learning techniques.Comment: Comments are welcom

Process design for optimizing text-based communication between physicians and nurses

Background and Aim Communication between physicians and nurses is a cornerstone of high-quality inpatient care. HIPAA-compliant text-based methods offer an alternative to the pager for communication between nurses and physicians. While messaging is popular in the personal setting, text-based professional communication in hospitals may increase the number of messages without improving coordination between care providers. (1) In addition, urgent messages that are more appropriately calls could be missed by the physician, leading to a delay in action. Other institutions use triage systems to communicate a question or clinical change by the urgency of expected physician response, which have attempted to mitigate this issue. (2) We aimed to improve bidirectional communication between housestaff and nursing with a communication process developed jointly by both parties using QI methods such as stakeholder analysis and a structured Work-Out session to brainstorm solutions