Phase space sampling and operator confidence with generative adversarial networks

We demonstrate that a generative adversarial network can be trained to produce Ising model configurations in distinct regions of phase space. In training a generative adversarial network, the discriminator neural network becomes very good a discerning examples from the training set and examples from the testing set. We demonstrate that this ability can be used as an anomaly detector, producing estimations of operator values along with a confidence in the prediction

Deep neural networks for direct, featureless learning through observation: the case of 2d spin models

We demonstrate the capability of a convolutional deep neural network in predicting the nearest-neighbor energy of the 4x4 Ising model. Using its success at this task, we motivate the study of the larger 8x8 Ising model, showing that the deep neural network can learn the nearest-neighbor Ising Hamiltonian after only seeing a vanishingly small fraction of configuration space. Additionally, we show that the neural network has learned both the energy and magnetization operators with sufficient accuracy to replicate the low-temperature Ising phase transition. We then demonstrate the ability of the neural network to learn other spin models, teaching the convolutional deep neural network to accurately predict the long-range interaction of a screened Coulomb Hamiltonian, a sinusoidally attenuated screened Coulomb Hamiltonian, and a modified Potts model Hamiltonian. In the case of the long-range interaction, we demonstrate the ability of the neural network to recover the phase transition with equivalent accuracy to the numerically exact method. Furthermore, in the case of the long-range interaction, the benefits of the neural network become apparent; it is able to make predictions with a high degree of accuracy, and do so 1600 times faster than a CUDA-optimized exact calculation. Additionally, we demonstrate how the neural network succeeds at these tasks by looking at the weights learned in a simplified demonstration

Opt-in overdraft regulation affects bank revenues

Amendments to Federal Regulation E, effective during 2010, limited banks ability to generate fee income from one-time debit card and ATM transactions. This research quantifies the estimated annual loss experienced by US and Arkansas banks

Evidence Based Evaluation Of A Ventilator Management Clinical Practice Protocol

Nursing care and management of mechanically ventilated patients requires specific care elements to be achieved in order for optimal patient outcomes. The ventilator management policy guides nursing care for this select group of patients. The ventilator management policy of a rural community acute healthcare facility was evaluated for evidence-base and validity using the AGREE GRS rating scale. The evaluation process included interview of the director of the inpatient unit responsible for care of ventilator patients, as well as review of the most current evidence-based practice protocols. Evaluation results indicate the facility’s policy met the needs of the patients, and incorporated the most up-to-date clinical recommendations.https://scholarworks.moreheadstate.edu/celebration_posters_2021/1023/thumbnail.jp

Optimizing thermodynamic trajectories using evolutionary and gradient-based reinforcement learning

Using a model heat engine, we show that neural network-based reinforcement learning can identify thermodynamic trajectories of maximal efficiency. We consider both gradient and gradient-free reinforcement learning. We use an evolutionary learning algorithm to evolve a population of neural networks, subject to a directive to maximize the efficiency of a trajectory composed of a set of elementary thermodynamic processes; the resulting networks learn to carry out the maximally-efficient Carnot, Stirling, or Otto cycles. When given an additional irreversible process, this evolutionary scheme learns a previously unknown thermodynamic cycle. Gradient-based reinforcement learning is able to learn the Stirling cycle, whereas an evolutionary approach achieves the optimal Carnot cycle. Our results show how the reinforcement learning strategies developed for game playing can be applied to solve physical problems conditioned upon path-extensive order parameters

Controlled Online Optimization Learning (COOL): Finding the ground state of spin Hamiltonians with reinforcement learning

Reinforcement learning (RL) has become a proven method for optimizing a procedure for which success has been defined, but the specific actions needed to achieve it have not. We apply the so-called "black box" method of RL to what has been referred as the "black art" of simulated annealing (SA), demonstrating that an RL agent based on proximal policy optimization can, through experience alone, arrive at a temperature schedule that surpasses the performance of standard heuristic temperature schedules for two classes of Hamiltonians. When the system is initialized at a cool temperature, the RL agent learns to heat the system to "melt" it, and then slowly cool it in an effort to anneal to the ground state; if the system is initialized at a high temperature, the algorithm immediately cools the system. We investigate the performance of our RL-driven SA agent in generalizing to all Hamiltonians of a specific class; when trained on random Hamiltonians of nearest-neighbour spin glasses, the RL agent is able to control the SA process for other Hamiltonians, reaching the ground state with a higher probability than a simple linear annealing schedule. Furthermore, the scaling performance (with respect to system size) of the RL approach is far more favourable, achieving a performance improvement of one order of magnitude on L=14x14 systems. We demonstrate the robustness of the RL approach when the system operates in a "destructive observation" mode, an allusion to a quantum system where measurements destroy the state of the system. The success of the RL agent could have far-reaching impact, from classical optimization, to quantum annealing, to the simulation of physical systems

On deep learning in physics

Machine learning, and most notably deep neural networks, have seen unprecedented success in recent years due to their ability to learn complex nonlinear mappings by ingesting large amounts of data through the process of training. This learning-by-example approach has slowly made its way into the physical sciences in recent years. In this dissertation I present a collection of contributions at the intersection of the fields of physics and deep learning. These contributions constitute some of the earlier introductions of deep learning to the physical sciences, and comprises a range of machine learning techniques, such as feed forward neural networks, generative models, and reinforcement learning. A focus will be placed on the lessons and techniques learned along the way that would influence future research projects

On deep learning in physics

Machine learning, and most notably deep neural networks, have seen unprecedented success in recent years due to their ability to learn complex nonlinear mappings by ingesting large amounts of data through the process of training. This learning-by-example approach has slowly made its way into the physical sciences in recent years. In this dissertation I present a collection of contributions at the intersection of the fields of physics and deep learning. These contributions constitute some of the earlier introductions of deep learning to the physical sciences, and comprises a range of machine learning techniques, such as feed forward neural networks, generative models, and reinforcement learning. A focus will be placed on the lessons and techniques learned along the way that would influence future research projects

Heteroleptic samarium(III) halide complexes probed by fluorescence-detected L3-edge X-ray absorption spectroscopy

Addition of various oxidants to the near-linear Sm(II) complex [Sm(N††)2] (1), where N†† is the bulky bis(triisopropylsilyl)amide ligand {N(SiiPr3)2}, afforded a family of heteroleptic three-coordinate Sm(III) halide complexes, [Sm(N††)2(X)] (X = F, 2-F; Cl, 2-Cl; Br, 2-Br; I, 2-I). In addition, the trinuclear cluster [{Sm(N††)}3(μ2-I)3(μ3-I)2] (3), which formally contains one Sm(II) and two Sm(III) centres, was isolated during the synthesis of 2-I. Complexes 2-X are remarkably stable towards ligand redistribution, which is often a facile process for heteroleptic complexes of smaller monodentate ligands in lanthanide chemistry, including the related bis(trimethylsilyl)amide {N(SiMe3)2} (N′′). Complexes 2-X and 3 have been characterised by single crystal X-ray diffraction, elemental analysis, multinuclear NMR, FTIR and electronic spectroscopy. The Lα1 fluorescence-detected X-ray absorption spectrum recorded at the Sm L3-edge for 2-X exhibited a resolved pre-edge peak defined as an envelope quadrupole-allowed 2p → 4f transition. The X-ray absorption spectral features were successfully reproduced using time-dependent density functional theoretical (TD-DFT) calculations that synergistically supports the experimental observations as well as the theoretical model upon which the electronic structure and bonding in lanthanide complexes is derived