Machine learning enabled reduced-order scenario generation for stochastic analysis of solar power forecasts
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A Prospective Quasi-Randomized Comparison of Intraoperatively Built Custom-Linked Seeds Versus Loose Seeds for Prostate Brachytherapy
Purpose: To compare dosimetric parameters, seed migration rates, operation times, and acute toxicities of intraoperatively built custom-linked (IBCL) seeds with those of loose seeds for prostate brachytherapy. Methods and Materials: Participants were 140 patients with low or intermediate prostate cancer prospectively allocated to an IBCL seed group (n=74) or a loose seed group (n=66), using quasirandomization (allocated by week of the month). All patients underwent prostate brachytherapy using an interactive plan technique. Computed tomography and plain radiography were performed the next day and 1 month after brachytherapy. The primary endpoint was detection of a 5% difference in dose to 90% of prostate volume on postimplant computed tomography 1 month after treatment. Seed migration was defined as a seed position >1 cm from the cluster of other seeds on radiography. A seed dropped into the seminal vesicle was also defined as a migrated seed. Results: Dosimetric parameters including the primary endpoint did not differ significantly between groups, but seed migration rate was significantly lower in the IBCL seed group (0%) than in the loose seed group (55%; P<.001). Mean operation time was slightly but significantly longer in the IBCL seed group (57 min) than in the loose seed group (50 min; P<.001). No significant differences in acute toxicities were seen between groups (median follow-up, 9 months). Conclusions: This prospective quasirandomized control trial showed no dosimetric differences between IBCL seed and loose seed groups. However, a strong trend toward decreased postimplant seed migration was shown in the IBCL seed group.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Ab Initio Methods for L-edge X-ray Absorption Spectroscopy
The theoretical prediction of X-ray absorption spectra (XAS) has become common- place in electronic structure theory. The ability to better model and understand L-edge spectra is of great interest in the study of transition metal complexes and a wide variety of solid state materials. However, until recently few rst-principles works have mod- eled L-edge XAS due to the presence of strong spin-orbit coupling in the 2p orbitals which splits the observed peaks into multiple groups of features. Therefore, a proper description of spin-orbit coupling is vital for the successful prediction of L-edge spectra. A number of new approaches that incorporate spin-orbit coupling have recently made advances in the computation of L-edge spectra. In this review, we describe recent work in computational L-edge XAS and how these methods may continue to improve in the future. Comparison of the advantages and disadvantages of the various approaches are considered, with special attention to not only the computational cost of the level of theory, but also the various approaches that can be used to compute the absorption spectra with a large number of high energy excited states.
Kasper, Joseph M.↗
Generalized scaling of spin qubit coherence in over 12,000 host materials
Significance Atomic defects in solid-state materials are promising candidates as quantum bits, or qubits. New materials are actively being investigated as hosts for new defect qubits; however, there are no unifying guidelines that can quantitatively predict qubit performance in a new material. One of the most critical property of qubits is their quantum coherence. While cluster correlation expansion (CCE) techniques are useful to simulate the coherence of electron spins in defects, they are computationally expensive to investigate broad classes of stable materials. Using CCE simulations, we reveal a general scaling relation between the electron spin coherence time and the properties of qubit host materials that enables rapid and quantitative exploration of new materials hosting spin defects.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Automated characterization of spatial and dynamical heterogeneity in supercooled liquids via implementation of machine learning
Abstract A computational approach by an implementation of the principle component analysis (PCA) with K -means and Gaussian mixture (GM) clustering methods from machine learning algorithms to identify structural and dynamical heterogeneities of supercooled liquids is developed. In this method, a collection of the average weighted coordination numbers ( W C N s ‾ ) of particles calculated from particles’ positions are used as an order parameter to build a low-dimensional representation of feature (structural) space for K -means clustering to sort the particles in the system into few meso-states using PCA. Nano-domains or aggregated clusters are also formed in configurational (real) space from a direct mapping using associated meso-states’ particle identities with some misclassified interfacial particles. These classification uncertainties can be improved by a co-learning strategy which utilizes the probabilistic GM clustering and the information transfer between the structural space and configurational space iteratively until convergence. A final classification of meso-states in structural space and domains in configurational space are stable over long times and measured to have dynamical heterogeneities. Armed with such a classification protocol, various studies over the thermodynamic and dynamical properties of these domains indicate that the observed heterogeneity is the result of liquid–liquid phase separation after quenching to a supercooled state.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Simulation and performance of an artificial retina for 40 MHz track reconstruction
We present the results of a detailed simulation of the artificial retina pattern-recognition algorithm, designed to reconstruct events with hundreds of charged-particle tracks in pixel and silicon detectors at LHCb with LHC crossing frequency of 40 MHz. Performances of the artificial retina algorithm are assessed using the official Monte Carlo samples of the LHCb experiment. We found performances for the retina pattern-recognition algorithm comparable with the full LHCb reconstruction algorithm.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment
We present a simulation-based study using deep convolutional neural networks (DCNNs) to identify neutrino interaction vertices in the MINERvA passive targets region, and illustrate the application of domain adversarial neural networks (DANNs) in this context. DANNs are designed to be trained in one domain (simulated data) but tested in a second domain (physics data) and utilize unlabeled data from the second domain so that during training only features which are unable to discriminate between the domains are promoted. MINERvA is a neutrino-nucleus scattering experiment using the NuMI beamline at Fermilab. A-dependent cross sections are an important part of the physics program, and these measurements require vertex finding in complicated events. To illustrate the impact of the DANN we used a modified set of simulation in place of physics data during the training of the DANN and then used the label of the modified simulation during the evaluation of the DANN. In conclusion, we find that deep learning based methods offer significant advantages over our prior track-based reconstruction for the task of vertex finding, and that DANNs are able to improve the performance of deep networks by leveraging available unlabeled data and by mitigating network performance degradation rooted in biases in the physics models used for training.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Exact closed-form unitary transformations of fermionic operators
Unitary transformations play a fundamental role in many-body physics, and except for special cases, they are not expressible in closed form. We present closed-form expressions for unitary transformations generated by a single fermionic operator for Hermitian and anti-Hermitian generators. We demonstrate the usefulness of these expressions in formal analyses of unitary transformations and numerical applications to Hamiltonian downfolding in quantum computing and Heisenberg dynamics. Furthermore, this work paves the way for new analytical treatments of unitary transformations and numerical many-body methods for fermions.
74 ATOMIC AND MOLECULAR PHYSICS↗
Interaction models and configurational entropies of binary MoTa and the MoNbTaW high entropy alloy
We introduce a simplified method to model the interatomic interactions of high entropy alloys based on a lookup table of cluster energies. Furthermore, these interactions are employed in replica exchange Monte Carlo simulations with histogram analysis to obtain thermodynamic properties across a broad temperature range. Kikuchi's cluster variation method entropy formalism is applied to directly calculate entropy from statistics on short- and long-range chemical order, and we discuss the convergence of the entropy as clusters of differing size are included. A high temperature series expansion aids in our understanding of the convergence. Computer codes implementing these methods, and supporting data, are freely available on the internet.
36 MATERIALS SCIENCE↗
Multilayer spectral graph clustering via convex layer aggregation
Multilayer graphs are commonly used for representing different relations between entities and handling heterogeneous data processing tasks. New challenges arise in multilayer graph clustering for assigning clusters to a common multilayer node set and for combining information from each layer. This paper presents a theoretical framework for multilayer spectral graph clustering of the nodes via convex layer aggregation. Under a novel multilayer signal plus noise model, we provide a phase transition analysis that establishes the existence of a critical value on the noise level that permits reliable cluster separation. The analysis also specifies analytical upper and lower bounds on the critical value, where the bounds become exact when the clusters have identical sizes. Numerical experiments on synthetic multilayer graphs are conducted to validate the phase transition analysis and study the effect of layer weights and noise levels on clustering reliability.
97 MATHEMATICS AND COMPUTING↗
Breast density quantification using magnetic resonance imaging (MRI) with bias field correction: A postmortem study
Purpose: Quantification of breast density based on three-dimensional breast MRI may provide useful information for the early detection of breast cancer. However, the field inhomogeneity can severely challenge the computerized image segmentation process. In this work, the effect of the bias field in breast density quantification has been investigated with a postmortem study. Methods: T1-weighted images of 20 pairs of postmortem breasts were acquired on a 1.5 T breast MRI scanner. Two computer-assisted algorithms were used to quantify the volumetric breast density. First, standard fuzzy c-means (FCM) clustering was used on raw images with the bias field present. Then, the coherent local intensity clustering (CLIC) method estimated and corrected the bias field during the iterative tissue segmentation process. Finally, FCM clustering was performed on the bias-field-corrected images produced by CLIC method. The left–right correlation for breasts in the same pair was studied for both segmentation algorithms to evaluate the precision of the tissue classification. Finally, the breast densities measured with the three methods were compared to the gold standard tissue compositions obtained from chemical analysis. The linear correlation coefficient, Pearson'sr, was used to evaluate the two image segmentation algorithms and the effect of bias field. Results: The CLIC method successfully corrected the intensity inhomogeneity induced by the bias field. In left–right comparisons, the CLIC method significantly improved the slope and the correlation coefficient of the linear fitting for the glandular volume estimation. The left–right breast density correlation was also increased from 0.93 to 0.98. When compared with the percent fibroglandular volume (%FGV) from chemical analysis, results after bias field correction from both the CLIC the FCM algorithms showed improved linear correlation. As a result, the Pearson'sr increased from 0.86 to 0.92 with the bias field correction. Conclusions: The investigated CLIC method significantly increased the precision and accuracy of breast density quantification using breast MRI images by effectively correcting the bias field. It is expected that a fully automated computerized algorithm for breast density quantification may have great potential in clinical MRI applications.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
DCA++
With the DCA++ project, we aim to gain insight into the fascinating physics of strongly correlated electron systems by employing modern quantum cluster methods. The DCA++ code provides a state of the art implementation of the dynamical cluster approximation (DCA) and its DCA+ extension. High scalability and portable performance allow to exploit today's leadership computing systems.
SciDAC↗
Fusion Model for Metagenomics
This work highlights the use of an embeddings approach that can encode multiple features and create efficient contextualization of profiled metagenomes derived from microbiome samples using computer vision models and image representations of the abundance profiles. The model's embeddings can be used to cluster existing samples based on multiple conditions and interpretations, and new embeddings can be quickly created for new samples and fitted to existing clusters to characterize them. This has practical applications for unknown, unlabeled microbiome samples. The model's embeddings can be used to cluster existing samples based on multiple conditions and interpretations, and new embeddings can be quickly created for new samples and fitted to existing clusters to characterize them. This has practical applications for unknown, unlabeled microbiome samples.
Valdes, CamiloA [Lawrence Livermore National Labor↗
Randomized Cholesky Preconditioning for Graph Partitioning Applications
A graph is a mathematical representation of a network; we say it consists of a set of vertices, which are connected by edges. Graphs have numerous applications in various fields, as they can model all sorts of connections, processes, or relations. For example, graphs can model intricate transit systems or the human nervous system. However, graphs that are large or complicated become difficult to analyze. This is why there is an increased interest in the area of graph partitioning, reducing the size of the graph into multiple partitions. For example, partitions of a graph representing a social network might help identify clusters of friends or colleagues. Graph partitioning is also a widely used approach to load balancing in parallel computing. The partitioning of a graph is extremely useful to decompose the graph into smaller parts and allow for easier analysis. There are different ways to solve graph partitioning problems. For this work, we focus on a spectral partitioning method which forms a partition based upon the eigenvectors of the graph Laplacian (details presented in Acer, et. al.). This method uses the LOBPCG algorithm to compute these eigenvectors. LOBPCG can be accelerated by an operator called a preconditioner. For this internship, we evaluate a randomized Cholesky (rchol) preconditioner for its effectiveness on graph partitioning problems with LOBPCG. We compare it with two standard preconditioners: Jacobi and Incomplete Cholesky (ichol). This research was conducted from August to December 2021 in conjunction with Sandia National Laboratories.
97 MATHEMATICS AND COMPUTING↗
The Spitzer-IRAC/MIPS Extragalactic survey (SIMES) IN the south ecliptic pole field
We present the Spitzer-IRAC/MIPS Extragalactic survey (SIMES) in the South Ecliptic Pole field. The large area covered (7.7 deg{sup 2}), together with one of the lowest Galactic cirrus emissions in the entire sky and a very extensive coverage by Spitzer, Herschel, Akari, and GALEX, make the SIMES field ideal for extragalactic studies. The elongated geometry of the SIMES area (≈4:1), allowing for significant cosmic variance reduction, further improves the quality of statistical studies in this field. Here we present the reduction and photometric measurements of the Spitzer/IRAC data. The survey reaches depths of 1.93 and 1.75 μJy (1σ) at 3.6 and 4.5 μm, respectively. We discuss the multiwavelength IRAC-based catalog, completed with optical, mid-, and far-IR observations. We detect 341,000 sources with F{sub 3.6μm}⩾3σ. Of these, 10% have an associated 24 μm counterpart, while 2.7% have an associated SPIRE source. We release the catalog through the NASA/IPAC Infrared Science Archive. Two scientific applications of these IRAC data are presented in this paper. First, we compute integral number counts at 3.6 μm. Second, we use the [3.6]–[4.5] color index to identify galaxy clusters at z > 1.3. We select 27 clusters in the full area, a result consistent with previous studies at similar depth.
79 ASTRONOMY AND ASTROPHYSICS↗
Numerical Study of Vortex Core in Turbulent Swirling Flow
Swirling flow has attracted a great deal of research interest. An application of swirling flow can be found in many engineering areas; such as gas cyclone and heat transfer enhancement. In nuclear engineering, swirling flow is used as heat transfer enhancement in rod bundle by installing grid spacer and mixing vane. Because of its extensive applications, there has been a great number of experimental and numerical investigations of swirling flow. McClusky et al., experimentally studied mapping of the lateral velocity in rod bundle using Laser Doppler Velocimetry (LDV) and Conner et al. predicted the flow field in rod bundle by CFD. To understand the characteristics of swirling flow such as tangential velocity and vortex core swirling flow, some researchers investigated swirling flow in a pipe instead of in rod bundle. This is due to the similarity between swirling flow in a pipe and rod bundle. In recent study, flow mapping of 2D velocity field of single-phase swirling flow had been conducted experimentally by Particle Image Velocimetry (PIV). However, this method needs calibration and complicated. The new measurement technique for flow mapping in swirling flow was done by phased array Ultrasonic Velocity Profiler (PAUVP). Hamdani et al., successfully plotted velocity field and determined the swirling core region (vortex core). This technique is nonintrusive and no need calibration. However, there was a limitation of the measurement because phased array UVP cannot capture 360 deg. velocity field inside the pipe. In addition, tangential velocity in CFD results was not agree well with experiment data due asymmetric problem. To evaluate this limitation of experimental method, the numerical simulations using Computational Fluid Dynamics (CFD) needs to be performed. The objective of this study is to use the commercial CFD code Fluent to model the developing turbulent swirling flow induced by twisted tape inside a straight pipe, and to gain an understanding of such a flow and the turbulence models involved in the simulation. Straight pipe is used instead of rod bundle for a simplification. In this work, the Renormalized Group (RNG) k-{epsilon} turbulence model and Reynolds Stress Model (RSM) turbulence model, are performed to study the phenomena of vortex core in a pipe with twisted tape inserts. Effects of the Reynolds number and asymmetric of twisted tape (inclination, {alpha} = 5 deg. and {alpha} = 20 deg.) on swirling core position are examined using water as the testing fluid. Furthermore, the Navier-Stokes equations will be solved to obtain results that can be validated with the previous experimental results. (authors)
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Graph Neural Network based Deep SpatialTemporal Clustering Ensemble
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Mathematics and Computing↗