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How Representative Are Uncrewed Aircraft System Measurements of the Convective Boundary Layer?
Abstract Uncrewed aircraft systems (UAS) demonstrate significant potential for filling data gaps in the atmospheric boundary layer. However, the extent to which UAS observations—typically vertical profiles taken over 15 min—are representative of the boundary layer as a whole remains poorly characterized. Using large eddy simulations (LES) of the daytime convective boundary layer (CBL), we quantify random errors in UAS measurements that occur due to insufficient statistical convergence of the time average to the true ensemble mean. Random errors in first‐order moments increase as the CBL becomes increasingly unstable, and are largest near the surface for most quantities. Errors are on the order of 2–6 m for wind speed, 15–60 for wind direction, 0.2–3 K for potential temperature, and 0.1–1 g for specific humidity, with errors in turbulent fluxes on the order of 50%–100%. Sampling strategies that mitigate random errors are discussed in light of our results.
An extended polynomial chaos expansion for PDF characterization and variation with aleatory and epistemic uncertainties
This paper presents an extended polynomial chaos formalism for epistemic uncertainties and a new framework for evaluating sensitivities and variations of output probability density functions (PDF) to uncertainty in probabilistic models of input variables. An ”extended” polynomial chaos expansion (PCE) approach is developed that accounts for both aleatory and epistemic uncertainties, modeled as random variables, thus allowing a unified treatment of both types of uncertainty. We explore in particular epistemic uncertainty associated with the choice of prior probabilistic models for input parameters. A PCE-based Kernel Density (KDE) construction provides a composite map from the PCE coefficients and germ to the PDF of quantities of interest (QoI). Here, the sensitivities of these PDF with respect to the input parameters are then evaluated. Input parameters of the probabilistic models are considered. By sampling over the epistemic random variable, a family of PDFs is generated and the failure probability is itself estimated as a random variable with its own PCE. Integrating epistemic uncertainties within the PCE framework results in a computationally efficient paradigm for propagation and sensitivity evaluation. Two typical illustrative examples are used to demonstrate the proposed approach.
An Empirical Quantile Estimation Approach for Chance-Constrained Nonlinear Optimization Problems
We investigate an empirical quantile estimation approach to solve chance-constrained nonlinear optimization problems. Our approach is based on the reformulation of the chance constraint as an equivalent quantile constraint to provide stronger signals on the gradient. In this approach, the value of the quantile function is estimated empirically from samples drawn from the random parameters, and the gradient of the quantile function is estimated via a finite-difference approximation on top of the quantile-function-value estimation. We establish a convergence theory of this approach within the framework of an augmented Lagrangian method for solving general nonlinear constrained optimization problems. The foundation of the convergence analysis is a concentration property of the empirical quantile process, and the analysis is divided based on whether or not the quantile function is differentiable. In contrast to the sampling-and-smoothing approach used in the literature, the method developed in this paper does not involve any smoothing function and hence the quantile-function gradient approximation is easier to implement and there are less accuracy-control parameters to tune. Furthermore, we demonstrate the effectiveness of this approach and compare it with a smoothing method for the quantile-gradient estimation. Numerical investigation shows that the two approaches are competitive for certain problem instances.
A data-driven approach to sampling matrix selection for compressive sensing
Sampling is a fundamental aspect of any implementation of compressive sensing. Typically, the choice of sampling method is guided by the reconstruction basis. However, this approach can be problematic with respect to certain hardware constraints and is not responsive to domain-specific context. We propose a method for defining an order for a sampling basis that is optimal with respect to capturing variance in data, thus allowing for meaningful sensing at any desired level of compression. We focus on the Walsh-Hadamard sampling basis for its relevance to hardware constraints, but our approach applies to any sampling basis of interest. We illustrate the effectiveness of our method on the Physical Sciences Inc. Fabry-Perot interferometer sensor multispectral dataset, the Johns Hopkins Applied Physics Lab FTIR-based longwave infrared sensor hyperspectral dataset, and a Colorado State University Swiss Ranger depth image dataset. The spectral datasets consist of simulant experiments, including releases of chemicals such as GAA and SF6. We combine our sampling and reconstruction with the adaptive coherence estimator (ACE) and bulk coherence for chemical detection and we incorporate an algorithmic threshold for ACE values to determine the presence or absence of a chemical. We compare results across sampling methods in this context. We have successful chemical detection at a compression rate of 90%. For all three datasets, we compare our sampling approach to standard orderings of sampling basis such as random, sequency, and an analog of sequency that we term `frequency.' In one instance, the peak signal to noise ratio was improved by over 30% across a test set of depth images.
Crystallographic and magnetic investigations of textured bismuth ferrite lead titanate layers
In present work we demonstrate the synthesis of textured 60BiFeO 3 –40PbTiO 3 (60:40 BFPT) by using the templated grain growth technique with platelets of BaTiO 3 . Synthesised 60:40 BFPT has been examined by High-energy x-ray diffraction utilizing a synchrotron x-ray source. The crystallographic structure refinement results revealed the co-existence of monoclinic and tetragonal phases. By employing an external electric field, the mixed monoclinic/tetragonal phases transformed to the predominantly tetragonal phase. In addition, a crystallographic texture refinement was completed on unpoled state of 60:40 BFPT which showed the sample had 1.3 multiples of random distribution (MRD) $\{100\}$ crystallographic textured in tetragonal phase. However, magnetic measurements showed isotropic ferromagnetism for the sample which is not in agreement with the crystallographic texture properties of the sample. Low temperature magnetic transition has been found in zero field cooled—field cooled (ZFC-FC) measurements. This could be due to the possible existence of a secondary magnetic phase, which dominated the magnetic result.
Motion Planning Algorithms for Safety and Quantum Computing Efficiency
Motion planning remains a fundamental problem in robotics. Sampling-based algorithms use randomization to allow efficient solutions to this complex problem. As mobile robots and autonomous vehicles become more prevalent in everyday life, motion planning must be applied to increasingly challenging scenarios. Safety has become a paramount concern in motion planning for ensuring robotic applications enrich human lives. To date, many motion planning techniques to increase safety in the face of uncertain and dynamic environments have been developed. This dissertation first addresses distributional safety of Rapidly-Exploring Random Trees (RRT) through our algorithm W-Safe RRT. To acknowledge distributional uncertainty and poor modeling, W-Safe RRT uses the Wasserstein metric to provide a probabilistic bound on the distributional distance between a robot and obstacles. Human-interpretable environmental agent classification allows online safety margin adaptation. We propose and analyze an integrating region method for online classification that increases actor labeling accuracy based on behavioral feature values when compared to state of the art methods. The method performs class assignments based on local maximum likelihood in a created behavioral feature-space, allowing a notion of classification uncertainty. Model-based methods with safety guarantees can quickly become computationally in tractable, especially with multiple agents, higher dimensions, and plentiful unknowns. Sampling based algorithms have been parallelized for computation with multi-core computers and GPUs. We consider the use of quantum algorithms and computers for sampling-based motion planning for the first time. Quantum computing performs operations on superpositions of states and can solve certain problems much more efficiently than classical computers, but introduces previously unseen challenges. With Quantum-RRT, we recast the motion planning problem into a database-search structure and use Quantum Amplitude Amplification to find reachable states in the database with a quadratic performance increase over classical methods. We address two error sources with this method: quantum measurement and quantum oracle errors. We then extend this method to Parallel Quantum-RRT, which uses a manager-worker architecture with multiple parallel quantum workers to increase database search efficiency. We compare algorithm architectures and characterize probabilities of multiple workers finding solutions. Lastly, we test in simulation the quantum algorithms against classical versions in a wide variety of scenarios, concluding that a similar parallelization improvement is to be found in the quantum case as was found in the parallelization of classical RRT.
Differentially Private Synthesis and Sharing of Network Data Via Bayesian Exponential Random Graph Models
Abstract Network data often contain sensitive relational information. One approach to protecting sensitive information while offering flexibility for network analysis is to share synthesized networks based on the information in originally observed networks. We employ differential privacy (DP) and exponential random graph models (ERGMs) and propose the DP-ERGM method to synthesize network data. We apply DP-ERGM to two real-world networks. We then compare the utility of synthesized networks generated by DP-ERGM, the DyadWise Randomized Response (DWRR) approach, and the Synthesis through Conditional distribution of Edge given nodal Attribute (SCEA) approach. In general, the results suggest that DP-ERGM preserves the original information significantly better than two other approaches in network structural statistics and inference for ERGMs and latent space models. Furthermore, DP-ERGM satisfies node DP through modeling the global network structure with ERGM, a stronger notion of privacy than the edge DP under which DWRR and SCEA operate.
Data Driven Temperature Control of Heat Exchangers
The following poster presents a couple of approaches for temperature control at heat exchanger’s exit. One of the approaches is based on using n-step ahead neural network while the other approach utilizes a combination of control + learning module for temperature control. Designing robust controllers for typical heat exchangers is very challenging primarily because of complexities associated with the synthesis of dynamics of industrial heat exchangers. Even if a dynamics model is synthesized, the accuracy of it would be called into question as there can be lot of unmodeled system dynamics and random noise in input parameters. In many situations, the degree of uncertainty in the model of system being controlled limits the utility of optimal control design. Controller can be manually tuned on the field but it is very difficult to determine manual adjustments that result in overall improvement. These practical challenges call for learning based approaches for temperature control that would result in optimal heat exchanger performance. We utilize the existing mathematical model for air-water system synthesized by Underwood and Crawford for testing our algorithms. Underwood and Crawford developed a model of heating coil by fitting a second-order non-linear equations to measurements of air and water temperature and flow rates obtained from the actual coil. The uncertainties in the model include air and water inlet temperatures and air-flow rate. These uncertainties are modeled as random variables that are sampled from a uniform distribution. The control variable is the water flow rate which is controlled for regulating the air-outlet temperature. Neural networks are frequently used in various literatures for learning time-series. We propose a modified version of neural networks known as n-step ahead neural network that take the state variables and output at time t as input to successfully predict output at time t+n. The variable n is a hyper-parameter which can be optimized based on performance constraints. The n-step ahead neural network was then trained on training data generated from experimental model to high training accuracies. This trained model performed appreciably in its objective to regulate outlet temperature of a heat exchanger. The second approach uses a combination of control and learning module for temperature regulation. The control module used is a proportional controller while the learning module used is steady state neural network. The steady state neural network is used to learn the controller output as a function of random variables and temperature set-point. The proportional controller is then used to compensate for transient behavior of linearized system around various operating points. The algorithm works effectively for temperature regulation as neural network captures the non-linear input-output behavior of closed loop system leaving proportional controller to compensate for steady state errors of a linearized plant. The proposed algorithm’s performances were evaluated against the performance of a PI controller. Further, the robustness of the algorithms were also studied in great detail under small perturbations around tuned parameters of the proposed algorithms.
Rain retrieval from dual-frequency radar Doppler spectra: validation and potential for a midlatitude precipitating case-study
A recently developed technique retrieving the binned raindrop size distributions (DSDs) and air state parameters from ground-based K a and W-band radars Doppler spectra profiles is improved and applied to a typical midlatitude rain event. The retrievals are thoroughly validated against DSD observations of a 2D video disdrometer and independent X-band observations. Here for this case-study, profiles of rain rate, R, mean volume diameter and concentration parameter are retrieved, with low bias and standard deviations. In light rain (0.1 < R < 1 mm h -1 ), the radar reflectivities must be calibrated with a collocated disdrometer which introduces random errors due to sampling mismatch between the two instruments. The best performances are obtained in moderate rain (1 < R < 20 mm h -1 ) where the retrieval is providing self-consistent estimates of the absolute calibration and of the attenuation caused by antenna or radome wetness for both radars.
Comparison of multiobjective optimization methods for the $\mathrm{LCLS-II}$ photoinjector
Particle accelerators are among some of the largest science experiments in the world and can consist of thousands of components with a wide variety of input ranges. These systems can easily become unwieldy optimization problems during design and operations studies. Starting in the early 2000s, searching for better beam dynamics configurations became synonymous with heuristic optimization methods in the accelerator physics community. Genetic algorithms and particle swarm optimization are currently the most widely used. These algorithms can take thousands of simulation evaluations to find optimal solutions for one machine prototype. For large facilities such as the Linac Coherent Light Source (LCLS) and others, this equates to a limited exploration of many possible design configurations. In this paper, the LCLS-II photoinjector is optimized with three optimization algorithms. All optimizations were started from both a uniform random and Latin hypercube sample. In all cases, the optimizations started from Latin hypercube samples outperformed optimizations started from uniform samples. All three algorithms were able to optimize the photoinjector, with the model-based methods approximating the Pareto front in fewer simulation evaluations. This work, in combination with previous optimization observations, indicates objective penalties have a strong impact on the efficiency of such methods. In general, we recommend heuristic methods for initial optimizations and model-based methods when information about the objective space is available.
Predictive Factor Analysis of Response-Adapted Radiation Therapy for Chemotherapy-Sensitive Pediatric Hodgkin Lymphoma: Analysis of the Children's Oncology Group AHOD 0031 Trial
Purpose: To evaluate whether clinical risk factors could further distinguish children with intermediate-risk Hodgkin lymphoma (HL) with rapid early and complete anatomic response (RER/CR) who benefit significantly from involved-field RT (IFRT) from those who do not, and thereby aid refinement of treatment selection. Methods and Materials: Children with intermediate-risk HL treated on the Children's Oncology Group AHOD 0031 trial who achieved RER/CR with 4 cycles of chemotherapy, and who were randomized to 21-Gy IFRT or no additional therapy (n=716) were the subject of this study. Recursive partitioning analysis was used to identify factors associated with clinically and statistically significant improvement in event-free survival (EFS) after randomization to IFRT. Bootstrap sampling was used to evaluate the robustness of the findings. Result: Although most RER/CR patients did not benefit significantly from IFRT, those with a combination of anemia and bulky limited-stage disease (n=190) had significantly better 4-year EFS with the addition of IFRT (89.3% vs 77.9% without IFRT; P=.019); this benefit was consistently reproduced in bootstrap analyses and after adjusting for other prognostic factors. Conclusion: Although most patients achieving RER/CR had favorable outcomes with 4 cycles of chemotherapy alone, those children with initial bulky stage I/II disease and anemia had significantly better EFS with the addition of IFRT as part of combined-modality therapy. Further work evaluating the interaction of clinical and biologic factors and imaging response is needed to further optimize and refine treatment selection.
Tunable electron beam pulser for picoseconds stroboscopic microscopy in transmission electron microscopes
For two decades, time-resolved transmission electron microscopes (TEM) have relied on pulsed-laser photoemission to generate electron bunches to explore sub-microsecond to sub-picosecond dynamics. Despite the vast successes of photoemission time-resolved TEMs, laser-based systems are inherently complex, thus tend not to be turn-key. In this paper, we report on the successful retrofit of a commercial 200 keV TEM, without an external laser, capable of producing continuously tunable pulsed electron beams with repetition rates from 0.1 GHz up to 12 GHz and a tunable bunch length from tens of nanoseconds down to 10 ps. This innovation enables temporal access into previously inaccessible regimes: i.e., high repetition rate stroboscopic experiments. Combination of a pair of RF-driven traveling wave stripline elements, quadrupole magnets, and a variable beam aperture enables operation of the instrument in (1) continuous waveform (CW) mode as though the instrument was never modified (i.e. convention TEM operation mode, where the electrons from the emission cathode randomly arrive at the sample without resolvable time information), (2) stroboscopic (pump-probe) mode, and (3) pulsed beam mode for dose rate sensitive materials. In order to analyze the effect of a pulsed beam on image quality, we examined Au nanoparticles using bright field, high-resolution TEM imaging and selected area diffraction in both continuous and pulsed-beam mode. Furthermore, in comparison of conventional TEMs, the add-on beam pulser enables the observation of ultrafast dynamic behavior in materials that are reversible under synchronized excitation.
Depolarized Scattering from Block Copolymer Grains Using Circularly Polarized Light
The grain structure of ordered block copolymer materials affects their viscoelastic, adhesive, optical, and electrical properties. Depolarized light scattering has proven to be an important method for characterizing this grain structure. In this paper, we use both theory and experiments to demonstrate the relationship between grain structure and depolarized light scattering from ordered block copolymer samples performed with crossed circular polarizers. We model the sample assuming it comprises randomly oriented ellipsoidal grains with optic axes coincident with the ellipsoid axes. We show that the scattering pattern obtained using circularly polarized (CP) light is azimuthally symmetric, in contrast to that obtained using linearly polarized (LP) light which exhibits 4-fold angular modulation. The integrated scattered power in the CP case is twice as large as that in the LP case. Finally, by simultaneously fitting CP and LP light scattering data, we obtain robust measures of parameters that characterize grain structures. In addition, CP light scattering can, in principle, be used to characterize nonrandom grain orientation distributions.
Modeling Wavelength Dependent Mid-infrared (5.5–25 μm) Optical Constants of Silicate Glasses: A Genetic Algorithm Approach
Wavelength-dependent mid-infrared (400–1,800 cm -1 ; 5.56–25 μm) optical constants (real and imaginary indices of refraction; n and k) are determined using reflectance spectra at a spectral sampling of 4 cm -1 for several silicate glasses of varying SiO 2 wt% which include (a) basaltic volcanic glass from Kilauea, Hawaii, (b) synthetic andesite, (c) two synthetic dacites, (d) obsidian volcanic glass from Mount Lassen, California, (e) synthetic rhyodacite, and (f) rhyolitic volcanic glass from Mexico. Because glasses are optically isotropic, no specific orientation was required for spectral measurements, and polished glass samples were measured at random orientations using micro-FTIR spectrometer. Lorentz-Lorenz dispersion theory and Fresnel reflectance model for high symmetry materials were used to model the optical constants by optimizing oscillator parameters in modeled spectra to match laboratory spectra. A genetic algorithm (GA) approach automatically finds the natural oscillators and their parameters for each spectrum, and then uses these parameters in a non-linear least squares optimization routine. The study compared spectral parameters such as Christiansen feature, reststrahlen bands (RBs), and the peaks centered around 860–1,100 (peak 1) and 400–480 cm -1 (peak 2) of n and k to their respective SiO 2 wt% of the glasses. CF, RBs, peak 1 of n and k, and peak 2 of n shift to higher wavenumbers with increased SiO 2 wt%, whereas peak 2 of k shifts to lower wavenumbers with increased SiO 2 wt%. Derived optical constants of these glasses will improve quantitative abundance mapping of volcanic materials on the surfaces of silicate targets in the Solar System.
Volume collapse phase transitions in cerium-praseodymium alloys under high pressure
Cerium-12at%Praseodymium(Ce 0.88 Pr 0.12 ) and Ce-50at%Praseodymium(Ce 0.50 Pr 0.50 ) alloy samples that contain a random solid-solution of Ce (4f1 (J=5/2)) and Pr (4f2 (J=4)) localized f-states have been studied by angle dispersive x-ray diffraction in a diamond anvil cell to a pressure of 65 GPa and 150 GPa respectively using a synchrotron source. Ce 0.88 Pr 0.12 alloy crystallizes in a face-centered cubic (γ-phase) structure at ambient conditions, while Ce 0.50 Pr 0.50 alloy crystallizes in the double hexagonal close packed (dhcp) structure at ambient conditions. Two distinct volume collapse transitions are observed in Ce 0.88 Pr 0.12 alloy at 1.5 GPa and 18 GPa with volume change of 8.5% and 3% respectively. In contrast, Ce 0.50 Pr 0.50 alloy shows only a single volume collapse of 5.6% at 20 GPa on phase transformation to α-Uranium structure under high pressure. Electrical transport measurements under high pressure show anomalies in electrical resistance at phase transitions for both compositions of this alloy.
Hamiltonian simulation in Zeno subspaces
Here, we investigate the quantum Zeno effect as a framework for designing and analyzing quantum algorithms for Hamiltonian simulation. We show that frequent projective measurements of an ancilla qubit register can be used to simulate quantum dynamics on a target qubit register with a circuit complexity similar to randomized approaches. The classical sampling overhead in the latter approaches is traded for ancilla qubit overhead in Zeno-based approaches. A second-order Zeno sequence is developed to improve scaling and implementations through unitary kicks are discussed. We derive rigorous error bounds that allow for identifying the associated circuit complexities for the first- and second-order Zeno sequences. We show that the circuits over the combined register can be identified as a subroutine commonly used in post-Trotter Hamiltonian simulation methods. We build on this observation to reveal connections between different Hamiltonian simulation algorithms.
The information content of projected galaxy fields
ABSTRACT The power spectrum of the non-linearly evolved large-scale mass distribution recovers only a minority of the information available on the mass fluctuation amplitude. We investigate the recovery of this information in 2D ‘slabs’ of the mass distribution averaged over ≈100 h−1 Mpc along the line of sight, as might be obtained from photometric redshift surveys. We demonstrate a Hamiltonian Monte Carlo method to reconstruct the non-Gaussian mass distribution in slabs, under the assumption that the projected field is a point-transformed Gaussian random field, Poisson-sampled by galaxies. When applied to the Quijote N-body suite at z = 0.5 and at a transverse resolution of 2 h−1 Mpc, the method recovers ∼30 times more information than the 2D power spectrum in the well-sampled limit, recovering the Gaussian limit on information. At a more realistic galaxy sampling density of 0.01 h3 Mpc−3, shot noise reduces the information gain to a factor of 5 improvement over the power spectrum at resolutions of 4 h−1 Mpc or smaller.