23 research outputs found
Estimating the Spectrum in Computed Tomography Via Kullback–Leibler Divergence Constrained Optimization
Purpose
We study the problem of spectrum estimation from transmission data of a known phantom. The goal is to reconstruct an x‐ray spectrum that can accurately model the x‐ray transmission curves and reflects a realistic shape of the typical energy spectra of the CT system. Methods
Spectrum estimation is posed as an optimization problem with x‐ray spectrum as unknown variables, and a Kullback–Leibler (KL)‐divergence constraint is employed to incorporate prior knowledge of the spectrum and enhance numerical stability of the estimation process. The formulated constrained optimization problem is convex and can be solved efficiently by use of the exponentiated‐gradient (EG) algorithm. We demonstrate the effectiveness of the proposed approach on the simulated and experimental data. The comparison to the expectation–maximization (EM) method is also discussed. Results
In simulations, the proposed algorithm is seen to yield x‐ray spectra that closely match the ground truth and represent the attenuation process of x‐ray photons in materials, both included and not included in the estimation process. In experiments, the calculated transmission curve is in good agreement with the measured transmission curve, and the estimated spectra exhibits physically realistic looking shapes. The results further show the comparable performance between the proposed optimization‐based approach and EM. Conclusions
Our formulation of a constrained optimization provides an interpretable and flexible framework for spectrum estimation. Moreover, a KL‐divergence constraint can include a prior spectrum and appears to capture important features of x‐ray spectrum, allowing accurate and robust estimation of x‐ray spectrum in CT imaging
Solution-Processed CuS Nanostructures for Solar Hydrogen Production
CuS is a promising solar energy conversion material due to its suitable optical properties, high elemental earth abundance, and nontoxicity. In addition to the challenge of multiple stable secondary phases, the short minority carrier diffusion length poses an obstacle to its practical application. This work addresses the issue by synthesizing nanostructured CuS thin films, which enables increased charge carrier collection. A simple solution-processing method involving the preparation of CuCl and CuCl molecular inks in a thiol-amine solvent mixture followed by spin coating and low-temperature annealing was used to obtain phase-pure nanostructured (nanoplate and nanoparticle) CuS thin films. The photocathode based on the nanoplate CuS (FTO/Au/CuS/CdS/TiO/RuO) reveals enhanced charge carrier collection and improved photoelectrochemical water-splitting performance compared to the photocathode based on the non-nanostructured CuS thin film reported previously. A photocurrent density of 3.0 mA cm at −0.2 versus a reversible hydrogen electrode (V) with only 100 nm thickness of a nanoplate CuS layer and an onset potential of 0.43 V were obtained. This work provides a simple, cost-effective, and high-throughput method to prepare phase-pure nanostructured CuS thin films for scalable solar hydrogen production
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High-dimensional estimation and optimization with multiple structured signals
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Fast and flexible estimation of effective migration surfaces
Spatial population genetic data often exhibits ‘isolation-by-distance,’ where genetic similarity tends to decrease as individuals become more geographically distant. The rate at which genetic similarity decays with distance is often spatially heterogeneous due to variable population processes like genetic drift, gene flow, and natural selection. Petkova et al., 2016 developed a statistical method called Estimating Effective Migration Surfaces (EEMS) for visualizing spatially heterogeneous isolation-by-distance on a geographic map. While EEMS is a powerful tool for depicting spatial population structure, it can suffer from slow runtimes. Here, we develop a related method called Fast Estimation of Effective Migration Surfaces (FEEMS). FEEMS uses a Gaussian Markov Random Field model in a penalized likelihood framework that allows for efficient optimization and output of effective migration surfaces. Further, the efficient optimization facilitates the inference of migration parameters per edge in the graph, rather than per node (as in EEMS). With simulations, we show conditions under which FEEMS can accurately recover effective migration surfaces with complex gene-flow histories, including those with anisotropy. We apply FEEMS to population genetic data from North American gray wolves and show it performs favorably in comparison to EEMS, with solutions obtained orders of magnitude faster. Overall, FEEMS expands the ability of users to quickly visualize and interpret spatial structure in their data
Adaptive wavelet distillation from neural networks through interpretations
Recent deep-learning models have achieved impressive prediction performance, but often sacrifice interpretability and computational efficiency. Interpretability is crucial in many disciplines, such as science and medicine, where models must be carefully vetted or where interpretation is the goal itself. Moreover, interpretable models are concise and often yield computational efficiency. Here, we propose adaptive wavelet distillation (AWD), a method which aims to distill information from a trained neural network into a wavelet transform. Specifically, AWD penalizes feature attributions of a neural network in the wavelet domain to learn an effective multi-resolution wavelet transform. The resulting model is highly predictive, concise, computationally efficient, and has properties (such as a multi-scale structure) which make it easy to interpret. In close collaboration with domain experts, we showcase how AWD addresses challenges in two real-world settings: cosmological parameter inference and molecular-partner prediction. In both cases, AWD yields a scientifically interpretable and concise model which gives predictive performance better than state-of-the-art neural networks. Moreover, AWD identifies predictive features that are scientifically meaningful in the context of respective domains. All code and models are released in a full-fledged package available on Github (https://github.com/Yu-Group/adaptive-wavelets)
Electrophoretic Deposition of Aged and Charge Controlled Colloidal Copper Sulfide Nanoparticles
Colloidal nanoparticles (NPs) have been recently spotlighted as building blocks for various nanostructured devices. Their collective properties have been exhibited by arranging them on a substrate to form assembled NPs. In particular, electrophoretic deposition (EPD) is an emerging fabrication method for such nanostructured films. To maximize the benefits of this method, further studies are required to fully elucidate the key parameters that influence the NP deposition. Herein, two key parameters are examined, namely: (i) the aging of colloidal NPs and (ii) the charge formation by surface ligands. The aging of Cu2-xS NPs changes the charge states, thus leading to different NP deposition behaviors. The SEM images of NP films, dynamic light scattering, and zeta potential results demonstrated that the charge control and restoration of interparticle interactions for aged NPs were achieved via simple ligand engineering. The charge control of colloidal NPs was found to be more dominant than the influence of aging, which can alter the surface charges of the NPs. The present results thus reveal that the charge formation on the colloidal NPs, which depends on the surface ligands, is an important controllable parameter in EPD
Transformation Importance with Applications to Cosmology
Published in ICLR 2020 Workshop on Fundamental Science in the era of AIMachine learning lies at the heart of new possibilities for scientific discovery, knowledge generation, and artificial intelligence. Its potential benefits to these fields requires going beyond predictive accuracy and focusing on interpretability. In particular, many scientific problems require interpretations in a domain-specific interpretable feature space (e.g. the frequency domain) whereas attributions to the raw features (e.g. the pixel space) may be unintelligible or even misleading. To address this challenge, we propose TRIM (TRansformation IMportance), a novel approach which attributes importances to features in a transformed space and can be applied post-hoc to a fully trained model. TRIM is motivated by a cosmological parameter estimation problem using deep neural networks (DNNs) on simulated data, but it is generally applicable across domains/models and can be combined with any local interpretation method. In our cosmology example, combining TRIM with contextual decomposition shows promising results for identifying which frequencies a DNN uses, helping cosmologists to understand and validate that the model learns appropriate physical features rather than simulation artifacts