Colorimetric sensors offer the prospect for on-demand sensing diagnostics in simple and low-cost form factors, enabling rapid spatiotemporal inspection by digital cameras or the naked eye. However, realizing strong dynamic color variations in response to small changes in sample properties has remained a considerable challenge, which is often pursued through the use of highly responsive materials under broadband illumination. In this work, we demonstrate a general colorimetric sensing technique that overcomes the performance limitations of existing chromatic and luminance-based sensing techniques. Our approach combines structural color optical filters as sensing elements alongside a multichromatic laser illuminant. We experimentally demonstrate our approach in the context of label-free biosensing and achieve ultrasensitive and perceptually enhanced chromatic color changes in response to refractive index changes and small molecule surface attachment. Using structurally enabled chromaticity variations, the human eye is able to resolve ∼0.1-nm spectral shifts with low-quality factor (e.g., Q ∼ 15) structural filters. This enables spatially resolved biosensing in large area (approximately centimeters squared) lithography-free sensing films with a naked eye limit of detection of ∼3 pg/mm², lower than industry standard sensors based on surface plasmon resonance that require spectral or angular interrogation. This work highlights the key roles played by both the choice of illuminant and design of structural color filter, and it offers a promising pathway for colorimetric devices to meet the strong demand for high-performance, rapid, and portable (or point-of-care) diagnostic sensors in applications spanning from biomedicine to environmental/structural monitoring. Significance Modern scientific instrumentation and sensors are often bulky, expensive, and/or cumbersome to operate. Colorimetric sensors on the other hand offer a promising solution to these problems, as they can be analyzed with high spatiotemporal resolution by digital cameras or the naked eye. However, the performance of colorimetric sensors is typically inferior to the bulky/expensive alternatives as it can be difficult to convert small input stimulus variations into a large color response. This work introduces a means to address and overcome this problem and opens the door to types of high-performance colorimetric sensors, which are competitive with and/or offer greater functionality than the bulky/expensive alternatives.

Electrical impedance tomography (EIT) is a well-known technique to estimate the conductivity distribution γ of a body Ω with unknown electromagnetic properties. EIT is a severely ill-posed inverse problem. In this paper, we formulate the EIT problem in the Bayesian framework using mixed total variation (TV) and non-convex ℓ _p regularization prior. We use the Markov Chain Monte Carlo (MCMC) method for sampling the posterior distribution to solve the ill-posed inverse problem in EIT. We present simulations to estimate the distribution for each pixel for the image reconstruction of the conductivity in EIT.

In many computational problems, using the Markov Chain Monte Carlo (MCMC) can be prohibitively time-consuming. We propose MCMC-Net, a simple yet efficient way to accelerate MCMC via neural networks. The key idea of our approach is to substitute the true likelihood function of the MCMC method with a neural operator based surrogate. We extensively evaluate the accuracy and speedup of our method on three different partial differential equation-based inverse problems where likelihood computations are computationally expensive, namely electrical impedance tomography, diffuse optical tomography, and quantitative photoacoustic tomography. MCMC-Net performs similar to the classical likelihood counterpart but with a significant speedup. We conjecture that the method can be applied to any problem with a sufficiently expensive likelihood function. We also analyze MCMC-Net in a theoretical setting for the different use cases. We prove a universal approximation theorem-type result to show that the proposed network can approximate the mapping resulting from forward model evaluations to a desired accuracy. Furthermore, we establish convergence of the surrogate posterior to the true posterior under Hellinger distance.

This paper aims to determine whether regularization improves image reconstruction in electrical impedance tomography (EIT) using a radial basis network. The primary purpose is to investigate the effect of regularization to estimate the network parameters of the radial basis function network to solve the inverse problem in EIT. Our approach to studying the efficacy of the radial basis network with regularization is to compare the performance among several different regularizations, mainly Tikhonov, Lasso, and Elastic Net regularization. We vary the network parameters, including the fixed and variable widths for the Gaussian used for the network. We also perform a robustness study for comparison of the different regularizations used. Our results include (1) determining the optimal number of radial basis functions in the network to avoid overfitting; (2) comparison of fixed versus variable Gaussian width with or without regularization; (3) comparison of image reconstruction with or without regularization, in particular, no regularization, Tikhonov, Lasso, and Elastic Net; (4) comparison of both mean square and mean absolute error and the corresponding variance; and (5) comparison of robustness, in particular, the performance of the different methods concerning noise level. We conclude that by looking at the R² score, one can determine the optimal number of radial basis functions. The fixed-width radial basis function network with regularization results in improved performance. The fixed-width Gaussian with Tikhonov regularization performs very well. The regularization helps reconstruct the images outside of the training data set. The regularization may cause the quality of the reconstruction to deteriorate; however, the stability is much improved. In terms of robustness, the RBF with Lasso and Elastic Net seem very robust compared to Tikhonov.

Fair lending practices and model interpretability are crucial concerns in the financial industry, especially given the increasing use of complex machine learning models. In response to the Consumer Financial Protection Bureau's (CFPB) requirement to protect consumers against unlawful discrimination, we introduce LDA-XGB1, a novel less discriminatory alternative (LDA) machine learning model for fair and interpretable binary classification. LDA-XGB1 is developed through biobjective optimization that balances accuracy and fairness, with both objectives formulated using binning and information value. It leverages the predictive power and computational efficiency of XGBoost while ensuring inherent model interpretability, including the enforcement of monotonic constraints. We evaluate LDA-XGB1 on two datasets: SimuCredit, a simulated credit approval dataset, and COMPAS, a real-world recidivism prediction dataset. Our results demonstrate that LDA-XGB1 achieves an effective balance between predictive accuracy, fairness, and interpretability, often outperforming traditional fair lending models. This approach equips financial institutions with a powerful tool to meet regulatory requirements for fair lending while maintaining the advantages of advanced machine learning techniques.

The material removal rates during milling operations are affected by the selection of the cutting depth and spindle speed. Poor selection of these parameters can result in chatter or suboptimal material removal rates. Stability Lobe Diagrams (SLDs) are the well-known approach to selecting appropriate chatter-free values for these parameters. The Physics-based stability lobe diagram is usually generated using the structural dynamics and the cutting parameters. However, since the machine dynamics are measured in the static state of the machine (zero speed), the generated SLD is not reliable as the machine behavior may vary during the cutting operations. Besides, measuring structural dynamics parameters under cutting conditions is difficult and needs new equipment. This study proposes a new approach to determining in-process structural dynamics parameters based on a multivariate Newton-Raphson method. The physics-based model is combined with empirical records to extract reliable structural dynamics parameters inversely. Some examples based on synthetic data are presented to illustrate this inverse approach. Also, the performance of the algorithm is evaluated on an empirical data set and its ability to improve the stability boundary is verified. Furthermore, the sensitivity analysis is performed to quantify the exposure of the SLD to the changes in each structural dynamics parameter.