Search for: All records

Recently, there has been growing concern about heavy‐tailed and skewed noise in biological data. We introduce RobustPALMRT, a flexible permutation framework for testing the association of a covariate of interest adjusted for control covariates. RobustPALMRT controls type I error rate for finite‐samples, even in the presence of heavy‐tailed or skewed noise. The new framework expands the scope of state‐of‐the‐art tests in three directions. First, our method applies to robust and quantile regressions, even with the necessary hyper‐parameter tuning. Second, by separating model‐fitting and model‐evaluation, we discover that performance improves when using a robust loss function in the model‐evaluation step, regardless of how the model is fit. Third, we allow fitting multiple models to detect specialized features of interest in a distribution. To demonstrate this, we introduce DispersionPALMRT, which tests for differences in dispersion between treatment and control groups. We establish theoretical guarantees, identify settings where our method has greater power than existing methods, and analyze existing immunological data on Long‐COVID patients. Using RobustPALMRT, we unveil novel differences between Long‐COVID patients and others even in the presence of highly skewed noise. 

Summary Digital MemComputing machines (DMMs), which employ nonlinear dynamical systems with memory (time non‐locality), have proven to be a robust and scalable unconventional computing approach for solving a wide variety of combinatorial optimization problems. However, most of the research so far has focused on the numerical simulations of the equations of motion of DMMs. This inevitably subjects time to discretization, which brings its own (numerical) issues that would be otherwise absent in actual physical systems operating in continuous time. Although hardware realizations of DMMs have been previously suggested, their implementation would require materials and devices that are not so easy to integrate with traditional electronics. Addressing this, our study introduces a novel hardware design for DMMs, utilizing readily available electronic components. This approach not only significantly boosts computational speed compared to current models but also exhibits remarkable robustness against additive noise. Crucially, it circumvents the limitations imposed by numerical noise, ensuring enhanced stability and reliability during extended operations. This paves a new path for tackling increasingly complex problems, leveraging the inherent advantages of DMMs in a more practical and accessible framework. 

Conformal prediction (CP) is an important tool for distribution-free predictive uncertainty quantification. Yet, a major challenge is to balance computational efficiency and prediction accuracy, particularly for multiple predictions. We propose Leave-One-Out Stable Conformal Prediction (LOO-StabCP), a novel method to speed up full conformal using algorithmic stability without sample splitting. By leveraging leave-one-out stability, our method is much faster in handling a large number of prediction requests compared to existing method RO-StabCP based on replace-one stability. We derived stability bounds for several popular machine learning tools: regularized loss minimization (RLM) and stochastic gradient descent (SGD), as well as kernel method, neural networks and bagging. Our method is theoretically justified and demonstrates superior numerical performance on synthetic and real-world data. We applied our method to a screening problem, where its effective exploitation of training data led to improved test power compared to state-of-the-art method based on split conformal. 

Abstract Due to the ubiquity of textiles in the lives, electronic textiles (E‐textiles) have emerged as a future technology capable of addressing a myriad of challenges from mixed reality interfaces, on‐garment climate control, patient diagnostics, and interactive athletic wear. However, providing sufficient electrical power in a textile form factor has remained elusive. To address this issue, different approaches are discussed, starting with supercapacitors' advantages and limitations and material choices for textile‐based supercapacitors before discussing proper data analysis and design considerations of textile‐based energy storage to power wearable electronics.