Voltage-Driven Translocation: Defining a Capture Radius. (arXiv:1911.11249v1 [physics.bio-ph])

Analyte translocation involves three phases: (i) diffusion in the loading solution; (ii) capture by the pore; (iii) threading. The capture process remains poorly characterized because it cannot easily be visualized or inferred from indirect measurements. The capture performance of a device is often described by a \textit{capture radius} generally defined as the radial distance $R^*$…

In a physics curriculum only introductory physics course grades show gender differences but do not predict future course performance for physics majors. (arXiv:1911.11262v1 [physics.ed-ph])

Analysis of institutional data for physics majors showing predictive relationships between required mathematics and physics courses in various years is important for contemplating how the courses build on each other and whether there is need to make changes to the curriculum for the majors to strengthen these relationships. We use 15 years of institutional data…

Multi-fidelity estimators for coronary circulation models under clinically-informed data uncertainty. (arXiv:1911.11266v1 [physics.med-ph])

Numerical models are increasingly used for non-invasive diagnosis and treatment planning in coronary artery disease, where service-based technologies have proven successful in identifying hemodynamically significant and hence potentially dangerous vascular anomalies. Despite recent progress towards clinical adoption, many results in the field are still based on a deterministic characterization of blood flow, with no quantitative…

Emulating tightly bound electrons in crystalline solids using mechanical waves. (arXiv:1911.11272v1 [cond-mat.mtrl-sci])

Solid state physics deals with systems composed of atoms with strongly bound electrons. The tunneling probability of each electron is determined by interactions that typically extend to neighboring sites, as their corresponding wave amplitudes decay rapidly away from an isolated atomic core. This kind of description is essential to material science, and it rules the…

Basic Physical Properties of Cubic Boron Arsenide. (arXiv:1911.11281v1 [cond-mat.mtrl-sci])

Cubic boron arsenide (BAs) is an emerging semiconductor material with a record-high thermal conductivity of 1300 W/mK. However, many fundamental properties of BAs remain unexplored experimentally. Here, for the first time, we report the systematic experimental measurements of important physical properties of BAs, including the bandgap, optical refractive index, stiffness, elastic modulus, shear modulus, Poisson…

Asymptotic properties of the maximum likelihood and cross validation estimators for transformed Gaussian processes. (arXiv:1911.11199v1 [math.ST])

The asymptotic analysis of covariance parameter estimation of Gaussian processes has been subject to intensive investigation. However, this asymptotic analysis is very scarce for non-Gaussian processes. In this paper, we study a class of non-Gaussian processes obtained by regular non-linear transformations of Gaussian processes. We provide the increasing-domain asymptotic properties of the (Gaussian) maximum likelihood…

Drift Estimation for a L\’evy-Driven Ornstein-Uhlenbeck Process with Heavy Tails. (arXiv:1911.11202v1 [math.ST])

We consider the problem of estimation of the drift parameter of an ergodic Ornstein–Uhlenbeck type process driven by a L\’evy process with heavy tails. The process is observed continuously on a long time interval $[0,T]$, $T\to\infty$. We prove that the statistical model is locally asymptotic mixed normal and the maximum likelihood estimator is asymptotically efficient.

One Man’s Trash is Another Man’s Treasure: Resisting Adversarial Examples by Adversarial Examples. (arXiv:1911.11219v1 [cs.LG])

Modern image classification systems are often built on deep neural networks, which suffer from adversarial examples–images with deliberately crafted, imperceptible noise to mislead the network’s classification. To defend against adversarial examples, a plausible idea is to obfuscate the network’s gradient with respect to the input image. This general idea has inspired a long line of…

Modeling Variables with a Detection Limit using a Truncated Normal Distribution with Censoring. (arXiv:1911.11221v1 [stat.AP])

When data are collected subject to a detection limit, observations below the detection limit may be considered censored. In addition, the domain of such observations may be restricted; for example, values may be required to be non-negative. We propose a regression method for censored observations that also accounts for domain restriction. The method finds maximum…

A Novel Visual Fault Detection and Classification System for Semiconductor Manufacturing Using Stacked Hybrid Convolutional Neural Networks. (arXiv:1911.11250v1 [cs.LG])

Automated visual inspection in the semiconductor industry aims to detect and classify manufacturing defects utilizing modern image processing techniques. While an earliest possible detection of defect patterns allows quality control and automation of manufacturing chains, manufacturers benefit from an increased yield and reduced manufacturing costs. Since classical image processing systems are limited in their ability…