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In-situ diffuse neutron scattering experiments revealed that when electric current is passed through single crystals of rutile TiO2 under conditions conducive to flash sintering, it induces the formation of parallel planes of oxygen vacancies. Specifically, a current perpendicular to the c-axis generates planes normal to the (132) reciprocal lattice vector, whereas currents aligned with the c-axis form planes normal to the (132) and to the (225) vector. The concentration of defects increases with incresing current. The structural modifications are linked to the appearance of signatures of interacting Ti3+ moments in magnetic susceptibility, signifying a structural collapse around the vacancy planes. Electrical conductivity measurements of the modified material reveal several electronic transitions between semiconducting states (via a metal-like intermediate state) with the smallest gap being 27 meV. Pristine TiO2 can be restored by heating followed by slow cooling in air. Our work suggests a novel paradigm for achieving switching of electrical conductivity related to the flash phenomenon

We apply machine learning to understand fundamental aspects of holographic duality, specifically the entropies obtained from the apparent and event horizon areas. We show that simple features of only the time series of the pressure anisotropy, namely the values and half-widths of the maxima and minima, the times these are attained, and the times of the first zeroes can predict the areas of the apparent and event horizons in the dual bulk geometry at all times with a fixed maximum length (30) of the input vector. Given that simple Vaidya-type metrics constructed just from the apparent and event horizon areas can be used to approximately obtain unequal time correlation functions, we argue that the corresponding entropy functions are the measures of information that need to be extracted from simple one-point functions to reconstruct specific aspects of correlation functions of the dual state with the best possible approximations., Comment: 10+10 pages, 1 Figure; Discussion improved, k-fold cross validation added

Interstitial lung diseases (ILD) present diagnostic challenges due to their varied manifestations and overlapping imaging features. To address this, we propose a machine learning approach that utilizes CLIP, a multimodal (image and text) self-supervised model, for ILD classification. We extensively integrate zero-shot CLIP throughout our workflow, starting from the initial extraction of image patches from volumetric CT scans and proceeding to ILD classification using "patch montages". Furthermore, we investigate how domain adaptive pretraining (DAPT) CLIP with task-specific images (CT "patch montages" extracted with ILD-specific prompts for CLIP) and/or text (lung-specific sections of radiology reports) affects downstream ILD classification performance. By leveraging CLIP-extracted "patch montages" and DAPT, we achieve strong zero-shot ILD classification results, including an AUROC of 0.893, without the need for any labeled training data. This work highlights the versatility and potential of multimodal models like CLIP for medical image classification tasks where labeled data is scarce., Comment: 11 pages, 11 figures

We demonstrate a novel approach to the structural and electronic property modification of perovskites, focusing on Pr$_2$CuO$_4$, an undoped parent compound of a class of electron-doped copper-oxide superconductors. Currents were passed parallel or perpendicular to the copper-oxygen layers with the voltage ramped up until a rapid drop in the resistivity was achieved, a process referred to as "flash". The current was then further increased tenfold in current-control mode. This state was quenched by immersion into liquid nitrogen. Flash can drive many compounds into different atomic structures with new properties, whereas the quench freezes them into a long-lived state. Single-crystal neutron diffraction of as-grown and modified Pr$_2$CuO$_4$ revealed a $\sqrt{10}$x$\sqrt{10}$ superlattice due to oxygen-vacancy order. The diffraction peak intensities of the superlattice of the modified sample were significantly enhanced relative to the pristine sample. Raman-active phonons in the modified sample were considerably sharper. Measurements of electrical resistivity, magnetization and two-magnon Raman scattering indicate that the modification affected only the Pr-O layers, but not the Cu-O planes. These results point to enhanced oxygen-vacancy order in the modified samples well beyond what can be achieved without passing electrical current. Our work opens a new avenue toward electric field/quench control of structure and properties of layered perovskite oxides.

Tuberculosis (TB), an infectious bacterial disease, is a significant cause of death, especially in low-income countries, with an estimated ten million new cases reported globally in $2020$. While TB is treatable, non-adherence to the medication regimen is a significant cause of morbidity and mortality. Thus, proactively identifying patients at risk of dropping off their medication regimen enables corrective measures to mitigate adverse outcomes. Using a proxy measure of extreme non-adherence and a dataset of nearly $700,000$ patients from four states in India, we formulate and solve the machine learning (ML) problem of early prediction of non-adherence based on a custom rank-based metric. We train ML models and evaluate against baselines, achieving a $\sim 100\%$ lift over rule-based baselines and $\sim 214\%$ over a random classifier, taking into account country-wide large-scale future deployment. We deal with various issues in the process, including data quality, high-cardinality categorical data, low target prevalence, distribution shift, variation across cohorts, algorithmic fairness, and the need for robustness and explainability. Our findings indicate that risk stratification of non-adherent patients is a viable, deployable-at-scale ML solution. As the official AI partner of India's Central TB Division, we are working on multiple city and state-level pilots with the goal of pan-India deployment., Comment: 11 pages

Surface tension and contact angle measurements are fundamental characterization techniques relevant to thermal and fluidic applications. Drop shape analysis techniques for the measurement of interfacial tension are powerful, versatile and flexible. Here we develop a Spline-based Interface Modeling and Optimization (SIMO) tool for estimating the surface tension and the contact angle from the profiles of sessile and pendant drops of various sizes. The employed strategy models the profile using a vector parametrized cubic spline which is then evolved to the eventual equilibrium shape using a novel thermodynamic free-energy minimization-based iterative algorithm. We perform experiments to show that in comparison, the typical fitting-based techniques are very sensitive to errors due to image acquisition, digitization and edge detection, and do not predict the correct surface tension and the contact angle values. We mimic these errors in theoretical drop profiles by applying the Gaussian noise and the smoothing filters. We then demonstrate that our optimization algorithm can even drive such inaccurate digitized profiles to the minimum energy equilibrium shape for the precise estimation of the surface tension and the contact angle values. We compare our scheme with software tools available in public domain to characterize the accuracy and the precision of SIMO., Comment: 28 pages, 8 Figures. Supporting Information can be accessed on: https://drive.google.com/open?id=13Tp0ILu0lOf0V7lxSX7ERjRMjFCDH3y4