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MLRegTest is a benchmark for machine learning systems on sequence classification, which contains training, development, and test sets from 1,800 regular languages. MLRegTest organizes its languages according to their logical complexity (monadic second order, first order, propositional, or monomial expressions) and the kind of logical literals (string, tier-string, subsequence, or combinations thereof). The logical complexity and choice of literal provides a systematic way to understand different kinds of long-distance dependencies in regular languages, and therefore to understand the capacities of different ML systems to learn such long-distance dependencies. 

Hierarchically microstructured tri-axial poly(vinyl alcohol)/graphene nanoplatelet (PVA/GNP) composite fibers were fabricated using a dry-jet wet spinning technique. The composites with distinct PVA/GNPs/PVA phases led to highly oriented and evenly distributed graphene nanoplatelets (GNPs) as a result of molecular chain-assisted interfacial exfoliation. With a concentration of 3.3 wt% continuously aligned GNPs, the composite achieved a ∼73.5% increase in Young's modulus (∼38 GPa), as compared to the pure PVA fiber, and an electrical conductivity of ∼0.38 S m −1 , one of the best mechanical/electrical properties reported for polymer/GNP nanocomposite fibers. This study has broader impacts on textile engineering, wearable robotics, smart sensors, and optoelectronic devices. 

The design and optimization of highly nonlinear and complex processes like plasma etching is challenging and timeconsuming. Significant effort has been devoted to creating plasma profile simulators to facilitate the development of etch recipes. Nevertheless, these simulators are often difficult to use in practice due to the large number of unknown parameters in the plasma discharge and surface kinetics of the etch material, the dependency of the etch rate on the evolving front profile, and the disparate length scales of the system. Here, we expand on the development of a previously published, data informed, Bayesian approach embodied in the platform RODEo (Recipe Optimization for Deposition and Etching). RODEo is used to predict etch rates and etch profiles over a range of powers, pressures, gas flow rates, and gas mixing ratios of an CF4/Ar gas chemistry. Three examples are shown: (1) etch rate predictions of an unknown material “X” using simulated experiments for a CF4/Ar chemistry, (2) etch rate predictions of SiO2 in a Plasma-Therm 790 RIE reactor for a CF4/Ar chemistry, and (3) profile prediction using level set methods. 

Multiphase flows in porous media are important in many natural and industrial processes. Pore-scale models for multiphase flows have seen rapid development in recent years and are becoming increasingly useful as predictive tools in both academic and industrial applications. However, quantitative comparisons between different pore-scale models, and between these models and experimental data, are lacking. Here, we perform an objective comparison of a variety of state-of-the-art pore-scale models, including lattice Boltzmann, stochastic rotation dynamics, volume-of-fluid, level-set, phase-field, and pore-network models. As the basis for this comparison, we use a dataset from recent microfluidic experiments with precisely controlled pore geometry and wettability conditions, which offers an unprecedented benchmarking opportunity. We compare the results of the 14 participating teams both qualitatively and quantitatively using several standard metrics, such as fractal dimension, finger width, and displacement efficiency. We find that no single method excels across all conditions and that thin films and corner flow present substantial modeling and computational challenges.