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Twinning is an essential mode of plastic deformation for achieving superior strength and ductility in metallic nanostructures. It has been generally believed that twinning-induced plasticity in body-centered cubic (BCC) metals is controlled by twin nucleation, but facilitated by rapid twin growth once the nucleation energy barrier is overcome. By performing in situ atomic-scale transmission electron microscopy straining experiments and atomistic simulations, we find that deformation twinning in BCC Ta nanocrystals larger than 15 nm in diameter proceeds by reluctant twin growth, resulting from slow advancement of twinning partials along the boundaries of finite-sized twin structures. In contrast, reluctant twin growth can be obviated by reducing the nanocrystal diameter to below 15 nm. As a result, the nucleated twin structure penetrates quickly through the cross section of nanocrystals, enabling fast twin growth via facile migration of twin boundaries leading to large uniform plastic deformation. The present work reveals a size-dependent transition in the nucleation- and growth-controlled twinning mechanism in BCC metals, and provides insights for exploiting twinning-induced plasticity and breaking strength-ductility limits in nanostructured BCC metals.

 

This study reported the application of an interactive Open Education Resource, namely, an open virtual experiment simulator education tool (OVESET), in teaching the kinetics of atom transfer radical polymerization (ATRP) in a polymer science classroom. The OVESET ATRP kinetic simulator aims at improving students’ inductive reasoning skills. Students were encouraged to perform virtual experiments to systematically examine the influence of each parameter, e.g., type of polymerization and concentrations of reagents, and to observe and make logical explanations of the general trends behind each series of experiments. The tool was designed to maximize accessibility and flexibility through open licensing. The simulator runs under the Jupyter Notebook environment, which is free to use, modify, and redistribute; therefore, instructors can adapt the simulator based on their teaching contexts. The simulator can be applied in a classroom setting without requiring any software installation and can be used across different operating systems. Assessment of the implementation demonstrated that students’ learning outcomes and STEM and polymer science identity were improved. Students also rated the tool as useful in increasing their understanding and inductive reasoning. The quick and in-place response of the notebook makes it ideal for both in-class demonstrations and after-class practices. The tool is freely available at https://bit.ly/ATRP-Simulator. 

Mixup is a data augmentation technique that relies on training using random convex combinations of data points and their labels. In recent years, Mixup has become a standard primitive used in the training of state-of-the-art image classification models due to its demonstrated benefits over empirical risk minimization with regards to generalization and robustness. In this work, we try to explain some of this success from a feature learning perspective. We focus our attention on classification problems in which each class may have multiple associated features (or views) that can be used to predict the class correctly. Our main theoretical results demonstrate that, for a non-trivial class of data distributions with two features per class, training a 2-layer convolutional network using empirical risk minimization can lead to learning only one feature for almost all classes while training with a specific instantiation of Mixup succeeds in learning both features for every class. We also show empirically that these theoretical insights extend to the practical settings of image benchmarks modified to have additional synthetic features. 

Recently, researchers observed that gradient descent for deep neural networks operates in an “edge-of-stability” (EoS) regime: the sharpness (maximum eigenvalue of the Hessian) is often larger than stability threshold 2/\eta (where \eta is the step size). Despite this, the loss oscillates and converges in the long run, and the sharpness at the end is just slightly below 2/\eta . While many other well-understood nonconvex objectives such as matrix factorization or two-layer networks can also converge despite large sharpness, there is often a larger gap between sharpness of the endpoint and 2/\eta . In this paper, we study EoS phenomenon by constructing a simple function that has the same behavior. We give rigorous analysis for its training dynamics in a large local region and explain why the fnal converging point has sharpness close to 2/\eta . Globally we observe that the training dynamics for our example have an interesting bifurcating behavior, which was also observed in the training of neural nets. 

Use of cold‐formed steel (CFS) framing as load‐bearing system for gravity and lateral loads in buildings is becoming increasingly common in the North American construction industry, notably in high seismic regions where light‐weight construction is an attractive option. Buildings framed with closely spaced and repetitively placed CFS members can be detailed to develop lateral resistance using a variety of sheathing options. A relatively new option involves the use of steel sheet as sheathing. Steel sheet sheathed CFS shear walls offer high lateral strength and stiffness, and provide ductility courtesy of tension field action within the steel sheet. Despite their acceptance, gaps in the understanding of their behavior do exist, notably, behavior under dynamic loading, the contribution of nonstructural architectural finishes, and the behavior of wall‐lines: shear walls placed inline with gravity walls. To this end, a two‐phased experimental effort was undertaken to advance understanding of the lateral response of CFS‐framed wall‐line systems. Specifically, a suite of wall‐lines, detailed for mid‐rise buildings, were evaluated through simulated seismic loading imposed via shake table and quasi‐static cyclic tests. Damage to the wall‐lines was largely manifested in the form of damage to fastener connections used for attaching the sheathing and gypsum panels, and separation of exterior finish layer. This paper documents and quantifies the progressively incurred physical damage observed in the tested wall‐line assemblies, and correlates it with the evolution of dynamic characteristics and hysteretic energy dissipated across a spectrum of performance levels.

 

Abstract Long-duration GRB 200829A was detected by Fermi-GBM and Swift-BAT/XRT, and then rapidly observed by other ground-based telescopes. It has a weak γ -ray emission in the very early phase and is followed by a bright spiky γ -ray emission pulse. The radiation spectrum of the very early emission is best fitted by a power-law function with index ∼−1.7. However, the bright spiky γ -ray pulse, especially the time around the peak, exhibits a distinct two-component radiation spectrum, i.e., Band function combined with a blackbody radiation spectrum. We infer the photospheric properties and reveal a medium magnetization at a photospheric position by adopting the initial size of the outflow as r 0 = 10 9 cm. It implies that the Band component in this pulse may be formed during the dissipation of the magnetic field. The power-law radiation spectra found in the very early prompt emission may imply the external-shock origination of this phase. Then, we perform the Markov Chain Monte Carlo method fitting on the light curves of this burst, where the jet corresponding to the γ -ray pulse at around 20 s is used to refresh the external shock. It is shown that the light curves of the very early phase and X-ray afterglow after 40 s, involving the X-ray bump at around 100 s, can be well modeled in the external-shock scenario. For the obtained initial outflow, we estimate the minimum magnetization factor of the jet based on the fact that the photospheric emission of this jet is missed in the very early phase.