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Predicting materials’ microstructure from the desired properties is critical for exploring new materials. Herein, a novel regression‐based prediction of scanning electron microscopy (SEM) images for the target hardness using generative adversarial networks (GANs) is demonstrated. This article aims at generating realistic SEM micrographs, which contain rich features (e.g., grain and neck shapes, tortuosity, spatial configurations of grain/pores). Together, these features affect material properties but are difficult to predict. A high‐performance GAN, named ‘Microstructure‐GAN’ (or M‐GAN), with residual blocks to significantly improve the details of synthesized micrographs is established . This algorithm was trained with experimentally obtained SEM micrographs of laser‐sintered alumina. After training, the high‐fidelity, feature‐rich micrographs can be predicted for an arbitrary target hardness. Microstructure details such as small pores and grain boundaries can be observed even at the nanometer scale (∼50 nm) in the predicted 1000× micrographs. A pretrained convolutional neural network (CNN) was used to evaluate the accuracy of the predicted micrographs with rich features for specific hardness. The relative bias of the CNN‐evaluated value of the generated micrographs was within 2.1%–2.7% from the values for experimental micrographs. This approach can potentially be applied to other microscopy data, such as atomic force, optical, and transmission electron microscopy. 

This paper investigates the relative timing of onset consonant and vowel gestures in Tibetan as spoken in the Tibetan diaspora. According to the coupled oscillator model of articulatory timing (Browman & Goldstein 2000, Nam & Saltzman 2003), the most readily-available coupling modes among gestures are in-phase (synchronous) or anti-phase (sequential) timing, with competition among these modes also giving rise to a stable timing pattern. The model predicts that other timing relations, i.e. ”eccentric timing”, are possible but not as readily available. Data gathered using electromagnetic articulography (EMA) shows relative C-V timing consistent with either competitive coupling or eccentric timing. Competitive coupling is a plausible explanation for CV syllables in a tone language (Gao 2008), but acoustic analysis showed that some speakers do not produce a pitch contrast corresponding to tone. In the apparent absence of a tone gesture, we conclude that these speakers exhibit eccentric C-V timing. 

This paper investigates the relative timing of onset consonant and vowel gestures in Tibetan as spoken in the Tibetan diaspora. According to the coupled oscillator model of articulatory timing (Browman & Goldstein 2000, Nam & Saltzman 2003), the most readily-available coupling modes among gestures are in-phase (synchronous) or anti-phase (sequential) timing, with competition among these modes also giving rise to a stable timing pattern. The model predicts that other timing relations, i.e. ”eccentric timing”, are possible but not as readily available. Data gathered using electromagnetic articulography (EMA) shows relative C-V timing consistent with either competitive coupling or eccentric timing. Competitive coupling is a plausible explanation for CV syllables in a tone language (Gao 2008), but acoustic analysis showed that some speakers do not produce a pitch contrast corresponding to tone. In the apparent absence of a tone gesture, we conclude that these speakers exhibit eccentric C-V timing.