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Inspired by humans’ exceptional ability to master arithmetic and generalize to new problems, we present a new dataset, Handwritten arithmetic with INTegers (HINT), to examine machines’ capability of learning generalizable concepts at three levels: perception, syntax, and semantics. In HINT, machines are tasked with learning how concepts are perceived from raw signals such as images (i.e., perception), how multiple concepts are structurally combined to form a valid expression (i.e., syntax), and how concepts are realized to afford various reasoning tasks (i.e., semantics), all in a weakly supervised manner. Focusing on systematic generalization, we carefully design a five-fold test set to evaluate both the interpolation and the extrapolation of learned concepts w.r.t. the three levels. Further, we design a few-shot learning split to determine whether or not models can rapidly learn new concepts and generalize them to more complex scenarios. To comprehend existing models’ limitations, we undertake extensive experiments with various sequence-to-sequence models, including RNNs, Transformers, and GPT-3 (with the chain of thought prompting). The results indicate that current models struggle to extrapolate to long-range syntactic dependency and semantics. Models exhibit a considerable gap toward human-level generalization when evaluated with new concepts in a few-shot setting. Moreover, we discover that it is infeasible to solve HINT by merely scaling up the dataset and the model size; this strategy contributes little to the extrapolation of syntax and semantics. Finally, in zero-shot GPT-3 experiments, the chain of thought prompting exhibits impressive results and significantly boosts the test accuracy. We believe the HINT dataset and the experimental findings are of great interest to the learning community on systematic generalization. 

Automatic food type recognition is an essential task of dietary monitoring. It helps medical professionals recognize a user’s food contents, estimate the amount of energy intake, and design a personalized intervention model to prevent many chronic diseases, such as obesity and heart disease. Various wearable and mobile devices are utilized as platforms for food type recognition. However, none of them has been widely used in our daily lives and, at the same time, socially acceptable enough for continuous wear. In this paper, we propose a food type recognition method that takes advantage of Airpods Pro, a pair of widely used wireless in-ear headphones designed by Apple, to recognize 20 different types of food. As far as we know, we are the first to use this socially acceptable commercial product to recognize food types. Audio and motion sensor data are collected from Airpods Pro. Then 135 representative features are extracted and selected to construct the recognition model using the lightGBM algorithm. A real-world data collection is conducted to comprehensively evaluate the performance of the proposed method for seven human subjects. The results show that the average f1-score reaches 94.4% for the ten-fold cross- validation test and 96.0% for the self-evaluation test. 

The physical properties of minerals are modified by the high temperatures of volcanic lightning during explosive eruptions. Alteration involves rapid heating and volatilization, melting, and fusion of ash grains within the discharge channel, followed by rapid quenching into new glassy textures. High current impulse experiments reveal that lightning alters not only the morphology and mineralogy of volcanic ash but also its magnetic properties. We investigate lightning‐induced magnetic changes in five igneous minerals (<32 μm powders of albite, labradorite, augite, hornblende, and magnetite) by comparing hysteresis parameters before and after impulse experiments conducted at peak currents of 25 and 40 kA. Both the paramagnetic and ferrimagnetic behaviors of the samples were altered, which we interpret as a superposition of two processes. (a) Rapid melting allows iron contained within inclusions of Fe‐oxides and Fe‐bearing silicates to diffuse into the newly formed melt, thereby increasing the paramagnetism of the resulting glass. (b) Nucleation and growth of magnetite from the newly formed melt increases the ferrimagnetic behavior of the post‐experimental samples. Nominally non‐Fe‐bearing silicates like albite and labradorite have significantly increased paramagnetism and ferrimagnetism. Fe‐bearing silicates like augite and hornblende contain higher concentrations of ferrimagnetic inclusions, from which Fe diffuses into the newly formed melt, thereby increasing paramagnetism while decreasing ferrimagnetism. Experiments conducted on magnetite produced new magnetite crystals with dendritic habits. Although specific to volcanic ash, these results provide important insights into the magnetism of other materials affected by lightning on Earth, the Moon, and throughout the solar system.

 

This chapter describes two mechanical expansion microscopy methods with accompanying step-by-step protocols. The first method, mechanically resolved expansion microscopy, uses non-uniform expansion of partially digested samples to provide the imaging contrast that resolves local mechanical properties. Examining bacterial cell wall with this method, we are able to distinguish bacterial species in mixed populations based on their distinct cell wall rigidity and detect cell wall damage caused by various physiological and chemical perturbations. The second method is mechanically locked expansion microscopy, in which we use a mechanically stable gel network to prevent the original polyacrylate network from shrinking in ionic buffers. This method allows us to use anti-photobleaching buffers in expansion microscopy, enabling detection of novel ultra-structures under the optical diffraction limit through super-resolution single molecule localization microscopy on bacterial cells and whole-mount immunofluorescence imaging in thick animal tissues. We also discuss potential applications and assess future directions. 

Traditional accelerated life test plans are typically based on optimizing the C-optimality for minimizing the variance of an interested quantile of the lifetime distribution. These methods often rely on some specified planning values for the model parameters, which are usually unknown prior to the actual tests. The ambiguity of the specified parameters can lead to suboptimal designs for optimizing the reliability performance of interest. In this paper, we propose a sequential design strategy for life test plans based on considering dual objectives. In the early stage of the sequential experiment, we suggest allocating more design locations based on optimizing the D-optimality to quickly gain precision in the estimated model parameters. In the later stage of the experiment, we can allocate more observations based on optimizing the C-optimality to maximize the precision of the estimated quantile of the lifetime distribution. We compare the proposed sequential design strategy with existing test plans considering only a single criterion and illustrate the new method with an example on the fatigue testing of polymer composites.