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In optical bands, the spectra of M giants often overlap with those of M dwarfs due to their similarities, especially for low or moderate resolution spectra. Traditionally, several feature indices, such as Na i, CaH, TiO5, and K i, are used to distinguish between M giants and M dwarfs. However, these features are selected by experience based on a small amount of standard spectra. Hence, it is not clear if these features are the most effective ones to detect M giants. In this paper, we use a machine-learning method, eXtreme Gradient Boosting (XGBoost), to discern M giants from M dwarfs for spectroscopic surveys. The important feature bands for distinguishing between M giants and M dwarfs are accurately identified by the XGBoost method through evaluating and quantifying the importance of each feature in spectra, including Na i, B1, and Ca ii, which are consistent with previous studies. Moreover, we find that a blend feature around 6564 Å (named B2) is sensitive to luminosity and that the feature combinations of both B1 versus CaH and B2 versus CaH, based on the average spectral flux, are important in distinguishing M giants from M dwarfs. Furthermore, our XGBoost prediction model achieves 99.79% overall accuracy and 96.87% recognition precision for M giants, outperforming the other three popular machine-learning algorithms (i.e., SVM, random forests, and ELM). Using such a prediction model, we detected 28,714 M-giant spectra from LAMOST DR5 and thus provided a larger amount of M giants for related scientific research.

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We report on systematic investigation of hot carrier dynamics in Ti 4 O 7 by ultrafast time-resolved optical reflectivity. We find the transient indication for its two-step insulator-metal (I-M) transition, in which two phase transitions occur from long-range order bipolaron low-temperature insulating (LI) phase to disordered bipolaron high-temperature insulating (HI) phase at T c1 and to free carrier metallic (M) phase at T c2 . Our results reveal that photoexcitation can effectively lower down both T c1 and T c2 with pump fluence increasing, allowing a light-control of I-M transition. We address a phase diagram that provides a framework for the photoinduced I-M transition and helps the potential use of Ti 4 O 7 for photoelectric and thermoelectric devices.

Regression adjustment is often used to estimate average treatment effect (ATE) in randomized experiments. Recently, some penalty-based regression adjustment methods have been proposed to handle the high-dimensional problem. However, these existing high-dimensional regression adjustment methods may fail to achieve satisfactory performance when the covariates are highly correlated. In this paper, we propose a novel adjustment estimation method for ATE by combining the semi-standard partial covariance (SPAC) and regression adjustment methods. Under some regularity conditions, the asymptotic normality of our proposed SPAC adjustment ATE estimator is shown. Some simulation studies and an analysis of HER2 breast cancer data are carried out to illustrate the advantage of our proposed SPAC adjustment method in addressing the highly correlated problem of the Rubin causal model.