Terrestrial Solar System planets either have high mean molecular weight atmospheres, as with Venus, Mars, and Earth, or no atmosphere at all, as with Mercury. We do not have sufficient observational information to know if this is typical of terrestrial planets or a phenomenon unique to the Solar System. The bulk of atmospheric exoplanet studies have focused on hot Jupiters and Neptunes, but recent discoveries of small, rocky exoplanets transiting small, nearby stars provide targets that are amenable to atmospheric study. GJ 1132b has a radius of 1.2 R⊕ and a mass of 1.6 M⊕, and orbits an M-dwarf 12 parsecs away from the Solar System. We present results from five transits of GJ 1132b taken with the Magellan Clay Telescope and the LDSS3C multi-object spectrograph. We jointly fit our five data sets when determining the best-fit transit parameters both for the white light curve and wavelength-binned light curves. We bin the light curves into 20 nm wavelength bands to construct the transmission spectrum. Our results disfavor a clear, 10× solar metallicity atmosphere at 3.7σ confidence and a 10% H₂O, 90% H₂ atmosphere at 3.5σ confidence. Our data are consistent with a featureless spectrum, implying that GJ 1132b has a high mean molecular weight atmosphere or no atmosphere at all, though we do not account for the possible presence of aerosols. This result is in agreement with theoretical work which suggests that a planet of GJ 1132b’s mass and insolation should not be able to retain a H₂ envelope.
Aspect based sentiment analysis (ABSA) can provide more detailed information than general sentiment analysis, because it aims to predict the sentiment polarities of the given aspects or entities in text. We summarize previous approaches into two subtasks: aspect-category sentiment analysis (ACSA) and aspect-term sentiment analysis (ATSA). Most previous approaches employ long short-term memory and attention mechanisms to predict the sentiment polarity of the concerned targets, which are often complicated and need more training time. We propose a model based on convolutional neural networks and gating mechanisms, which is more accurate and efficient. First, the novel Gated Tanh-ReLU Units can selectively output the sentiment features according to the given aspect or entity. The architecture is much simpler than attention layer used in the existing models. Second, the computations of our model could be easily parallelized during training, because convolutional layers do not have time dependency as in LSTM layers, and gating units also work independently. The experiments on SemEval datasets demonstrate the efficiency and effectiveness of our models.   The code and data is available at https://github.com/wxue004cs/GCAE
A geotechnical studies has been carried out on the typically weathered Kenny Hill sedimentary rock. The objectives are to determine the geotechnical properties related to wet tropical weathering. The mandatory classification by weathering grade was adopted to classify the interbedded rock mass which is mainly constitutes of sandstone and shale. The purposes are to characterize and classify the engineering properties, stiffness characteristics and behavior of weathered sandstones and shale and consequently their behaviour as a composite rock material. A series of rock material strength tests were carried out, i.e. a uniaxial compressive strength and point load strength. The empirical strength models were developed based on sandstone and shale experimental data. Thus the rock materials composite strength, elastic modulus and stiffness were mathematically modeled with respect to their respective weathering grades. Conclusively it was found that as a composite rock the strength models, modulus and stiffness were influenced by their state of weathering and deteriorated non-linearly.