Optical properties of 4 A single-walled carbon nanotubes inside the zeolite channels studied from first principles calculations

The structural, electronic, and optical properties of 4 A single-walled carbon nanotubes (SWNTs) contained inside the zeolite channels have been studied based upon the density-functional theory in the local-density approximation (LDA). Our calculated results indicate that the relaxed geometrical structures for the smallest SWNTs in the zeolite channels are much different from those of the ideal isolated SWNTs, producing a great effect on their physical properties. It is found that all three kinds of 4 A SWNTs can possibly exist inside the Zeolite channels. Especially, as an example, we have also studied the coupling effect between the ALPO_4-5 zeolite and the tube (5,0) inside it, and found that the zeolite has real effects on the electronic structure and optical properties of the inside (5,0) tube.Comment: 9 pages, 6figure

network pruning via transformable architecture search

Network pruning reduces the computation costs of an over-parameterized network without performance damage. Prevailing pruning algorithms pre-define the width and depth of the pruned networks, and then transfer parameters from the unpruned network to pruned networks. To break the structure limitation of the pruned networks, we propose to apply neural architecture search to search directly for a network with flexible channel and layer sizes. The number of the channels/layers is learned by minimizing the loss of the pruned networks. The feature map of the pruned network is an aggregation of K feature map fragments (generated by K networks of different sizes), which are sampled based on the probability distribution.The loss can be back-propagated not only to the network weights, but also to the parameterized distribution to explicitly tune the size of the channels/layers. Specifically, we apply channel-wise interpolation to keep the feature map with different channel sizes aligned in the aggregation procedure. The maximum probability for the size in each distribution serves as the width and depth of the pruned network, whose parameters are learned by knowledge transfer, e.g., knowledge distillation, from the original networks. Experiments on CIFAR-10, CIFAR-100 and ImageNet demonstrate the effectiveness of our new perspective of network pruning compared to traditional network pruning algorithms. Various searching and knowledge transfer approaches are conducted to show the effectiveness of the two components. Code is at: https://github.com/D-X-Y/NAS-Projects.Comment: Published in the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019

Late Holocene forcing of the Asian winter and summer monsoon as evidenced by proxy records from the northern Qinghai-Tibetan Plateau

Little is known about decadal- to centennial-scale climate variability and its associated forcing mechanisms on the Qinghai-Tibetan Plateau. A decadal-resolution record of total organic carbon (TOC) and grainsize retrieved from a composite piston core from Kusai Lake, NW China, provides solid evidence for decadal- to centennial-scale Asian monsoon variability for the Northern Qinghai-Tibetan Plateau during the last 3770 yr. Intensified winter and summer monsoons are well correlated with respective reductions and increases in solar irradiance. A number of intensified Asian winter monsoon phases are potentially correlated with North Atlantic climatic variations including Bond events 0 to 2 and more recent subtle climate changes from the Medieval Warm Period to the Little Ice Age. Our findings indicate that Asian monsoon changes during the late Holocene are forced by changes in both solar output and oceanic-atmospheric circulation patterns. Our results demonstrate that these forcing mechanisms operate not only in low latitudes but also in mid-latitude regions (the Northern Qinghai-Tibetan Plateau)

Studies on the expectorant, antitussive and antiasthmatic properties of asterosaponin extracted from Luidia quinaria

The aim of this study was to analyze the expectorant, antitussive and antiasthmatic effects of asterosaponin from Luidia quinaria through secretion of phenol red from mouse tracheas, frequency of cough caused by ammonia in mice and asthma induced by histamine in guinea pig, respectively. Resultsshowed that asterosaponin extracted from L. quinaria at doses of 20 mg/kg and 40 mg/kg could significantly increase secretion of phenol red from mouse tracheas, prolonged the latent period of asthma induced by histamine, and decreased the frequency of cough caused by ammonia. In conclusion,asterosaponin from L. quinaria has obvious antitussive, antiasthmatic and expectorant effects

Style Aggregated Network for Facial Landmark Detection

© 2018 IEEE. Recent advances in facial landmark detection achieve success by learning discriminative features from rich deformation of face shapes and poses. Besides the variance of faces themselves, the intrinsic variance of image styles, e.g., grayscale vs. color images, light vs. dark, intense vs. dull, and so on, has constantly been overlooked. This issue becomes inevitable as increasing web images are collected from various sources for training neural networks. In this work, we propose a style-aggregated approach to deal with the large intrinsic variance of image styles for facial landmark detection. Our method transforms original face images to style-aggregated images by a generative adversarial module. The proposed scheme uses the style-aggregated image to maintain face images that are more robust to environmental changes. Then the original face images accompanying with style-aggregated ones play a duet to train a landmark detector which is complementary to each other. In this way, for each face, our method takes two images as input, i.e., one in its original style and the other in the aggregated style. In experiments, we observe that the large variance of image styles would degenerate the performance of facial landmark detectors. Moreover, we show the robustness of our method to the large variance of image styles by comparing to a variant of our approach, in which the generative adversarial module is removed, and no style-aggregated images are used. Our approach is demonstrated to perform well when compared with state-of-the-art algorithms on benchmark datasets AFLW and 300-W. Code is publicly available on GitHub: https://github.com/D-X-Y/SAN

Observational evidence of a change in radiative forcing due to the indirect aerosol effect

Anthropogenic aerosols enhance cloud reflectivity by increasing the number concentration of cloud droplets, leading to a cooling effect on climate known as the indirect aerosol effect. Observational support for this effect is based mainly on evidence that aerosol number concentrations are connected with droplet concentrations, but it has been difficult to determine the impact of these indirect effects on radiative forcing(1-3). Here we provide observational evidence for a substantial alteration of radiative fluxes due to the indirect aerosol effect. We examine the effect of aerosols on cloud optical properties using measurements of aerosol and cloud properties at two North American sites that span polluted and clean conditions-a continental site in Oklahoma with high aerosol concentrations, and an Arctic site in Alaska with low aerosol concentrations. We determine the cloud optical depth required to fit the observed shortwave downward surface radiation. We then use a cloud parcel model to simulate the cloud optical depth from observed aerosol properties due to the indirect aerosol effect. From the good agreement between the simulated indirect aerosol effect and observed surface radiation, we conclude that the indirect aerosol effect has a significant influence on radiative fluxes.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62920/1/nature02234.pd