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Micro-expression recognition (MER) has attracted much attention with various practical applications, particularly in clinical diagnosis and interrogations. In this paper, we propose a three-stream convolutional neural network (TSCNN) to recognize MEs by learning ME-discriminative features in three key frames of ME videos. We design a dynamic-temporal stream, static-spatial stream, and local-spatial stream module for the TSCNN that respectively attempt to learn and integrate temporal, entire facial region, and facial local region cues in ME videos with the goal of recognizing MEs. In addition, to allow the TSCNN to recognize MEs without using the index values of apex frames, we design a reliable apex frame detection algorithm. Extensive experiments are conducted with five public ME databases: CASME II, SMIC-HS, SAMM, CAS(ME) 2 , and CASME. Our proposed TSCNN is shown to achieve more promising recognition results when compared with many other methods. INDEX TERMS Micro-expression recognition, convolutional neural networks, apex frame location, spatiotemporal information.

We present a morphological classification of 14,245 radio active galactic nuclei (AGNs) into six types, i.e., typical Fanaroff-Riley Class I / II (FRI/II), FRI/II-like bent-tailed, X-shaped radio galaxy, and ringlike radio galaxy, by designing a convolutional neural network (CNN) based autoencoder, namely MCRGNet, and applying it to a labeled radio galaxy (LRG) sample containing 1442 AGNs and an unlabeled radio galaxy (unLRG) sample containing 14,245 unlabeled AGNs selected from the Best-Heckman sample. We train MCRGNet and implement the classification task by a three-step strategy, i.e., pre-training, fine-tuning, and classification, which combines both unsupervised and supervised learnings. A four-layer dichotomous tree is designed to classify the radio AGNs, which leads to a significantly better performance than the direct six-type classification. On the LRG sample, our MCRGNet achieves a total precision of ∼ 93% and an averaged sensitivity of ∼ 87%, which are better than those obtained in previous works. On the unLRG sample, whose labels have been human-inspected, the neural network achieves a total precision of ∼ 80%. Also, using the Sloan Digital Sky Survey (SDSS) Data Release 7 (DR7) to calculate the r-band absolute magnitude (M opt ) and using the flux densities to calculate the radio luminosity (L radio ), we find that the distributions of the unLRG sources on the L radio -M opt plane do not show an apparent redshift evolution and could confirm with a sufficiently large sample that there could not exist an abrupt separation between FRIs and FRIIs as reported in some previous works.

The targeted delivery of hydrophobic therapeutic drugs to tumors is one of the major challenges in drug development. The use of natural proteins as drug delivery vehicles holds great promise due to various functionalities of proteins. In the current study, we exploited a natural protein, GroEL, which possesses a double layer cage structure, as a hydrophobic drug container, which is switchable by ATP binding to a hydrophilic status, to design a novel and intelligent hydrophobic drug delivery molecular machine with a controlled drug release profile. When loaded with the hydrophobic antitumor drug, Doxorubicin (Dox), GroEL was able to shield the drug from the aqueous phase of blood, releasing the drug once in the presence of a critical concentration of ATP at the tumor site. Unexpectedly, we found that GroEL has a specific affinity for the cell structural protein, plectin, which is expressed at abnormally elevated levels on the membranes of tumor cells but not in normal cells. This finding, in combination with the ATP sensitivity, makes GroEL a superior natural tumor targeting nanocarrier. Our data show that GroEL-Dox is able to effectively, and highly selectively, deliver the hydrophobic drug to fast growing tumors without overt adverse effects on the major organs. GroEL is therefore a promising drug delivery platform that can overcome the obstacles to hydrophobic drug targeting and delivery.

In this paper, we review the status of the multifunctional experimental platform at the National Laboratory of High Power Laser and Physics (NLHPLP). The platform, including the SG-II laser facility, SG-II 9th beam, SG-II upgrade (SG-II UP) facility, and SG-II 5 PW facility, is operational and available for interested scientists studying inertial confinement fusion (ICF) and a broad range of high-energy-density physics. These facilities can provide important experimental capabilities by combining different pulse widths of nanosecond, picosecond, and femtosecond scales. In addition, the SG-II UP facility, consisting of a single petawatt system and an eight-beam nanosecond system, is introduced including several laser technologies that have been developed to ensure the performance of the facility. Recent developments of the SG-II 5 PW facility are also presented.

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When applying the foreground removal methods to uncover the faint cosmological signal from the epoch of reionization (EoR), the foreground spectra are assumed to be smooth. However, this assumption can be seriously violated in practice since the unresolved or mis-subtracted foreground sources, which are further complicated by the frequency-dependent beam effects of interferometers, will generate significant fluctuations along the frequency dimension. To address this issue, we propose a novel deep-learning-based method that uses a nine-layer convolutional denoising autoencoder (CDAE) to separate the EoR signal. After being trained on the SKA images simulated with realistic beam effects, the CDAE achieves excellent performance as the mean correlation coefficient (ρ) between the reconstructed and input EoR signals reaches 0.929 ± 0.045. In comparison, the two representative traditional methods, namely the polynomial fitting method and the continuous wavelet transform method, both have difficulties in modelling and removing the foreground emission complicated with the beam effects, yielding onlyρ poly = 0.296 ± 0.121 andρ cwt = 0.198 ± 0.160, respectively. We conclude that, by hierarchically learning sophisticated features through multiple convolutional layers, the CDAE is a powerful tool that can be used to overcome the complicated beam effects and accurately separate the EoR signal. Our results also exhibit the great potential of deep-learning-based methods in future EoR experiments.