Fast and Accurate Neural Word Segmentation for Chinese

Neural models with minimal feature engineering have achieved competitive performance against traditional methods for the task of Chinese word segmentation. However, both training and working procedures of the current neural models are computationally inefficient. This paper presents a greedy neural word segmenter with balanced word and character embedding inputs to alleviate the existing drawbacks. Our segmenter is truly end-to-end, capable of performing segmentation much faster and even more accurate than state-of-the-art neural models on Chinese benchmark datasets.Comment: To appear in ACL201

Characterization of a fatal feline panleukopenia virus derived from giant panda with broad cell tropism and zoonotic potential

Represented by feline panleukopenia virus (FPV) and canine parvovirus (CPV), the species carnivore protoparvovirus 1 has a worldwide distribution through continuous ci13rculation in companion animals such as cats and dogs. Subsequently, both FPV and CPV had engaged in host-to-host transfer to other wild animal hosts of the order Carnivora. In the present study, we emphasized the significance of cross-species transmission of parvoviruses with the isolation and characterization of an FPV from giant panda displaying severe and fatal symptoms. The isolated virus, designated pFPV-sc, displayed similar morphology as FPV, while phylogenetic analysis indicated that the nucleotide sequence of pFPV-sc clades with Chinese FPV isolates. Despite pFPV-sc is seemingly an outcome of a spillover infection event from domestic cats to giant pandas, our study also provided serological evidence that FPV or other parvoviruses closely related to FPV could be already prevalent in giant pandas in 2011. Initiation of host transfer of pFPV-sc is likely with association to giant panda transferrin receptor (TfR), as TfR of giant panda shares high homology with feline TfR. Strikingly, our data also indicate that pFPV-sc can infect cell lines of other mammal species, including humans. To sum up, observations from this study shall promote future research of cross-host transmission and antiviral intervention of Carnivore protoparvovirus 1, and necessitate surveillance studies in thus far unacknowledged potential reservoirs

Meta-Knowledge and Multi-Task Learning-Based Multi-Scene Adaptive Crowd Counting

In this paper, we propose a multi-scene adaptive crowd counting method based on meta-knowledge and multi-task learning. In practice, surveillance cameras are stationarily deployed in various scenes. Considering the extensibility of a surveillance system, the ideal crowd counting method should have a strong generalization capability to be deployed in unknown scenes. On the other hand, given the diversity of scenes, it should also effectively suit each scene for better performance. These two objectives are contradictory, so we propose a coarse-to-fine pipeline including meta-knowledge network and multi-task learning. Specifically, at the coarse-grained stage, we propose a generic two-stream network for all existing scenes to encode meta-knowledge especially inter-frame temporal knowledge. At the fine-grained stage, the regression of the crowd density map to the overall number of people in each scene is considered a homogeneous subtask in a multi-task framework. A robust multi-task learning algorithm is applied to effectively learn scene-specific regression parameters for existing and new scenes, which further improve the accuracy of each specific scenes. Taking advantage of multi-task learning, the proposed method can be deployed to multiple new scenes without duplicated model training. Compared with two representative methods, namely AMSNet and MAML-counting, the proposed method reduces the MAE by 10.29% and 13.48%, respectively

Deep Reinforcement Learning-Based Resource Allocation for Satellite Internet of Things with Diverse QoS Guarantee

Large-scale terminals’ various QoS requirements are key challenges confronting the resource allocation of Satellite Internet of Things (S-IoT). This paper presents a deep reinforcement learning-based online channel allocation and power control algorithm in an S-IoT uplink scenario. The intelligent agent determines the transmission channel and power simultaneously based on contextual information. Furthermore, the weighted normalized reward concerning success rate, power efficiency, and QoS requirement is adopted to balance the performance between increasing resource efficiency and meeting QoS requirements. Finally, a practical deployment mechanism based on transfer learning is proposed to promote onboard training efficiency and to reduce computation consumption of the training process. The simulation demonstrates that the proposed method can balance the success rate and power efficiency with QoS requirement guaranteed. For S-IoT’s normal operation condition, the proposed method can improve the power efficiency by 60.91% and 144.44% compared with GA and DRL_RA, while its power efficiency is only 4.55% lower than that of DRL-EERA. In addition, this method can be transferred and deployed to a space environment by merely 100 onboard training steps

Deep Reinforcement Learning-Based Resource Allocation for Satellite Internet of Things with Diverse QoS Guarantee

Large-scale terminals’ various QoS requirements are key challenges confronting the resource allocation of Satellite Internet of Things (S-IoT). This paper presents a deep reinforcement learning-based online channel allocation and power control algorithm in an S-IoT uplink scenario. The intelligent agent determines the transmission channel and power simultaneously based on contextual information. Furthermore, the weighted normalized reward concerning success rate, power efficiency, and QoS requirement is adopted to balance the performance between increasing resource efficiency and meeting QoS requirements. Finally, a practical deployment mechanism based on transfer learning is proposed to promote onboard training efficiency and to reduce computation consumption of the training process. The simulation demonstrates that the proposed method can balance the success rate and power efficiency with QoS requirement guaranteed. For S-IoT’s normal operation condition, the proposed method can improve the power efficiency by 60.91% and 144.44% compared with GA and DRL_RA, while its power efficiency is only 4.55% lower than that of DRL-EERA. In addition, this method can be transferred and deployed to a space environment by merely 100 onboard training steps

Astragaloside IV Synergizing with Ferulic Acid Ameliorates Pulmonary Fibrosis by TGF-β1/Smad3 Signaling

Objective. The study aims to research the interventional effect and mechanism of astragaloside IV (Ast) synergizing with ferulic acid (FA) on idiopathic pulmonary fibrosis (IPF) induced by bleomycin in mice. Methods. The mice were randomly divided into seven groups with 10 mice in each group, namely, a sham operation group, a model group, a miRNA-29b (miR-29) group, a miR-29b negative control group (NC group), a FA group, an Ast group, and a combination group. A mouse model of pulmonary fibrosis was established by intratracheal instillation of bleomycin. Samples were collected after 28 days of continuous administration. Hematoxylin and eosin (HE) and Masson staining were used to observe pathological changes in the lung tissue, and the degree of fibrosis was evaluated using the hydroxyproline content. Changes in transforming growth factor-β1 (TGF-β1) and Smad3 in the lung were observed using immunohistochemistry. Enzyme-linked immunosorbent assay (ELISA) was used to detect the level of reactive oxygen species (ROS), malondialdehyde (MDA), and superoxide dismutase (SOD) in the serum. PCR was used to detect the expression of the miR-29b, TGF-β1, Smad3, and nuclear factor E2-related factor 2 (Nrf2) genes. Western blotting was used to detect the content of the TGF-β/Smad3 protein. Results. Ferulic acid combined with astragaloside IV reduced the degree of pulmonary fibrosis and the synthesis of hydroxyproline in lung tissue. The combination of the two also regulated the oxidative stress response ， TGF-β1/Smad3 pathway and miR-29b in lung tissue. Conclusion. Astragaloside IV combined with ferulic acid regulated the oxidative stress of lung tissues and TGF-β1/Smad3 signaling through miR-29b, thereby reducing the degree of pulmonary fibrosis. This provides a reference direction for the clinical treatment of IPF patients

Hierarchical Porous Carbon Fibers for Enhanced Interfacial Electron Transfer of Electroactive Biofilm Electrode

The nanoporous carbon fiber materials derived from electrospun polyacrylonitrile (PAN) fibers doped with zeolitic imidazolate framework are developed here and applied in the microbe fuel cell anode for enhanced interfacial electron transfer. Zeolitic imidazolate fram-8 (ZIF-8) could introduce a large number of mesopores into fibers, which significantly promote indirect electron transfer mediated by flavins (IET). Moreover, it is noted that thinner fibers are more suitable for cytochromes-based direct electron transfer (DET). Furthermore, the enlarged fiber interspace strengthens the amount of biofilm loading but a larger interspace between thick fibers would hinder the formation of continuous biofilm. Consequently, the nanoporous carbon fiber derived from PAN/ZIF-8 composite with a 1:1 wt ratio shows the best performance according to its suitable mesoporous structure and optimal fiber diameter, which delivers a 10-fold higher maximum power density in microbial fuel cells compared to carbon fabric. In this work, we reveal that the proportion of IET and DET in the interfacial electron transfer process varies with different porous structures and fiber diameters, which may provide some insights for designing porous fiber electrodes for microbial fuel cells and also other devices of bioelectrochemical systems