RNN Language Model with Word Clustering and Class-based Output Layer

The recurrent neural network language model (RNNLM) has shown significant promise for statistical language modeling. In this work, a new class-based output layer method is introduced to further improve the RNNLM. In this method, word class information is incorporated into the output layer by utilizing the Brown clustering algorithm to estimate a class-based language model. Experimental results show that the new output layer with word clustering not only improves the convergence obviously but also reduces the perplexity and word error rate in large vocabulary continuous speech recognition

Changes of pore structure and chloride content in cement pastes after pore solution expression

Pore solution expression is a widely accepted approach to extract pore solution of cement-based materials by appllying high pressure. In this study, the variations of pore solution distribution and chloride content in cement pastes before and after pore solution expression were examined. The results showed that the value of chloride concentration index N-c were mostly higher than 1.0 for cement pastes immersed in NaCl solution, and decreased with the chloride concentration of soaking solution and water-to-binder (w/b) ratio. During the pore solution expression, the pores larger than 40 nm were totally removed and the porosity of smaller pore was decreased. Based on a proposed physical model on structure of cement paste, the value of N-c was calculated according to the variations of pore structure and chloride content during pore solution expression. The calculated results showed similar trend as the experimental results obtained by pore solution expression method

StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts

Inferring spatial relations in natural language is a crucial ability an intelligent system should possess. The bAbI dataset tries to capture tasks relevant to this domain (task 17 and 19). However, these tasks have several limitations. Most importantly, they are limited to fixed expressions, they are limited in the number of reasoning steps required to solve them, and they fail to test the robustness of models to input that contains irrelevant or redundant information. In this paper, we present a new Question-Answering dataset called StepGame for robust multi-hop spatial reasoning in texts. Our experiments demonstrate that state-of-the-art models on the bAbI dataset struggle on the StepGame dataset. Moreover, we propose a Tensor-Product based Memory-Augmented Neural Network (TP-MANN) specialized for spatial reasoning tasks. Experimental results on both datasets show that our model outperforms all the baselines with superior generalization and robustness performance.Comment: AAAI 2022 Camera Read

Preparation, characterization, and in vivo evaluation of a self-nanoemulsifying drug delivery system (SNEDDS) loaded with morin-phospholipid complex

Background: As a poorly water-soluble drug, the oral application of morin is limited by its low oral bioavailability. In this study, a new self-nanoemulsifying drug delivery system (SNEDDS), based on the phospholipid complex technique, was developed to improve the oral bioavailability of morin. Methods: Morin-phospholipid complex (MPC) was prepared by a solvent evaporation method and characterized by infrared spectroscopy and X-ray diffraction. After formation of MPC, it was found that the liposolubility of morin was significantly increased, as verified through solubility studies. An orthogonal design was employed to screen the blank SNEDDS, using emulsifying rate and particle size as evaluation indices. Ternary phase diagrams were then constructed to investigate the effects of drug loading on the self-emulsifying performance of the optimized blank SNEDDS. Subsequently, in vivo pharmacokinetic parameters of the morin-phospholipid complex self-nanoemulsifying drug delivery system (MPC-SNEDDS) were investigated in Wistar rats (200 mg/kg of morin by oral administration). Results: The optimum formulation was composed of Labrafil (R) M 1944 CS, Cremophor (R) RH 40, and Transcutol (R) P (3: 5: 3, w/w), which gave a mean particle size of approximately 140 nm. Oral delivery of the MPC-SNEDDS exhibited a significantly greater C(max) (28.60 mu g/mL) than the morin suspension (5.53 mu g/mL) or MPC suspension (23.74 mu g/mL) (all P < 0.05). T(max) was prolonged from 0.48 to 0.77 hours and to 1 hour for MPC and MPC-SNEDDS, respectively. In addition, the relative oral bioavailability of morin formulated in the MPC-SNEDDS was 6.23-fold higher than that of the morin suspension, and was significantly higher than that of the MPC suspension (P < 0.05). Conclusion: The study demonstrated that a SNEDDS combined with the phospholipid complex technique was a promising strategy to enhance the oral bioavailability of morin.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000298166900001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Nanoscience & NanotechnologyPharmacology & PharmacySCI(E)24ARTICLE3405-3414