Twenty compounds, including 14 new α-acid derivatives, a new chromone, and five known compounds, were identified from the pistillate inflorescence of Humulus lupulus (hops), and their structures were elucidated via physical data analysis. The absolute configurations of new α-acid derivatives 1-11b were determined by comparing their computed and experimental electronic circular dichroism spectra using TDDFT and NMR spectroscopic data. A putative biosynthetic pathway for the identified components was deduced. Their antineuroinflammatory effects were assayed systematically, and their structure-activity relationships are discussed briefly. Among the identified compounds, compound 14 displayed moderate inhibitory effects against nitric oxide production with an IC value of 7.92 μM.

As an interesting and challenging problem, generating image caption automatically has attracted increasingly attention in natural language processing and computer vision communities. In this paper, we propose an end-to-end deep learning approach for image caption generation. We leverage image feature information at specific location every moment and generate the corresponding caption description through a semantic attention model. The end-to-end framework allows us to introduce an independent recurrent structure as an attention module, derived by calculating the similarity between image feature sequence and semantic word sequence. Additionally, our model is designed to transfer the knowledge representation obtained from the English portion into the Chinese portion to achieve the cross-lingual image captioning. We evaluate the proposed model on the most popular benchmark datasets. We report an improvement of 3.9% over existing state-of-the-art approaches for cross-lingual image captioning on the Flickr8k CN dataset on CIDEr metric. The experimental results demonstrate the effectiveness of our attention model.

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Motivated by a wide range of real world applications of hand writing digital recognition, e.g., postal code recognition, the past decades have seen its great progress. The related approaches are generally composed of two components, feature extraction and identification methods. We note that the previous approaches are limited by the following two aspects: (1) the feature is not adaptive enough to cover the great variance within data; (2) the recognition methods are suffered from local minima solution. Inspired by these observations and to overcome these limitations, we in this paper propose an approach HMM-MLR by exploiting hidden Markov model (HMM) and modified logistic regression (MLR). In the proposed approach, HMM is employed to model the trace of handwriting digital, which is able to model the large variance within digitals and can adapt to the data distribution. Then the features are extracted based on HMM and then delivered into MLR for recognition. Benefitting from the global optimum solution of MLR, the proposed approach could reach highly stable results. To verify the effectiveness of the proposed approach, we experimentally compare our proposed approach with other state-of-the-art approaches over Semeion handwritten digit dataset. The experimental results show that, over both recognition accuracy and recall, for different rounds of experiments and different number of training samples, our HMM-MLR exhibits significant improvement over others. Index Terms-Handwriting Digital Recognition; Hidden Markov Model; Modified Logistic RegressionI.