No abstract

Recognizing freehand sketches with high arbitrariness is such a great challenge that the automatic recognition rate has reached a ceiling in recent years. In this paper, we explicitly explore the shape properties of sketches, which has almost been neglected before in the context of deep learning, and propose a sequential dual learning strategy that combines both shape and texture features. We devise a two-stage recurrent neural network to balance these two types of features. Our architecture also considers stroke orders of sketches to reduce the intra-class variations of input features. Extensive experiments on the TU-Berlin benchmark set show that our method achieves over 90% recognition rate for the first time on this task, outperforming both humans and state-of-the-art algorithms by over 19 and 7.5 percentage points, respectively. Especially, our approach can distinguish the sketches with similar textures but different shapes more effectively than recent deep networks. Based on the proposed method, we develop an on-line sketch retrieval and imitation application to teach children or adults to draw. The application is available as Sketch.Draw. CCS CONCEPTS • Computing methodologies → Object recognition.

While lung cancer poses a serious threat to human health, non-small-cell lung cancer (NSCLC) is the most common type of lung cancer. Danggui Buxue Decoction (DBD) is a classical traditional antitumor medicine commonly used in China. However, the potential mechanism of DBD against NSCLC has not yet been expounded. Therefore, this study clarified the potential molecular mechanism and key targets of DBD in NSCLC treatment through several technological advances, such as network pharmacology, molecular docking, and bioinformatics. Firstly, the relative active ingredients and key DBD targets were analyzed, and subsequently, a drug-ingredient-target-disease network diagram was constructed for NSCLC treatment with DBD, resulting in the identification of five main active ingredients and ten core targets according to the enrichment degree. The enrichment analysis revealed that DBD can achieve the purpose of treating NSCLC through the AGE-RAGE signaling pathway in diabetic complications. Secondly, the molecular docking approach predicted that quercetin and hederagenin have the best working mechanisms with PDE3A and PTGS1, while the survival analysis results depicted that high PDE3A gene expression has a relatively poor prognosis for NSCLC patients ( p < 0.05 ). Additionally, PDE3A is mainly distributed in the LU65 cell line that originated from Asian population. In summary, our study results showed that DBD can treat NSCLC through the synergistic correlation between multiple ingredients, multiple targets, and multiple pathways, thus effectively improving NSCLC prognosis. This study not only reflected the medicinal value of DBD but also provided a solid structural basis for future new drug developments and targeted therapies.

Point-of-interest (POI) retrieval that searches for relevant destination locations plays a significant role in on-demand ridehailing services. Existing solutions to POI retrieval mainly retrieve and rank POIs based on their semantic similarity scores. Although intuitive, quantifying the relevance of a Query-POI pair by single-field semantic similarity is subject to inherent limitations. In this paper, we propose a novel Query-POI relevance model for effective POI retrieval for ondemand ride-hailing services. Different from existing relevance models, we capture and represent multi-field and local&global semantic features of a Query-POI pair to measure the semantic similarity. Besides, we observe a hidden correlation between origin-destination locations in ride-hailing scenarios, and propose two location embeddings to characterize the specific correlation. By incorporating the geographic correlation with the semantic similarity, our model achieves better performance in POI ranking. Experimental results on two real-world click-through datasets demonstrate the improvements of our model over state-of-the-art methods.

Recognizing freehand sketches with high arbitrariness is greatly challenging. Most existing methods either ignore the geometric characteristics or treat sketches as handwritten characters with fixed structural ordering. Consequently, they can hardly yield high recognition performance even though sophisticated learning techniques are employed. In this paper, we propose a sequential deep learning strategy that combines both shape and texture features. A coded shape descriptor is exploited to characterize the geometry of sketch strokes with high flexibility, while the outputs of constitutional neural networks (CNN) are taken as the abstract texture feature. We develop dual deep networks with memorable gated recurrent units (GRUs), and sequentially feed these two types of features into the dual networks, respectively. These dual networks enable the feature fusion by another gated recurrent unit (GRU), and thus accurately recognize sketches invariant to stroke ordering. The experiments on the TU-Berlin data set show that our method outperforms the average of human and state-of-the-art algorithms even when significant shape and appearance variations occur.