Tooth-marked tongue or crenated tongue can provide valuable diagnostic information for traditional Chinese Medicine doctors. However, tooth-marked tongue recognition is challenging. The characteristics of different tongues are multiform and have a great amount of variations, such as different colors, different shapes, and different types of teeth marks. The regions of teeth mark only appear along the lateral borders. Most existing methods make use of concave regions information to classify the tooth-marked tongue which leads to inconstant performance when the region of teeth mark is not concave. In this paper, we try to solve these problems by proposing a three-stage approach which first makes use of concavity information to propose the suspected regions, then use a convolutional neural network to extract deep features and at last use a multiple-instance classifier to make the final decision. Experimental results demonstrate the effectiveness of the proposed method.
Recently deep learning has been successfully adopted in many applications such as speech recognition and image classification. In this work, we explore the possibility of employing deep learning in graph clustering. We propose a simple method, which first learns a nonlinear embedding of the original graph by stacked autoencoder, and then runs $k$-means algorithm on the embedding to obtain the clustering result. We show that this simple method has solid theoretical foundation, due to the similarity between autoencoder and spectral clustering in terms of what they actually optimize. Then, we demonstrate that the proposed method is more efficient and flexible than spectral clustering. First, the computational complexity of autoencoder is much lower than spectral clustering: the former can be linear to the number of nodes in a sparse graph while the latter is super quadratic due to eigenvalue decomposition. Second, when additional sparsity constraint is imposed, we can simply employ the sparse autoencoder developed in the literature of deep learning; however, it is non-straightforward to implement a sparse spectral method. The experimental results on various graph datasets show that the proposed method significantly outperforms conventional spectral clustering which clearly indicates the effectiveness of deep learning in graph clustering.
Existing recommendation algorithms mostly focus on optimizing traditional recommendation measures, such as the accuracy of rating prediction in terms of RMSE or the quality of top-k recommendation lists in terms of precision, recall, MAP, etc. However, an important expectation for commercial recommendation systems is to improve the nal revenue/pro t of the system. Traditional recommendation targets such as rating prediction and top-k recommendation are not directly related to this goal.In this work, we blend the fundamental concepts in online advertising and micro-economics into personalized recommendation for pro t maximization. Speci cally, we propose value-aware recommendation based on reinforcement learning, which directly optimizes the economic value of candidate items to generate the recommendation list. In particular, we generalize the basic concept of click conversion rate (CVR) in computational advertising into the conversation rate of an arbitrary user action (XVR) in E-commerce, where the user actions can be clicking, adding to cart, adding to wishlist, etc. In this way, each type of user action is mapped to its monetized economic value. Economic values of di erent user actions are further integrated as the reward of a ranking list, and reinforcement learning is used to optimize the recommendation list for the maximum total value. Experimental results in both o ine benchmarks and online commercial systems veri ed the improved performance of our framework, in terms of both traditional top-k ranking tasks and the economic pro ts of the system.
A series of novel perylene tetra sec-alkyl ester compounds were successfully designed and synthesised. The photophysical properties were investigated and the UV absorption and fluorescence emission spectra displayed a mirror-image relationship. The compound PS8 showed the highest fluorescent quantum yield, while the fluorescence of PS8 was quenched in the aggregated state in mixed solvents. Moreover, the electrochemical properties of the perylene derivatives were studied to determine the molecules’ highest occupied molecular orbital and lowest unoccupied molecular orbital levels by cyclic voltammetry. The most important result was that PS8 exhibited a columnar phase at room temperature and was responsive to an electric field. PS8 could perpendicularly orient to an applied electric field. In addition, highly oriented face-on alignment was achieved on indium tin oxide-covered glass by thermal annealing.
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