Multi-Label Zero-Shot Learning with Structured Knowledge Graphs

In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input instance. Inspired by the way humans utilize semantic knowledge between objects of interests, we propose a framework that incorporates knowledge graphs for describing the relationships between multiple labels. Our model learns an information propagation mechanism from the semantic label space, which can be applied to model the interdependencies between seen and unseen class labels. With such investigation of structured knowledge graphs for visual reasoning, we show that our model can be applied for solving multi-label classification and ML-ZSL tasks. Compared to state-of-the-art approaches, comparable or improved performances can be achieved by our method.Comment: CVPR 201

Learning Deep Latent Spaces for Multi-Label Classification

Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input instance. We propose a novel deep neural networks (DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this task. Aiming at better relating feature and label domain data for improved classification, we uniquely perform joint feature and label embedding by deriving a deep latent space, followed by the introduction of label-correlation sensitive loss function for recovering the predicted label outputs. Our C2AE is achieved by integrating the DNN architectures of canonical correlation analysis and autoencoder, which allows end-to-end learning and prediction with the ability to exploit label dependency. Moreover, our C2AE can be easily extended to address the learning problem with missing labels. Our experiments on multiple datasets with different scales confirm the effectiveness and robustness of our proposed method, which is shown to perform favorably against state-of-the-art methods for multi-label classification.Comment: published in AAAI-201

Radiographic Disclosure of Fournier’s Gangrene

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The influence of the technical dimension, functional dimension, and tenant satisfaction on tenant loyalty: an analysis based on the theory of planned behavior

This study primarily explored the influence of the technical dimension, functional dimension, and tenant satisfaction on tenant loyalty. The theory of planned behavior served as the basis of this study, and the three aforementioned factors (the technical dimension, the functional dimension, and tenant satisfaction) were incorporated into a conceptual framework for tenant loyalty. Structural equation modeling (SEM) was employed for parameter estimation. The participants consisted of tenants residing in eight administrative districts in Kaohsiung City. 315 questionnaires were administered, all of which were returned. After removing 15 invalid responses, there were 300 valid responses, which indicated an effective recovery rate of 95.2%. The results showed that the technical dimension, the functional dimension, and attitude significantly and positively influenced tenant satisfaction. Tenant satisfaction, perceived behavioral control, and social norms significantly and positively influenced tenant loyalty. Tenant satisfaction mediated the influence of the technical dimension and the functional dimension on tenant loyalty; the mediating effect of the functional dimension on tenant loyalty was greater than that of the technical dimension. The findings of this study highlight the measures rental companies should adopt in order to enhance the technical dimension, functional dimension, and tenant satisfaction, as this is crucial to maintaining sustainable operations

A maximum principle for second order nonlinear differential inequalities and its applications

AbstractLet y(t) be a nontrivial solution of the second order differential inequality y(t){(r(t)y′(t))′ + ƒ(t,y(t))} ⩽ 0We show that the zeros of y(t) are simple; y(t) and y′(t) have at most finite number of zeros on any compact interval [a, b] under suitable conditions on r and f. Using the main result, we establish some nonlinear maximum principles and a nonlinear Levin's comparison theorem, which extend some results of Protter, Weinberger, and Levin