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Abstract-Predicting specific household characteristics (e.g., age of person, household income, cooking style, etc) from their everyday electricity consumption (i.e., smart meter data) enables energy provider to develop many intelligent business applications or help consumers to reduce their energy consumption. However, most existing works intend to predict single household characteristic via smart meter data independently, and ignore the joint analysis of different characteristics. In this paper, we consider each characteristic as an independent task and intend to predict multiple household characteristics simultaneously by designing a new multi-task learning formulation: Discriminative MultiTask Relationship Learning (DisMTRL). Specifically, two main challenges need to be handled: 1) task relationship, that is the embedded structure of relationships among different characteristics; 2) feature learning, there exist redundant features in original training data. To achieve these, our DisMTRL model aims to obtain a simple but robust weight matrix through capturing the intrinsic relatedness among different characteristics by task covariance matrix (MTRL) and incorporating the discriminative features via feature covariance matrix (Dis). For model optimization, we employ an alternating minimization strategy to learn the optimal weight matrix as well as the relationship between tasks by converting feature learning regularization as trace minimization problem. For evaluation, we adopt a smart meter dataset collected from 4232 households in Ireland at a 30min granularity over an interval of 1.5 years. The experimental results justify the effectiveness of our proposed model.

In this paper, we develop a novel 3D object recognition algorithm to perform detection and pose estimation jointly. We focus on analyzing the advantages of the 3D point cloud relative to the RGB-D image and try to eliminate the unpredictability of output values that inevitably occurs in regression tasks. To achieve this, we first adopt the Truncated Signed Distance Function (TSDF) to encode the point cloud and extract low compact discriminative feature via unsupervised deep learning network. This approach can not only eliminate the dense scale sampling for offline model training but also reduce the distortion by mapping the 3D shape to the 2D plane and overcome the dependence on color cues. Then, we train a Hough forests to achieve multi-object detection and 6-DoF pose estimation simultaneously. In addition, we propose a robust multilevel verification strategy that effectively reduces the unpredictability of output values which occurs in the hough regression module. Experiments on public datasets demonstrate that our approach provides effective results comparable to the state-of-the-arts.

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Abstract-Goal: Most state-of-the-art computer-aided endoscopic diagnosis methods require pixelwise labeled data to train various supervised machine learning models. However, it is a tedious and time-consuming work to collect sufficient precisely labeled image data. Fortunately, we can easily obtain huge endoscopic medical reports including the diagnostic text and images, which can be considered as weakly labeled data. Methods: In this paper, our motivation is to design a new computer-aided endoscopic diagnosis system without human specific labeling; in comparison with most state of the arts, ours only depends on the endoscopic images with weak labels mined from the diagnostic text. To achieve this, we first cast the endoscopic image folder and included images as bag and instances and represent each instance based on the global bag-of-words model. We then adopt a feature mapping scheme to represent each bag by mining the most suspicious lesion instance from each positive bag automatically. In order to achieve self-online updating from sequential new coming data, an online metric learning method is used to optimize the bag-level classification. Results: Our computer-aided endoscopic diagnosis system achieves an AUC of 0.93 on a new endoscopic image dataset captured from 424 volunteers with more than 12k images. Conclusion: The system performance outperforms other state of the arts when we mine the most positive instances from positive bags and adopt the online phase to mine more information from the unseen bags. Significance: We present the first weakly labeled endoscopic image dataset for computer-aided endoscopic diagnosis and a novel system that is suitable for use in clinical settings.Index Terms-Computer-aided endoscopic diagnosis, multiple instance learning (MIL), online metric learning, weakly labeled data.