ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering

We propose a novel attention based deep learning architecture for visual question answering task (VQA). Given an image and an image related natural language question, VQA generates the natural language answer for the question. Generating the correct answers requires the model's attention to focus on the regions corresponding to the question, because different questions inquire about the attributes of different image regions. We introduce an attention based configurable convolutional neural network (ABC-CNN) to learn such question-guided attention. ABC-CNN determines an attention map for an image-question pair by convolving the image feature map with configurable convolutional kernels derived from the question's semantics. We evaluate the ABC-CNN architecture on three benchmark VQA datasets: Toronto COCO-QA, DAQUAR, and VQA dataset. ABC-CNN model achieves significant improvements over state-of-the-art methods on these datasets. The question-guided attention generated by ABC-CNN is also shown to reflect the regions that are highly relevant to the questions

Privacy Preserving Utility Mining: A Survey

In big data era, the collected data usually contains rich information and hidden knowledge. Utility-oriented pattern mining and analytics have shown a powerful ability to explore these ubiquitous data, which may be collected from various fields and applications, such as market basket analysis, retail, click-stream analysis, medical analysis, and bioinformatics. However, analysis of these data with sensitive private information raises privacy concerns. To achieve better trade-off between utility maximizing and privacy preserving, Privacy-Preserving Utility Mining (PPUM) has become a critical issue in recent years. In this paper, we provide a comprehensive overview of PPUM. We first present the background of utility mining, privacy-preserving data mining and PPUM, then introduce the related preliminaries and problem formulation of PPUM, as well as some key evaluation criteria for PPUM. In particular, we present and discuss the current state-of-the-art PPUM algorithms, as well as their advantages and deficiencies in detail. Finally, we highlight and discuss some technical challenges and open directions for future research on PPUM.Comment: 2018 IEEE International Conference on Big Data, 10 page

Seismic test and numerical verification of the scaled-down reinforced concrete containment vessel

According to the ASME-359 code, a scaled-down structure of Reinforced Concrete Containment Vessel (RCCV) of Advanced Boiling Water Reactor (ABWR) building is constructed for the seismic test on the shaking table. Several acceleration time history satisfing design response spectrum with different magnitudes are used in the test. Besides, the numerical finite element model of RCCV is built by SAP2000 for calculating the dynamic responses numerically

Fluoroquinolones are associated with delayed treatment and resistance in tuberculosis: a systematic review and meta-analysis

SummaryBackgroundCurrent guidelines for treating community-acquired pneumonia recommend the use of fluoroquinolones for high-risk patients. Previous studies have reported controversial results as to whether fluoroquinolones are associated with delayed diagnosis and treatment of pulmonary tuberculosis (TB) and the development of fluoroquinolone-resistant Mycobacterium tuberculosis. We performed a systematic review and meta-analysis to clarify these issues.MethodsThe following databases were searched through September 30, 2010: PubMed, EMBASE, CINAHL, Cochrane Library, Web of Science, BIOSIS Previews, and the ACP Journal Club. We considered studies that addressed the issues of delay in diagnosis and treatment of TB and the development of resistance.ResultsNine eligible studies (four for delays and five for resistance issues) were included in the meta-analysis from the 770 articles originally identified in the database search. The mean duration of delayed diagnosis and treatment of pulmonary TB in the fluoroquinolone prescription group was 19.03 days, significantly longer than that in the non-fluoroquinolone group (95% confidence interval (CI) 10.87 to 27.18, p<0.001). The pooled odds ratio of developing a fluoroquinolone-resistant M. tuberculosis strain was 2.70 (95% CI 1.30 to 5.60, p=0.008). No significant heterogeneity was found among studies in the meta-analysis.ConclusionsEmpirical fluoroquinolone prescriptions for pneumonia are associated with longer delays in diagnosis and treatment of pulmonary TB and a higher risk of developing fluoroquinolone-resistant M. tuberculosis

Seismic test and numerical verification of the scaled-down reinforced concrete containment vessel

According to the ASME-359 code, a scaled-down structure of Reinforced Concrete Containment Vessel (RCCV) of Advanced Boiling Water Reactor (ABWR) building is constructed for the seismic test on the shaking table. Several acceleration time history satisfing design response spectrum with different magnitudes are used in the test. Besides, the numerical finite element model of RCCV is built by SAP2000 for calculating the dynamic responses numerically

SPGNet: Semantic Prediction Guidance for Scene Parsing

Multi-scale context module and single-stage encoder-decoder structure are commonly employed for semantic segmentation. The multi-scale context module refers to the operations to aggregate feature responses from a large spatial extent, while the single-stage encoder-decoder structure encodes the high-level semantic information in the encoder path and recovers the boundary information in the decoder path. In contrast, multi-stage encoder-decoder networks have been widely used in human pose estimation and show superior performance than their single-stage counterpart. However, few efforts have been attempted to bring this effective design to semantic segmentation. In this work, we propose a Semantic Prediction Guidance (SPG) module which learns to re-weight the local features through the guidance from pixel-wise semantic prediction. We find that by carefully re-weighting features across stages, a two-stage encoder-decoder network coupled with our proposed SPG module can significantly outperform its one-stage counterpart with similar parameters and computations. Finally, we report experimental results on the semantic segmentation benchmark Cityscapes, in which our SPGNet attains 81.1% on the test set using only 'fine' annotations.Comment: ICCV 201