58 research outputs found
Anatomical Structure Sketcher for Cephalograms by Bimodal Deep Learning
The lateral cephalogram is a commonly used medium to acquire patient-specific morphology for diagnose and treatment planning in clinical dentistry. The robust anatomical structure detection and accurate annotation remain challenging considering the personal skeletal variations and image blurs caused by device-specific projection magnification, together with structure overlapping in the lateral cephalograms. We propose a novel cephalogram sketcher system, where the contour extraction of anatomical structures is formulated as a cross-modal morphology transfer from regular image patches to arbitrary curves. Specifically, the image patches of structures of interest are located by a hierarchical pictorial model. The automatic contour sketcher converts the image patch to a morphable boundary curve via a bimodal deep Boltzmann machine. The deep machine learns a joint representation of patch textures and contours, and forms a path from one modality (patches) to the other (contours). Thus, the sketcher can infer the contours by alternating Gibbs sampling along the path in a manner similar to the data completion. The proposed method is robust not only to structure detection, but also tends to produce accurate structure shapes and landmarks even in blurry X-ray images. The experiments performed on clinically captured cephalograms demonstrate the effectiveness of our method.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000346352700099&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Computer Science, Artificial IntelligenceCPCI-S(ISTP)
Robust Visual Question Answering: Datasets, Methods, and Future Challenges
Visual question answering requires a system to provide an accurate natural
language answer given an image and a natural language question. However, it is
widely recognized that previous generic VQA methods often exhibit a tendency to
memorize biases present in the training data rather than learning proper
behaviors, such as grounding images before predicting answers. Therefore, these
methods usually achieve high in-distribution but poor out-of-distribution
performance. In recent years, various datasets and debiasing methods have been
proposed to evaluate and enhance the VQA robustness, respectively. This paper
provides the first comprehensive survey focused on this emerging fashion.
Specifically, we first provide an overview of the development process of
datasets from in-distribution and out-of-distribution perspectives. Then, we
examine the evaluation metrics employed by these datasets. Thirdly, we propose
a typology that presents the development process, similarities and differences,
robustness comparison, and technical features of existing debiasing methods.
Furthermore, we analyze and discuss the robustness of representative
vision-and-language pre-training models on VQA. Finally, through a thorough
review of the available literature and experimental analysis, we discuss the
key areas for future research from various viewpoints.Comment: IEEE TPAMI (Under Review
A New Dynamic Path Planning Approach for Unmanned Aerial Vehicles
Dynamic path planning is one of the key procedures for unmanned aerial vehicles (UAV) to successfully fulfill the diversified missions. In this paper, we propose a new algorithm for path planning based on ant colony optimization (ACO) and artificial potential field. In the proposed algorithm, both dynamic threats and static obstacles are taken into account to generate an artificial field representing the environment for collision free path planning. To enhance the path searching efficiency, a coordinate transformation is applied to move the origin of the map to the starting point of the path and in line with the source-destination direction. Cost functions are established to represent the dynamically changing threats, and the cost value is considered as a scalar value of mobile threats which are vectors actually. In the process of searching for an optimal moving direction for UAV, the cost values of path, mobile threats, and total cost are optimized using ant optimization algorithm. The experimental results demonstrated the performance of the new proposed algorithm, which showed that a smoother planning path with the lowest cost for UAVs can be obtained through our algorithm.
(PDF) A New Dynamic Path Planning Approach for Unmanned Aerial Vehicles. Available from: https://www.researchgate.net/publication/328765418_A_New_Dynamic_Path_Planning_Approach_for_Unmanned_Aerial_Vehicles [accessed Nov 20 2018]
Towards Deeper Graph Neural Networks
Graph neural networks have shown significant success in the field of graph
representation learning. Graph convolutions perform neighborhood aggregation
and represent one of the most important graph operations. Nevertheless, one
layer of these neighborhood aggregation methods only consider immediate
neighbors, and the performance decreases when going deeper to enable larger
receptive fields. Several recent studies attribute this performance
deterioration to the over-smoothing issue, which states that repeated
propagation makes node representations of different classes indistinguishable.
In this work, we study this observation systematically and develop new insights
towards deeper graph neural networks. First, we provide a systematical analysis
on this issue and argue that the key factor compromising the performance
significantly is the entanglement of representation transformation and
propagation in current graph convolution operations. After decoupling these two
operations, deeper graph neural networks can be used to learn graph node
representations from larger receptive fields. We further provide a theoretical
analysis of the above observation when building very deep models, which can
serve as a rigorous and gentle description of the over-smoothing issue. Based
on our theoretical and empirical analysis, we propose Deep Adaptive Graph
Neural Network (DAGNN) to adaptively incorporate information from large
receptive fields. A set of experiments on citation, co-authorship, and
co-purchase datasets have confirmed our analysis and insights and demonstrated
the superiority of our proposed methods.Comment: 11 pages, KDD202
State Control and the Effects of Foreign Relations on Bilateral Trade
Do states use trade to reward and punish partners? WTO rules and the pressures of globalization restrict states’ capacity to manipulate trade policies, but we argue that governments can link political goals with economic outcomes using less direct avenues of influence over firm behavior. Where governments intervene in markets, politicization of trade is likely to occur. In this paper, we examine one important form of government control: state ownership of firms. Taking China and India as examples, we use bilateral trade data by firm ownership type, as well as measures of bilateral political relations based on diplomatic events and UN voting to estimate the effect of political relations on import and export flows. Our results support the hypothesis that imports controlled by state-owned enterprises (SOEs) exhibit stronger responsiveness to political relations than imports controlled by private enterprises. A more nuanced picture emerges for exports; while India’s exports through SOEs are more responsive to political tensions than its flows through private entities, the opposite is true for China. This research holds broader implications for how we should think about the relationship
between political and economic relations going forward, especially as a number of countries with partially state-controlled economies gain strength in the global economy
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