Wireless sensor networks consist of a large number of sensor nodes that have low power and limited transmission range and can be used in various scenario. The nodes can be deployed in the long and narrow region, such ...
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Recursive least-squares temporal difference algorithm (RLS-TD) is deduced, which can use data more efficiently with fast convergence and less computational burden. Reinforcement learning based on recursive least-squar...
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In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive lab.led data. However, collecting and annotating images from the real world is too demanding in term...
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In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive lab.led data. However, collecting and annotating images from the real world is too demanding in terms of lab.r and money investments, and is usually inflexible to build datasets with specific characteristics, such as small area of objects and high occlusion level. Under the framework of Parallel Vision, this paper presents a purposeful way to design artificial scenes and automatically generate virtual images with precise annotations.A virtual dataset named Parallel Eye is built, which can be used for several computer vision tasks. Then, by training the DPM(Deformable parts model) and Faster R-CNN detectors, we prove that the performance of models can be significantly improved by combining Parallel Eye with publicly availab.e real-world datasets during the training phase. In addition, we investigate the potential of testing the trained models from a specific aspect using intentionally designed virtual datasets, in order to discover the flaws of trained models. From the experimental results, we conclude that our virtual dataset is viable to train and test the object detectors.
Quality score (QS) plays a critical role in sponsored search advertising (SSA) auctions, and in practice is closely correlated to the historical click-through rate (CTR) of an advertisement. The CTR-QS correlation may...
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Quality score (QS) plays a critical role in sponsored search advertising (SSA) auctions, and in practice is closely correlated to the historical click-through rate (CTR) of an advertisement. The CTR-QS correlation may impose great influence on advertisers' positioning strategies of selecting the targeting slots in the sponsored list. In the literature, however, QS is implicitly assumed to be an independent variable and exogenously assigned by Web search engines, so that little theoretical or managerial insights can be offered to help understand the positioning dynamics in SSA auctions with CTR-QS correlation. We strive to bridge this research gap in this paper. Based on a discrete time-dependent optimal control model, which explicitly captures the relationship between the historical CTR and QS, we determine the optimal strategy for revenue-maximizing advertisers' QS-based positioning decisions through a policy-iteration-based numerical approximation method. We also investigate two practically-used heuristic strategies, namely the greedy and farsighted positioning strategies, aiming to examine and help understand advertisers' real-world positioning dynamics. Our analysis indicates that both the optimal and greedy positioning strategies lead advertisers to monotonically increase or decrease their targeting slots over time, which may cause a polarization trend emerging in SSA markets. Meanwhile, the farsighted positioning strategy can accelerate the polarization. Our simulations show that both the greedy and farsighted strategies have good revenue performance. Our findings indicate that advertisers should monotonically adjust their targeting positions to maximize their revenue in CTR-QS correlated SSA auctions. Our findings also highlight the need for Web search engine companies to set a lowered weight for historical CTRs or use position-normalized CTRs in their QS measurements, so as to suppress the polarization trend.
Cooperative behavior has been of great concern in evolutionary game researches because of its important role in social life and natural evolution. Since memory can greatly influence the emergence of cooperative behavi...
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Real time bidding (RTB) is emerged with the rapid development and integration of Internet and big data, and it has become the most important business model for online computational advertising. In RTB-based advertisin...
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Real time bidding (RTB) is emerged with the rapid development and integration of Internet and big data, and it has become the most important business model for online computational advertising. In RTB-based advertising markets, Demand Side Platforms (DSPs) aim to help the advertisers buy ad impressions matched with their target audiences. Due to the existence of discount rate, the advertising effect may be diminished when displaying the advertisements multiple times to the same target audience. As such, frequency capping is widely considered as a crucial issue faced by most advertisers. In this paper, we mainly consider the frequency capping problems in RTB advertising markets, and establish a two-stage optimization model for advertisers and DSPs. Utilizing the computational experiment approach, we design two experiments to validate our model. The experimental results show that under different discount rates, the optimal frequency caps are different. Moreover, when considering all the discount rates, there exists an optimal frequency cap, at which the expected maximum revenue can be obtained in the long run.
This paper presents a grasping convolutional neural network with image segmentation for mobile manipulating robot. The proposed method is cascaded by a feature pyramid network FPN and a grasping network DrGNet. The FP...
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Internet inquiry is playing an increasingly important role as the complement of the traditional medical service system, especially the similar cases recommendation. It can not only save the patients' waiting time,...
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ISBN:
(纸本)9781467384148
Internet inquiry is playing an increasingly important role as the complement of the traditional medical service system, especially the similar cases recommendation. It can not only save the patients' waiting time, but also make use of the historical resources, for many cases with the same purpose have been solved perfectly. However, because of the diversity and non-standard of the patients' descriptions, the inquiry platform cannot find the cases with similar semantic easily. Most traditional retrieval methods require the overlap of two sentences, and this is not suitable with the diversity and non-standard descriptions. In this paper, we try to utilize the sentences' semantic representation in a continuous space to understand the cases, and then recommend the similar cases. We also incorporate it into query likelihood language models, trying to get better results. Our experimental data are all collected from a real internet inquiry platform, and the results show that our methods significantly outperform the state-of-the-art translation based methods for similar cases recommendation.
Automatic diagnosis based on medical imaging necessitates both lesion segmentation and disease classification. Lesion segmentation requires pixel-level annotations while disease classification only requires image-leve...
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Automatic diagnosis based on medical imaging necessitates both lesion segmentation and disease classification. Lesion segmentation requires pixel-level annotations while disease classification only requires image-level annotations. The two tasks are usually studied separately despite the latter problem relies on the former. Motivated by the close correlation between them, we propose a mixed-supervision guided method and a residual-aided classification U-Net model (ResCU-Net) for joint segmentation and benign-malignant classification. By coupling the strong supervision in the form of segmentation mask and weak supervision in the form of benign-malignant lab.l through a simple annotation procedure, our method efficiently segments tumor regions while simultaneously predicting a discriminative map for identifying the benign-malignant types of tumors. Our network, ResCU-Net, extends U-Net by incorporating the residual module and the SegNet architecture to exploit multilevel information for achieving improved tissue identification. With experiments on a public mammogram database of INbreast, we validate the effectiveness of our method and achieve consistent improvements over state-of-the-art models.
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