A moderate deviation principle for stochastic Volterra equation

Li, Yumeng; Wang, Ran; Yao, Nian; Zhang, Shuguang

doi:10.1016/j.spl.2016.10.033

Cited by 18 publications

(7 citation statements)

References 13 publications

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“…Hence, we use the weak convergence method to prove large and moderate deviation principles (c.f. [8,11,14,15]). Fourthly, in order to obtain the moderate deviation principle for Eq.…”

Section: Introductionmentioning

confidence: 99%

Deviation estimates for multivalued McKean-Vlasov stochastic differential equations

Fang¹,

Qiao²

2022

Preprint

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“…Hence, we use the weak convergence method to prove large and moderate deviation principles (c.f. [8,11,14,15]). Fourthly, in order to obtain the moderate deviation principle for Eq.…”

Section: Introductionmentioning

confidence: 99%

Deviation estimates for multivalued McKean-Vlasov stochastic differential equations

Fang¹,

Qiao²

2022

Preprint

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“…Recently, there has been a surge of interest in using stochastic Volterra equations for financial modelling, with asymptotic approximations being a popular subject of research; see the introductions of [13,12] for many references. While small-noise large deviations for such equations are well studied for Lipschitz coefficients [17,18,20,21], results for processes that involve non-Lipschitz functions in their dynamics are scarce. In the papers [9] and [11], concrete models with finite-dimensional parameter spaces are considered, whereas [5,10,13,12,14] study models where volatility is a function of a Gaussian process.…”

Section: Introductionmentioning

confidence: 99%

Large deviations for fractional volatility models with non-Gaussian volatility driver

Gerhold¹,

Gerstenecker²,

Gulisashvili³

2020

Preprint

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We study stochastic volatility models in which the volatility process is a function of a continuous fractional stochastic process, which is an integral transform of the solution of an SDE satisfying the Yamada-Watanabe condition. We establish a small-noise large deviation principle for the log-price, and, for a special case of our setup, obtain logarithmic call price asymptotics for large strikes.

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“…There are also a large number of valuable research results, which a large number of researchers have done semantic segmentation and realistic applications based on deep learning. Besides, the deep learning techniques have been applied to various fields of image processing such as other works, making a great breakthrough in traditional image processing.…”

Section: Introductionmentioning

confidence: 99%

Research of animals image semantic segmentation based on deep learning

Liu

et al. 2018

Concurrency and Computation

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It is imperative for us to develop the technology of image semantic segmentation with the increasing demand in the image processing. Nowadays, the development of deep learning is of great significance to the improvement of image segmentation. Furthermore, the paper discussed the relationship between image semantic segmentation and animal image research based on the actual situation, and found that animal image processing technology plays a more important role in the field of protecting precious animals. The end-to-end network training of this paper is consisted of Fully Convolutional Network (FCN) for the front end and Conditional Random Fields as Recurrent Neural Networks (CRF-RNN) for the back end via comparing a variety of research methods. The experiments achieved desired outcome for the semantic segmentation of animal images by utilizing Caffe deep learning framework and explained the implementation details from the aspects of training and testing. KEYWORDS Caffe, CRF-RNN, FCN, image semantic segmentation INTRODUCTIONTraditionally, the segmentation of the image is only directly on the surface layer, such as the appearance features based on texture, color, and shape, and they are separated after aggregating the similar features. Among them, the segmentation method based on regions, because the selection of seed points will lead to low efficiency of the algorithm, the segmented results have a certain flaw and can not reach the expected effect. However, the histogram-based threshold segmentation method ignores certain image space information, and it is very likely to segment a discontinuous and meaningless regions, making the segmentation result less satisfying. Therefore, with the demand for image segmentation technology, the semantic segmentation is indispensable.In semantic segmentation, the method of deep learning can be well escaped from the traditional processing methods, and the limitation of using only the underlying features is eliminated, making process in the level of understanding for image. The deep learning framework of Caffe 1 can handle semantic segmentation well. As the number of treasured animals continues to decline, the application of science and technology to protect them is imminent. In particular, it is necessary to collect a set of quality and distinctive animal image data for a specific animal, and the collection of a large number of data sets depends not only on human resources but also requires the support of the relevant sensors, cameras, and other hardware in the wild places it can automatically detect in the field or other places. One solution is to establish a corresponding index for the existing animal picture database. For the protected animal, it can use image processing technology to quickly identify for these animals, and then semantic segmentation is exploited to make a good recognition of animal images. This will not only increase the efficiency of the staff, but reduce the situation in which Concurrency Computat Pract Exper. 2020;32:e4892. wileyonlinelibrary.com/journal/cp...

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A moderate deviation principle for stochastic Volterra equation

Cited by 18 publications

References 13 publications

Deviation estimates for multivalued McKean-Vlasov stochastic differential equations

Deviation estimates for multivalued McKean-Vlasov stochastic differential equations

Large deviations for fractional volatility models with non-Gaussian volatility driver

Research of animals image semantic segmentation based on deep learning

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