scholarly journals A Phase Shift Deep Neural Network for High Frequency Approximation and Wave Problems

2020 ◽  
Vol 42 (5) ◽  
pp. A3285-A3312
Author(s):  
Wei Cai ◽  
Xiaoguang Li ◽  
Lizuo Liu
Keyword(s):  
Neural Network ◽  
Phase Shift ◽  
High Frequency ◽  
Deep Neural Network ◽  
Wave Problems ◽  
Frequency Approximation

scholarly journals Real-Time Detection of Volatility in Liquidity Provision

2020 ◽  
Vol 9 ◽  
pp. 143-156
Author(s):  
Matt Brigida
Keyword(s):  
Neural Network ◽  
Real Time ◽  
High Frequency ◽  
Prediction Error ◽  
Deep Neural Network ◽  
Price Volatility ◽  
Market Participant ◽  
Liquidity Provision ◽  
Real Time Detection

Previous research has found that high-frequency traders will vary the bid or offer price rapidly overperiods of milliseconds. This is a benefit to fast traders who can time thier trades with microsecondprecision, however it is a cost to the average market participant due to increased trade execution priceuncertainty. In this analysis we attempt to construct real-time methods for determining whether theliquidity of a security is being altered by high-frequency traders. We find a four-state Markov switchingmodel identifies a state consistent with high-frequency traders affecting liquidity. Moreover, we find thisstate is positicely corrrelated with the prediction error from a deep neural network. This state can beused as a signal to delay market participant orders until the price volatility subsides. This delay wouldonly last tens of milliseconds, and so would not be noticable by the average market participant.

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

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