Parameter-Mapping Sonification of Tick-Data

2019 ◽  
pp. 237-252
Author(s):  
David Worrall
Keyword(s):  
Parameter Mapping ◽  
Tick Data

The Asset Pricing Effects of UK Market Liquidity Shocks: Evidence from Tick Data

2014 ◽  
Author(s):  
Jason Foran ◽  
Mark C. Hutchinson ◽  
Niall O'Sullivan
Keyword(s):  
Asset Pricing ◽  
Market Liquidity ◽  
Liquidity Shocks ◽  
Tick Data

Magnetic resonance parameter mapping using model‐guided self‐supervised deep learning

2021 ◽  
Vol 85 (6) ◽  
pp. 3211-3226
Author(s):  
Fang Liu ◽  
Richard Kijowski ◽  
Georges El Fakhri ◽  
Li Feng
Keyword(s):  
Deep Learning ◽  
Magnetic Resonance ◽  
Resonance Parameter ◽  
Parameter Mapping ◽  
Magnetic Resonance Parameter

Long correlations and fractional difference analysis applied to the study of memory effects in high-frequency (tick) data

2015 ◽  
Vol 15 (8) ◽  
pp. 1365-1374 ◽  
Author(s):  
Maria Pia Beccar Varela ◽  
Francis Biney ◽  
Ionut Florescu
Keyword(s):  
High Frequency ◽  
Memory Effects ◽  
Fractional Difference ◽  
Difference Analysis ◽  
Tick Data

scholarly journals Limits to Ionization-parameter Mapping as a Diagnostic of Hii Region Optical Depth

2021 ◽  
Vol 923 (1) ◽  
pp. 78
Author(s):  
Amit N. Sawant ◽  
Eric W. Pellegrini ◽  
M. S. Oey ◽  
Jesús López-Hernández ◽  
Genoveva Micheva
Keyword(s):  
Optical Depth ◽  
Strong Dependence ◽  
Light Flux ◽  
Emission Lines ◽  
Ionization State ◽  
Hii Region ◽  
Parameter Mapping ◽  
Ionization Structure ◽  
The Galaxy ◽  
Ionization Parameter

Abstract We employ ionization-parameter mapping (IPM) to infer the optical depth of H ii regions in the northern half of M33. We construct [O iii]λ5007/[O ii]λ3727 and [O iii]λ5007/[S ii]λ6724 ratio maps from narrowband images continuum-subtracted in this way, from which we classify the H ii regions by optical depth to ionizing radiation, based on their ionization structure. This method works relatively well in the low-metallicity regime, 12 + log ( O / H ) ≤ 8.4 , where [O iii]λ λ4959, 5007 is strong. However, at higher metallicities, the method breaks down due to the strong dependence of the [O iii]λ λ4959, 5007 emission lines on the nebular temperature. Thus, although O++ may be present in metal-rich H ii regions, these commonly used emission lines do not serve as a useful indicator of its presence, and hence the O ionization state. In addition, IPM as a diagnostic of optical depth is limited by spatial resolution. We also report a region of highly excited [O iii] extending over an area ∼1 kpc across and [O iii]λ5007 luminosity of 4.9 ± 1.5 × 1038 erg s−1, which is several times higher than the ionizing budget of any potential sources in this portion of the galaxy. Finally, this work introduces a new method for continuum subtraction of narrowband images based on the dispersion of pixels around the mode of the diffuse-light flux distribution. In addition to M33, we demonstrate the method on C iii]λ1909 imaging of Haro 11, ESO 338-IG004, and Mrk 71.

Enhancing intravoxel incoherent motion parameter mapping in the brain using k -b PCA

2018 ◽  
Vol 31 (12) ◽  
pp. e4008 ◽  
Author(s):  
Georg R. Spinner ◽  
Johannes F.M. Schmidt ◽  
Constantin von Deuster ◽  
Christian Federau ◽  
Christian T. Stoeck ◽  
...  
Keyword(s):  
Motion Parameter ◽  
Intravoxel Incoherent Motion ◽  
Parameter Mapping ◽  
The Brain

Volatility and Microstructure Noise

2014 ◽  
Author(s):  
Yacine Aïıt-Sahalia ◽  
Jean Jacod
Keyword(s):  
Microstructure Noise ◽  
Underlying Process ◽  
One Dimensional ◽  
Integrated Volatility ◽  
Tick Data ◽  
Efficient Price ◽  
The Difference

This chapter covers the various problems arising in the estimation of the integrated volatility when the observations are contaminated by a noise. The approach used is quite partial, and fundamentally phenomenological, in contrast with a microeconomical approach. That is, the authors assumed the existence of an underlying (nonobservable) efficient price, and what is called noise below is by definition the difference between the observed price and the efficient price. Henceforth, it certainly does not apply to tick-by-tick data, even if these were regularly spaced in time (which they are not). In the whole chapter, with the exception of one section, the underlying process X is one-dimensional.

scholarly journals Accelerating parameter mapping with a locally low rank constraint

2014 ◽  
Vol 73 (2) ◽  
pp. 655-661 ◽  
Author(s):  
Tao Zhang ◽  
John M. Pauly ◽  
Ives R. Levesque
Keyword(s):  
Low Rank ◽  
Parameter Mapping ◽  
Rank Constraint
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