scholarly journals A STOCHASTIC CASCADE MODEL FOR FX DYNAMICS

2000 ◽  
Vol 03 (03) ◽  
pp. 357-360 ◽  
Author(s):  
WOLFGANG BREYMANN ◽  
SHOALEH GHASHGHAIE ◽  
PETER TALKNER
Keyword(s):  
Information Flow ◽  
Cascade Model ◽  
Time Interval ◽  
Hydrodynamic Turbulence ◽  
Time Horizons ◽  
Structural Peculiarities ◽  
Heavy Tailed Distribution ◽  
Heavy Tailed ◽  
Short Time ◽  
Multiplicative Cascade

A time series model for the FX dynamics is presented which takes into account structural peculiarities of the market, namely its heterogeneity and an information flow from long to short time horizons. The model emerges from an analogy between FX dynamics and hydrodynamic turbulence. The heterogeneity of the market is modeled in the form of a multiplicative cascade of time scales ranging from several minutes to a few months, analogous to the Kolmogorov cascade in turbulence. The model reproduces well the important empirical characteristics of FX rates for major currencies, as the heavy-tailed distribution of returns, their change in shape with the increasing time interval, and the persistence of volatility.

scholarly journals Neuro-Inspired Computing with Spin-VCSELs

2021 ◽  
Vol 11 (9) ◽  
pp. 4232
Author(s):  
Krishan Harkhoe ◽  
Guy Verschaffelt ◽  
Guy Van der Sande
Keyword(s):  
Processing Speed ◽  
Delay Time ◽  
High Speed ◽  
State Of The Art ◽  
Polarization Modulation ◽  
Time Interval ◽  
Computing Technique ◽  
High Modulation ◽  
Short Time ◽  
Modulation Speed

Delay-based reservoir computing (RC), a neuromorphic computing technique, has gathered lots of interest, as it promises compact and high-speed RC implementations. To further boost the computing speeds, we introduce and study an RC setup based on spin-VCSELs, thereby exploiting the high polarization modulation speed inherent to these lasers. Based on numerical simulations, we benchmarked this setup against state-of-the-art delay-based RC systems and its parameter space was analyzed for optimal performance. The high modulation speed enabled us to have more virtual nodes in a shorter time interval. However, we found that at these short time scales, the delay time and feedback rate heavily influence the nonlinear dynamics. Therefore, and contrary to other laser-based RC systems, the delay time has to be optimized in order to obtain good RC performances. We achieved state-of-the-art performances on a benchmark timeseries prediction task. This spin-VCSEL-based RC system shows a ten-fold improvement in processing speed, which can further be enhanced in a straightforward way by increasing the birefringence of the VCSEL chip.

scholarly journals Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1213
Author(s):  
Ahmed Aljanad ◽  
Nadia M. L. Tan ◽  
Vassilios G. Agelidis ◽  
Hussain Shareef
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Solar Irradiance ◽  
Particle Swarm ◽  
Time Interval ◽  
Time Intervals ◽  
Swarm Optimization ◽  
Backpropagation Neural Networks ◽  
Proposed Model ◽  
Short Time

Hourly global solar irradiance (GSR) data are required for sizing, planning, and modeling of solar photovoltaic farms. However, operating and controlling such farms exposed to varying environmental conditions, such as fast passing clouds, necessitates GSR data to be available for very short time intervals. Classical backpropagation neural networks do not perform satisfactorily when predicting parameters within short intervals. This paper proposes a hybrid backpropagation neural networks based on particle swarm optimization. The particle swarm algorithm is used as an optimization algorithm within the backpropagation neural networks to optimize the number of hidden layers and neurons used and its learning rate. The proposed model can be used as a reliable model in predicting changes in the solar irradiance during short time interval in tropical regions such as Malaysia and other regions. Actual global solar irradiance data of 5-s and 1-min intervals, recorded by weather stations, are applied to train and test the proposed algorithm. Moreover, to ensure the adaptability and robustness of the proposed technique, two different cases are evaluated using 1-day and 3-days profiles, for two different time intervals of 1-min and 5-s each. A set of statistical error indices have been introduced to evaluate the performance of the proposed algorithm. From the results obtained, the 3-days profile’s performance evaluation of the BPNN-PSO are 1.7078 of RMSE, 0.7537 of MAE, 0.0292 of MSE, and 31.4348 of MAPE (%), at 5-s time interval, where the obtained results of 1-min interval are 0.6566 of RMSE, 0.2754 of MAE, 0.0043 of MSE, and 1.4732 of MAPE (%). The results revealed that proposed model outperformed the standalone backpropagation neural networks method in predicting global solar irradiance values for extremely short-time intervals. In addition to that, the proposed model exhibited high level of predictability compared to other existing models.

scholarly journals Identifying bidirectional total and non-linear information flow in functional corticomuscular coupling during a dorsiflexion task: a pilot study

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Tie Liang ◽  
Qingyu Zhang ◽  
Xiaoguang Liu ◽  
Bin Dong ◽  
Xiuling Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Information Flow ◽  
Gamma Band ◽  
Motor Dysfunction ◽  
Beta Band ◽  
Short Time Series ◽  
Information Interaction ◽  
Stroke Group ◽  
Short Time ◽  
Maximal Information Coefficient

Abstract Background The key challenge to constructing functional corticomuscular coupling (FCMC) is to accurately identify the direction and strength of the information flow between scalp electroencephalography (EEG) and surface electromyography (SEMG). Traditional TE and TDMI methods have difficulty in identifying the information interaction for short time series as they tend to rely on long and stable data, so we propose a time-delayed maximal information coefficient (TDMIC) method. With this method, we aim to investigate the directional specificity of bidirectional total and nonlinear information flow on FCMC, and to explore the neural mechanisms underlying motor dysfunction in stroke patients. Methods We introduced a time-delayed parameter in the maximal information coefficient to capture the direction of information interaction between two time series. We employed the linear and non-linear system model based on short data to verify the validity of our algorithm. We then used the TDMIC method to study the characteristics of total and nonlinear information flow in FCMC during a dorsiflexion task for healthy controls and stroke patients. Results The simulation results showed that the TDMIC method can better detect the direction of information interaction compared with TE and TDMI methods. For healthy controls, the beta band (14–30 Hz) had higher information flow in FCMC than the gamma band (31–45 Hz). Furthermore, the beta-band total and nonlinear information flow in the descending direction (EEG to EMG) was significantly higher than that in the ascending direction (EMG to EEG), whereas in the gamma band the ascending direction had significantly higher information flow than the descending direction. Additionally, we found that the strong bidirectional information flow mainly acted on Cz, C3, CP3, P3 and CPz. Compared to controls, both the beta-and gamma-band bidirectional total and nonlinear information flows of the stroke group were significantly weaker. There is no significant difference in the direction of beta- and gamma-band information flow in stroke group. Conclusions The proposed method could effectively identify the information interaction between short time series. According to our experiment, the beta band mainly passes downward motor control information while the gamma band features upward sensory feedback information delivery. Our observation demonstrate that the center and contralateral sensorimotor cortex play a major role in lower limb motor control. The study further demonstrates that brain damage caused by stroke disrupts the bidirectional information interaction between cortex and effector muscles in the sensorimotor system, leading to motor dysfunction.

scholarly journals Optimal Perturbations of an Oceanic Vortex Lens

Fluids ◽  
2018 ◽  
Vol 3 (3) ◽  
pp. 63 ◽  
Author(s):  
Thomas Meunier ◽  
Claire Ménesguen ◽  
Xavier Carton ◽  
Sylvie Le Gentil ◽  
Richard Schopp
Keyword(s):  
Normal Mode ◽  
Growth Rates ◽  
Time Interval ◽  
Time Intervals ◽  
Horizontal Structure ◽  
Azimuthal Wave ◽  
Wave Numbers ◽  
Non Linear ◽  
Long Time ◽  
Short Time

The stability properties of a vortex lens are studied in the quasi geostrophic (QG) framework using the generalized stability theory. Optimal perturbations are obtained using a tangent linear QG model and its adjoint. Their fine-scale spatial structures are studied in details. Growth rates of optimal perturbations are shown to be extremely sensitive to the time interval of optimization: The most unstable perturbations are found for time intervals of about 3 days, while the growth rates continuously decrease towards the most unstable normal mode, which is reached after about 170 days. The horizontal structure of the optimal perturbations consists of an intense counter-shear spiralling. It is also extremely sensitive to time interval: for short time intervals, the optimal perturbations are made of a broad spectrum of high azimuthal wave numbers. As the time interval increases, only low azimuthal wave numbers are found. The vertical structures of optimal perturbations exhibit strong layering associated with high vertical wave numbers whatever the time interval. However, the latter parameter plays an important role in the width of the vertical spectrum of the perturbation: short time interval perturbations have a narrow vertical spectrum while long time interval perturbations show a broad range of vertical scales. Optimal perturbations were set as initial perturbations of the vortex lens in a fully non linear QG model. It appears that for short time intervals, the perturbations decay after an initial transient growth, while for longer time intervals, the optimal perturbation keeps on growing, quickly leading to a non-linear regime or exciting lower azimuthal modes, consistent with normal mode instability. Very long time intervals simply behave like the most unstable normal mode. The possible impact of optimal perturbations on layering is also discussed.

Estimation of Time-Dependent Turning Fractions at Signalized Intersections

1998 ◽  
Vol 1644 (1) ◽  
pp. 142-149 ◽  
Author(s):  
Gang-Len Chang ◽  
Xianding Tao
Keyword(s):  
Signalized Intersections ◽  
Efficient Estimation ◽  
Estimation Accuracy ◽  
Time Interval ◽  
Short Time Interval ◽  
Simulation Experiments ◽  
Proposed Model ◽  
Short Time ◽  
Estimation Of Time ◽  
Additional Constraints

An effective method for estimating time-varying turning fractions at signalized intersections is described. With the inclusion of approximate intersection delay, the proposed model can account for the impacts of signal setting on the dynamic distribution of intersection flows. To improve the estimation accuracy, the use of preestimated turning fractions from a relatively longer time interval has been proposed to serve as additional constraints for the same estimation but over a short time interval. The results of extensive simulation experiments indicated that the proposed method can yield sufficiently accurate as well as efficient estimation of dynamic turning fractions for signalized intersections.

Micro-Expression Recognition using 3D - CNN

2020 ◽  
pp. 5-13
Author(s):  
Vishal Dubey ◽  
◽  
◽  
◽  
Bhavya Takkar ◽  
...  
Keyword(s):  
Emotional Response ◽  
High Speed ◽  
Actual State ◽  
Time Interval ◽  
Short Time Interval ◽  
Expression Recognition ◽  
Specific Emotion ◽  
3D Cnn ◽  
Short Time ◽  
Micro Expression

Micro-expression comes under nonverbal communication, and for a matter of fact, it appears for minute fractions of a second. One cannot control micro-expression as it tells about our actual state emotionally, even if we try to hide or conceal our genuine emotions. As we know that micro-expressions are very rapid due to which it becomes challenging for any human being to detect it with bare eyes. This subtle-expression is spontaneous, and involuntary gives the emotional response. It happens when a person wants to conceal the specific emotion, but the brain is reacting appropriately to what that person is feeling then. Due to which the person displays their true feelings very briefly and later tries to make a false emotional response. Human emotions tend to last about 0.5 - 4.0 seconds, whereas micro-expression can last less than 1/2 of a second. On comparing micro-expression with regular facial expressions, it is found that for micro-expression, it is complicated to hide responses of a particular situation. Micro-expressions cannot be controlled because of the short time interval, but with a high-speed camera, we can capture one's expressions and replay them at a slow speed. Over the last ten years, researchers from all over the globe are researching automatic micro-expression recognition in the fields of computer science, security, psychology, and many more. The objective of this paper is to provide insight regarding micro-expression analysis using 3D CNN. A lot of datasets of micro-expression have been released in the last decade, we have performed this experiment on SMIC micro-expression dataset and compared the results after applying two different activation functions.

scholarly journals Inter-rater and inter-device reliability of mechanical thresholds measurement with the Electronic von Frey Anaesthesiometer and the SMALGO in healthy cats

2018 ◽  
Vol 21 (10) ◽  
pp. 979-984 ◽  
Author(s):  
Chiara Adami ◽  
Elena Lardone ◽  
Paolo Monticelli
Keyword(s):  
Small Animal ◽  
Time Interval ◽  
Short Time Interval ◽  
Sensory Thresholds ◽  
Additional Tool ◽  
Bland Altman Method ◽  
Device Reliability ◽  
Short Time ◽  
Mechanical Thresholds ◽  
Level Of Agreement

Objectives The aim of this study was to compare the Electronic von Frey Anaesthesiometer (EVF) and the Small Animal ALGOmeter (SMALGO), used to measure sensory thresholds in 13 healthy cats at both the stifle and the lumbosacral joint, in terms of inter-rater and inter-device reliability. Methods Two independent observers carried out the sets of measurements in a randomised order, with a 45 min interval between them, in each cat. The inter-rater and inter-device reliability were evaluated by calculating the inter-rater correlation coefficient (ICC) for each pair of measurements. The Bland–Altman method was used as an additional tool to assess the level of agreement between the two algometers. Results The mean ± SD sensory thresholds measured with the EVF were 311 ± 116 g and 378 ± 178 g for the stifle and for the lumbosacral junction, respectively, whereas those measured with the SMALGO were 391 ±172 g and 476 ± 172 g. The inter-rater reliability was fair (ICC >0.4) for each pair of measurements except those taken at the level of the stifle with the SMALGO, for which the level of agreement between observers A and B was poor (ICC = 0.01). The inter-device reliability was good (ICC = 0.73; P = 0.001). The repetition of the measurements affected reliability, as the thresholds obtained after the 45 min break were consistently lower than those measured during the first part of the trial ( P = 0.02). Conclusions and relevance The EVF and the SMALGO may be used interchangeably in cats, especially when the area to be tested is the lumbosacral joint. However, when the thresholds are measured at the stifle, the inter-observer reliability is better with the EVF than with the SMALGO. The reliability decreases when the measurements are repeated within a short time interval, suggesting a limited clinical applicability of quantitative sensory testing with both algometers in cats.

Approximate exit probabilities for a Brownian bridge on a short time interval, and applications

1989 ◽  
Vol 21 (1) ◽  
pp. 1-19 ◽  
Author(s):  
H. R. Lerche ◽  
D. Siegmund
Keyword(s):  
Virial Coefficient ◽  
Large Deviation ◽  
Brownian Bridge ◽  
Exit Time ◽  
Second Virial Coefficient ◽  
Dimensional Euclidean Space ◽  
Time Interval ◽  
Short Time Interval ◽  
First Exit Time ◽  
Short Time

Let T be the first exit time of Brownian motion W(t) from a region ℛ in d-dimensional Euclidean space having a smooth boundary. Given points ξ0 and ξ1 in ℛ, ordinary and large-deviation approximations are given for Pr{T < ε |W(0) = ξ0, W(ε) = ξ 1} as ε → 0. Applications are given to hearing the shape of a drum and approximating the second virial coefficient.

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