The drift model of “magnetars”

2007 ◽  
pp. 467-469
Author(s):  
I. F. Malov ◽  
G. Z. Machabeli
Keyword(s):  
Drift Model

scholarly journals Effects of memristive synapse radiation interactions on learning in spiking neural networks

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
Sumedha Gandharava Dahl ◽  
Robert C. Ivans ◽  
Kurtis D. Cantley
Keyword(s):  
Action Potentials ◽  
Radiation Effects ◽  
Synaptic Weight ◽  
Learning Rule ◽  
Spike Timing ◽  
Spiking Neural Network ◽  
Synaptic Action ◽  
Drift Model ◽  
Spatio Temporal ◽  
Post Synaptic Neuron

AbstractThis study uses advanced modeling and simulation to explore the effects of external events such as radiation interactions on the synaptic devices in an electronic spiking neural network. Specifically, the networks are trained using the spike-timing-dependent plasticity (STDP) learning rule to recognize spatio-temporal patterns (STPs) representing 25 and 100-pixel characters. Memristive synapses based on a TiO2 non-linear drift model designed in Verilog-A are utilized, with STDP learning behavior achieved through bi-phasic pre- and post-synaptic action potentials. The models are modified to include experimentally observed state-altering and ionizing radiation effects on the device. It is found that radiation interactions tend to make the connection between afferents stronger by increasing the conductance of synapses overall, subsequently distorting the STDP learning curve. In the absence of consistent STPs, these effects accumulate over time and make the synaptic weight evolutions unstable. With STPs at lower flux intensities, the network can recover and relearn with constant training. However, higher flux can overwhelm the leaky integrate-and-fire post-synaptic neuron circuits and reduce stability of the network.

scholarly journals Reaction-Drift Model for Switching Transients in Pr₀.₇Ca₀.₃MnO₃-Based Resistive RAM

2020 ◽  
Vol 67 (9) ◽  
pp. 3610-3617
Author(s):  
Vivek Saraswat ◽  
Shankar Prasad ◽  
Abhishek Khanna ◽  
Ashwin Wagh ◽  
Ashwin Bhat ◽  
...  
Keyword(s):  
Resistive Ram ◽  
Drift Model

Outcome Framing in Intertemporal Choice: The DRIFT Model

2011 ◽  
Author(s):  
Daniel Read ◽  
Shane Frederick ◽  
Marc Scholten
Keyword(s):  
Intertemporal Choice ◽  
Drift Model

Fluid turbulence simulations of divertor heat load for ITER hybrid scenario using BOUT++

2021 ◽  
Author(s):  
Xueyun Wang ◽  
Xueqiao Xu ◽  
Philip B Snyder ◽  
Zeyu Li
Keyword(s):  
Heat Flux ◽  
Steady State ◽  
Energy Loss ◽  
Heat Load ◽  
Type I ◽  
Fluid Turbulence ◽  
Drift Model ◽  
Steady State Operation ◽  
Different Types ◽  
The One

Abstract The BOUT++ six-field turbulence code is used to simulate the ITER 11.5MA hybrid scenario and a brief comparison is made among ITER baseline, hybrid and steady-state operation (SSO) scenarios. Peeling-ballooning instabilities with different toroidal mode numbers dominate in different scenarios and consequently yield different types of ELMs. The energy loss fractions (ΔWped/Wped) caused by unmitigated ELMs in the baseline and hybrid scenarios are large (~2%) while the one in the SSO scenario is dramatically smaller (~1%), which are consistent with the features of type-I ELMs and grassy ELMs respectively. The intra ELM divertor heat flux width in the three scenarios given by the simulations is larger than the estimations for inter ELM phase based on Goldston’s heuristic drift model. The toroidal gap edge melting limit of tungsten monoblocks of divertor targets imposes constraints on ELM energy loss, giving that the ELM energy loss fraction should be smaller than 0.4%, 1.0%, and 1.2% for ITER baseline, hybrid and SSO scenarios, correspondingly. The simulation shows that only the SSO scenario with grassy ELMs may satisfy the constraint.

Accumulative Drifting Model of Inertial Navigation Platform under Elliptic Vibration

2014 ◽  
Vol 613 ◽  
pp. 269-278
Author(s):  
Zhen Xian Fu ◽  
Yu Rong Lin ◽  
Yang Liu ◽  
Xing Lin Chen
Keyword(s):  
Real Time ◽  
Time Synchronization ◽  
Inertial Navigation ◽  
Parameter Calibration ◽  
Testing Environment ◽  
Drift Model ◽  
General Elliptic ◽  
Elliptic Vibration ◽  
Proposed Model ◽  
Error Parameter

To facilitate the calibration of a precision inertial navigation platform, the drifting of the platform under vibratory testing environment is analyzed, and a simplified drift model is developed which features the accumulative rather than instantaneous impact of the vibration on the platform drifting. When applied to error parameter calibration for the platform, the proposed model entails much less computing load in drifting prediction, and removes the requirement of strict real-time synchronization between the vibration generating device and the drift-predicting programs. The form of vibration can be assumed to be elliptic, a relatively general one which allows the shaker to vibrate sinuoidally in two directions perpendicular to each other and with phase difference of 90 degree. Under certain circumstances, the elliptic vibration can be simplified to a linear or circular one, as is typical in practice. Simulations of the platform drifting error under linear, circular and general elliptic vibration shows that the accumulative model can well serve as an alternative to the conventional one in such test environments, and the merits of the proposed model become more prominent when the frequency of vibration gets higher.

scholarly journals Asymmetric cell division-dominant neutral drift model for normal intestinal stem cell homeostasis

2019 ◽  
Vol 316 (1) ◽  
pp. G64-G74 ◽  
Author(s):  
Yoshitatsu Sei ◽  
Jianying Feng ◽  
Carson C. Chow ◽  
Stephen A. Wank
Keyword(s):  
Cell Division ◽  
Mitotic Spindle ◽  
Asymmetric Cell Division ◽  
Dominant Mode ◽  
Lineage Tracing ◽  
Intestinal Stem Cells ◽  
In Vivo Studies ◽  
Neutral Drift ◽  
Drift Model

The normal intestinal epithelium is continuously regenerated at a rapid rate from actively cycling Lgr5-expressing intestinal stem cells (ISCs) that reside at the crypt base. Recent mathematical modeling based on several lineage-tracing studies in mice shows that the symmetric cell division-dominant neutral drift model fits well with the observed in vivo growth of ISC clones and suggests that symmetric divisions are central to ISC homeostasis. However, other studies suggest a critical role for asymmetric cell division in the maintenance of ISC homeostasis in vivo. Here, we show that the stochastic branching and Moran process models with both a symmetric and asymmetric division mode not only simulate the stochastic growth of the ISC clone in silico but also closely fit the in vivo stem cell dynamics observed in lineage-tracing studies. In addition, the proposed model with highest probability for asymmetric division is more consistent with in vivo observations reported here and by others. Our in vivo studies of mitotic spindle orientations and lineage-traced progeny pairs indicate that asymmetric cell division is a dominant mode used by ISCs under normal homeostasis. Therefore, we propose the asymmetric cell division-dominant neutral drift model for normal ISC homeostasis. NEW & NOTEWORTHY The prevailing mathematical model suggests that intestinal stem cells (ISCs) divide symmetrically. The present study provides evidence that asymmetric cell division is the major contributor to ISC maintenance and thus proposes an asymmetric cell division-dominant neutral drift model. Consistent with this model, in vivo studies of mitotic spindle orientation and lineage-traced progeny pairs indicate that asymmetric cell division is the dominant mode used by ISCs under normal homeostasis.

scholarly journals Closing a Bitcoin Trade Optimally under Partial Information: Performance Assessment of a Stochastic Disorder Model

Mathematics ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 157
Author(s):  
Zehra Eksi ◽  
Daniel Schreitl
Keyword(s):  
Performance Analysis ◽  
Performance Assessment ◽  
Partial Information ◽  
Exit Time ◽  
Exit Strategy ◽  
Random Drift ◽  
Expected Return ◽  
Drift Model ◽  
Out Of Sample ◽  
Partially Observable

The Bitcoin market exhibits characteristics of a market with pricing bubbles. The price is very volatile, and it inherits the risk of quickly increasing to a peak and decreasing from the peak even faster. In this context, it is vital for investors to close their long positions optimally. In this study, we investigate the performance of the partially observable digital-drift model of Ekström and Lindberg and the corresponding optimal exit strategy on a Bitcoin trade. In order to estimate the unknown intensity of the random drift change time, we refer to Bitcoin halving events, which are considered as pivotal events that push the price up. The out-of-sample performance analysis of the model yields returns values ranging between 9% and 1153%. We conclude that the return of the initiated Bitcoin momentum trades heavily depends on the entry date: the earlier we entered, the higher the expected return at the optimal exit time suggested by the model. Overall, to the extent of our analysis, the model provides a supporting framework for exit decisions, but is by far not the ultimate tool to succeed in every trade.

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