Distributed Stochastic Power Control for Time-Varying Long-Term and Short-Term Fading Wireless Networks

This work analyzes the frequency-dependent network structure of Economic Policy Uncertainties (EPU) across G-7 countries between January 1998 and April 2021. We implement an approach that builds dynamic networks relying on a locally stationary Time-Varying Parameter-Vector Autoregressive model using Quasi-Bayesian Local Likelihood methods. We compute short-, medium-, and long-term network connectedness of G-7 EPUs over a period covering several economic/financial turmoils. Furthermore, we structure short-term network topologies for the Global Financial Crisis (GFC) and the COVID-19 pandemic periods. Findings of the study indicate amplified interdependencies between G-7 EPUs around well-known economic/geopolitical incidents, frequency-dependent connectedness networks among them, and stronger interdependencies than the medium-, and long-term linkages. Finally, we find that short-term spillovers are not persistent in the long-term for both turmoil periods.

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Representational content determines the long-term dynamics of the CA1 spatial code

10.21203/rs.3.rs-238563/v1 ◽

2021 ◽

Author(s):

Alexandra Keinath ◽

Coralie-Anne Mosser ◽

Mark Brandon

Keyword(s):

Episodic Memory ◽

Prior Experience ◽

Representational Content ◽

Spatial Code ◽

Time Varying ◽

Short Term ◽

Daily Experience ◽

Neural Ensembles ◽

Spatial Geometry

Abstract Hippocampal subregion CA1 is thought to support episodic memory by reinstating a stable spatial code. Yet recent experiments have demonstrated that this code is largely unstable on a timescale of days, challenging its presumed function. While these dynamics may indeed reflect homogenous drift within the population, they may alternatively reflect distinct time-varying representational component(s) which coexists alongside other stable components. Here we adjudicate between these possibilities. To this end, we characterized the mouse CA1 spatial code over more than a month of daily experience in an extended geometric morph paradigm. We find that this code is governed by distinct representational components with different long-term dynamics, including stable components representing spatial geometry and prior experience. These components are mediated by separate neural ensembles with similar short-term spatial reliability and precision. Together, these results demonstrate that the long-term dynamics of the CA1 spatial code are defined by representational content, not homogenous drift.

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On the stability of a power control algorithm for wireless networks in the presence of time-varying delays

2009 European Control Conference (ECC) ◽

10.23919/ecc.2009.7074854 ◽

2009 ◽

Cited By ~ 4

Author(s):

Themistoklis Charalambous ◽

Yassine Ariba

Keyword(s):

Wireless Networks ◽

Power Control ◽

Control Algorithm ◽

Time Varying ◽

Power Control Algorithm ◽

The Stability

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Variance Reduction in Analytical Processes in the Clinical Laboratory by Digital Filtering

Annals of Clinical Biochemistry International Journal of Laboratory Medicine ◽

10.1177/000456328302000310 ◽

1983 ◽

Vol 20 (3) ◽

pp. 174-181 ◽

Cited By ~ 7

Author(s):

R T P Jansen ◽

P J M Bonants

Keyword(s):

Variance Reduction ◽

Clinical Laboratory ◽

Digital Filtering ◽

Process Variations ◽

Mean Value ◽

Time Varying ◽

Short Term ◽

Serum Samples ◽

Filtering Procedure

A model is postulated describing the fluctuations in analytical chemical processes in the clinical laboratory. In this model the process variations are described by a non-stationary stochastic process with a significant time-varying mean value. Experiments demonstrate a short-term variance within a run and a long-term variance between runs determined by the time-varying mean value. For four different analytical systems used for determining six serum analytes between-run variance was demonstrated to be significantly greater than within-run variance. Based on the model a digital filtering procedure is presented which in each run estimates the process mean and subsequently corrects serum samples for its deviation. Thus significant variance reductions are obtained. The filtering procedure was tested for the determination in inorganic phosphate with a continuous-flow system in an experimental environment.

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Joint Power Control and Rate Adaptation for Video Streaming in Wireless Networks With Time-Varying Interference

IEEE Transactions on Vehicular Technology ◽

10.1109/tvt.2015.2480785 ◽

2016 ◽

Vol 65 (8) ◽

pp. 6315-6329 ◽

Cited By ~ 13

Author(s):

Ayaz Ahmad ◽

Naveed Ul Hassan ◽

Mohamad Assaad ◽

Hamidou Tembine

Keyword(s):

Wireless Networks ◽

Power Control ◽

Video Streaming ◽

Rate Adaptation ◽

Time Varying ◽

Joint Power ◽

Joint Power Control

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Extended experience reveals distinct representational dynamics governing the CA1 spatial code

10.1101/2021.01.12.426395 ◽

2021 ◽

Author(s):

Alexandra T. Keinath ◽

Coralie-Anne Mosser ◽

Mark P. Brandon

Keyword(s):

Episodic Memory ◽

Calcium Imaging ◽

Prior Experience ◽

Representational Content ◽

Spatial Code ◽

Time Varying ◽

Short Term ◽

Neural Ensembles ◽

Free Exploration

SummaryHippocampal subregion CA1 is thought to support episodic memory by reinstating a stable spatial code. However, recent calcium imaging experiments have challenged this presumed function by demonstrated that this code is largely unstable on a timescale of days. This turnover may reflect homogenous drift within the population; alternatively, it may reflect distinct time-varying representational component(s) which coexists alongside other stable components. Here we characterized the mouse CA1 spatial code over more than a month of daily free exploration experience in an extended geometric morph paradigm. We find that this code is governed by distinct representational components with different long-term dynamics, including stable components representing the shape of space and prior experience. These components are mediated by separate neural ensembles with similar short-term spatial reliability and precision. Together, these results demonstrate that the long-term dynamics of the CA1 spatial code are defined by representational content, not homogenous drift.

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