Spatial and temporal uncertainty as determinants of vigilance behavior.

1964 ◽  
Vol 67 (2) ◽  
pp. 127-131 ◽  
Author(s):  
Jack A. Adams ◽  
Lawrence R. Boulter
Keyword(s):  
Temporal Uncertainty ◽  
Vigilance Behavior

scholarly journals Dynamic Control of Response Criterion in Premotor Cortex during Perceptual Detection under Temporal Uncertainty

Neuron ◽  
2015 ◽  
Vol 86 (4) ◽  
pp. 1067-1077 ◽  
Author(s):  
Federico Carnevale ◽  
Victor de Lafuente ◽  
Ranulfo Romo ◽  
Omri Barak ◽  
Néstor Parga
Keyword(s):  
Premotor Cortex ◽  
Dynamic Control ◽  
Response Criterion ◽  
Temporal Uncertainty

Vigilance Behavior of Pronghorns in Different Habitats

1990 ◽  
Vol 71 (3) ◽  
pp. 460-462 ◽  
Author(s):  
A. E. Goldsmith
Keyword(s):  
Vigilance Behavior

CSMC

2021 ◽  
Vol 5 (4) ◽  
pp. 1-22
Author(s):  
Hai Wang ◽  
Baoshen Guo ◽  
Shuai Wang ◽  
Tian He ◽  
Desheng Zhang
Keyword(s):  
Large Scale ◽  
Mobile Network ◽  
Signal Propagation ◽  
Spectrum Management ◽  
External Information ◽  
Temporal Uncertainty ◽  
Large Area ◽  
Fine Grained ◽  
Map Construction ◽  
Spatio Temporal

The rise concern about mobile communication performance has driven the growing demand for the construction of mobile network signal maps which are widely utilized in network monitoring, spectrum management, and indoor/outdoor localization. Existing studies such as time-consuming and labor-intensive site surveys are difficult to maintain an update-to-date finegrained signal map within a large area. The mobile crowdsensing (MCS) paradigm is a promising approach for building signal maps because collecting large-scale MCS data is low-cost and with little extra-efforts. However, the dynamic environment and the mobility of the crowd cause spatio-temporal uncertainty and sparsity of MCS. In this work, we leverage MCS as an opportunity to conduct the city-wide mobile network signal map construction. We propose a fine-grained city-wide Cellular Signal Map Construction (CSMC) framework to address two challenges including (i) the problem of missing and unreliable MCS data; (ii) spatio-temporal uncertainty of signal propagation. In particular, CSMC captures spatio-temporal characteristics of signals from both inter- and intra- cellular base stations and conducts missing signal recovery with Bayesian tensor decomposition to build large-area fine-grained signal maps. Furthermore, CSMC develops a context-aware multi-view fusion network to make full use of external information and enhance signal map construction accuracy. To evaluate the performance of CSMC, we conduct extensive experiments and ablation studies on a large-scale dataset with over 200GB MCS signal records collected from Shanghai. Experimental results demonstrate that our model outperforms state-of-the-art baselines in the accuracy of signal estimation and user localization.

scholarly journals Temporal Uncertainty During Overshadowing

2011 ◽  
pp. 46-55
Author(s):  
Dómhnall J. Jennings ◽  
Eduardo Alonso ◽  
Esther Mondragón ◽  
Charlotte Bonardi
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Associative Learning ◽  
Learning Theories ◽  
Temporal Difference ◽  
Temporal Uncertainty ◽  
Learning Problem ◽  
Difference Model ◽  
Distribution Form ◽  
Temporal Properties

Standard associative learning theories typically fail to conceptualise the temporal properties of a stimulus, and hence cannot easily make predictions about the effects such properties might have on the magnitude of conditioning phenomena. Despite this, in intuitive terms we might expect that the temporal properties of a stimulus that is paired with some outcome to be important. In particular, there is no previous research addressing the way that fixed or variable duration stimuli can affect overshadowing. In this chapter we report results which show that the degree of overshadowing depends on the distribution form - fixed or variable - of the overshadowing stimulus, and argue that conditioning is weaker under conditions of temporal uncertainty. These results are discussed in terms of models of conditioning and timing. We conclude that the temporal difference model, which has been extensively applied to the reinforcement learning problem in machine learning, accounts for the key findings of our study.

scholarly journals Spatio-temporal uncertainty and cortical-hippocampal interactions: fMRI study

2015 ◽  
Vol 15 (12) ◽  
pp. 991
Author(s):  
Nisha Dalal ◽  
Virginia Flanagin ◽  
Stefan Glasauer
Keyword(s):  
Temporal Uncertainty ◽  
Fmri Study ◽  
Spatio Temporal
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