Velocity-Robust Gait Analysis for Human Identification through Constrained Learning of Stochastic Switched Auto-Regressive Model

2013 ◽  
Vol 6 (5) ◽  
pp. 331-340
Author(s):  
Dapeng ZHANG ◽  
Shinkichi INAGAKI ◽  
Tatsuya SUZUKI
Keyword(s):  
Gait Analysis ◽  
Human Identification ◽  
Regressive Model ◽  
Auto Regressive ◽  
Constrained Learning ◽  
Auto Regressive Model

scholarly journals Estimation of Excess Mortality and Years of Life Lost to COVID-19 in Norway and Sweden between March and November 2020

2021 ◽  
Vol 18 (8) ◽  
pp. 3913
Author(s):  
Martin Rypdal ◽  
Kristoffer Rypdal ◽  
Ola Løvsletten ◽  
Sigrunn Holbek Sørbye ◽  
Elinor Ytterstad ◽  
...  
Keyword(s):  
Excess Mortality ◽  
Age Groups ◽  
Years Of Life Lost ◽  
Regressive Model ◽  
Epidemic Period ◽  
All Cause Mortality ◽  
Auto Regressive ◽  
Expected Mortality ◽  
Auto Regressive Model ◽  
Excess Deaths

We estimate the weekly excess all-cause mortality in Norway and Sweden, the years of life lost (YLL) attributed to COVID-19 in Sweden, and the significance of mortality displacement. We computed the expected mortality by taking into account the declining trend and the seasonality in mortality in the two countries over the past 20 years. From the excess mortality in Sweden in 2019/20, we estimated the YLL attributed to COVID-19 using the life expectancy in different age groups. We adjusted this estimate for possible displacement using an auto-regressive model for the year-to-year variations in excess mortality. We found that excess all-cause mortality over the epidemic year, July 2019 to July 2020, was 517 (95%CI = (12, 1074)) in Norway and 4329 [3331, 5325] in Sweden. There were 255 COVID-19 related deaths reported in Norway, and 5741 in Sweden, that year. During the epidemic period of 11 March–11 November, there were 6247 reported COVID-19 deaths and 5517 (4701, 6330) excess deaths in Sweden. We estimated that the number of YLL attributed to COVID-19 in Sweden was 45,850 [13,915, 80,276] without adjusting for mortality displacement and 43,073 (12,160, 85,451) after adjusting for the displacement accounted for by the auto-regressive model. In conclusion, we find good agreement between officially recorded COVID-19 related deaths and all-cause excess deaths in both countries during the first epidemic wave and no significant mortality displacement that can explain those deaths.

scholarly journals Dynamically Engaged Smiling Predicts Cooperation Above and Beyond Average Smiling Levels

2017 ◽  
Author(s):  
Alexander Francois Danvers ◽  
Michelle N. Shiota
Keyword(s):  
Prisoner's Dilemma ◽  
Prisoner’S Dilemma ◽  
Honest Signal ◽  
Same Sex ◽  
Dyadic Data ◽  
Regressive Model ◽  
Auto Regressive ◽  
Auto Regressive Model

Smiling has been conceptualized as a signal of cooperative intent, yet smiles are easy to fake. We suggest that contextually appropriate, dynamically engaged smiling imposes an attentional cost, thereby making engaged smiling a plausible “honest signal” of cooperative intent. To test this hypothesis, we analyzed data from 123 pairs of same-sex strangers having “getting-to-know-you” conversations who subsequently played a one-shot prisoner’s dilemma together. We calculated the strength of engagement in smiling using a cross-lagged auto-regressive model for dyadic data. We found that when an individual’s partner (the signaler) tended to smile in a more responsive way, that individual (the receiver) was more likely to cooperate. Conversely, when a signaler tended to smile in a less responsive way, the receiver was less likely to cooperate. These effects were present over-and-above the effects of average levels of smiling and self-reported liking, which also predicted likelihood of cooperation. However, dynamically engaged smiling did not predict cooperation on the part of the signaler, suggesting that receivers weight the importance of engagement more highly than they should, or even that engaged smiling might be a manipulative display. These results illustrate how conversational dynamics can influence evolutionary signaling.

Image adaptive filtering using general auto-regressive model

2014 ◽  
Vol 22 (1) ◽  
pp. 186-192
Author(s):  
郝飞 HAO Fei ◽  
史金飞 SHI Jin-fei ◽  
张志胜 ZHANG Zhi-sheng ◽  
陈茹雯 CHEN Ru-wen
Keyword(s):  
Adaptive Filtering ◽  
Regressive Model ◽  
Auto Regressive ◽  
Auto Regressive Model
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