On Stein’s Factors for Poisson Approximation in Wasserstein Distance with Nonlinear Transportation Costs

In this study, two operational methodologies to extract thinned woods were investigated in the Nasunogahara area, Tochigi Prefecture, Japan. Methodology one included manual extraction and light truck transportation. Methodology two included mini-forwarder forwarding and four-ton truck transportation. Furthermore, a newly introduced chipper was investigated. As a result, costs of manual extractions within 10 m and 20 m were JPY942/m3 and JPY1040/m3, respectively. On the other hand, the forwarding cost of the mini-forwarder was JPY499/m3, which was significantly lower than the cost of manual extractions. Transportation costs with light trucks and four-ton trucks were JPY7224/m3 and JPY1298/m3, respectively, with 28 km transportation distances. Chipping operation costs were JPY1036/m3 and JPY1160/m3 with three and two persons, respectively. Finally, the total costs of methodologies one and two from extraction within 20 m to chipping were estimated as JPY9300/m3 and JPY2833/m3, respectively, with 28 km transportation distances and three-person chipping operations (EUR1 = JPY126, as of 12 August 2020).

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On the Impact of Transportation Costs on Trade in a Multilateral World

Southern Economic Journal ◽

10.1002/j.2325-8012.2005.tb00661.x ◽

2005 ◽

Vol 71 (3) ◽

pp. 592-606

Author(s):

Peter Egger

Keyword(s):

Transportation Costs ◽

The Impact

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Wasserstein distance-based distributionally robust optimal scheduling in rural microgrid considering the coordinated interaction among source-grid-load-storage

Energy Reports ◽

10.1016/j.egyr.2021.05.073 ◽

2021 ◽

Author(s):

Changming Chen ◽

Jianxu Xing ◽

Qinchao Li ◽

Shengyuan Liu ◽

Jien Ma ◽

...

Keyword(s):

Wasserstein Distance ◽

Optimal Scheduling ◽

Grid Load ◽

Distributionally Robust

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279 WACSAW: A Statistical and Individually Adaptive Method to Determine Sleep and Wakefulness from Activity

SLEEP ◽

10.1093/sleep/zsab072.278 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A111-A112

Author(s):

Austin Vandegriffe ◽

V A Samaranayake ◽

Matthew Thimgan

Keyword(s):

Time Series ◽

Optimal Transport ◽

Activity Patterns ◽

Adaptive Method ◽

Wasserstein Distance ◽

Activity Data ◽

Commercial Grade ◽

Movement Data ◽

The Difference ◽

Levene Test

Abstract Introduction Technological innovations have broadened the type and amount of activity data that can be captured in the home and under normal living conditions. Yet, converting naturalistic activity patterns into sleep and wakefulness states has remained a challenge. Despite the successes of current algorithms, they do not fill all actigraphy needs. We have developed a novel statistical approach to determine sleep and wakefulness times, called the Wasserstein Algorithm for Classifying Sleep and Wakefulness (WACSAW), and validated the algorithm in a small cohort of healthy participants. Methods WACSAW functional routines: 1) Conversion of the triaxial movement data into a univariate time series; 2) Construction of a Wasserstein weighted sum (WSS) time series by measuring the Wasserstein distance between equidistant distributions of movement data before and after the time-point of interest; 3) Segmenting the time series by identifying changepoints based on the behavior of the WSS series; 4) Merging segments deemed similar by the Levene test; 5) Comparing segments by optimal transport methodology to determine the difference from a flat, invariant distribution at zero. The resulting histogram can be used to determine sleep and wakefulness parameters around a threshold determined for each individual based on histogram properties. To validate the algorithm, participants wore the GENEActiv and a commercial grade actigraphy watch for 48 hours. The accuracy of WACSAW was compared to a detailed activity log and benchmarked against the results of the output from commercial wrist actigraph. Results WACSAW performed with an average accuracy, sensitivity, and specificity of >95% compared to detailed activity logs in 10 healthy-sleeping individuals of mixed sexes and ages. We then compared WACSAW’s performance against a common wrist-worn, commercial sleep monitor. WACSAW outperformed the commercial grade system in each participant compared to activity logs and the variability between subjects was cut substantially. Conclusion The performance of WACSAW demonstrates good results in a small test cohort. In addition, WACSAW is 1) open-source, 2) individually adaptive, 3) indicates individual reliability, 4) based on the activity data stream, and 5) requires little human intervention. WACSAW is worthy of validating against polysomnography and in patients with sleep disorders to determine its overall effectiveness. Support (if any):

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Wasserstein Distance-Based Auto-Encoder Tracking

Neural Processing Letters ◽

10.1007/s11063-021-10507-9 ◽

2021 ◽

Author(s):

Long Xu ◽

Ying Wei ◽

Chenhe Dong ◽

Chuaqiao Xu ◽

Zhaofu Diao

Keyword(s):

Wasserstein Distance

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On the rate of Poisson process approximation to a Bernoulli process

Journal of Applied Probability ◽

10.2307/3215005 ◽

1997 ◽

Vol 34 (4) ◽

pp. 898-907 ◽

Cited By ~ 4

Author(s):

Aihua Xia

Keyword(s):

Poisson Process ◽

Point Process ◽

Discrete Time ◽

Wasserstein Distance ◽

Stein’S Method ◽

Upper Bounds ◽

Stein's Method ◽

Bernoulli Process ◽

Logarithmic Factor ◽

Process Approximation

This note gives the rate for a Wasserstein distance between the distribution of a Bernoulli process on discrete time and that of a Poisson process, using Stein's method and Palm theory. The result here highlights the possibility that the logarithmic factor involved in the upper bounds established by Barbour and Brown (1992) and Barbour et al. (1995) may be superfluous in the true Wasserstein distance between the distributions of a point process and a Poisson process.

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