Spectral-temporal dependence of the power of pulsed radiation of semiconductor emitters

1997 ◽  
Vol 40 (3) ◽  
pp. 246-247
Author(s):  
L. S. Lovinskii
Keyword(s):  
Temporal Dependence ◽  
Semiconductor Emitters

scholarly journals Modeling Best Practice Life Expectancy Using Gumbel Autoregressive Models

Risks ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 51
Author(s):  
Anthony Medford
Keyword(s):  
Time Series ◽  
Life Expectancy ◽  
Extreme Value Theory ◽  
Autoregressive Model ◽  
Best Practice ◽  
Gumbel Distribution ◽  
Value Theory ◽  
Model Diagnostics ◽  
Temporal Dependence ◽  
Simulation Results

Best practice life expectancy has recently been modeled using extreme value theory. In this paper we present the Gumbel autoregressive model of order one—Gumbel AR(1)—as an option for modeling best practice life expectancy. This class of model represents a neat and coherent framework for modeling time series extremes. The Gumbel distribution accounts for the extreme nature of best practice life expectancy, while the AR structure accounts for the temporal dependence in the time series. Model diagnostics and simulation results indicate that these models present a viable alternative to Gaussian AR(1) models when dealing with time series of extremes and merit further exploration.

Attention-based spatio-temporal dependence learning network

2019 ◽  
Vol 503 ◽  
pp. 92-108 ◽  
Author(s):  
Qianli Ma ◽  
Shuai Tian ◽  
Jia Wei ◽  
Jiabing Wang ◽  
Wing W.Y. Ng
Keyword(s):  
Temporal Dependence ◽  
Learning Network ◽  
Spatio Temporal

scholarly journals Improving Computational Efficiency in Crowded Task Allocation Games with Coupled Constraints

2019 ◽  
Vol 9 (10) ◽  
pp. 2117
Author(s):  
Ming Chong Lim ◽  
Han-Lim Choi
Keyword(s):  
Task Allocation ◽  
Random Sampling ◽  
Scheduling Algorithm ◽  
Stochastic Algorithm ◽  
Speed Of Convergence ◽  
Temporal Dependence ◽  
Real World Applications ◽  
Coupled Constraints ◽  
Multi Agent ◽  
Feasible Solutions

Multi-agent task allocation is a well-studied field with many proven algorithms. In real-world applications, many tasks have complicated coupled relationships that affect the feasibility of some algorithms. In this paper, we leverage on the properties of potential games and introduce a scheduling algorithm to provide feasible solutions in allocation scenarios with complicated spatial and temporal dependence. Additionally, we propose the use of random sampling in a Distributed Stochastic Algorithm to enhance speed of convergence. We demonstrate the feasibility of such an approach in a simulated disaster relief operation and show that feasibly good results can be obtained when the confirmation and sample size requirements are properly selected.

Size and temporal dependence of the metastable decay probabilities of ionized rare gas clusters

1992 ◽  
Vol 96 (5) ◽  
pp. 3624-3627 ◽  
Author(s):  
Y. Ji ◽  
M. Foltin ◽  
C. H. Liao ◽  
T. D. Märk
Keyword(s):  
Rare Gas ◽  
Temporal Dependence ◽  
Metastable Decay ◽  
Gas Clusters ◽  
Rare Gas Clusters

scholarly journals Modelling of Bi-Directional Spatio-Temporal Dependence and Users’ Dynamic Preferences for Missing POI Check-In Identification

2019 ◽  
Vol 33 ◽  
pp. 5458-5465 ◽  
Author(s):  
Dongbo Xi ◽  
Fuzhen Zhuang ◽  
Yanchi Liu ◽  
Jingjing Gu ◽  
Hui Xiong ◽  
...  
Keyword(s):  
Large Scale ◽  
Real Life ◽  
Location Prediction ◽  
Temporal Dependence ◽  
Great Opportunity ◽  
Mobility Data ◽  
Proposed Model ◽  
Poi Recommendation ◽  
Spatio Temporal ◽  
Dynamic Preferences

Human mobility data accumulated from Point-of-Interest (POI) check-ins provides great opportunity for user behavior understanding. However, data quality issues (e.g., geolocation information missing, unreal check-ins, data sparsity) in real-life mobility data limit the effectiveness of existing POIoriented studies, e.g., POI recommendation and location prediction, when applied to real applications. To this end, in this paper, we develop a model, named Bi-STDDP, which can integrate bi-directional spatio-temporal dependence and users’ dynamic preferences, to identify the missing POI check-in where a user has visited at a specific time. Specifically, we first utilize bi-directional global spatial and local temporal information of POIs to capture the complex dependence relationships. Then, target temporal pattern in combination with user and POI information are fed into a multi-layer network to capture users’ dynamic preferences. Moreover, the dynamic preferences are transformed into the same space as the dependence relationships to form the final model. Finally, the proposed model is evaluated on three large-scale real-world datasets and the results demonstrate significant improvements of our model compared with state-of-the-art methods. Also, it is worth noting that the proposed model can be naturally extended to address POI recommendation and location prediction tasks with competitive performances.

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