Adaptive forgetting in recursive identification through multiple models

2005 ◽  
pp. 171-185 ◽  
Author(s):  
P. Andersson
Keyword(s):  
Recursive Identification ◽  
Multiple Models ◽  
Adaptive Forgetting

Organising Modern Synagogues: A Case of Multiple Models

2001 ◽  
Vol 34 (2) ◽  
Author(s):  
Margaret Harris
Keyword(s):  
Multiple Models

Multiple Models Direct Adaptive Decoupling Controller for a Stochastic System

2010 ◽  
Vol 36 (9) ◽  
pp. 1295-1304 ◽  
Author(s):  
Yi-Hui ZHENG ◽  
Xin WANG ◽  
Shao-Yuan LI ◽  
Jian-Guo JIANG
Keyword(s):  
Stochastic System ◽  
Multiple Models

Cloud Provider's Response to Multiple Models of Attack Behaviors

2019 ◽  
Vol 13 (4) ◽  
pp. 325-333
Author(s):  
Xu Liu ◽  
Xiaoqiang Di ◽  
Jinqing Li ◽  
Huamin Yang ◽  
Ligang Cong ◽  
...  
Keyword(s):  
Stackelberg Game ◽  
Multiple Models ◽  
Theoretic Model ◽  
Cloud Provider ◽  
Game Model ◽  
Attack Behavior ◽  
Behavior Model ◽  
Protection Strategy ◽  
Behavior Models ◽  
Optimal Protection

Background: User behavior models have been widely used to simulate attack behaviors in the security domain. We revised all patents related to response to attack behavior models. How to decide the protected target against multiple models of attack behaviors is studied. Methods: We utilize one perfect rational and three bounded rational behavior models to simulate attack behaviors in cloud computing, and then investigate cloud provider’s response based on Stackelberg game. The cloud provider plays the role of defender and it is assumed to be intelligent enough to predict the attack behavior model. Based on the prediction accuracy, two schemes are built in two situations. Results: If the defender can predict the attack behavior model accurately, a single-objective game model is built to find the optimal protection strategy; otherwise, a multi-objective game model is built to find the optimal protection strategy. Conclusion: The numerical results prove that the game theoretic model performs better in the corresponding situation.

Mechanisms, Models, and Kinds

2017 ◽  
Author(s):  
Stuart Glennan
Keyword(s):  
Natural Kinds ◽  
Multiple Models

This chapter explores how mechanisms and their constituents can be classified into kinds. It argues for a weakly realist account of natural kinds—one which suggests that classification into kinds is based upon real similarities between instances of those kinds, but which denies that kinds have essences or have some reality apart from their instances. I introduce a models-first account of kinds, which suggests that two things are of the same kind to the extent that they can be represented by the same model. Because target entities can be represented by multiple models, they will belong to multiple kinds. I illustrate the approach by showing how the entities and activities that make up mechanisms can be classified into kinds.

Population Viability of Rorippa columbiae: Multiple Models and Spatial Trend Data

1998 ◽  
Vol 12 (5) ◽  
pp. 1054-1065 ◽  
Author(s):  
Elizabeth E. Crone ◽  
Janet L. Gehring
Keyword(s):  
Population Viability ◽  
Multiple Models ◽  
Spatial Trend ◽  
Trend Data ◽  
Rorippa Columbiae

scholarly journals Integrative oncogene-dependency mapping identifies RIT1 vulnerabilities and synergies in lung cancer

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Athea Vichas ◽  
Amanda K. Riley ◽  
Naomi T. Nkinsi ◽  
Shriya Kamlapurkar ◽  
Phoebe C. R. Parrish ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Spindle Assembly Checkpoint ◽  
Spindle Assembly ◽  
Genetic Data ◽  
Multiple Models ◽  
Ras Pathway ◽  
Functional Interactions ◽  
Genome Wide ◽  
A Genome ◽  
Mutant Cells

AbstractCRISPR-based cancer dependency maps are accelerating advances in cancer precision medicine, but adequate functional maps are limited to the most common oncogenes. To identify opportunities for therapeutic intervention in other rarer subsets of cancer, we investigate the oncogene-specific dependencies conferred by the lung cancer oncogene, RIT1. Here, genome-wide CRISPR screening in KRAS, EGFR, and RIT1-mutant isogenic lung cancer cells identifies shared and unique vulnerabilities of each oncogene. Combining this genetic data with small-molecule sensitivity profiling, we identify a unique vulnerability of RIT1-mutant cells to loss of spindle assembly checkpoint regulators. Oncogenic RIT1M90I weakens the spindle assembly checkpoint and perturbs mitotic timing, resulting in sensitivity to Aurora A inhibition. In addition, we observe synergy between mutant RIT1 and activation of YAP1 in multiple models and frequent nuclear overexpression of YAP1 in human primary RIT1-mutant lung tumors. These results provide a genome-wide atlas of oncogenic RIT1 functional interactions and identify components of the RAS pathway, spindle assembly checkpoint, and Hippo/YAP1 network as candidate therapeutic targets in RIT1-mutant lung cancer.

Vehicle traffic delay prediction in ferry terminal based on Bayesian multiple models combination method

2017 ◽  
Vol 13 (5) ◽  
pp. 467-490 ◽  
Author(s):  
Weibin Zhang ◽  
Yong Qi ◽  
Kristian Henrickson ◽  
Jinjun Tang ◽  
Yinhai Wang
Keyword(s):  
Combination Method ◽  
Multiple Models ◽  
Vehicle Traffic ◽  
Traffic Delay ◽  
Delay Prediction
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