scholarly journals Policy Transfer across Visual and Dynamics Domain Gaps via Iterative Grounding

2021 ◽  
Author(s):  
Grace Zhang ◽  
Linghan Zhong ◽  
Youngwoon Lee ◽  
Joseph Lim
Keyword(s):  
Policy Transfer

Evaluating Cross-Country Policy Learning in Public Administration: The Case of Qatar-Singapore Partnership in Regional Training Centre for Public Administration (RTCPA)

2019 ◽  
Author(s):  
M. Evren Tok ◽  
Duygu Sever
Keyword(s):  
Middle East ◽  
Public Administration ◽  
Policy Transfer ◽  
Policy Learning ◽  
Training Center ◽  
Training Centre ◽  
Cross Country

This study investigates the case of Qatar Singapore Regional Training Center for Public Administration.As a tool for this process of policy transfer, the article further evaluates the case of Singapore- Qatar Asia-Middle East Dialogue (AMED) Regional Training Centre for Public Administration (RTCPA) in Doha, Qatar, as a mechanism to foster this policy transferThe study suggests that this evaluation would be a fruitful example in revealing the strengths and weakness of such initiatives and can offer a scheme for insights regarding effective tools of policy learning.

scholarly journals Automated Ventricular System Segmentation in Paediatric Patients Treated for Hydrocephalus Using Deep Learning Methods

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Michał Klimont ◽  
Mateusz Flieger ◽  
Jacek Rzeszutek ◽  
Joanna Stachera ◽  
Aleksandra Zakrzewska ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Augmentation ◽  
Ct Images ◽  
Policy Transfer ◽  
Training Data ◽  
Intraobserver Variability ◽  
Practical Applications ◽  
Brain Scans ◽  
Rate Policy ◽  
Ct Brain

Hydrocephalus is a common neurological condition that can have traumatic ramifications and can be lethal without treatment. Nowadays, during therapy radiologists have to spend a vast amount of time assessing the volume of cerebrospinal fluid (CSF) by manual segmentation on Computed Tomography (CT) images. Further, some of the segmentations are prone to radiologist bias and high intraobserver variability. To improve this, researchers are exploring methods to automate the process, which would enable faster and more unbiased results. In this study, we propose the application of U-Net convolutional neural network in order to automatically segment CT brain scans for location of CSF. U-Net is a neural network that has proven to be successful for various interdisciplinary segmentation tasks. We optimised training using state of the art methods, including “1cycle” learning rate policy, transfer learning, generalized dice loss function, mixed float precision, self-attention, and data augmentation. Even though the study was performed using a limited amount of data (80 CT images), our experiment has shown near human-level performance. We managed to achieve a 0.917 mean dice score with 0.0352 standard deviation on cross validation across the training data and a 0.9506 mean dice score on a separate test set. To our knowledge, these results are better than any known method for CSF segmentation in hydrocephalic patients, and thus, it is promising for potential practical applications.

The process of transfer: The micro-influences of power, time and learning

2019 ◽  
Vol 35 (4) ◽  
pp. 445-464 ◽  
Author(s):  
David Peter Dolowitz ◽  
Rodica Plugaru ◽  
Sabine Saurugger
Keyword(s):  
Empirical Study ◽  
Transfer Process ◽  
Policy Transfer ◽  
Interactive Effects ◽  
Dynamic Effects ◽  
Governance Framework ◽  
Theoretical Assumptions ◽  
Hospital Construction ◽  
Micro Level ◽  
Over Time

To date, there have been a number of studies that have examined how policies move from one jurisdiction to another. However, few of these studies have examined the micro-interactive effects of actors. This is necessary to understand how actors shape outcomes over time. The aim of this paper is to engage with this micro-level literature through an empirical study of policy transfer in the field of architectural norms in hospital construction in post-Soviet states. To do this, we generate several theoretical assumptions to link the transfer literature to wider debates in the governance framework. The goal is to discover how the power of actors interacts in the policymaking processes to influence outcomes over time and in light of learning. What we hope to do is bring the interactive and dynamic effects that occur between agents attempting to shape the transfer process back into the transfer picture. The aim is to show that power flows and that these flows alter the shape and outcome of the transfer process.

scholarly journals Zero-Shot Autonomous Vehicle Policy Transfer: From Simulation to Real-World via Adversarial Learning

2020 ◽  
Author(s):  
Behdad Chalaki ◽  
Logan E. Beaver ◽  
Ben Remer ◽  
Kathy Jang ◽  
Eugene Vinitsky ◽  
...  
Keyword(s):  
Real World ◽  
Autonomous Vehicle ◽  
Policy Transfer ◽  
Adversarial Learning

Policy Transfer from Simulation to Real World for Autonomous Control of an Omni Wheel Robot

2020 ◽  
Author(s):  
Yuto Ushida ◽  
Hafiyanda Razan ◽  
Takuto Sakuma ◽  
Shohei Kato
Keyword(s):  
Real World ◽  
Policy Transfer ◽  
Autonomous Control ◽  
Omni Wheel
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