Abstract SP112: Spatial variance signatures/Intra-tumor zonation in TNBC

Author(s):  
M Park ◽  
C Martínez Ramirez ◽  
Y Yang ◽  
A Blanchet-Cohen ◽  
H Kuasne ◽  
...  
Keyword(s):  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Stefano Anile ◽  
Sébastien Devillard

An amendment to this paper has been published and can be accessed via a link at the top of the paper.


2008 ◽  
Vol 18 (6) ◽  
pp. 1331-1337 ◽  
Author(s):  
Michael A. Litzow ◽  
J. Daniel Urban ◽  
Benjamin J. Laurel

2019 ◽  
Vol 1 (01) ◽  
pp. 11-19 ◽  
Author(s):  
James Deva Koresh H

The paper puts forward a real time traffic sign sensing (detection and recognition) frame work for enhancing the vehicles capability in order to have a save driving, path planning. The proposed method utilizes the capsules neural network that outperforms the convolutional neural network by eluding the necessities for the manual effort. The capsules network provides a better resistance for the spatial variance and the high reliability in the sensing of the traffic sign compared to the convolutional network. The evaluation of the capsule network with the Indian traffic data set shows a 15% higher accuracy when compared with the CNN and the RNN.


Author(s):  
Daniel Kerekes

The study uses the 2017 parliamentary elections results to analyses spatial patterns of votes in the city of Prague. A unique approach combining contextual and compositional data is introduced. Census data and data indicating the quality of life are reassigned to a shared entity – an address point, and analysed via automatic linear modelling. The model explained 69 % of spatial variance of votes share for the conservative TOP 09 party and the winning ANO 2011 movement, but only 19  % for the Pirate Party and the Mayors and Independence movement. Future research might focus on finding variables which would explain spatial variance of these parties’ vote shares. Abother possibility is the development of a methodology for studying votes spatiality within urban areas, in order to develop a robust theory.


2016 ◽  
Vol 36 (5) ◽  
pp. 0528002 ◽  
Author(s):  
高飞 Gao Fei ◽  
李松辉 Li Songhui ◽  
李婉婉 Li Wanwan ◽  
汪丽 Wang Li ◽  
辛文辉 Xin Wenhui ◽  
...  

2008 ◽  
Vol 65 (12) ◽  
pp. 3621-3635 ◽  
Author(s):  
Robin J. Hogan

Abstract A fast, approximate method is described for the calculation of the intensity of multiply scattered lidar returns from clouds. At each range gate it characterizes the outgoing photon distribution by its spatial variance, the variance of photon direction, and the covariance of photon direction and position. The result is that for an N-point profile the calculation is O(N) efficient yet it implicitly includes all orders of scattering, in contrast with the O(Nm/m!) efficiency of models that explicitly consider each scattering order separately for truncation at m-order scattering. It is also shown how the shape of the scattering phase function near 180° may be taken into account for both liquid water droplets and ice particles. The model considers only multiple scattering due to small-angle forward-scattering events, which is suitable for most ground-based and airborne lidars because of their small footprint on the cloud. For spaceborne lidar, it must be used in combination with the wide-angle multiple scattering model described in Part II of this two-part paper.


Sensors ◽  
2016 ◽  
Vol 16 (7) ◽  
pp. 1091 ◽  
Author(s):  
Ze Yu ◽  
Peng Lin ◽  
Peng Xiao ◽  
Lihong Kang ◽  
Chunsheng Li

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