The Cigar-Box Papers: A Local View of the Centennial Electoral Scandals

1976 ◽  
Vol 55 (3) ◽  
pp. 256-269
Author(s):  
Roger Olmsted
Keyword(s):  
Local View

scholarly journals A local view of the laser induced magnetic domain dynamics in CoPd stripe domains at the picosecond time scale

2020 ◽  
Vol 32 (46) ◽  
pp. 465801
Author(s):  
V López-Flores ◽  
M-A Mawass ◽  
J Herrero-Albillos ◽  
A A Uenal ◽  
S Valencia ◽  
...  
Keyword(s):  
Time Scale ◽  
Magnetic Domain ◽  
Local View ◽  
Picosecond Time Scale ◽  
Domain Dynamics

scholarly journals Global-Local View: Scalable Consistency for Concurrent Data Types

2018 ◽  
pp. 492-504 ◽  
Author(s):  
Deepthi Akkoorath ◽  
José Brandão ◽  
Annette Bieniusa ◽  
Carlos Baquero
Keyword(s):  
Data Types ◽  
Local View

scholarly journals Sensor Fusion-Based Cooperative Trail Following for Autonomous Multi-Robot System

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 823 ◽  
Author(s):  
Mingyang Geng ◽  
Shuqi Liu ◽  
Zhaoxia Wu
Keyword(s):  
Sensor Fusion ◽  
Sensor Data ◽  
Great Success ◽  
Trail Following ◽  
Robust Performance ◽  
Robot System ◽  
Threshold Mechanism ◽  
Local View ◽  
In The Wild

Autonomously following a man-made trail in the wild is a challenging problem for robotic systems. Recently, deep learning-based approaches have cast the trail following problem as an image classification task and have achieved great success in the vision-based trail-following problem. However, the existing research only focuses on the trail-following task with a single-robot system. In contrast, many robotic tasks in reality, such as search and rescue, are conducted by a group of robots. While these robots are grouped to move in the wild, they can cooperate to lead to a more robust performance and perform the trail-following task in a better manner. Concretely, each robot can periodically exchange the vision data with other robots and make decisions based both on its local view and the information from others. This paper proposes a sensor fusion-based cooperative trail-following method, which enables a group of robots to implement the trail-following task by fusing the sensor data of each robot. Our method allows each robot to face the same direction from different altitudes to fuse the vision data feature on the collective level and then take action respectively. Besides, considering the quality of service requirement of the robotic software, our method limits the condition to implementing the sensor data fusion process by using the “threshold” mechanism. Qualitative and quantitative experiments on the real-world dataset have shown that our method can significantly promote the recognition accuracy and lead to a more robust performance compared with the single-robot system.

scholarly journals SVM-Based Deep Stacking Networks

2019 ◽  
Vol 33 ◽  
pp. 5273-5280 ◽  
Author(s):  
Jingyuan Wang ◽  
Kai Feng ◽  
Junjie Wu
Keyword(s):  
Neural Networks ◽  
High Performance ◽  
Optimal Solution ◽  
Data Sets ◽  
Complex Data ◽  
Layer By Layer ◽  
Deep Network ◽  
Local View ◽  
Linear Svm ◽  
Network Approaches

The deep network model, with the majority built on neural networks, has been proved to be a powerful framework to represent complex data for high performance machine learning. In recent years, more and more studies turn to nonneural network approaches to build diverse deep structures, and the Deep Stacking Network (DSN) model is one of such approaches that uses stacked easy-to-learn blocks to build a parameter-training-parallelizable deep network. In this paper, we propose a novel SVM-based Deep Stacking Network (SVM-DSN), which uses the DSN architecture to organize linear SVM classifiers for deep learning. A BP-like layer tuning scheme is also proposed to ensure holistic and local optimizations of stacked SVMs simultaneously. Some good math properties of SVM, such as the convex optimization, is introduced into the DSN framework by our model. From a global view, SVM-DSN can iteratively extract data representations layer by layer as a deep neural network but with parallelizability, and from a local view, each stacked SVM can converge to its optimal solution and obtain the support vectors, which compared with neural networks could lead to interesting improvements in anti-saturation and interpretability. Experimental results on both image and text data sets demonstrate the excellent performances of SVM-DSN compared with some competitive benchmark models.

Solid Bridging during Pattern Collapse (Stiction) Studied on Silicon Nanoparticles

2011 ◽  
Vol 1299 ◽  
Author(s):  
Daniel Peter ◽  
Michael Dalmer ◽  
Andriy Lotnyk ◽  
Lorenz Kienle ◽  
Alfred Lechner ◽  
...  
Keyword(s):  
Silicon Nanoparticles ◽  
High Surface ◽  
Volume Ratio ◽  
Model System ◽  
Core Shell ◽  
Surface Phenomena ◽  
Drying Process ◽  
Sio2 Shell ◽  
Local View ◽  
Shell Particles

ABSTRACTThe high surface to volume ratio of nanoparticles allows a detailed experimental study of the surface phenomena associated with solid bridging. Besides bulk analyses, the local view on the structure and composition via HRTEM is particularly essential. 50 nm core shell particles consisting of a silicon (Si) core and a SiO2 shell were used as model system to understand surface phenomena appearing for Si-based nanostructures. Evaporative drying from de-ionized water shows the most significant bridging effect based on SiO2. There is only a localized deposition of oxides between the particles during the drying process and no overall oxidation. For the deposition material, silicates are the most likely candidates.

Central-Local Relations in British Parties: A Local View

1971 ◽  
Vol 19 (4) ◽  
pp. 440-446 ◽  
Author(s):  
M. Parkinson
Keyword(s):  
Local View
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