Validation of the DAPT score in large-scale consecutive and contemporary patients population in the real world

Platelets ◽  
2020 ◽  
pp. 1-8
Author(s):  
Guofeng Gao ◽  
Yanyan Zhao ◽  
Dong Zhang ◽  
Chenxi Song ◽  
Weihua Song ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
The Real

scholarly journals City brain: practice of large‐scale artificial intelligence in the real world

2019 ◽  
Vol 1 (1) ◽  
pp. 28-37 ◽  
Author(s):  
Jianfeng Zhang ◽  
Xian‐Sheng Hua ◽  
Jianqiang Huang ◽  
Xu Shen ◽  
Jingyuan Chen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Large Scale ◽  
The Real

The ontogeny of a mammalian cognitive map in the real world

Science ◽  
2020 ◽  
Vol 369 (6500) ◽  
pp. 194-197 ◽  
Author(s):  
Lee Harten ◽  
Amitay Katz ◽  
Aya Goldshtein ◽  
Michal Handel ◽  
Yossi Yovel
Keyword(s):  
Real World ◽  
Large Scale ◽  
Cognitive Maps ◽  
Cognitive Map ◽  
Interindividual Differences ◽  
The Real ◽  
Fruit Bat ◽  
Wild Fruit ◽  
In The Wild

How animals navigate over large-scale environments remains a riddle. Specifically, it is debated whether animals have cognitive maps. The hallmark of map-based navigation is the ability to perform shortcuts, i.e., to move in direct but novel routes. When tracking an animal in the wild, it is extremely difficult to determine whether a movement is truly novel because the animal’s past movement is unknown. We overcame this difficulty by continuously tracking wild fruit bat pups from their very first flight outdoors and over the first months of their lives. Bats performed truly original shortcuts, supporting the hypothesis that they can perform large-scale map-based navigation. We documented how young pups developed their visual-based map, exemplifying the importance of exploration and demonstrating interindividual differences.

scholarly journals Logo-2K+: A Large-Scale Logo Dataset for Scalable Logo Classification

2020 ◽  
Vol 34 (04) ◽  
pp. 6194-6201
Author(s):  
Jing Wang ◽  
Weiqing Min ◽  
Sujuan Hou ◽  
Shengnan Ma ◽  
Yuanjie Zheng ◽  
...  
Keyword(s):  
Image Recognition ◽  
Real World ◽  
Large Scale ◽  
Data Augmentation ◽  
Ground Truth ◽  
Classification Task ◽  
The Real ◽  
Product Recommendation ◽  
Contextual Advertising ◽  
Benchmark Datasets

Logo classification has gained increasing attention for its various applications, such as copyright infringement detection, product recommendation and contextual advertising. Compared with other types of object images, the real-world logo images have larger variety in logo appearance and more complexity in their background. Therefore, recognizing the logo from images is challenging. To support efforts towards scalable logo classification task, we have curated a dataset, Logo-2K+, a new large-scale publicly available real-world logo dataset with 2,341 categories and 167,140 images. Compared with existing popular logo datasets, such as FlickrLogos-32 and LOGO-Net, Logo-2K+ has more comprehensive coverage of logo categories and larger quantity of logo images. Moreover, we propose a Discriminative Region Navigation and Augmentation Network (DRNA-Net), which is capable of discovering more informative logo regions and augmenting these image regions for logo classification. DRNA-Net consists of four sub-networks: the navigator sub-network first selected informative logo-relevant regions guided by the teacher sub-network, which can evaluate its confidence belonging to the ground-truth logo class. The data augmentation sub-network then augments the selected regions via both region cropping and region dropping. Finally, the scrutinizer sub-network fuses features from augmented regions and the whole image for logo classification. Comprehensive experiments on Logo-2K+ and other three existing benchmark datasets demonstrate the effectiveness of proposed method. Logo-2K+ and the proposed strong baseline DRNA-Net are expected to further the development of scalable logo image recognition, and the Logo-2K+ dataset can be found at https://github.com/msn199959/Logo-2k-plus-Dataset.

scholarly journals In the Shadow of the Palm: Dispersed Ontologies among Marind, West Papua

2018 ◽  
Vol 33 (4) ◽  
pp. 621-649 ◽  
Author(s):  
Sophie Chao
Keyword(s):  
Real World ◽  
Oil Palm ◽  
Large Scale ◽  
Well Being ◽  
Life Forms ◽  
Human Species ◽  
Multispecies Ethnography ◽  
The Real ◽  
Human Exceptionalism ◽  
West Papua

This article explores how indigenous Marind of West Papua conceptualize the radical socio-environmental transformations wrought by large-scale deforestation and oil palm expansion on their customary lands and forests. Within the ecology of the Marind lifeworld, oil palm constitutes a particular kind of person, endowed with particular agencies and affects. Its unwillingness to participate in symbiotic socialities with other species jeopardizes the well-being of the life forms populating a dynamic multispecies cosmology, including humans. Drawing from ontological theories and the multispecies approach, I show how people in a remote place engage with adverse environmental transformations enacted by an other-than-human actor. Assumptions of human exceptionalism come under question in the context of a vegetal being that is exceptional in its own particular and destructive ways. Arguing for greater attention to other-than-human species that are unloving rather than unloved, I explore the epistemological frictions that arise from combining the anthropology of ontology with multispecies ethnography. I also attend to the implications of these theoretical positions in the real world of advocacy for those struggling in and against growing social and ecological precariousness.

Ergonomic criteria in large-scale engineering design—II Evaluating and applying requirements in the real world of design

1999 ◽  
Vol 30 (3) ◽  
pp. 207-221 ◽  
Author(s):  
Ingrid Anette Wulff ◽  
Rolf H. Westgaard ◽  
Bente Rasmussen
Keyword(s):  
Engineering Design ◽  
Real World ◽  
Large Scale ◽  
The Real

Adaptive Reinforcement Learning Integrating Exploitation-and Exploration-oriented Learning

1999 ◽  
Vol 3 (6) ◽  
pp. 474-478
Author(s):  
Satoshi Kurihara ◽  
◽  
Rikio Onai ◽  
Toshiharu Sugawara ◽  
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robots ◽  
Real World ◽  
Large Scale ◽  
Autonomous Systems ◽  
Learning System ◽  
The Internet ◽  
Changing Environment ◽  
The Real ◽  
Exploitation And Exploration

We propose and evaluate an adaptive reinforcement learning system that integrates both exploitation- and exploration-oriented learning (ArLee). Compared to conventional reinforcement learning, ArLee is more robust in a dynamically changing environment and conducts exploration-oriented learning efficiently even in a large-scale environment. It is thus well suited for autonomous systems, for example, software agents and mobile robots, that operate in dynamic, large-scale environments, such as the real world and the Internet. Simulation demonstrates the learning system’s basic effectiveness.

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