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 Reasoning with linguistic preferences using NPN logic

2010 ◽  
Vol 7 (3) ◽  
pp. 511-528 ◽  
Author(s):  
Goran Devedzic ◽  
Danijela Milosevic ◽  
Lozica Ivanovic ◽  
Dragan Adamovic ◽  
Miodrag Manic
Keyword(s):  
Side Effects ◽  
Decision Analysis ◽  
Decision Maker ◽  
Real World ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Basic Notion ◽  
The Real ◽  
Alternative Framework ◽  
Linguistic Models

Negative-positive-neutral logic provides an alternative framework for fuzzy cognitive maps development and decision analysis. This paper reviews basic notion of NPN logic and NPN relations and proposes adaptive approach to causality weights assessment. It employs linguistic models of causality weights activated by measurement-based fuzzy cognitive maps? concepts values. These models allow for quasi-dynamical adaptation to the change of concepts values, providing deeper understanding of possible side effects. Since in the real-world environments almost every decision has its consequences, presenting very valuable portion of information upon which we also make our decisions, the knowledge about the side effects enables more reliable decision analysis and directs actions of decision maker.

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

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.

scholarly journals Edge-assisted Collaborative Image Recognition for Mobile Augmented Reality

2022 ◽  
Vol 18 (1) ◽  
pp. 1-31
Author(s):  
Guohao Lan ◽  
Zida Liu ◽  
Yunfan Zhang ◽  
Tim Scargill ◽  
Jovan Stojkovic ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Image Recognition ◽  
Large Scale ◽  
Data Augmentation ◽  
Recognition Accuracy ◽  
Image Distortion ◽  
Mobile Augmented Reality ◽  
The Real ◽  
In The Wild ◽  
Mobile Ar

Mobile Augmented Reality (AR), which overlays digital content on the real-world scenes surrounding a user, is bringing immersive interactive experiences where the real and virtual worlds are tightly coupled. To enable seamless and precise AR experiences, an image recognition system that can accurately recognize the object in the camera view with low system latency is required. However, due to the pervasiveness and severity of image distortions, an effective and robust image recognition solution for “in the wild” mobile AR is still elusive. In this article, we present CollabAR, an edge-assisted system that provides distortion-tolerant image recognition for mobile AR with imperceptible system latency . CollabAR incorporates both distortion-tolerant and collaborative image recognition modules in its design. The former enables distortion-adaptive image recognition to improve the robustness against image distortions, while the latter exploits the spatial-temporal correlation among mobile AR users to improve recognition accuracy. Moreover, as it is difficult to collect a large-scale image distortion dataset, we propose a Cycle-Consistent Generative Adversarial Network-based data augmentation method to synthesize realistic image distortion. Our evaluation demonstrates that CollabAR achieves over 85% recognition accuracy for “in the wild” images with severe distortions, while reducing the end-to-end system latency to as low as 18.2 ms.

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

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
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