„Er Muss Experimentieren“ – Die Kunst Im Gegenwärtigen Kapitalismus / “He Must Experiment” – Art in Present-Day Capitalism

2016 ◽  
pp. 78-91
Author(s):  
Christoph Menke
Keyword(s):  
2020 ◽  
Vol 63 (3) ◽  
pp. 131-141
Author(s):  
Alexander A. Pisarev

This article reviews the possible role of the universal basic income in the transformation of experience in gender and age perspectives. The universal basic income has been particularly hotly debated in recent decades. Regardless of the position, the common tone of the debates is the imperative “we must experiment.” Such a close interest in the universal basic income derives from the fact that it threatens to change the “generic” for humans situation of finiteness of resources and the need to work. Thus, it is able to change the experience of what it means to be human. Since the universal basic income allows to separate labor from income, it is likely that its introduction will return value to the currently stigmatized or devalued types of labor, such as child care, elderly care or domestic work. It creates opportunities for experience redistribution in gender perspective: care and leaving (career break), affective connection, and sensitivity could become the business of both parents, not just mothers. Another experience redistribution is possible in age perspective. Along with automation of labor, population ageing is a universal process that will sooner or later affect all the countries. Alarmist narratives that present this process as a threat and a problem now prevail. They are largely based on outdated ideas about old age and what it means to be old. However, in fact, ageing is the maturation of the population as a whole. With a proper re-evaluation of the meaning and significance of old age, the introduction of the universal basic income could create material conditions for the transfer of experience from the elderly to the younger – for the first time since traditional societies.


2013 ◽  
Vol 12 (1) ◽  
pp. 117-124 ◽  
Author(s):  
Yoshihide Tominaga ◽  
Satoru Iizuka ◽  
Masashi Imano ◽  
Hiroto Kataoka ◽  
Akashi Mochida ◽  
...  
Keyword(s):  

2019 ◽  
Vol 1 (1) ◽  
pp. 277-283 ◽  
Author(s):  
Zinoviy Blikharskyy ◽  
Jacek Selejdak ◽  
Yaroslav Blikharskyy ◽  
Roman Khmil

AbstractIn this article presented results of researching corrosion of steel bars in aggressive environment in time under loading. For researching were used special equipment. The experience and research works shown that steel bars in the crack cross-section area can be corrode. With increasing width of crack in re-bars and power of aggressive of environment increased the level of corrosion and decreased time of progress. The level of danger of corrosion in the crack in depend of specialty of steel bars. It is geometry parameters of steel bars and characteristic of corrosive behaviour. The general tendency of the influence of various defects on the strength of steels is widely studied experimentally and theoretically only for geometrically correct stress concentrators. For damages that are irregular in shape, such as corrosion ulcers, significantly less researching in each case must experiment to find their effect on the mechanical properties of steels. In this work the influence of simultaneous action of the aggressive environment and loading on strength of steel re-bars has been described.


Author(s):  
Jiayue Guo ◽  
Yang Feng ◽  
Meng Wang ◽  
Jinglong Wu

The visual system is the part of the central nervous system that gives organisms the ability to process visual details and enables the formation of several non-image photo response functions. It detects and explains information from visible by the light to build a representation of the surrounding environment. One reason why the visual system is so important is that it enables us to perceive information at a distance. We need not be in immediate contact with a stimulus to process it. We must experiment with visual equipment to understand how we process visual information. This article summarizes current visual system equipment and how this equipment can be used to determine how the visual system functions.


Author(s):  
Fernando Camelli ◽  
Rainald Lohner ◽  
Steven Hanna
Keyword(s):  

2011 ◽  
Vol 44 (1/2/3/4) ◽  
pp. 376 ◽  
Author(s):  
Riccardo Buccolieri ◽  
Silvana Di Sabatino
Keyword(s):  

2017 ◽  
Vol 20 (60) ◽  
pp. 51 ◽  
Author(s):  
Loubna Benchikhi ◽  
Mohamed Sadgal ◽  
Aziz Elfazziki ◽  
Fatimaezzahra Mansouri

Computer vision applications require choosing operators and their parameters, in order to provide the best outcomes. Often, the users quarry on expert knowledge and must experiment many combinations to find manually the best one. As performance, time and accuracy are important, it is necessary to automate parameter optimization at least for crucial operators. In this paper, a novel approach based on an adaptive discrete cuckoo search algorithm (ADCS) is proposed. It automates the process of algorithms’ setting and provides optimal parameters for vision applications. This work reconsiders a discretization problem to adapt the cuckoo search algorithm and presents the procedure of parameter optimization. Some experiments on real examples and comparisons to other metaheuristic-based approaches: particle swarm optimization (PSO), reinforcement learning (RL) and ant colony optimization (ACO) show the efficiency of this novel method.


Author(s):  
Vedang Naik ◽  
◽  
Rohit Sahoo ◽  
Sameer Mahajan ◽  
Saurabh Singh ◽  
...  

Reinforcement learning is an artificial intelligence paradigm that enables intelligent agents to accrue environmental incentives to get superior results. It is concerned with sequential decision-making problems which offer limited feedback. Reinforcement learning has roots in cybernetics and research in statistics, psychology, neurology, and computer science. It has piqued the interest of the machine learning and artificial intelligence groups in the last five to ten years. It promises that it allows you to train agents using rewards and penalties without explaining how the task will be completed. The RL issue may be described as an agent that must make decisions in a given environment to maximize a specified concept of cumulative rewards. The learner is not taught which actions to perform but must experiment to determine which acts provide the greatest reward. Thus, the learner has to actively choose between exploring its environment or exploiting it based on its knowledge. The exploration-exploitation paradox is one of the most common issues encountered while dealing with Reinforcement Learning algorithms. Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. We describe how to utilize several deep reinforcement learning (RL) algorithms for managing a Cartpole system used to represent episodic environments and Stock Market Trading, which is used to describe continuous environments in this study. We explain and demonstrate the effects of different RL ideas such as Deep Q Networks (DQN), Double DQN, and Dueling DQN on learning performance. We also look at the fundamental distinctions between episodic and continuous activities and how the exploration-exploitation issue is addressed in their context.


Author(s):  
Anthony J. Maeder

Many different techniques for visualizing data exist, and often users must experiment with several before selecting the most effective one for their problem. Knowledge of the characteristics of the human visual system can assist in our choice of visualization techniques. Limits imposed by our visual "cognitive bandwidth" mean that only detail up to these limits needs to be generated in a visualization scene. Some aspects of our visual process will be discussed and an approach will be described for modeling scene detail, which takes visual limits of such aspects into account.


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