Enter the Hybrid (Kleist)

Forms of Life ◽  
2020 ◽  
pp. 191-226
Author(s):  
Andreas Gailus
Keyword(s):  
Human Life ◽  
The Self ◽  
Heinrich Von Kleist ◽  
Ordinary Life ◽  
Mode Of Life ◽  
Power Of Language ◽  
Poetic Practice ◽  
Self Organizing

This chapter examines the radicalization of this violent dimension in Kleist. While Kant and Goethe model life as a self-organizing form, Heinrich von Kleist highlights its divided and conflictual nature, depicting it as driving beyond form into the territory of deformation and disarticulation. In Kleist, this anti-organicism manifests in a poetic practice that emphasizes both the self-interrupting power of language and the prosthetic character of human life. Whereas Kant's and Goethe's autopoetic models seek to reconcile art and life, Kleist's heteropoietics frames art as an artificially intensified mode of life: art exceeds ordinary life, not by providing it with a beautiful form, but by extracting and magnifying its capacity to exceed itself, to break its own form, to become hybrid.

How to grow a self: development of the self in a Bayesian brain

2019 ◽  
Author(s):  
Mateusz Wozniak
Keyword(s):  
Conceptual Development ◽  
Human Life ◽  
Predictive Coding ◽  
Body Representation ◽  
Developmental Trajectory ◽  
The Self ◽  
Learning Mechanisms ◽  
Bayesian Brain ◽  
Third Person ◽  
Complex Forms

The last two decades have brought several attempts to explain the self as a part of the Bayesian brain, typically within the framework of predictive coding. However, none of these attempts have looked comprehensively at the developmental aspect of self-representation. The goal of this paper is to argue that looking at the developmental trajectory is crucial for understanding the structure of an adult self-representation. The paper argues that the emergence of the self should be understood as an instance of conceptual development, which in the context of a Bayesian brain can be understood as a process of acquisition of new internal models of hidden causes of sensory input. The paper proposes how such models might emerge and develop over the course of human life by looking at different stages of development of bodily and extra-bodily self-representations. It argues that the self arises gradually in a series of discrete steps: from first-person multisensory representations of one’s body to third-person multisensory body representation, and from basic forms of the extended and social selves to progressively more complex forms of abstract self-representation. It discusses how each of them might emerge based on domain-general learning mechanisms, while also taking into account the potential role of innate representations. Finally it suggests how the conceptual structure of self-representation might inform the debate about the structure of self-consciousness.

scholarly journals An Intelligent Evaluation Method to Analyze the Competitiveness of Airlines

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jun Zhao ◽  
Xumei Chen
Keyword(s):  
Neural Network ◽  
Prior Knowledge ◽  
Evaluation Method ◽  
Standard Sample ◽  
Reference Value ◽  
The Self ◽  
Index Data ◽  
Som Neural Network ◽  
Simulation Results ◽  
Self Organizing

An intelligent evaluation method is presented to analyze the competitiveness of airlines. From the perspective of safety, service, and normality, we establish the competitiveness indexes of traffic rights and the standard sample base. The self-organizing mapping (SOM) neural network is utilized to self-organize and self-learn the samples in the state of no supervision and prior knowledge. The training steps of high convergence speed and high clustering accuracy are determined based on the multistep setting. The typical airlines index data are utilized to verify the effect of the self-organizing mapping neural network on the airline competitiveness analysis. The simulation results show that the self-organizing mapping neural network can accurately and effectively classify and evaluate the competitiveness of airlines, and the results have important reference value for the allocation of traffic rights resources.

scholarly journals Self-exciting multifractional processes

2021 ◽  
Vol 58 (1) ◽  
pp. 22-41
Author(s):  
Fabian A. Harang ◽  
Marc Lagunas-Merino ◽  
Salvador Ortiz-Latorre
Keyword(s):  
Stochastic Process ◽  
Brownian Motion ◽  
Rate Of Convergence ◽  
Existence And Uniqueness ◽  
Volterra Equation ◽  
The Self ◽  
The Past ◽  
Stochastic Volterra Equation ◽  
Multifractional Brownian Motion ◽  
Self Organizing

AbstractWe propose a new multifractional stochastic process which allows for self-exciting behavior, similar to what can be seen for example in earthquakes and other self-organizing phenomena. The process can be seen as an extension of a multifractional Brownian motion, where the Hurst function is dependent on the past of the process. We define this by means of a stochastic Volterra equation, and we prove existence and uniqueness of this equation, as well as giving bounds on the p-order moments, for all $p\geq1$. We show convergence of an Euler–Maruyama scheme for the process, and also give the rate of convergence, which is dependent on the self-exciting dynamics of the process. Moreover, we discuss various applications of this process, and give examples of different functions to model self-exciting behavior.

scholarly journals The Spread of the COVID-19 Outbreak in Brazil: An Overview by Kohonen Self-Organizing Map Networks

Medicina ◽  
2021 ◽  
Vol 57 (3) ◽  
pp. 235
Author(s):  
Diego Galvan ◽  
Luciane Effting ◽  
Hágata Cremasco ◽  
Carlos Adam Conte-Junior
Keyword(s):  
The Self ◽  
Self Organizing Map ◽  
Virus Spread ◽  
Self Organizing Maps ◽  
The North ◽  
Som Clustering ◽  
Unsupervised Neural Networks ◽  
Mining Tools ◽  
Temporal Spread ◽  
Self Organizing

Background and objective: In the current pandemic scenario, data mining tools are fundamental to evaluate the measures adopted to contain the spread of COVID-19. In this study, unsupervised neural networks of the Self-Organizing Maps (SOM) type were used to assess the spatial and temporal spread of COVID-19 in Brazil, according to the number of cases and deaths in regions, states, and cities. Materials and methods: The SOM applied in this context does not evaluate which measures applied have helped contain the spread of the disease, but these datasets represent the repercussions of the country’s measures, which were implemented to contain the virus’ spread. Results: This approach demonstrated that the spread of the disease in Brazil does not have a standard behavior, changing according to the region, state, or city. The analyses showed that cities and states in the north and northeast regions of the country were the most affected by the disease, with the highest number of cases and deaths registered per 100,000 inhabitants. Conclusions: The SOM clustering was able to spatially group cities, states, and regions according to their coronavirus cases, with similar behavior. Thus, it is possible to benefit from the use of similar strategies to deal with the virus’ spread in these cities, states, and regions.

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