Order and information in the patterns of spinning magnetic micro-disks at the air-water interface

A local measure based on the Shannon entropy establishes connections among information, structures, and interactions.

Communication regimes of the Central Asian states in the context of the national security interests of the Russian Federation

The article reveals the content of the destructive influence of Western countries on the communication regimes and information space of the friendly Central Asian states, which poses a threat to their national security and contradicts the national interests of the Russian Federation. The importance of joint efforts with the states of the Central Asian region, including those in the format of the SCO and CSTO regional organizations, to ensure information security and a positive impact on the communication regimes of this region is noted. Attention is drawn to the role of national mass media in the arsenal of tools for influencing the minds and psychology of the population of Central Asian countries and in this regard gives a description of Western information structures that exercise such influence, the mechanisms of their functioning and the way to neutralize their activities. The dynamism of the matter under study and its subjection to changes in the balance of power in the international arena are noted. The role of the state in ensuring information security and protecting citizens from distorted information and communication influence is analyzed separately. The paper suggests considerations regarding the organization of a systematic counteraction to the destructive actions of individual states in the information field of countries in Central Asia, in particular, it suggests the need to create a comprehensive system, together with the Russian Federation, to block and neutralize malicious information and propaganda materials, and also a proposal regarding the creation of a global communication order based on the formation of an international legal framework for rational civilized regulation of country communication regimes at the global and regional levels.

INTERNET USER’S BEHAVIOR FROM THE STANDPOINT OF THE NEURAL NETWORK THEORY OF SOCIETY: PREREQUISITES FOR THE META-EDUCATION CONCEPT FORMATION

An analysis of the behavior of Internet users from the point of view of their preferences in the choice of information sources and the effectiveness of their impact is presented. It is shown that the modern infocommunication space has undergone qualitative changes in the most recent time, and these transformations are already having a pronounced impact on higher education, mainly through the factor of competition between information sources. It is shown that these transformations can be interpreted as the evolution of the noosphere, which is considered as a global infocommunication network, in which non-trivial transpersonal information objects are formed. Their existence leads to the fact that the human intellect has a dual nature - both individual and collective principles are present in it at the same time. The latter is responsible for such phenomena as the collective unconscious, understood in the sense of Jung. It is shown that the neural network model of the noosphere makes it possible to formulate a similar concept of "professional collective unconscious", which is responsible for professional intuition, acts of creativity, etc. In turn, the existence of the professional collective unconscious forces us to radically reconsider the content of what is called training and move to the concept of meta-learning, which, among other things, involves stimulating transitions from one level of interaction with transpersonal information structures that make up the professional collective unconscious to another.

What lies underneath: Precise classification of brain states using time-dependent topological structure of dynamics

The self-organising global dynamics underlying brain states emerge from complex recursive nonlinear interactions between interconnected brain regions. Until now, all efforts of capturing the causal mechanistic generating principles have proven elusive, since they have been unable to describe the non-stationarity of brain dynamics, i.e. time-dependent changes. Here, we present a novel framework able to characterise brain states with high specificity, precisely by modelling the time-dependent dynamics. Through describing the topological structure of the brain at each moment in time (its ‘information structure’), we are able to classify different brain states by using the statistics across time of these exact ‘information structures’ hitherto hidden in the neuroimaging dynamics. Proving the strong potential of this framework, we were able to classify the neuroimaging data from two classes of comatose patients (minimally conscious state and unresponsive wakefulness syndrome) compared with healthy controls with very high precision.

What lies underneath: Precise classification of brain states using time-dependent topological structure of dynamics

The self-organising global dynamics underlying brain states emerge from complex recursive nonlinear interactions between interconnected brain regions. Until now, all efforts of capturing the causal mechanistic generating principles have proven elusive, since they have been unable to describe the non-stationarity of brain dynamics, i.e. time-dependent changes. Here, we present a novel framework able to characterise brain states with high specificity, precisely by modelling the time-dependent dynamics. Through describing the topological structure of the brain at each moment in time (its ‘information structure’), we are able to classify different brain states by using the statistics across time of these exact ‘information structures’ hitherto hidden in the neuroimaging dynamics. Proving the strong potential of this framework, we were able to classify the neuroimaging data from two classes of comatose patients (minimally conscious state and unresponsive wakefulness syndrome) compared with healthy controls with very high precision.

What lies underneath: Precise classification of brain states using time-dependent topological structure of dynamics

The self-organising global dynamics underlying brain states emerge from complex recursive nonlinear interactions between interconnected brain regions. Until now, all efforts of capturing the causal mechanistic generating principles have proven elusive, since they have been unable to describe the non-stationarity of brain dynamics, i.e. time-dependent changes. Here, we present a novel framework able to characterise brain states with high specificity, precisely by modelling the time-dependent dynamics. Through describing the topological structure of the brain at each moment in time (its ‘information structure’), we are able to classify different brain states by using the statistics across time of these exact ‘information structures’ hitherto hidden in the neuroimaging dynamics. Proving the strong potential of this framework, we were able to classify the neuroimaging data from two classes of comatose patients (minimally conscious state and unresponsive wakefulness syndrome) compared with healthy controls with very high precision.

Naive Learning Through Probability Overmatching

Naïve Learning in a Binary Action, Social Network Environment In “Naïve Learning Through Probability Overmatching,” I. Arieli, Y. Babichenko, and M. Mueller-Frank consider an environment where privately informed agents select a binary action repeatedly observing the past actions of their neighbors in a social network. Rational inference has been shown to be exceedingly complex in this environment. Instead, this paper focuses on boundedly rational agents that form beliefs according to discretized DeGroot updating and apply a decision rule that assigns a (mixed) action to each belief. It is shown that naïve learning, where the long run actions of all agents are optimal given their pooled private information, can be achieved in any strongly connected network if beliefs satisfy a high level of inertia and the decision rule coincides with probability overmatching. The main difference to existing naïve learning results is that here it is shown to hold (1) for binary rather than uncountable action spaces and (2) even for network and information structures where Bayesian agents fail to learn.

Measures of Uncertainty for a Four-Hybrid Information System and Their Applications

A four-hybrid information system (4HIS) is an information system (IS) where the dataset of object descriptions consists of categorical, boolean, real-valued and missing data or attributes. This paper studies measures of uncertainty for a 4HIS and its application in attribute reduction. The distance function for each type of attribute in a 4HIS is first provided. Then, this distance is used to produce the tolerance relation induced by a given subsystem in a 4HIS. Next, information structure of this subsystem is proposed in terms of a set vector and dependence between information structures is introduced. Moreover, granulation and entropy measures in a 4HIS are investigated on the basis of information structures. In order to verify the feasibility of the proposed measures, effectiveness analysis is performed from a statistical perspective. Finally, an application of the proposed measures for attribute reduction in a 4HIS is given.

Information structures and entropy measurement for a fuzzy probabilistic information system

An information system (IS), an important model in the field of artificial intelligence, takes information structure as the basic structure. A fuzzy probabilistic information system (FPIS) is the combination of some fuzzy relations in the same universe that satisfy probability distribution. A FPIS as an IS with fuzzy relations includes three types of uncertainties (i.e., roughness, fuzziness and probability). This paper studies information structures in a FPIS from the perspective of granular computing (GrC). Firstly, two types of information structures in a FPIS are defined by set vectors. Then, equality, dependence and independence between information structures in a FPIS are proposed, and they are depicted by means of the inclusion degree. Next, information distance between information structures in a FPIS is presented. Finally, entropy measurement for a FPIS is investigated based on the proposed information structures. These results may be helpful for understanding the nature of structures and uncertainty in a FPIS.