Information Flow Structure Research Articles

Specific inhibition and disinhibition in the higher-order structure of a cortical connectome.

Neurons are thought to act as parts of assemblies with strong internal excitatory connectivity. Conversely, inhibition is often reduced to blanket inhibition with no targeting specificity. We analyzed the structure of excitation and inhibition in the MICrONS $mm^{3}$ dataset, an electron microscopic reconstruction of a piece of cortical tissue. We found that excitation was structured around a feed-forward flow in large non-random neuron motifs with a structure of information flow from a small number of sources to a larger number of potential targets. Inhibitory neurons connected with neurons in specific sequential positions of these motifs, implementing targeted and symmetrical competition between them. None of these trends are detectable in only pairwise connectivity, demonstrating that inhibition is structured by these large motifs. While descriptions of inhibition in cortical circuits range from non-specific blanket-inhibition to targeted, our results describe a form of targeting specificity existing in the higher-order structure of the connectome. These findings have important implications for the role of inhibition in learning and synaptic plasticity.

Holding diverse market beliefs by firms: Information flow, profit performances, and channel structure

We investigate the impact of diverse market beliefs held by supply chain firms on information flow and channel efficiency. In presentative settings, suppliers sell substitutable products through retailers to a market with uncertain demand and market competition is in quantity. Structure B comprises a monopolistic supplier and a monopolistic retailer. With respect to structure B, structure U includes competition between suppliers, structure D includes competition between retailers, while structure SC includes competition between two supply chains. A retailer has access to a demand signal useful for updating the forecast of market uncertainty. A supplier has no signal access but can offer a payment to a retailer, who decides whether to accept the payment and disclose signal to the supplier, termed information sharing. Suppliers and retailers hold diverse beliefs about market conditions. Knowing each other's market beliefs influences firms’ ex-post operations policies and ex-ante profit perception. We demonstrate that holding diverse beliefs about market uncertainty by firms facilitates information sharing except in the presence of only retailer competition, in which case it deters information sharing. Exchanging market beliefs about market uncertainty among firms strengthens their incentive to engage in information sharing, but it exerts mixed effects on their actual profits. By comparison, suppliers and retailers are more likely to benefit from exchanging market beliefs about market size, although it is inconsequential to information sharing. Moreover, we pinpoint the impacts of holding diverse market beliefs by firms on their relative performances across channel structures.

Mobilising transformative community-based climate change adaptation

Under-resourced and marginalised communities often have limited government support, and therefore, must decide by themselves if and how they adapt to the uncertainties of climate change. Community-based climate change adaptation (CbA) forms a plausible approach here. In practice, however, CbA projects often fail to address the underlying causes of vulnerability like power relations and sociocultural structures that influence adaptation measures adopted and outcomes. In response, this perspective paper explores the concept of transformative community-based climate change adaption (TCbA), a reflexive approach that focuses on empowering communities by reframing the decision-making context. It discusses pathways of mobilising this shift to transformative CbA in three steps: (1) specifying the characteristics of a TCbA; (2) exploring the leverage points to mobilise it, and (3) recommending a set of actions and processes that facilitate co-decided interventions at the identified leverage points. These leverage points include "the structure of information flow", "rules of the system" and “power to change system structure or self-organise". The paper concludes by emphasising that the shift to transformative CbA begins when facilitators and members of the community undertaking a CbA project are reflexive about how the societal context of decision processes, like power relations, influence community adaptation decision-making, planning and implementation.

Investigating cortical complexity and connectivity in rats with schizophrenia.

The above studies indicate that the SCZ animal model has abnormal gamma oscillations and abnormal functional coupling ability of brain regions at the cortical level. However, few researchers have focused on the correlation between brain complexity and connectivity at the cortical level. In order to provide a more accurate representation of brain activity, we studied the complexity of electrocorticogram (ECoG) signals and the information interaction between brain regions in schizophrenic rats, and explored the correlation between brain complexity and connectivity. We collected ECoG signal from SCZ rats. The frequency domain and time domain functional connectivity of SCZ rats were evaluated by magnitude square coherence and mutual information (MI). Permutation entropy (PE) and permutation Lempel-Ziv complexity (PLZC) were used to analyze the complexity of ECoG, and the relationship between them was evaluated. In addition, in order to further understand the causal structure of directional information flow among brain regions, we used phase transfer entropy (PTE) to analyze the effective connectivity of the brain. Firstly, in the high gamma band, the complexity of brain regions in SCZ rats is higher than that in normal rats, and the neuronal activity is irregularity. Secondly, the information integration ability of SCZ rats decreased and the communication of brain network information was hindered at the cortical level. Finally, compared with normal rats, the causal relationship between brain regions of SCZ rats was closer, but the information interaction center was not clear. The above findings suggest that at the cortical level, complexity and connectivity are valid biomarkers for identifying SCZ. This bridges the gap between peak potentials and EEG. This may help to understand the pathophysiological mechanisms at the cortical level in schizophrenics.

Can a Single Line of Code Change Society? The Systemic Risks of Optimizing Engagement in Recommender Systems on Global Information Flow, Opinion Dynamics and Social Structures

Can a Single Line of Code Change Society? The Systemic Risks of Optimizing Engagement in Recommender Systems on Global Information Flow, Opinion Dynamics and Social Structures

Методичні аспекти підготовки інтегрованої звітності

Enterprises carry out financial and economic activities under constant changes, which directly or indirectly affect the content, volume and structure of information flows arising from identifying, measuring and formalising data related to economic operations and their results. The article aims to identify the elements of the preparation method of integrated reporting, the application of which by the accountant will help increase the degree of satisfaction of users' information needs. It was established that the preparation of integrated reporting includes three stages: determining users' information needs, obtaining relevant information for forming indicators, and directly filling out forms of integrated reporting. The first stage's goal is to establish the characteristics that should be concise, understandable for most users, and meaningful enough to make adequate management decisions on their basis. An essential aspect of the second stage is the determination of the sources of obtaining the necessary data with the identification of factors that contribute to reducing the cost of acquiring data, ensuring their relevance in terms of the time of receipt and intended purpose, as well as availability and a high level of trust. The preparation of integrated reports involves the selection of an acceptable conceptual basis that would consider the branch and territorial aspects of the functioning of business entities, their organisational and functional structure, policies and procedures for implementing individual financial and economic operations, and segments of activity. An accountant's application in practical activities of the approach to the preparation of integrated reporting disclosed in the article will allow the assessment of the expediency of obtaining individual data based on their importance for users, the ratio of benefits from receiving them, and related costs. Qualitatively prepared integrated reporting will give users confidence that formalised and properly systematised indicators characterise the enterprise's economic activity, taking into account the influence of quantitative, qualitative and cost factors.

Cortical Synchrony and Information Flow during Transition from Wakefulness to Light Non-Rapid Eye Movement Sleep.

Sleep is a highly stereotyped phenomenon, requiring robust spatiotemporal coordination of neural activity. Understanding how the brain coordinates neural activity with sleep onset can provide insights into the physiological functions subserved by sleep and the pathologic phenomena associated with sleep onset. We quantified whole-brain network changes in synchrony and information flow during the transition from wakefulness to light non-rapid eye movement (NREM) sleep, using MEG imaging in a convenient sample of 14 healthy human participants (11 female; mean 63.4 years [SD 11.8 years]). We furthermore performed computational modeling to infer excitatory and inhibitory properties of local neural activity. The transition from wakefulness to light NREM was identified to be encoded in spatially and temporally specific patterns of long-range synchrony. Within the delta band, there was a global increase in connectivity from wakefulness to light NREM, which was highest in frontoparietal regions. Within the theta band, there was an increase in connectivity in fronto-parieto-occipital regions and a decrease in temporal regions from wakefulness to Stage 1 sleep. Patterns of information flow revealed that mesial frontal regions receive hierarchically organized inputs from broad cortical regions upon sleep onset, including direct inflow from occipital regions and indirect inflow via parieto-temporal regions within the delta frequency band. Finally, biophysical neural mass modeling demonstrated changes in the anterior-to-posterior distribution of cortical excitation-to-inhibition with increased excitation-to-inhibition model parameters in anterior regions in light NREM compared with wakefulness. Together, these findings uncover whole-brain corticocortical structure and the orchestration of local and long-range, frequency-specific cortical interactions in the sleep-wake transition.SIGNIFICANCE STATEMENT Our work uncovers spatiotemporal cortical structure of neural synchrony and information flow upon the transition from wakefulness to light non-rapid eye movement sleep. Mesial frontal regions were identified to receive hierarchically organized inputs from broad cortical regions, including both direct inputs from occipital regions and indirect inputs via the parieto-temporal regions within the delta frequency range. Biophysical neural mass modeling revealed a spatially heterogeneous, anterior-posterior distribution of cortical excitation-to-inhibition. Our findings shed light on the orchestration of local and long-range cortical neural structure that is fundamental to sleep onset, and support an emerging view of cortically driven regulation of sleep homeostasis.

Spreading Dynamics of Capital Flow Transfer in Complex Financial Networks.

The financial system, a complex network, operates primarily through the exchange of capital, where the role of information is critical. This study utilizes the transfer entropy method to examine the strength and direction of information flow among different capital flow time series and investigate the community structure within the transfer networks. Moreover, the spreading dynamics of the capital flow transfer networks are observed, and the importance and traveling time of each node are explored. The results imply a dominant role for the food and drink industry within the Chinese market, with increased attention towards the computer industry starting in 2014. The community structure of the capital flow transfer networks significantly differs from those constructed from stock prices, with the main sector predominantly encompassing industry leaders favored by primary funds with robust capital flow connections. The average traveling time from sectors such as food and drink, coal, and utilities to other sectors is the shortest, and the dynamic flow between these sectors displays a significant role. These findings highlight that comprehension of information flow and community structure within the financial system can offer valuable insights into market dynamics and help to identify key sectors and companies.

Evolution of norms for judging social behavior

Reputations provide a powerful mechanism to sustain cooperation, as individuals cooperate with those of good social standing. But how should someone's reputation be updated as we observe their social behavior, and when will a population converge on a shared norm for judging behavior? Here, we develop a mathematical model of cooperation conditioned on reputations, for a population that is stratified into groups. Each group may subscribe to a different social norm for assessing reputations and so norms compete as individuals choose to move from one group to another. We show that a group initially comprising a minority of the population may nonetheless overtake the entire population-especially if it adopts the Stern Judging norm, which assigns a bad reputation to individuals who cooperate with those of bad standing. When individuals do not change group membership, stratifying reputation information into groups tends to destabilize cooperation, unless individuals are strongly insular and favor in-group social interactions. We discuss the implications of our results for the structure of information flow in a population and for the evolution of social norms of judgment.

Distributed MPC-Based String Stable Platoon Control of Networked Vehicle Systems

In this paper, the string stable platoon control problem of discrete-time networked vehicle systems is considered by using distributed model predictive control (MPC) based method. An optimization problem is established to minimize the cost function associated to the system trajectories. The last-step shifting method is applied to set the local optimal solution as the assumed solution and send it to the neighbor vehicles. By using the sum of the cost function as Lyapunov function, the stability of the closed-loop platoon system is studied. Comparing with existing results, the string stability, which is the unique characteristics of the platoon system, is guaranteed under the bidirectional-based structure as well as the predecessor-follower-based information flow structure. Finally, several simulations are presented to demonstrate the effectiveness of the proposed algorithms.

IEF-CSNET: Information Enhancement and Fusion Network for Compressed Sensing Reconstruction.

The rapidly growing requirement for data has put forward Compressed Sensing (CS) to realize low-ratio sampling and to reconstruct complete signals. With the intensive development of Deep Neural Network (DNN) methods, performance in image reconstruction from CS measurements is constantly increasing. Currently, many network structures pay less attention to the relevance of before- and after-stage results and fail to make full use of relevant information in the compressed domain to achieve interblock information fusion and a great receptive field. Additionally, due to multiple resamplings and several forced compressions of information flow, information loss and network structure redundancy inevitably result. Therefore, an Information Enhancement and Fusion Network for CS reconstruction (IEF-CSNET) is proposed in this work, and a Compressed Information Extension (CIE) module is designed to fuse the compressed information in the compressed domain and greatly expand the receptive field. The Error Comprehensive Consideration Enhancement (ECCE) module enhances the error image by incorporating the previous recovered error so that the interlink among the iterations can be utilized for better recovery. In addition, an Iterative Information Flow Enhancement (IIFE) module is further proposed to complete the progressive recovery with loss-less information transmission during the iteration. In summary, the proposed method achieves the best effect, exhibits high robustness at this stage, with the peak signal-to-noise ratio (PSNR) improved by 0.59 dB on average under all test sets and sampling rates, and presents a greatly improved speed compared with the best algorithm.

DuFNet: Dual Flow Network of Real-Time Semantic Segmentation for Unmanned Driving Application of Internet of Things

The application of unmanned driving in the Internet of Things is one of the concrete manifestations of the application of artificial intelligence technology. Image semantic segmentation can help the unmanned driving system by achieving road accessibility analysis. Semantic segmentation is also a challenging technology for image understanding and scene parsing. We focused on the challenging task of real-time semantic segmentation in this paper. In this paper, we proposed a novel fast architecture for real-time semantic segmentation named DuFNet. Starting from the existing work of Bilateral Segmentation Network (BiSeNet), DuFNet proposes a novel Semantic Information Flow (SIF) structure for context information and a novel Fringe Information Flow (FIF) structure for spatial information. We also proposed two kinds of SIF with cascaded and paralleled structures, respectively. The SIF encodes the input stage by stage in the ResNet18 backbone and provides context information for the feature fusion module. Features from previous stages usually contain rich low-level details but high-level semantics for later stages. The multiple convolutions embed in Parallel SIF aggregate the corresponding features among different stages and generate a powerful global context representation with less computational cost. The FIF consists of a pooling layer and an upsampling operator followed by projection convolution layer. The concise component provides more spatial details for the network. Compared with BiSeNet, our work achieved faster speed and comparable performance with 72.34% mIoU accuracy and 78 FPS on Cityscapes Dataset based on the ResNet18 backbone.

Социально-медийное пространство Северного Кавказа: структура и внутри региональные особенности

In the article, on the example of the territories of the North Caucasus, their representation in social media is considered. Modern approaches to the use of data from social media in sociogeographical research are analyzed. A number of research terms and concepts used to analyze the images of territories in social media have been identified and formed. The concept of „socialmediaspace“ is proposed and substantiated, tools and metrics for its internal studyare defined. With the help of the data collected by the information- analytical system «Medialogy», a study was made of the geography of publications of their authors, their audience at the federal, regional and local levels. On the basis of this material, the intensity and tonality of the information flow were determined, and the leading regional centers for the formation of social media content were identified. A geographic analysis of the features of the formation of information messages was carried out, which made it possible to identify territories with a predominance of an external or internal image. Based on it, characteristic types of information flow structure are proposed. The parametrization of the social media space is implemented through indicators of concentration, differentiation and connectivity. A typology of the municipalities of the North Caucasus has been carried out according to the peculiarities of the development of the social media space, information centers and information peripher have been identified. The developed method of geographical analysis of the territory in social media can be applied to other territories of various levels. The results obtained are of practical importance for territorial branding and promotion of territories in social media.

Smart Grid and wind generators: an overview of cyber threats and vulnerabilities of power supply networks

The subject of this study is the cyber vulnerability of wind generators, as part of the cyberphysical system of intelligent power supply networks, Smart Grid. Wind generators produce electricity for further distribution in the network between «smart» electricity consumers, which often include autonomous power systems in medical institutions, autonomous power supply of homes, charging stations for cars, etc. Wind generators operate in two aspects: in the physical and information space. Thus, a violation of the security of the information flow of a wind generator can affect the physical performance of electricity generation, and disable equipment. The study aims to identify types of cyber threats in the wind generator network based on the analysis of known attack incidents, analysis of the Smart Grid network structure, network devices, protocols, and control mechanisms of a wind generator. The tasks of the work are: review and analyze known cyberattack incidents; review the classification of cyber threats to wind farms; consider the most common methods of attacks on the cyberphysical system of wind farms; consider ways of intrusions into the information flow of the cyberphysical system wind generator; consider resilience mechanisms of wind generators in case of a cyberattack, consider the directions of further research. The methods are a systematic approach that provides a comprehensive study of the problem, quantitative and qualitative analysis of incidents of cyber attacks on wind generators, and methods of attacks. The following results were obtained: 11 large-scale known incidents of cyber attacks on the cyberphysical systems of the energy sector and smart power supply networks were analyzed, and information flow features and structure of the wind generators were considered. Main communication interfaces of the Smart Grid network were reviewed, control mechanisms for the physical parts of the wind generator system such as automatic voltage regulator, and automatic generation control were observed, vulnerable data transmission protocols, DNP3 in particular, were analyzed, possible consequences in the case of a cyber-intrusion into the network were considered. Conclusions: wind farms, as part of the Smart Grid system, are a convenient target for cyberattacks, as the number of potential ways to interfere with the information flow of the cyberphysical system is growing due to an increase in the number of sensors, communication channels in the network. This is especially important for the further development of wind farm security systems, which at the time, are not able to provide high accuracy of intrusion detection into the information flow.

Estimating Sentence-like Structure in Synthetic Languages Using Information Topology.

Estimating sentence-like units and sentence boundaries in human language is an important task in the context of natural language understanding. While this topic has been considered using a range of techniques, including rule-based approaches and supervised and unsupervised algorithms, a common aspect of these methods is that they inherently rely on a priori knowledge of human language in one form or another. Recently we have been exploring synthetic languages based on the concept of modeling behaviors using emergent languages. These synthetic languages are characterized by a small alphabet and limited vocabulary and grammatical structure. A particular challenge for synthetic languages is that there is generally no a priori language model available, which limits the use of many natural language processing methods. In this paper, we are interested in exploring how it may be possible to discover natural ‘chunks’ in synthetic language sequences in terms of sentence-like units. The problem is how to do this with no linguistic or semantic language model. Our approach is to consider the problem from the perspective of information theory. We extend the basis of information geometry and propose a new concept, which we term information topology, to model the incremental flow of information in natural sequences. We introduce an information topology view of the incremental information and incremental tangent angle of the Wasserstein-1 distance of the probabilistic symbolic language input. It is not suggested as a fully viable alternative for sentence boundary detection per se but provides a new conceptual method for estimating the structure and natural limits of information flow in language sequences but without any semantic knowledge. We consider relevant existing performance metrics such as the F-measure and indicate limitations, leading to the introduction of a new information-theoretic global performance based on modeled distributions. Although the methodology is not proposed for human language sentence detection, we provide some examples using human language corpora where potentially useful results are shown. The proposed model shows potential advantages for overcoming difficulties due to the disambiguation of complex language and potential improvements for human language methods.

SDN-assisted technique for traffic control and information execution in vehicular adhoc networks

Recently, applying Software-Defined Networking (SDN) in automotive industry has sparked a lot of interest due to its capacity to ensure focused organization across the board coupled with its vast versatility of isolating data from the control plane. However, the major concern in traffic network is that varied crisis and diversion messages are expected to be shared or brought into by the connected vehicles. Thus, disclosure of this information results in an evaluation of the steering and control of such messages. This research aims at employing SDN-assisted technique in heterogeneous vehicular organizations in executing information traffic-using optimal course determination and guiding in situations like a blockage in Vehicle Ad Hoc Networks (VANETs). To realize this, a workable traffic reproduction model is proposed and the experiments realized a re-enactment findings in terms of Round Trip Time (RTT) and Packet Delivery Ratio (PDR) and improves the conventional information flow structure.

Phase-resolved characteristics of bubbles in cloud cavitation shedding cycles

To investigate the complex physics and mixture properties inside sheet/cloud cavities, we performed an experimental study that used phase-resolved optical probes and high-speed photography to acquire both vapor-water information and large-scale flow structures. We found distinctive characteristics at different scales: the large scale of cavitation shedding, and the small scale of bubbles. Large-scale cavitation behavior was examined using the mean void fraction profile and raw signal history, characterized by the shedding of cavity cloud. Small-scale information on bubbles and probability density function of bubble size distributions were quite similar at various measurement locations. Based on the experimental data, we proposed an empirical bubble size distribution and discussed the corresponding statistical quantities for cloud cavitation.

Organizational and Economic Factors of Health System Optimization

The article discusses approaches to optimizing the health care system in the context of reform. In the article identified institutional and organizational constraints to shape the health care system model with the necessary components: quality assurance, financial constraints, and optimization of budgetary resources. In the article developed the approaches to the formation of the health care system at the regional level. To this aim, the factors that influence the efficiency of the organization and functioning of the medical system are identified. Particular attention is paid to the organization of quality medical care in the united territorial communities of the Sumy region. The factorial analysis made it possible to form models of organization of medical institutions in the united territorial communities, taking into account financial and organizational constraints. In the process of research used the conditionally constant and variable factors in the model to adapt effectively to changes in the external environment. Modeling is based on a functional approach (model based on data flow construction). This approach allows you to identify functional problems of the system, to determine the basic requirements of stakeholders, to determine the structure of information flows in the model. In this case, the health care system is considered as a set of processes (related or independent) that achieve this goal. Then the overall management of the system can be represented as the management of a set of these processes in order to obtain the end result (with the specified characteristics). The peculiarity of this approach to modeling is to summarize a large array of input information, to establish direct and feedback between economic, demographic, geographical, social indicators of community development and medical indicators (results) that characterize the level of public health. The introduction of an integral indicator in the calculation model will solve both general institutional tasks, such as temporal dynamics within the created unified territorial community and creating a competitive position in comparison with other unified territorial communities of the region, and making local management decisions based on the influence of individual factors on the functioning of the health care system. The application of the model makes the practical importance in the control of deviation of the actual value of the integral indicator from the normative and planned result.

ОПЫТ ВНУТРИРЕГИОНАЛЬНОГО АНАЛИЗА ОБРАЗОВ ТЕРРИТОРИИ В ДИСКУРСЕ СОЦИАЛЬНЫХ МЕДИА (НА ПРИМЕРЕ СЕВЕРО-КАВКАЗСКОГО ФЕДЕРАЛЬНОГО ОКРУГА)

The article examines the territorial features of the representation of the municipalities of the North Caucasus Federal District in leading social media. We analyze the main approaches to defining the geographical image of a territory; explore the theoretical foundations underlying the study of social media from the perspective of geographical science; identify a set of research terms and concepts used to analyze images of territories in social media. The paper proposes a new method of multi-scale monitoring of the territory’s image in social media, which can be adapted for other regions of Russia. The analysis of information in social media makes it possible to identify the peculiarities of the representation of a particular territory, as well as the mental connections between municipalities, cities, regions, and the degree of their influence on the geographical image of each other. With the data collected by the Medialogia information and analytical system, the geography of publications, their authors, their audience at the federal, regional, and local levels were studied. Based on this material, the intensity, both absolute and relative, and the tonality of the information flow for each municipality of the North Caucasus Federal District were determined. The leading regional centers for the formation of social media content were identified. The most frequently mentioned topics, words, and hashtags in relation to each territory were considered. A geographical analysis of the features of the formation of information messages was carried out, which made it possible to identify territories with a predominance of an external or internal image. On the basis of the analysis, the characteristic types of the information flow structure were proposed. The main problems of forming a positive image of the North Caucasus territory in social media are were identified. The results obtained are of practical significance for territorial branding and promotion of the region's territories in social media.

Understanding the Transformation to a Knowledge-Based Health Bioeconomy: Exploring Dynamics Linked to Preventive Medicine in Kenya

The bioeconomy transition is seen as a means to achieving industrial competitiveness. Targeted actions on leverage points can have specific effects on transitional changes in system dynamics; these actions have yet to be identified in the context of the knowledge-based health bioeconomy in Kenya. This paper employs system dynamics and grounded theory to identify causations linked to the feedback mechanisms in a complex adaptive system specific to preventive medicine in Kenya. The causal relations identified will allow for extended empirical interrogations. We conducted sixteen semi-structured interviews with key informants using purposive and theoretical sampling. Through these interviews, we obtained detailed information on trends for leverage points for a transition to a bioeconomy in Kenya. We developed three qualitative themes along the structure of information flows, rules, and goals of the system. In addition, we determined the overall perception of the health bioeconomy and elaborated stakeholder-specific applications. We identified a dissociation as a general perception that knowledge generation is the preservation of the public sector. Government effectiveness was found to affect public-service turnaround time, transparency, and regulatory interventions. Finally, we identified weak network failures as the key system failures whose functional deficiencies can be exploited for future policy legitimation.