Mechanism Of Information Transfer Research Articles

How Does the Comprehensive Social Public Participation Affect Perceptions of Social Justice?

This study reveals the complex effects of the government service perception and different types of the comprehensive social public participation on the comprehensive perception of social justice and its sub-dimensions through OLS regression. Specifically, we find that the government service perception exhibits extremely significant and robust positive effects in all models. At the same time, the impact of different types of the comprehensive social public participation on perceptions of social equity shows significant variability. Rights advocacy participation demonstrates a negative effect in the model of the sense of legal and opportunity equality. This finding that suggests we need to further examine and optimize the advocacy mechanisms. The negative role of communication engagement in all sub-dimension models reveals the inadequacy of information transfer and communication mechanisms in building social trust. In contrast, the positive effect of community participation in the model of the sense of legal and opportunity equality highlights the positive role of communities in promoting social fairness, while the overall positive effect of electoral participation, although weakly significant in some sub-dimensions, suggests the potential value of the electoral system in enhancing the sense of social fairness.

Does climate policy uncertainty drive the extreme spillovers of carbon-energy-shipping markets?

In addressing the ramifications of climate change, the shipping industry, reliant on energy, has been integrated into the Emissions Trading System (ETS). This study utilizes the quantile connectedness model to investigate the information spillover mechanisms and extreme time-varying interconnections among carbon, energy, and shipping markets. Whether climate policy uncertainty drives the extreme interconnections is also discussed during both pre- and post-Paris Agreement periods, by using GARCH-MIDAS model. The empirical findings underscore the following key points: (i) the systemic connectedness is highly sensitive to market conditions and major events, increasing significantly under extreme market conditions; (ii) following the implementation of the Paris Agreement, an elevated level of informational interdependence has manifested between the carbon market and the energy and shipping sectors; (iii) the information transfer mechanism between carbon and shipping sectors creates direct and indirect spillover paths, with crude oil market mediating the indirect path; (iv) climate policy uncertainty greatly affects the extreme time-varying interconnections, and this impact has decreased after the Paris Agreement came into effect. These results offer valuable insights for market policymakers and shipping companies in achieving a balance between carbon emission reduction and shipping business, particularly amidst heightened climate policy uncertainty.

Superoscillations in high energy physics and gravity

We explore superoscillations within the context of classical and quantum field theories, presenting novel solutions to Klein–Gordon’s, Dirac’s, Maxwell’s and Einstein’s equations. In particular, we illustrate a procedure of second quantization of fields and how to construct a Fock space which encompasses Superoscillating states. Furthermore, we extend the application of superoscillations to quantum tunnelings, scatterings and mixings of particles, squeezed states and potential advancements in laser interferometry, which could open new avenues for experimental tests of quantum gravity effects. By delving into the relationship among superoscillations and phenomena such as Hawking radiation, the black hole (BH) information and the Firewall paradox, we propose an alternative mechanism for information transfer across the BH event horizon.

Towards the Disappearing Truth: Fine-Grained Joint Causal Influences Learning with Hidden Variable-Driven Causal Hypergraphs in Time Series

Causal discovery under Granger causality framework has yielded widespread concerns in time series analysis task. Nevertheless, most previous methods are unaware of the underlying causality disappearing problem, that is, certain weak causalities are less focusable and may be lost during the modeling process, thus leading to biased causal conclusions. Therefore, we propose to introduce joint causal influences (i.e., causal influences from the union of multiple variables) as additional causal indication information to help identify weak causalities. Further, to break the limitation of existing methods that implicitly and coarsely model joint causal influences, we propose a novel hidden variable-driven causal hypergraph neural network to meticulously explore the locality and diversity of joint causal influences, and realize its explicit and fine-grained modeling. Specifically, we introduce hidden variables to construct a causal hypergraph for explicitly characterizing various fine-grained joint causal influences. Then, we customize a dual causal information transfer mechanism (encompassing a multi-level causal path and an information aggregation path) to realize the free diffusion and meticulous aggregation of joint causal influences and facilitate its adaptive learning. Finally, we design a multi-view collaborative optimization constraint to guarantee the characterization diversity of causal hypergraph and capture remarkable forecasting relationships (i.e., causalities). Experiments are conducted to demonstrate the superiority of the proposed model.

Stereocontrolled Self-Assembly of a Helicate-Bridged CuI12L4 Cage That Emits Circularly Polarized Light.

Control over the stereochemistry of metal-organic cages can give rise to useful functions that are entwined with chirality, such as stereoselective guest binding and chiroptical applications. Here, we report a chiral CuI12L4 pseudo-octahedral cage that self-assembled from condensation of triaminotriptycene, aminoquinaldine, and diformylpyridine subcomponents around CuI templates. The corners of this cage consist of six head-to-tail dicopper(I) helicates whose helical chirality can be controlled by the addition of enantiopure 1,1'-bi-2-naphthol (BINOL) during the assembly process. Chiroptical and nuclear magnetic resonance (NMR) studies elucidated the process and mechanism of stereochemical information transfer from BINOL to the cage during the assembly process. Initially formed CuI(BINOL)2 thus underwent stereoselective ligand exchange during the formation of the chiral helicate corners of the cage, which determined the overall cage stereochemistry. The resulting dicopper(I) helicate corners of the cage were also shown to generate circularly polarized luminescence.

Criticality supports cross-frequency cortical-thalamic information transfer during conscious states.

Consciousness is thought to be regulated by bidirectional information transfer between the cortex and thalamus, but the nature of this bidirectional communication - and its possible disruption in unconsciousness - remains poorly understood. Here, we present two main findings elucidating mechanisms of corticothalamic information transfer during conscious states. First, we identify a highly preserved spectral channel of cortical-thalamic communication that is present during conscious states, but which is diminished during the loss of consciousness and enhanced during psychedelic states. Specifically, we show that in humans, mice, and rats, information sent from either the cortex or thalamus via δ/θ/α waves (∼1-13 Hz) is consistently encoded by the other brain region by high γ waves (52-104 Hz); moreover, unconsciousness induced by propofol anesthesia or generalized spike-and-wave seizures diminishes this cross-frequency communication, whereas the psychedelic 5-methoxy-N,N-dimethyltryptamine (5-MeO-DMT) enhances this low-to-high frequency interregional communication. Second, we leverage numerical simulations and neural electrophysiology recordings from the thalamus and cortex of human patients, rats, and mice to show that these changes in cross-frequency cortical-thalamic information transfer may be mediated by excursions of low-frequency thalamocortical electrodynamics toward/away from edge-of-chaos criticality, or the phase transition from stability to chaos. Overall, our findings link thalamic-cortical communication to consciousness, and further offer a novel, mathematically well-defined framework to explain the disruption to thalamic-cortical information transfer during unconscious states.

Predicting the cognitive function status in end-stage renal disease patients at a functional subnetwork scale.

Brain functional networks derived from functional magnetic resonance imaging (fMRI) provide a promising approach to understanding cognitive processes and predicting cognitive abilities. The topological attribute parameters of global networks are taken as the features from the overall perspective. It is constrained to comprehend the subtleties and variances of brain functional networks, which fell short of thoroughly examining the complex relationships and information transfer mechanisms among various regions. To address this issue, we proposed a framework to predict the cognitive function status in the patients with end-stage renal disease (ESRD) at a functional subnetwork scale (CFSFSS). The nodes from different network indicators were combined to form the functional subnetworks. The area under the curve (AUC) of the topological attribute parameters of functional subnetworks were extracted as features, which were selected by the minimal Redundancy Maximum Relevance (mRMR). The parameter combination with improved fitness was searched by the enhanced whale optimization algorithm (E-WOA), so as to optimize the parameters of support vector regression (SVR) and solve the global optimization problem of the predictive model. Experimental results indicated that CFSFSS achieved superior predictive performance compared to other methods, by which the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were up to 0.5951, 0.0281 and 0.9994, respectively. The functional subnetwork effectively identified the active brain regions associated with the cognitive function status, which offered more precise features. It not only helps to more accurately predict the cognitive function status, but also provides more references for clinical decision-making and intervention of cognitive impairment in ESRD patients.

An enhanced bacterial colony optimization with dynamic multi‐leader co‐evolution for multiobjective optimization problems

AbstractThe information transfer mechanism within the population is an essential factor for population‐based multiobjective optimization algorithms. An efficient leader selection strategy can effectively help the population to approach the true Pareto front. However, traditional population‐based multiobjective optimization algorithms are restricted to a single global leader and cannot transfer information efficiently. To overcome those limitations, in this paper, a multiobjective bacterial colony optimization with dynamic multi‐leader co‐evolution (MBCO/DML) is proposed, and a novel information transfer mechanism is developed within the group for adaptive evolution. Specifically, to enhance convergence and diversity, a multi‐leaders learning mechanism is designed based on a dynamically evolving elite archive via direction‐based hierarchical clustering. Finally, adaptive bacterial elimination is proposed to enable bacteria to escape from the local Pareto front according to convergence status. The results of numerical experiments show the superiority of the proposed algorithm in comparison with related population‐based multiobjective optimization algorithms on 24 frequently used benchmarks. This paper demonstrates the effectiveness of our dynamic leader selection in information transfer for improving both convergence and diversity to solve multiobjective optimization problems, which plays a significant role in information transfer of population evolution. Furthermore, we confirm the validity of the co‐evolution framework to the bacterial‐based optimization algorithm, greatly enhancing the searching capability for bacterial colony.

Structural basis of gRNA stabilization and mRNA recognition in trypanosomal RNA editing.

In Trypanosoma brucei, the editosome, composed of RNA-editing substrate-binding complex (RESC) and RNA-editing catalytic complex (RECC), orchestrates guide RNA (gRNA)-programmed editing to recode cryptic mitochondrial transcripts into messenger RNAs (mRNAs). The mechanism of information transfer from gRNA to mRNA is unclear owing to a lack of high-resolution structures for these complexes. With cryo-electron microscopy and functional studies, we have captured gRNA-stabilizing RESC-A and gRNA-mRNA-binding RESC-B and RESC-C particles. RESC-A sequesters gRNA termini, thus promoting hairpin formation and blocking mRNA access. The conversion of RESC-A into RESC-B or -C unfolds gRNA and allows mRNA selection. The ensuing gRNA-mRNA duplex protrudes from RESC-B, likely exposing editing sites to RECC-catalyzed cleavage, uridine insertion or deletion, and ligation. Our work reveals a remodeling event facilitating gRNA-mRNA hybridization and assembly of a macromolecular substrate for the editosome's catalytic modality.

Tuning Charge Transport of Oligopeptide Junctions via Interfacial Amino Acids†

Comprehensive SummaryThe charge transport through peptides can imitate the corresponding processes in more complicated proteins, enabling us to develop high‐performance bioelectronic devices and to understand the mechanisms of biomolecular recognition and information transfer. While charge transport modulation through individual peptides has been achieved via various covalent strategies, the intermolecular modulation is still very challenging, which may capture the charge transport between proteins. To tackle this challenge, we used well‐defined self‐assembled monolayers (SAMs) of oligopeptides as a model to imitate the interface of proteins and explored an interfacial amino acid strategy for charge transport modulation. We showed that non‐covalently interfaced charged amino acids (e.g., arginine) effectively attenuated the charge transport of glutamic acid terminated polyglycine peptide SAMs. By analyzing the relationship of the charge transport with the molecular frontier orbital relative to the Fermi energy level of the electrode, the molecule‐electrodes coupling (Γ), and the trends in skewness and kurtosis with voltage and the dielectric constant (εr), we showed that the attenuation was from the decreased Γ and the reduced polarizability. We present an efficient strategy to modulate the charge transport of oligopeptide‐SAM junctions by intermolecular interactions, which will advance our understanding of charge transport in biological systems and facilitate developing future electronics.

Combined knowledge transfer and adaptive coordinate systems approach for evolutionary bilevel optimization

Reducing the number of lower-level function evaluations (LFEs) is crucial to designing a bilevel evolutionary algorithm (BLEA) for bilevel optimization. Direct neighbor solution transfer (DST) is regularly used in BLEA because it can improve the lower-level search efficiency and reduce the number of LFEs. But this method still faces the challenge of misleading searches to make the algorithm converge to the wrong optimum. This paper proposes a bilevel improved multi-operator differential evolution algorithm (BL-IMODE) for bilevel optimization, which uses the knowledge transfer and adaptive coordinate systems approach. Specifically, this paper proposes a lower-level search knowledge transfer (LLS-KT) and a lower-level (LL) optimal solution distribution information transfer (LLO-DIT) mechanism to implement knowledge transfer. In LLS-KT, transferring LL solutions to the LL optimizer is similar to DST, but the LLS-KT transfers multiple solutions and thus has less possibility of misleading searches than DST. Moreover, the LLO-DIT transfers the LL optimal solution distribution information to the LL optimizer for generating promising offspring solutions. Since this method does not directly transfer LL solutions, it avoids misleading searches. Additionally, the adaptive coordinate systems (ACS) approach is used in two-level optimizers to suit different fitness landscapes and further reduce the number of LFEs. In ACS, three differential evolution mutation operators are produced in two coordinate systems. An adaptive strategy is used to select the suitable differential evolution mutation operator to generate promising offspring solutions in the Eigen coordinate or original coordinate system. Experimental studies on three test suites benchmark problems for comparison with seven state-of-the-art algorithms are conducted. The results show BL-IMODE has a significant advantage over the comparative algorithms. Finally, BL-IMODE is applied to tackle the inverse optimal control problem.

Risk assessment of infectious disease epidemic based on fuzzy Bayesian network.

The prevention and control of infectious disease epidemic (IDE) is an important task for every country and region. Risk assessment is significant for the prevention and control of IDE. Fuzzy Bayesian networks (FBN) can capture complex causality and uncertainty. The study developed a novel FBN model, integrating grounded theory, interpretive structural model, and expert weight determination algorithm for the risk assessment of IDE. The algorithm is proposed by the authors for expert weighting in fuzzy environment. The proposed FBN model comprehensively takes into account the risk factors and the interaction among them, and quantifies the uncertainty of IDE risk assessment, so as to make the assessment results more reliable. Taking the epidemic situation of COVID-19 in Wuhan as a case, the application of the proposed model is illustrated. And sensitivity analysis is performed to identify the important risk factors of IDE. Moreover, the effectiveness of the model is checked by the three-criterion-based quantitative validation method including variation connection, consistent effect, and cumulative limitation. Results show that the probability of the outbreak of COVID-19 in Wuhan is as high as 82.26%, which is well-matched with the actual situation. "Information transfer mechanism," "coordination and cooperation among various personnel," "population flow," and "ability of quarantine" are key risk factors. The constructed model meets the above three criteria. The application potential and effectiveness of the developed FBN model are demonstrated. The study provides decision support for preventing and controlling IDE.

A Method for In-Vivo Mapping of Axonal Diameter Distributions in the Human Brain Using Diffusion-Based Axonal Spectrum Imaging (AxSI).

In this paper we demonstrate a generalized and simplified pipeline called axonal spectrum imaging (AxSI) for in-vivo estimation of axonal characteristics in the human brain. Whole-brain estimation of the axon diameter, in-vivo and non-invasively, across all fiber systems will allow exploring uncharted aspects of brain structure and function relations with emphasis on connectivity and connectome analysis. While axon diameter mapping is important in and of itself, its correlation with conduction velocity will allow, for the first time, the explorations of information transfer mechanisms within the brain. We demonstrate various well-known aspects of axonal morphometry (e.g., the corpus callosum axon diameter variation) as well as other aspects that are less explored (e.g., axon diameter-based separation of the superior longitudinal fasciculus into segments). Moreover, we have created an MNI based mean axon diameter map over the entire brain for a large cohort of subjects providing the reference basis for future studies exploring relation between axon properties, its connectome representation, and other functional and behavioral aspects of the brain.

Dynamic network link prediction based on learning continuous time events

Dynamic network link prediction is a hot research problem in complex networks. Network representation learning can effectively learn the similarity of nodes and can be used for link prediction. Existing dynamic network representation learning methods mainly discrete window dynamic networks and then model them on static network snapshot graphs, which is difficult to effectively deal with dynamic networks with fine-grained temporal characteristics. In this paper, we propose a link prediction model that can learn complex temporal properties in dynamic networks. The model uses continuous time event sequences to represent dynamic networks, learns continuous temporal information and structural evolution features in the networks, and proposes a temporal attention-based information transfer mechanism to model the diffusion and aggregation of information in the networks. Finally, it transforms link prediction into a classification problem. The experiments are conducted on four real dynamic network datasets and simulated networks, using ap and auc as evaluation metrics. The experimental results of real networks demonstrate that the model can effectively learn the continuity of network evolution and obtain an effective node representation, thus improving the link prediction effect. The experimental results of the simulated network show that the effect of link prediction is related to the network model, but the model in this paper can still obtain better prediction results.

Information-transfer characteristics in network motifs

Information processing in biological systems is realized by the appropriate transmission of information flows over complex networks, such as gene regulatory, signal transduction, and neural networks. These information flows are affected by the input-signal characteristics and structural properties of network systems, such as the network topology, regulation rules, and intrinsic and environmental noise. Many biological networks frequently include several typical patterns called network motifs, which are considered to play important roles in biological functions. However, their information-theoretic properties, particularly the dependence of the information flows in each network on the input signal, remain poorly understood. In our previous study [Mori and Okada, Phys. Rev. Res. 2, 043432 (2020)], we developed a graphical expansion method to describe transfer entropy (TE), a measure of information flow, in Boolean networks in terms of multiple information pathways. There, the input signal was limited to a simple case, and the effect of the input-signal characteristics on TE was not clarified. In this paper, we improve our method to render it applicable to Boolean networks that receive input signals with arbitrary stochastic characteristics. Our formula expresses how TE is determined by the input-signal characteristics, the assignment of Boolean functions, and the noise magnitude. We find that, in both positive and negative feedback loops, TE hardly depends on the signal timescale. In contrast, coherent and incoherent feedforward loops show low- and high-pass filtering properties, respectively, for a time-varying signal, which is consistent with previous reports. The emergence of either low- or high-pass filtering is determined by the Fourier components of the Boolean functions on specific pathways transmitting information flows. Thus our formula reveals the mechanism of information transfer in network motifs and provides insights into the origin of information processing in biological networks.

Постмемориальная эстетика: синтез литературного и визуального в творчестве художников-аутсайдеров (на примере Евфросинии Керсновской)

In the process of implementing Stalin's policy, various stratums of the population were subjected to repressions: from ordinary workers to representatives of the creative and scientific intelligentsia. The art created by the artists during their imprisonment in the forced labor camps of the USSR and after their release is gradually going through the process of legitimation in contemporary artistic discourse. During the imprisonment in the camps, as well as after the release and restoration of civil rights, artists and poets reflected on the events experienced in the Gulag. For many of them, in conditions of lack of freedom, creativity became a kind of reaction to trauma. In the 20th century, the information transfer mechanism that contributes to overcoming trauma began to be called the term “postmemory” (postmemory) and used in studies of the memory of traumatic events for generations of eyewitnesses (participants) and postgenerations. The works of visual culture, broadcasting narratives associated with the structure of post-memory, have post-memorial aesthetics. Painting or literature, created in conditions of captivity or after liberation, was not just an instrument of escapism, but also had an important psychotherapeutic function, contributing to the process of retraumatization. Even after the death of the eyewitness himself, who captured his traumatic memories on paper, these narratives of history and the emotional layer of information about the past, albeit indirectly, are transmitted to representatives of post-generations, transforming their role from spectators to witnesses. At the same time, such art often differed from the recognized canons of Soviet aesthetics, which is partly due to the fact that it was created by both professional artists and outsider artists (self-taught, amateurs). One of the clearest examples is Evfrosiniya Kersnovskaya's memoirs “How Much Does a Man Cost” illustrated by the author, written by her in the 1960s and published in the 1990s. A distinctive feature of the memoirs is the combination of the literary language with fine art, which contributes to a deeper immersion of the reader into the context of not only the personal history of Euphrosyne Kersnovskaya, but also the history of the Gulag.

Cross-Mix Monitoring for Medical Image Segmentation With Limited Supervision

Image segmentation is a fundamental building block of automatic medical applications. It has been greatly improved since the emergence of deep neural networks. However, deep-learning-based models often require a large amount of manual annotations, which has seriously hindered their practical usage. To alleviate this problem, numerous works were proposed by utilizing unlabeled data based on semi-supervised frameworks. Recently, the Mean-Teacher (MT) model has been successfully applied in many scenarios due to its effective learning strategy. Nevertheless, the existing MT model still has certain limitations. Firstly, to gain extra generalization ability through consistency training, various sorts of perturbations are often added to the training data. However, if the variation is too weak, it may cause the Lazy Student Phenomenon, and cause fluctuations in the learning model. On the contrary, large image perturbations may enlarge the performance gap between the teacher and student. In this case, the student may lose its learning momentum, and more seriously, drag down the overall performance of the whole system. In order to address these issues, we introduce a novel semi-supervised medical image segmentation framework, in which a Cross-Mix Teaching paradigm is proposed to provide extra data flexibility, thus effectively avoid Lazy Student Phenomenon. Moreover, a lightweight Transductive Monitor is applied to serve as the bridge that connects the teacher and student for active knowledge distillation. In the light of this cross-network information mixing and transfer mechanism, our method is able to continuously explore the discriminative information contained in unlabeled data. Extensive experiments on challenging medical image data sets demonstrate that our method outperforms current state-of-the-art semi-supervised segmentation methods under severe lack of supervision.

Social capital and capital allocation efficiency

AbstractBased on the social norms and structural theories of social capital, this study examines the relationship between community social capital and the firms’ capital allocation efficiency. We hypothesize and find that the community social capital of a firm's headquarter area has a negative and statistically significant impact on its capital allocation inefficiency, which is robust to alternative proxies for community social capital and capital allocation inefficiency, propensity score matching and instrumental variable regressions. In addition, we find that the effect of community social capital is more pronounced for firms with poor internal ethical culture and weak network connections to outside executives and directors, implying that community social capital becomes important in these situations. This finding links prior social norms and networks literature to capital allocation studies in that the norms and networks components of community social capital discipline self‐interested managers’ behavior and reduce information asymmetry‐two channels of capital allocation efficiency. Overall, community social capital works as a compensatory monitoring and information transfer mechanism and improves the firms’ capital allocation efficiency.

Classification of tumor from computed tomography images: A brain-inspired multisource transfer learning under probability distribution adaptation.

Preoperative diagnosis of gastric cancer and primary gastric lymphoma is challenging and has important clinical significance. Inspired by the inductive reasoning learning of the human brain, transfer learning can improve diagnosis performance of target task by utilizing the knowledge learned from the other domains (source domain). However, most studies focus on single-source transfer learning and may lead to model performance degradation when a large domain shift exists between the single-source domain and target domain. By simulating the multi-modal information learning and transfer mechanism of human brain, this study designed a multisource transfer learning feature extraction and classification framework, which can enhance the prediction performance of the target model by using multisource medical data (domain). First, this manuscript designs a feature extraction network that takes the maximum mean difference based on the Wasserstein distance as an adaptive measure of probability distribution and extracts the domain-specific invariant representations between source and target domain data. Then, aiming at the random generation of parameters bringing uncertainties to prediction accuracy and generalization ability of extreme learning machine network, the 1-norm regularization is used to implement sparse constraints of the output weight matrix and improve the robustness of the model. Finally, some experiments are carried out on the data of two medical centers. The experimental results show that the area under curves (AUCs) of the method are 0.958 and 0.929 in the two validation cohorts, respectively. The method in this manuscript can provide doctors with a better diagnostic reference, which has certain practical significance.

Choroid plexus-derived extracellular vesicles exhibit brain targeting characteristics

The brain is protected against invading organisms and other unwanted substances by tightly regulated barriers. However, these central nervous system (CNS) barriers impede the delivery of drugs into the brain via the blood circulation and are therefore considered major hurdles in the treatment of neurological disorders. Consequently, there is a high need for efficient delivery systems that are able to cross these strict barriers. While most research focuses on the blood-brain barrier (BBB), the design of drug delivery platforms that are able to cross the blood-cerebrospinal fluid (CSF) barrier, formed by a single layer of choroid plexus epithelial cells, remains a largely unexplored domain. The discovery that extracellular vesicles (EVs) make up a natural mechanism for information transfer between cells and across cell layers, has stimulated interest in their potential use as drug delivery platform. Here, we report that choroid plexus epithelial cell-derived EVs exhibit the capacity to home to the brain after peripheral administration. Moreover, these vesicles are able to functionally deliver cargo into the brain. Our findings underline the therapeutic potential of choroid plexus-derived EVs as a brain drug delivery vehicle via targeting of the blood-CSF interface.