Narrow Results

The study of connectivity patterns of a system’s variables, such as multi-channel electroencephalograms (EEG), is of utmost importance towards a better understanding of its internal evolutionary mechanisms. Here, the problem of estimating the connectivity network from multivariate time series in the presence of prominent unobserved variables is addressed. The causality measure of partial mutual information from mixed embedding (PMIME), designed to estimate direct lag-causal effects in the presence of many observed variables, is adapted to estimate also zero-lag effects, the so-called instantaneous causality. We term the proposed advanced method, PMIME0. The estimation of instantaneous causality by PMIME0 is a signature of the presence of hidden source in the observed system, as demonstrated analytically in a toy model. It is further demonstrated that the PMIME0 identifies the true instantaneous with great accuracy in a variety of high-dimensional dynamical systems. The method is applied to EEG data with epileptiform discharges (EDs), and the results imply a strong impact of unobserved confounders during the EDs. This finding comes as a possible explanation for the increased levels of causality during epileptic seizures estimated by some measures affected by the presence of a common source.

In the study of complex systems one of the major concerns is the detection and characterization of causal interdependencies and couplings between different subsystems. The nature of such dependencies is typically not only nonlinear but also asymmetric and thus makes the use of symmetric and linear methods ineffective. Moreover, signals sampled from real world systems are noisy and short, posing additional constraints on the estimation of the underlying couplings. In this article, we compare a set of six recently introduced methods for quantifying the causal structure of bivariate time series extracted from systems with complex dynamical behavior. We discuss the usefulness of the methods for detecting asymmetric couplings and directional flow of information in the context of uni- and bidirectionally coupled deterministic chaotic systems.

An extension of transfer entropy, called partial transfer entropy (PTE), is proposed to detect causal effects among observed interacting systems, and particularly to distinguish direct from indirect causal effects. PTE is compared to a linear direct causality measure, the Partial Directed Coherence (PDC), on known linear stochastic systems and nonlinear deterministic systems. PTE performs equally well as PDC on the linear systems and better than PDC on the nonlinear systems, both being dependent on the selection of the measure specific parameters. PTE and PDC are applied to electroencephalograms of epileptic patients during the preictal, ictal and postictal states, and PTE turns out to detect better changes of the strength of the direct causality at specific pairs of electrodes and for the different states.

One of the main features of information flow control is to ensure the enforcement of privacy and regulated accessibility. However, most information flow models that have been proposed do not provide substantial assurance to enforce end-to-end confidentiality policies or they are too restrictive, overprotected, and inflexible. This paper presents an approach to control flow information in object-oriented systems using versions, thus allowing considerable flexibility without compromising system security by leaking sensitive information. Models based on message filtering intercept every message exchanged among objects to control the flow of information. Versions are proposed to provide flexibility and avoid unnecessary and undesirable blocking of messages during the filtering process. Two options of operations are supported by versions — cloning reply and non-cloning reply. Furthermore, we present an algorithm which enforces message filtering through these operations.

Controlling information flows to prevent information leakage within an application is essential. According to the maturity of object-oriented techniques, many models were developed for the control in object-oriented systems. Since objects may be dynamically instantiated during program execution, controlling information flows among objects is difficult. Our research revealed that association is useful in the control. We developed an association-based information flow control model for object-oriented systems. It precisely controls information flows among objects through associations and constraints. It also offers features such as controlling method invocation through argument sensitivity, allowing declassification, allowing purpose-oriented method invocation, and precisely controlling write access. This paper proposes the model and the implementation of the model, which is composed of the language AbFlow (association-based flow) and its supporting environment.

A time series model for the FX dynamics is presented which takes into account structural peculiarities of the market, namely its heterogeneity and an information flow from long to short time horizons. The model emerges from an analogy between FX dynamics and hydrodynamic turbulence. The heterogeneity of the market is modeled in the form of a multiplicative cascade of time scales ranging from several minutes to a few months, analogous to the Kolmogorov cascade in turbulence.

The model reproduces well the important empirical characteristics of FX rates for major currencies, as the heavy-tailed distribution of returns, their change in shape with the increasing time interval, and the persistence of volatility.

Cointegration tests and ex ante trading rules are applied to study cross-market linkages between the Taiwan Index futures contracts listed on the Singapore Exchange and the Taiwan Stock Exchange Capitalization-weighted Stock Index futures contracts listed on the Taiwan Futures Exchange. The exchange rate-adjusted returns of the two futures series do not differ significantly in mean but in variances, and show significant mean-reverting tendencies between them. Our trading strategies are able to generate statistically significant, if economically insignificant, profits, while our Granger causality tests demonstrate that information flows primarily from the Singapore market to the Taiwan market, a result confirming other research.

We discuss a novel role locking protocol (RLP) to prevent illegal information flow among objects in a role-based access control (RBAC) model. In this paper, we define a conflicting relation among roles "a role R₁ conflicts with another role R₂" to show that illegal information flow may occur if a transaction associated with role R₁ is performed before another transaction with role R₂. Here, we introduce a role lock on an object to abort a transaction with role R₁ if another transaction with role R₂ had been already performed on the object. Role locks are not released even if transactions issuing the role locks commit. After data in an object o₁ flow to another object o₂, if the object o₁ is updated, the data in the object o₂ is independent of the object o₁, i.e. obsolete. A role lock on an object can be released if information brought into the object is obsolete. We discuss how to release obsolete role locks. We also discuss how to implement the role locking protocol in single-server and multi-server systems.

The total energy the brain consumed and the intensities of information flows across different brain regions in an intellectual activity may help to explain an individual’s intelligence level. To verify this assumption, 43 students aged 18–25 were recruited as the research subjects. Their intelligence quotients (IQ) were scored by using Wechsler Adult Intelligence Scale (WAIS), while their electroencephalogram (EEG) signals were recorded simultaneously by using Neuroscan system. The total energy and distribution patterns of EEG signals were acquired in Curry 8.0. The intensities of information flow across different brain regions were measured by Phase Slope Index (PSI). 20 channels and 190 channel combinations were selected for data analysis. The results show that the IQ score negatively correlates to the EEG energy and positively correlates to the intensities of information flows at specific frequency bands in specific channel pairs, especially in some long distance (18–24cm) channel pairs.

We use a notion of causal independence based on intervention, which is a fundamental concept of the theory of causal networks, to define a measure for the strength of a causal effect. We call this measure "information flow" and compare it with known information flow measures such as transfer entropy.

The logistic regression model is one of the most widely used classification models. In some practical situations, few samples and massive uncertain information bring more challenges to the application of the traditional logistic regression. This paper takes advantages of the hesitant fuzzy set (HFS) in depicting uncertain information and develops the logistic regression model under hesitant fuzzy environment. Considering the complexity and uncertainty in the application of this logistic regression, the concept of hesitant fuzzy information flow (HFIF) and the correlation coefficient between HFSs are introduced to determine the main factors. In order to better manage situations with small samples, a new optimized method based on the maximum entropy estimation is also proposed to determine the parameters. Then the Levenberg–Marquardt Algorithm (LMA) under hesitant fuzzy environment is developed to solve the parameter estimation problem with fewer samples and uncertain information in the logistic regression model. A specific implementation process for the optimized logistic regression model based on the maximum entropy estimation under the hesitant fuzzy environment is also provided. Moreover, we apply the proposed model to the prediction problem of Emergency Extreme Air Pollution Event (EEAPE). A comparative analysis and a sensitivity analysis are further conducted to illustrate the advantages of the optimized logistic regression model under hesitant fuzzy environment.

Information sharing is an important factor for effectiveness within the internal supply chain. In this paper we use a methodology for mapping information flows in an internal supply chain, and case studies of two Swedish multinational organisations. Eight retrospective cases were used to map, describe and analyse the information flow that supports the physical material flow from the receipt of an order to point of delivery. Every involved person was interviewed on at least one occasion each. The interviews were conducted to map and describe the information and physical material flow. The aim was to identify factors that could improve and rationalise information flows and generate a better flow within the organisation.

The study shows the importance of an integrated information system, but also clearly indicates the importance of a collaborative culture and an awareness of the human–technology interface. The study also shows that three factors of interface distortions are most frequent in the cases: (1) changes registered in the database trigger no action among the staff, (2) new knowledge to staff is stored only orally and not in the database, and (3) interface between the paper system and the database, and between the old and the new information storage culture.

RFID (Radio Frequency Identification) technology is put forward as a new data collection method to bridge the gap between information flow and material flow. The data achieved by RFID can be shared by both MES and ERP simultaneously. A simulated WIP (Work In Process) machining process application case study is used in the paper to show how the synchronization is realized.

Variations of the bath energy are compared with the information flow in local dephasing channels. Special correlated initial conditions are prepared from the thermal equilibrium of the whole system, by performing a selective measurement on the qubit. The spectral densities under study are ohmic-like at low frequencies and include logarithmic perturbations of the power-law profiles. The bath and the correlation energy alternately increase or decrease, monotonically, over long times, according to the value of the ohmicity parameter, following logarithmic and power laws. Consider initial conditions such that the environment is in a thermal state, factorized from the state of the qubit. In the superohmic regime the long-time features of the information flow are transferred to the bath and correlation energy, by changing the initial condition from the factorized to the specially correlated, even with different temperatures. In fact, the low-frequency structures of the spectral density that provide information backflow with the factorized initial condition, induce increasing (decreasing) bath (correlation) energy with the specially correlated initial configuration. By performing the same change of initial conditions, the spectral properties providing information loss, produce decrease (increase) of the bath (correlation) energy.

The energy of the bosonic bath and the flow of quantum information in local dephasing channels are studied over short and long times in case the distribution of frequency modes of the bosonic bath exhibits a low-frequency gap. The initial conditions consist in special correlations between the qubit and the bosonic bath or are factorized, and involve thermal states of the whole system or of the bath. The low-frequency gap generates damped oscillations of the bath energy around the asymptotic value, for the correlated initial conditions, and induces the open system to alternately loose and gain information, for the factorized initial configurations. The long-time oscillations of the bath energy become regular and the frequency of the oscillations coincides with the upper cut-off frequency of the spectral gap. Regular long-time sequences of intervals are found over which the bath energy increases (decreases), for the correlated initial conditions, and information is lost (gained) by the open system, for the factorized initial configurations, even at different temperatures. This relation is reversed, if compared to the one obtained without the low-frequency gap, and can fail if the spectral density is tailored near the spectral gap according to power laws with odd natural powers.

Stock indices are key indicators of the economy since they indicate the strength of a country’s stock market. For this reason, causality, information flow and co-movement analysis of stock indices gain importance in comparing countries’ economies. Here, we apply a novel approach by analyzing the results of two different methodologies; in wavelet coherence (WTC) analysis, the co-movement between stock indices provided and coherent areas can be shown, and information flow is indicated for five-year periods, especially on coherent zones by Transfer Entropy (TE), which detects cause-and-effect relations. This paper analyzed the information flow and co-movement among FTSE100 in the United Kingdom, the DAX in Germany and S&P500 Index in the United States stock indices. Three different results are obtained as follows: (1) DAX is on the leading side in general for five-year periods, (2) bidirectional information flows arise for every pair in the coherent periods and (3) TE-guided WTC analysis shows that TE sign change can be explained by phase angle direction obtained with WTC. These results indicate that both the methods yield proper outcomes in coherent time zones and during financial crisis like the COVID period, which we have faced for two years; for this reason, the results were also obtained for the COVID period, and in general, that shows DAX dominated other indices. We published this study to help researchers understand the connectedness between stock indices and investors avoiding risk in their stock portfolios, especially during financial crisis periods.

The information flow property of Non-Interference was recently relaxed into Abstract Non-Interference (ANI), a weakened version where attackers can only observe properties of data, rather than their exact value. ANI was originally defined on integers: a property models the set of numbers satisfying it. The present work proposes an Object-Oriented, Java-based formulation of ANI, where data take the form of objects, and the observed property comes to be their class. Relevant data are stored in fields; the execution of a program is taken to be the invocation of some (public) method by an external user; a class is secure if, for all its public methods, the class of its public data after the execution does not depend on the initial class of its private data. The relation ANI lies in the representation of abstract domains as class hierarchies: upper closure operators map objects into the smallest class they belong to. An analyzer for a non-trivial subset of Java is illustrated, which is sound since programs are never misclassified as secure.

Communication requirements are considered for the cooperative control of wide area search munitions where resource allocation is performed by an iterative network flow. We briefly outline both the single and iterative network flow assignment algorithms and their communication requirements. Then, using the abstracted communication framework recently incorporated into AFRL's MultiUAV simulation package, a model is constructed to investigate the peak and average data rates occurring in a sequence of vehicle-target scenarios using an iterative network flow for task allocation, implemented as a redundant, centralized optimization, that assumes perfect communication.

For the quantification of strength and identification of direction of coupling between two sub-systems of a complex dynamical system, observed from bivariate time series, a number of measures have been proposed that can be grouped in measures of phase synchronization, state space and information. We review all these measures and, in particular, for the information measures we examine different estimates of the probability distributions. We propose also a modification of the transfer entropy measure to span larger time windows and thus be more appropriate for flows. Simulations on systems of different types and for varying coupling strengths showed that information measures, and the modified transfer entropy measure in particular, detect best the coupling strength and direction. This is also found when applying the measures to pairs of EEG channels in order to detect the propagation of pre-epileptic brain activity.

Information causality measures, i.e. transfer entropy and symbolic transfer entropy, are modified using the concept of surrogate data in order to identify correctly the presence and direction of causal effects. The measures are evaluated on multiple bivariate time series of known coupled systems of varying complexity and on a range of embedding dimensions. The proposed modifications of the causality measures are found to reduce the bias in the estimation of the measures and preserve the zero level in the absence of coupling.