133 research outputs found
Critical temperatures in driven binary mixtures with conserved and non-conserved dynamics
No full textGranger causality and the inverse Ising problem
No full textThe inference of the couplings of an Ising model with given means and correlations is called
the inverse Ising problem. This approach has received a lot of attention as a tool to analyze
neural data. We show that autoregressive methods may be used to learn the couplings of
an Ising model, also in the case of asymmetric connections and for multispin interactions.
We find that, for each link, the linear Granger causality is two times the corresponding
transfer entropy (i.e., the information flow on that link) in the weak coupling limit. For
sparse connections and a low number of samples, the `1 regularized least squares method
is used to detect the interacting pairs of spins. Nonlinear Granger causality is related to
multispin interactions
Kernel Method for Nonlinear Granger Causality
No full textImportant information on the structure of complex systems can be obtained by measuring to what extent
the individual components exchange information among each other. The linear Granger approach, to
detect cause-effect relationships between time series, has emerged in recent years as a leading statistical
technique to accomplish this task. Here we generalize Granger causality to the nonlinear case using the
theory of reproducing kernel Hilbert spaces. Our method performs linear Granger causality in the feature
space of suitable kernel functions, assuming arbitrary degree of nonlinearity.We develop a new strategy to
cope with the problem of overfitting, based on the geometry of reproducing kernel Hilbert spaces.
Applications to coupled chaotic maps and physiological data sets are presented
Causal Information Approach to Partial Conditioning in Multivariate Data Sets
No full textWhen evaluating causal influence from one time series to another in a multivariate data set it is necessary to take into account
the conditioning effect of the other variables. In the presence of many variables and possibly of a reduced number of samples, full
conditioning can lead to computational and numerical problems. In this paper, we address the problem of partial conditioning to
a limited subset of variables, in the framework of information theory. The proposed approach is tested on simulated data sets and
on an example of intracranial EEG recording from an epileptic subject. We show that, in many instances, conditioning on a small
number of variables, chosen as the most informative ones for the driver node, leads to results very close to those obtained with
a fully multivariate analysis and even better in the presence of a small number of samples. This is particularly relevant when the
pattern of causalities is sparse
Kernel-Granger causality and the analysis of dynamical networks
No full textWe propose a method of analysis of dynamical networks based on a recent measure of Granger causality
between time series, based on kernel methods. The generalization of kernel-Granger causality to the multivariate
case, here presented, shares the following features with the bivariate measures: i the nonlinearity of the
regression model can be controlled by choosing the kernel function and ii the problem of false causalities,
arising as the complexity of the model increases, is addressed by a selection strategy of the eigenvectors of a
reduced Gram matrix whose range represents the additional features due to the second time series. Moreover,
there is no a priori assumption that the network must be a directed acyclic graph. We apply the proposed
approach to a network of chaotic maps and to a simulated genetic regulatory network: it is shown that the
underlying topology of the network can be reconstructed from time series of node’s dynamics, provided that a
sufficient number of samples is available. Considering a linear dynamical network, built by preferential attachment
scheme, we show that for limited data use of the bivariate Granger causality is a better choice than
methods using L1 minimization. Finally we consider real expression data from HeLa cells, 94 genes and 48
time points. The analysis of static correlations between genes reveals two modules corresponding to wellknown
transcription factors; Granger analysis puts in evidence 19 causal relationships, all involving genes
related to tumor development
Blind post-test analysis performed by RELAP5/MOD2 code of the LOBI-MOD2 small break test BL-44 high power counterpart to LOBI BL-34 experiment
No full textThe document deals with the description of results obtained by the Relap5 code in the ‘blind post-test’ (i.e. without any code-user modification of code-related input parameters) of the Counterpart Test (CT) BL-44 performed in the Pressurized Water Reactor (PWR) experimental simulator LOBI installed at the EURATOM JRC Research Center in Ispra, Italy. The Relap5 is the well-known computer code developed at Idaho National Laboratory in US: the code is in use at UNIPI since more than a decade. The LOBI loop is an Integral Test Facility (ITF) simulating with full height, full pressure, full linear power a German 4-loop KWU-Siemens PWR. The concerned test is the high power CT of the LOBI test BL-34 which was the CT of experiments performed in SPES, BETHSY and LSTF ITF. Because of the maximum power limitations of BETHSY and LSTF, the test BL-34 was performed starting from a ‘low power condition’ (about 20% nominal power): thus, the initial 10-50 seconds of the experiments were ‘highly’ distorted with respect to the test scenario expected in the reference Nuclear Power Plant (NPP), the prototype. Therefore a new test was performed (in the same way in SPES ITF, CT SP-SB-03 and SP-SB-04, low and high power test were performed) and called BL-44.. The present document discusses the comparison between code prediction and measured data in test BL-44
Post-test analysis performed by RELAP5/MOD2 code of the LOBI/MOD2 small break test BL-34 counterpart to LSTF, SPES and BETHSY Experiments
No full textThe document deals with the description of results obtained by the Relap5 code in the post test analysis of the Counterpart Test (CT) BL-34 performed in the Pressurized Water Reactor (PWR) experimental simulator LOBI installed at the EURATOM JRC Research Center of Ispra in Italy. The Relap5 is the well-known computer code developed at Idaho National Laboratory in US: the code is in use at UNIPI since more than a decade. The LOBI loop is an Integral Test Facility (ITF) simulating with full height, full pressure, full linear power a German type 4-loop KWU-Siemens PWR. The concerned test is the low power CT of experiments performed in SPES, BETHSY and LSTF ITF. Because of the maximum power limitations of BETHSY and LSTF, the test BL-34 was performed starting from a ‘low power condition’ (about 20% nominal power): thus, the initial 10-50 seconds of the experiments were ‘highly’ distorted with respect to the test scenario expected in the reference Nuclear Power Plant (NPP), the prototype. Therefore a new test was subsequently performed at high power and called BL-44. The present document discusses the results of code application to the analysis of measured data in BL-34
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