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Compressive Information Extraction: A Dynamical Systems Approach1
2012
IFAC Proceedings Volumes
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time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. ...
Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently ...
2.1.1 Robust identification of hybrid systems: As outlined before, the main idea that drove this research was to treat the observed data as the output of an underlying switched dynamical system, with events ...
doi:10.3182/20120711-3-be-2027.00430
fatcat:4xxsi2orf5bdlpddghh3fpl65e
Handling missing data in multivariate time series using a vector autoregressive model-imputation (VAR-IM) algorithm
2018
Neurocomputing
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The objective of this paper is to introduce a new algorithm for handling missing data from multivariate time series datasets. ...
Generally, the VAR-IM method achieved significant improvement of the imputation tasks as compared with the other two methods. ...
MARSS incorporates an expectation-maximization (EM) algorithm. ...
doi:10.1016/j.neucom.2017.03.097
fatcat:axuyb2vnjzdhpfkmkktsd2lgiy
A state-space approach to sparse dynamic network reconstruction
[article]
2018
arXiv
pre-print
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Furthermore, we avoid various difficulties arising in gradient computation by using the Expectation Minimization (EM) algorithm instead. ...
., using ARX models) requires a large amount of parameters in model selection. ...
EXPECTATION MAXIMIZATION In the "outer-loop" EM algorithm, we choose z(t) as the latent variable, whose values at t 1 , . . . , t N are the "missing data", denoted by Z N {z 1 , . . . , z N } = 0 I n×p ...
arXiv:1811.08677v1
fatcat:3d27dyzu7rafxdh7e4izhtmi6m
Analysis and Computational Dissection of Molecular Signature Multiplicity
2010
PLoS Computational Biology
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The new algorithm identifies exactly the set of optimal signatures in controlled experiments and yields signatures with significantly better predictivity and reproducibility than previous algorithms in ...
Multiplicity is a special form of data analysis instability in which different analysis methods used on the same data, or different samples from the same population lead to different but apparently maximally ...
It operates by repeated application of a signature extraction algorithm to resampled data (e.g., via bootstrapping) [6, 12, 13] . ...
doi:10.1371/journal.pcbi.1000790
pmid:20502670
pmcid:PMC2873900
fatcat:bsuohl3k35bijii2phlt2tuoxm
Subspace-based Identification Algorithm for Characterizing Causal Networks in Resting Brain
[article]
2011
arXiv
pre-print
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To simultaneously satisfy these two concerns, in this paper, we introduce a novel state-space system identification approach for studying causal interactions among brain regions in the absence of explicit ...
Our simulations demonstrate that Subspace-based Identification Algorithm (SIA) is sufficiently robust against above-mentioned factors, and can reliably unravel the underlying causal interactions of resting-state ...
We are thankful to Jason Smith for providing the implementation of EM algorithm and helpful discussions. ...
arXiv:1108.4644v2
fatcat:g4p3aoa4mzestfw4yesmleeaua
An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems
[article]
2018
arXiv
pre-print
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We demonstrate that the algorithmic information content of a system is deeply connected to its potential dynamics, thus affording an avenue for moving systems in the information-theoretic space and controlling ...
The method consists in finding and applying a series of controlled interventions to a dynamical system to estimate how its algorithmic information content is affected when every one of its elements are ...
This process of identification of algorithmic contributing elements allows systems to be 'peeled back' to their most likely causal origin, unveiling their generating principles (Supplement Section 2), ...
arXiv:1709.05429v11
fatcat:hldyt3tygjcqtft543scvbaamm
Feature selection for the accurate prediction of septic and cardiogenic shock ICU mortality in the acute phase
[article]
2018
biorxiv/medrxiv
pre-print
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Missing data imputation techniques and machine learning models were used, followed by feature selection from different data subsets. ...
It requires immediate treatment, which is why the development of tools for planning therapeutic interventions is required to deal with shock in the critical care environment. ...
Acknowledgments This research was supported by the European FP7 project Shockomics (Nr. 602706), Multiscale approach to the identification of molecular biomarkers in acute heart failure induced by shock ...
doi:10.1101/337261
fatcat:bndqicbz45huve7ws37h44vojq
Feature selection for the accurate prediction of septic and cardiogenic shock ICU mortality in the acute phase
2018
PLoS ONE
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Missing data imputation techniques and machine learning models were used, followed by feature selection from different data subsets. ...
It requires immediate treatment, which is why the development of tools for planning therapeutic interventions is required to deal with shock in the critical care environment. ...
Acknowledgments This research was supported by the European FP7 project Shockomics (Nr. 602706), Multiscale approach to the identification of molecular biomarkers in acute heart failure induced by shock ...
doi:10.1371/journal.pone.0199089
pmid:30457997
pmcid:PMC6245679
fatcat:lozfxfpfszbrnjmfx32bbtmvsa
An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems
2019
iScience
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Next, the model-based interventional or causal calculus is evaluated and validated using (1) a huge large set of small graphs, (2) a number of larger networks with different topologies, and finally (3) ...
The method consists of applying a series of controlled interventions to a networked system while estimating how the algorithmic information content is affected. ...
AUTHOR CONTRIBUTIONS
DECLARATION OF INTERESTS The authors declare no competing interests. ...
doi:10.1016/j.isci.2019.07.043
pmid:31541920
pmcid:PMC6831824
fatcat:xpeyzezusretlka6vnxecqloru
Sparse Nonlinear MIMO Filtering and Identification
[chapter]
2013
Signals and Communication Technology
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In this chapter system identification algorithms for sparse nonlinear multi input multi output (MIMO) systems are developed. ...
In other cases it appears as a pragmatic modelling approach that seeks to cope with the curse of dimensionality, particularly acute in nonlinear systems like Volterra type series. ...
Acknowledgements This research has been co-financed by the European Union (European Social Fund -ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National ...
doi:10.1007/978-3-642-38398-4_7
fatcat:a7awuvl2prfvfkylo2za6cprz4
Simulation-based methods for blind maximum-likelihood filter identification
1999
Signal Processing
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This paper presents a survey of recent stochastic algorithms, related to the expectation-maximization (EM) principle, that make it possible to estimate the parameters of the unknown linear system in the ...
Blind linear system identification consists in estimating the parameters of a linear time-invariant system given its (possibly noisy) response to an unobserved input signal. ...
by maximization using the expected values of the sufficient statistics as if they had been computed from observed values of the missing data (see [19, 62] , the historical review of [32] , as well as ...
doi:10.1016/s0165-1684(98)00182-0
fatcat:odgii7yg3jckbb7smvfh4m4kem
OptiMissP: A dashboard to assess missingness in proteomic data-independent acquisition mass spectrometry
2021
PLoS ONE
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Missing values are a key issue in the statistical analysis of proteomic data. ...
This is complemented by topological data analysis which provides additional insight to the structure of the data and their missingness. ...
Missing values imputation can be performed with any of five implemented methods: Lowest Value, MissForest, MICE, Probabilistic PCA and Expectation-Maximization algorithm. ...
doi:10.1371/journal.pone.0249771
pmid:33857200
fatcat:c5fyljjg35hurn64wjz6fliw3y
An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems
[article]
2017
bioRxiv
pre-print
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This new approach yields a suite of powerful parameter-free algorithms of wide applicability, ranging from the discovery of causality, dimension reduction, feature selection, model generation, a maximal ...
We highlight the capability of the methods to pinpoint key elements related to cell function and cell development, conforming with biological knowledge and experimentally validated data, and demonstrate ...
This process of identification of algorithmic contributing elements allows systems to be 'peeled back' to their most likely causal origin, unveiling their generating principles (Supplement Section 2), ...
doi:10.1101/185637
fatcat:7ew3vutxargtpisjmbp2hojjiu
Causal set generator and action computer
2018
Computer Physics Communications
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2017 pre-print arXiv:1709.03013v1 fatcat:akpiqdonnzhrfn5ciokpochzauOther Versions
We highlight several important features of the code, including the compact data structures, the O(N^2) causal set generation process, and several implementations of the O(N^3) algorithm to compute the ...
We also analyze the scaling of the algorithms' running times with respect to the problem size and available resources, with suggestions on how to modify the code for future hardware architectures. ...
All of the algorithms mentioned so far may be easily optimized for a system with (512-bit) ZMM registers, and we should expect the greatest speedup for the set operations. ...
doi:10.1016/j.cpc.2018.06.008
fatcat:mftxbq5hdvgungymfi6exhrtyu
Maximum Entropy Vector Kernels for MIMO system identification
[article]
2016
arXiv
pre-print
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Recent contributions have framed linear system identification as a nonparametric regularized inverse problem. ...
In contrast with previous literature on reweighted nuclear norm penalties, our kernel is described by a small number of hyper-parameters, which are iteratively updated through marginal likelihood maximization ...
This approach has then been extended to the case of missing input and output data [33] or to short data records [55] . ...
arXiv:1508.02865v2
fatcat:z2n45p2mwnh5xh7shxn5tfwaze
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