Filters








6,710 Hits in 11.0 sec

Compressive Information Extraction: A Dynamical Systems Approach1

Mario Sznaier
2012 IFAC Proceedings Volumes  
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

Faraj Bashir, Hua-Liang Wei
2018 Neurocomputing  
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]

Zuogong Yue, Johan Thunberg, Lennart Ljung, Jorge Goncalves
2018 arXiv   pre-print
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

Alexander Statnikov, Constantin F. Aliferis, Scott Markel
2010 PLoS Computational Biology  
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]

Shahab Kadkhodaeian Bakhtiari, Gholam-Ali Hossein-Zadeh
2011 arXiv   pre-print
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]

Hector Zenil, Narsis A. Kiani, Francesco Marabita, Yue Deng, Szabolcs Elias, Angelika Schmidt, Gordon Ball, Jesper Tegnér
2018 arXiv   pre-print
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]

Alexander Aushev, Vicent Ribas Ripoll, Alfredo Vellido, Federico Aletti, Bernardo Bollen Pinto, Karim Bendjelid, Antoine Herpain, Emiel Hendrik Post, Eduardo Romay Medina, Ricard Ferrer, Giuseppe Baselli
2018 biorxiv/medrxiv   pre-print
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

Alexander Aushev, Vicent Ribas Ripoll, Alfredo Vellido, Federico Aletti, Bernardo Bollen Pinto, Antoine Herpain, Emiel Hendrik Post, Eduardo Romay Medina, Ricard Ferrer, Giuseppe Baselli, Karim Bendjelid
2018 PLoS ONE  
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

Hector Zenil, Narsis A. Kiani, Francesco Marabita, Yue Deng, Szabolcs Elias, Angelika Schmidt, Gordon Ball, Jesper Tegnér
2019 iScience  
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]

G. Mileounis, N. Kalouptsidis
2013 Signals and Communication Technology  
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

Olivier Cappé, Arnaud Doucet, Marc Lavielle, Eric Moulines
1999 Signal Processing  
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

Angelica Arioli, Arianna Dagliati, Bethany Geary, Niels Peek, Philip A. Kalra, Anthony D. Whetton, Nophar Geifman, Qi Wu
2021 PLoS ONE  
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]

Hector Zenil, Narsis A. Kiani, Francesco Marabita, Yue Deng, Szabolcs Elias, Angelika Schmidt, Gordon Ball, Jesper Tegner
2017 bioRxiv   pre-print
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

William J. Cunningham, Dmitri Krioukov
2018 Computer Physics Communications  
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]

Giulia Prando, Gianluigi Pillonetto, Alessandro Chiuso
2016 arXiv   pre-print
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
« Previous Showing results 1 — 15 out of 6,710 results