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An Approach to Sparse Continuous-time System Identification from Unevenly Sampled Data [article]

Rui Teixeira Ribeiro, Alexandre Mauroy, Jorge Goncalves
2018 arXiv   pre-print
In this work, we address the problem of identifying sparse continuous-time dynamical systems when the spacing between successive samples (the sampling period) is not constant over time.  ...  The proposed approach combines the leave-one-sample-out cross-validation error trick from machine learning with an iterative subset growth method to select the subset of basis functions that governs the  ...  To identify continuous-time dynamical systems, two types of approaches are typically used: a) indirect approach, where an equivalent discrete-time system is first inferred from samples and is then converted  ... 
arXiv:1802.10348v1 fatcat:rwp7ohx2xngqbpsxa4wu2n5geu

Interpolation and extrapolation using a high-resolution discrete Fourier transform

M.D. Sacchi, T.J. Ulrych, C.J. Walker
1998 IEEE Transactions on Signal Processing  
For irregular sampling (unevenly sampled and/or gapped data), the algorithm uses one Cholesky decomposition per iteration.  ...  We present an iterative nonparametric approach to spectral estimation that is particularly suitable for estimation of line spectra.  ...  Application to Unevenly Sampled Data and to Gap Filling Two important problems in time series analysis are spectral estimation of unevenly sampled data and gap filling.  ... 
doi:10.1109/78.651165 fatcat:vbkjucz6hjairjrpfz4baywehu

A Unified SVM Framework for Signal Estimation [article]

José Luis Rojo-Álvarez, Manel Martínez-Ramón, Jordi Muñoz-Marí, Gustavo Camps-Valls
2013 arXiv   pre-print
First, when a specific time-signal structure is assumed to model the underlying system that generated the data, the linear signal model (so called Primal Signal Model formulation) is first stated and analyzed  ...  We illustrate the usefulness of this methodology by defining SVM algorithms for linear and non-linear system identification, spectral analysis, nonuniform interpolation, sparse deconvolution, and array  ...  Additionally, note that this signal model equation allows us to consider the spectral analysis of continuous-time unevenly sampled time series.  ... 
arXiv:1311.5406v1 fatcat:v4cc5zs4fvfr5eiftrkv7oggoy

Characterizing Variable Stars in a Single Night with LSST [article]

Eric D. Feigelson, Frederica Bianco, Sara Bonito
2019 arXiv   pre-print
The most effective features can then inform the wider LSST community on the best approaches to variable star identification and classification from the sparse, irregular cadences that dominate the LSST  ...  A powerful array of statistical procedures can be applied to the ensemble of lightcurves from the long-standing fields of time series analysis, signal processing and econometrics.  ...  Therefore individual star analysis is supplemented by an ensemble approach involving extraction of scalar values from the lightcurves and analysis products, such as signal-to-noise ratio of Fourier and  ... 
arXiv:1901.08009v1 fatcat:ubbm3zpufvghrdm2v2uttga5by

Clustering and visualization approaches for human cell cycle gene expression data analysis

F. Napolitano, G. Raiconi, R. Tagliaferri, A. Ciaramella, A. Staiano, G. Miele
2008 International Journal of Approximate Reasoning  
A preprocessing based on a Robust Principal Component Analysis Neural Network for feature extraction of unevenly sampled data is used.  ...  The method, which provides a user-friendly visualization interface in both 2 and 3 dimensions, can work on noisy data with missing points, and represents an automatic procedure to get, with no a priori  ...  Summarizing, in our analysis this phase is accomplished by applying an on-line Robust PCA NN for each gene that corresponds to an unevenly sampled sequence.  ... 
doi:10.1016/j.ijar.2007.03.013 fatcat:rd4n3tsdcjdbxpo7q5kcehisya

Speaker identification using multimodal neural networks and wavelet analysis

Noor Almaadeed, Amar Aggoun, Abbes Amira
2015 IET Biometrics  
to real-time, text-independent speaker identification systems. www.ietdl.org 18 This is an open access article published by the IET under the Creative Commons Attribution License  ...  Performance tests conducted using the GRID database corpora have shown that this approach has faster identification time and greater accuracy compared with traditional approaches, and it is applicable  ...  Speaker identification is an expert system based on the single biometric of voice data. It first extracts the audio from the raw audio data.  ... 
doi:10.1049/iet-bmt.2014.0011 fatcat:4aox64fgsna7hd36lg3655s6tm

A generic multivariate framework for the integration of microbiome longitudinal studies with other data types [article]

Antoine Bodein, Olivier Chapleur, Arnaud Droit, Kim-Anh Lê Cao
2019 bioRxiv   pre-print
Reduction in costs now enables longitudinal or time course studies on the same biological material or system.  ...  The challenges include few time points that may be unevenly spaced and unmatched between different data types, a small number of unique individual biospecimens and high individual variability.  ...  We assume the data are in raw count formats resulting from bioinformatics pipelines such as QIIME (Caporaso et al., 2010) or FROGS (Escudié et al., 2017) for 16S amplicon data.  ... 
doi:10.1101/585802 fatcat:vtr2xv6t7jfpbhxmbwmfabg5rq

Multi-omics Data Integration, Interpretation, and Its Application

Indhupriya Subramanian, Srikant Verma, Shiva Kumar, Abhay Jere, Krishanpal Anamika
2020 Bioinformatics and Biology Insights  
To study complex biological processes holistically, it is imperative to take an integrative approach that combines multi-omics data to highlight the interrelationships of the involved biomolecules and  ...  With the advent of high-throughput techniques and availability of multi-omics data generated from a large set of samples, several promising tools and methods have been developed for data integration and  ...  Authors would also like to thank Pratap Sanap for his input on tools categorization. We thank the anonymous reviewers for their useful comments and valuable suggestions.  ... 
doi:10.1177/1177932219899051 pmid:32076369 pmcid:PMC7003173 fatcat:dchnmbmzh5di7jcuc7ilxjsk3e

Inferring human mobility from sparse low accuracy mobile sensing data

Andrea Cuttone, Sune Lehmann, Jakob Eg Larsen
2014 Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing Adjunct Publication - UbiComp '14 Adjunct  
In this paper we study the feasibility of inferring human mobility from sparse, low accuracy mobile sensing data.  ...  We validate our results using participants' location diaries, and analyze the inferred geographical networks, the time spent at different places, and the number of unique places over time.  ...  Location is acquired in an opportunistic manner, so every time another app requests a location, this sample is also recorded in our system.  ... 
doi:10.1145/2638728.2641283 dblp:conf/huc/CuttoneLL14 fatcat:7ygx7ie3fvcjladg7dvl5o2jrq

Predicting Complications in Critical Care Using Heterogeneous Clinical Data

Vijay Huddar, Bapu Koundinya Desiraju, Vaibhav Rajan, Sakyajit Bhattacharya, Shourya Roy, Chandan K. Reddy
2016 IEEE Access  
However, clinical data from heterogeneous sources within hospitals pose significant modeling challenges.  ...  Digitized clinical data in electronic medical records can be effectively used to develop machine learning models to identify patients at risk of complications early and provide prioritized care to prevent  ...  Modeling the temporality of multivariate clinical data that is noisy, sparse and unevenly sampled presents significant challenges. For example, Mao et al.  ... 
doi:10.1109/access.2016.2618775 fatcat:gvp2rpdj4fd77n5664h65titqu

A Generic Multivariate Framework for the Integration of Microbiome Longitudinal Studies With Other Data Types

Antoine Bodein, Olivier Chapleur, Arnaud Droit, Kim-Anh Lê Cao
2019 Frontiers in Genetics  
Reduction in costs now enables longitudinal or time course studies on the same biological material or system.  ...  The challenges include few time points that may be unevenly spaced and unmatched between different data types, a small number of unique individual biospecimens, and high individual variability.  ...  The authors collected an average of 26 stool samples from healthy full-term infants.  ... 
doi:10.3389/fgene.2019.00963 pmid:31803221 pmcid:PMC6875829 fatcat:b5o5y74kszbq7emzdmdwz4fehm

Bayesian state space models for dynamic genetic network construction across multiple tissues

Yulan Liang, Arpad Kelemen
2016 Statistical Applications in Genetics and Molecular Biology  
The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time  ...  The unevenly spaced short time courses with unseen time points are treated as hidden state variables.  ...  To mimic the real observed gene expression data, we generate "time course gene expression data with 16 unevenly spaced time points" with the data format similar to our real gene expression data from liver  ... 
doi:10.1515/sagmb-2014-0055 pmid:27343475 fatcat:qftv6sjwjzbujooxltaheu2gge

An opinion on imaging challenges in phenotyping field crops

Derek Kelly, Avimanyou Vatsa, Wade Mayham, Linh Ngô, Addie Thompson, Toni Kazic
2015 Machine Vision and Applications  
These are disambiguating one plant from another; assigning an individual plant's organs to it; and identifying field phenotypes from those shown in archival images.  ...  approaches, and decide on the next steps.  ...  We gratefully acknowlege grants from the U.  ... 
doi:10.1007/s00138-015-0728-4 fatcat:tekaasittvfifc57yoq5i2ekaa

Blind testing of cross-linking/mass spectrometry hybrid methods in CASP11

Michael Schneider, Adam Belsom, Juri Rappsilber, Oliver Brock
2016 Proteins: Structure, Function, and Bioinformatics  
This approach exploits the chemistry of a highly reactive, photoactivatable cross-linker to produce an order of magnitude more cross-links than homobifunctional cross-linkers.  ...  over the protein-were largely unknown to the prediction groups and their approaches were not yet tailored to this kind of data.  ...  They express their gratitude to the following researchers for generously providing samples for the target proteins:  ... 
doi:10.1002/prot.25028 pmid:26945814 pmcid:PMC5042049 fatcat:iuffdy57zng4jnrcf6uugn2wca

Informed baseline subtraction of proteomic mass spectrometry data aided by a novel sliding window algorithm

Tyman E. Stanford, Christopher J. Bagley, Patty J. Solomon
2016 Proteome Science  
Acknowledgements Thank you to the creators and custodians of the publicly available data used in this manuscript.  ...  We would also like to thank the anonymous reviewer for their time and constructive comments that have improved this manuscript.  ...  Other names for unevenly spaced data include unevenly sampled, non-equispaced, non-uniform, inhomogeneous, irregularly sampled or non-synchronous data.  ... 
doi:10.1186/s12953-016-0107-8 pmid:27980460 pmcid:PMC5142289 fatcat:ghhtpeo2njclzaxncjwedhe27e
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