2,505 research outputs found
Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems
Development of robust dynamical systems and networks such as autonomous
aircraft systems capable of accomplishing complex missions faces challenges due
to the dynamically evolving uncertainties coming from model uncertainties,
necessity to operate in a hostile cluttered urban environment, and the
distributed and dynamic nature of the communication and computation resources.
Model-based robust design is difficult because of the complexity of the hybrid
dynamic models including continuous vehicle dynamics, the discrete models of
computations and communications, and the size of the problem. We will overview
recent advances in methodology and tools to model, analyze, and design robust
autonomous aerospace systems operating in uncertain environment, with stress on
efficient uncertainty quantification and robust design using the case studies
of the mission including model-based target tracking and search, and trajectory
planning in uncertain urban environment. To show that the methodology is
generally applicable to uncertain dynamical systems, we will also show examples
of application of the new methods to efficient uncertainty quantification of
energy usage in buildings, and stability assessment of interconnected power
networks
Target recognition for coastal surveillance based on radar images and generalised Bayesian inference
For coastal surveillance, this study proposes a novel approach to identify moving vessels from radar images with the use of a generalised Bayesian inference technique, namely the evidential reasoning (ER) rule. First of all, the likelihood information about radar blips is obtained in terms of the velocity, direction, and shape attributes of the verified samples. Then, it is transformed to be multiple pieces of evidence, which are formulated as generalised belief distributions representing the probabilistic relationships between the blip's states of authenticity and the values of its attributes. Subsequently, the ER rule is used to combine these pieces of evidence, taking into account their corresponding reliabilities and weights. Furthermore, based on different objectives and verified samples, weight coefficients can be trained with a non-linear optimisation model. Finally, two field tests of identifying moving vessels from radar images have been conducted to validate the effectiveness and flexibility of the proposed approach
Applying MDL to Learning Best Model Granularity
The Minimum Description Length (MDL) principle is solidly based on a provably
ideal method of inference using Kolmogorov complexity. We test how the theory
behaves in practice on a general problem in model selection: that of learning
the best model granularity. The performance of a model depends critically on
the granularity, for example the choice of precision of the parameters. Too
high precision generally involves modeling of accidental noise and too low
precision may lead to confusion of models that should be distinguished. This
precision is often determined ad hoc. In MDL the best model is the one that
most compresses a two-part code of the data set: this embodies ``Occam's
Razor.'' In two quite different experimental settings the theoretical value
determined using MDL coincides with the best value found experimentally. In the
first experiment the task is to recognize isolated handwritten characters in
one subject's handwriting, irrespective of size and orientation. Based on a new
modification of elastic matching, using multiple prototypes per character, the
optimal prediction rate is predicted for the learned parameter (length of
sampling interval) considered most likely by MDL, which is shown to coincide
with the best value found experimentally. In the second experiment the task is
to model a robot arm with two degrees of freedom using a three layer
feed-forward neural network where we need to determine the number of nodes in
the hidden layer giving best modeling performance. The optimal model (the one
that extrapolizes best on unseen examples) is predicted for the number of nodes
in the hidden layer considered most likely by MDL, which again is found to
coincide with the best value found experimentally.Comment: LaTeX, 32 pages, 5 figures. Artificial Intelligence journal, To
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Light intensity modulates the regulatory network of the shade avoidance response in Arabidopsis.
Plants such as Arabidopsis thaliana respond to foliar shade and neighbors who may become competitors for light resources by elongation growth to secure access to unfiltered sunlight. Challenges faced during this shade avoidance response (SAR) are different under a light-absorbing canopy and during neighbor detection where light remains abundant. In both situations, elongation growth depends on auxin and transcription factors of the phytochrome interacting factor (PIF) class. Using a computational modeling approach to study the SAR regulatory network, we identify and experimentally validate a previously unidentified role for long hypocotyl in far red 1, a negative regulator of the PIFs. Moreover, we find that during neighbor detection, growth is promoted primarily by the production of auxin. In contrast, in true shade, the system operates with less auxin but with an increased sensitivity to the hormonal signal. Our data suggest that this latter signal is less robust, which may reflect a cost-to-robustness tradeoff, a system trait long recognized by engineers and forming the basis of information theory
Customized Uncertainty Quantification of Parking Duration Predictions for EV Smart Charging
As Electric Vehicle (EV) demand increases, so does the demand for efficient Smart Charging (SC) applications. However, SC is only acceptable if the EV userâs mobility requirements and risk preferences are fulfilled, i.e. their respective EV has enough charge to make their planned journey. To fulfill these requirements and risk preferences, the SC application must consider the predicted parking duration at a given location and the uncertainty associated with this prediction. However, certain regions of uncertainty are more critical than others for user-centric SC applications, and therefore, such uncertainty must be explicitly quantified. Therefore, the present paper presents multiple approaches to customize the uncertainty quantification of parking duration predictions specifically for EV user-centric SC applications. We decompose parking duration prediction errors into a critical component which results in undercharging, and a non-critical component. Furthermore, we derive quantile-based security levels that can minimize the probability of a critical error given a userâs risk preferences. We evaluate our customized uncertainty quantification with four different probabilistic prediction models on an openly available semi-synthetic mobility data set and a data set consisting of real EV trips. We show that our customized uncertainty quantification can regulate critical errors, even in challenging real-world data with high fluctuation and uncertainty
Computational approaches for single-cell omics and multi-omics data
Single-cell omics and multi-omics technologies have enabled the study of cellular heterogeneity with unprecedented resolution and the discovery of new cell types. The core of identifying heterogeneous cell types, both existing and novel ones, relies on efficient computational approaches, including especially cluster analysis. Additionally, gene regulatory network analysis and various integrative approaches are needed to combine data across studies and different multi-omics layers. This thesis comprehensively compared Bayesian clustering models for single-cell RNAsequencing (scRNA-seq) data and selected integrative approaches were used to study the cell-type specific gene regulation of uterus. Additionally, single-cell multi-omics data integration approaches for cell heterogeneity analysis were investigated.
Article I investigated analytical approaches for cluster analysis in scRNA-seq data, particularly, latent Dirichlet allocation (LDA) and hierarchical Dirichlet process (HDP) models. The comparison of LDA and HDP together with the existing state-of-art methods revealed that topic modeling-based models can be useful in scRNA-seq cluster analysis. Evaluation of the cluster qualities for LDA and HDP with intrinsic and extrinsic cluster quality metrics indicated that the clustering performance of these methods is dataset dependent.
Article II and Article III focused on cell-type specific integrative analysis of uterine or decidual stromal (dS) and natural killer (dNK) cells that are important for successful pregnancy. Article II integrated the existing preeclampsia RNA-seq studies of the decidua together with recent scRNA-seq datasets in order to investigate cell-type-specific contributions of early onset preeclampsia (EOP) and late onset preeclampsia (LOP). It was discovered that the dS marker genes were enriched for LOP downregulated genes and the dNK marker genes were enriched for upregulated EOP genes. Article III presented a gene regulatory network analysis for the subpopulations of dS and dNK cells. This study identified novel subpopulation specific transcription factors that promote decidualization of stromal cells and dNK mediated maternal immunotolerance.
In Article IV, different strategies and methodological frameworks for data integration in single-cell multi-omics data analysis were reviewed in detail. Data integration methods were grouped into early, late and intermediate data integration strategies. The specific stage and order of data integration can have substantial effect on the results of the integrative analysis. The central details of the approaches were presented, and potential future directions were discussed.
âLaskennallisia menetelmiĂ€ yksisolusekvensointi- ja multiomiikkatulosten analyyseihin
Yksisolusekvensointitekniikat mahdollistavat solujen heterogeenisyyden tutkimuksen ennennÀkemÀttömÀllÀ resoluutiolla ja uusien solutyyppien löytÀmisen. Solutyyppien tunnistamisessa keskeisessÀ roolissa on ryhmittely eli klusterointianalyysi. Myös geenien sÀÀtelyverkostojen sekÀ eri molekyylidatatasojen yhdistÀminen on keskeistÀ analyysissÀ. VÀitöskirjassa verrataan bayesilaisia klusterointimenetelmiÀ ja yhdistetÀÀn eri menetelmillÀ kerÀttyjÀ tietoja kohdun solutyyppispesifisessÀ geeninsÀÀtelyanalyysissÀ. LisÀksi yksisolutiedon integraatiomenetelmiÀ selvitetÀÀn kattavasti.
Julkaisu I keskittyy analyyttisten menetelmien, erityisesti latenttiin Dirichletallokaatioon (LDA) ja hierarkkiseen Dirichlet-prosessiin (HDP) perustuvien mallien tutkimiseen yksisoludatan klusterianalyysissÀ. Kattava vertailu nÀiden kahden mallin sekÀ olemassa olevien menetelmien kanssa paljasti, ettÀ aihemallinnuspohjaiset menetelmÀt voivat olla hyödyllisiÀ yksisoludatan klusterianalyysissÀ. Menetelmien suorituskyky riippui myös kunkin analysoitavan datasetin ominaisuuksista.
Julkaisuissa II ja III keskitytÀÀn naisen lisÀÀntymisterveydelle tÀrkeiden kohdun stroomasolujen ja NK-immuunisolujen solutyyppispesifiseen analyysiin. Artikkelissa II yhdistettiin olemassa olevia tuloksia pre-eklampsiasta viimeisimpiin yksisolusekvensointituloksiin ja löydettiin varhain alkavan pre-eklampsian (EOP) ja myöhÀÀn alkavan pre-eklampsian (LOP) solutyyppispesifisiÀ vaikutuksia. Havaittiin, ettÀ erilaistuneen strooman markkerigeenien ilmentyminen vÀhentyi LOP:ssa ja NK-markkerigeenien ilmentyminen lisÀÀntyi EOP:ssa. Julkaisu III analysoi strooman ja NK-solujen alapopulaatiospesifisiÀ geeninsÀÀtelyverkostoja ja niiden transkriptiofaktoreita. Tutkimus tunnisti uusia alapopulaatiospesifisiÀ sÀÀtelijöitÀ, jotka edistÀvÀt strooman erilaistumista ja NK-soluvÀlitteistÀ immunotoleranssia
Julkaisu IV tarkastelee yksityiskohtaisesti strategioita ja menetelmiÀ erilaisten yksisoludatatasojen (multi-omiikka) integroimiseksi. IntegrointimenetelmÀt ryhmiteltiin varhaisen, myöhÀisen ja vÀlivaiheen strategioihin ja kunkin lÀhestymistavan menetelmiÀ esiteltiin tarkemmin. LisÀksi keskusteltiin mahdollisista tulevaisuuden suunnista
Bayesian Network Modeling and Inference in Plant Gene Networks And Analysis of Sequencing and Imaging Data
Scientific and technological advancements over the years have made curing, preventing or managing all diseases, a goal that seems to be within reach. The approach to manipulating biological systems is multifaceted. This dissertation focuses on two problems that pose fundamental challenges in developing methods to control biological systems: the first is to model complex interactions in biological systems; the second is faithful representation and analysis of biological data obtained from scientific equipments.
The first part of this dissertation is a discussion on modeling and inference in gene networks, and Bayesian inference. Then we describe the application of Bayesian network modeling to represent interactions among genes, and integrating gene expression data in order to identify potential points of intervention in the gene network. We conclude with a summary of evolving directions for modeling gene interactions.
The second topic this dissertation focuses on is taming biological data to obtain actionable insights. We introduce the challenges in representation and analysis of high throughput sequencing data and proceeds to describe the analysis of imaging data in the dynamic environment of cancer cells. Then we discuss tackling the problem of analyzing high throughput RNA sequencing data in order to pinpoint genes that exhibit different behaviors under monitored experimental conditions. Then we address the interesting problem of deciphering and quantifying gene-level activity from epifluorescent imaging data
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