Search results for "Data model"
showing 10 items of 162 documents
Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes
2018
In this work we evaluate multi-output (MO) Gaussian Process (GP) models based on the linear model of coregionalization (LMC) for estimation of biophysical parameter variables under a gap filling setup. In particular, we focus on LAI and fAPAR over rice areas. We show how this problem cannot be solved with standard single-output (SO) GP models, and how the proposed MO-GP models are able to successfully predict these variables even in high missing data regimes, by implicitly performing an across-domain information transfer.
Requirement analysis for an artificial intelligence model for the diagnosis of the COVID-19 from chest X-ray data
2021
There are multiple papers published about different AI models for the COVID-19 diagnosis with promising results. Unfortunately according to the reviews many of the papers do not reach the level of sophistication needed for a clinically usable model. In this paper I go through multiple review papers, guidelines, and other relevant material in order to generate more comprehensive requirements for the future papers proposing a AI based diagnosis of the COVID-19 from chest X-ray data (CXR). Main findings are that a clinically usable AI needs to have an extremely good documentation, comprehensive statistical analysis of the possible biases and performance, and an explainability module.
Warped Gaussian Processes in Remote Sensing Parameter Estimation and Causal Inference
2018
This letter introduces warped Gaussian process (WGP) regression in remote sensing applications. WGP models output observations as a parametric nonlinear transformation of a GP. The parameters of such a prior model are then learned via standard maximum likelihood. We show the good performance of the proposed model for the estimation of oceanic chlorophyll content from multispectral data, vegetation parameters (chlorophyll, leaf area index, and fractional vegetation cover) from hyperspectral data, and in the detection of the causal direction in a collection of 28 bivariate geoscience and remote sensing causal problems. The model consistently performs better than the standard GP and the more a…
Joint Gaussian Processes for Biophysical Parameter Retrieval
2017
Solving inverse problems is central to geosciences and remote sensing. Radiative transfer models (RTMs) represent mathematically the physical laws which govern the phenomena in remote sensing applications (forward models). The numerical inversion of the RTM equations is a challenging and computationally demanding problem, and for this reason, often the application of a nonlinear statistical regression is preferred. In general, regression models predict the biophysical parameter of interest from the corresponding received radiance. However, this approach does not employ the physical information encoded in the RTMs. An alternative strategy, which attempts to include the physical knowledge, co…
Consistent Regression of Biophysical Parameters with Kernel Methods
2020
This paper introduces a novel statistical regression framework that allows the incorporation of consistency constraints. A linear and nonlinear (kernel-based) formulation are introduced, and both imply closed-form analytical solutions. The models exploit all the information from a set of drivers while being maximally independent of a set of auxiliary, protected variables. We successfully illustrate the performance in the estimation of chlorophyll content.
Semantic HMC for Big Data Analysis
2014
International audience; Analyzing Big Data can help corporations to im-prove their efficiency. In this work we present a new vision to derive Value from Big Data using a Semantic Hierarchical Multi-label Classification called Semantic HMC based in a non-supervised Ontology learning process. We also proposea Semantic HMC process, using scalable Machine-Learning techniques and Rule-based reasoning.
Explainable Student Agency Analytics
2021
Several studies have shown that complex nonlinear learning analytics (LA) techniques outperform the traditional ones. However, the actual integration of these techniques in automatic LA systems remains rare because they are generally presumed to be opaque. At the same time, the current reviews on LA in higher education point out that LA should be more grounded to the learning science with actual linkage to teachers and pedagogical planning. In this study, we aim to address these two challenges. First, we discuss different techniques that open up the decision-making process of complex techniques and how they can be integrated in LA tools. More precisely, we present various global and local e…
Measuring the Rate of Information Transfer in Point-Process Data: Application to Cardiovascular Interactions
2021
We present the implementation to cardiovascular variability of a method for the information-theoretic estimation of the directed interactions between event-based data. The method allows to compute the transfer entropy rate (TER) from a source to a target point process in continuous time, thus overcoming the severe limitations associated with time discretization of event-based processes. In this work, the method is evaluated on coupled cardiovascular point processes representing the heartbeat dynamics and the related peripheral pulsation, first using a physiologically-based simulation model and then studying real point-process data from healthy subjects monitored at rest and during postural …
Firefly optimization used to identify hysteresis parameter on rotational MR-damper
2014
In his paper the physical properties and mathematical models of a semi-active magnetorheological (MR) damper is studied. The considered models are the Dahl model and Bouc-Wen model. The parameters for these models are found by using a firefly optimization algorithm that minimizes the difference between experimental and simulated data. The objective of his paper is to compare different mathematical MR-damper models with the experimental data. The simulation results illustrate he effectiveness of he proposed optimization algorithm.
On Using Conceptual Modeling for Ontologies
2004
Are database concepts and techniques suitable for ontology design and management? The question has been on the floor for some time already. It gets a new emphasis today, thanks to the focus on ontologies and ontology services due to the spread of web services as a new paradigm for information management. This paper analyzes some of the arguments that are relevant to the debate, in particular the question whether conceptual data models would adequately support the design and use of ontologies. It concludes suggesting a hybrid approach, combining databases and logic-based services.