Search results for "Data model"
showing 10 items of 162 documents
Joint Gaussian processes for inverse modeling
2017
Solving inverse problems is central in geosciences and remote sensing. Very often a mechanistic physical model of the system exists that solves the forward problem. Inverting the implied radiative transfer model (RTM) equations numerically implies, however, challenging and computationally demanding problems. Statistical models tackle the inverse problem and predict the biophysical parameter of interest from radiance data, exploiting either in situ data or simulated data from an RTM. We introduce a novel nonlinear and nonparametric statistical inversion model which incorporates both real observations and RTM-simulated data. The proposed Joint Gaussian Process (JGP) provides a solid framework…
Convolutional Neural Networks for Cloud Screening: Transfer Learning from Landsat-8 to Proba-V
2018
Cloud detection is a key issue for exploiting the information from Earth observation satellites multispectral sensors. For Proba-V, cloud detection is challenging due to the limited number of spectral bands. Advanced machine learning methods, such as convolutional neural networks (CNN), have shown to work well on this problem provided enough labeled data. However, simultaneous collocated information about the presence of clouds is usually not available or requires a great amount of manual labor. In this work, we propose to learn from the available Landsat −8 cloud masks datasets and transfer this learning to solve the Proba-V cloud detection problem. CNN are trained with Landsat images adap…
Diving into the Past: A Paleo Data–Model Comparison Workshop on the Late Glacial and Holocene
2019
Understanding changes in the climate of the late Pleistocene and the Holocene has long been a research topic. Studies rely on different sources of information, ranging from terrestrial and marine archives to a hierarchy of climate modeling activities. In contrast to the climate of the last millennium, novel approaches are necessary to bridge the different temporal and spatial representations of the various archives and the climate models, and to achieve a robust understanding of climate variability and climate processes on centennial-to-millennial time scales.
Co-Designing Social Simulation Models For Policy Advise: Lessons Learned From the INFSO-SKIN Study
2019
This paper describes a participatory approach to co-designing social simulation models with policymakers using a case study of modeling European Commission policy. Managing the collaboration of a wide range of individuals or organizations is challenging but increasingly important as policy making becomes more complex. A framework for a co-design process based on a participatory approach is proposed. The framework suggests that the collaborative design should go through the following phases: Identifying user questions, data provision, model discussion for validation, visualization of results and discussing scope and limitations with stakeholders. Key findings are that the co-design process r…
A proposed mapping method for aligning machine execution data to numerical control code
2019
The visions of the digital thread and smart manufacturing have boosted the potential of relating downstream data to upstream decisions in design. However, to date, the tools and methods to robustly map across the related data representations is significantly lacking. In response, we propose a mapping technique for standard manufacturing data representations. Specifically, we focus on relating controller data from machining tools in the form of MTConnect, an emerging standard that defines the vocabulary and semantics as well as communications protocols for execution data, and G-Code, the most widely used standard for numerical control (NC) instructions. We evaluate the efficacy of our mappin…
DESDEO: The Modular and Open Source Framework for Interactive Multiobjective Optimization
2021
Interactive multiobjective optimization methods incorporate preferences from a human decision maker in the optimization process iteratively. This allows the decision maker to focus on a subset of solutions, learn about the underlying trade-offs among the conflicting objective functions in the problem and adjust preferences during the solution process. Incorporating preference information allows computing only solutions that are interesting to the decision maker, decreasing computation time significantly. Thus, interactive methods have many strengths making them viable for various applications. However, there is a lack of existing software frameworks to apply and experiment with interactive …
Parallel Pairwise Epistasis Detection on Heterogeneous Computing Architectures
2016
This is a post-peer-review, pre-copyedit version of an article published in IEEE Transactions on Parallel and Distributed Systems. The final authenticated version is available online at: http://dx.doi.org/10.1109/TPDS.2015.2460247. [Abstract] Development of new methods to detect pairwise epistasis, such as SNP-SNP interactions, in Genome-Wide Association Studies is an important task in bioinformatics as they can help to explain genetic influences on diseases. As these studies are time consuming operations, some tools exploit the characteristics of different hardware accelerators (such as GPUs and Xeon Phi coprocessors) to reduce the runtime. Nevertheless, all these approaches are not able t…
Discovering Differential Equations from Earth Observation Data
2020
Modeling and understanding the Earth system is a constant and challenging scientific endeavour. When a clear mechanistic model is unavailable, complex or uncertain, learning from data can be an alternative. While machine learning has provided excellent methods for detection and retrieval, understanding the governing equations of the system from observational data seems an elusive problem. In this paper we introduce sparse regression to uncover a set of governing equations in the form of a system of ordinary differential equations (ODEs). The presented method is used to explicitly describe variable relations by identifying the most expressive and simplest ODEs explaining data to model releva…
Automatic Identification System data - potential resource for marine vessels drift studies in the Baltic Sea
2020
Automatic Identification System (AIS) has been employed for increasing the safety at the sea. Nowadays general information about the marine traffic can be tracked in operational mode using www.marinetraffic.com web portal. Additional information can be purchased for commercial and research purposes. Our study highlights a possibility to study the drift using an additional information available at www.marinetraffic.com - selected data on vessel position during its status “not under command”, which corresponds to the drifting vessels. Trajectories of the drift from AIS have been compared with mathematically modeled point-wise drifter paths. Copernicus Marine Environmental Monitoring Service's…
First M87 Event Horizon Telescope Results. VII. Polarization of the Ring
2021
Full list of authors: Akiyama, Kazunori; Algaba, Juan Carlos; Alberdi, Antxon; Alef, Walter; Anantua, Richard; Asada, Keiichi; Azulay, Rebecca; Baczko, Anne-Kathrin; Ball, David; Baloković, Mislav; Barrett, John; Benson, Bradford A.; Bintley, Dan; Blackburn, Lindy; Blundell, Raymond; Boland, Wilfred; Bouman, Katherine L.; Bower, Geoffrey C.; Boyce, Hope Bremer, Michael; Brinkerink, Christiaan D.; Brissenden, Roger; Britzen, Silke; Broderick, Avery E.; Broguiere, Dominique; Bronzwaer, Thomas; Byun, Do-Young; Carlstrom, John E.; Chael, Andrew; Chan, Chi-kwan; Chatterjee, Shami; Chatterjee, Koushik; Chen, Ming-Tang; Chen, Yongjun; Chesler, Paul M.; Cho, Ilje; Christian, Pierre; Conway, John E.…