Algebraic effects are computational effects that can be described with a set of basic operations and equations between them. As many interesting effect handlers do not respect these equations, most approaches assume a trivial theory, sacrificing both reasoning power and safety. We present an alternative approach where the type system tracks equations that are observed in subparts of the program, yielding a sound and flexible logic, and paving a way for practical optimisations and reasoning tools.
The central task in modeling complex dynamical systems is parameter estimation. This task is an optimization task that involves numerous evaluations of a computationally expensive objective function. Surrogate-based optimization introduces a computationally efficient predictive model that approximates the value of the objective function. The standard approach involves learning a surrogate from training examples that correspond to past evaluations of the objective function. Current surrogate-based optimization methods use static, predefined substitution strategies to decide when to use the surrogate and when the true objective. We introduce a meta-model framework where the substitution strategy is dynamically adapted to the solution space of the given optimization problem. The meta model encapsulates the objective function, the surrogate model and the model of the substitution strategy, as well as components for learning them. The framework can be seamlessly coupled with an arbitrary optimization algorithm without any modification: It replaces the objective function and autonomously decides how to evaluate a given candidate solution. We test the utility of the framework on three tasks of estimating parameters of real-world models of dynamical systems. The results show that the meta model significantly improves the efficiency of optimization, reducing the total number of evaluations of the objective function up to an average of 77%. INDEX TERMS Differential equations, meta models, numerical optimization, parameter estimation, surrogate models.
<p>Analysing EO data is a complex process, and solutions often require custom tailored algorithms. On top of that, in the EO domain most problems come with an additional challenge: How can the solution be applied on a large scale?</p> <p>Within the H2020 project Global Earth Monitor (GEM) we have updated and extended&#160;eo-learn&#160;with additional functionalities that allow for new approaches to scalable and cost-effective Earth Observation data processing. We have tied it with the Sentinel Hub&#8217;s unified main data interface (Process API), the Data Cube processing engine for constructing analysis-ready adjustable data cubes using Batch Process API, and, finally, the Statistical API and Batch Statistical API to streamline access to spatio-temporally aggregated satellite data.</p> <p>As part of GEM processing framework, we have built eo-grow which facilitates extraction of valuable information from satellite imagery. eo-grow tackles the issues of scalability by enabling coordination of clusters to run the EO workflows over large areas using &#160;Ray. At the same time the framework provides reproducibility and traceability of the experiments using schemed input configurations and their validation.</p> <p>In eo-grow a workflow based solution is wrapped into a pipeline object, which takes care of parametrization, logging, storage, multi-processing, data management and more. The pipeline object is configured via a well-defined schema allowing straightforward experimentation and scaling up: going to larger area of interest, running on different time interval, or tweak of any other pipeline parameter becomes just a matter of updating (json) configuration, which additionally serve as record of the experiment.</p> <p>eo-grow library has been publicly released on GitHub: https://github.com/sentinel-hub/eo-grow. The documentation available in the repository provides the overview of the eo-grow general structure, its core objects, and instructions on installation and using eo-grow with command line interface. Additional repository, https://github.com/sentinel-hub/eo-grow-examples showcases eo-grow on a few use-cases.</p> <p>In the presentation we will introduce the framework and showcase its usability on concrete examples. We will illustrate how eo-grow is used in large-scale research experiments, explain its role in reproducibility and show how the no-code approach and code reuse facilitate the productionalization of the workflows.</p>
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