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48284 results in Computer Science

Page 255 of 2415

  • Introduction: Living Systems, Causality, and Information

  • Hector Zenil, University of Cambridge, Narsis A. Kiani, Karolinska Institutet, Stockholm, Jesper Tegnér, King Abdullah University of Science and Technology, Saudi Arabia
  • Book:
    Algorithmic Information Dynamics
    Published online:
    11 May 2023
    Print publication:
    25 May 2023, pp 1-6
    • Chapter
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  • 11 - Asynchronous Communication

  • from Part III - Extensions and Variations
  • Fabrizio Montesi, University of Southern Denmark
  • Book:
    Introduction to Choreographies
    Published online:
    11 May 2023
    Print publication:
    25 May 2023, pp 191-200

  • Summary

    Communication in the languages presented so far is synchronous: a sending action blocks the sender until it can interact with a compatible receiving action at the intended receiver. In this chapter, we consider an alternative semantics for interactions: asynchronous communication. Asynchronous communication allows for a sending action to be executed without waiting for the receiver to be ready by storing the sent message in a message queue that the intended receiver can later read.

  • 2 - Simple Choreographies

  • from Part I - Foundations
  • Fabrizio Montesi, University of Southern Denmark
  • Book:
    Introduction to Choreographies
    Published online:
    11 May 2023
    Print publication:
    25 May 2023, pp 39-54

  • Summary

    We introduce our first choreography language, Simple Choreographies, which allows for writing sequences of interactions between processes. The key aspect of the language is that interactions are syntactically manifest in choreographies. A semantics of choreographies is obtained in terms of a labelled transition system.

  • A data-driven method for automated data superposition with applications in soft matter science

  • Kyle R. Lennon, Gareth H. McKinley, James W. Swan
  • Journal:
    Data-Centric Engineering / Volume 4 / 2023
    Published online by Cambridge University Press:
    25 May 2023, e13
    • Article
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      • Open access
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  • The superposition of data sets with internal parametric self-similarity is a longstanding and widespread technique for the analysis of many types of experimental data across the physical sciences. Typically, this superposition is performed manually, or recently through the application of one of a few automated algorithms. However, these methods are often heuristic in nature, are prone to user bias via manual data shifting or parameterization, and lack a native framework for handling uncertainty in both the data and the resulting model of the superposed data. In this work, we develop a data-driven, nonparametric method for superposing experimental data with arbitrary coordinate transformations, which employs Gaussian process regression to learn statistical models that describe the data, and then uses maximum a posteriori estimation to optimally superpose the data sets. This statistical framework is robust to experimental noise and automatically produces uncertainty estimates for the learned coordinate transformations. Moreover, it is distinguished from black-box machine learning in its interpretability—specifically, it produces a model that may itself be interrogated to gain insight into the system under study. We demonstrate these salient features of our method through its application to four representative data sets characterizing the mechanics of soft materials. In every case, our method replicates results obtained using other approaches, but with reduced bias and the addition of uncertainty estimates. This method enables a standardized, statistical treatment of self-similar data across many fields, producing interpretable data-driven models that may inform applications such as materials classification, design, and discovery.

  • 1 - Inference Systems

  • from Part I - Foundations
  • Fabrizio Montesi, University of Southern Denmark
  • Book:
    Introduction to Choreographies
    Published online:
    11 May 2023
    Print publication:
    25 May 2023, pp 15-38

  • Summary

    Before venturing into the study of choreographies, we introduce the formalism of inference systems. Inference systems are widely used in the fields of formal logic and programming languages and they were later applied to theory of choreographies as well.

Page 255 of 2415