[ANN] Special issue on the Julia programming language

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In case you missed it, there was an announcement last year on a special issue on the Julia programming language. It is now moving to the next stage: Special issue on Julia? - #46 by PetrKryslUCSD

Edited: More information about the Journal.

The Journal is Advances in Engineering Software (https://www.journals.elsevier.com/advances-in-engineering-software).

The objective of this journal is to communicate recent and projected advances in computer-based engineering techniques. The fields covered include mechanical, aerospace, civil and environmental engineering, with an emphasis on research and development leading to practical problem-solving.

The scope of the journal includes:
• Innovative computational strategies and numerical algorithms for large-scale engineering problems
• Analysis and simulation techniques and systems
• Model and mesh generation
• Control of the accuracy, stability and efficiency of computational process
• Exploitation of new computing environments (eg distributed heterogeneous and collaborative computing)
• Advanced visualization techniques, virtual environments and prototyping
• Applications of AI, knowledge-based systems, computational intelligence, including fuzzy logic, neural networks and evolutionary computations
• Application of object-oriented technology to engineering problems
• Intelligent human computer interfaces
• Design automation, multidisciplinary design and optimization
• CAD, CAE and integrated process and product development systems
• Quality and reliability

Economics (and econometrics) have also appeared in this journal. Papers have been published on biology and bioengineering. But as the above list suggests, the selection of topics heavily tilts towards engineering.

Please refer to the Guide for Authors for details on the preparation of the manuscript
https://www.elsevier.com/journals/advances-in-engineering-software/0965-9978/guide-for-authors

A typical paper is around ten pages (two-column typesetting).

Timeframe

I would like to get a really short abstract within let’s say a week (please submit the abstract to me via the “Messages” tool). i would compile the list of abstracts and submit to the editors for final approval of the general look and feel of the issue, and with that the authors can start writing for real. My preliminary estimate of the delivery of the manuscript is somewhere in July. The manuscripts will undergo the usual peer-reviewed process, so getting the go-ahead on the manuscript does not necessarily mean the manuscript will be accepted.

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I realize I left the issue of time too vague. I would like to get the REALLY short abstracts plus titles within let’s say a week. This would be submitted to the editors for final approval, and with that it is off to the races for the authors. My preliminary estimate of the delivery of the manuscript is somewhere in July.

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I think @mkborregaard may be interested?

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I think you are right, Michael expressed interest in the special issue. And how about you?

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Title: “Is Julia Good for AI?”

Really Short Abstract:
An exposé, breaking down the large field of AI, with the discussion of the kinds of algorithms involved, and if Julia’s unique, or otherwise, features make it a good choice for implementing them. Consideration in particular will be given to Machine learning, Symbolic Logic, Search and Planning, as well as the classical application areas of computer vision and natural language processing.

No promise, that I will actually deliver this, I have a thesis due,
but I have started writing on it before.

It would be a very expository and speculative article, with little hard data (beyond reference to papers implemented in julia). Rather than an article discussing how julia was used to solve a particular problem.

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That sounds particularly good, AI has been a focus for this journal in the past.
It has the feel of the review article (or did you have in mind a research article?).
Would you mind sending this to the Messages so that I have all this in one place? Thanks.

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I guess yes, much more of a review article than a research article.

You can see some of then content I’ve prepared before as a draft for a blog post,
https://github.com/oxinabox/oxinabox.github.io/blob/master/_drafts/Is_julia_good_for_AI.md

Obviously as a blog post the style is all wrong.
And I’m not entirely happy with everything in it – it is why it remains a draft rather than me putting it out on my blog.

But the content is mostly what I would want to cover.
(Ideally with reference to some of the most significant papers, and discussion as to if these can be implemented easily in julia.)

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I can maybe work with @mkborregaard – I think we could write something up about using Julia for ecological research, and discuss uses for (i) data synthesis and crunching and (ii) modelling and simulations.

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It’s a nice idea, and we’ve talked about it for some time, but it’s hard for me to see the fit to the scope of the journal as outlined in the first post, or how it would reach an interested readership there.

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Hi

Title: Solving a quantised state system in Julia

Really Short Abstract:
The quantised state system methods are an alternative family of numerical integration solvers based on the idea of state quantization. They can have many advantages compared to classical algorithms: due to their discrete-event nature discontinuities can be easily modelled, detection of zero-crossings can be done explicitly, … Existing libraries in Julia as TaylorSeries, SimJulia, … make the implementation of both non-stiff and stiff solvers straightforward. The macro facilities of Julia allows the end user to construct a complex model in an easy way.

All material is available I only have to find some time to write the paper;)

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Oh, we’re posting here? Here’s an abstract.

Stiffness Detection and Automatic Algorithm Switching for Ordinary, Delay, and Stochastic Differential Equations in Julia

Christopher Rackauckas, Yingbo Ma, Qing Nie

Stiffness is a property of many differential equations which reduces the efficiency of explicit numerical integration routines through step size restrictions. Implicit methods circumvent these restrictions but require solving a nonlinear system every step which is costly and thus inefficient when not required. To mitigate these issues, we implemented algorithms for stiffness detection and automatic algorithm switching for ordinary, delay, and stochastic differential equations in Julia as part of DifferentialEquations.jl. Multiple methods for approximate stiffness detection are tested on a suite of test problems to show tradeoffs between efficiency and accuracy of detection. With these detectors we tested the efficiency of multiple automatic switching schemes between explicit Runge-Kutta and (semi) implicit Rosenbrock and Singly Diagonally Implicit Runge-Kutta (SDIRK) integrators. The resulting methods are benchmarked against pure explicit and implicit integrators to show efficiency improvements on test problems which are classically designated as non-stiff/stiff. In addition, benchmarks against the classic multiparadigm LSODA algorithm and demonstrate the effectiveness of these new methods.

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No harm in posting here, it may give other people an idea of what is being thought of, but if you don’t mind please send me these abstracts in messages. Easier to keep track of it that way. Thanks.

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I agree. There was talk at one point with @Ward9250, @bicycle1885 and some others in the BioJulia community about trying to do a similar thing in eg PLoS Computational Biology, but not sure that went anywhere. Having multiple special issues for different domains doesn’t seem like the worst thing (and provide opportunities for citation circle jerks :stuck_out_tongue_closed_eyes: )

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DiffEq has quite a bit of Bio stuff as well. Please keep us posted since that sounds interesting!

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Agreed. Spreading the word about Julia in multiple journals is the right thing to do in any case. :slight_smile:

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Title: “Uncertainty propagation with functionally correlated quantities”

Author: Mosè Giordano

Abstract: Many uncertainty propagation software exist, written in different programming languages, but not all of them are able to handle functional correlation between quantities. In this paper we review one strategy to deal with uncertainty propagation of quantities that are functionally correlated, and introduce a new software offering this feature: the Julia package Measurements.jl. It supports real and complex numbers with uncertainty, arbitrary-precision calculations, mathematical and linear algebra operations with matrices and arrays.

E-print: [1610.08716] Uncertainty propagation with functionally correlated quantities

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Not speaking for the entire board of PLOS Comp Biol (but in my capacity as a software editor there), but I don’t think that Julia meets the criteria for broad use and impact (yet). I’m quite sure that @mkborregaard and I could write something up for our community (Methods Ecol Evol or Ecography are obvious choices), or we can onboard other biologists like @kevbonham and (are there other biologists around) and aim for something broader. This is starting to deviate from the original topic, so we should move this to another thread if there is interest.

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Please make a topic. I’m interested if its broad enough to include systems biology (I can make it population related though, but I know applications to populations of cells a lot more than populations of organisms :smile:)

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Any update on the timeline @PetrKryslUCSD?