The format for the session is as follows:
The live part of the session will be carried out with the software BigBlueButton provided by TU Braunschweig. Every session will get their own BigBlueButton room.
Additionally there will be a slack channel for this session for the duration of the entire conference.
Modern mathematics research includes more than just pen and paper. Mathematical research data such as numerical models, scripts, software, and other digital artifacts became the daily bread and butter of working mathematicians. However, professional organizations primarily focus on academic publications as a vehicle for knowledge transfer and reward distribution. In this ICMS session, we address the problem of sharing mathematical research data other than publications.
Topics include but are not limited to:
If you would like to give a talk in this ICMS session, you must first submit a proposed title and short abstract to one of the session organizers by email. If accepted, you then have the option to submit also an extended abstract to be included in the conference proceedings: these will be submitted to the ICMS easychair page and undergo review. The deadlines and instructions for proceedings submission are on the main ICMS 2020 webpage.
The deadline for the extended abstract submission is 27 March 2020.
The Open Research Knowledge Graph (ORKG) provides machine-actionable access to scholarly literature that habitually is written in prose. Following the FAIR principles, the ORKG makes traditional, human-coded knowledge findable, accessible, interoperable, and reusable in a structured manner in accordance with the Linked Open Data paradigm. At the moment, in ORKG papers are described manually, but in the long run the semantic depth of the literature at scale needs automation. Operational Research is a suitable test case for this vision because the mathematical field and, hence, its publication habits are highly structured: A mundane problem is formulated as a mathematical model, solved or approximated numerically, and evaluated systematically. We study the existing literature with respect to the Assembly Line Balancing Problem and derive a semantic description in accordance with the ORKG. Eventually, selected papers are ingested to test the semantic description and refine it further.
Software, and software source code in particular, is widely used in modern research. It must be properly archived, referenced, described and cited in order to build a stable and long lasting corpus of scientific knowledge. In this article we show how the Software Heritage universal source code archive provides a means to fully address the first two concerns, by archiving seamlessly all publicly available software source code, and by providing intrinsic persistent identifiers that allow to reference it at various granularities in a way that is at the same time convenient and effective. We call upon the research community to adopt widely this approach.
The empirical Gramian framework -- emgr -- is a numerical software toolbox for
the approximate computation of system-theoretic operators, the so-called system
Gramians, which characterize input-output systems, the object of interest in
mathematical system theory. Due to their data-driven computation, the empirical
system Gramians happen to be compatible with a wide range of systems and
facilitate various scientific and engineering applications.
While this numerical software package is topically focused, the observations and experiences made during its more than eight years of continued advancement also relate to mathematical software in general, and encompass scientific and technical aspects of development and dissemination. In this contribution, these findings are classified and summarized from a mathematical and software perspective.
Scientists increasingly rely on computer algebra systems and digital mathematical libraries to compute, validate, or experiment with mathematical formulae. However, the focus in digital mathematical libraries and scientific documents often lies more on an accurate presentation of the formulae rather than providing uniform access to the semantic information. But, presentational math formats do not provide exclusive access to the underlying semantic meanings. One has to derive the semantic information from the context. As a consequence, the workflow of experimenting and publishing in the Sciences often includes time-consuming, error-prone manual conversions between presentational and computational math formats. As a contribution to improve this workflow, we propose a context-sensitive approach that extracts semantic information from a given context, embeds the information into the given input, and converts the semantically enhanced expressions to computer algebra systems.
We discuss design aspects of the open-source Basic Polynomial Algebra Subprograms (BPAS) library. We build on standard C++11 template mechanisms to improve ease of use and accessibility. The BPAS computer algebra library looks to enable end-users to do work more easily and efficiently through optimized C code wrapped in an object-oriented and user-friendly C++ interface. Two key aspects of this interface to be discussed are the encoding of the algebraic hierarchy as a class hierarchy and a mechanism to support the combination of algebraic types as a new type. Existing libraries, if encoding the algebraic hierarchy at all, use runtime value checks to determine if two elements belong to the same ring for an incorrect false sense of type safety in an otherwise statically-typed language. On the contrary, our template metaprogramming mechanism provides true compile-time type safety and compile-time code generation. The details of this mechanism are transparent to end-users, providing a very natural interface for an end-user mathematician.
The purpose of this project is to test and evaluate an approach for Formula Concept Discovery (FCD). FCD aims at retrieving a formula concept (in the form of a Wikidata item) together with its defining formula within documents, in this case 100 English Wikipedia articles. To correctly identify the defining formula of a Wikipedia article, this approach searches for shared formulae across Wikipedia articles available in different languages. The formula shared in the most languages is then assumed to be the defining formula. The results show that neither this approach alone nor a combination with an existing approach that considers the order of the formulae inside an article leads to satisfying results. It is thus concluded that the number of times a formula is shared across a Wikipedia article in different languages is not a good indicator to determine the defining formula with the current approach. Consequently, several ideas for further research are proposed which could improve the results.
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