arXiv subjects

Fabian Mueller

Publications and source records attributed to Fabian Mueller.

4 recordsLinked to original sources

arXiv2021

Discovering Mathematical Objects of Interest -- A Study of Mathematical Notations

Mathematical notation, i.e., the writing system used to communicate concepts in mathematics, encodes valuable information for a variety of information search and retrieval systems. Yet, mathematical notations remain mostly unutilized by today's systems. In this paper, we present the first in-depth study on the distributions of mathematical notation in two large scientific corpora: the open access arXiv (2.5B mathematical objects) and the mathematical reviewing service for pure and applied mathematics zbMATH (61M mathematical objects). Our study lays a foundation for future research projects on mathematical information retrieval for large scientific corpora. Further, we demonstrate the relevance of our results to a variety of use-cases. For example, to assist semantic extraction systems, to improve scientific search engines, and to facilitate specialized math recommendation systems. The contributions of our presented research are as follows: (1) we present the first distributional analysis of mathematical formulae on arXiv and zbMATH; (2) we retrieve relevant mathematical objects for given textual search queries (e.g., linking $P_{n}^{(α, β)}\!\left(x\right)$ with `Jacobi polynomial'); (3) we extend zbMATH's search engine by providing relevant mathematical formulae; and (4) we exemplify the applicability of the results by presenting auto-completion for math inputs as the first contribution to math recommendation systems. To expedite future research projects, we have made available our source code and data.

cs.DL↗

arXiv2021

zbMATH Open: API Solutions and Research Challenges

We present zbMATH Open, the most comprehensive collection of reviews and bibliographic metadata of scholarly literature in mathematics. Besides our website https://zbMATH.org which is openly accessible since the beginning of this year, we provide API endpoints to offer our data. The API improves interoperability with others, i.e., digital libraries, and allows using our data for research purposes. In this article, we (1) illustrate the current and future overview of the services offered by zbMATH; (2) present the initial version of the zbMATH links API; (3) analyze potentials and limitations of the links API based on the example of the NIST Digital Library of Mathematical Functions; (4) and finally, present the zbMATH Open dataset as a research resource and discuss connected open research problems.

cs.DL↗

arXiv2020

Development of a multi-channel power supply for silicon photo-multipliers used with inorganic scintillators

The motivation of the current R&D project is based upon the requirements of the JEDI international collaboration aiming to measure Electric Dipole Moments (EDMs) of charged particles in storage rings. One of the most important elements of such an experiment will be a specially designed polarimeter with the detection system based on a modular inorganic scintillator (LYSO crystal) calorimeter. The calorimeter modules are read out by Silicon Photo Multipliers (SiPMs). This paper describes the development of a multi-channel power supply for the polarimeter modules, providing very stable and clean bias voltages for SiPMs. In order to ensure the best possible performance of SiPMs in conjunction with the crystal-based calorimeter modules and to guarantee the required level of calorimeter stability, several quality requirements have to be met by the power supply. Additionally, it is required to provide features including remote control via the network, ramping of the output voltage, measuring and sending the information about its output voltages and currents, etc. The obtained results demonstrate that the goals for the JEDI polarimeter are met. The developed hardware will be useful in other fields of fundamental and applied research, medical diagnostic techniques and industry, where SiPMs are used.

physics.ins-det↗

arXiv2018

Cost-Sensitive Learning for Predictive Maintenance

In predictive maintenance, model performance is usually assessed by means of precision, recall, and F1-score. However, employing the model with best performance, e.g. highest F1-score, does not necessarily result in minimum maintenance cost, but can instead lead to additional expenses. Thus, we propose to perform model selection based on the economic costs associated with the particular maintenance application. We show that cost-sensitive learning for predictive maintenance can result in significant cost reduction and fault tolerant policies, since it allows to incorporate various business constraints and requirements.

cs.LG↗