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Structure preservation for the Deep Neural Network Multigrid Solver. ETNA - Electronic Transactions on Numerical Analysis
No full textThe simulation of partial differential equations is a central subject of numerical analysis and an indispensable tool in science, engineering, and related fields. Existing approaches, such as finite elements, provide (highly) efficient tools but deep neural network-based techniques emerged in the last few years as an alternative with very promising results. We investigate the combination of both approaches for the approximation of the Navier-Stokes equations and to what extent structural properties such as divergence freedom can and should be respected. Our work is based on DNN-MG, a deep neural network multigrid technique, that we introduced recently and which uses a neural network to represent fine grid fluctuations not resolved by a geometric multigrid finite element solver. Although DNN-MG provides solutions with very good accuracy and is computationally highly efficient, we noticed that the neural network-based corrections substantially violate the divergence freedom of the velocity vector field. In this contribution, we discuss these findings and analyze three approaches to address the problem: a penalty term to encourage divergence freedom of the network output; a penalty term for the corrected velocity field; and a network that learns the stream function and which hence yields divergence-free corrections by construction. Our experimental results show that the third approach based on the stream function outperforms the other two and not only improves the divergence freedom but also the overall fidelity of the simulation
Habitat suitability evaluation for Paeonia decomposita, based on a MaxEnt model. eco.mont (Journal on Protected Mountain Areas Research and Management)|eco.mont Vol. 14 No. 1 14 1|
No full textPaeonia decomposita is on the IUCN’s Red List of endangered species, and occurs only in the northwest part of Sichuan Province, China. For the effective protection of the species, it is important to evaluate the suitability of potential habitats for P. decomposita and natural factors that influence the species. Based on the actual distribution points of P. decomposita in northwest Sichuan from 2016 to 2018, the Maximum Entropy Model (MaxEnt) was used to analyse the main factors affecting its habitat, and to predict suitable habitats. The results show that: (1) the model has high accuracy and is suitable for the prediction and evaluation of habitat suitability for P. decomposita; (2) temperature, slope, precipitation and moisture index will all greatly affect P. decomposita’s distribution; (3) the areas that are potentially suitable for P. decomposita are mainly in Mianyang, Aba, Ganzi and Liangshan, which are greatly affected by human activities; effective protection measures have not been taken. It is proposed that new reserves for the introduction of P. decomposita should be established in areas of high or moderately high suitability. A programme of cultivation of this rare species should also be set up
Extracting and Geocoding Locations in Social Media Posts: A Comparative Analysis. GI_Forum|GI_Forum 2021, Volume 2|
No full textGeo-social media have become an established data source for spatial analysis of geographic and social processes in various fields. However, only a small share of geo-social media data are explicitly georeferenced, which often compromises the reliability of the analysis results by excluding large volumes of data from the analysis. To increase the number of georeferenced tweets, inferred locations can be extracted from the texts of social media posts. We propose a customized workflow for location extraction from tweets and subsequent geocoding. We compare the results of two methods: DBpedia Spotlight (using linked Wikipedia entities), and spaCy combined with the geocoding methods of OpenStreetMap Nominatim. The results suggest that the workflow using spaCy and Nominatim identifies more locations than DBpedia Spotlight. For 50,616 tweets posted within California, USA, the granularity of the extracted locations is reasonable. However, several directions for future research were identified, including improved semantic analysis, the creation of a cascading workflow, and the need to integrate different data sources in order to increase reliability and spatial accuracy
Orchard Meadow Trees: Tree Detection Using Deep Learning in ArcGIS Pro. GI_Forum|GI_Forum 2021, Volume 2|
No full text‘Orchard meadows’ refers to the combination of extensively managed fruit trees in combination with fields and pastures. In many regions, among others in Germany, Austria and Switzerland, they are a landscape-defining element and of particular ecological, economic and social importance. However, the numbers of orchard meadows and fruit trees have been decreasing for quite some time. Current and detailed data that allow for the identification of suitable countermeasures to maintain this cultural landscape element are often missing. Such data can be obtained through deep learning. Various deep learning frameworks can now be used in the context of ArcGIS Pro. But what exactly does the use of deep learning involve, in the context of ArcGIS Pro, to get an insight into the stocks of orchard meadow trees? What are the challenges? Initial analyses were carried out using selected areas in Franconian Switzerland (Northern Bavaria) as an example. The results confirm the potential of the approach, but also that training data, model and output data must be refined
„Judenrein“? Zum Antisemitismus an der Wiener Rechtsund Staatswissenschaftlichen Fakultät vor 1938. Beiträge zur Rechtsgeschichte Österreichs|Beiträge zur Rechtsgeschichte Österreichs Band 1 / 2021|
No full textThe article deals primarily with anti‐Semitism at the University of Vienna in the 19th and 20th centuries, especiallyat the Faculty of Law and State. It also gives an overview on the different methods that were used to exclude specificgroups of persons from university studies or academic employment. Although Jews were admitted to the Universityof Vienna in the late 18th century, they faced many difficulties – legal as well as factual ones – if they wanted topursue an academic career. Although the legal obstacles were abolished at the end of the 19th century, Jewish scholars’chances to obtain a professorship were small due to the rising anti‐Semitism at the University of Vienna. Theconsequences of the anti‐Semitic atmosphere and schemes are exemplified by the cases of Hans Kelsen and StephanBrassloff, among others
Automatic Detection of Driving–Lane Geometry Based on Aerial Images and Existing Spatial Data. GI_Forum|GI_Forum 2021, Volume 2|
No full textSpatial data are a key element of geographic information systems (GIS). With the growing computational power of modern GIS, the demand for accurate and up-to-date high definition (HD) spatial data grows accordingly and increases the requirements of data acquisition. To simplify and automate the process of obtaining HD road data, several methods have been created with different approaches and stages of automation. A new method combining high resolution aerial images and existing linear road data is presented in this article. The method models roads in a vector environment at the level of single driving lanes. Object-based image analysis (OBIA) is used to identify road surface markings (RSMs) in aerial images; the geometry of RSM polygons is analysed (skeletonization, neighbourhood and context analysis, pattern recognition) in order to obtain a coherent network of driving lanes. The technique is able to distinguish automatically between solid and broken lines. The method proposed was tested and proven to satisfactorily model driving lanes, including in complex situations like junctions, roundabouts or over- or underpasses
