scholarly journals A 3D geological model of a structurally complex Alpine region as a basis for interdisciplinary research

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
James M. Thornton ◽  
Gregoire Mariethoz ◽  
Philip Brunner
Keyword(s):  
Interdisciplinary Research ◽  
Iterative Process ◽  
Input Data ◽  
State Of The Art ◽  
Alpine Region ◽  
Swiss Alps ◽  
Bedrock Geology ◽  
Geological Models ◽  
Geological Information ◽  
Alpine Research

Abstract Certain applications, such as understanding the influence of bedrock geology on hydrology in complex mountainous settings, demand 3D geological models that are detailed, high-resolution, accurate, and spatially-extensive. However, developing models with these characteristics remains challenging. Here, we present a dataset corresponding to a renowned tectonic entity in the Swiss Alps - the Nappe de Morcles - that does achieve these criteria. Locations of lithological interfaces and formation orientations were first extracted from existing sources. Then, using state-of-the-art algorithms, the interfaces were interpolated. Finally, an iterative process of evaluation and re-interpolation was undertaken. The geology was satisfactorily reproduced; modelled interfaces correspond well with the input data, and the estimated volumes seem plausible. Overall, 18 formations, including their associated secondary folds and selected faults, are represented at 10 m resolution. Numerous environmental investigations in the study area could benefit from the dataset; indeed, it is already informing integrated hydrological (snow/surface-water/groundwater) simulations. Our work demonstrates the potential that now exists to develop complex, high-quality geological models in support of contemporary Alpine research, augmenting traditional geological information in the process.

scholarly journals Technical Note: Quantification of interferences of wet chemical HONO LOPAP measurements under simulated polar conditions

2008 ◽  
Vol 8 (22) ◽  
pp. 6813-6822 ◽  
Author(s):  
J. Kleffmann ◽  
P. Wiesen
Keyword(s):  
Technical Note ◽  
Swiss Alps ◽  
Pollution Level ◽  
Research Station ◽  
Polar Regions ◽  
Alpine Research ◽  
Absorption Photometer ◽  
Present Pilot Study ◽  
Good Agreement

Abstract. In the present pilot study, an optimized LOPAP instrument (LOng Path Absorption Photometer) for the detection of nitrous acid (HONO) in the atmosphere (DL 0.2 pptV) was tested at the high alpine research station Jungfraujoch at 3580 m altitude in the Swiss Alps under conditions comparable to polar regions. HONO concentrations in the range <0.5–50 pptV with an average of 7.5 pptV were observed at the Jungfraujoch. The diurnal profiles obtained exhibited clear maxima at noon and minima with very low concentration during the night supporting the proposed photochemical production of HONO. In good agreement with recent measurements at the South Pole, it was demonstrated, that interferences of chemical HONO instruments can significantly influence the measurements and lead to considerable overestimations, especially for low pollution level. Accordingly, the active correction of interferences is of paramount importance for the determination of reliable HONO data.

scholarly journals Estimating Efforts and Success of Symmetry-Seeing Machines by Use of Synthetic Data

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 227
Author(s):  
Eckart Michaelsen ◽  
Stéphane Vujasinovic
Keyword(s):  
Extraction Method ◽  
Input Data ◽  
State Of The Art ◽  
Recognition Performance ◽  
Human Subjects ◽  
Synthetic Data ◽  
Ground Truth ◽  
Comparative Test ◽  
Real Imagery ◽  
Dominant Part

Representative input data are a necessary requirement for the assessment of machine-vision systems. For symmetry-seeing machines in particular, such imagery should provide symmetries as well as asymmetric clutter. Moreover, there must be reliable ground truth with the data. It should be possible to estimate the recognition performance and the computational efforts by providing different grades of difficulty and complexity. Recent competitions used real imagery labeled by human subjects with appropriate ground truth. The paper at hand proposes to use synthetic data instead. Such data contain symmetry, clutter, and nothing else. This is preferable because interference with other perceptive capabilities, such as object recognition, or prior knowledge, can be avoided. The data are given sparsely, i.e., as sets of primitive objects. However, images can be generated from them, so that the same data can also be fed into machines requiring dense input, such as multilayered perceptrons. Sparse representations are preferred, because the author’s own system requires such data, and in this way, any influence of the primitive extraction method is excluded. The presented format allows hierarchies of symmetries. This is important because hierarchy constitutes a natural and dominant part in symmetry-seeing. The paper reports some experiments using the author’s Gestalt algebra system as symmetry-seeing machine. Additionally included is a comparative test run with the state-of-the-art symmetry-seeing deep learning convolutional perceptron of the PSU. The computational efforts and recognition performance are assessed.

State-of-the-Art of Thermal Recovery Models

1981 ◽  
Vol 103 (4) ◽  
pp. 296-300
Author(s):  
S. M. Farouq Ali ◽  
J. Ferrer
Keyword(s):  
Oil Recovery ◽  
Input Data ◽  
State Of The Art ◽  
Front Tracking ◽  
Steam Injection ◽  
Thermal Recovery ◽  
In Situ Combustion ◽  
Cyclic Steam Stimulation ◽  
Recovery Models

Thermal recovery models for oil recovery consist of steam injection and in-situ combustion simulators. At the present time, steam injection simulators have been developed to a point where it is possible to reliably simulate portions of a fieldwide flood. Cyclic steam stimulation simulation still entails a number of questionable assumptions. Formation parting cannot be simulated in either case. In-situ combustion simulators lack the capability for front tracking. Even though the models are rather sophisticated, process mechanism description and input data are inadequate.

scholarly journals Active Learning for Constrained Document Clustering with Uncertainty Region

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
M. A. Balafar ◽  
R. Hazratgholizadeh ◽  
M. R. F. Derakhshi
Keyword(s):  
Iterative Process ◽  
Clustering Algorithm ◽  
State Of The Art ◽  
Distance Matrix ◽  
Support Vector ◽  
Constrained Clustering ◽  
Similarity Matrix ◽  
Improve Accuracy ◽  
Degree Of Similarity ◽  
Accuracy And Stability

Constrained clustering is intended to improve accuracy and personalization based on the constraints expressed by an Oracle. In this paper, a new constrained clustering algorithm is proposed and some of the informative data pairs are selected during an iterative process. Then, they are presented to the Oracle and their relation is answered with “Must-link (ML) or Cannot-link (CL).” In each iteration, first, the support vector machine (SVM) is utilized based on the label produced by the current clustering. According to the distance of each document from the hyperplane, the distance matrix is created. Also, based on cosine similarity of word2vector of each document, the similarity matrix is created. Two types of probability (similarity and degree of similarity) are calculated and they are smoothed for belonging to neighborhoods. Neighborhoods form the samples that are labeled by Oracle, to be in the same cluster. Finally, at the end of each iteration, the data with a greater level of uncertainty (in term of probability) is selected for questioning the oracle. In order to evaluate, the proposed method is compared with famous state-of-the-art methods based on two criteria and over a standard dataset. The result demonstrates an increased accuracy and stability of the obtained result with fewer questions.

scholarly journals The Nature of Corruption: An Interdisciplinary Perspective

2016 ◽  
Vol 17 (1) ◽  
pp. 53-72 ◽  
Author(s):  
Eugen Dimant ◽  
Thorben Schulte
Keyword(s):  
Interdisciplinary Research ◽  
State Of The Art ◽  
Macro Level ◽  
Interdisciplinary Perspective ◽  
Big Picture ◽  
Empirical Perspective ◽  
The Many ◽  
Corrupt Behavior ◽  
Shed Light

In response to the many facets of corruption, many scholars have produced interdisciplinary research from both the theoretical and empirical perspective. This paper provides a comprehensive state-of-the-art survey of existing literature on corruption, utilizing these interdisciplinary insights. Specifically, we shed light on corruption research including insights from, among others, the fields of economics, psychology, and criminology. Our systematic discussion of the antecedents and effects of corruption at the micro, meso, and macro level allows us to capture the big picture of not only what drives corrupt behavior, but also its substantial ramifications.

scholarly journals 3D Maps Representation Using GNG

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Vicente Morell ◽  
Miguel Cazorla ◽  
Sergio Orts-Escolano ◽  
Jose Garcia-Rodriguez
Keyword(s):  
Input Data ◽  
Mobile Robotics ◽  
State Of The Art ◽  
Growing Neural Gas ◽  
Input Space ◽  
Neural Gas ◽  
Space Experiments ◽  
Space Adaptation

Current RGB-D sensors provide a big amount of valuable information for mobile robotics tasks like 3D map reconstruction, but the storage and processing of the incremental data provided by the different sensors through time quickly become unmanageable. In this work, we focus on 3D maps representation and propose the use of the Growing Neural Gas (GNG) network as a model to represent 3D input data. GNG method is able to represent the input data with a desired amount of neurons or resolution while preserving the topology of the input space. Experiments show how GNG method yields a better input space adaptation than other state-of-the-art 3D map representation methods.

scholarly journals MAGO: A NEW APPROACH FOR ORTHOPHOTOS PRODUCTION BASED ON ADAPTIVE MESH RECONSTRUCTION

2019 ◽  
Vol XLII-2/W11 ◽  
pp. 533-538
Author(s):  
S. Gagliolo ◽  
B. Federici ◽  
I. Ferrando ◽  
D. Sguerso
Keyword(s):  
Iterative Process ◽  
Point Cloud ◽  
Input Data ◽  
Adaptive Mesh ◽  
Test Case ◽  
New Approach ◽  
Surface Models ◽  
Best Fitting ◽  
Mesh Reconstruction ◽  
Orientation Parameters

<p><strong>Abstract.</strong> Orthophotos are one of the most common and typical products of a photogrammetric post-processing and, since the diffusion of specific software, their generation and usage have become even more widespread. In spite of it, some issues remain on the accuracy of orthophoto reconstruction, which is often downgraded by the introduction of meshes and Digital Surface Models to be used as surfaces representing the object. The use of a more accurate and reliable input, such as a point cloud, makes these approximations avoidable. For this reason, a new approach, termed MAGO (Adaptive Mesh for Orthophoto Reconstruction), is here delineated and proposed. The input data of the procedure are the user-defined orthophoto plane, the image and its internal and external orientation parameters, and a point cloud representing the object. Each pixel of the image is projected on the orthophoto plane at its original resolution via an iterative process, which builds an adaptive mesh, defined by means of the three best fitting points, where the collinearity rays and the point cloud intersect. After an overview on the method and its innovative features, an example on a test case is reported, together with a comparison between MAGO’s and another photogrammetric software results.</p>

scholarly journals Collaborative Filtering Based on Bi-Relational Data Representation

2013 ◽  
Vol 38 (1) ◽  
pp. 67-83 ◽  
Author(s):  
Andrzej Szwabe ◽  
Pawel Misiorek ◽  
Michal Ciesielczyk ◽  
Czeslaw Jedrzejek
Keyword(s):  
Collaborative Filtering ◽  
Input Data ◽  
State Of The Art ◽  
User Profile ◽  
Data Representation ◽  
User Feedback ◽  
Behavioral Data ◽  
Input Matrix ◽  
Feedback Data

Abstract Widely-referenced approaches to collaborative filtering (CF) are based on the use of an input matrix that represents each user profile as a vector in a space of items and each item as a vector in a space of users. When the behavioral input data have the form of (userX, likes, itemY) and (userX, dislikes, itemY) triples one has to propose a representation of the user feedback data that is more suitable for the use of propositional data than the ordinary user-item ratings matrix. We propose to use an element-fact matrix, in which columns represent RDF-like behavioral data triples and rows represent users, items, and relations. By following such a triple-based approach to the bi-relational behavioral data representation we are able to improve the quality of collaborative filtering. One of the key findings of the research presented in this paper is that the proposed bi-relational behavioral data representation, while combined with reflective matrix processing, significantly outperforms state-of-the-art collaborative filtering methods based on the use of a ‘standard’ user-item matrix.

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