1,721,021 research outputs found
A Time-Vertex Signal Processing Framework
Get PDFAn emerging way to deal with high-dimensional non-euclidean data is to assume that the underlying structure can be captured by a graph. Recently, ideas have begun to emerge related to the analysis of time-varying graph signals. This work aims to elevate the notion of joint harmonic analysis to a full-fledged framework denoted as Time-Vertex Signal Processing, that links together the time-domain signal processing techniques with the new tools of graph signal processing. This entails three main contributions: (a) We provide a formal motivation for harmonic time-vertex analysis as an analysis tool for the state evolution of simple Partial Differential Equations on graphs. (b) We improve the accuracy of joint filtering operators by up-to two orders of magnitude. (c) Using our joint filters, we construct time-vertex dictionaries analyzing the different scales and the local time-frequency content of a signal. The utility of our tools is illustrated in numerous applications and datasets, such as dynamic mesh denoising and classification, still-video inpainting, and source localization in seismic events. Our results suggest that joint analysis of time-vertex signals can bring benefits to regression and learningLTS
Replication Data for: The 3-billion fossil question: How to automate classification of microfossils
No full textThis dataset consists of 100,000 PNG images of individual microfossils extracted from whole slide images of fossils from a single wellbore. The well in question belongs to the Mikkel field on the Norwegian continental shelf. The wellbore is located at 64 degrees north-south, 7 degrees east-west with ID NO 6407-6-5. All images in this dataset are of RGB format with a 224-by-224 pixel resolution. There are no labels associated with these images, thus the number of different species represented in this dataset is unknown and likely to be in the order of 1000. The dataset was created using the method described in the associated paper. The purpose was to create a medium sized dataset of preprocessed microfossil crops for use in self-supervised training of small to medium sized deep learning models, for which this dataset is suffiently big. For large scale training of e.g. Vision Transformers, more data will be required. See the following fact page for more geological information: https://factpages.sodir.no/en/wellbore/PageView/Exploration/With/PalySlides/3921. Note that the original whole slide images from which this dataset is created were provided by the Norwegian national data repository for petroleum data (Diskos) under the Norwegian Licence for Open Government Data (NLOD) 2.0
Tracking Time-Vertex Propagation using Dynamic Graph Wavelets
Get PDFGraph Signal Processing generalizes classical signal processing to signal or data indexed by the vertices of a weighted graph. So far, the research efforts have been focused on static graph signals. However numerous applications involve graph signals evolving in time, such as spreading or propagation of waves on a network. The analysis of this type of data requires a new set of methods that fully takes into account the time and graph dimensions. We propose a novel class of wavelet frames named Dynamic Graph Wavelets, whose time-vertex evolution follows a dynamic process. We demonstrate that this set of functions can be combined with sparsity based approaches such as compressive sensing to reveal information on the dynamic processes occurring on a graph. Experiments on real seismological data show the efficiency of the technique, allowing to estimate the epicenter of earthquake events recorded by a seismic network.LTS
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
DC-Approximated Power System Reliability Predictions with Graph Convolutional Neural Networks
Get PDFThe current standard operational strategy within electrical power systems is done following deterministic reliability practices. These practices are deemed to be secure under most operating situations when considering power system security, but as the deterministic practices do not consider the probability and consequences of operation, the operating situation may often become either too strict or not strict enough. This can in periods lead to inefficient operation when regarding the socio-economic aspects. With the continuous integration of renewable energy sources to the electrical power system coupled with the increasing demand for electricity, the power systems have been pushed to operating closer to their stability limit. This poses a challenge for the operation and planning of the power system. Research is therefore being invested into finding more flexible operational strategies which operates according to probabilistic reliability criteria, taking the probability of future events into consideration while also aiming to minimize the expected cost and defining limits for probabilistic reliability indicators.
To reliably plan and operate the systems according to a probabilistic reliability criterion, numerical problems such as the Optimal Power Flow (OPF) and the Power Flow (PF) equations are used. These tools are helpful as they are used to determine the optimal way of producing and transporting power. These tools are also used in contingency analyses, where the effect of occurring contingencies is analyzed and evaluated. Due to the non-linearity of the PF equations, the solution is often found through iterative numerical methods such as the Gauss-Seidel method or the Newton-Raphson method. These numerical methods are often computationally expensive, and convergence to the global minimum is not guaranteed either. In recent years, various Machine Learning (ML) models have gathered a lot of attention due to their success in different numerical tasks, particularly Graph Convolutional Networks (GCNs) due to the model’s ability to utilize the topology and learn localized features. As the field of GCN is new, extensive research is being committed to identify the GCNs ability to work on applications such as the electrical power system.
This thesis seeks to conduct preliminary experiments where Graph Convolutional Networks (GCN) models are used as a substitution for the numerical DC-OPFs which are used to determine values such as the system load shedding due to contingencies. The GCN models are trained and tested on multiple datasets on both a system- and a node-level, where the goal is to test the models' ability to generalize across perturbations of different system-parameters, such as the system load, the number of induced contingencies and different system topologies.
The experiments of the thesis show that the GCNs can predict the load-shedding values across multiple system-parameter perturbations such as the number of induced contingencies, increasing load-variation and a modified system-topology with a high accuracy, without having to be retrained for those specific situations. Though, the further the system-parameters were perturbated, the less accurate the model's predictions became. This reduction in accuracy per system-parameter perturbation was caused by a change in the load-shedding pattern as additional parameters were perturbated, which the models were unable to comprehend. Lastly, this thesis also shows that the GCN models are substantially faster than the numerical methods which they seek to replace
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