1,720,966 research outputs found
Unraveling Anomalies in Time (Version 1.0.0)
No full textThis repository contains the accompanying code to reproduce the results of the publication:
Rewicki et al: Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of Anomalous Behavior in Bio-regenerative Life Support System Telemetry. In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2024, https://doi.org/10.1007/978-3-031-70378-2_13
In this work, we present a pipeline for deriving different anomaly types and identify recurring anomalous behavior from unlabeled telemetry data stemming from the EDEN ISS research greenhouse. This software contains the implementation of the proposed pipeline
Machine Learning for Space Gardening
No full textSustained human presence in space requires the development of new technologies to maintain environment control, provide water, oxygen, food and to keep astronauts healthy and psychologically fit. The EDEN NEXT GEN project works along the roadmap of building a flight-ready bio-regenerative life support system within this decade. Being part of that project, we are concerned with detecting unhealthy system states and plant stress in the context of extraterrestrial horticulture. In this talk, I will introduce different classical and deep-learning-based methods for finding anomalies in time series and present our latest results on differences regarding the types of anomalies these methods can find
Estimating Uncertainty of Deep Learning Multi-Label Classifications using Laplace Approximation
Full text linkWith the huge successes of deep learning and its application in critical areas such as medical diagnosis or autonomous driving and in fields with noisy and very varying data such as remote sensing, the need for reliable confidence statements about such model's predictions becomes apparent. Therefore, uncertainty estimation methods for neural networks have raised rising interest in the machine learning community. While various methods for regression and multi-class classification tasks have been published, the field of multi-label classification has hardly been considered yet. In this work, we derive the Kronecker-factored Laplace approximation in the multi-label setting, a method to approximate the intractable posterior distribution over the parameters of neural networks. We employ this method in the remote sensing domain and estimate the model uncertainty of eight deep neural networks that have been trained on an aerial scene classification dataset. By comparing the probabilistic classifiers to their deterministic counterparts, we evaluate the potential for using the uncertainty estimates to improve the calibration of those classifiers as well as the out-of-distribution detection. We found that we can improve the calibration for overconfident classifiers whereas for underconfident ones, this method might not be beneficial. Furthermore, the ability to improve the separation from in- and out-of-distribution data seems to be depending on the depth of the neural network within one model family
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
Estimating Uncertainty of Deep Learning Multi-Label Classifications Using Laplace Approximation
No full textDeep learning methods have become valuable tools in remote
sensing for tasks like aerial scene classification or land cover
analysis. Dealing with noisy and very varying data, the need
for reliable confidence statements becomes apparent. While
deep learning models are known to yield overconfident pre-
dictions, quantifying the model uncertainty of those classi-
fiers can help mitigating that effect. Although uncertainty es-
timation methods for multi-class classification have been pub-
lished, multi-label classification - the task of labelling data
with multiple class labels simultaneously - has hardly been
considered yet. In this study, we use multi-label Laplace Ap-
proximation to estimate the model uncertainty of deep multi-
label classifiers and show how this method can improve cali-
bration and out-of-distribution detection in the remote sensing
domain
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe 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
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