1,015 research outputs found
The Spoken Wikipedia Corpora
No full textThe Spoken Wikipedia project unites volunteer readers of Wikipedia articles. Hundreds of spoken articles in multiple languages are available to users who are – for one reason or another – unable or unwilling to consume the written version of the article. Our resource, the Spoken Wikipedia Corpus, consolidates the Spoken Wikipediae, adding text segmentation, normalization, time-alignment and further annotations, making it accessible for research and fostering new ways of interacting with the material.
Timo Baumann and Arne Köhn and Felix Hennig. 2018. The Spoken Wikipedia Corpus Collection: Harvesting, Alignment and an Application to Hyperlistening, in Language Resources and Evaluation, Special Issue representing significant contributions of LREC 2016.
Arne Köhn, Florian Stegen, Timo Baumann. 2016. Mining the Spoken Wikipedia for Speech Data and Beyond, in Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016).
CLARIN Metadata summary for The Spoken Wikipedia Corpora (CMDI-based)
Title: The Spoken Wikipedia Corpora
Description: The Spoken Wikipedia project unites volunteer readers of Wikipedia articles. Hundreds of spoken articles in multiple languages are available to users who are – for one reason or another – unable or unwilling to consume the written version of the article. Our resource, the Spoken Wikipedia Corpus, consolidates the Spoken Wikipediae, adding text segmentation, normalization, time-alignment and further annotations, making it accessible for research and fostering new ways of interacting with the material.
Publication date: 2017
Data owner: Timo Baumann - Universität Hamburg
Contributors: Timo Baumann (author), Arne Köhn (author), Florian Stegen (author)
Languages: English (eng), German (deu), Dutch (nld)
Size: 5397 article, 1005 hour
Segmentation units: other
Genre: encyclopedia
Modality: spoken
References: Timo Baumann; Arne Köhn; Felix Hennig (2018) The Spoken Wikipedia Corpus Collection: Harvesting, Alignment and an Application to Hyperlistening References: Arne Köhn; Florian Stegen; Timo Baumann (2016) Mining the Spoken Wikipedia for Speech Data and Beyon
Replication Data for: Efficient Application of Accelerator Cards for the Coupling Library preCICE
No full textThis dataset contains all testcase setup files and result files for the measurements presented in the Master's thesis with the title "Efficient Application of Accelerator Cards for the Coupling Library preCICE" (Author: Timo Pierre Schrader).
Furthermore, it contains the version of preCICE used throughout this thesis.
The thesis revolves around GPU acceleration of RBF data mapping in preCICE. See the README for more information how to build and run the testcase
Characterization of regional differences in resting-state fMRI with a data-driven network model of brain dynamics
No full textThe code and data associated with the study Sip V., Hashemi M., Dickscheid T., Amunts K., Petkoski S., Jirsa V.: Characterization of regional differences in resting-state fMRI with a data-driven network model of brain dynamics. To appear. Preprint at https://doi.org/10.1101/2021.09.01.458521
The multilevel human brain atlas in EBRAINS - an overview
No full textThe multilevel human brain atlas in EBRAINS - an overview Katrin Amunts, Timo DickscheidBrain atlases enable the localization and analysis of data from different sources in a common reference system, making them an essential research tool for understanding the structural and functional organization of the brain. EBRAINS offers a multilevel atlas of the human brain, which captures different principles of brain organization in a comprehensive anatomical framework. It integrates the Julich-Brain probabilistic cytoarchitectonic maps and the BigBrain microscopic 3D model as core elements, and links them with multimodal data features describing microstructure, connectivity and function. The atlas is deeply integrated into the EBRAINS infrastructure, making use of its sustainable data sharing capabilities and cloud resources. This session provides a conceptual introduction to the multilevel human brain atlas, and an overview of the EBRAINS research infrastructure as a sustainable platform for accessing, operating and developing the atlas
Timo de Rijk: 'We plant the seed'; interview
No full textArt historian Timo de Rijk was appointed Professor of Design, Culture and Society in Delft and Leiden last September. He calls this combination ‘a real breakthrough’. ‘Leiden University studies the workings of culture, while TU Delft aims at creating new things. These are fundamentally different approaches. I am the bridge between the two.’Industrial Design Engineerin
Approaching phase retrieval with deep learning
No full textPhase retrieval is the process of reconstructing images from only magnitude measurements. The problem is particularly challenging as most of the information about the image is contained in the missing phase. An important phase retrieval problem is Fourier phase retrieval, where the magnitudes of the Fourier transform are given. This problem is relevant in many areas of science, e.g., in X-ray crystallography, astronomy, microscopy, array imaging, and optics. In addition to Fourier phase retrieval, we also take a closer look at two additional phase retrieval problems: Fourier phase retrieval with a reference image and compressive Gaussian phase retrieval.
Most methods for phase retrieval, e.g., the error-reduction algorithm or Fienup's hybrid-input output algorithms are optimization-based algorithms which solely minimize an error-function to reconstruct the image. These methods usually make strong assumptions about the measured magnitudes which do not always hold in practice. Thus, they only work reliably for easy instances of the phase retrieval problems but fail drastically for difficult instances.
With the recent advances in the development of graphics processing units (GPUs), deep neural networks (DNNs) have become fashionable again and have led to breakthroughs in many research areas. In this thesis, we show how DNNs can be applied to solve the more difficult instances of phase retrieval problems when training data is available. On the one hand, we show how supervised learning can be used to greatly improve the reconstruction quality when training images and their corresponding measurements are available. We analyze the ability of these methods to generalize to out-of-distribution data. On the other hand, we take a closer look at an existing unsupervised method that relies on generative models. Unsupervised methods are agnostic toward the measurement process which is particularly useful for Gaussian phase retrieval. We apply this method to the Fourier phase retrieval problem and demonstrate how the reconstruction performance can be further improved with different initialization schemes. Furthermore, we demonstrate how optimizing intermediate representations of the underlying generative model can help overcoming the limited range of the model and, thus, can help to reach better solutions. Finally, we show how backpropagation can be used to learn reference images using a modification of the well-established error-reduction algorithm and discuss whether learning a reference image is always efficient. As it is common in machine learning research, we evaluate all methods on benchmark image datasets as it allows for easy reproducibility of the experiments and comparability to related methods. To better understand how the methods work, we perform extensive ablation experiments, and also analyze the influence of measurement noise and missing measurements
Educación Artística Comunitaria en Finlandia: entrevista a Timo Jokela
No full textEste artículo presenta un estudio sobre la educación artística comunitaria en la Universidad de Laponia en Finlandia. En primer lugar se analizan las líneas principales de este modelo formativo y de su plan de estudios. En segundo lugar se presenta una entrevista con Timo Jokela, Decano de la Facultad de Arte y Diseño de la Universidad de Laponia y director del Departamento de Educación Artística en esa misma Facultad. En la entrevista Timo Jokela habla de las relaciones entre arte, medio ambiente, comunidad y educación partiendo de su propia experiencia como artista y centrado en el contexto finlandés. También sobre los aspectos sociales, culturales, artísticos y educativos que están implícitos en el modelo de educación artística comunitaria que se pone en práctica, como itinerario formativo, en la Universidad de Laponia.
This article presents a study on community-based art education at the University of Lapland in Finland. First, the main guidelines of this training model and its curriculum are analysed. Second, the author includes an interview with Timo Jokela, Dean of the Faculty of Art and Design of The University of Lapland in Finland and Director of the Department of Art Education at the same faculty. In the interview Timo Jokela draws upon his own experience as an artist in Finland to talk about the relationships between art, the environment, community and education. He also talks about the social, cultural, artistic and educational aspects, which are central to the community-based art education scheme in place at The University of Lapland
Ei-ladattavan hybridiauton esilämmitys : vaikutukset järjestelmän toimintakykyyn
No full textTämän opinnäytetyön tarkoituksena oli tutkia ja selvittää sähköisen esilämmityksen vaikutuksia hybridijärjestelmän toimintaan. Työssä tarkasteltiin erityisesti vaikutuksia matkaan joka ajettiin pelkästään käyttämällä sähköä auton hybridiakustosta. Samalla seurattiin auton polttoaineen keskikulutuksen muutoksia. Nämä vaikutukset saatiin parhaiten näkyviin, ajamalla samoja reittejä esilämmitetyllä ja ilman esilämmitystä olevalla autolla sekä vertailemalla näistä saatuja tuloksia.
Työssä käytettävä hybridiauto on tekijän omistuksessa ja omassa käytössä. Autoa käytetään pääsääntöisesti taajamaliikenteessä, mutta myös maantie- ja moottoritienopeuksissa. Ajaminen koostuu tekijän normaalista auton käytöstä. Työhön kuuluu kolme eri vaihetta. Ensimmäisessä vaiheessa autoa esilämmitettiin ennen jokaista kotona suoritettua liikkeellelähtöä, työn tekemisen aikana voimassa olleiden suosituksien mukaisesti. Toisessa vaiheessa autoa ei esilämmitetty ennen ajoon lähtöä. Kolmannessa vaiheessa tulokset kirjattiin muistiin ja analysoitiin.
Hybridijärjestelmän käyttö vähentää polttoaineen kulutusta ja päästöjä verrattuna pelkästään polttomoottorilla varustettuun autoon. Auton esilämmitys parantaa sekä ajoturvallisuutta että mukavuutta, kun lämpötila on pakkasella tai lähellä nollaa. Esilämmitetty moottori saavuttaa nopeammin normaalin käyntilämpötilan verrattuna moottoriin, jota ei ole esilämmitetty ennen ajoon lähtöä. Kun moottori käy normaalilla käyntilämpötilallaan, päästöt ja polttoaineen kulutus pienentyvät verrattuna kylmänä käyvään moottoriin. Myös käynnistyksen päästöjä saadaan pienennettyä esilämmityksellä.
Autolla on ajettu pääsääntöisesti lyhyitä matkoja, jolloin järjestelmän polttomoottori ei ole saavuttanut normaalia käyntilämpötilaansa. Työn tuloksena havaittiin, että esilämmitys vaikuttaa myönteisesti hybridijärjestelmän kykyyn käyttää pelkästään sähkömoottoria auton liikuttamiseen. Tämä tulos on ennakko-odotusten mukainen.The purpose of the thesis was to study the effects of electronic preheating on the operation of a hybrid system. The impact of the trip, which was driven by using only electricity from the car hybrid battery, was specifically examined in this thesis. At the same time, changes in the average fuel consumption of the car were tracked. The effects were best noticed driving the same routes with a preheated car and the car without preheating, and comparing the results.
The hybrid car used in the work is owned and used by the author. The car is mainly used in urban area but also at highway speeds. Driving consists of the author´s normal car use. The work involved three different phases. In the first phase, the car was preheated before each start-up at home, in accordance with the recommendations in force at the time of the work. In the second phase, the car was not preheated before start-ups. In the third phase, the results were reported and analyzed.
Using a hybrid system in a car reduces fuel consumption and emissions compared to a car with an internal combustion engine only. Car preheating improves both driving safely and comfort when the temperature is low or near zero degrees. A preheated engine reaches faster its normal operating temperature compared to an engine that has not been preheated before driving. When the engine is running at its normal operating temperature, emissions and fuel consumption are reduced compared to a cold engine. Start-up emissions can also be reduced by preheating.
The car has been mainly driven for short distances, whereupon the internal combustion engine of the system has not reached its normal operating temperature. The study shows that preheating has a positive effect on the ability of the hybrid system to use only an electric motor to move the car. This result is in line with the expectations
Robust Wide-Baseline Stereo Matching for Sparsely Textured Scenes
No full textThe task of wide baseline stereo matching algorithms is to identify corresponding elements in pairs of overlapping images taken from significantly different viewpoints. Such algorithms are a key ingredient to many computer vision applications, including object recognition, automatic camera orientation, 3D reconstruction and image registration. Although today's methods for wide baseline stereo matching produce reliable results for typical application scenarios, they assume properties of the image data that are not always granted, for example a significant amount of distinctive surface texture. For such problems, highly advanced algorithms have been proposed, which are often very problem specific, difficult to implement and hard to transfer to new matching problems. The motivation for our work comes from the belief that we can find a generic formulation for robust wide baseline image matching that is able to solve difficult matching problems and at the same time applicable to a variety of applications. It should be easy to implement, and have good semantic interpretability. Therefore our key contribution is the development of a generic statistical model for wide baseline stereo matching, which seamlessly integrates different types of image features, similarity measures and spatial feature relationships as information cues. It unifies the ideas of existing approaches into a Bayesian formulation, which has a clear statistical interpretation as the MAP estimate of a binary classification problem. The model ultimately takes the form of a global minimization problem that can be solved with standard optimization techniques. The particular type of features, measures, and spatial relationships however is not prescribed. A major advantage of our model over existing approaches is its ability to compensate weaknesses in one information cue implicitly by exploiting the strength of others. In our experiments we concentrate on images of sparsely textured scenes as a specifically difficult matching problem. Here the amount of stable image features is typically rather small, and the distinctiveness of feature descriptions often low. We use the proposed framework to implement a wide baseline stereo matching algorithm that can deal better with poor texture than established methods. For demonstrating the practical relevance, we also apply this algorithm to a system for automatic image orientation. Here, the task is to reconstruct the relative 3D positions and orientations of the cameras corresponding to a set of overlapping images. We show that our implementation leads to more successful results in case of sparsely textured scenes, while still retaining state of the art performance on standard datasets.Robuste Merkmalszuordnung für Bildpaare schwach texturierter Szenen mit deutlicher Stereobasis Die Aufgabe von Wide Baseline Stereo Matching Algorithmen besteht darin, korrespondierende Elemente in Paaren überlappender Bilder mit deutlich verschiedenen Kamerapositionen zu bestimmen. Solche Algorithmen sind ein grundlegender Baustein für zahlreiche Computer Vision Anwendungen wie Objekterkennung, automatische Kameraorientierung, 3D Rekonstruktion und Bildregistrierung. Die heute etablierten Verfahren für Wide Baseline Stereo Matching funktionieren in typischen Anwendungsszenarien sehr zuverlässig. Sie setzen jedoch Eigenschaften der Bilddaten voraus, die nicht immer gegeben sind, wie beispielsweise einen hohen Anteil markanter Textur. Für solche Fälle wurden sehr komplexe Verfahren entwickelt, die jedoch oft nur auf sehr spezifische Probleme anwendbar sind, einen hohen Implementierungsaufwand erfordern, und sich zudem nur schwer auf neue Matchingprobleme übertragen lassen. Die Motivation für diese Arbeit entstand aus der Überzeugung, dass es eine möglichst allgemein anwendbare Formulierung für robustes Wide Baseline Stereo Matching geben muß, die sich zur Lösung schwieriger Zuordnungsprobleme eignet und dennoch leicht auf verschiedenartige Anwendungen angepasst werden kann. Sie sollte leicht implementierbar sein und eine hohe semantische Interpretierbarkeit aufweisen. Unser Hauptbeitrag besteht daher in der Entwicklung eines allgemeinen statistischen Modells für Wide Baseline Stereo Matching, das verschiedene Typen von Bildmerkmalen, Ähnlichkeitsmaßen und räumlichen Beziehungen nahtlos als Informationsquellen integriert. Es führt Ideen bestehender Lösungsansätze in einer Bayes'schen Formulierung zusammen, die eine klare Interpretation als MAP Schätzung eines binären Klassifikationsproblems hat. Das Modell nimmt letztlich die Form eines globalen Minimierungsproblems an, das mit herkömmlichen Optimierungsverfahren gelöst werden kann. Der konkrete Typ der verwendeten Bildmerkmale, Ähnlichkeitsmaße und räumlichen Beziehungen ist nicht explizit vorgeschrieben. Ein wichtiger Vorteil unseres Modells gegenüber vergleichbaren Verfahren ist seine Fähigkeit, Schwachpunkte einer Informationsquelle implizit durch die Stärken anderer Informationsquellen zu kompensieren. In unseren Experimenten konzentrieren wir uns insbesondere auf Bilder schwach texturierter Szenen als ein Beispiel schwieriger Zuordnungsprobleme. Die Anzahl stabiler Bildmerkmale ist hier typischerweise gering, und die Unterscheidbarkeit der Merkmalsbeschreibungen schlecht. Anhand des vorgeschlagenen Modells implementieren wir einen konkreten Wide Baseline Stereo Matching Algorithmus, der besser mit schwacher Textur umgehen kann als herkömmliche Verfahren. Um die praktische Relevanz zu verdeutlichen, wenden wir den Algorithmus für die automatische Bildorientierung an. Hier besteht die Aufgabe darin, zu einer Menge überlappender Bilder die relativen 3D Kamerapositionen und Kameraorientierungen zu bestimmen. Wir zeigen, dass der Algorithmus im Fall schwach texturierter Szenen bessere Ergebnisse als etablierte Verfahren ermöglicht, und dennoch bei Standard-Datensätzen vergleichbare Ergebnisse liefert
Robust Wide-Baseline Stereo Matching for Sparsely Textured Scenes
No full textThe task of wide baseline stereo matching algorithms is to identify corresponding elements in pairs of overlapping images taken from significantly different viewpoints. Such algorithms are a key ingredient to many computer vision applications, including object recognition, automatic camera orientation, 3D reconstruction and image registration. Although today’s methods for wide baseline stereo matching produce reliable results for typical application scenarios, they assume properties of the image data that are not always granted, for example a significant amount of distinctive surface texture. For such problems, highly advanced algorithms have been proposed, which are often very problem specific, difficult to implement and hard to transfer to new matching problems. The motivation for our work comes from the belief that we can find a generic formulation for robust wide baseline image matching that is able to solve difficult matching problems and at the same time applicable to a variety of applications. It should be easy to implement, and have good semantic interpretability. Therefore our key contribution is the development of a generic statistical model for wide baseline stereo matching, which seamlessly integrates different types of image features, similarity measures and spatial feature relationships as information cues. It unifies the ideas of existing approaches into a Bayesian formulation, which has a clear statistical interpretation as the MAP estimate of a binary classification problem. The model ultimately takes the form of a global minimization problem that can be solved with standard optimization techniques. The particular type of features, measures, and spatial relationships however is not prescribed. A major advantage of our model over existing approaches is its ability to compensate weaknesses in one information cue implicitly by exploiting the strength of others. In our experiments we concentrate on images of sparsely textured scenes as a specifically difficult matching problem. Here the amount of stable image features is typically rather small, and the distinctiveness of feature descriptions often low. We use the proposed framework to implement a wide baseline stereo matching algorithm that can deal better with poor texture than established methods. For demonstrating the practical relevance, we also apply this algorithm to a system for automatic image orientation. Here, the task is to reconstruct the relative 3D positions and orientations of the cameras corresponding to a set of overlapping images. We show that our implementation leads to more successful results in case of sparsely textured scenes, while still retaining state of the art performance on standard datasets.Robuste Merkmalszuordnung für Bildpaare schwach texturierter Szenen mit deutlicher StereobasisDie Aufgabe von Wide Baseline Stereo Matching Algorithmen besteht darin, korrespondierende Elemente in Paaren überlappender Bilder mit deutlich verschiedenen Kamerapositionen zu bestimmen. Solche Algorithmen sind ein grundlegender Baustein für zahlreiche Computer Vision Anwendungen wie Objekterkennung, automatische Kameraorientierung, 3D Rekonstruktion und Bildregistrierung. Die heute etablierten Verfahren für Wide Baseline Stereo Matching funktionieren in typischen Anwendungsszenarien sehr zuverlässig. Sie setzen jedoch Eigenschaften der Bilddaten voraus, die nicht immer gegeben sind, wie beispielsweise einen hohen Anteil markanter Textur. Für solche Fälle wurden sehr komplexe Verfahren entwickelt, die jedoch oft nur auf sehr spezifische Probleme anwendbar sind, einen hohen Implementierungsaufwand erfordern, und sich zudem nur schwer auf neue Matchingprobleme übertragen lassen. Die Motivation für diese Arbeit entstand aus der Überzeugung, dass es eine möglichst allgemein anwendbare Formulierung für robustes Wide Baseline Stereo Matching geben muß, die sich zur Lösung schwieriger Zuordnungsprobleme eignet und dennoch leicht auf verschiedenartige Anwendungen angepasst werden kann. Sie sollte leicht implementierbar sein und eine hohe semantische Interpretierbarkeit aufweisen. Unser Hauptbeitrag besteht daher in der Entwicklung eines allgemeinen statistischen Modells für Wide Baseline Stereo Matching, das verschiedene Typen von Bildmerkmalen, Ähnlichkeitsmaßen und räumlichen Beziehungen nahtlos als Informationsquellen integriert. Es führt Ideen bestehender Lösungsansätze in einer Bayes’schen Formulierung zusammen, die eine klare Interpretation als MAP Schätzung eines binären Klassifikationsproblems hat. Das Modell nimmt letztlich die Form eines globalen Minimierungsproblems an, das mit herkömmlichen Optimierungsverfahren gelöst werden kann. Der konkrete Typ der verwendeten Bildmerkmale, Ähnlichkeitsmaße und räumlichen Beziehungen ist nicht explizit vorgeschrieben. Ein wichtiger Vorteil unseres Modells gegenüber vergleichbaren Verfahren ist seine Fähigkeit, Schwachpunkte einer Informationsquelle implizit durch die Stärken anderer Informationsquellen zu kompensieren. In unseren Experimenten konzentrieren wir uns insbesondere auf Bilder schwach texturierter Szenen als ein Beispiel schwieriger Zuordnungsprobleme. Die Anzahl stabiler Bildmerkmale ist hier typischerweise gering, und die Unterscheidbarkeit der Merkmalsbeschreibungen schlecht. Anhand des vorgeschlagenen Modells implementieren wir einen konkreten Wide Baseline Stereo Matching Algorithmus, der besser mit schwacher Textur umgehen kann als herkömmliche Verfahren. Um die praktische Relevanz zu verdeutlichen, wenden wir den Algorithmus für die automatische Bildorientierung an. Hier besteht die Aufgabe darin, zu einer Menge überlappender Bilder die relativen 3D Kamerapositionen und Kameraorientierungen zu bestimmen. Wir zeigen, dass der Algorithmus im Fall schwach texturierter Szenen bessere Ergebnisse als etablierte Verfahren erm¨oglicht, und dennoch bei Standard-Datensätzen vergleichbare Ergebnisse liefert
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