Refine
Has Fulltext
- yes (4490) (remove)
Year of publication
Document Type
- Postprint (2089)
- Doctoral Thesis (1684)
- Monograph/Edited Volume (221)
- Preprint (126)
- Working Paper (117)
- Article (86)
- Master's Thesis (56)
- Habilitation Thesis (38)
- Part of Periodical (26)
- Conference Proceeding (25)
Language
- English (4490) (remove)
Is part of the Bibliography
- yes (4490) (remove)
Keywords
- climate change (68)
- Klimawandel (46)
- machine learning (39)
- Modellierung (34)
- diffusion (28)
- morphology (27)
- climate (25)
- model (25)
- Germany (24)
- German (22)
Institute
- Institut für Biochemie und Biologie (530)
- Institut für Physik und Astronomie (520)
- Mathematisch-Naturwissenschaftliche Fakultät (482)
- Institut für Geowissenschaften (443)
- Institut für Chemie (362)
- Humanwissenschaftliche Fakultät (207)
- Extern (201)
- Strukturbereich Kognitionswissenschaften (174)
- Wirtschaftswissenschaften (169)
- Institut für Mathematik (166)
Define real, Moron!
(2011)
Academic language should not be a ghetto dialect at odds with ordinary language, but rather an extension that is compatible with lay-language. To define ‘game’ with the unrealistic ambition of satisfying both lay-people and experts should not be a major concern for a game ontology, since the field it addresses is subject to cultural evolution and diachronic change. Instead of the impossible mission of turning the common word into an analytic concept, a useful task for an ontology of games is to model game differences, to show how the things we call games can be different from each other in a number of different ways.
Hepcidin-25 was identified as themain iron regulator in the human body, and it by binds to the sole iron-exporter ferroportin. Studies showed that the N-terminus of hepcidin is responsible for this interaction, the same N-terminus that encompasses a small copper(II) binding site known as the ATCUN (amino-terminal Cu(II)- and Ni(II)-binding) motif. Interestingly, this copper-binding property is largely ignored in most papers dealing with hepcidin-25. In this context, detailed investigations of the complex formed between hepcidin-25 and copper could reveal insight into its biological role. The present work focuses on metal-bound hepcidin-25 that can be considered the biologically active form. The first part is devoted to the reversed-phase chromatographic separation of copper-bound and copper-free hepcidin-25 achieved by applying basic mobile phases containing 0.1% ammonia. Further, mass spectrometry (tandemmass spectrometry (MS/MS), high-resolutionmass spectrometry (HRMS)) and nuclear magnetic resonance (NMR) spectroscopy were employed to characterize the copper-peptide. Lastly, a three-dimensional (3D)model of hepcidin-25with bound copper(II) is presented. The identification of metal complexes and potential isoforms and isomers, from which the latter usually are left undetected by mass spectrometry, led to the conclusion that complementary analytical methods are needed to characterize a peptide calibrant or referencematerial comprehensively. Quantitative nuclear magnetic resonance (qNMR), inductively-coupled plasma mass spectrometry (ICP-MS), ion-mobility spectrometry (IMS) and chiral amino acid analysis (AAA) should be considered among others.
An exploration of rhythmic grouping of speech sequences by french- and german-learning infants
(2016)
Rhythm in music and speech can be characterized by a constellation of several acoustic cues. Individually, these cues have different effects on rhythmic perception: sequences of sounds alternating in duration are perceived as short-long pairs (weak-strong/iambicpattern), whereas sequences of sounds alternating in intensity or pitch are perceived as loud-soft, or high-low pairs (strong-weak/trochaic pattern). This perceptual bias-called the lambic-Trochaic Law (ITL) has been claimed to be an universal property of the auditory system applying in both the music and the language domains. Recent studies have shown that language experience can modulate the effects of the ITL on rhythmic perception of both speech and non-speech sequences in adults, and of non-speech sequences in 7.5-month-old infants. The goal of the present study was to explore whether language experience also modulates infants' grouping of speech. To do so, we presented sequences of syllables to monolingual French- and German-learning 7.5-month-olds. Using the Headturn Preference Procedure (HPP), we examined whether they were able to perceive a rhythmic structure in sequences of syllables that alternated in duration, pitch, or intensity. Our findings show that both French- and German-learning infants perceived a rhythmic structure when it was cued by duration or pitch but not intensity. Our findings also show differences in how these infants use duration and pitch cues to group syllable sequences, suggesting that pitch cues were the easier ones to use. Moreover, performance did not differ across languages, failing to reveal early language effects on rhythmic perception. These results contribute to our understanding of the origin of rhythmic perception and perceptual mechanisms shared across music and speech, which may bootstrap language acquisition.
We report on the detection of very high energy (VHE; E > 100 GeV) gamma-ray emission from the BL Lac objects KUV 00311-1938 and PKS 1440-389 with the High Energy Stereoscopic System (H.E.S.S.). H.E.S.S. observations were accompanied or preceded by multiwavelength observations with Fermi/LAT, XRT and UVOT onboard the Swift satellite, and ATOM. Based on an extrapolation of the Fermi/LAT spectrum towards the VHE gamma-ray regime, we deduce a 95 per cent confidence level upper limit on the unknown redshift of KUV 00311-1938 of z < 0.98 and of PKS 1440-389 of z < 0.53. When combined with previous spectroscopy results, the redshift of KUV 00311-1938 is constrained to 0.51 <= z < 0.98 and of PKS 1440-389 to 0.14 (sic) z < 0.53.
Proceedings of KogWis 2010 : 10th Biannual Meeting of the German Society for Cognitive Science
(2010)
As the latest biannual meeting of the German Society for Cognitive Science (Gesellschaft für Kognitionswissenschaft, GK), KogWis 2010 at Potsdam University reflects the current trends in a fascinating domain of research concerned with human and artificial cognition and the interaction of mind and brain. The Plenary talks provide a venue for questions of the numerical capacities and human arithmetic (Brian Butterworth), of the theoretical development of cognitive architectures and intelligent virtual agents (Pat Langley), of categorizations induced by linguistic constructions (Claudia Maienborn), and of a cross-level account of the “Self as a complex system“ (Paul Thagard). KogWis 2010 integrates a wealth of experimental research, cognitive modelling, and conceptual analysis in 5 invited symposia, over 150 individual talks, 6 symposia, and more than 40 poster contributions. Some of the invited symposia reflect local and regional strenghts of research in the Berlin-Brandenburg area: the two largests research fields of the university Cognitive Sciences Area of Excellence in Potsdam are represented by an invited symposium on “Information Structure” by the Special Research Area 632 (“Sonderforschungsbereich”, SFB) of the same name, of Potsdam University and Humboldt-University Berlin, and by a satellite conference of the research group “Mind and Brain Dynamics”. The Berlin School of Mind and Brain at Humboldt-University Berlin takes part with an invited symposium on “Decision Making” from a perspective of cognitive neuroscience and philosophy and the DFG Cluster of Excellence “Languages of Emotion” of Free University presents interdisciplinary research results in an invited symposium on “Symbolising Emotions”.
A central insight from psychological studies on human eye movements is that eye movement patterns are highly individually characteristic. They can, therefore, be used as a biometric feature, that is, subjects can be identified based on their eye movements. This thesis introduces new machine learning methods to identify subjects based on their eye movements while viewing arbitrary content. The thesis focuses on probabilistic modeling of the problem, which has yielded the best results in the most recent literature. The thesis studies the problem in three phases by proposing a purely probabilistic, probabilistic deep learning, and probabilistic deep metric learning approach. In the first phase, the thesis studies models that rely on psychological concepts about eye movements. Recent literature illustrates that individual-specific distributions of gaze patterns can be used to accurately identify individuals. In these studies, models were based on a simple parametric family of distributions. Such simple parametric models can be robustly estimated from sparse data, but have limited flexibility to capture the differences between individuals. Therefore, this thesis proposes a semiparametric model of gaze patterns that is flexible yet robust for individual identification. These patterns can be understood as domain knowledge derived from psychological literature. Fixations and saccades are examples of simple gaze patterns. The proposed semiparametric densities are drawn under a Gaussian process prior centered at a simple parametric distribution. Thus, the model will stay close to the parametric class of densities if little data is available, but it can also deviate from this class if enough data is available, increasing the flexibility of the model. The proposed method is evaluated on a large-scale dataset, showing significant improvements over the state-of-the-art. Later, the thesis replaces the model based on gaze patterns derived from psychological concepts with a deep neural network that can learn more informative and complex patterns from raw eye movement data. As previous work has shown that the distribution of these patterns across a sequence is informative, a novel statistical aggregation layer called the quantile layer is introduced. It explicitly fits the distribution of deep patterns learned directly from the raw eye movement data. The proposed deep learning approach is end-to-end learnable, such that the deep model learns to extract informative, short local patterns while the quantile layer learns to approximate the distributions of these patterns. Quantile layers are a generic approach that can converge to standard pooling layers or have a more detailed description of the features being pooled, depending on the problem. The proposed model is evaluated in a large-scale study using the eye movements of subjects viewing arbitrary visual input. The model improves upon the standard pooling layers and other statistical aggregation layers proposed in the literature. It also improves upon the state-of-the-art eye movement biometrics by a wide margin. Finally, for the model to identify any subject — not just the set of subjects it is trained on — a metric learning approach is developed. Metric learning learns a distance function over instances. The metric learning model maps the instances into a metric space, where sequences of the same individual are close, and sequences of different individuals are further apart. This thesis introduces a deep metric learning approach with distributional embeddings. The approach represents sequences as a set of continuous distributions in a metric space; to achieve this, a new loss function based on Wasserstein distances is introduced. The proposed method is evaluated on multiple domains besides eye movement biometrics. This approach outperforms the state of the art in deep metric learning in several domains while also outperforming the state of the art in eye movement biometrics.
We establish elements of a new approach to ellipticity and parametrices within operator algebras on manifolds with higher singularities, only based on some general axiomatic requirements on parameter-dependent operators in suitable scales of spaes. The idea is to model an iterative process with new generations of parameter-dependent operator theories, together with new scales of spaces that satisfy analogous requirements as the original ones, now on a corresponding higher level. The "full" calculus involves two separate theories, one near the tip of the corner and another one at the conical exit to infinity. However, concerning the conical exit to infinity, we establish here a new concrete calculus of edge-degenerate operators which can be iterated to higher singularities.
Linked Open Data (LOD) comprises very many and often large public data sets and knowledge bases. Those datasets are mostly presented in the RDF triple structure of subject, predicate, and object, where each triple represents a statement or fact. Unfortunately, the heterogeneity of available open data requires significant integration steps before it can be used in applications. Meta information, such as ontological definitions and exact range definitions of predicates, are desirable and ideally provided by an ontology. However in the context of LOD, ontologies are often incomplete or simply not available. Thus, it is useful to automatically generate meta information, such as ontological dependencies, range definitions, and topical classifications. Association rule mining, which was originally applied for sales analysis on transactional databases, is a promising and novel technique to explore such data. We designed an adaptation of this technique for min-ing Rdf data and introduce the concept of “mining configurations”, which allows us to mine RDF data sets in various ways. Different configurations enable us to identify schema and value dependencies that in combination result in interesting use cases. To this end, we present rule-based approaches for auto-completion, data enrichment, ontology improvement, and query relaxation. Auto-completion remedies the problem of inconsistent ontology usage, providing an editing user with a sorted list of commonly used predicates. A combination of different configurations step extends this approach to create completely new facts for a knowledge base. We present two approaches for fact generation, a user-based approach where a user selects the entity to be amended with new facts and a data-driven approach where an algorithm discovers entities that have to be amended with missing facts. As knowledge bases constantly grow and evolve, another approach to improve the usage of RDF data is to improve existing ontologies. Here, we present an association rule based approach to reconcile ontology and data. Interlacing different mining configurations, we infer an algorithm to discover synonymously used predicates. Those predicates can be used to expand query results and to support users during query formulation. We provide a wide range of experiments on real world datasets for each use case. The experiments and evaluations show the added value of association rule mining for the integration and usability of RDF data and confirm the appropriateness of our mining configuration methodology.
Unique column combinations of a relational database table are sets of columns that contain only unique values. Discovering such combinations is a fundamental research problem and has many different data management and knowledge discovery applications. Existing discovery algorithms are either brute force or have a high memory load and can thus be applied only to small datasets or samples. In this paper, the wellknown GORDIAN algorithm and "Apriori-based" algorithms are compared and analyzed for further optimization. We greatly improve the Apriori algorithms through efficient candidate generation and statistics-based pruning methods. A hybrid solution HCAGORDIAN combines the advantages of GORDIAN and our new algorithm HCA, and it significantly outperforms all previous work in many situations.
As digital media infiltrate an increasingly greater proportion of our lives, concern about the possibility of various forms of technology addictions has emerged. For technology addiction, researchers have developed a variety of self-reported scales in areas such as Internet, smartphones, videogames, social network sites (SNS) or television. However, no uniform criteria or definition exists for technology addiction. Utilized dimensions of technology addiction, to measure specific outcomes, lack a conceptual standard. Therefore, linkages between technology areas dimensions have not been examined in a broader way by the research community, in order to develop a uniform technology addiction scale.
In this regard, firstly, a theoretical model was developed in order to extract common technology dimensions. Secondly, a systematic literature review in the areas of Internet, smartphone, video games and SNS was conducted in order to extract the dimensions used. To identify relevant studies, nine databases (GoogleScholar, ScienceDirect, PubMed, EmeraldInsight, Wiley, SpringerLink, ACM, iEEE and JSTOR) were searched, producing 4698 results, and 50 studies met the inclusion criteria. Thirdly, the developed theoretical model was utilized in order to determine the dimension in each of the identified scales.
Based on analysis of the dimensional distributions, the findings suggest that there are common dimensions across areas of technology such as “compulsive use” and “negative outcomes” but also differences in dimensions across areas such as “social comfort” and “mood regulation”, which are more used in the area of SNS. Moreover, new dimensions such as “cognitive absorption” or “utility and function loss" for technology addiction were extracted, which should be considered as these have not yet been researched in a broader way. In addition, no gold standard for the conceptual criteria or definition for technology addiction has been developed yet.