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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”.
Historischer November in Palästina : Zwei-Staaten-Lösung versus dauerhafte Besatzung durch Israel
(2013)
Der November 2012 wird sicherlich als ein historischer Monat in die palästinensische Geschichte eingehen. Im November begann die israelische Armee mit der gezielten Tötung eines hohen Funktionärs der Hamas einen Luftkrieg gegen den Gazastreifen. Nach acht Tagen wurde Israel durch internationalen Druck gezwungen, einen Waffenstillstand mit der Hamas zu akzeptieren. Einmal mehr zeigte sich, dass der Nahostkonflikt nicht mit militärischer Gewalt zu lösen ist. Dies kann nur mit einer Zwei-Staaten-Lösung erreicht werden, der substanzielle und ehrliche Verhandlungen vorausgehen.
Lamprophyre sind porphyrische, aus Mantelschmelzen gebildete Gesteine, die meist in Form von Gängen auftreten. Sie zeichnen sich durch auffällige und charakteristische texturelle, chemische und mineralogische Eigenschaften aus. Als ehemalige Mantelschmelzen liefern sie Information sowohl über Bedingungen der Schmelzbildung im Mantel als auch über geodynamische Prozesse, die zu metasomatischer Veränderung des Mantels geführt haben. Im Saxothuringikum Mitteleuropas, am Nordrand des Böhmischen Massivs, gibt es zahlreiche Lamprophyrvorkommen, die hier zur Charakterisierung der Mantelentwicklung während der variszischen Orogenese dienen. Die vorliegende Arbeit befaßt sich mit den mineralogischen, geochemischen und isotopischen (Sr-Nd-Pb) Signaturen von spätvariszischen kalkalkalischen Lamprophyren, von postvariszischen ultramafischen Lamprophyren, von Alkalibasalten der Lausitz und, zum Vergleich, von prävariszischen Gabbros. Darüberhinaus nutzt die Arbeit Lithium-Isotopensignaturen kombiniert mit Sr-Nd-Pb–Isotopendaten spätvariszischer kalkalkalischer Lamprophyre aus drei variszischen Domänen (Erzgebirge, Lausitz, Sudeten) zur Erkundung der lokalen Mantelüberprägungen während der variszischen Orogenese.
Deep metric learning employs deep neural networks to embed instances into a metric space such that distances between instances of the same class are small and distances between instances from different classes are large. In most existing deep metric learning techniques, the embedding of an instance is given by a feature vector produced by a deep neural network and Euclidean distance or cosine similarity defines distances between these vectors. This paper studies deep distributional embeddings of sequences, where the embedding of a sequence is given by the distribution of learned deep features across the sequence. The motivation for this is to better capture statistical information about the distribution of patterns within the sequence in the embedding. When embeddings are distributions rather than vectors, measuring distances between embeddings involves comparing their respective distributions. The paper therefore proposes a distance metric based on Wasserstein distances between the distributions and a corresponding loss function for metric learning, which leads to a novel end-to-end trainable embedding model. We empirically observe that distributional embeddings outperform standard vector embeddings and that training with the proposed Wasserstein metric outperforms training with other distance functions.
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.
Different properties of programs, implemented in Constraint Handling Rules (CHR), have already been investigated. Proving these properties in CHR is fairly simpler than proving them in any type of imperative programming language, which triggered the proposal of a methodology to map imperative programs into equivalent CHR. The equivalence of both programs implies that if a property is satisfied for one, then it is satisfied for the other. The mapping methodology could be put to other beneficial uses. One such use is the automatic generation of global constraints, at an attempt to demonstrate the benefits of having a rule-based implementation for constraint solvers.
Say it with double flowers
(2020)
Every year, lovers world-wide rely on mutants to show their feelings on Valentine's Day. This is because many of the most popular ornamental flowering plants have been selected to form extra petals at the expense of reproductive organs to enhance their attractiveness and aesthetic value to humans. This so-called 'double flower' (DF) phenotype, first described more than 2000 years ago (Meyerowitz et al., 1989) is present, for example, in many modern roses, carnations, peonies, and camellias. Gattolin et al. (2020) now identify a unifying explanation for the molecular basis of many of these DF cultivars.
Phytochemical investigation of the dichloromethane/methanol (1:1) extract of the roots of Bulbine frutescens led to the isolation of a new xanthone, 8-hydroxy-6-methylxanthone-1-carboxylic acid (1) and a new phenylanthraquinone, 6',8-O-dimethylknipholone (2) along with six known compounds. The structures were elucidated on the basis of NMR and MS spectral data analyses. The structure of compound 1 was confirmed through X-ray crystallography which was then used as a reference to propose the revision of the structures of six seco-anthraquinones into xanthones. The isolated compounds were evaluated for cytotoxicity against human cervix carcinoma KB-3-1 cells with the phenylanthraquinone knipholone being the most active (IC50 = 0.43 mu M). Two semi-synthetic knipholone derivatives, knipholone Mannich base and knipholone-1,3-oxazine, were prepared and tested for cytotoxic activity; both showed moderate activities (IC50 value of 1.89 and 2.50 mu M, respectively). (C) 2014 Phytochemical Society of Europe. Published by Elsevier B.V. All rights reserved.