Titlebook: Hilbert Modular Surfaces; Gerard Geer Book 1988 Springer-Verlag Berlin Heidelberg 1988 Canon.Compactification.Dimension.Division.Divisor.F

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索賠

The Classification of Hilbert Modular Surfaces,nera, which roughly speaking is a way of measuring the ampleness of the canonical divisor class. On Hilbert modular surfaces we have in some sense two “canonical divisor classes”, the usual canonical class . . . . . and the class . .(. .(log.)) with . the divisor resolving the cusps. The second one

propose

Humbert Surfaces,belian surfaces are Hilbert modular surfaces. Since the moduli spaces of polarized complex abelian varieties are Siegel modular varieties we find natural maps of Hilbert modular surfaces to Siegel modular threefolds. The images are called Humbert surfaces, after G. Humbert, who studied abelian surfa

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Moduli of Abelian Schemes with Real Multiplication, of Hilbert modular varieties over (schemes of) number rings. These models live in a somewhat larger category than that of schemes: they are algebraic stacks. They are however not proper over their base schemes. This is due to the fact that the abelian varieties which we consider can degenerate. Mum

anesthesia

Gerard van der Geerificial Intelligence DFKI, Saarland University), providing us with new developments in the field. We are very grateful to the contributions of such pioneer scie978-3-540-36713-0978-3-540-36861-8Series ISSN 0302-9743 Series E-ISSN 1611-3349

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plied by the text, but not mentioned directly. We have also collected two datasets to train and test the corresponding models. These datasets are publicly available at .. We have compared the performance of our algorithm with several existing approaches which might be used for extracting the locatio

索賠

Gerard van der Geers, sentences, tokens, pauses, and unfinished words; the relative frequencies of parts of speech and some grammatical categories (animacy, number, aspect, involvement, mood, person, tense), and the statistics of positive and negative words use. The Mann-Whitney U test was used to assess differences i

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FLAGR

Gerard van der Geerext corpus. We show that these representations can be easily added to existing models and be combined with other word representation features. We evaluate our model on FactRuEval-2016 dataset for named entity recognition in Russian and achieve state of the art results.

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