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Metadata --- Digital Assets --- Embedding
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Metadata --- Digital Assets --- Embedding --- Metadata --- Digital Assets --- Embedding
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Ordered algebraic structures --- Algebra, Universal. --- Embedding theorems. --- Magic squares. --- Quasigroups.
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MET Methods & Techniques --- botanical collections --- embedding in resin --- methods & techniques
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Breast Neoplasms --- Receptors, Estrogen --- Paraffin Embedding --- therapy --- analysis
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Reinforcing bars. --- Bonding strength --- Bond stress --- Bars --- Lateral pressure --- Embedding --- Compressive strength
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Let X be an abstract not necessarily compact orientable CR manifold of dimension 2n-1, ngeqslant 2, and let L^k be the k-th tensor power of a CR complex line bundle L over X. Given qin {0,1,ldots ,n-1}, let Box ^{(q)}_{b,k} be the Gaffney extension of Kohn Laplacian for (0,q) forms with values in L^k. For lambda geq 0, let Pi ^{(q)}_{k,leq lambda} :=E((-infty ,lambda ]), where E denotes the spectral measure of Box ^{(q)}_{b,k}. In this work, the author proves that Pi ^{(q)}_{k,leq k^{-N_0}}F^*_k, F_kPi ^{(q)}_{k,leq k^{-N_0}}F^*_k, N_0geq 1, admit asymptotic expansions with respect to k on the non-degenerate part of the characteristic manifold of Box ^{(q)}_{b,k}, where F_k is some kind of microlocal cut-off function. Moreover, we show that F_kPi ^{(q)}_{k,leq 0}F^*_k admits a full asymptotic expansion with respect to k if Box ^{(q)}_{b,k} has small spectral gap property with respect to F_k and Pi^{(q)}_{k,leq 0} is k-negligible away the diagonal with respect to F_k. By using these asymptotics, the authors establish almost Kodaira embedding theorems on CR manifolds and Kodaira embedding theorems on CR manifolds with transversal CR S^1 action.
Embedding theorems. --- CR submanifolds. --- Manifolds (Mathematics) --- Embeddings (Mathematics) --- Kernel functions. --- Asymptotic expansions.
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Detecting corefering events and entities in texts is an important task in NLP, where it plays a role in many other tasks and applications. In this work, we build on a joint approach of entity and event coreference resolution, pioneered by H. Lee, Recasens, et al. 2012 and matured by Barhom et al. 2019 using a neural architecture. In particular we look at coreference resolution across documents, more complicated and less researched than coreference resolution within documents. Using the Barhom et al. 2019’s model, we propose a series of extensions to improve its results. This is done by increasing the amount of information provided to the model, in particular the joint nature of the modelling and by improving entity and event representation with the use of document embedding. As a secondary problem, we investigate ways to improve the model’s time performance through compressing the mention representations. Our results are compared with other works tackling the problem of cross document coreference resolution on the ECB+ dataset, the standard dataset for cross document entity and event coreference resolution.
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517.982 --- 517.982 Linear spaces with topology and order or other structures --- Linear spaces with topology and order or other structures --- Integral representations --- Representations, Integral --- Imbedding theorems --- Theorems, Embedding --- Theorems, Imbedding --- Embeddings (Mathematics) --- Functions of several real variables. --- Invariant embedding. --- Functions of several real variables --- Lebesgue integration --- Invariant imbedding --- Invariant imbedding. --- Embedding theorems --- Functions of several complex variables --- Algebraic number theory --- Crystallography, Mathematical --- Representations of groups --- Complex variables --- Several complex variables, Functions of --- Functions of complex variables --- Functional analysis --- Invariant embedding
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