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When dinosaurs hear like barn owls: pitfalls and caveats in assessing hearing in dinosaurs. [PDF]
Biol LettManley GA, Köppl C.
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SHINE: Deterministic Many-to-Many Clustering of Molecular Pathways. [PDF]
J Chem Inf ModelChen L +4 more
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Optimally scrambling chiral spin-chain with effective black hole geometry. [PDF]
Sci RepDaniel A +3 more
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Quantum coordinates, localisation of events, and the quantum hole argument. [PDF]
Commun PhysKabel V +6 more
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The Unstable Relationship Between Drought Status and Leaf Water Content Complicates the Remote Sensing of Tree Drought Stress. [PDF]
Glob Chang BiolBoving I +5 more
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Distance Metric Learning for Large Margin Nearest Neighbor Classification
Neural Information Processing Systems, 2005The accuracy of k-nearest neighbor (kNN) classification depends significantly on the metric used to compute distances between different examples. In this paper, we show how to learn a Mahalanobis distance metric for kNN classification from labeled ...
Kilian Q. Weinberger, L. Saul
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An Introduction to Functional Analysis, 2020
As calculus developed, eventually turning into analysis, concepts first explored on the real line (e.g., a limit of a sequence of real numbers) eventually extended to other spaces (e.g., a limit of a sequence of vectors or of functions), and in the early
Christian Clason
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As calculus developed, eventually turning into analysis, concepts first explored on the real line (e.g., a limit of a sequence of real numbers) eventually extended to other spaces (e.g., a limit of a sequence of vectors or of functions), and in the early
Christian Clason
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Mathematical programming, 2015
We consider stochastic programs where the distribution of the uncertain parameters is only observable through a finite training dataset. Using the Wasserstein metric, we construct a ball in the space of (multivariate and non-discrete) probability ...
Peyman Mohajerin Esfahani, D. Kuhn
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We consider stochastic programs where the distribution of the uncertain parameters is only observable through a finite training dataset. Using the Wasserstein metric, we construct a ball in the space of (multivariate and non-discrete) probability ...
Peyman Mohajerin Esfahani, D. Kuhn
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2015
The aim of this introductory chapter is to present some basic aspects of analysis and topology for the space R(M) of complete Riemannian metrics on a smooth manifold M. We also consider the corresponding moduli space M(M), which is the quotient of R(M) by the action of the diffeomorphism group Diff(M).
David J. Wraith, Wilderich Tuschmann
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The aim of this introductory chapter is to present some basic aspects of analysis and topology for the space R(M) of complete Riemannian metrics on a smooth manifold M. We also consider the corresponding moduli space M(M), which is the quotient of R(M) by the action of the diffeomorphism group Diff(M).
David J. Wraith, Wilderich Tuschmann
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2009
The Encyclopedia of Databases, a comprehensive work, provides easy access to relevant information on all aspects of very large databases. This encyclopedia features alphabetical organization of concepts covering main areas of very large databases. These 1000 entries offer convenient access to information in the field of databases with definitions and ...
Vlastislav Dohnal +2 more
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The Encyclopedia of Databases, a comprehensive work, provides easy access to relevant information on all aspects of very large databases. This encyclopedia features alphabetical organization of concepts covering main areas of very large databases. These 1000 entries offer convenient access to information in the field of databases with definitions and ...
Vlastislav Dohnal +2 more
openaire +1 more source