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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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12 results for 2-reductive

A geometric construction of Sullivan's Stiefel-Whitney homology classes of a real analytic variety XX is given by means of the conormal cycle of an embedding of XX in a smooth variety. We prove that the Stiefel-Whitney classes define additive natural transformations from certain constructible functions to homology. W…

1995-08-21abs ↗pdf ↗

We introduce an invariant for trivalent fatgraph spines of a once bordered surface, which takes values in the first homology of the surface. This invariant is the secondary object coming from two 1-cocycles on the dual fatgraph complex, one introduced by Morita and Penner in 2008, and the other by Penner, Turaev, and t…

2015-11-03abs ↗pdf ↗

Hybrid neural-tree networks reduce IoT model size and computation by 52.2% and 11.1% respectively.

problem Power and storage constraints in IoT devices limit the deployment of modern neural networks.
method Combines neural and tree-based learning with ternary quantization.
result Significant reduction in model size and computation with minimal accuracy loss.

We review a cochain-free treatment of the classical van Kampen obstruction θto embeddability of an n-polyhedron into R^{2n} and consider several analogues and generalizations of θ, including an extraordinary lift of θwhich in the manifold case has been studied by J.-P. Dax. The following results are obtained. - The mod…

2006-12-04abs ↗pdf ↗

The paper finds Riemannian metric representatives for Stiefel-Whitney classes.

problem Finding representatives of Stiefel-Whitney classes using Riemannian metrics.
method Using Whitney's criteria and properties of Riemannian metrics, the paper constructs representatives for all Stiefel-Whitney classes.
result The representatives of Stiefel-Whitney classes are derived from the determinant of the metric and other geometric properties.

BottleNet++ compresses deep learning features for efficient mobile inference.

problem Balancing computation and communication in mobile devices with deep learning models.
method End-to-end architecture with encoder, non-trainable channel layer, and decoder.
result Achieves high compression ratio and bandwidth reduction with minimal accuracy loss.