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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.

168,742 papers · 148 categories

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1122 · Nov 201919922001200920172026
8 results for G-opers

We review the properties of transversality of distributions with respect to submersions. This allows us to construct a convolution product for a large class of distributions on Lie groupoids. We get a unital involutive algebra $\cE\_{r,s}'(G,Ω^{1/2})$ enlarging the convolution algebra C_c(G,Ω1/2)C^\infty\_c(G,Ω^{1/2}) associate…

2015-02-06abs ↗pdf ↗

As announced in [12], we develop a calculus of Fourier integral G-operators on any Lie groupoid G. For that purpose, we study convolability and invertibility of Lagrangian conic submanifolds of the symplectic groupoid T * G. We also identify those Lagrangian which correspond to equivariant families parametrized by the …

2016-01-04abs ↗pdf ↗

For a complex simple simply connected Lie group GG, and a compact Riemann surface CC, we consider two sorts of families of flat GG-connections over CC. Each family is determined by a point u{\mathbf u} of the base of Hitchin's integrable system for (G,C)(G,C). One family ,u\nabla_{\hbar,{\mathbf u}} consists of GG-o…

2016-07-07abs ↗pdf ↗

We obtain several essential self-adjointness conditions for a Schroedinger type operator D*D+V acting in sections of a vector bundle over a manifold M. Here V is a locally square-integrable bundle map. Our conditions are expressed in terms of completeness of certain metrics on M; these metrics are naturally associated …

2002-01-24abs ↗pdf ↗

A model predicts solar irradiance without local data using satellite and weather forecasts.

problem Forecasting solar irradiance without local measurements for geographically dispersed solar generators.
method Uses satellite data and weather forecasts with a deep neural network trained on a subset of ground data.
result Proposed model performs as well or better than local models across 25 locations and prediction horizons.

This paper proposes a method to compress and adapt CNNs for real-world applications.

problem Differences in data distributions and high computational costs limit CNN adoption.
method Joint optimization of CNNs for unsupervised domain adaptation and knowledge distillation.
result The proposed method achieves the highest accuracy with comparable or lower time complexity.