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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.

168,742 papers · 148 categories

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1122 · May 202619922001200920172026
9 results for operator-adaptive

New method speeds up NIR spectroscopy calibration by 400x.

problem Efficient preprocessing selection in NIR spectroscopy.
method Operator-adaptive PLS and Ridge regression.
result Significant reduction in fitting time with comparable prediction quality.

On a Kähler spin manifold Kählerian twistor spinors are a natural analogue of twistor spinors on Riemannian spin manifolds. They are defined as sections in the kernel of a first order differential operator adapted to the Kähler structure, called Kählerian twistor (Penrose) operator. We study Kählerian twistor spinors a…

2008-12-17abs ↗pdf ↗

Geometric framework for Milnor classifying spaces in diffeological spaces.

problem Milnor classifying spaces in diffeological spaces.
method Developed spherical and projective models with natural diffeological structures, constructed Riemannian metrics, defined differential forms, and introduced Clifford structures.
result Established a coherent geometric setting combining classifying spaces, diffeology, and higher geometric structures.

HGP-SL pools and learns graph structure for hierarchical representation learning.

problem Graph pooling is overlooked in GNN models, limiting hierarchical representation learning.
method Integrates graph pooling and structure learning into a unified module.
result HGP-SL improves graph classification performance on benchmarks.

The paper explores self-adjointness of Laplace-Beltrami operator on special geometric manifolds.

problem Characterizing self-adjoint extensions of the Laplace-Beltrami operator on αα-Grushin manifolds.
method Introducing an exotic calculus of pseudodifferential operators adapted to the geometry of the singularity.
result Criterion for essential self-adjointness and determination of several self-adjoint extensions.

Self-ONNs adapt nodal operators during training for higher diversity and efficiency.

problem Limited network heterogeneity and high computational demand in ONNs.
method Self-organized ONNs with generative neurons that adapt nodal operators during training.
result Self-ONNs achieve utmost heterogeneity and computational efficiency.