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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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5 results for “cross-coupling”
On Simultaneous Long-Short Stock Trading Controllers with Cross-Couplingq-fin.ST

This paper extends SLS controllers to two stocks, proving the RPE property with cross-coupling.

problem Extending SLS controllers to two stocks without exploiting correlations.
method Developed a novel architecture for cross-coupling two SLS controllers, derived a closed-form expected value, and proved the RPE property.
result Guaranteed RPE property with cross-coupling for a large class of stock dynamics.
SLS controllerscross-couplingstock tradingRPE propertystock dynamics
2020-11-18abs ↗pdf ↗
Gradient-Free Learning Based on the Kernel and the Range Spacecs.LG

In this article, we show that solving the system of linear equations by manipulating the kernel and the range space is equivalent to solving the problem of least squares error approximation. This establishes the ground for a gradient-free learning search when the system can be expressed in the form of a linear matrix e…

2018-10-27abs ↗pdf ↗
Cross-Spectral Witness for Hidden Nonequilibrium Beyond the Scalar Ceilingcond-mat.stat-mech

A new method detects hidden driving forces in systems with multiple observables.

problem Hidden driving forces in systems with multiple observables cannot be detected by scalar statistics.
method Cross-spectral witness for hidden nonequilibrium.
result Two simultaneously observed channels retain an off-diagonal cross-spectral sector inaccessible to scalar reductions.
hidden driving forcescross-spectral witnessnonequilibrium physicstime-irreversibilitystochastic systems
2026-04-04abs ↗pdf ↗
Noisy, sparse, nonlinear: Navigating the Bermuda Triangle of physical inference with deep filteringphysics.chem-ph

Deep filtering improves robustness of models from noisy, sparse data.

problem Challenges in understanding microscopic interactions from noisy, sparse, and biased data.
method Statistical approach based on deep filtering of nonlinear feature networks.
result Physicochemical models are more robust, transparent, and generalize better.
deep filteringnonlinear feature networksmachine learningnoisy datasparse data
2019-11-19abs ↗pdf ↗