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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.

168,657 papers · 148 categories

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0111 · Feb 200319922001200920172026
9 results for non-orderability

Study the topology of loops of contactomorphisms and Legendrians in non-orderable manifolds.

problem Global topology of loops of contactomorphisms and Legendrians in non-orderable manifolds.
method Filtering loops by positivity and analyzing subspaces of the filtration.
result Homotopy groups of the space of loops are subgroups of the positive loops subspace.

The classical reduced-form and filtration expansion framework in credit risk is extended to the case of multiple, non-ordered defaults, assuming that conditional densities of the default times exist. Intensities and pricing formulas are derived, revealing how information driven default contagion arises in these models.…

2011-04-27abs ↗pdf ↗

Study callable convertible bonds with liquidity constraints, generalizing previous work.

problem Callable convertible bond problem with liquidity constraints.
method Introduced a new technique to handle non-ordered payoff situations.
result Complete solution to callable convertible bond problem with liquidity constraint.

We show that several torsion free 3-manifold groups are not left-orderable. Our examples are groups of cyclic branched covers of S^3 branched along links. The figure eight knot provides simple nontrivial examples. The groups arising in these examples are known as Fibonacci groups which we show not to be left-orderable.…

2003-02-10abs ↗pdf ↗

The study proves properties of spectral selectors for contact manifolds and applies them to contact big fibers and geodesics.

problem Properties of spectral selectors for contact manifolds.
method Algebraic properties of spectral selectors for strongly orderable contact manifolds.
result Established contact big fiber theorem and constructed norms on contactomorphism group universal cover.

Framework analyzes neural network dynamics for better understanding and optimization.

problem Understanding the fundamental mechanisms of deep neural networks.
method Dynamical systems theory, transformation units, attraction basins.
result Different transformation modes lead to distinct learning phases and network performance.