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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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326597129 · May 202619922001200920172026
48 results for secondary stability

New stability theorem for nonorientable surfaces mapping class groups.

problem Stability of homology groups of mapping class groups of nonorientable surfaces.
method Galatius--Kupers--Randal-Williams framework of cellular E2E_2-algebras.
result New best known stability range for homology of nonorientable surfaces.

Chow stability is one notion of Mumford's Geometric Invariant Theory for studying the moduli space of polarized varieties. Kapranov, Sturmfels and Zelevinsky detected that Chow stability of polarized toric varieties is determined by its inherent {\it secondary polytope}, which is a polytope whose vertices correspond to…

2013-06-19abs ↗pdf ↗

In this paper we define K-theoretic secondary invariants attached to a Lie groupoid GG. The K-theory of Cr(Gad0)C^*_r(G_{ad}^0) (where Gad0G_{ad}^0 is the adiabatic deformation GG restricted to the interval [0,1)[0,1)) is the receptacle for K-theoretic secondary invariants. We give a Lie groupoid version of construction given b…

2016-09-26abs ↗pdf ↗

We extend the notion of connection in order to be able to study singular geometric structures, namely, we consider a notion of connection on a Lie algebroid which is a natural extension of the usual concept of connection. Using connections, we are able to define holonomy of the orbit foliation of a Lie algebroid and pr…

2000-07-21abs ↗pdf ↗

Machine learning improves RNA secondary structure prediction.

problem Stagnant performance of RNA secondary structure prediction methods.
method Machine learning, especially deep learning, is used to predict RNA secondary structures.
result Machine learning methods have improved the prediction of RNA secondary structures.

We study smooth maps between smooth manifolds with only fold points as their singularities, and clarify the obstructions to the existence of such a map in a given homotopy class for certain dimensions. The obstructions are described in terms of characteristic classes, which arise as Postnikov invariants, and can be int…

2010-03-14abs ↗pdf ↗

Study of spectral flow in symmetric Toeplitz operator families.

problem Understanding spectral flow in families of symmetric Toeplitz operators.
method Analog of Atiyah-Singer-Robbin-Salamon theorem for Z2\mathbb{Z}_2-valued spectral flow.
result Graded secondary spectral flow equals secondary index of a Callias-type operator.

The knot invariant Upsilon, defined by Ozsvath, Stipsicz, and Szabo, induces a homomorphism from the smooth knot concordance group to the group of piecewise linear functions on the interval [0,2]. Here we define a set of related secondary invariants, each of which assigns to a knot a piecewise linear function on [0,2].…

2016-10-17abs ↗pdf ↗

The paper tackles online learning with two types of losses and shows it's impossible without certain assumptions.

problem Online learning with primary and secondary losses where the secondary loss is bounded by a linear threshold.
method Analyzes the feasibility of achieving low regret with respect to the primary loss while keeping the secondary loss within a linear threshold.
result Achieving the goal is impossible without bounded variance assumption on the secondary loss.

ABM simulates OTC government bond market dynamics, enhancing liquidity and stability.

problem Understanding and ensuring market stability and liquidity in OTC government bond markets.
method Developed a bespoke ABM to simulate market-maker interactions and test hypotheses.
result Greater agent diversity enhances market liquidity and reducing market-making costs improves stability.

Fuses ITRs for primary and secondary outcomes to minimize harm.

problem Learn an ITR maximizing primary outcome while minimizing harm to secondary outcomes.
method Introduces fusion penalty to encourage similar recommendations for different outcomes. Two algorithms estimate the ITR using surrogate loss functions.
result Agreement rate between primary and secondary optimal ITRs converges faster than ignoring secondary outcomes.

We introduce two invariants called the secondary cuspidal curvature and the bias on 5/25/2-cuspidal edges, and investigate their basic properties. While the secondary cuspidal curvature is an analog of the cuspidal curvature of (ordinary) cuspidal edges, there are no invariants corresponding to the bias. We prove that t…

2017-10-16abs ↗pdf ↗

In this study, we generalize double tangent bundles to double jet bundles. We present a secondary vector bundle structure on a 1-jet of a vector bundle. We show that 1-jet of a vector bundle carries two vector bundle structures, namely primary and secondary structures. We also show that the manifold charts induced by p…

2016-01-17abs ↗pdf ↗

Secondary Calculus formalizes PDEs using cohomology, simplifying their study.

problem Formalizing and simplifying the study of partial differential equations (PDEs).
method Using cohomology of diffieties to formalize PDEs and their properties.
result Differential calculus on PDE solution spaces is homotopy calculus on horizontal De Rham algebras of diffieties.

Paper constructs a cohomology class related to McDuff's secondary class, proving it transgresses to the Euler class of foliated sphere bundles.

problem Finding higher-dimensional analogs of the Calabi invariant and its transgression to the Euler class.
method Constructing a cohomology class of volume-preserving diffeomorphisms and proving transgression to the Euler class of foliated sphere bundles.
result The cohomology class transgresses to the Euler class of foliated sphere bundles.

The study improves credit evaluation in peer-to-peer lending using machine learning.

problem Traditional credit histories are insufficient for distinguishing good from bad borrowers.
method Used machine learning classification and clustering algorithms to predict creditworthiness.
result Achieved 65% F1 and 73% AUC on LendingClub data, identifying key secondary attributes.

A famous construction of Gelfand, Kapranov and Zelevinsky associates to each finite point configuration ARdA \subset \mathbb{R}^d a polyhedral fan, which stratifies the space of weight vectors by the combinatorial types of regular subdivisions of AA. That fan arises as the normal fan of a convex polytope. In a complete…

2017-08-29abs ↗pdf ↗

E2Efold predicts RNA secondary structures better than previous methods.

problem RNA secondary structure prediction with constraints.
method End-to-end deep learning model using unrolled algorithms to enforce constraints.
result E2Efold predicts significantly better structures, especially for pseudoknotted structures.

PS8-Net improves eight-state protein secondary structure prediction accuracy.

problem Precise prediction of eight-state protein secondary structure (PSS) is crucial in bioinformatics.
method PS8-Net is a new deep convolutional neural network (DCNN) that uses a PS8 module with skip connections to enhance accuracy.
result PS8-Net achieves 76.89% Q8 accuracy on benchmark datasets.

Defines and proves CR invariants on five-manifolds.

problem Defines and studies CR invariants on CR five-manifolds.
method Defines global secondary CR invariants and proves their linear combination.
result Any global secondary CR invariant is a linear combination of total QQ'-curvature, total I\mathcal{I}'-curvature, and a local CR invariant.

Deep learning helps remove secondary BB-mode polarization to detect primordial gravitational waves.

problem Removing secondary BB-mode polarization from CMB data to detect primordial gravitational waves.
method Applied deep learning (ResUNet-CMB) to estimate and remove multiple sources of secondary BB-mode polarization.
result Deep learning can produce nearly optimal, unbiased estimates of the amplitude of primordial gravitational waves.

In this paper we construct an infinite family of knots with vanishing Upsilon invariant ΥΥ, although their secondary Upsilon invariants Υ2Υ^2 show that they are linearly independent in the smooth knot concordance group. We also prove a conjecture in a paper by Allen.

2018-05-23abs ↗pdf ↗

In this paper, we introduce six axioms for relative Bott-Chern secondary characteristic classes and prove the uniqueness and existence theorem for them. Such a work provides us a natural way to understand and hence to prove the arithmetic Grothendieck-Riemann-Roch theorem.

1998-10-19abs ↗pdf ↗

We define and study the secondary Chern-Euler class for a general submanifold of a Riemannian manifold. Using this class, we define and study index for a vector field with non-isolated singularities on a submanifold. As an application, our studies give conceptual proofs of a classical result of Chern.

2009-06-22abs ↗pdf ↗

The Law of Vector Fields is a term coined by Gottlieb for a relative Poincaré-Hopf theorem. It was first proved by Morse and expresses the Euler characteristic of a manifold with boundary in terms of the indices of a generic vector field and the inner part of its tangential projection on the boundary. We give two diffe…

2009-09-25abs ↗pdf ↗

In this paper we study the variability and rigidity of secondary characteristic classes which arise from flat connections on a manifold. Considering the connection as a Lie-algebra valued one-form, we study the characteristic map from Lie algebra cohomology to de Rham cohomology of the manifold, and prove that if the L…

1999-04-22abs ↗pdf ↗

For a Riemannian foliation on a closed manifold, the first secondary invariant of Molino's central sheaf is an obstruction to tautness. Another obstruction is the class defined by the basic component of the mean curvature with respect to some metric. Both obstructions are proved to be the same up to a constant, and oth…

2013-11-14abs ↗pdf ↗

Symmetric CNNs improve sequential recommendation and protein structure prediction.

problem Improving prediction accuracy in sequential recommendation and protein structure inference.
method Developed a CNN architecture that preserves symmetry in convolutional layers, using parameterized convolutional kernels.
result Symmetric structured CNNs achieve better performance with fewer parameters.

We refine the intersection product in homology to an equivariant setting, which unifies several known constructions. As an application, we give a common generalisation of the Chas-Sullivan string product on a manifold and the Chataur-Menichi string product on the classifying space by defining a string product on the Bo…

2015-06-01abs ↗pdf ↗

We give a survey of the approaches to classifying foliations, starting with the Haefliger classifying spaces and the various results and examples about the secondary classes of foliations. Various dynamical properties of foliations are introduced and discussed, including expansion rate, local entropy, and orbit growth …

2008-04-08abs ↗pdf ↗

For a manifold with boundary, the restriction of Chern's transgression form of the Euler curvature form over the boundary is closed. Its cohomology class is called the secondary Chern-Euler class and used by Sha to formulate a relative Poincaré-Hopf theorem, under the condition that the metric on the manifold is locall…

2009-01-17abs ↗pdf ↗

Multi-task learning (MTL) has recently contributed to learning better representations in service of various NLP tasks. MTL aims at improving the performance of a primary task, by jointly training on a secondary task. This paper introduces automated tasks, which exploit the sequential nature of the input data, as second…

2017-09-16abs ↗pdf ↗

Let xi be a smooth oriented vector bundle, with n-dimensional fibre, over a smooth manifold M. Denote by xi-hat the fibrewise one-point compactification of xi. The main purpose of this paper is to define geometrically a canonical element Upsilon(xi) in H^n(xi-hat,Q) (H^n(xi-hat,Z) tensor 1/2, to be more precise). The e…

1999-11-01abs ↗pdf ↗

We study primary and secondary invariants of leafwise Dirac operators on foliated bundles. Given such an operator, we begin by considering the associated regular self-adjoint operator DmD_m on the maximal Connes-Skandalis Hilbert module and explain how the functional calculus of DmD_m encodes both the leafwise calculus…

2008-09-12abs ↗pdf ↗

Constructs combinatorial 2D topological field theories from cyclic A-infinity algebras.

problem Developing a combinatorial framework for 2D topological field theories.
method Using triangulations and polygonal decompositions, constructing cochains on a CW complex.
result Existence of combinatorial 2D topological field theories based on cyclic A-infinity algebras.