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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.

169,051 papers · 148 categories

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295886115 · May 202619922001200920172026
48 results for ranking interlacement

New risk-dependent centrality measures assess node importance in financial networks.

problem Understanding how external risk levels affect network node importance.
method Developed risk-dependent centrality measures based on SI model of epidemics.
result Observed ranking interlacement phenomenon where nodes can swap positions due to external risk changes.

We characterize those unions of embedded disjoint circles in the 2-sphere which can be the multiple point set of a generic immersion of the 2-sphere into 3-dimensional space in terms of the interlacement of the given circles. Our result is the one higher dimensional analogue of Rosenstiehl's characterization of words b…

2017-04-19abs ↗pdf ↗

A knot diagram has an associated looped interlacement graph, obtained from the intersection graph of the Gauss diagram by attaching loops to the vertices that correspond to negative crossings. This construction suggests an extension of the Kauffman bracket to an invariant of looped graphs, and an extension of Reidemeis…

2008-08-25abs ↗pdf ↗

Loops in surfaces and chord diagrams are studied with graph factorizations and grammars.

problem Understanding loops in surfaces and their properties.
method Factorization of filoops into spheric and toric sums, and grammars generating chordiagraphs.
result Minimal genus of filoops and stability properties under factorizations.

New concept of regular separation for ODEs leads to improved Hardy field results.

problem Understanding solutions of definable ODEs with specific properties.
method Introducing regular separation and proving its implications for ODEs and vector fields.
result The regular separation property leads to improved Hardy field results and non-empty sets of trajectories.

In this paper, we study distribution of the zeros of the Alexander polynomials of knots and links in S^3. We call a knot or link "real stable" (resp. "circular stable") if all the zeros of its Alexander polynomial are real (resp. unit complex). We give a general construction of real stable and circular stable knots and…

2013-07-05abs ↗pdf ↗

Gabai showed that the Whitehead manifold is the union of two submanifolds each of which is homeomorphic to R3\mathbb R^3 and whose intersection is again homeomorphic to R3\mathbb R^3. Using a family of generalizations of the Whitehead Link, we show that there are uncountably many contractible 3-manifolds with this doub…

2017-11-14abs ↗pdf ↗

Study surfaces with constant ratio of principal curvatures in Euclidean and isotropic geometries.

problem Characterize surfaces with constant ratio of principal curvatures in different geometries.
method Differential geometry, line geometry, Lie sphere geometry, ordinary differential equations, algebraic geometry.
result Characterized various types of surfaces like rotational, channel, ruled, helical, and translational.

This paper establishes certain existence and classification results for solutions to SU(n)SU(n) Toda systems with three singular sources at 0, 1, and \infty. First, we determine the necessary conditions for such an SU(n)SU(n) Toda system to be related to an nnth order hypergeometric equation. Then, we construct solutions …

2016-10-11abs ↗pdf ↗

We propose a model in which a spliced vector bundle (with an arbitrary number of gauge structures in the splice) possesses a geometry which do not split. The model employs connection 1-forms with values in a space-product of Lie algebras, and therefore interlaces the various gauge structures in a non-trivial manner. Sp…

1997-04-16abs ↗pdf ↗

The present paper is a review of the current state of Graph-Link Theory (graph-links are also closely related to homotopy classes of looped interlacement graphs), dealing with a generalisation of knots obtained by translating the Reidemeister moves for links into the language of intersection graphs of chord diagrams. I…

2010-01-03abs ↗pdf ↗

Paper proposes a robust framework for detecting multiple periodic components in time series.

problem Detecting multiple periodic components in time series with interlaced patterns and external noise.
method Applying maximal overlap discrete wavelet transform to isolate periodic components, ranking them by wavelet variance, and detecting single periodicity robustly.
result The proposed algorithm outperforms other methods for both single and multiple periodicity detection.

The paper examines circle graphs of Gauss diagrams and finds counterexamples to previous descriptions.

problem Problems with previous descriptions of realizable Gauss diagrams.
method Experimental checking and formulation of new descriptions of realizable circle graphs.
result New descriptions of realizable circle graphs and an algorithm for checking realizability.

Solves Gursky-Streets equations for σkσ_k Yamabe problem in dimensions n2kn \geq 2k.

problem Solving the σkσ_k Yamabe problem in dimensions n2kn \geq 2k.
method Introduced and solved the Gursky-Streets equations with uniform C1,1C^{1, 1} estimates using concavity of the operator and Garding's theory of hyperbolic polynomials.
result Established the uniqueness of the solution to the degenerate equations for the first time.

This work explains how tempering improves Bayesian neural networks by reducing the impact of data augmentation.

problem Improper sharpening of Bayesian neural networks leads to suboptimal performance.
method Theoretical analysis and empirical evaluations of simplified settings and group convolutions.
result Tempering reduces the misspecification due to modeling augmentations as independent and identically distributed (i.i.d.) data.

This paper addresses the problem of rank aggregation, which aims to find a consensus ranking among multiple ranking inputs. Traditional rank aggregation methods are deterministic, and can be categorized into explicit and implicit methods depending on whether rank information is explicitly or implicitly utilized. Surpri…

2013-09-26abs ↗pdf ↗

We study the problem of rank aggregation: given a set of ranked lists, we want to form a consensus ranking. Furthermore, we consider the case of extreme lists: i.e., only the rank of the best or worst elements are known. We impute missing ranks by the average value and generalise Spearman's ρto extreme ranks. Our main …

2014-10-16abs ↗pdf ↗

The geometric approach [1312.1262] to iterated variations of local functionals -- e.g., of the (master-)action functional -- resulted in an extension of the deformation quantisation technique to the set-up of Poisson models of field theory [IHES/M/15/13]. It also allowed of a rigorous proof ([1312.1262],[1210.0726]) fo…

2014-10-01abs ↗pdf ↗

We study the problem of learning to rank from multiple information sources. Though multi-view learning and learning to rank have been studied extensively leading to a wide range of applications, multi-view learning to rank as a synergy of both topics has received little attention. The aim of the paper is to propose a c…

2018-01-31abs ↗pdf ↗

Study of WKB asymptotics of Stokes matrices and spectral curves, proving rhombus inequalities.

problem Analyzing WKB asymptotics of Stokes matrices and spectral curves.
method Using spectral network theory, Poisson geometry, and cluster structures.
result Real parts of leading WKB exponents satisfy rhombus inequalities.

Paper tackles underranking in group-fair ranking systems, proving a trade-off and presenting an algorithm.

problem Underranking in group-fair ranking systems can worsen social and economic inequalities.
method Formulated underranking as a new problem, proved a lower bound, and presented a fair ranking algorithm.
result Algorithm achieves best of underranking and group fairness, confirming theoretical trade-off.

Introduces nondecreasing rank for matrices and tensors, developing methods and applications.

problem Finding low-rank approximations for matrices and tensors with monotonic constraints.
method Developed a variant of hierarchical alternating least squares algorithm for finding low ND rank approximations.
result Low ND rank factorizations can be found and interpreted for real-world datasets.

Sparse reduced-rank regression selects variables and ranks via manifold optimization.

problem Traditional rank selection fails when true rank is high.
method Sparse regularization and manifold optimization for rank and variable selection.
result Accurate estimation of coefficient parameter with high true rank.

The paper tackles learning true rankings from noisy, incomplete data.

problem Learning true rankings from incomplete and noisy data.
method Introduces a selective Mallows model for noisy rankings and derives upper and lower bounds on sample complexity.
result Strong asymptotically tight bounds on sample complexity for learning complete rankings and top-k rankings.

Paper introduces GAMs for interpretable learning-to-rank models.

problem Need for transparent ranking models in legal or policy scenarios.
method Developed generalized additive models (GAMs) for ranking tasks using neural networks.
result Neural ranking GAMs achieve better performance than traditional GAMs while maintaining interpretability.

Proposes tensor Q-rank for better tensor rank recovery in complex data.

problem Improving tensor rank recovery for complex data with low sampling rate.
method Introduces tensor Q-rank and two selection methods for Q\mathbf{Q}, proposing VMTQN and MOTQN models.
result Demonstrates superior performance in tensor completion problems compared to TNN-based methods.

Low-rank framework for task-specific LLM ranking from sparse comparisons.

problem Challenges in reliable task-specific ranking of LLMs under sparse, imbalanced comparisons.
method Low-rank modeling of task-by-model ability matrix, max-norm accurate estimator, task-wise top-K recovery guarantees, uncertainty quantification framework.
result Improves sample efficiency and produces tighter, better-calibrated ranking certificates.

New method solves nonsmooth low-rank matrix optimization problems efficiently.

problem Nonsmooth and low-rank matrix optimization problems in statistics and machine learning.
method Low-rank Extragradient Method with warm-start initialization.
result The extragradient method converges to an optimal solution with rate O(1/t)O(1/t) and requires only two low-rank SVDs per iteration.