Research
On-device research index

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

Trend · papers per month

3774111148 · Jun 202019922001200920182026
48 results for central part

This research focuses on optimizing expensive computer experiments with multiple objectives, targeting the central part of the Pareto front.

problem Optimizing complex systems with limited experiments and conflicting objectives.
method A Bayesian multi-objective optimization method that directs the search towards the central part of the Pareto front.
result The method, C-EHI, better locates the central part of the Pareto front compared to state-of-the-art algorithms.

We analyze the degree-two part of the Torelli group's associated graded.

problem Understanding the structure of the degree-two part of the Torelli group's associated graded.
method We use algebraic topology and group theory to analyze the structure of the degree-two part of the Torelli group's associated graded.
result The abelian group Γ2I/Γ3IΓ_2 \mathcal{I} / Γ_3 \mathcal{I} is torsion-free and described as a lattice in a rational vector space.

Study on braid groups' congruence subgroups and their crystallographic quotients.

problem Understanding congruence subgroups and crystallographic quotients of braid groups.
method Investigation of lower central series of congruence braid groups related to B3B_3.
result Quotients of congruence braid groups are almost crystallographic.

This paper extends rack and quandle covering theory using higher categorical Galois theory.

problem Developing a higher covering theory of racks and quandles.
method Applying techniques from higher categorical Galois theory to extend and clarify the foundations of rack and quandle coverings.
result Identification of meaningful higher-dimensional centrality conditions defining higher coverings of racks and quandles.

A new subdivision scheme for Heisenberg group values with central smoothness loss.

problem Regularity of limit curves in Heisenberg group-valued subdivision schemes.
method Interpolatory subdivision scheme with central correction based on group law.
result Central part of limit curve converges to a continuous limit with logarithmic modulus of continuity.

In this paper we prove that the Kähler-Einstein metrics for a degeneration family of Kähler manifolds with ample canonical bundles Gromov-Hausdorff converge to the complete Kähler-Einstein metric on the smooth part of the central fiber when the central fiber has only normal crossing singularities inside smooth total sp…

2003-03-10abs ↗pdf ↗

We consider returns of two Korean stock market indices, KOSPI and KOSDAQ index. Central parts of the probability distribution function of returns are well fitted by the Lorentzian distribution function. However, tail parts of the probability distribution function follow a power law behavior well. We found that the prob…

2004-07-16abs ↗pdf ↗

The paper derives the QGS equations using stochastic central extensions.

problem Deriving the viscous quasi-geostrophic equations on the torus.
method Central extensions of Lie groups and Lie algebras, stochastic Lagrangian formulation, and Euler-Poincaré reduction.
result Stochastic perturbations to the central extension lead to solutions of the QGS equations.

We introduce a model for the adaptive evolution of a network of company ownerships. In a recent work it has been shown that the empirical global network of corporate control is marked by a central, tightly connected "core" made of a small number of large companies which control a significant part of the global economy.…

2013-06-14abs ↗pdf ↗

ARA combines aggregated RAPPOR and Tf-Idf estimation for centralized DP analysis.

problem Gap between local and central DP approaches in terms of data storage, analysis speed, and amount of data.
method Collects RAPPOR reports from multiple clients, pushes them to a Tf-Idf estimation model, and analyzes them for centralized DP.
result Successfully and efficiently analyzed major truth values from multiple clients.

Study feasibility of deep neural networks for Euro banknote classification.

problem Meeting special requirements for banknote classification by central banks.
method Training and testing deep neural networks on state-of-the-art GPU hardware for few classes and 0-class rejection.
result Deep neural networks can meet central bank requirements for banknote classification.

Liouville domains have become central objects in symplectic and contact geometry. However, the auxiliary data they involve --- namely, Liouville forms --- and the non-compactness of their completions generate some inconvenience. The notion of ideal Liouville domains is designed to suppress these awkward aspects and to …

2017-08-29abs ↗pdf ↗

Study on when the lower central series stops for various groups, including braid groups.

problem Understanding when the lower central series stops for different groups.
method Various techniques applied to braid groups and related groups.
result Complete computation of the lower central series for most groups studied.

We consider the scaling behaviors for fluctuations of the number of Korean firms bankrupted in the period from August 1 2002 to October 28 2003. We observe a power law for the distribution of the number of the bankrupted firms. The Pareto exponent is close to unity. We also consider the daily increments of the number o…

2007-01-26abs ↗pdf ↗

We present a preferential attachment growth model to obtain the distribution P(K)P(K) of number of units KK in the classes which may represent business firms or other socio-economic entities. We found that P(K)P(K) is described in its central part by a power law with an exponent φ=2+b/(1b)φ=2+b/(1-b) which depends on the probabil…

2006-09-04abs ↗pdf ↗

Network metrics form a fundamental part of the network analysis toolbox. Used to quantitatively measure different aspects of the network, these metrics can give insights into the underlying network structure and function. In this work, we connect network metrics to modern probabilistic machine learning. We focus on the…

2014-09-15abs ↗pdf ↗

Unified framework deciphers global central bank communications.

problem Misinterpretations of central bank communications can disproportionately impact vulnerable populations.
method Developed the World Central Banks (WCB) dataset, annotated and reviewed sentences, defined tasks, and benchmarked models.
result A model trained on aggregated data across banks outperforms models trained on individual bank data.

In this paper we prove that the Kähler-Einstein metrics for a toroidal canonical degeneration family of Kähler manifolds with ample canonical bundles Gromov-Hausdorff converge to the complete Kähler-Einstein metric on the smooth part of the central fiber when the base locus of the degeneration family is empty. We also …

2003-03-10abs ↗pdf ↗

A foliation on a manifold M can be informally thought of as a partition of M into injectively immersed submanifolds, called leaves. In this thesis we study foliations whose leaves carry some specific geometric structures. The thesis consists of two parts. In the first part we classify foliations on open manifolds whose…

2014-09-11abs ↗pdf ↗

The study uses Hidden Markov Models to analyze student enrollment patterns and academic performance.

problem Limited understanding of how enrollment patterns affect academic performance.
method Applied Hidden Markov Models to categorize enrollment strategies and compare academic outcomes.
result Mixed enrollment strategies lead to better academic performance, especially during part-time semesters.

Paper analyzes convergence of decentralized algorithms with noise and bias.

problem Finite time convergence analysis of decentralized stochastic approximation schemes.
method Separated iterates into consensual parts and consensus error; bounded consensus error in terms of stationarity.
result Decentralized SA scheme converges at O(logT/T){\cal O}(\log T/ \sqrt{T} ) rate.

New algorithm detects cores in graphs with community structure, improving vertex selection for better clustering.

problem Understanding and detecting core-periphery structures in graphs with community structure.
method Introduces relative centrality to detect cores in graphs with community and core-periphery structures.
result Relative centrality solves bias issues in core detection, leading to better vertex selection and improved clustering performance.

We bound the higher-order Dehn functions and other filling invariants of certain Carnot groups using approximation techniques. These groups include the higher-dimensional Heisenberg groups, jet groups, and central products of two-step nilpotent groups. Some consequences of this work are a construction of groups with ar…

2006-08-07abs ↗pdf ↗

Negative interest rates stabilize economies, but physical cash limits their effectiveness.

problem Lack of effectiveness of negative interest rates in stabilizing economies.
method Simplified stock-flow consistent model, simulation evidence, discussion of alternative solutions.
result Negative interest rates can stabilize economies, but physical cash limits their effectiveness.

This survey paper concerns mainly with some asymptotic topological properties of finitely presented discrete groups: quasi-simple filtration (QSF), geometric simple connectivity (GSC), topological inverse-representations, and the notion of easy groups. As we will explain, these properties are central in the theory of d…

2018-04-14abs ↗pdf ↗

Quantum computing aids in optimizing currency reserves for central banks.

problem Optimizing currency composition in foreign exchange reserves.
method Comparison of quantum and classical algorithms for portfolio optimization.
result Quantum algorithms outperform classical methods in currency optimization.

Wide and Deep GNN learns from distributed graphs and retrain online.

problem Decentralized graph support changes over time, causing mismatch between training and testing graphs.
method Wide and Deep GNN architecture with distributed online learning.
result Convergence guarantees for online retraining of the wide part of the GNN.

The study shows portfolios based on core-periphery stock structure outperform traditional strategies.

problem Optimizing stock portfolios using mesoscale structures.
method Constructing portfolios based on the core-periphery profile of stocks from Pearson correlations.
result Portfolios based on the core-periphery profile of stocks outperform traditional strategies.

Let V be a compact real analytic surface with isolated singularities embedded in RNR^N, and assume its smooth part is equipped with a Riemannian metric that is induced from some analytic Riemannian metric on RNR^N. We prove: 1. Each point of V has a neighborhood which is quasi-isometric (naturally and 'almost isometric…

1999-01-15abs ↗pdf ↗

A new hybrid Newton algorithm improves convergence in logistic regression.

problem Solving large-scale binary classification problems efficiently.
method Proposes a hybrid stochastic Newton algorithm with two weighted components in the Hessian matrix estimation.
result Proves almost sure convergence to the true parameter of logistic regression.

Consider the problem of a central bank that wants to manage the exchange rate between its domestic currency and a foreign one. The central bank can purchase and sell the foreign currency, and each intervention on the exchange market leads to a proportional cost whose instantaneous marginal value depends on the current …

2017-12-06abs ↗pdf ↗

New statistical inference method for high-dimensional Hawkes processes.

problem Uncertainty evaluation of network estimates in high-dimensional point process data.
method Develops a new statistical inference procedure using concentration inequalities and martingale central limit theory.
result Characterizes the convergence rate of test statistics for high-dimensional Hawkes processes.

Transformers learn to play games in-context, proving Nash equilibrium.

problem Understanding in-context game-playing capabilities of pre-trained transformers.
method Theoretical guarantees and constructional results for transformer architecture in multi-agent games.
result Pre-trained transformers can learn Nash equilibrium in-context for two-player zero-sum games.