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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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255176101 · May 202619922001200920172026
48 results for causal velocity

New method distinguishes cause from effect using causal velocity.

problem Inferring causal direction from bivariate data.
method Parametrization of bivariate SCMs in terms of causal velocity, using tools from measure transport.
result Method extends beyond known model classes and requires no assumptions on noise distributions.

Study improves paddy rice yield predictions in Peru using sparse regression and climatic variables.

problem Improving precision of paddy rice yield forecasts in Peru.
method Sparse regression, Elastic-Net regularization, climatic variables, dynamic transformations.
result Improved predictive performance of paddy rice yield forecasts.

In the first part of this paper we consider expanding vacuum cosmological spacetimes with a free TNT^N-action. Among them, we give evidence that Gowdy spacetimes have AVTD (asymptotically velocity term dominated) behavior for their initial geometry, in any dimension. We then give sufficient conditions to reach a simila…

2019-08-06abs ↗pdf ↗

We discuss some aspects about the computation of kinematic, spectroscopic, Fermi and astrometric relative velocities that are geometrically defined in general relativity. Mainly, we state that kinematic and spectroscopic relative velocities only depend on the 4-velocities of the observer and the test particle, unlike F…

2011-09-01abs ↗pdf ↗

RFM improves CNFs by adding a boundary constraint term and matching velocity fields.

problem Flow matching on constrained domains leads to unnatural samples.
method RFM adds a boundary constraint term and matches velocity fields in a simulation-free manner.
result RFM achieves comparable or better results on standard image benchmarks and produces high-quality samples.

Optimal self-distillation improves generative models' velocity risk and mode recovery.

problem Improving generative models' velocity risk and mode recovery.
method Proved optimal self-distillation for rectified flow via linear probing, derived mixing coefficient, and provided validation tuning.
result Optimal self-distillation improves velocity risk and mode recovery.

New method estimates velocity fields for minimizing ff-divergences without overfitting.

problem Minimizing statistical discrepancies between target and particle distributions.
method Directly estimate velocity fields using interpolation techniques, proving consistency under mild conditions.
result Consistent estimators of velocity fields improve accuracy in applications like domain adaptation and missing data imputation.

Proves strong solutions for graphical Brakke flows with L2L^2 normal velocity.

problem Proving strong solutions for graphical Brakke flows with specific velocity conditions.
method Combining L2L^2 normal velocity with parabolic regularity theory.
result Graphical Brakke flows with forcing term in Lp,qL^{p,q} and C0,αC^{0,α} are strong and classical solutions.

SAGE generates subsurface velocity models from sparse well logs and seismic images.

problem Lack of high-quality subsurface velocity models due to limited data availability.
method Subsurface AI-driven geostatistical extraction using proxy posterior.
result SAGE produces geologically plausible and statistically accurate velocity realizations.

This paper detects Markov violations in RL with noise, improving policy development.

problem Partial observability and sensor/actuator noise invalidate Markovian assumptions in RL.
method Combines PCMCI causal discovery with Markov Violation score (MVS).
result Even substantial noise doesn't always disrupt multi-step dependencies.

LFIS uses a time-dependent velocity field to sample from complex distributions.

problem Sampling from unnormalized density functions.
method LFIS learns a time-dependent velocity field to transport samples from a simple initial distribution to a complex target distribution.
result LFIS achieves state-of-the-art performance on various benchmark problems.

NN-Turb generates turbulent velocity statistics using neural networks.

problem Creating a 1D field with turbulent velocity statistics.
method Fully-convolutional neural network (NN-Turb) to generate the field.
result NN-Turb generates a 1D field that satisfies Kolmogorov's 2/3 and 4/5 laws, exhibiting intermittency.

Bitcoin's monetary velocity is constrained by network friction, leading to significant utility contraction during shocks.

problem Bitcoin's monetary velocity is limited by network congestion, causing significant utility loss during economic shocks.
method Empirical analysis using Transaction Cost Index and threshold regression to identify structural breaks and velocity contraction.
result Network friction significantly reduces Bitcoin's monetary velocity, leading to a net utility contraction of -9.39% during shocks.

The determinants of the velocity of money have been examined based on life-cycle hypothesis. The velocity of money can be expressed by reciprocal of the average value of holding time which is defined as interval between participating exchanges for one unit of money. This expression indicates that the velocity is govern…

2005-07-21abs ↗pdf ↗

A conservative drifting method improves generative modeling by using KDE gradients, proving convergence rates.

problem Improving generative modeling by addressing non-conservatism issues.
method Proposes a conservative drifting method using kernel density estimator gradients to address non-conservatism.
result Proves finite-particle convergence rates for the conservative method, providing explicit quadrature constants.

A new method interprets astrophysical spectra using geometric paths to distinguish line profiles.

problem Tackling the indistinguishability of spectral line profiles under scalar summaries.
method Introduces a geometric representation of line profiles using rough path theory, mapping profiles to a common velocity grid and defining descriptors from path properties.
result Compact descriptors separate morphologies with similar scalar summaries, revealing ordered line structures.

Develops scalable model for learning velocity fields in complex traffic scenarios.

problem Learning heterogeneous and dynamic velocity fields in complex traffic scenarios.
method Nonparametric Bayesian modeling with hierarchical Dirichlet process and infinite hidden Markov model, Gaussian process prior, and scalable approximate inference.
result Demonstrates effective scalability and applicability to real-world traffic data.

Generative sampler learns velocity fields for efficient posterior inference.

problem Sampling from complex posterior distributions in high dimensions.
method Generative multivariate posterior sampler via flow matching, learning a velocity field for a deterministic transport map.
result Conditional Brenier map enables fast generation of credible sets with theoretical consistency guarantees.

Time dilation 11v2\frac{1}{\sqrt{1-v^2}} and relative velocity vv are observationally indistinguishable in the special theory of relativity, a duality that carries over into the general theory under Fermi coordinates along a curve (in coordinate-independent language, in the tangent Minkowski space along the curve). For …

2005-12-05abs ↗pdf ↗

An (r,n)(r,n)-velocity is an rr-jet with source at 0Rn0 \in \R^n, and target in a manifold YY. An (r,n)(r,n)-velocity is said to be regular, if it has a representative which is an immersion at 0Rn0 \in \R^{n}. The manifold TnrYT^{r}_{n}Y of (r,n)(r,n)-velocities as well as its open, LnrL^{r}_{n}-invariant, dense submanifold $\Imm …

1997-08-26abs ↗pdf ↗

Study uses neural networks to predict wall quantities in turbulent flows.

problem Predicting wall quantities in turbulent open channel flows.
method Training convolutional neural networks (FCN) and a proposed R-Net architecture to predict wall-shear-stress and wall pressure.
result R-Net architecture performs better and predicts wall quantities with around 10% error.

In this study, the concept of dual Lorentzian homotetic exponential motions in is discussed and their velocities, accelerations obtained. Also, some geometric results between velocity and acceleration vectors of a point in a spatial motion are obtained. Finally, the theorems related to acceleration and acceleration cen…

2013-11-03abs ↗pdf ↗

The paper develops a new class of financial market models. These models are based on generalized telegraph processes: Markov random flows with alternating velocities and jumps occurring when the velocities are switching. While such markets may admit an arbitrage opportunity, the model under consideration is arbitrage-f…

2007-12-20abs ↗pdf ↗

We have studied the statistical mechanics of money circulation in a closed economic system. An explicit statistical formulation of the circulation velocity of money is presented for the first time by introducing the concept of holding time of money. The result indicates that the velocity is governed by behavior pattern…

2005-07-20abs ↗pdf ↗

In this paper the dependence of wealth distribution and the velocity of money on the required reserve ratio is examined based on a random transfer model of money and computer simulations. A fractional reserve banking system is introduced to the model where money creation can be achieved by bank loans and the monetary a…

2005-07-21abs ↗pdf ↗

Clarifies method of phase synchronization for decoupling linear differential equations.

problem Velocity-dependent transformations in linear second-order differential equations.
method Linear transformation of coordinates and velocities.
result Velocity-dependent transformations do not preserve second-order character and define their own system.

GPMI method interpolates uncertain atrial conduction velocity on non-Euclidean manifolds.

problem Uncertainty in atrial conduction velocity calculations.
method Gaussian Process Manifold Interpolation (GPMI) on human atrial manifolds.
result GPMI accounts for atrial topology and calculates CV uncertainty.

Neural networks predict flow and elastic stresses in viscoelastic turbulence.

problem Predicting flow and elastic stresses in viscoelastic turbulent flows using limited experimental data.
method Convolutional neural networks trained on wall-normal velocity and pressure data.
result Neural networks accurately predict flow and elastic stresses, especially during low-drag events.

The square root velocity framework is a method in shape analysis to define a distance between curves and functional data. Identifying two curves if they differ by a reparametrisation leads to the quotient space of unparametrised curves. In this paper we study analytical and topological aspects of this construction for …

2015-07-09abs ↗pdf ↗

Generative models are reinterpreted in statistical terms, enabling better understanding and inference.

problem Insufficient interpretability of generative models in statistical terms.
method Flow matching and orthogonalization/cross-fitting in double/debiased machine learning.
result Generative models can be used to estimate nuisance components while maintaining inferential validity.

MeanFlow training is unstable due to misusing conditional velocity, leading to variance issues.

problem Unstable training of MeanFlow due to variance problems.
method Theoretical analysis and derivation of optimal coefficient in closed form.
result The optimal coefficient in MeanFlow training minimizes variance but not necessarily quality.

Vortex induced vibrations of bluff bodies occur when the vortex shedding frequency is close to the natural frequency of the structure. Of interest is the prediction of the lift and drag forces on the structure given some limited and scattered information on the velocity field. This is an inverse problem that is not str…

2018-08-26abs ↗pdf ↗

The chapter reviews metrics for comparing curves, focusing on quotient elastic and square root velocity metrics.

problem Comparing and analyzing shapes of curves.
method Construction and theoretical properties of quotient elastic metrics, special case of square root velocity metric, numerical approaches for estimation.
result Simplified expression for the square root velocity metric distance.

A theorem proves a surface evolution graph satisfies a PDE under specific conditions.

problem Prove a surface evolution graph satisfies a PDE under specific conditions.
method Use Brakke's formulation of velocity and analyze the distributional time derivative of the graph.
result The graph satisfies the PDE pointwise under the given conditions.

Study invariant λλ-translators in Lorentz-Minkowski space.

problem Characterize λλ-translators invariant under translations and rotations.
method Analyze 1-parameter group of translations and rotations, find explicit parametrizations, and solve non-linear autonomous systems.
result Explicit parametrizations and qualitative properties of invariant λλ-translators.

Study proves existence of multiple geodesics in a specific metric space.

problem Existence of multiple geodesics in a manifold with a Randers-Kropina metric.
method Lusternik-Schnirelman theory applied to a homotopy type of solutions of an affine control system.
result Proves existence of infinitely many geodesics between two points in a non-contractible manifold.

We propose a predictive neural network architecture that can be utilized to update reference velocity models as inputs to the full waveform inversion. Deep learning models are explored to augment velocity model building workflows during processing the 3D seismic volume in salt-prone environments. Specifically, a neural…

2019-08-11abs ↗pdf ↗

The paper explores rectified flows and their relation to optimal transport.

problem Understanding the connection between rectified flows and optimal transport.
method Investigates invariance properties, explicit constructions, and analysis of rectified flows in various settings.
result Rectified flows, when gradient constrained, do not generally solve optimal transport problems.