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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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326597129 · May 202619922001200920182026
48 results for apparent flow

The paper studies curves in surfaces using flow-spines and apparent contours.

problem Understanding curves in arbitrary surfaces using flow-spines and apparent contours.
method By considering generic curves and their apparent contours relative to a traversing flow, the paper reconstructs curves and allows them to vary up to homotopy.
result A finite set of local moves on decorated graphs allows for the reconstruction and variation of curves within a fixed generic flow.

Semi-supervised learning classifies cardiac pathology using motion features from cine MRI.

problem Classifying cardiac pathology based on motion features from cine MRI.
method Semi-supervised learning of apparent flow to generate motion features from non-segmented images.
result The model achieves 95% classification accuracy on ACDC test set.

Study constructs solutions for evolving hypersurfaces using inverse spacetime mean curvature.

problem Evolution of hypersurfaces in spacetime.
method Weak solutions for hypersurfaces evolving along inverse spacetime mean curvature in asymptotically flat maximal initial data sets.
result Weak solution detects both future- and past-trapped apparent horizons.

It is well-known that the LIE(Locally Induction Equation) admit soliton-type solutions and same soliton solutions arise from different and apparently irrelevant physical models. By comparing the solitons of LIE and Killing magnetic geodesics, we observe that these solitons are essentially decided by two families of iso…

2012-12-17abs ↗pdf ↗

Paper studies apparent horizon dynamics and introduces a null comparison principle.

problem Global dynamics of apparent horizon and local achronality.
method Constructing apparent horizon by solving MOTS along null hypersurfaces, using Klainerman-Szeftel estimates and null comparison principle.
result Smooth, asymptotically null, and converging apparent horizon proven.

In this paper, we assume that the permanent market impact of metaorders is linear and that the price is a martingale. Those two hypotheses enable us to derive the evolution of the price from the dynamics of the flow of market orders. For example, if the market order flow is assumed to follow a nearly unstable Hawkes pr…

2014-02-06abs ↗pdf ↗

Construct opers with apparent singularities from λ-connections on Riemann surfaces.

problem Constructing opers with apparent singularities from λ-connections.
method Using Riemann surfaces and λ-connections to define rational maps and Poisson structures.
result Defines a rational map capturing important data of λ-connections and apparent singularities.

In this paper we characterize the intrinsic geometry of apparent horizons (outermost marginally outer trapped surfaces) in asymptotically flat spacetimes; that is, the Riemannian metrics on the two sphere which can arise. Furthermore we determine the minimal ADM mass of a spacetime containing such an apparent horizon. …

2014-12-01abs ↗pdf ↗

Study metrics with specific spectral properties to compute Bartnik and Bartnik-Bray masses efficiently.

problem Compute Bartnik and Bartnik-Bray masses efficiently for metrics with specific spectral properties.
method Spectral generalization of positive scalar curvature, applying Codá Marques's path-connectedness theorem, and efficient constructions for scalar-nonnegative fill-in problem.
result Compute Bartnik and Bartnik-Bray masses efficiently for metrics with -Δ + kR ≥ 0.

Solves Einstein constraint equations on compact manifolds with specified boundaries.

problem Solving Einstein constraint equations with specified boundaries.
method Studies conformal constraint equations with low regularity assumptions.
result Solves Einstein constraint equations on compact manifolds with specified boundaries.

In this paper we find new examples of Riemannian manifolds with outermost apparent horizons with nonspherical topology, in dimensions four and above. More precisely, for any n,m1n,m\ge1, we construct asymptotically flat, scalar flat Riemannian manifolds containing smooth outermost minimal hypersurfaces with topology $S^n…

2007-04-18abs ↗pdf ↗

Paper analyzes blowup of regularized Jang solutions and constant expansion surfaces.

problem Blowup behavior of regularized solutions to Jang equation inside apparent horizons.
method Two geometric treatments: dilation and translation. Characterization of limits of rescaled and translated solutions.
result Limits of properly rescaled solutions are constant expansion surfaces.

Causal autoregressive flows enable accurate causal inference and prediction.

problem Causal discovery and interventional predictions in machine learning.
method Autoregressive normalizing flows with fixed variable orderings.
result Causal models derived from autoregressive flows are identifiable and allow for accurate interventional and counterfactual predictions.

We study the long memory of order flow for each of three liquid currency pairs on a large electronic trading platform in the foreign exchange (FX) spot market. Due to the extremely high levels of market activity on the platform, and in contrast to existing empirical studies of other markets, our data enables us to perf…

2015-04-16abs ↗pdf ↗

Constructs initial data leading to apparent horizons and tests Penrose Inequality.

problem Testing Penrose Inequality in dynamical spacetimes.
method Scale critical initial data for Einstein vacuum system, constructing Cauchy data.
result Penrose Inequality holds in an open region of the future of initial data.

Gradient methods work well on overparameterized diagonal linear networks.

problem Understanding why gradient-based methods work well in overparameterized models.
method Study of Deep Diagonal Linear Networks with gradient flow analysis.
result Gradient flow on layer parameters induces a mirror-flow dynamic in the effective parameter space, leading to explicit convergence guarantees.

The paper shows how to create Schwarzschild initial data with degenerate apparent horizons.

problem Creating Schwarzschild initial data with degenerate apparent horizons.
method Modifying the construction of Mantoulidis-Schoen to handle the degenerate case.
result The first eigenvalue of the operator LgL_g must be zero for Schwarzschild initial data with degenerate apparent horizons.

Paper proves existence of anisotropic dynamical horizons in gravitational collapse.

problem Existence of apparent horizons in gravitational collapse.
method Scale-critical hyperbolic method and non-perturbative elliptic techniques.
result Smooth and spacelike apparent horizons emerge from general initial data in gravitational collapse.

For asymptotically flat initial data of Einstein's equations satisfying an energy condition, we show that the Penrose inequality holds between the ADM mass and the area of an outermost apparent horizon, if the data are restricted suitably. We prove this by generalizing Geroch's proof of monotonicity of the Hawking mass…

2002-01-08abs ↗pdf ↗

Study shows formation of Kerr black holes with complete apparent horizons and proves Penrose inequalities.

problem Formation of Kerr black holes and Penrose inequalities.
method Combining gravitational-collapse and Kerr stability results with new coordinate changes and elliptic arguments.
result Proves dynamical and spacetime Penrose inequalities in black hole formation spacetimes.

Using a proprietary dataset of meta-orders and prediction signals, and assuming a quasi-linear impact model, we deconvolve market impact from past correlated trades and a predictable return component to elicit the temporal dependence of the market impact of a single daily meta-order, over a ten day horizon in various e…

2014-07-12abs ↗pdf ↗

This paper uses probability tensors for efficient path planning in complex scenarios.

problem Efficient path planning in complex environments with obstacles and multiple goals.
method Probability tensors are used to model agent motion and decision-making, incorporating past and future information.
result The model finds solutions in complex scenarios, demonstrating realistic emergent behaviors.

Reducing volatility proxy improves apparent market correlation dynamics.

problem Attributing apparent slow collective market dynamics to intrinsic or driver inheritance.
method Coupled Ornstein-Uhlenbeck model with VIX proxy, decomposing and controlling for autocorrelation.
result VIX-coupled model reduces effective relaxation time from 298 to 61 trading days, improving fit over bare mean reversion.

Paper introduces a method to assess liquidity risk in meme tokens using entity-linked address analysis.

problem High market volatility and vulnerability to manipulation in meme tokens.
method Multi-dimensional approach integrating fund flow analysis, behavioral similarity, and anomalous transaction detection.
result Significant disparities between apparent and actual liquidity in meme token markets.

Generalizes Rips' result on hyperbolic spaces to metric spaces, showing collapses for tree metrics.

problem Understanding the contractibility of Vietoris-Rips complexes in metric spaces.
method Extending Rips' result using geodesic defect and apparent pairs gradient.
result Vietoris-Rips complexes collapse to subforests for finite tree metrics.

New method proves instability of naked singularity and censors it.

problem Proving instability and censoring naked singularity.
method Einstein-scalar field system, hyperbolic short-pulse method, non-perturbative elliptic arguments.
result Tiny anisotropic perturbation leads to anisotropic apparent horizon censoring the naked singularity.

Background doesn't affect personality predictions in deep networks.

problem Understanding how background images influence personality attribution in deep learning models.
method Explicitly studied the effect of background images on personality prediction in deep residual networks, controlling for confounds.
result Adding background information to input decreases model performance for personality trait prediction.

Bayesian method finds voids in galaxy surveys with deep neural networks.

problem Finding genuine matter underdensities in sparse galaxy surveys is underconstrained.
method Deep graph neural network evolves 'test particles' to sample from stochastic void definitions.
result Trained model performs well and finds Bayes-optimal void mappings.

In a paper \cite{P} in 1973, R. Penrose made a physical argument that the total mass of a spacetime which contains black holes with event horizons of total area AA should be at least A/16π\sqrt{A/16π}. An important special case of this physical statement translates into a very beautiful mathematical inequality in Riemann…

2003-04-18abs ↗pdf ↗

The paper addresses causal estimation for text data with apparent overlap violations.

problem Estimating causal effects from text data with unknown confounders and apparent overlap.
method Uses supervised representation learning to create a representation that preserves confounding information while eliminating predictive information, satisfying overlap assumptions.
result Shows how to obtain robust causal estimation in the presence of apparent overlap violations.