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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.

168,742 papers · 148 categories

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12.5%25.0%37.5%50.0% · Jan 199419922001200920172026
48 results for aerosol vertical profiles

Develops a method to disaggregate aerosol optical depth into vertical extinction profiles.

problem Uncertainty in measuring aerosol vertical distributions due to limited observations.
method Bayesian nonparametric Gaussian process modeling using meteorological predictors.
result Model reconstructs realistic extinction profiles with well-calibrated uncertainty, outperforming idealized baselines.

Deep learning detects cloud changes due to human aerosols.

problem Uncertainty in the effect of anthropogenic aerosols on cloud properties and Earth's energy balance.
method Deep convolutional neural networks to analyze cloud images.
result Identified and characterized specific cloud perturbations due to human aerosols.

Reanalysis datasets combining numerical physics models and limited observations to generate a synthesised estimate of variables in an Earth system, are prone to biases against ground truth. Biases identified with the NASA Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) aerosol optic…

2019-10-14abs ↗pdf ↗

Modeling wildfire aerosols using satellite data to predict solar radiation reduction.

problem Accurately estimate and predict AOD propagation from wildfires using multi-source satellite data.
method Physics-informed statistical modeling integrating multi-source satellite data with an advection-diffusion equation.
result The proposed approach accurately predicts AOD propagation and demonstrates model interpretability.

Efficiently matches random graphs with inhomogeneous edge probabilities.

problem Matching latent vertex correspondence between two correlated random graphs with inhomogeneous edge probabilities.
method Inspired by Ding et al. (2021), an efficient matching algorithm is developed with conditions on minimal average degree and minimal correlation.
result An efficient matching algorithm is obtained as long as the minimal average degree is at least Ω(log2n)Ω(\log^{2} n) and the minimal correlation is at least 1O(log2n)1 - O(\log^{-2} n).

In a previous paper the author introduced the notion of TreadmillSled of a curve, which is an operator that takes regular curves in R^2 to curves in R^2. This operator turned out to be very useful to describe helicoidal surfaces, for example, it provides an interpretation for the profile curve of helicoidal surfaces wi…

2011-05-17abs ↗pdf ↗

Robustly detects and attributes climate change impacts under interventions.

problem Detect and attribute climate change impacts from observations robustly.
method Supervised learning with anchor regression for robust predictions under interventions.
result CO2 forcing can be robustly predicted from temperature patterns under strong solar forcing interventions.

The paper calculates the number of triangulations and quadrangulations of surfaces based on their profiles.

problem Counting triangulations and quadrangulations of surfaces based on their vertex indices.
method Using a variation of Hodge structure and results from J. Kollár on curvature and Chern classes.
result The number of triangulations and quadrangulations with specific profiles scales with the genus and profile length.

The study examines constant mean curvature tubes around geodesics in specific 3-manifolds.

problem Investigating constant mean curvature surfaces in homogeneous 3-manifolds.
method Analyzing horizontal tubes foliating spaces under certain conditions.
result Horizontal tubes foliate spaces under specific curvature conditions.

New comparison theorems for rotationally symmetric self-shrinkers help in proving the uniqueness of the Angenent torus.

problem Uniqueness of the Angenent torus in rotationally symmetric self-shrinkers
method Analyzing profile curves and vertical points of rotationally symmetric self-shrinkers
result Proving the existence and monotonicity of horizontal-point trajectories

The paper develops methods to infer membership probabilities and rank network nodes using the DCMM model.

problem Understanding the latent structure of network data, especially in mixed-membership models.
method Degree-Corrected Mixed Membership (DCMM) model, novel finite-sample expansion, asymptotic distributions, confidence intervals, multiplier bootstrap method.
result Valid inference on membership probabilities and node rankings, quantifying uncertainty.

Profile entropy measures learnability and compressibility of discrete distributions.

problem Understanding the learnability and compressibility of discrete distributions.
method Investigates profile entropy, showing its role in estimation, inference, and compression.
result Profile entropy is a fundamental measure unifying estimation, inference, and compression.

Method controls extrapolation in prediction profiles for statistical and machine learning models.

problem Avoiding invalid predictions due to extrapolation in prediction profiles.
method Genetic algorithm optimization over constrained factor regions.
result Optimal factor settings without constraint are often invalid and extrapolated.

Different optimizer choices lead to different financial model predictions.

problem The impact of optimizer choice on neural network models in financial time series.
method Analysis of large-scale volatility forecasting for S&P 500 stocks using various model-training-pipeline pairs.
result Optimizer choice reshapes non-linear response profiles and temporal dependence in financial models, leading to different functional outcomes.

We equip many non compact non simply connected surfaces with smooth Riemannian metrics whose isoperimetric profile is smooth, a highly non generic property. The computation of the profile is based on a calibration argument, a rearrangement argument, the Bol-Fiala curvature dependent inequality, together with new result…

2007-01-07abs ↗pdf ↗

We introduce a spectrum of monotone coarse invariants for metric measure spaces called Poincaré profiles. The two extremes of this spectrum determine the growth of the space, and the separation profile as defined by Benjamini--Schramm--Timár. In this paper we focus on properties of the Poincaré profiles of groups with …

2017-07-07abs ↗pdf ↗

Logarithmic separation profile in hyperbolic groups shows hierarchical structure.

problem Understanding hierarchical structure in hyperbolic groups with logarithmic separation.
method Proving groups with logarithmic separation split over cyclic groups and providing counterexamples.
result Not all groups with hierarchical structure have logarithmic separation profile.

Framework detects shape shifts in functional profiles using Fréchet mean and shape invariant model.

problem Detecting shape shifts in functional profiles.
method Combining Fréchet mean and shape invariant model for interpretable parameterization of profile deviations.
result Potential shifts in shape deformation process distinguished by significant shifts in amplitude and/or phase.

Study compares isoperimetric profiles on manifolds with integral Ricci curvature bounds.

problem Comparing isoperimetric profiles on manifolds with integral Ricci curvature bounds.
method Extending previous work, the study uses integral bounds on Ricci curvature to prove comparison results for isoperimetric profile functions.
result Comparison results for the Isoperimetric profile function in manifolds with integral bounds on Ricci curvature.

GraphHull models networks with clear multi-scale explanations of community structure.

problem Lack of self-explainable models in graph machine learning.
method Two-level convex hulls with global archetypes and local prototypes.
result GraphHull models networks with clear multi-scale explanations.

Estimates lower bounds for isoperimetric profiles and improves on previous estimates for specific manifolds.

problem Estimating lower bounds for isoperimetric profiles of specific Riemannian manifolds.
method Explicit lower bounds for isoperimetric profiles of Riemannian product manifolds.
result Improved lower bounds for isoperimetric profiles and Yamabe constants.

In the context of sub-Riemannian Heisenberg groups Hn, n \geq 1, we shall study Isoperimetric Profiles, which are closed compact hypersurfaces having constant horizontal mean curvature, very similar to ellipsoids. Our main goal is to study the stability of Isoperimetric Profiles.

2011-10-04abs ↗pdf ↗

Paper describes profiles of multivariate normal distributions and novel estimators for mutual information.

problem Estimating mutual information for complex distributions.
method Analytical description of profiles, introduction of Bend and Mix Models, Monte Carlo estimation.
result Bend and Mix Models accurately estimate mutual information profiles and provide Bayesian estimates.

Random layer-wise pruning profiles are as effective as metric-based ones for various datasets.

problem Reduction of model size and computational resources in neural networks.
method Conducted baseline experiments, developed RL-based search algorithm for finding transferable layer-wise pruning profiles.
result RL-based layer-wise pruning profiles are as good or better than best profiles found on the original dataset via exhaustive search.

The study analyzes how large language models form and express investor risk profiles.

problem Understanding how large language models (LLMs) form and express investor risk profiles.
method Examined three LLMs (GPT, Gemini, and Llama) and assessed their responses to a standardized risk questionnaire under varying prompts.
result LLMs generally form long-term investment profiles, but they exhibit different risk tolerance levels.

Uniform Lipschitz continuity of isoperimetric profiles in evolving surfaces.

problem Uniform Lipschitz continuity of isoperimetric profiles in evolving surfaces.
method Normalized Ricci flow on compact surfaces.
result Uniform Lipschitz continuity of isoperimetric profiles under normalized Ricci flow.

A new framework for adaptive behavior using reusable value profiles.

problem Adaptive behavior in changing environments requires switching among value-control regimes, but maintaining separate parameters for each situation is impractical.
method Introduces value profiles: reusable bundles of parameters assigned to hidden states, allowing for state-conditional strategy recruitment without independent parameters for each context.
result Profile-based models outperform simpler alternatives in probabilistic reversal learning, suggesting belief-dependent control of adaptive behavior.

The hypercube's perimeter is significantly larger than expected near half volume.

problem Understanding the isoperimetric profile of the hypercube.
method Analytical proof of perimeter bounds and comparison to Gaussian isoperimetric profile.
result The isoperimetric profile of the hypercube does not converge to the Gaussian profile as dimension increases.

With the maturation of metabolomics science and proliferation of biobanks, clinical metabolic profiling is an increasingly opportunistic frontier for advancing translational clinical research. Automated Machine Learning (AutoML) approaches provide exciting opportunity to guide feature selection in agnostic metabolic pr…

2017-10-09abs ↗pdf ↗

A geodesic net with 4 boundary vertices and 25 balanced vertices is constructed.

problem Constructing geodesic nets with specific vertex types and properties.
method Novel approach to increase the number of balanced vertices from 16 to 25.
result First net with four boundary vertices and 25 balanced vertices, including non-symmetric balanced vertices.

We study the shape of inflated surfaces introduced in \cite{B1} and \cite{P1}. More precisely, we analyze profiles of surfaces obtained by inflating a convex polyhedron, or more generally an almost everywhere flat surface, with a symmetry plane. We show that such profiles are in a one-parameter family of curves which w…

2009-07-29abs ↗pdf ↗