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.

168,982 papers · 148 categories

Trend · papers per month

12.5%25.0%37.5%50.0% · Jan 199419922001200920172026
48 results for center count

Estimates point counts in Teichmüller space for mapping class groups.

problem Counting points in Teichmüller space under mapping class group actions.
method Quantitative estimates with power saving error terms for Teichmüller metric balls.
result Effectivizes asymptotic counting results of Athreya et al.

The paper counts conjugacy classes of pseudo-Anosov homeomorphisms in Teichmüller space.

problem Counting conjugacy classes of pseudo-Anosov homeomorphisms in Teichmüller space.
method Analyzes the asymptotic behavior of conjugacy classes as the radius of a ball in Teichmüller space increases.
result Asymptotics for the number of pseudo-Anosov homeomorphisms conjugate to a given homeomorphism within a ball of radius R centered at X.

OpFlow predicts robust OD flows by learning choice potentials conditioned on spatial exposures.

problem Deep models trained on raw counts are vulnerable to distribution shift.
method OpFlow learns row-centered choice potentials and reconstructs flows by combining them with a calibrated origin scale.
result OpFlow improves robustness under environment shifts, as shown by controlled synthetic shifts and a real-world experiment.

The paper counts mapping classes by Nielsen-Thurston type, finding growth rates for different subsets.

problem Counting mapping classes in Teichmüller space with different subsets.
method Introduced complexity length to measure negative curvature of curve complexes.
result Growth rates for finite-order, reducible, and multitwists subsets.

In the first of these two lectures, I use a comparison to symplectic Khovanov homology to motivate the idea that the Jones polynomial and Khovanov homology of knots can be defined by counting the solutions of certain elliptic partial differential equations in 4 or 5 dimensions. The second lecture is devoted to a descri…

2016-03-12abs ↗pdf ↗

Paper explores online k-means clustering, finding optimal bounds for center count.

problem Understanding the effects of online k-means clustering with unknown n.
method Analyzes different cases of online k-means clustering, proving optimal bounds.
result Optimal bounds for center count in various scenarios, showing Theta(log n) centers suffice.

Lomonaco and Kauffman introduced knot mosaic system to give a definition of quantum knot system. This definition is intended to represent an actual physical quantum system. A knot (m,n)(m,n)-mosaic is an m×nm \times n matrix of mosaic tiles which are T0T_0 through T10T_{10} depicted as below, representing a knot or a link b…

2013-12-14abs ↗pdf ↗

New methods model gamma-ray data to better understand Galactic emissions.

problem Uncertain diffuse Galactic gamma-ray emissions bias data interpretation.
method Gaussian processes and variational inference for flexible modeling.
result More robust interpretation of gamma-ray sky, especially dark matter signals.

A fast method estimates Gaussian mixture components without iterative fitting.

problem Estimating the number of components in high-dimensional Gaussian mixtures.
method Center data, compute singular values, and count above a threshold.
result The estimator consistently recovers the true number of components under mild separation condition.

A CNN-based method detects and counts corn kernels from images.

problem Manual counting of corn kernels is labor-intensive and prone to error.
method Sliding window approach with CNN for detection and NMS for overlapping removal.
result The method successfully detects and counts kernels with low error.

Efficient algorithm for graph matching in correlated stochastic block models.

problem Graph matching in correlated stochastic block models with balanced communities.
method Extends previous work on centered subgraph counts to handle estimation errors and edge correlation.
result First efficient algorithm for graph matching in the logarithmic average degree regime, matching all but a vanishing fraction of vertices with high probability.

CDL index improves clustering validation for non-convex data.

problem Selecting clustering algorithms and hyperparameters without labeled data.
method CDL uses compactness, centers, and covariances to compute a probabilistic description length bound.
result CDL outperforms conventional CVIs on synthetic and image benchmarks.

Language models allocate information storage, not collapsing into uniform representations.

problem Incomplete neural collapse in language model representations.
method Analyzing variance and information sharing across 14 models, proving an information floor.
result Within-class variance is allocated information storage, not collapsed into uniform representations.

The study examines the growth of conjugacy classes in negatively curved manifolds.

problem Growth of conjugacy classes in manifolds with variable negative curvature.
method Analyzes the number of conjugacy class orbits in a ball of radius T centered at a point in the universal cover of a manifold.
result Exponentially small error terms for the count of conjugacy class orbits in 2D or high-dimensional manifolds with specific curvature bounds.

Flow Matching for count data improves sample quality and efficiency.

problem Mapping between count distributions across batches or time points in high-dimensional count data.
method count-FM, a flow-matching framework based on a continuous-time birth-death process with local unit jumps.
result count-FM achieves better sample quality than representative baselines while using fewer parameters.

Optimism stabilizes Thompson Sampling for adaptive inference in multi-armed bandits.

problem Subtle inferential properties of Thompson Sampling under adaptive data collection.
method Introduced optimism as a key mechanism to restore stability and validity of inference.
result Suitably implemented optimism stabilizes Thompson Sampling and enables asymptotically valid Wald inference.

ExCIR provides efficient, consistent, and scalable explainability for complex models.

problem Complex models lack transparency and require efficient, stable, and scalable explainability methods.
method ExCIR uses correlation-aware feature attribution with robust centering and groupwise aggregation.
result ExCIR delivers trustworthy agreement with global baselines and full model rankings, reduces runtime, and scales to large datasets.

The paper develops a stationary-distribution theory for Random Forest ensemble size selection.

problem Determining the optimal number of trees in Random Forests.
method Modeling the ensemble size as a birth-death Markov chain and deriving its stationary distribution.
result The stationary ensemble size BB_* scales as O(ε2)O(\varepsilon^{-2}) as ε0\varepsilon\downarrow 0.

The k-means++ algorithm is generalized by choosing the most distant point from the nearest center.

problem Improving the initialization of k-means clustering.
method Generalizing the center initialization process by selecting the most distant point from the nearest center.
result Choosing the most distant point from the nearest center achieves similar clustering quality to k-means++.

Center identified in stated skein algebra for quantum traces.

problem Understanding the center of the stated skein algebra.
method Analyzing the algebra as a generalization of Kauffman bracket skein algebra, focusing on the case when the quantum parameter is a root of unity.
result Simple description and dimension calculation of the center over the center module.

The object of our investigation is a point that gives the maximum value of a potential with a strictly decreasing radially symmetric kernel. It defines a center of a body in Rm. When we choose the Riesz kernel or the Poisson kernel as the kernel, such centers are called a radial center or an illuminating center, respec…

2016-03-09abs ↗pdf ↗

Refines geometric center of mass analysis for Einstein field equations.

problem Analyzing the geometric center of mass of Willmore surfaces in initial data for Einstein field equations.
method Refined Lyapunov-Schmidt analysis to study geometric center of mass of area-constrained Willmore surfaces.
result The geometric center of mass agrees with the Hamiltonian center of mass under specific conditions.

A new method, Count-MORL, improves offline reinforcement learning by using state-action frequency.

problem Improving offline reinforcement learning performance.
method Integrates count-based conservatism into model-based offline reinforcement learning.
result The learned policy is near-optimal and outperforms existing methods.

Proposes a method to reconcile count time series forecasts.

problem No formal framework for probabilistic reconciliation of count time series.
method Generalizes Bayes' rule for reconciling real-valued and count variables.
result Improves forecast accuracy for count variables compared to Gaussian reconciliation.

Let f:M->M be a partially hyperbolic diffeomorphism such that all of its center leaves are compact. We prove that Sullivan's example of a circle foliation that has arbitrary long leaves cannot be the center foliation of f. This is proved by thorough study of the accessible boundaries of the center-stable and the center…

2011-04-28abs ↗pdf ↗

Graph neural networks struggle with counting certain substructures in graphs.

problem Detecting and counting specific substructures in graphs.
method Study of graph neural networks' ability to count attributed graph substructures.
result Graph neural networks like MPNNs, 2-WL, and 2-IGNs have limitations in counting certain substructures.

The paper proposes count echo state networks for forecasting graduate student enrollments.

problem Forecasting graduate student enrollments from historical data.
method Developed hierarchical count echo state networks and compared them to Poisson autoregressions and negative binomial models.
result Hierarchical negative binomial based echo state network is the superior model.

We investigate centers of a body (the closure of a bounded open set) defined as maximum points of potentials. In particular, we study centers defined by the Riesz potential and by Poisson's integral. These centers, in general, depend on parameters and move with respect to the parameters. We give a necessary and suffici…

2016-03-28abs ↗pdf ↗

Counted essential surfaces in a knot's exterior, finding a unique pattern.

problem Counting essential surfaces in a knot's exterior.
method Counted essential surfaces by genus, using Euler totient function. Showed normal surfaces are connected by counting their components. Used Agol, Hass, and Thurston's tools to convert component counting into orbit counting.
result Found a unique pattern in the number of essential surfaces by genus.

A new method for brain tissue segmentation across medical centers using a smoothness prior.

problem Tissue segmentation challenges due to center-specific acquisition protocols.
method Developed a smoothness prior that is fit to segmentations from another medical center, integrated into an unsupervised Bayesian model.
result Segmentations are similarly smooth across centers, improving generalization.

We prove the existence of a center, or continuous selection of a point, in the relative interior of C1C^1 embedded kk-disks in Riemannian nn-manifolds. If k3k\le 3 the center can be made equivariant with respect to the isometries of the manifold, and under mild assumptions the same holds for k=4=nk=4=n. By contrast, for…

2017-06-25abs ↗pdf ↗