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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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48 results for Denjoy-Wolff point

Characterizes convergence of orbits of holomorphic semigroups in the unit disc.

problem Understanding the convergence behavior of orbits of holomorphic semigroups.
method Analyzes the shape of the starlike domain image of the Koenigs function and uses properties of quasi-geodesics.
result Characterizes convergence types (non-tangential and tangential) in terms of the domain's shape and quasi-geodesic properties.

While Multiple Instance (MI) data are point patterns -- sets or multi-sets of unordered points -- appropriate statistical point pattern models have not been used in MI learning. This article proposes a framework for model-based MI learning using point process theory. Likelihood functions for point pattern data derived …

2017-03-07abs ↗pdf ↗

Study on connection points on double regular polygons, providing coordinates and proving non-connection points.

problem Identifying connection points on double regular polygons.
method Examined coordinates in trace field, provided constructive proof for prime nn.
result For n=7n=7, conjectured all remaining points are connection points; for n7n \geq 7 prime, provided explicit separatrix.

This paper reverses a construction by merging boundary critical points into an interior one.

problem Pushing interior critical points to the boundary and splitting them into two boundary points.
method Specific assumptions allow merging two boundary critical points into one interior critical point.
result Merging two boundary critical points into a single interior critical point.

PINNACLE optimizes point selection for PINNs, improving accuracy.

problem Challenges in selecting points for training Physics-Informed Neural Networks (PINNs).
method Introduces PINNACLE, an algorithm that jointly optimizes collocation and experimental points selection, adjusting point proportions dynamically.
result PINNACLE outperforms existing methods in forward, inverse, and transfer learning problems.

The minimal number of critical points is studied for smooth functions on closed manifolds.

problem Determining the minimal number of critical points for smooth functions on closed manifolds.
method Investigates cylindrical ball neighborhoods and exotic critical points, proving the conjecture for certain types of critical points.
result The minimal number of critical points is the same for smooth functions without exotic critical points on closed manifolds of dimension at least 6.

Unified analysis of EG and OGDA for saddle point problems using proximal point method.

problem Solving saddle point problems in bilinear and strongly convex-strongly concave settings.
method Unified analysis as approximations of the proximal point method.
result Unified analysis of EG and OGDA for saddle point problems.

Proposes a method to explain deep neural networks by identifying representer points in the training set.

problem Explaining the predictions of deep neural networks.
method Identifying representer points in the training set to decompose neural network predictions.
result Provides a deeper understanding of neural network predictions through positive and negative representer values.

Adding a point to configurations in closed balls depends on the number of points and their ordering.

problem When can a new point be added to configurations of n distinct points in a closed ball?
method Analyzes the conditions for adding a point based on the number of points and their ordering.
result The possibility of adding a point depends on the number of points and their ordering.

Estimating boundaries from point clouds with improved accuracy and rigorous error estimates.

problem Identifying the boundary of a domain from point cloud samples.
method Developed new estimators for normal vectors, distances, and boundary tests; provided error estimates.
result Efficient and accurate estimators for boundary properties on point clouds.

The paper classifies circle actions on 6D manifolds with isolated fixed points.

problem Classifying circle actions on 6D manifolds with isolated fixed points.
method Performing equivariant connected sums at fixed points with specific manifolds.
result A sequence of operations can reduce the fixed point data to the empty collection.

A limit point p of a discrete group of Mobius transformations acting on S^n is called a concentration point if for any sufficiently small connected open neighborhood U of p, the set of translates of U contains a local basis for the topology of S^n at p. For the case of Fuchsian groups (n = 1), every concentration point…

1998-06-23abs ↗pdf ↗

Self-focal points on ellipsoids of dimension 3 or higher are rare.

problem Existence of self-focal points on Riemannian manifolds of dimension 3 or higher.
method Analyzing geodesics and umbilic points on ellipsoids of various dimensions.
result Ellipsoids of dimension 3 or higher with at least 4 distinct axes have no self-focal points.

The paper introduces a method to probabilistically select inducing points in sparse Gaussian processes.

problem The challenge is selecting the optimal number of inducing points in sparse Gaussian processes.
method A point process prior is applied to the inducing points, and the posterior is approximated using stochastic variational inference.
result The model learns which and how many inducing points to use, leading to fewer inducing points being preferred as they become less informative.

Heavy-ball algorithms can always avoid saddle points with random initialization.

problem Optimizing nonconvex functions with saddle points.
method Developed a new mapping to interpret heavy-ball algorithms as iterations, proving they can escape saddle points.
result Heavy-ball algorithms can escape saddle points with random initialization.

The paper proves Γ\Gamma-convergence of discrete tangent-point energies to continuous energies and ropelength, with applications to biarc curves.

problem Proving convergence of discrete tangent-point energies to continuous energies and ropelength.
method Using biarc curves and interpolation, the paper proves Γ\Gamma-convergence of discretized tangent-point energies to the continuous tangent-point energies and ropelength functional.
result Discrete almost minimizing biarc curves converge to ropelength minimizers and minimizers of continuous tangent-point energies.

Characterizes Lebesgue points using nearest neighbor methods.

problem Consistency of classification algorithms based on nearest neighbors.
method Characterization of Lebesgue points via 1-Nearest Neighbor regression.
result Proves convergence of 1-Nearest Neighbor classification algorithms in metric spaces.

In recent decades, the use of 3D point clouds has been widespread in computer industry. The development of techniques in analyzing point clouds is increasingly important. In particular, mapping of point clouds has been a challenging problem. In this paper, we develop a discrete analogue of the Teichmüller extremal mapp…

2015-11-20abs ↗pdf ↗

Continuous functions on Riemannian manifolds with poles have fixed points.

problem Extending continuous functions on Riemannian manifolds with poles.
method Simple geometrical technique to generalize Brouwer fixed point theorem.
result Any continuous function on the boundary of a convex domain of a 2D Riemannian manifold with a pole has a fixed point that can be extended to the domain.

The wide adoption of DNNs has given birth to unrelenting computing requirements, forcing datacenter operators to adopt domain-specific accelerators to train them. These accelerators typically employ densely packed full precision floating-point arithmetic to maximize performance per area. Ongoing research efforts seek t…

2018-04-04abs ↗pdf ↗

We introduce flip points to interpret neural networks, providing detailed explanations and confidence measures.

problem Lack of interpretability in neural networks for important applications.
method Investigating flip points, the boundary between two output classes, to provide detailed interpretation and confidence measures.
result Flip points enable detailed interpretation and measure confidence in neural network outputs.

Quantized neural networks can represent all fixed-point functions under certain conditions.

problem Expressive power of quantized neural networks under fixed-point arithmetic.
method Analyzing necessary and sufficient conditions for quantized networks to represent all fixed-point functions.
result Various popular activation functions satisfy the sufficient condition for representing all fixed-point functions.

Study finds central points of double heptagon surface are not connection points.

problem Identifying connection points on double heptagon translation surfaces.
method Used a gcd algorithm to determine hyperbolic directions and found non-connection points.
result Central points of heptagons are not connection points on double heptagon translation surfaces.