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.
Optimizes UAV deployment for VLC-enabled UAVs considering illumination distribution.
SAIL reduces design evaluations, producing diverse high-performing designs.
New illumination bodies defined for ball-convex shapes, proving convexity and establishing surface area measures.
Extends illumination bodies to non-Euclidean spaces and proves their volume derivative defines surface area.
Prove a generalization of Werner's formula for the volume of illumination bodies on Riemannian manifolds.
We propose an automatic method to infer high dynamic range illumination from a single, limited field-of-view, low dynamic range photograph of an indoor scene. In contrast to previous work that relies on specialized image capture, user input, and/or simple scene models, we train an end-to-end deep neural network that di…
For a Veech surface (x,ω), we characterize subspaces of X^n, invariant under the diagonal action of the affine group of X. We prove that non-arithmetic Veech surfaces have only finitely many invariant subspaces of very particular shape (in any dimension). Among other consequences we find copies of (X,ω) embedded in the…
Efficient model for foggy scene understanding in vehicles.
Enhances 2D face recognition with 3D features using active illumination.
The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features defined by the user. This technique has the potential to be a powerful tool for design space exploration, but is limited by the need for numerous evaluations. The Surrogate-Assisted Illumination algorithm (SAIL), introd…
Visual perception is a challenging problem in part due to illumination variations. A possible solution is to first estimate an illumination invariant representation before using it for recognition. The object albedo and surface normals are examples of such representations. In this paper, we introduce a multilayer gener…
GANPOP uses deep learning to estimate optical properties from single images, improving accuracy over existing methods.
Automatic video modification to hide faces while maintaining pose, illumination, and expression.
New approach for camera-specific color constancy using few-shot meta-learning.
This research classifies Teichmüller curves in genus 2, proving parity conjectures for specific cases.
In this paper we use 3-manifold techniques to illuminate the structure of the category of tangles. In particular, we show that every idempotent morphism in such a category naturally splits as such that is an identity morphism.
Robust visual tracking for long video sequences is a research area that has many important applications. The main challenges include how the target image can be modeled and how this model can be updated. In this paper, we model the target using a covariance descriptor, as this descriptor is robust to problems such as p…
DeepBark improves tree bark re-identification accuracy.
New tool helps analyze complex financial data.
Top 8 robotic vision systems tackled lifelong object recognition challenges.
In this paper we use 3-manifold techniques to illuminate the structure of the string link monoid. In particular, we give a prime decomposition theorem for string links on two components as well as give necessary conditions for string links to commute under the stacking operation.
Recently, two-dimensional canonical correlation analysis (2DCCA) has been successfully applied for image feature extraction. The method instead of concatenating the columns of the images to the one-dimensional vectors, directly works with two-dimensional image matrices. Although 2DCCA works well in different recognitio…
Mounting evidences are being gathered suggesting that income and wealth distribution in various countries or societies follow a robust pattern, close to the Gibbs distribution of energy in an ideal gas in equilibrium, but also deviating significantly for high income groups. Application of physics models seem to provide…
We unify f-divergences, Bregman divergences, surrogate loss bounds (regret bounds), proper scoring rules, matching losses, cost curves, ROC-curves and information. We do this by systematically studying integral and variational representations of these objects and in so doing identify their primitives which all are rela…
Deep RL fails on deceptive games, revealing algorithm weaknesses.
A new CNN-based algorithm improves Fourier ptychography for faster, more robust image reconstruction.
The basic theorems of vector calculus are illuminated when we replace the original 3 stooges of vector calculus: Grad, Div, and Curl, with combinatorial substitutes. In addition to providing simple proofs of Green's theorem and the equivalence of the integral and derivative definitions of curl, we also provide a brief …
Let K be a knot in S^3. We study the iterated Bing doubles of K, giving a new proof for the following statement: If BD_n(K) is slice for some n, then K is algebraically slice. This result was first proved by Cha and Kim using covering link calculus. We also use this tool, but our proof is substantially simpler and illu…
Two new coding schemes improve the efficient communication of noisy data.
Increasingly, a huge amount of statistics have been gathered which clearly indicates that income and wealth distributions in various countries or societies follow a robust pattern, close to the Gibbs distribution of energy in an ideal gas in equilibrium. However, it also deviates in the low income and more significantl…
We present a simple and fast geometric method for modeling data by a union of affine subspaces. The method begins by forming a collection of local best-fit affine subspaces, i.e., subspaces approximating the data in local neighborhoods. The correct sizes of the local neighborhoods are determined automatically by the Jo…
The paper derives inequalities on Finsler manifolds, influenced by their curvatures.
Transformers can implement reinforcement learning algorithms from data without updates.
Quality-Diversity algorithms explore multiple high-performing solutions in a search space.
Graph matching---aligning a pair of graphs to minimize their edge disagreements---has received wide-spread attention from both theoretical and applied communities over the past several decades, including combinatorics, computer vision, and connectomics. Its attention can be partially attributed to its computational dif…
Improved ELM for robust object tracking with dynamic weights and forgetting factor.
Study connects curvature to graph theory and reveals differences.
Paper tackles offline SSP with value iteration for policy evaluation and learning.
New RL algorithm gives tighter bounds without domain knowledge.
This paper improves radar performance against jammers using RL.
Noise addition prevents overfitting in adaptive data analysis.
In recent years, random matrices have come to play a major role in computational mathematics, but most of the classical areas of random matrix theory remain the province of experts. Over the last decade, with the advent of matrix concentration inequalities, research has advanced to the point where we can conquer many (…
This paper tackles hard exploration in the game Pommerman, improving RL learning.
Explains differences between WL and folklore-WL formulations in graph neural networks.
The importance of Einstein's geometrization philosophy, as an alternative to the least action principle, in constructing general relativity (GR), is illuminated. The role of differential identities in this philosophy is clarified. The use of Bianchi identity to write the field equations of GR is shown. Another similar …
Paper proposes a graph model for optimal AP deployment in indoor optical wireless networks.
Transformers can emulate various algorithms by prompting, proving universality.
Bayesian probabilistic numerical methods are a set of tools providing posterior distributions on the output of numerical methods. The use of these methods is usually motivated by the fact that they can represent our uncertainty due to incomplete/finite information about the continuous mathematical problem being approxi…