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.
Optical ESNs enable flexible, efficient machine learning with reduced energy.
The accurate characterization of the business cycles in the nonlinear dynamic financial and economic systems in the time of globalization represents a formidable research problem. The central banks and other financial institutions make their decisions on the minimum capital requirements, countercyclical capital buffer …
Brillouin zones were introduced by Brillouin in the thirties to describe quantum mechanical properties of crystals, that is, in a lattice in . They play an important role in solid-state physics. It was shown by Bieberbach that Brillouin zones tile the underlying space and that each zone has the same area. We gene…
A self-contained introduction is presented of the notion of the (abstract) differentiable manifold and its tangent vector fields. The way in which elementary topological ideas stimulated the passage from Euclidean (vector) spaces and linear maps to abstract spaces (manifolds) and diffeomorphisms is emphasized. Necessar…
Linear problems appear in a variety of disciplines and their application for the transmission matrix recovery is one of the most stimulating challenges in biomedical imaging. Its knowledge turns any random media into an optical tool that can focus or transmit an image through disorder. Here, converting an input-output …
The analysis of the theoretical material revealed the lack of consensus on defini-tion of the tax stimulation of innovation-active business entities within the re-gional taxation. The definition tax stimulation of innovation-active business en-tities is specified.
Spinal cord stimulation has enabled humans with motor complete spinal cord injury (SCI) to independently stand and recover some lost autonomic function. Quantifying the quality of bipedal standing under spinal stimulation is important for spinal rehabilitation therapies and for new strategies that seek to combine spina…
General Motors or a local business, which one is better to be stimulated in post-crisis recessions, where government stimulation is meant to overcome recessions? Due to the budget constraints, it is quite relevant to ask how one can increase the chance of economic recovery. One of the key elements to answer this questi…
This article aims to discuss some basics in field of credit modeling, specifically the pricing issue of FtD contract. We demonstrate how the popular copula approach is used in pricing FtD contract, and give a stimulation example of such practice based on SAS 9.1.
Graph Convolutional Networks improve prosthetic sensation interpretation.
Electromagnetic stimulation of the human brain is a key tool for the neurophysiological characterization and diagnosis of several neurological disorders. Transcranial magnetic stimulation (TMS) is one procedure that is commonly used clinically. However, personalized TMS requires a pipeline for accurate head model gener…
Research analyzes Georgia's tax system and suggests improvements.
Machine learning detects epilepsy development from EEG before seizures.
A new geometric framework resolves singularities in anomalous transport.
We introduce scattering-symplectic manifolds, manifolds with a type of minimally degenerate Poisson structure that is not too restrictive so as to have a large class of examples, yet restrictive enough for standard Poisson invariants to be computable. This paper will demonstrate the potential of the scattering symplect…
We introduce general scattering transforms as mathematical models of deep neural networks with l2 pooling. Scattering networks iteratively apply complex valued unitary operators, and the pooling is performed by a complex modulus. An expected scattering defines a contractive representation of a high-dimensional probabil…
Paper explains scattering diagrams' role in mirror symmetry.
New method uses broken scattering to uniquely identify Finsler manifolds.
GSAN learns adaptive node representations using geometric scattering and attention.
New method learns soliton dynamics from scattering data without assuming known equations.
New K-theory approach classifies anyonic topological phases in 2D semimetals.
Scattering representations simplify SBI for images without extra compression.
The paper establishes scattering theory for wave equations on Schwarzschild spacetime.
Paper develops formulas for shape derivatives in wave scattering.
The Euclidean scattering transform was introduced nearly a decade ago to improve the mathematical understanding of the success of convolutional neural networks (ConvNets) in image data analysis and other tasks. Inspired by recent interest in geometric deep learning, which aims to generalize ConvNets to manifold and gra…
Study scattering rigidity on stationary manifolds using geodesics.
MODWST improves classification tasks with wavelet scattering.
Study on scattering geodesics on modular surface and their sojourn times.
Scattering theory on Riemann surfaces with explicit matrix and generalized period mappings.
Study on recovering Lorentzian metrics from scattering data.
We construct a theoretical model for equilibrium distribution of workers across sectors with different labor productivity, assuming that a sector can accommodate a limited number of workers which depends only on its productivity. A general formula for such distribution of productivity is obtained, using the detail-bala…
The Euclidean scattering transform was introduced nearly a decade ago to improve the mathematical understanding of convolutional neural networks. Inspired by recent interest in geometric deep learning, which aims to generalize convolutional neural networks to manifold and graph-structured domains, we define a geometric…
Bayesian Scattering offers a simple baseline for image data uncertainty.
Efficiently estimates neural connectivity using ensemble stimulation.
Scattering theory for linearised gravity on Schwarzschild black hole exterior.
The scattering transform is a multilayered wavelet-based deep learning architecture that acts as a model of convolutional neural networks. Recently, several works have introduced generalizations of the scattering transform for non-Euclidean settings such as graphs. Our work builds upon these constructions by introducin…
We construct continuous families of scattering manifolds with the same scattering phase. The manifolds are compactly supported metric perturbations of Euclidean for . The metric perturbation may have arbitrarily small support.
Autoencoders identify brain networks linked to stress and genotype.
Scattering theory for harmonic one-forms on Riemann surfaces.
Paper shows how scattering maps of Schrödinger equations relate to metrics.
The problem of recovering the asymptotics of a short range perturbation of the Euclidean metric on R^n from fixed energy scattering data is studied. It is shown that if two such metrics, g1, g2, have scattering data at some fixed energy which are equal up to smoothing, then there exists a diffeomorphism ψ`fixing infini…
Scattering theory developed for linearised gravity near Schwarzschild black hole.
Establishes scattering theory for de Sitter vacuum solutions in even dimensions.
We develop a geometric scattering theory for a geometrically finite group acting on (a vector bundle over) a symmetric space of negative curvature. In particular, we obtain the meromorphic continuation of Eisenstein series and scattering matrices and their functional equations.
Optimal design portfolios improve energy efficiency and reduce risk in uncertain reservoirs.
We develop the scattering theory of general conformally compact metrics. For low frequencies, the domain of the scattering matrix is shown to be frequency dependent. In particular, generalized eigenfunctions exhibit L^2 decay in directions where the asymptotic curvature is sufficiently negative. The scattering matrix i…
Analytic metrics are uniquely determined by their scattering map.
Wavelet scattering spectra model non-Gaussian time-series, proving scale invariance for self-similar processes.