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.
Physics-informed neural network identifies and characterizes surface cracks in metals.
New method recovers compressed crack images for automatic segmentation.
This research develops efficient surrogate models for predicting crack growth in metal structures.
Bayesian model predicts crack evolution on rails with uncertainties.
Study uses neural processes to predict and classify crack patterns in moving disks.
In modern building infrastructures, the chance to devise adaptive and unsupervised data-driven health monitoring systems is gaining in popularity due to the large availability of big data from low-cost sensors with communication capabilities and advanced modeling tools such as Deep Learning. The main purpose of this pa…
Study on wave-breaking phenomena in solutions of the Camassa-Holm equation.
This paper improves Gaussian process predictions by integrating prior knowledge.
Deep-CAPTCHA cracks visual CAPTCHAs using deep learning.
Early SGD hyperparameters affect deep neural network training, showing a break-even point.
We construct singular solutions to special Lagrangian equa- tions with subcritical phases and minimal surface systems. A priori estimate breaking families of smooth solutions are also produced cor- respondingly. A priori estimates for special Lagrangian equations with certain convexity are largely known by now.
In this paper we prove explicit formulas for all Willmore surfaces of revolution and demonstrate their use in the discussion of the associated Dirichlet boundary value problems. It is shown by an explicit example that symmetric Dirichlet boundary conditions do in general not entail the symmetry of the surface. In addit…
Given data over the joint distribution of two random variables and , we consider the problem of inferring the most likely causal direction between and . In particular, we consider the general case where both and may be univariate or multivariate, and of the same or mixed data types. We take an inf…
In this letter we investigate the information provided by the "compass rose" (Crack, T.F. and Ledoit, O. (1996), Journal of Finance, 51(2), pg. 751-762) patterns revealed in phase portraits of daily stock returns. It has been initially suggested that the compass rose is just a manifestation of price clustering and disc…
In this paper, five different approaches for reduced-order modeling of brittle fracture in geomaterials, specifically concrete, are presented and compared. Four of the five methods rely on machine learning (ML) algorithms to approximate important aspects of the brittle fracture problem. In addition to the ML algorithms…
The paper proposes a thermodynamic potential to guide training of generative models, breaking ergodicity to improve functionality.
DAFNO learns surrogates for complex systems on irregular geometries.
In this paper we give a complete description of the set of discrete faithful representations SH(M) uniformizing a compact, orientable, hyperbolizable 3-manifold M with incompressible boundary, equipped with the strong topology, with the description given in term of the end invariants of the quotient manifolds. As part …
Geometric mechanism mimics physics' symmetry breaking.
The paper models Gasoil options using Brent benchmarks, improving volatility estimation.
Improved aircraft structure prediction using derivative-enhanced sparse Cholesky GP method.
Researchers create BPS monopoles with any desired symmetry breaking.
The paper classifies and proves properties of symmetry breaking operators for specific groups.
We consider the reduction along two compact directions of a twisted N=4 gauge theory on a 4-dimensional orientable manifold which is not a global product of two surfaces but contains a non-orientable surface. The low energy theory is a sigma-model on a 2-dimensional worldsheet with a boundary which lives on branes cons…
The paper calculates critical points of systole function on Teichmüller space.
We give a complete classification of intertwining operators (symmetry breaking operators) between spherical principal series representations of G=O(n+1,1) and G'=O(n,1). We construct three meromorphic families of the symmetry breaking operators, and find their distribution kernels and their residues at all poles explic…
Investigates spontaneous symmetry breaking in non-equilibrium systems.
MATLAB toolbox pde2path solves geometric PDEs and finds bifurcations in immersed surfaces.
We investigate the "compass rose" (Crack, T.F. and Ledoit, O. (1996), Journal of Finance, 51(2), pg. 751-762) patterns revealed in phase portraits (delay plots) of stock returns. The structures observed in these diagrams have been attributed mainly to price clustering and discreteness. Using wavelet based denoising, we…
Proves and tests methods for learning time-series with breaks.
According to Courant's theorem, an eigenfunction as\-sociated with the -th eigenvalue has at most nodal domains. A footnote in the book of Courant and Hilbert, states that the same assertion is true for any linear combination of eigenfunctions associated with eigenvalues less than or equal to . We c…
The paper analyzes XVA reduction strategies in financial crises using Mandatory Breaks, Restructuring, and Resets.
We introduce the concept of spontaneous symmetry breaking to arbitrage modeling. In the model, the arbitrage strategy is considered as being in the symmetry breaking phase and the phase transition between arbitrage mode and no-arbitrage mode is triggered by a control parameter. We estimate the control parameter for mom…
We present Bernstein-Sato identities for scalar-, spinor- and differential form-valued distribution kernels on Euclidean space associated to conformal symmetry breaking operators. The associated Bernstein-Sato operators lead to partially new formulae for conformal symmetry breaking differential operators on functions, …
Paper detects and estimates breaks in high-dimensional functional time series.
The Chinese restaurant process (CRP) and the stick-breaking process are the two most commonly used representations of the Dirichlet process. However, the usual proof of the connection between them is indirect, relying on abstract properties of the Dirichlet process that are difficult for nonexperts to verify. This shor…
SymPE breaks symmetries in equivariant networks, improving performance across various tasks.
Bayesian ARMA model with directional shifts captures structural breaks in compositional time series.
Owing to the expeditious growth in the information and communication technologies, smart cities have raised the expectations in terms of efficient functioning and management. One key aspect of residents' daily comfort is assured through affording reliable traffic management and route planning. Comprehensively, the majo…
Noether's framework reveals symmetry-breaking in neural networks.
A new method to estimate local volatility from high-frequency data.
Part I. We prove a one-to-one correspondence between differential symmetry breaking operators for equivariant vector bundles over two homogeneous spaces and certain homomorphisms for representations of two Lie algebras, in connection with branching problems of the restriction of representations. We develop a new method…
New method breaks symmetry in neural networks, improving sample efficiency.
The present paper shows a solution to the problem of automatic distress detection, more precisely the detection of holes in paved roads. To do so, the proposed solution uses a weightless neural network known as Wisard to decide whether an image of a road has any kind of cracks. In addition, the proposed architecture al…
The beta-Bernoulli process provides a Bayesian nonparametric prior for models involving collections of binary-valued features. A draw from the beta process yields an infinite collection of probabilities in the unit interval, and a draw from the Bernoulli process turns these into binary-valued features. Recent work has …
The paper presents the comparative study of the nature of stock markets in short-term and long-term time scales with and without structural break in the stock data. Structural break point has been identified by applying Zivot and Andrews structural trend break model to break the original time series (TSO) into time ser…
Classifies patterns of symmetry breaking and vacuum degeneracy in scalar and gauge fields.
We provide some insights in the study of branching problems of reductive groups, and a method of investigations into symmetry breaking operators. First, we give geometric criteria for finiteness property of linearly independent continuous (respectively, differential) operators that intertwine two induced representation…