arXiv research

New non-separable covariance kernels for spatiotemporal data derived from harmonic oscillator physics.

Momentum is a simple and widely used trick which allows gradient-based optimizers to pick up speed along low curvature directions. Its performance depends crucially on a damping coefficient $β$. Large $β$ values can potentially deliver much larger speedups, but are prone to oscillations and instability; hence one typic…

Autoencoder estimates parameters of noisy, multi-component damped signals.

Using a modified damped harmonic oscillator model equivalent to a model of market dynamics with price expectations, we analyze the reaction of financial markets to shocks. In order to do this, we gather data from indices of a variety of financial markets for the 1987 Black Monday, the Russian crisis of 1998, the crash …

We prove a global smooth isometric immersion for negatively curved surfaces with finite total curvature.

For dynamical systems that can be modelled as asymptotically stable linear systems forced by Gaussian noise, this paper develops methods to infer or estimate their modes from observations in real time. The modes can be real or complex. For a real mode, we wish to infer its damping rate and mode shape. For a complex mod…

This paper proposes an alternative to the classical price-adjustment mechanism (called "tâtonnement" after Walras) that is second-order in time. The proposed mechanism, an analogue to the damped harmonic oscillator, provides a dynamic equilibration process that depends only on local information. We show how such a proc…

A new real-time method estimates system matrices and states using Kalman filter.

We investigate the behavior of stocks in daily price-limited stock markets by purposing a quantum spatial-periodic harmonic model. The stock price is presumed to oscillate and damp in a quantum spatial-periodic harmonic oscillator potential well. Complicated non-linear relations including inter-band positive correlatio…

Graph-Coupled Oscillator Networks (GraphCON) tackles graph-based learning problems.

We pursue the quantum-mechanical challenge to the efficient market hypothesis for the stock market by employing the quantum Brownian motion model. We utilize the quantum Caldeira-Leggett master equation as a possible phenomenological model for the stock-market-prices fluctuations while introducing the external harmonic…

In the past 20 years, momentum or trend following strategies have become an established part of the investor toolbox. We introduce a new way of analyzing momentum strategies by looking at the information ratio (IR, average return divided by standard deviation). We calculate the theoretical IR of a momentum strategy, an…

A new FFT method for Heston model option pricing with explicit error bounds.

A new method uses higher-order Langevin dynamics with critical damping for better generative modeling.

TOLD++ improves convergence of diffusion models by critically damping the forward transition matrix.

New damping technique improves deep learning models by reducing noise in flat directions.

We prove a Weyl-type fractal upper bound for the spectrum of the damped wave equation, on a negatively curved compact manifold. It is known that most of the eigenvalues have an imaginary part close to the average of the damping function. We count the number of eigenvalues in a given horizontal strip deviating from this…

Neural networks require a careful design in order to perform properly on a given task. In particular, selecting a good activation function (possibly in a data-dependent fashion) is a crucial step, which remains an open problem in the research community. Despite a large amount of investigations, most current implementat…

Unified method for calculating financial option prices from characteristic functions.

Study shows how neural networks learn eigenfunctions of the NTK in underparameterized settings.

DistillKac generates images quickly using damped wave equations.

Spatial statisticians and quantitative investors use the same mathematical object: a Schur complement, damped by one parameter.

We study the dynamics of a version of the batch minority game, with random external information and with different types of inhomogeneous decision noise (additive and multiplicative), using generating functional techniques à la De Dominicis. The control parameters in this model are the ratio $α=p/N$ of the number $p$ o…

The maximum a posteriori (MAP) configuration of binary variable models with submodular graph-structured energy functions can be found efficiently and exactly by graph cuts. Max-product belief propagation (MP) has been shown to be suboptimal on this class of energy functions by a canonical counterexample where MP conver…

A new method optimizes Fourier pricing for multi-asset options using adaptive quadrature.

In this paper, we present an algorithm for the sparse signal recovery problem that incorporates damped Gaussian generalized approximate message passing (GGAMP) into Expectation-Maximization (EM)-based sparse Bayesian learning (SBL). In particular, GGAMP is used to implement the E-step in SBL in place of matrix inversio…

Develops geometric framework for dissipative field equations.

A new oscillator measures trending behavior of financial instruments.

Minimal vector fields on oscillator groups studied, with specific conditions for minimality.

Study examines boundedness of oscillating singular integrals on specific Lie groups.

Log-periodic oscillations have been used to predict price trends and crashes on financial markets. So far two types of log-periodic oscillations have been associated with the real markets. The first type are oscillations which accompany a rising market and which ends in a crash. The second type oscillations, called "an…

Estimates box dimension of fractal interpolation surfaces using oscillation vectors.

Researchers classify lattices in a specific four-dimensional group.

The energy in a square membrane $Ω$ subject to constant viscous damping on a subset $ω\subset Ω$ decays exponentially in time as soon as $ω$ satisfies a geometrical condition known as the "Bardos-Lebeau-Rauch" condition. The rate $τ(ω)$ of this decay satisfies $τ(ω)= 2 \min(-μ(ω), g(ω))$ (see Lebeau [Math. Phys. Stud. …

Unified ODE model explains residual and non-residual networks.

Newton's method solves variational problems on manifolds.

We analyse four consecutive cycles observed in the USA for employment and inflation. They are driven by three oil price shocks and an intended interest rate shock. Non-linear coupling between the rate equations for consumer products as prey and consumers as predators provides the required instability, but its natural d…

A new method improves flow matching by dynamically weighting density estimates.

Improved sampling in generative models using CLDs with a hyperparameter.

The Duffing oscillator's parameters are identified online using variational message passing.

New method linearizes nonlinear coupled oscillators on graphs.

D-LinOSS models learn to dissipate energy, improving performance on long-range tasks.

We introduce deep learning models to estimate the masses of the binary components of black hole mergers, $(m_1,m_2)$, and three astrophysical properties of the post-merger compact remnant, namely, the final spin, $a_f$, and the frequency and damping time of the ringdown oscillations of the fundamental $\ell=m=2$ bar mo…

Improved generative models using critically-damped Langevin diffusion.

The present paper introduces a majority orienting model in which the dealers' behavior changes based on the influence of the price to show the oscillation of stock price in the stock market. We show the oscillation of the price for the model by applying the van der Pol equation which is a deterministic approximation of…

We discuss stochastic modeling of volatility persistence and anti-correlations in electricity spot prices, and for this purpose we present two mean-reverting versions of the multifractal random walk (MRW). In the first model the anti-correlations are modeled in the same way as in an Ornstein-Uhlenbeck process, i.e. via…

The study derives generalization bounds for neural oscillators, improving their performance with regularization.

Study finds the spectrum of a cubic Dirac operator on specific oscillator group manifolds.