arXiv research

Study reveals how travel times on cylindrical boundaries can identify spacetime structure.

Evolution of planar curves under a nonlocal geometric equation is investigated. It models the simultaneous contraction and growth of carbonate particles called ooids in geosciences. Using classical ODE results and a bijective mapping we demonstrate that the steady parameters associated with the physical environment det…

New method learns time-invariant rewards from demonstrations.

Paper tests if beta coefficients in AMF model are consistent over time.

A method for learning with autoregressive chain-of-thoughts.

The paper uses curvature to assess stability of linear systems.

We build a simple diagnostic criterion for approximate factor structure in large cross-sectional equity datasets. Given a model for asset returns with observable factors, the criterion checks whether the error terms are weakly cross-sectionally correlated or share at least one unobservable common factor. It only requir…

The paper introduces a new method to assess system stability using torsion of state trajectories.

New test uncovers causal links in rare event dynamics.

SPAQL improves RL by adaptively partitioning state-action space and learning a time-invariant policy.

Paper derives an error bound for stochastic LTI systems.

The paper tackles joint learning of linear systems, improving accuracy with pooled data.

Paper tackles temporal overfitting in wind power curve modeling.

Paper tackles efficient off-policy evaluation in long-horizon settings.

ODENets are more robust to perturbations and adversarial attacks compared to CNNs.

ForecastNet uses a time-variant deep feed-forward neural network for better multi-step-ahead time series forecasting.

New model stabilizes asynchronous LTI systems, independent of synchronous stability.

This work represents an application of constant mean curvature graphs (as solutions of the mean curvature PDE) to non-linear non-Darcy flows in porous media. It relates time invariant pressure distribution graphs to graphs of constant mean curvature surfaces. This differential geometric interpretation provides an impor…

AR model forecasts partially observed dynamical time series by estimating evolution function and imputing missing variables.

Study static Einstein-Maxwell space invariant by translation.

Proposes a method to prove closing of periodic orbits in dynamical systems.

Autoencoder detects subtle changes in time series data.

Paper proposes a method for learning and planning in time-varying environments.

We prove that stochastic gradient descent efficiently converges to the global optimizer of the maximum likelihood objective of an unknown linear time-invariant dynamical system from a sequence of noisy observations generated by the system. Even though the objective function is non-convex, we provide polynomial running …

In this paper we construct the differential equations of the stream lines that characterize plasma regarded as a non-isotropic medium geometrized by a jet rheonomic time-invariant Berwald-Moor metric. Section 1 contains historical notes regarding the Plasma Physics and its geometrical description. Section 2 analyzes th…

New method calibrates asynchronous, error-prone covariates for longitudinal data.

Tutorial on using concentration inequalities for linear system identification.

Improved speech separation and enhancement using neural beamforming.

We study the problem of controlling linear time-invariant systems with known noisy dynamics and adversarially chosen quadratic losses. We present the first efficient online learning algorithms in this setting that guarantee $O(\sqrt{T})$ regret under mild assumptions, where $T$ is the time horizon. Our algorithms rely …

We consider the problem of learning a realization for a linear time-invariant (LTI) dynamical system from input/output data. Given a single input/output trajectory, we provide finite time analysis for learning the system's Markov parameters, from which a balanced realization is obtained using the classical Ho-Kalman al…

Study constructs solutions for evolving hypersurfaces using inverse spacetime mean curvature.

Over the past few years, we developed a mathematically rigorous method to study the dynamical processes associated to nonlinear Forchheimer flows for slightly compressible fluids. We have proved the existence of a geometric transformation which relates constant mean curvature surfaces and time-invariant pressure distri…

We develop a cross-sectional research design to identify causal effects in the presence of unobservable heterogeneity without instruments. When units are dense in physical space, it may be sufficient to regress the "spatial first differences" (SFD) of the outcome on the treatment and omit all covariates. The identifyin…

Extends IPC to time-variant systems, revealing information processing in diverse dynamics.

New measure EC assesses node contributions in nonlinear, time-varying systems.

We forecast S&P 500 excess returns using a flexible Bayesian econometric state space model with non-Gaussian features at several levels. More precisely, we control for overparameterization via novel global-local shrinkage priors on the state innovation variances as well as the time-invariant part of the state space mod…

This paper provides a geometrical derivation of the Hybrid Minimum Principle (HMP) for autonomous hybrid systems whose state manifolds constitute Lie groups $(G,\star)$ which are left invariant under the controlled dynamics of the system, and whose switching manifolds are defined as smooth embedded time invariant subma…

dynoGP uses deep Gaussian processes for dynamic system identification.

Let $M^m$ be an oriented manifold, let $N^{m-1}$ be an oriented closed manifold, and let $p$ be a point in $M^m$. For a smooth map $f:N^{m-1} \to M^m, p \not\in Im f,$ we introduce an invariant $awin_p(f)$ that can be regarded as a generalization of the classical winding number of a planar curve around a point. We show…

The paper proposes a method for interpretable mixture density estimation using a tree structure.

This paper presents a Bayesian approach to symbol and phase inference in a phase-unsynchronized digital receiver. It primarily extends [Quinn 2011] to the multi-symbol case, using the variational Bayes (VB) approximation to deal with the combinatorial complexity of the phase inference in this case. The work provides a …

Safety filter for unknown discrete-time systems with learned models and noise covariance.

We consider the problem of discrete-time signal denoising, focusing on a specific family of non-linear convolution-type estimators. Each such estimator is associated with a time-invariant filter which is obtained adaptively, by solving a certain convex optimization problem. Adaptive convolution-type estimators were dem…

The study analyzes momentum-based optimization algorithms from dynamical systems perspective.

Iterative method learns unknown constraints for MPC control.

New insights into spectral statistics of sample covariance matrix for stable linear systems.

Many real-world applications require robust algorithms to learn point processes based on a type of incomplete data --- the so-called short doubly-censored (SDC) event sequences. We study this critical problem of quantitative asynchronous event sequence analysis under the framework of Hawkes processes by leveraging the …

Time-variant value function transfer method for RL.