arXiv research

Clarifies method of phase synchronization for decoupling linear differential equations.

A time schedule simplifies learning in flow-based models for high-dimensional data.

Proves well-posedness for hard phase model in general relativity.

Neural net reconstructs dark matter density from halo velocities.

We study velocity correlations induced by diffusion and dissipation in a simple dissipative dynamical system. We observe that diffusion, as a result of time reversible microscopic processes, leads to correlations with different spatial parity from those caused by dissipation, consisting of time irreversible microscopic…

We extend Routh's reduction procedure to an arbitrary Lagrangian system (that is, one whose Lagrangian is not necessarily the difference of kinetic and potential energies) with a symmetry group which is not necessarily Abelian. To do so we analyse the restriction of the Euler-Lagrange field to a level set of momentum i…

The introduction of a covariant derivative on the velocity phase space is needed for a global expression of Euler-Lagrange equations. The aim of this paper is to show how its torsion tensor turns out to be involved in such a version.

SL(N,C) is the phase space of the Poisson SU(N). We calculate explicitly the symplectic structure of SL(N,C), define an analogue of the Hamiltonian of the free motion on SU(N) and solve the corresponding equations of motion. Velocity is related to the momentum by a non-linear Legendre transformation.

Study reconstructs Riemannian metric from Cherenkov radiation in complex media.

Anomalous diffusion in SGD reveals interactions between hyperparameters and Hessian.

In this study, it is generalized the concept of Lagrangian mechanics with constraints to complex case. To be beginning, it is considered a Kaehlerian manifold as a velocity-phase space. Then a non-holonomic constraint is given by 1-form on it. If the form is closed, it is found that the constraint is (locally) holonomi…

Gonogo offers tools for sensitivity experiments in R.

The paper accelerates ISTA and FISTA algorithms for composite optimization problems.

Recently, the Deep Planning Network (PlaNet) approach was introduced as a model-based reinforcement learning method that learns environment dynamics directly from pixel observations. This architecture is useful for learning tasks in which either the agent does not have access to meaningful states (like position/velocit…

Starting from the characterization of the past time evolution of market prices in terms of two fundamental indicators, price velocity and price acceleration, we construct a general classification of the possible patterns characterizing the deviation or defects from the random walk market state and its time-translationa…

A new algorithm reconstructs population dynamics from coarse samples.

Seismic phase association is a fundamental task in seismology that pertains to linking together phase detections on different sensors that originate from a common earthquake. It is widely employed to detect earthquakes on permanent and temporary seismic networks, and underlies most seismicity catalogs produced around t…

The paper constructs chaotic solutions to the Euler equations on high-dimensional manifolds.

We discuss some aspects about the computation of kinematic, spectroscopic, Fermi and astrometric relative velocities that are geometrically defined in general relativity. Mainly, we state that kinematic and spectroscopic relative velocities only depend on the 4-velocities of the observer and the test particle, unlike F…

DSNE visualizes data velocity in lower dimensions.

RFM improves CNFs by adding a boundary constraint term and matching velocity fields.

Log-ergodic model improves velocity of money prediction.

It is well known that Lagrangian dynamical systems naturally arise in describing wave front dynamics in the limit of short waves (which is called pseudoclassical limit or limit of geometrical optics). Wave fronts are the surfaces of constant phase, their points move along lines which are called rays. In non-homogeneous…

In this paper, we study the herding phenomena in financial markets arising from the combined effect of (1) non-coordinated collective interactions between the market players and (2) concurrent reactions of market players to dynamic market signals. By interpreting the expected rate of return of an asset and the favorabi…

Optimal self-distillation improves generative models' velocity risk and mode recovery.

New method estimates velocity fields for minimizing $f$-divergences without overfitting.

We prove that, in a space-time of dimension n>3 with a velocity field that is shear-free, vorticity-free and acceleration-free, the covariant divergence of the Weyl tensor is zero if the contraction of the Weyl tensor with the velocity is zero. The other way, if the covariant divergence of the Weyl tensor is zero, then…

Proves strong solutions for graphical Brakke flows with $L^2$ normal velocity.

CCVFM uses coreset to improve generative models by refining residual flows.

SAGE generates subsurface velocity models from sparse well logs and seismic images.

New method distinguishes cause from effect using causal velocity.

The superfamily phenomenon of time series with different dynamics can be characterized by the motif rank patterns observed in the nearest-neighbor networks of the time series in phase space. However, the determinants of superfamily classification are unclear. We attack this problem by studying the influence of linear t…

LFIS uses a time-dependent velocity field to sample from complex distributions.

NN-Turb generates turbulent velocity statistics using neural networks.

Bitcoin's monetary velocity is constrained by network friction, leading to significant utility contraction during shocks.

Novel weak solutions for volume-preserving mean curvature flow established.

New method recovers radar and communication signals from overlaid data.

The determinants of the velocity of money have been examined based on life-cycle hypothesis. The velocity of money can be expressed by reciprocal of the average value of holding time which is defined as interval between participating exchanges for one unit of money. This expression indicates that the velocity is govern…

A conservative drifting method improves generative modeling by using KDE gradients, proving convergence rates.

A new method interprets astrophysical spectra using geometric paths to distinguish line profiles.

Neural solver computes Wasserstein geodesics and velocity fields efficiently.

Develops scalable model for learning velocity fields in complex traffic scenarios.

Generative sampler learns velocity fields for efficient posterior inference.

Time dilation $\frac{1}{\sqrt{1-v^2}}$ and relative velocity $v$ are observationally indistinguishable in the special theory of relativity, a duality that carries over into the general theory under Fermi coordinates along a curve (in coordinate-independent language, in the tangent Minkowski space along the curve). For …

An $(r,n)$-velocity is an $r$-jet with source at $0 \in \R^n$, and target in a manifold $Y$. An $(r,n)$-velocity is said to be regular, if it has a representative which is an immersion at $0 \in \R^{n}$. The manifold $T^{r}_{n}Y$ of $(r,n)$-velocities as well as its open, $L^{r}_{n}$-invariant, dense submanifold $\Imm …

We show how the Dixon's system of first order equations of motion for the particle with inner dipole structure together with the side Mathisson constraint follows from rather general construction of the 'Hamilton system' developed by Weyssenhoff, Rund and Grässer to describe the phase space counterpart of the evolution…

Study uses neural networks to predict wall quantities in turbulent flows.

In this study, the concept of dual Lorentzian homotetic exponential motions in is discussed and their velocities, accelerations obtained. Also, some geometric results between velocity and acceleration vectors of a point in a spatial motion are obtained. Finally, the theorems related to acceleration and acceleration cen…