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arXiv research

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.

168,742 papers · 148 categories

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2.1%4.2%6.3%8.3% · Oct 199519922001200920172026
48 results for rigid-body movement

New equations for rigid body motion on infinite-dimensional spaces of operators.

problem Integrating rigid body dynamics on infinite-dimensional spaces of operators.
method Introducing pseudo-Riemannian metrics and adapting classical integrability theory.
result Existence of geodesics and integrals of motion for the rigid body equations.

Study of rigid body displacements in a projective space over dual numbers with geometric interpretations.

problem Understanding rigid body displacements in a novel geometric space.
method Projective differential geometry over the ring of dual numbers.
result Existence of non-straight curves with multiple osculating tangents.

Study on symmetries and equilibria in Poisson manifolds, with applications to rigid body dynamics.

problem Characterizing conformal relative equilibria on Poisson manifolds.
method Introducing conformally Poisson actions and momentum maps, establishing algebraic criteria.
result Classification of nontrivial conformal relative equilibria in Lie algebras, with applications to rigid body dynamics.

Paper derives and applies a parallel transport equation on Lie groups.

problem Efficiently solving parallel transport on Lie groups with left-invariant metrics.
method Derives a parallel transport equation in Lie algebra, applies it to SE(3), and compares to existing methods.
result Stable and efficient parallel transport implementation on Lie groups.

Extended Regge complex for linearized Riemann-Cartan geometry and cohomology.

problem Cohomology of the Regge complex in three dimensions.
method Constructing a discrete version of linearized Riemann-Cartan geometry on any triangulation.
result The cohomology of the Regge complex is isomorphic to the infinitesimal-rigid-body-motion-valued de~Rham cohomology.

Integrates magnetic geodesic and sub-Riemannian flows on Stefel variety, proving integrability and Lax presentations.

problem Integrability of magnetic geodesic and sub-Riemannian flows on Vn,2V_{n,2}.
method Proves integrability of magnetic geodesic and sub-Riemannian flows on Vn,2V_{n,2} with respect to magnetic field ηdαη\, dα.
result Integrable cases of a heavy rigid body with a gyrostat are derived.

Suppose that the initial triangle formed by the three moving masses of the three-body problem is similar to the triangle formed at some later time. We derive a simple integral formula for the overall rotation relating the two triangles. The formula is based on the fact that the space of similarity classes of triangles …

1995-10-16abs ↗pdf ↗

Optimizes angular velocity transfers for rigid bodies under deadline constraints.

problem Stochastic guidance of spin states of rigid bodies over a hard deadline.
method Structural analysis of Kantorovich optimal coupling formulation for nonlinear dynamics.
result Derives the ground cost for optimal transport of angular velocity.

The aim of this note is to present simple proofs of the completeness of Manakov's integrals for a motion of a rigid body fixed at a point in Rn\mathbb R^n, as well as for geodesic flows on a class of homogeneous spaces SO(n)/SO(n1)××SO(nr)SO(n)/SO(n_1)\times\dots\times SO(n_r).

2015-04-27abs ↗pdf ↗

Model learns Lagrangian dynamics from images for better prediction and control.

problem Lack of interpretability and applicability to high-dimensional data like images.
method Unsupervised neural network model that learns Lagrangian dynamics from images using a coordinate-aware VAE.
result Model infers interpretable Lagrangian dynamics, enabling long-term prediction and synthesis of controllers.

Paper reduces movement primitive dimensionality in parameter space.

problem High dimensionality of movement primitives makes policy optimization expensive.
method Investigates dimensionality reduction in parameter space, identifying principal movements.
result Dimensionality reduction in parameter space is more effective than in configuration space.

We recast the Calabi flow in DeGiorgi's language of minimizing movements. We establish the long time existence of minimizing movements for K-energy with arbitrary initial condition. Furthermore we establish some a priori regularity of these solutions, and that sufficiently regular minimizing movements are smooth soluti…

2012-08-13abs ↗pdf ↗

We give a criterion of (micro-)kroneckerity of the linear Poisson pencil on g\frak{g}^* related to an algebraic Nijenhuis operator N:ggN:\frak{g}\to \frak{g} on a finite-dimensional Lie algebra g\frak{g}. As an application we get a series of examples of completely integrable systems on semisimple Lie algebras related t…

2005-04-16abs ↗pdf ↗

Taureau uses Twitter sentiment analysis to predict stock market movement.

problem Predicting stock market movement using public opinion on Twitter.
method Obtained historical tweets, filtered and labeled, generated word embeddings, assessed sentiment scores, correlated with stock price movement, designed and evaluated predictive model.
result Taureau can predict stock price movement from lagged sentiment scores.

We propose a sliding surface for systems on the Lie group SO(3)×R3SO(3)\times \mathbb{R}^3 . The sliding surface is shown to be a Lie subgroup. The reduced-order dynamics along the sliding subgroup have an almost globally asymptotically stable equilibrium. The sliding surface is used to design a sliding-mode controller for t…

2019-05-14abs ↗pdf ↗

This paper presents a novel approach for incremental semiparametric inverse dynamics learning. In particular, we consider the mixture of two approaches: Parametric modeling based on rigid body dynamics equations and nonparametric modeling based on incremental kernel methods, with no prior information on the mechanical …

2016-01-18abs ↗pdf ↗

The study models market price movement based on investors' expectations.

problem Understanding the dynamics of investors' expectations and market price movement.
method Developed a non-linear evolutionary equation linking investors' expectations and market asset price movement.
result Model predictions co-integrated with asset time series, suggesting potential for price movement forecasting.

Paper proves minimizing movements match smooth droplet flow in 3D.

problem Consistency of minimizing movements with smooth mean curvature flow.
method Proved minimizing movements coincide with smooth droplet flow.
result Minimizing movements and smooth mean curvature flow are consistent in 3D.

MPANF improves naive forecast by incorporating directional information.

problem Challenging to surpass naive forecast in financial time series.
method Combines naive forecast with movement prediction and accuracy.
result MPANF generally outperforms common benchmarks.

Framework infers coordination strategies from movement data.

problem Inferring individual movement strategies from group data.
method Formalizes Coordination Strategy Inference Problem; provides methodology to infer strategies.
result Framework accurately infers strategies in simulated and real-world datasets.

The Hamilton-Jacobi problem is revisited bearing in mind the consequences arising from a possible bi-Hamiltonian structure. The problem is formulated on the tangent bundle for Lagrangian systems in order to avoid the bias of the existence of a natural symplectic structure on the cotangent bundle. First it is developed …

2006-04-26abs ↗pdf ↗

The study introduces a new stickiness parameter for stock prices using a non-linear model.

problem Understanding how closely individual stocks follow a stock index's price movements.
method Developed a non-linear pricing model inspired by tectonic plate movements to measure stickiness.
result Defined a stickiness parameter for stock price returns using a novel model.

Bayesian method infers local rules for collective animal movement.

problem Learn local rules governing long-term group behaviors.
method Bayesian Inverse Reinforcement Learning with Linearly-Solvable Markov Decision Process.
result Recover true costs and find value of collective movement.

NoTMF forecasts sparse urban road movement speeds with nonstationary temporal matrix factorization.

problem Sparse and nonstationary movement speed data from urban roads.
method Nonstationary Temporal Matrix Factorization (NoTMF) model.
result NoTMF outperforms baseline models in forecasting urban road movement speeds.

This paper presents a semi-parametric algorithm for online learning of a robot inverse dynamics model. It combines the strength of the parametric and non-parametric modeling. The former exploits the rigid body dynamics equa- tion, while the latter exploits a suitable kernel function. We provide an extensive comparison …

2016-03-17abs ↗pdf ↗

New algorithm reduces dynamic regret in time-varying movement costs.

problem Dynamic regret in online convex optimization with time-varying movement costs.
method Introduced a novel algorithm for time-varying movement costs, achieving comparator-adaptive dynamic regret bound.
result Established first comparator-adaptive dynamic regret bound of O~((M2+MPT)(T+tλt))\widetilde{\mathcal{O}}(\sqrt{(M^2+MP_T)(T+\sum_t λ_t)}).