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.
New algorithm efficiently trains machine learning models to atomic forces data.
Improved CG force-field learning from all-atom data.
New method uses normalizing flows to improve force fields for coarse-grained molecular dynamics.
CAMEL embeds data into a manifold using curvature-augmented forces.
Weibull weight-scale parameter evolves during AdamW training, with alignment, injection, and decay forces driving its growth and relaxation.
We provide a benchmark dataset for hand gesture recognition using force myography.
Bayesian modelling of dynamic systems must achieve a compromise between providing a complete mechanistic specification of the process while retaining the flexibility to handle those situations in which data is sparse relative to model complexity, or a full specification is hard to motivate. Latent force models achieve …
GDML learns effective CG models from all-atom data.
The paper integrates dissipative and curl forces using geometric methods.
In this paper we contribute a novel algorithm family, which generalizes many unsupervised techniques including unnormalized and energy models, and allows us to infer different statistical modalities (e.g. data likelihood and ratio between densities) from data samples. The proposed unsupervised technique, named Probabil…
Latent force models are a class of hybrid models for dynamic systems, combining simple mechanistic models with flexible Gaussian process (GP) perturbations. An extension of this framework to include multiplicative interactions between the state and GP terms allows strong a priori control of the model geometry at the ex…
Paper introduces a PDE-free method for decomposing forces in any dimension.
Proposes linking energy and force uncertainty in deep learning potentials.
Labor productivity in developed countries is analyzed and modeled. Modeling is based on our previous finding that the rate of labor force participation is a unique function of GDP per capita. Therefore, labor productivity is fully determined by the rate of economic growth, and thus, is a secondary economic variable. In…
We consider a generalization of the notion of a natural mechanical system to the case of additional forces of gyroscopic type. Such forces appear, for example, as a result of global reduction of a natural system with symmetry. We study symmetries in the systems with gyroscopic forces to find out when these systems admi…
The paper analyzes errors in mechanical systems with external forces.
The evolution of the rate of price inflation and unemployment in Japan has been modeled within the Phillips curve framework. As an extension to the Phillips curve, we represent both variables as linear functions of the change rate of labor force. All models were first estimated in 2005 for the period between 1980 and 2…
Study curve flows with global forcing terms using a distance comparison principle.
Cointegration helps insurers understand long-range mortality patterns.
Smooth flows for physical systems with smooth energies and forces.
In this paper we provide a variational derivation of the Euler-Poincaré equations for systems subjected to external forces using an adaptation of the techniques introduced by Galley and others. Moreover, we study in detail the underlying geometry which is related to the notion of Poisson groupoid. Finally, we apply the…
An important task in structural design is to quantify the structural performance of an object under the external forces it may experience during its use. The problem proves to be computationally very challenging as the external forces' contact locations and magnitudes may exhibit significant variations. We present an e…
Study reveals hidden infections and infection dynamics from early data.
Develops Hamiltonian Score Matching and Generative Flows for machine learning.
We show that under suitable non-degeneracy conditions, complete gradient flow lines of the scalar curvature functional of a riemannian manifold perturb into eternal forced mean curvature flows with large forcing term.
The paper finds that circles and logarithmic spirals are the only constant-speed ramps for a specific force field.
The Teacher Forcing algorithm trains recurrent networks by supplying observed sequence values as inputs during training and using the network's own one-step-ahead predictions to do multi-step sampling. We introduce the Professor Forcing algorithm, which uses adversarial domain adaptation to encourage the dynamics of th…
Proposes a new model to price options considering market forces beyond Black-Scholes.
Proves uniqueness of blowups for forced mean curvature flow.
We present a novel learning framework that consistently embeds underlying physics while bypassing a significant drawback of most modern, data-driven coarse-grained approaches in the context of molecular dynamics (MD), i.e., the availability of big data. The generation of a sufficiently large training dataset poses a co…
The paper simplifies complex mechanical systems with external forces.
Neural network models colloidal particle dynamics in non-equilibrium systems.
New insights into Hessian structure of neural networks reveal two forces.
We apply the potential force estimation method to artificial time series of market price produced by a deterministic dealer model. We find that dealers' feedback of linear prediction of market price based on the latest mean price changes plays the central role in the market's potential force. When markets are dominated…
This work tackles force control for contact-rich manipulation tasks with rigid robots using RL.
In this paper, we consider the mean curvature flow of convex hypersurfaces in Euclidean spaces with a general forcing term. We show that the flow may shrink to a point in finite time if the forcing term is small, or exist for all times and expand to infinity if the forcing term is large enough. The flow can also conver…
Deep learning predicts adhesive forces in soft viscoelastic contacts quickly and accurately.
The problem of combined state and input estimation of linear structural systems based on measured responses and a priori knowledge of structural model is considered. A novel methodology using Gaussian process latent force models is proposed to tackle the problem in a stochastic setting. Gaussian process latent force mo…
Study compares employers with and without anticipating strategic labor force responses.
We are able to derive the equations of motion for forced mechanical systems in a purely variational setting, both in the context of Lagrangian or Hamiltonian mechanics, by duplicating the variables of the system as introduced by Galley [2013], Galley, Tsang, and Stein [2014]. We show that this construction is useful to…
The paper explains emergent phenomena in deep learning using entropic forces.
Feature normalization prevents collapse in non-contrastive learning dynamics.
In this paper we study the blow up sequence of mean curvature flow of surfaces in with additional forces. We prove that the blow up limit of a mean curvature flow of smoothly embedded surfaces with additional forces with finite entropy is a smoothly embedded self-shrinker.
Latent force models (LFM) are principled approaches to incorporating solutions to differential equations within non-parametric inference methods. Unfortunately, the development and application of LFMs can be inhibited by their computational cost, especially when closed-form solutions for the LFM are unavailable, as is …
Bayesian filtering approach identifies nonlinear restoring forces in dynamic systems.
We develop SHOPPER, a sequential probabilistic model of shopping data. SHOPPER uses interpretable components to model the forces that drive how a customer chooses products; in particular, we designed SHOPPER to capture how items interact with other items. We develop an efficient posterior inference algorithm to estimat…
The forcing relation of braids has been introduced for a 2-dimensional analogue of the Sharkovskii order on periods for maps of the interval. In this paper, by making use of the Nielsen fixed point theory and a representation of braid groups, we deduce a trace formula for the computation of the forcing order.
MACE architecture outperforms alternatives in various molecular and materials science tasks.