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.
Exact solution found for two-body financial dealer model using kinetic theory.
The two-body problem with a central interaction on simply connected constant curvature spaces of an arbitrary dimension is considered. The explicit expression for the quantum two-body Hamiltonian via a radial differential operator and generators of the isometry group is found. We construct a self-adjoint extension of t…
We consider the two body problem with central interaction on two point homogeneous spaces from point of view of the invariant differential operators theory. The representation of the two particle Hamiltonian in terms of the radial differential operator and invariant operators on the symmetry group is found. The connect…
We consider the class of short rate interest rate models for which the short rate is proportional to the exponential of a Gaussian Markov process x(t) in the terminal measure r(t) = a(t) exp(x(t)). These models include the Black, Derman, Toy and Black, Karasinski models in the terminal measure. We show that such intere…
A new theory explains large associative memory with biological plausibility.
The study finds the best elliptical trajectory for planets using a variation of the hodograph theorem.
Let be the identity component of the isometry group for an arbitrary curved two-point homogeneous space . We consider algebras of -invariant differential operators on bundles of unit spheres over . The generators of this algebra and the corresponding relations for them are found. The connection of these ge…
I analyze the one-dimensional, cubic Schrödinger equation, with nonlinearity constructed from the current density, rather than, as is usual, from the charge density. A soliton solution is found, where the soliton moves only in one direction. Relation to higher-dimensional Chern--Simons theory is indicated. The theory i…
We build CSI-Net, a unified Deep Neural Network~(DNN), to learn the representation of WiFi signals. Using CSI-Net, we jointly solved two body characterization problems: biometrics estimation (including body fat, muscle, water, and bone rates) and person recognition. We also demonstrated the application of CSI-Net on tw…
We consider the ideal-gas models of trading markets, where each agent is identified with a gas molecule and each trading as an elastic or money-conserving (two-body) collision. Unlike in the ideal gas, we introduce saving propensity of agents, such that each agent saves a fraction of its money and trades with t…
We consider the ideal-gas models of trading markets, where each agent is identified with a gas molecule and each trading as an elastic or money-conserving (two-body) collision. Unlike in the ideal gas, we introduce saving propensity of agents, such that each agent saves a fraction of its money and trades with t…
We have numerically simulated the ideal-gas models of trading markets, where each agent is identified with a gas molecule and each trading as an elastic or money-conserving two-body collision. Unlike in the ideal gas, we introduce (quenched) saving propensity of the agents, distributed widely between the agents ($0 \le…
On a natural circle bundle T(M) over a 4-dimensional manifold M equipped with a split signature metric g, whose fibers are real totally null selfdual 2-planes, we consider a tautological rank 2 distribution D obtained by lifting each totally null plane horizontally to its point in the fiber. Over the open set where g i…
We study the non-asymptotic behavior of a Coulomb gas on a compact Riemannian manifold. This gas is a symmetric n-particle Gibbs measure associated to the two-body interaction energy given by the Green function. We encode such a particle system by using an empirical measure. Our main result is a concentration inequalit…
We propose a method for recovering the structure of a sparse undirected graphical model when very few samples are available. The method decides about the presence or absence of bonds between pairs of variable by considering one pair at a time and using a closed form formula, analytically derived by calculating the post…
MACE uses higher-order messages to create fast, accurate force fields.
Meta learning enables cross-domain Hamiltonian dynamics.
We propose a formula of time-series prediction by means of three states random field Ising model (RFIM). At the economic crisis due to disasters or international disputes, the stock price suddenly drops. The macroscopic phenomena should be explained from the corresponding microscopic view point because there are existi…
New method disentangles high-order effects in feature importance.
Study shows limitations and universality of equivariant QNNs with -equivariant gates.
New black hole solutions with positive and negative masses in 4 and 5 dimensions.
The paper introduces BCART models for aggregate claim amount, improving frequency-severity and joint modeling.
The paper uses model-based trees to create interpretable surrogate models for complex machine learning models.
Gauge Flow Models use a learnable Gauge Field in Generative Flow Models.
The study examines how model predictions hold up under model extensions.
Revises Bayesian model averaging for foundation models.
Paper introduces symmetric divergence link models for probability distributions.
New method to handle credit portfolio model uncertainties.
The paper tests stock return models and uses LSTM to predict stock returns.
Researchers review challenges in interpreting additive models, especially neural additive models.
Novel hybrid modeling combines ML and physics for real-time diagnosis.
CRS model improves ranking data modeling with theoretical guarantees.
Sigma models linked to Gross-Neveu models via quiver varieties.
Interpretable machine learning has become a strong competitor for traditional black-box models. However, the possible loss of the predictive performance for gaining interpretability is often inevitable, putting practitioners in a dilemma of choosing between high accuracy (black-box models) and interpretability (interpr…
Simple models are preferred over complex models, but over-simplistic models could lead to erroneous interpretations. The classical approach is to start with a simple model, whose shortcomings are assessed in residual-based model diagnostics. Eventually, one increases the complexity of this initial overly simple model a…
Matryoshka hides secret models in a carrier model, achieving high capacity and robustness.
Seq2Seq models speed up epidemic model predictions.
This work develops scalable model selection methods with fast update and selection.
Paper proposes BMPO to optimize policies using bidirectional models.
Copulas outperform marginal models in multivariate risk forecasting, reducing model risk by narrowing down the set of models.
BayesBlend blends multiple models' predictions for better insurance loss predictions.
The paper identifies when larger models improve predictions and proposes a switcher model.
Improved diffusion model generation speed with speculative sampling.
We propose a generalization of neural network sequence models. Instead of predicting one symbol at a time, our multi-scale model makes predictions over multiple, potentially overlapping multi-symbol tokens. A variation of the byte-pair encoding (BPE) compression algorithm is used to learn the dictionary of tokens that …
The paper extends statistical inference methods for black-box generative models.
PMM uses Bayesian inference to generate data from noisy approximations.
Unified model improves sampling speed and quality.
In science and especially in economics, agent-based modeling has become a widely used modeling approach. These models are often formulated as a large system of difference equations. In this study, we discuss two aspects, numerical modeling and the probabilistic description for two agent-based computational economic mar…