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.
Study motion of discrete interfaces on triangular lattice using Almgren, Taylor, and Wang's approach.
This paper explores the information-theoretic limitations of graph property testing in zero-field Ising models. Instead of learning the entire graph structure, sometimes testing a basic graph property such as connectivity, cycle presence or maximum clique size is a more relevant and attainable objective. Since property…
The paper studies structure detection in high-temperature ferromagnetic models.
Graphical models are a rich language for describing high-dimensional distributions in terms of their dependence structure. While there are algorithms with provable guarantees for learning undirected graphical models in a variety of settings, there has been much less progress in the important scenario when there are lat…
We study the motion of discrete interfaces driven by ferromagnetic interactions in a two-dimensional periodic environment by coupling the minimizing movements approach by Almgren, Taylor and Wang and a discrete-to-continuous analysis. The case of a homogeneous environment has been recently treated by Braides, Gelli and…
The relation between differential geometry of surfaces and some Heisenberg ferromagnet models is considered.
Geometrical flows (GF) play an important role in modern mathematics and physics. In this letter we have considered some integrable isotropic GF -- Ricci flows (RF) and mean curvature flows (MCF) -- which are related with integrable Heisenberg ferromagnets. In 2+1 dimensions, these GF have a singularity at .
Topological Skyrmions as intricate spin textures were observed experimentally in helimagnets on 2d plane. Theoretical foundation of such solitonic states to appear in pure ferromagnetic model, as exact solutions expressed through any analytic function, was made long ago by Belavin and Polyakov (BP). We propose an innov…
Prior distributions of binarized natural images are learned by using a Boltzmann machine. According the results of this study, there emerges a structure with two sublattices in the interactions, and the nearest-neighbor and next-nearest-neighbor interactions correspondingly take two discriminative values, which reflect…
Quantum annealing is a generic solver of the optimization problem that uses fictitious quantum fluctuation. Its simulation in classical computing is often performed using the quantum Monte Carlo simulation via the Suzuki--Trotter decomposition. However, the negative sign problem sometimes emerges in the simulation of q…
We analyze income tax evasion dynamics in a standard model of statistical mechanics, the Ising model of ferromagnetism. However, in contrast to previous research, we use an inhomogeneous multi-dimensional Ising model where the local degrees of freedom (agents) are subject to a specific social temperature and coupled to…
Belief propagation quickly converges to global optima for ferromagnetic Ising models.
Machine learning identifies phase transitions in condensed matter physics.
We study the motion of discrete interfaces driven by ferromagnetic interactions in a two-dimensional low-contrast periodic environment, by coupling the minimizing movements approach by Almgren, Taylor and Wang and a discrete-to-continuum analysis. As in a recent paper by Braides and Scilla dealing with high-contrast pe…
In this paper we investigate the computational complexity of learning the graph structure underlying a discrete undirected graphical model from i.i.d. samples. We first observe that the notoriously difficult problem of learning parities with noise can be captured as a special case of learning graphical models. This lea…
Algorithm learns RBMs with arbitrary external fields, improving on previous constraints.
We study long wave limits for general Schrodinger maps systems into Kahler manifolds with a constraining potential vanishing on a Lagrangian submanifold. We obtain KdV type systems set on the tangent space of the submanifold. Our general theory is applied to study the long wave limit of the Gross-Pitaevskii equation, a…
Approximate algorithms for structured prediction problems---such as LP relaxations and the popular alpha-expansion algorithm (Boykov et al. 2001)---typically far exceed their theoretical performance guarantees on real-world instances. These algorithms often find solutions that are very close to optimal. The goal of thi…
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…
Dynamic sampling from changing graphical models.
This paper introduces a new specialized algorithm for equilibrium Monte Carlo sampling of binary-valued systems, which allows for large moves in the state space. This is achieved by constructing self-avoiding walks (SAWs) in the state space. As a consequence, many bits are flipped in a single MCMC step. We name the alg…
Generalized belief propagation converges to optimal solutions on graphs with motifs.
The Gibbs sampler is a particularly popular Markov chain used for learning and inference problems in Graphical Models (GMs). These tasks are computationally intractable in general, and the Gibbs sampler often suffers from slow mixing. In this paper, we study the Swendsen-Wang dynamics which is a more sophisticated Mark…
New algorithm samples from Ising models efficiently, even with outliers.
Study null curves and their motion in 3D flat space-time, leading to integrable hierarchies.
We present a method for scalable and fully 3D magnetic field simultaneous localisation and mapping (SLAM) using local anomalies in the magnetic field as a source of position information. These anomalies are due to the presence of ferromagnetic material in the structure of buildings and in objects such as furniture. We …
Model analyzes corruption dynamics on an Ising lattice.
Paper presents a privacy-preserving algorithm for estimating peer effects using the Ising model.
Optimal SQ bounds for learning binary product distributions and Ising models.
We prove a mapping between dual and primal factor graph marginals for efficient estimation.
Paper optimizes change detection in unnormalized distributions.
A neural network model predicts the critical point of the Ising phase transition.
Inspired by the bankruptcy of Lehman Brothers and its consequences on the global financial system, we develop a simple model in which the Lehman default event is quantified as having an almost immediate effect in worsening the credit worthiness of all financial institutions in the economic network. In our stylized desc…
Improves magnetic field mapping using an array of magnetometers with noisy input.
This article considers a model for alternative processes for securities prices and compares this model with actual return data of several securities. The distributions of returns that appear in the model can be Gaussian as well as non-Gaussian; in particular they may have two peaks. We consider a discrete Markov chain …
IFM improves recommender systems by learning flexible feature interactions.
Surrogate-based analysis of interactions via local effect smooths
Symmetry-electronic fingerprints reveal competing magnetic phases in two-dimensional materials.
Local mappings relate dual and primal factor graphs for efficient marginal probability estimation.
A new method detects interactions in neural networks using topological analysis.
Interactive learning framework for various settings.
InteractE improves link prediction in KGs by increasing feature interactions.
Enhances MTGP for better hierarchical latent interactions.
iKF method uncovers complex variable interactions for scientific discovery.
Graph neural network predicts vehicle interactions and trajectories for autonomous driving.
A graph neural network detects beneficial feature interactions for recommender systems.
We describe and extract time-ordered multibody interactions from complex systems.
Dropout regularizes against high-order interactions by canceling interaction rates.