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 phases identified in neural scaling laws with compute limits.
The study uses the Merton model to estimate PD and finds a phase transition affecting convergence speed.
A learning algorithm optimizes beamforming for holographic transceivers in far-field communication.
The Allen-Cahn system on manifolds yields multiple phase distributions.
Precise scientific analysis in collider-based particle physics is possible because of complex simulations that connect fundamental theories to observable quantities. The significant computational cost of these programs limits the scope, precision, and accuracy of Standard Model measurements and searches for new phenome…
A neural network learns phase space properties for time series analysis.
Existence proved for complex 3-folds equation on a specific phase range.
We employ unsupervised machine learning techniques to learn latent parameters which best describe states of the two-dimensional Ising model and the three-dimensional XY model. These methods range from principal component analysis to artificial neural network based variational autoencoders. The states are sampled using …
We describe a bottom-up framework, based on the identification of appropriate order parameters and determination of phase diagrams, for understanding progressively refined agent-based models and simulations of financial markets. We illustrate this framework by starting with a deterministic toy model, whereby indepe…
Unsupervised learning is a discipline of machine learning which aims at discovering patterns in big data sets or classifying the data into several categories without being trained explicitly. We show that unsupervised learning techniques can be readily used to identify phases and phases transitions of many body systems…
A neural network model predicts the critical point of the Ising phase transition.
The stability of money value is an important requisite for a functioning economy, yet it critically depends on the actions of participants in the market themselves. Here we model the value of money as a dynamical variable that results from trading between agents. The basic trading scenario can be recast into an Ising t…
We present a simple model of firm rating evolution. We consider two sources of defaults: individual dynamics of economic development and Potts-like interactions between firms. We show that such a defined model leads to phase transition, which results in collective defaults. The existence of the collective phase depends…
In this paper we explore the functional correlation approach to operational risk. We consider networks with heterogeneous a-priori conditional and unconditional failure probability. In the limit of sparse connectivity, self-consistent expressions for the dynamical evolution of order parameters are obtained. Under equil…
Study on phase transitions on surfaces using Allen-Cahn equation.
Diffusion maps help learn complex quantum phase transitions from data.
The classification of phase transitions is a central and challenging task in condensed matter physics. Typically, it relies on the identification of order parameters and the analysis of singularities in the free energy and its derivatives. Here, we propose an alternative framework to identify quantum phase transitions,…
We introduce the concept of spontaneous symmetry breaking to arbitrage modeling. In the model, the arbitrage strategy is considered as being in the symmetry breaking phase and the phase transition between arbitrage mode and no-arbitrage mode is triggered by a control parameter. We estimate the control parameter for mom…
We construct a two-parameter covariant differential calculus on the quantum -exterior plane. We also give a deformation of the two-dimensional fermionic phase space.
Estimates spherical functions on SL(3,R) improving previous results.
Quantum phase diagrams for Chern topological insulators show jumps at critical loci.
DP-SGD can update fewer coordinates while maintaining privacy.
Study on estimating signals from shifted and noisy copies in high dimensions, revealing a phase transition.
New model shows natural language exhibits phase transition similar to physics.
Gradient descent dynamics in quadratic regression models are analyzed, revealing five phases: monotonic, catapult, periodic, chaotic, and divergent.
New model explains market dynamics with phase transitions and non-linear interactions.
New method uses transport maps for efficient Bayesian inference.
Bayesian and simulation methods predict credit default probabilities.
In order to model volatile real-world network behavior, we analyze phase-flipping dynamical scale-free network in which nodes and links fail and recover. We investigate how stochasticity in a parameter governing the recovery process affects phase-flipping dynamics, and find the probability that no more than q% of nodes…
We examine the out-of-equilibrium phase reported by Plerou {\it et. al.} in Nature, {\bf 421}, 130 (2003) using the data of the New York stock market (NYSE) between the years 2001 --2002. We find that the observed two phase phenomenon is an artifact of the definition of the control parameter coupled with the nature of …
Algorithm finds frequencies, amplitudes, and phases of sinusoids in noisy data.
Deep learning has become an area of interest in most scientific areas, including physical sciences. Modern networks apply real-valued transformations on the data. Particularly, convolutions in convolutional neural networks discard phase information entirely. Many deterministic signals, such as seismic data or electrica…
We introduce a noncommutative differential calculus on the two-parameter -superplane via a contraction of the (p,q)-superplane. We manifestly show that the differential calculus is covariant under transformations. We also give a two-parameter deformation of the (1+1)-dimensional phase space alge…
Improved neural networks by averaging late-stage weights.
Study uniform rates for estimating Gaussian mixtures without separation assumption.
Continuous phase transitions identified in Doi-Onsager, noisy transformer, and Hegselmann-Krause models.
A time schedule simplifies learning in flow-based models for high-dimensional data.
Autoencoder estimates parameters of noisy, multi-component damped signals.
Paper eliminates warm-up phase for PO in linear MDPs, achieving optimal regret.
We analyze the linear response of a market network to shocks based on the bipartite market model we introduced in an earlier paper, which we claimed to be able to identify the time-line of the 2009-2011 Eurozone crisis correctly. We show that this model has three distinct phases that can broadly be categorized as "stab…
Modeling financial markets with memory using fractional calculus and Brownian motion.
Minimizing non-convex and high-dimensional objective functions is challenging, especially when training modern deep neural networks. In this paper, a novel approach is proposed which divides the training process into two consecutive phases to obtain better generalization performance: Bayesian sampling and stochastic op…
The complex wave representation (CWR) converts unsigned 2D distance transforms into their corresponding wave functions. Here, the distance transform S(X) appears as the phase of the wave function φ(X)---specifically, φ(X)=exp(iS(X)/τwhere τis a free parameter. In this work, we prove a novel result using the higher-orde…
The paper establishes a sub-additive inequality for volume and ε-phase-transition spectra of Riemannian manifolds.
Weight decay stabilizes training dynamics by slowing progressive sharpening.
Improved regret bounds for bandit phase retrieval.
We analyze the stock prices of the S&P market from 1987 until 2012 with the covariance matrix of the firm returns determined in time windows of several years. The eigenvector belonging to the leading eigenvalue (market) exhibits in its long term time dependence a phase transition with an order parameter which can be in…
GE-autoencoder identifies spontaneous symmetry breaking in systems.