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.
We present and study a Minority Game based model of a financial market where adaptive agents -- the speculators -- interact with deterministic agents -- called producers. Speculators trade only if they detect predictable patterns which grant them a positive gain. Indeed the average number of active speculators grows wi…
LNK improves uncertainty estimation for molecular dynamics, reducing errors by up to 2.5 times.
Improved sampling for gauge theory with SNFs.
The distribution of wealth among the members of a society is herein assumed to result from two fundamental mechanisms, trade and investment. An empirical distribution of wealth shows an abrupt change between the low-medium range, that may be fitted by a non-monotonic function with an exponential-like tail such as a Gam…
This work explains crises in markets without external news using bounded rational agents.
In this communication, complex systems with a near trivial dynamics are addressed. First, under the hypothesis of equiprobability in the asymptotic equilibrium, it is shown that the (hyper) planar geometry of an -dimensional multi-agent economic system implies the exponential (Boltzmann-Gibss) wealth distribution an…
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 …
Unbiased wealth exchanges always lead to inequality.
Stochastic normalizing flows improve lattice field theory simulations.
This Colloquium reviews statistical models for money, wealth, and income distributions developed in the econophysics literature since the late 1990s. By analogy with the Boltzmann-Gibbs distribution of energy in physics, it is shown that the probability distribution of money is exponential for certain classes of models…
Using the mechanics of creep in material sciences as a metaphor, we present a general framework to understand the evolution of financial, economic and social systems and to construct scenarios for the future. In a nutshell, highly non-linear out-of-equilibrium systems subjected to exogenous perturbations tend to exhibi…
Machine learning helps create accurate models of neutron star postmerger signals.
A popular approach for predicting the future of dynamical systems involves mapping them into a lower-dimensional "latent space" where prediction is easier. We show that the information-theoretically optimal approach uses different mappings for present and future, in contrast to state-of-the-art machine-learning approac…
Spectral analysis detects structural changes in financial networks.
A probing scheme is considered with an accessible and controllable qubit, used to probe an out-of equilibrium system consisting of a second qubit interacting with an environment. Quantum spontaneous synchronization between the probe and the system emerges in this model and, by tuning the probe frequency, can occur both…
Although classical economic theory is based on the concept of stable equilibrium, real economic systems appear to be always out of equilibrium. Indeed, they share many of the dynamical features of other complex systems, e.g., ecological food-webs. We focus on the relation between increasing complexity of the economic n…
The mutual fund industry manages about a quarter of the assets in the U.S. stock market and thus plays an important role in the U.S. economy. The question of how much control is concentrated in the hands of the largest players is best quantitatively discussed in terms of the tail behavior of the mutual fund size distri…
Proposes linking energy and force uncertainty in deep learning potentials.
Stochastic gradient descent (SGD) is widely believed to perform implicit regularization when used to train deep neural networks, but the precise manner in which this occurs has thus far been elusive. We prove that SGD minimizes an average potential over the posterior distribution of weights along with an entropic regul…
Agent Based Modeling (ABM) has become a widespread approach to model complex interactions. In this chapter after briefly summarizing some features of ABM the different approaches in modeling spatial interactions are discussed. It is stressed that agents can interact either indirectly through a shared environment and/or…
AI simplifies trading strategies, potentially making markets more efficient.
We introduce an evolutionary game with feedback between perception and reality, which we call the reality game. It is a game of chance in which the probabilities for different objective outcomes (e.g., heads or tails in a coin toss) depend on the amount wagered on those outcomes. By varying the `reality map', which rel…
Economic integration, globalization and financial crises represent examples of processes whose understanding requires the analysis of the underlying network structure. Of particular interest is establishing whether a real economic network is in a state of (quasi)stationary equilibrium, i.e. characterized by smooth stru…
We introduce a minimal agent-based model to qualitatively conceptualize the allocation of limited wealth among more abundant opportunities. We study the interplay of power, satisfaction and frustration in distribution, concentration, and inequality of wealth. Our framework allows us to compare subjective measures of fr…
Study links cognitive effort to thermodynamic principles, optimizing decision-making.
New method reconstructs non-equilibrium stochastic systems from data.
New link detection results using closures of 3-braids.
Deep learning improves anomaly detection across various fields.
Graph energy helps detect communities in networks better than traditional methods.
Monitoring gas turbine combustors health, in particular, early detecting abnormal behaviors and incipient faults, is critical in ensuring gas turbines operating efficiently and in preventing costly unplanned maintenance. One popular means of detecting combustor abnormalities is through continuously monitoring exhaust g…
New ML-based detection improves PMH signal detection in load-modulated MIMO systems.
Develops slope detection for 3-manifolds with torus boundaries.
2DSig-Detect detects adversarial perturbations in images.
Develops a method to detect changes in linear systems with temporal correlations.
Tackles the computational hardness of HPC detection, conjecturing equivalence to PC detection.
Real-time fuel leakage detection framework MOCPD improves accuracy.
Study on detecting hierarchical community structures in networks.
Deep object detection improves mitotic nucleus detection in breast cancer biopsies.
ECAD detects anomalies without data exchangeability, improving traffic flow detection.
Autonomous driving requires 3D perception of vehicles and other objects in the in environment. Much of the current methods support 2D vehicle detection. This paper proposes a flexible pipeline to adopt any 2D detection network and fuse it with a 3D point cloud to generate 3D information with minimum changes of the 2D d…
PAC-Wrap provides provable guarantees for semi-supervised anomaly detection.
This paper offers a distribution-free method for post-detection changepoint localization.
The study defines backdoor detection in ML and proves its infeasibility.
Detects data drift in deep learning models using neural embeddings.
It follows from earlier work of Silver-Williams and the authors that twisted Alexander polynomials detect the unknot and the Hopf link. We now show that twisted Alexander polynomials also detect the trefoil and the figure-8 knot, that twisted Alexander polynomials detect whether a link is split and that twisted Alexand…
This research identifies flaws in drift detection methods and creates adversarial data streams to exploit them.
Multimodal deep learning improves flaw detection in software programs.
New method detects OOD samples using neural network trajectories.