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.
Paper uses neural networks to analyze oil price impact on Iranian stock and industry indices.
Investment risk on a regulated market is influenced by gold prices and oil trading.
Solves non-Abelian Rainich problem for SU(2) gauge fields.
We investigate relationship between annual electric power consumption per capita and gross domestic production (GDP) per capita for 131 countries. We found that the relationship can be fitted with a power-law function. We examine the relationship for 47 prefectures in Japan. Furthermore, we investigate values of annual…
Paper extends port-Hamiltonian model to include internal energy for compressible and incompressible flow.
This study analyzes global oil trade networks to assess their efficiency and robustness.
New model estimates Gibbs free energies using machine learning and isobaric-isothermal flows.
Negative screening is one method to avoid interactions with inappropriate entities. For example, financial institutions keep investment exclusion lists of inappropriate firms that have environmental, social, and government (ESG) problems. They create their investment exclusion lists by gathering information from variou…
The paper studies scaling limits of Wasserstein metrics on Gaussian mixture models.
New method for selecting clusters in residential electricity data.
Energy consumption in Ecuador has increased significantly during the last decades, affecting negatively the financial position of the country since large energy consumption subsidies are provided in its internal market and Ecuador is mostly a crude oil exporter and oil derivatives importer country. This research seeks …
We introduce and begin the study of new knot energies defined on knot diagrams. Physically, they model the internal energy of thin metallic solid tori squeezed between two parallel planes. Thus the knots considered can perform the second and third Reidemeister moves, but not the first one. The energy functionals consid…
One major hurdle in the road toward a low carbon economy is the present entanglement of developed economies with oil. This tight relationship is mirrored in the correlation between most of economic indicators with oil price. This paper addresses the role of oil compared to the other three main energy commodities -coal,…
Study on existence of ground states on curved spaces with conditions on potential growth.
Gravity derived from thermodynamics via optimal transport.
We address a class of schemes for the Euler equations with the following features: the space discretization is staggered, possible upwinding is performed with respect to the material velocity only and the internal energy balance is solved, with a correction term designed on consistency arguments. These schemes have bee…
ELS framework improves safety alignment by dynamically steering LLMs towards helpful responses.
The paper develops a method to identify LLM-generated text without training.
Theory models nonlinear soft tissue elasticity and remodeling using extended Finsler geometry.
COMPAS recidivism predictions show racial bias against African Americans, study finds.
Recidivism prediction scores are used across the USA to determine sentencing and supervision for hundreds of thousands of inmates. One such generator of recidivism prediction scores is Northpointe's Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) score, used in states like California and F…
New system assigns vehicles to routes for cost and energy efficiency.
Smooth flows for physical systems with smooth energies and forces.
We present an analysis of oil prices in US$ and in other major currencies that diagnoses unsustainable faster-than-exponential behavior. This supports the hypothesis that the recent oil price run-up has been amplified by speculative behavior of the type found during a bubble-like expansion. We also attempt to unravel t…
Differential conservation laws in Lagrangian field theory are usually related to symmetries of a Lagrangian density and are obtained if the Lie derivative of a Lagrangian density by a certain class of vector fields on a fiber bundle vanishes. However, only two field models meet this property in fact. In gauge theory of…
Graph energy helps detect communities in networks better than traditional methods.
The paper deals with the problem of identifying the internal dependencies and similarities among a large number of random processes. Linear models are considered to describe the relations among the time series and the energy associated to the corresponding modeling error is the criterion adopted to quantify their simil…
Modified Gibbs-Helmholtz equation geometric models for thermodynamics.
CNN detects phase transitions in Potts models without prior knowledge.
Moment Pooling reduces latent space dimensions in machine learning models.
Spiking neural networks (SNNs) are distributed trainable systems whose computing elements, or neurons, are characterized by internal analog dynamics and by digital and sparse synaptic communications. The sparsity of the synaptic spiking inputs and the corresponding event-driven nature of neural processing can be levera…
The paper finds shape modes for vortices in a specific sigma model.
Internal Lagrangians derived from variational principles.
Model predicts internal fraud in retail banking is cyclical and influenced by corruption.
It is well-known that the precision of data, hyperparameters, and internal representations employed in learning systems directly impacts its energy, throughput, and latency. The precision requirements for the training algorithm are also important for systems that learn on-the-fly. Prior work has shown that the data and…
FEPS models agents to learn and act in complex environments without deep neural networks.
We study warped compactifications of string/M theory with the help of effective potentials, continuing previous work of the last two authors and Michael R. Douglas presented in arXiv:1206.1885. The dynamics of the conformal factor of the internal metric, which is responsible for instabilities in these constructions, is…
In this paper, we provide an integrated systems modeling approach to analyzing global externalities from a microeconomic perspective. Various forms of policy (fiscal, monetary, etc.) have addressed flaws and market failures in models, but few have been able to successfully eliminate modern externalities that remain an …
A key question for machine learning approaches in particle physics is how to best represent and learn from collider events. As an event is intrinsically a variable-length unordered set of particles, we build upon recent machine learning efforts to learn directly from sets of features or "point clouds". Adapting and spe…
Neural networks are based on a simplified model of the brain. In this project, we wanted to relax the simplifying assumptions of a traditional neural network by making a model that more closely emulates the low level interactions of neurons. Like in an RNN, our model has a state that persists between time steps, so tha…
Examines international taxation's impact on Georgian businesses.
Neural network architectures are at the core of powerful automatic speech recognition systems (ASR). However, while recent researches focus on novel model architectures, the acoustic input features remain almost unchanged. Traditional ASR systems rely on multidimensional acoustic features such as the Mel filter bank en…
The paper develops models to understand sensory coding and cortical topography.
The discovery of novel materials and functional molecules can help to solve some of society's most urgent challenges, ranging from efficient energy harvesting and storage to uncovering novel pharmaceutical drug candidates. Traditionally matter engineering -- generally denoted as inverse design -- was based massively on…
Optimized execution model using interbank and internal liquidity.
While the fundamental object in Riemannian geometry is a metric, closed string theories call for us to put a two-form gauge field and a scalar dilaton on an equal footing with the metric. Here we propose a novel differential geometry which treats the three objects in a unified manner, manifests not only diffeomorphism …
This study assesses how economic shocks affect the efficiency and robustness of international pesticide trade networks.
A new training method for efficient Boltzmann generators.