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.
The study applies wealth thermalization hypothesis to social networks and explains inequality.
Deep learning adapts HVAC models to new buildings.
Study chaotic dynamics in social stratification models leading to thermalization and turbulence.
CNN accurately reconstructs lattice topology with strong thermal fluctuations.
Learning influence pathways of a network of dynamically related processes from observations is of considerable importance in many disciplines. In this article, influence networks of agents which interact dynamically via linear dependencies are considered. An algorithm for the reconstruction of the topology of interacti…
Power and thermal management are critical components of High-Performance-Computing (HPC) systems, due to their high power density and large total power consumption. The assessment of thermal dissipation by means of compact models directly from the thermal response of the final device enables more robust and precise the…
Study examines how twisting graphene nanoribbons affects their thermal conductivity.
Paper addresses LSTM stability for thermal systems using infinity-norm.
ARX models predict thermal behavior of WBG semiconductors accurately.
Study examines how economic policy uncertainty impacts stock markets.
Modeling buildings' heat dynamics is a complex process which depends on various factors including weather, building thermal capacity, insulation preservation, and residents' behavior. Gray-box models offer a causal inference of those dynamics expressed in few parameters specific to built environments. These parameters …
The study investigates noise effects on parameter estimation for Ornstein-Uhlenbeck processes.
Study on uniquely determining thermal properties from boundary temperature and heat flux measurements.
With the fast growth in the visual surveillance and security sectors, thermal infrared images have become increasingly necessary ina large variety of industrial applications. This is true even though IR sensors are still more expensive than their RGB counterpart having the same resolution. In this paper, we propose a d…
Generative thermal design learns optimal shapes using multi-agent reinforcement learning.
A modified GAN improves thermal comfort classification models by balancing imbalanced datasets.
We present an agent behavior based microscopic model that induces jumps, spikes and high volatility phases in the price process of a traded asset. We transfer dynamics of thermally activated jumps of an unexcited/ excited two state system discussed in the context of quantum mechanics to agent socio-economic behavior an…
Study finds almost contact structures in thermal QCD-like theories at intermediate coupling.
This work generates synthetic 3D thermal facial data using 2D facial data and deep learning.
On curved spaces, viscous fluids reach equilibrium quickly.
The world GDP distribution is described using thermodynamics principles.
The thermal subsystem of the Mars Express (MEX) spacecraft keeps the on-board equipment within its pre-defined operating temperatures range. To plan and optimize the scientific operations of MEX, its operators need to estimate in advance, as accurately as possible, the power consumption of the thermal subsystem. The re…
Paper uses VAEs to detect radar targets in complex noise.
Study of -theory dual of thermal QCD-like theories at intermediate coupling.
Personal income distribution in the USA has a well-defined two-class structure. The majority of population (97-99%) belongs to the lower class characterized by the exponential Boltzmann-Gibbs ("thermal") distribution, whereas the upper class (1-3% of population) has a Pareto power-law ("superthermal") distribution. By …
Paper uses deep learning to improve thermal-hydraulic simulations.
Thermalizer stabilizes autoregressive models for long-term predictions in chaotic systems.
As energy markets begin clearing at sub-hourly rates, their interaction with load control systems becomes a potentially important consideration. A simple model for the control of thermal systems using market-based power distribution strategies is proposed, with particular attention to the behavior and dynamics of elect…
Method infers causal structure from system behaviors using RKHS and kernel -machines.
We show that the dynamical equations describing the collective behavior of the model introduced by Cavagna et al. i) are not their Eqs. (5,6) but rather ii) are the same as those of the minority game (MG). As a consequence the analytic solution of the MG presented in [PRL, 84, 1824 (2000)] holds also for this model. Fi…
The large thermal capacity of buildings enables heating, ventilating, and air-conditioning (HVAC) systems to be exploited as demand response (DR) resources. Optimal DR of HVAC units is challenging, particularly for multi-zone buildings, because this requires detailed physics-based models of zonal temperature variations…
Improved person detection in occluded conditions with AOS images.
Thermal preferences vary from person to person and may change over time. The main objective of this paper is to sequentially pose intelligent queries to occupants in order to optimally learn the indoor air temperature values which maximize their satisfaction. Our central hypothesis is that an occupant's preference rela…
New approach connects quantum phases to VQA trainability, enabling better scaling.
We introduce a novel non-parametric methodology to test for the dynamical time evolution of the lag-lead structure between two arbitrary time series. The method consists in constructing a distance matrix based on the matching of all sample data pairs between the two time series. Then, the lag-lead structure is searched…
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…
Combines ocean surface and interior data to study ocean dynamics.
New MBL hidden Born machine learns various tasks.
New framework reveals thermodynamic principles for LLM training.
In the emerging advancement in the branch of autonomous robotics, the ability of a robot to efficiently localize and construct maps of its surrounding is crucial. This paper deals with utilizing thermal-infrared cameras, as opposed to conventional cameras as the primary sensor to capture images of the robot's surroundi…
New method predicts heat load in thermal grids using latent variables.
New formalism solves kinematical constraints in curved backgrounds and non-trivial states.
Machine learning models accurately predict molecular magnetic anisotropy tensors.
Generalizes embedding formalism for CFTs on curved backgrounds.
Meta-learning algorithms prepare quantum Gibbs states efficiently for NISQ devices.
Recent results and interpretations are presented for the thermal minority game, concentrating on deriving and justifying the fundamental stochastic differential equation for the microdynamics.
Quantum annealers aim at solving non-convex optimization problems by exploiting cooperative tunneling effects to escape local minima. The underlying idea consists in designing a classical energy function whose ground states are the sought optimal solutions of the original optimization problem and add a controllable qua…
We consider the problem of optimal trading for a power producer in the context of intraday electricity markets. The aim is to minimize the imbalance cost induced by the random residual demand in electricity, i.e. the consumption from the clients minus the production from renewable energy. For a simple linear price impa…