arXiv research

Study finds almost contact structures in thermal QCD-like theories at intermediate coupling.

Study of ${\cal M}$-theory dual of thermal QCD-like theories at intermediate coupling.

Supervised learning with a deep convolutional neural network is used to identify the QCD equation of state (EoS) employed in relativistic hydrodynamic simulations of heavy-ion collisions from the simulated final-state particle spectra $ρ(p_T,Φ)$. High-level correlations of $ρ(p_T,Φ)$ learned by the neural network act a…

Geometric QCD framework establishes stable vacuum for quark confinement.

We show that the baryon number of N=2 supersymmetric QCD can be twisted in order to couple the topological field theory of non-abelian monopoles to $Spin^c$-structures. To motivate the construction, we also consider some aspects of the twisting procedure as a gauging of global currents in two and four dimensions, in pa…

Compactification of AdS5 allows studying meson behavior in QCD.

ARX models predict thermal behavior of WBG semiconductors accurately.

In this paper the dynamics of the classical chiral $QCD_{2}$ currents is studied. We describe how the dynamics of the theory can be summarized in an equation of the Lax form, thereby demonstrating the existence of an infinite set of conserved quantities. Next, the $r$ matrix of a fundamental Poisson relation is obtaine…

The study applies wealth thermalization hypothesis to social networks and explains inequality.

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…

New method uses neural maps to efficiently sample lattice QCD distributions.

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…

Generative thermal design learns optimal shapes using multi-agent reinforcement learning.

A modified GAN improves thermal comfort classification models by balancing imbalanced datasets.

This work generates synthetic 3D thermal facial data using 2D facial data and deep learning.

Jets from boosted heavy particles have a typical angular scale which can be used to distinguish them from QCD jets. We introduce a machine learning strategy for jet substructure analysis using a spectral function on the angular scale. The angular spectrum allows us to scan energy deposits over the angle between a pair …

Paper addresses LSTM stability for thermal systems using infinity-norm.

CNN accurately reconstructs lattice topology with strong thermal fluctuations.

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…

Recent progress in applying machine learning for jet physics has been built upon an analogy between calorimeters and images. In this work, we present a novel class of recursive neural networks built instead upon an analogy between QCD and natural languages. In the analogy, four-momenta are like words and the clustering…

Geometric QHD tests improve hub detection in correlated data.

Paper uses VAEs to detect radar targets in complex noise.

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 …

Thermal dynamics modeling has been a critical issue in building heating, ventilation, and air-conditioning (HVAC) systems, which can significantly affect the control and maintenance strategies. Due to the uniqueness of each specific building, traditional thermal dynamics modeling approaches heavily depending on physics…

Study chaotic dynamics in social stratification models leading to thermalization and turbulence.

Thermalizer stabilizes autoregressive models for long-term predictions in chaotic systems.

We obtain the best known quantitative estimates for the $L^p$-Poincaré and log-Sobolev inequalities on domains in various sub-Riemannian manifolds, including ideal Carnot groups and in particular ideal generalized H-type Carnot groups and the Heisenberg groups, corank $1$ Carnot groups, the Grushin plane, and various H…

Study examines how twisting graphene nanoribbons affects their thermal conductivity.

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…

Non-parametric estimators improve quickest changepoint detection under irregular sequence lengths.

Many four-dimensional supersymmetric compactifications of F-theory contain gauge groups that cannot be spontaneously broken through geometric deformations. These "non-Higgsable clusters" include realizations of $SU(3)$, $SU(2)$, and $SU(3) \times SU(2)$, but no $SU(n)$ gauge groups or factors with $n> 3$. We study poss…

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 formalism solves kinematical constraints in curved backgrounds and non-trivial states.

Study examines how economic policy uncertainty impacts stock markets.

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.

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…

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…

Deep learning enhances Hamiltonian Monte Carlo for sampling gauge field configurations.

Dual ML approach predicts peak temperatures in AFSD, improving process optimization.

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 …