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.
A modified GAN improves thermal comfort classification models by balancing imbalanced datasets.
New deep learning model optimizes energy use in buildings.
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…
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…
Increasing energy efficiency in buildings can reduce costs and emissions substantially. Historically, this has been treated as a local, or single-agent, optimization problem. However, many buildings utilize the same types of thermal equipment e.g. electric heaters and hot water vessels. During operation, occupants in t…
The paper optimizes air conditioning setpoints using machine learning.
Transfer learning improves HVAC fault detection with minimal labeled data.
A model used for velocity control during car following was proposed based on deep reinforcement learning (RL). To fulfil the multi-objectives of car following, a reward function reflecting driving safety, efficiency, and comfort was constructed. With the reward function, the RL agent learns to control vehicle speed in …
SECRM-2D improves RL-based autonomous driving with safety guarantees.
ARX models predict thermal behavior of WBG semiconductors accurately.
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…
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.
SBO uses dual voting to build consensus in noisy feedback settings.
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.
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…
Paper uses VAEs to detect radar targets in complex noise.
Study of -theory dual of thermal QCD-like theories at intermediate coupling.
This paper presents a case study of a recommender system that can be used to save energy in smart homes without lowering the comfort of the inhabitants. We present an algorithm that uses consumer behavior data only and uses machine learning to suggest actions for inhabitants to reduce the energy consumption of their ho…
Let be a complex manifold and be an embedding of complex submanifold. Assuming that the embedding is -linearizable or -comfortably embedded, we construct via the deformation to the normal cone a diffeomorphism from a small neighborhood of the zero section in the normal bundle …
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.
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.
In coming years residential consumers will face real-time electricity tariffs with energy prices varying day to day, and effective energy saving will require automation - a recommender system, which learns consumer's preferences from her actions. A consumer chooses a scenario of home appliance use to balance her comfor…
Study examines how twisting graphene nanoribbons affects their thermal conductivity.
Improved person detection in occluded conditions with AOS images.
Future buildings will offer new convenience, comfort, and efficiency possibilities to their residents. Changes will occur to the way people live as technology involves into people's lives and information processing is fully integrated into their daily living activities and objects. The future expectation of smart build…
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.
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…
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 …