arXiv research

This paper measures temperature in agent systems using volatility.

Machine learning and deep learning infer surface/groundwater exchange from temperature data.

The paper prices weather contracts using a complex temperature model.

Investigates how extreme temperature events affect global equity portfolios.

The minute fluctuations of of S&P 500 and NASDAQ 100 indices display Boltzmann statistics over a wide range of positive as well as negative returns, thus allowing us to define a {\em market temperature} for either sign. With increasing time the sharp Boltzmann peak broadens into a Gaussian whose volatility $σ$ measure…

Researchers use Gaussian Process Regression to improve accuracy of a low-cost hot-wire anemometer.

Model uncertainty obtained by variational Bayesian inference with Monte Carlo dropout is prone to miscalibration. The uncertainty does not represent the model error well. In this paper, temperature scaling is extended to dropout variational inference to calibrate model uncertainty. Expected uncertainty calibration erro…

TemperatureGAN generates hourly atmospheric temperature data with high fidelity.

Understanding local currents in the North Atlantic region of the ocean is a key part of modelling heat transfer and global climate patterns. Satellites provide a surface signature of the temperature of the ocean with a high horizontal resolution while in situ autonomous probes supply high vertical resolution, but horiz…

We find necessary and sufficient conditions for existence of a locally isometric embedding of a vacuum space-time into a conformally-flat 5-space. We explicitly construct such embeddings for any spherically symmetric Lorentzian metric in $3+1$ dimensions as a hypersurface in $R^{4, 1}$. For the Schwarzschild metric the…

Quantum computing at room temperature achieves high accuracy in image classification.

Over the last 23 years, the U.S. Securities and Exchange Commission has required over 34,000 companies to file over 165,000 annual reports. These reports, the so-called "Form 10-Ks," contain a characterization of a company's financial performance and its risks, including the regulatory environment in which a company op…

New method calibrates classifier probabilities with guaranteed coverage.

The paper tackles sampling from Gibbs measures with constrained support, providing a sampling guarantee.

ARX models predict thermal behavior of WBG semiconductors accurately.

FAKI improves gradient-free inference for inverse problems.

In the present paper and the companion paper [9] a probabilistic (statistical-mechanical) approach to the construction of canonical metrics on a complex algebraic varieties X is introduced, by sampling "temperature deformed" determinantal point processes. The main new ingredient is a large deviation principle for Gibbs…

Water managers in the western United States (U.S.) rely on longterm forecasts of temperature and precipitation to prepare for droughts and other wet weather extremes. To improve the accuracy of these longterm forecasts, the U.S. Bureau of Reclamation and the National Oceanic and Atmospheric Administration (NOAA) launch…

Recently, Deep Neural Networks (DNNs) have been achieving impressive results on wide range of tasks. However, they suffer from being well-calibrated. In decision-making applications, such as autonomous driving or medical diagnosing, the confidence of deep networks plays an important role to bring the trust and reliabil…

Gas demand forecasting is a critical task for energy providers as it impacts on pipe reservation and stock planning. In this paper, the one-day-ahead forecasting of residential gas demand at country level is investigated by implementing and comparing five models: Ridge Regression, Gaussian Process (GP), k-Nearest Neigh…

Study on convergence of exponential probability measures with applications to maximum entropy models and SGLD.

Paper develops a new method to improve model calibration under distribution shifts.

A new topology design improves zero-shot classification performance in contrastive learning.

Study convergence of simulated annealing in continuous and discrete settings.

Machine learning identifies melting points in thermocouples for automatic calibration.

Method reveals dissimilarity in alloys' Curie temperatures.

Temperature scaling improves model uncertainty but not diversity in LLMs.

Introduce a thermodynamically informed, temperature-transferable MLCG framework for proteins.

Study reveals structural differences in financial networks near and far from crises using balance theory.

Study local sensitivity of HDD and CDD temperature derivatives prices.

We introduce a temperature into the exponential function and replace the softmax output layer of neural nets by a high temperature generalization. Similarly, the logarithm in the log loss we use for training is replaced by a low temperature logarithm. By tuning the two temperatures we create loss functions that are non…

A new method selects optimal temperature for Bayesian Deep Learning.

Study on uniquely determining thermal properties from boundary temperature and heat flux measurements.

Climate volatility reduces economic growth, especially in poorer countries.

New adaptive temperature selection improves parallel tempering efficiency.

Logit-GFN accelerates GFlowNets training by scaling logits based on temperature.

Attention temperature improves robustness of ICL in high-dimensional settings.

Trends in terrestrial temperature variability are perhaps more relevant for species viability than trends in mean temperature. In this paper, we develop methodology for estimating such trends using multi-resolution climate data from polar orbiting weather satellites. We derive two novel algorithms for computation that …

Stochastic model prices weather derivatives for Indian states, highlighting temperature volatility impacts.

The paper analyzes extreme temperature forecasting using machine learning models.

The paper analyzes the InfoNCE loss under different temperature schedules using Langevin dynamics.

The study optimizes simulated annealing's cooling schedule for better performance.

The Gibbs algorithm's generalization error is bounded, improving with prior volume in low temperatures.

Optimizes contrastive learning with individualized temperatures for better performance on imbalanced datasets.

New method uses RBM flows to find critical temperatures in Ising models.

Algorithm learns Sherrington-Kirkpatrick model parameters at low temperatures.

Develops a new stochastic volatility model for temperature derivatives.

Study shows sample complexity for logistic regression with normal covariates.