Develops algorithms to estimate trends in global temperature variability.
problem Estimating trends in cloud reflectance temperature variability.
method Two novel algorithms for dense, gridded observations over space and time.
result Evaluation of methods with simulated and real-world data.
A spin model relating physical to financial variables is presented. This work is the first to introduce the concept of negative absolute temperature into stock market dynamics by establishing a rigorous formal analogy between physical and financial variables. Based on this model, an algorithm evaluating negative temper…
This paper measures temperature in agent systems using volatility.
problem How to measure temperature in agent systems.
method Examined an agent system with two decision options in a news environment, established the measurement equation, and outlined the concept of temperature measurement.
result Illustrated a strategy for influencing average opinion in competing subsystems.
Researchers derive the relation between temperature and volatility in ideal agent systems.
problem Deriving the exact algebraic relation between temperature and volatility in ideal agent systems.
method Analogy with spin systems from statistical physics.
result Derive the exact algebraic relation between temperature and volatility for an ideal agent system.
Researchers use Gaussian Process Regression to improve accuracy of a low-cost hot-wire anemometer.
problem Improving accuracy of low-cost hot-wire anemometers in varying temperatures.
method Probabilistic calibration using Gaussian Process Regression.
result The method provides good performance in estimating actual wind speeds, including uncertainty.
New model uses heteroscedastic Gaussian process for alkenone SST proxy.
problem Restoring historical sea surface temperatures using proxies.
method Heteroscedastic Gaussian process regression method.
result Nonparametric approach handles variable noise patterns and outliers.
The paper studies structure detection in high-temperature ferromagnetic models.
problem Distinguishing between empty models and models with a specific subgraph structure.
method Matching upper and lower bounds for minimax testing, and computational hardness results.
result Arboricity drives the testability of the problem, and there are no polynomial time tests under certain conditions.
Decadal climate predictions, which are initialized with observed conditions, are characterized by two main sources of uncertainties--internal and model variabilities. Using an ensemble of climate model simulations from the CMIP5 decadal experiments, we quantified the total uncertainty associated with these predictions …
Modeling temperature dynamics for weather derivatives using a novel regime-switching model.
problem Basis risk in weather derivatives due to poor design and modelling of temperature.
method Novel time-varying mean-reversion Lévy regime-switching model.
result The proposed model accurately models deseasonalized temperature data.
Tangent Works won GEFCom 2017 using automatic model building.
problem Forecasting time series with historical temperature shuffling.
method Automatic model building using Tangent Information Modeller (TIM) with historical temperature shuffling and decision on trend variable.
result Automated model building setup won the competition.
Bayesian framework quantifies uncertainty in portfolio temperature alignment.
problem Uncertainty in portfolio temperature alignment models.
method X-Degree Compatibility (XDC) approach with FaIR climate model, adaptive MCMC, deep learning emulator.
result Robust parametric uncertainty quantification for FaIR model.
CNN detects phase transitions in Potts models without prior knowledge.
problem Detecting phase transitions in q-state Potts models using deep learning. method Trained a deep CNN on Ising model spin configurations and temperatures, then tested on Potts model images.
result Deep CNN accurately detects phase transitions in Potts models, including high- and low-temperature regions.
Weather derivatives help farmers hedge against crop yield risks.
problem High basis risks in weather derivatives pricing models.
method Machine learning ensemble technique to determine yield-weather relationships; mean-reverting model with local temperature dependence.
result Average temperature is the most significant weather variable affecting maize yield.
Variational inference (VI) combined with data subsampling enables approximate posterior inference over large data sets, but suffers from poor local optima. We first formulate a deterministic annealing approach for the generic class of conditionally conjugate exponential family models. This approach uses a decreasing te…
Bayesian neural networks use temperature adjustments to improve predictive performance.
problem Lack of theoretical generalization guarantees for Bayesian neural networks.
method Temperature adjustments to balance likelihood and prior regularization.
result Improved predictive performance through temperature adjustments.
VARENN visualizes climate data in 2D images for analysis.
problem Lack of integrated spatiotemporal data in climate models.
method VARENN uses convolutional neural networks to summarize monthly climate data into 2D color images.
result VARENN models accurately classify temperature and precipitation changes.
Partition functions of probability distributions are important quantities for model evaluation and comparisons. We present a new method to compute partition functions of complex and multimodal distributions. Such distributions are often sampled using simulated tempering, which augments the target space with an auxiliar…
Machine learning improves subseasonal forecasts for Western U.S. climate.
problem Improving long-term forecasts for drought and wet weather extremes in the Western U.S.
method Ensemble of two regression models using meteorological data and historical measurements.
result Ensemble skill exceeds top competitor's for temperature and precipitation forecasts.
Model predicts viscosity of multicomponent systems efficiently.
problem Expensive experimental viscosity measurements in various industries.
method Artificial neural networks trained on a database of chemical systems and temperatures.
result Model Viskositas provides more accurate predictions with lower errors, variability, and outliers.
A new model predicts wind speed using multiple meteorological variables.
problem Precise wind speed forecasting for wind power producers and grid operators.
method Multi-variable Stacked Long Short-Term Memory (LSTM) network.
result The proposed MSLSTM model outperforms traditional methods in wind speed prediction.
TSCoNet forecasts correlated geophysical fields with uncertainty estimates.
problem Accurate and reliable forecasts of correlated geophysical fields across many locations.
method Two-stage CNN-LSTM coupled with Gaussian copula.
result Calibrated prediction intervals without sacrificing point accuracy.
Introduces bi-temperature logistic loss for more robust training.
problem Training neural nets with noise robustness.
method Introduces two temperatures into logistic loss and Bregman divergences.
result Training becomes more robust to noise with bi-temperature loss.
Neural networks improve post-processing of ensemble weather forecasts.
problem Improving reliability and accuracy of probabilistic weather forecasts.
method Flexible neural network approach for estimating forecast distribution parameters.
result Neural network approach significantly outperforms traditional methods in 2-meter temperature forecasts.
Combines GANs and EVT for better modeling of spatial climate extremes.
problem Modeling dependencies between climate extremes, especially in high-dimensional spaces.
method Generative Adversarial Networks (GANs) combined with Extreme Value Theory (EVT).
result evtGAN outperforms classical GANs and statistical approaches in modeling spatial extremes.
Temperature scaling improves model uncertainty but not diversity in LLMs.
problem Improving the calibration and stochasticity of probabilistic models.
method Investigates theoretical properties of temperature scaling in classification and LLMs.
result Temperature scaling increases model uncertainty but not diversity in LLMs.
The study optimizes simulated annealing's cooling schedule for better performance.
problem Designing optimal cooling schedules for simulated annealing to improve its performance.
method Analyzed the cooling schedule's impact on simulated annealing's performance and provided sample and simulation complexity results.
result Optimal cooling schedules can be found with a small number of samples, improving the algorithm's runtime or success rate.
Introduce a thermodynamically informed, temperature-transferable MLCG framework for proteins.
problem Temperature transferability of MLCG models for proteins.
method Explicit decomposition of CG potential into energetic and entropic components.
result Reproduces temperature-dependent quantities like heat capacity.
Study local sensitivity of HDD and CDD temperature derivatives prices.
problem Understanding how temperature derivatives prices change with small temperature changes.
method Analyzes sensitivity of HDD and CDD futures and options prices to temperature perturbations using a CAR process.
result Identifies the order of the CAR process and its impact on temperature derivatives prices.
Contrastive learning adapts to data intrinsic dimensions, learning low-dimensional representations.
problem Learning high-dimensional representations from multi-modal data.
method Multi-modal contrastive learning with temperature optimization.
result Contrastive learning adapts to intrinsic dimensions of data, not specified dimensions.
A new method selects optimal temperature for Bayesian Deep Learning.
problem Finding the optimal temperature for improving predictive performance in Bayesian Deep Learning.
method Data-driven approach to estimate temperature as a model parameter.
result Our method performs comparably to grid search but at a fraction of the cost.
Climate volatility reduces economic growth, especially in poorer countries.
problem Impact of climate volatility on economic growth.
method Exploiting data on 133 countries over 59 years, controlling for temperature changes.
result A 1 degree C increase in temperature volatility leads to a 0.3% decline in GDP growth.
New adaptive temperature selection improves parallel tempering efficiency.
problem Enhancing mixing in multi-modal distributions using parallel tempering.
method Adaptive temperature selection using policy gradient approach.
result Lower integrated autocorrelation times achieved compared to traditional methods.
Logit-GFN accelerates GFlowNets training by scaling logits based on temperature.
problem Training temperature-conditional GFlowNets is numerically challenging.
method Logit-GFN uses a learned function of temperature to scale policy logits.
result Logit-GFN greatly accelerates GFlowNets training and improves generalization and mode discovery.
New method learns differential equations from data with hidden variables.
problem Learning differential equations from data with hidden variables.
method Sparse linear regression optimization problem with higher order time derivatives and dictionary of functions.
result High quality short-term forecasts with orders of magnitude faster than competing methods.
MTL improves multi-dimensional regression in luminescence sensing.
problem Challenges in modeling multi-dimensional regression problems with classical methods.
method Multi-task learning (MTL) with feed-forward neural networks (FFNNs).
result MTL allows predicting multiple parameters from a single set of measurements.
Information-theoretic quantities, such as entropy, are used to quantify the amount of information a given variable provides. Entropies can be used together to compute the mutual information, which quantifies the amount of information two variables share. However, accurately estimating these quantities from data is extr…
Attention temperature improves robustness of ICL in high-dimensional settings.
problem ICL robustness failure under distribution shift in high dimensions.
method Analyzed a Transformer with approximate softmax attention, derived a closed-form error expression, and showed optimal temperature minimizes error.
result Optimal attention temperature minimizes ICL generalization error under distribution shift.
The paper analyzes extreme temperature forecasting using machine learning models.
problem Forecasting extreme temperatures in U.S. cities.
method Auto-Regressive Integrated Moving Average, Exponential Smoothing, Multilayer Perceptrons, Gaussian Processes.
result Multilayer Perceptrons were found to be the most effective approach for forecasting extreme temperatures.
We present a novel Metropolis-Hastings method for large datasets that uses small expected-size minibatches of data. Previous work on reducing the cost of Metropolis-Hastings tests yield variable data consumed per sample, with only constant factor reductions versus using the full dataset for each sample. Here we present…
Automated model predicts C. elegans escape behavior.
problem Complex escape behavior of C. elegans due to rising temperature.
method Phenomenological modeling using Sir Isaac dynamical inference platform.
result Inferred model accurately predicts worm behavior and is biologically interpretable.
The paper analyzes the InfoNCE loss under different temperature schedules using Langevin dynamics.
problem Understanding the dynamics of InfoNCE loss under fixed versus annealed temperature schedules.
method Modeling embedding evolution under Langevin dynamics on a compact Riemannian manifold, with theoretical guarantees for convergence.
result Slow logarithmic inverse-temperature schedules ensure convergence to globally optimal representations, while faster schedules risk suboptimal minima.
Embeds spherically symmetric space-times into 5D flat space, matching Hawking temperature.
problem Finding conditions for embedding spherically symmetric space-times into flat 5D space.
method Explicitly constructs embeddings for any spherically symmetric Lorentzian metric in 3+1 dimensions.
result Hawking temperature matches Unruh temperature for Schwarzschild metric.
The Gibbs algorithm's generalization error is bounded, improving with prior volume in low temperatures.
problem Bounding the generalization error of the Gibbs algorithm in low temperature regimes.
method Analyzes the Gibbs algorithm's performance, extending known high-temperature bounds to low-temperature scenarios.
result With high probability, the generalization error decreases with the total prior volume of similar hypotheses.
Optimizes contrastive learning with individualized temperatures for better performance on imbalanced datasets.
problem The common practice of using a global temperature parameter ignores the varying semantic similarity across different anchor data.
method Proposes a new robust contrastive loss inspired by distributionally robust optimization (DRO) and an efficient stochastic algorithm for automatic temperature individualization.
result Our method automatically learns a suitable temperature for each sample, improving performance on imbalanced datasets.
New method resolves nonidentifiability in mixture models.
problem Nonidentifiability in marginal models of mixture models.
method Introducing an effective temperature to generalize the marginal likelihood.
result Maximization of the generalized likelihood leads to unique results.
New method uses RBM flows to find critical temperatures in Ising models.
problem Detecting critical temperatures in RBM flows without model topology information.
method Iterative sampling from RBM mapped on Ising model temperature space using a neural network thermometer.
result Flow of RBM trained on Ising spin configurations approaches critical temperature around kBTc/J≈2.269. The paper prices weather contracts using a complex temperature model.
problem Accurate pricing of weather contracts under temperature dynamics.
method Time-changed Levy model with mean-reverting dynamics, Fourier expansion, Esscher transform.
result An accurate approximation of weather contract prices.
Deep GMRFs improve spatial data modeling and prediction.
problem Modeling spatial dependencies in data.
method Established connection between GMRFs and CNNs, allowing for multi-layer architectures.
result Deep GMRFs outperform state-of-the-art models in satellite temperature prediction.