arXiv research

Investigates how extreme temperature events affect global equity portfolios.

Monitoring gas turbine combustors health, in particular, early detecting abnormal behaviors and incipient faults, is critical in ensuring gas turbines operating efficiently and in preventing costly unplanned maintenance. One popular means of detecting combustor abnormalities is through continuously monitoring exhaust g…

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 present a novel unsupervised deep learning approach that utilizes the encoder-decoder architecture for detecting anomalies in sequential sensor data collected during industrial manufacturing. Our approach is designed not only to detect whether there exists an anomaly at a given time step, but also to predict what wi…

EagleEye detects localized density anomalies in multivariate data.

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 …

The coefficient of the logarithmic term in the entropy on even spheres is re-computed by the local technique of integrating the finite temperature energy density up to the horizon on static d--dimensional de Sitter space and thence finding the entropy by thermodynamics. Numeric evaluation yields the known answer i.e. (…

Study improves accuracy of weather data for real-time building simulations.

We consider the problem of signal recovery on graphs as graphs model data with complex structure as signals on a graph. Graph signal recovery implies recovery of one or multiple smooth graph signals from noisy, corrupted, or incomplete measurements. We propose a graph signal model and formulate signal recovery as a cor…

Temperature scaling improves model uncertainty but not diversity in LLMs.

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

Study combines variational inference and transformers for seasonal climate predictions.

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.

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.

This paper measures temperature in agent systems using volatility.

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 …

The paper analyzes extreme temperature forecasting using machine learning models.

Framework reconstructs missing spatio-temporal data for extreme value prediction.

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

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.

The paper prices weather contracts using a complex temperature model.

Algorithm learns Sherrington-Kirkpatrick model parameters at low temperatures.

Develops a new stochastic volatility model for temperature derivatives.

A scalable Gaussian process clustering method for large datasets.

Study shows sample complexity for logistic regression with normal covariates.

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…

Improves anomaly detection with contaminated unlabeled data.

In the spirit of behavioral finance, we study the process of opinion formation among investors using a variant of the 2D Voter Model with a tunable social temperature. Further, a feedback acting on the temperature is introduced, such that social temperature reacts to market imbalances and thus becomes time dependent. I…

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…

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

Researchers derive the relation between temperature and volatility in ideal agent systems.

Deep RL detects anomalies from few labeled examples and large unlabeled data.

We propose a supervised anomaly detection method for data with inexact anomaly labels, where each label, which is assigned to a set of instances, indicates that at least one instance in the set is anomalous. Although many anomaly detection methods have been proposed, they cannot handle inexact anomaly labels. To measur…

Recent semi-supervised anomaly detection methods that are trained using small labeled anomaly examples and large unlabeled data (mostly normal data) have shown largely improved performance over unsupervised methods. However, these methods often focus on fitting abnormalities illustrated by the given anomaly examples on…

New method estimates Nishimori temperature for node classification in weighted graphs.

A new method assigns anomaly scores to features for better interpretation.

Although deep learning has been applied to successfully address many data mining problems, relatively limited work has been done on deep learning for anomaly detection. Existing deep anomaly detection methods, which focus on learning new feature representations to enable downstream anomaly detection methods, perform in…

Cold posteriors improve Bayesian neural networks by reducing overestimation of aleatoric uncertainty.

Temperature scaling fails for distributions with class overlaps, while Mixup improves calibration.

We present a model of financial markets originally proposed for a turbulent flow, as a dynamic basis of its intermittent behavior. Time evolution of the price change is assumed to be described by Brownian motion in a power-law potential, where the `temperature' fluctuates slowly. The model generally yields a fat-tailed…

Improved PDMP samplers for multi-modal distributions using tempering.