Machine learning detects underwater gas leaks.
problem Early detection of gas leaks in underwater reservoirs.
method Machine learning and Passive Acoustic Monitoring (PAM).
result Classification algorithms achieve good performance in detecting leaks.
In this paper, we first give the regular point reduction and the two types of Hamilton-Jacobi equation for a regular controlled Hamiltonian (RCH) system with symmetry and momentum map on the generalization of a semidirect product Lie group. Next, as an application of the theoretical results, we consider the underwater …
Noncritical soft-faults and model deviations are a challenge for Fault Detection and Diagnosis (FDD) of resident Autonomous Underwater Vehicles (AUVs). Such systems may have a faster performance degradation due to the permanent exposure to the marine environment, and constant monitoring of component conditions is requi…
New mortgage contracts reduce underwater default by adjusting loan balances, but must balance prepayment incentives.
problem Underwater default incentives in mortgages.
method Analyzes automatic balance adjustment and prepayment penalties in mortgage contracts.
result Automatic balance adjustments are preferable to traditional contracts at certain spreads, reducing underwater default.
MC-pix2pix generates high-quality synthetic sonar data for ATR systems.
problem Generating realistic synthetic sonar data for ATR systems.
method Markov Conditional pix2pix (MC-pix2pix) method.
result MC-pix2pix-generated data is almost indistinguishable from real sonar data.
This paper investigates the effect of leak in spiking neural networks.
problem Comparative analysis of leaky and non-leaky spiking neuron models.
method Experimental and frequency domain analysis of spiking neural networks.
result Leaky spiking neuron models provide improved robustness and generalization but decrease sparsity of computation.
Deep learning detects schools of herring from echograms.
problem Manual interpretation of echograms is time-consuming and inconsistent.
method Deep learning framework for automatic detection.
result Deep learning outperforms traditional machine learning.
Current auto loans converge to super-prime credit despite remaining underwater.
problem Inefficient consumer behavior in auto loans leading to suboptimal credit risk.
method Large-sample statistical hypothesis test on transition matrix between risk bands.
result All current risk bands converge to super-prime credit, despite remaining underwater.
We present a method for Temporal Difference (TD) learning that addresses several challenges faced by robots learning to navigate in a marine environment. For improved data efficiency, our method reduces TD updates to Gaussian Process regression. To make predictions amenable to online settings, we introduce a sparse app…
How to self-localize large teams of underwater nodes using only noisy range measurements? How to do it in a distributed way, and incorporating dynamics into the problem? How to reject outliers and produce trustworthy position estimates? The stringent acoustic communication channel and the accuracy needs of our geophysi…
Optimizes UUV hull design with a two-orders-of-magnitude speedup.
problem Designing efficient underwater vehicle hulls using CFD simulations.
method Bayesian Optimization-LCB algorithm and DNN-based surrogate model.
result Two-orders-of-magnitude speedup in design optimization process.
Framework improves marine mammal monitoring in noisy underwater environments.
problem Underwater bioacoustic monitoring challenges due to overlapping calls and variable noise.
method Multi-step attention-guided framework with segmentation and mid-level fusion.
result Improved signal discrimination, reduced false positives, reliable representations.
This paper presents the R package GAS for the analysis of time series under the Generalized Autoregressive Score (GAS) framework of Creal et al. (2013) and Harvey (2013). The distinctive feature of the GAS approach is the use of the score function as the driver of time-variation in the parameters of nonlinear models. T…
Fault diagnosis method for refrigerant leaks using scaling law.
problem Difficulty in improving fault-detection model generalization due to complex system configuration and insufficient data.
method Derive a scaling law based on physical modeling and control mechanism, apply to other systems without modification.
result Scaling exponents of different air-conditioning systems are equivalent, indicating the proposed method's applicability.
GAS models have been recently proposed in time-series econometrics as valuable tools for signal extraction and prediction. This paper details how financial risk managers can use GAS models for Value-at-Risk (VaR) prediction using the novel GAS package for R. Details and code snippets for prediction, comparison and back…
For a given real generic curve $\ga: S^1\to \Bbb {RP}^n$ let $D_\ga$ denote the ruled hypersurface in RPn consisting of all osculating subspaces to $\ga$ of codimension 2. A curve $\ga: S^1\to \Bbb {RP}^n$ is called convex if the total number of its intersection points (counted with multiplicities) with any h…
Green bond leaks impact equity markets, altering investor reactions.
problem Green bond leaks affect equity market reactions.
method Identified 259 instances of pre-announcement leaks in 2,036 green bond headlines.
result News leaks significantly alter equity trading dynamics and investor reactions.
This paper analyzes Ethereum's gas fees and their derivatives, providing a comprehensive model.
problem Understanding and predicting gas fees on the Ethereum blockchain.
method Analyzed Ethereum's gas fee structure and used a fractional Ornstein-Uhlenbeck process to model gas prices.
result A model for pricing and trading gas fee derivatives to hedge against volatility.
Geodesic X-ray transform proves injective for smooth one-forms on gas giant manifolds.
problem Injectivity of geodesic X-ray transform for one-forms on specific manifolds.
method Pestov identity and asymptotic analysis of short geodesics.
result Geodesic X-ray transform is solenoidally injective for smooth one-forms on gas giant manifolds.
Deep learning improves combustor anomaly detection in gas turbines.
problem Improving anomaly detection performance in gas turbine combustors.
method Hierarchically learned features from exhaust gas temperature sensor measurements using deep learning.
result Deep learning-based anomaly detection significantly improved combustor anomaly detection performance.
Study uses AUVs and RL to map river plumes over multiple days.
problem Long-term mapping of dynamic river plumes with multiple AUVs.
method Multi-agent reinforcement learning with spatiotemporal GPR.
result Multi-agent approach outperforms single-agent and benchmarks.
Study the Hessian geometry of an ideal gas in a centrifuge.
problem Understanding the Hessian geometry of an ideal gas in a centrifuge.
method Investigate the Hessian geometry associated with an ideal gas in a spherical centrifuge, using the action of the Euclidean rotation group.
result The Hessian geometry of a spherical rigid body is isometric to a hyperbolic space in the high angular velocity limit.
The prediction of the gas production from mature gas wells, due to their complex end-of-life behavior, is challenging and crucial for operational decision making. In this paper, we apply a modified deep LSTM model for prediction of the gas flow rates in mature gas wells, including the uncertainties in input parameters.…
Introduces GA-P/E, a growth-adjusted stock valuation measure.
problem Evaluating stock value and predicting future returns.
method Computes a payback period adjusted for earnings growth, using a sorted portfolio methodology.
result Low GA-P/E stocks outperform high GA-P/E stocks in absolute and risk-adjusted returns.
Improved GAS models using trees and forests for better forecasts.
problem Improving forecasts from GAS models to avoid curse of dimensionality.
method Localized parameters using decision trees and random forests.
result Significantly outperform baseline GAS model in empirical analyses.
Improved gas demand forecasting using ensemble methods.
problem Short-term prediction of gas demand components.
method Nine base forecasters (Ridge Regression, GP, NN, ANN, Torus, LASSO, Elastic Net, RF, SVR) and four ensemble predictors (simple, weighted, subset, SVR aggregation) were evaluated.
result Ensemble predictors outperformed individual base forecasters and TSO predictions.
Paper improves gas species identification in complex mixtures using neural networks.
problem Identifying gas species in multi-gas mixtures with high accuracy.
method Multi-label neural networks with optimal thresholding for IR spectroscopy.
result Optimal thresholding improves classification performance over conventional methods.
This study compares GNNs and GA-MLPs, finding GA-MLPs can distinguish graphs but not count walks.
problem Comparing expressive power and graph isomorphism testing capabilities of GNNs and GA-MLPs.
method GA-MLPs augment node features with multi-hop operators and apply MLPs node-wise; GNNs are compared as a baseline.
result GA-MLPs can distinguish almost all non-isomorphic graphs but cannot count attributed walks, unlike GNNs.
Autonomous systems can be used to search for sparse signals in a large space; e.g., aerial robots can be deployed to localize threats, detect gas leaks, or respond to distress calls. Intuitively, search algorithms may increase efficiency by collecting aggregate measurements summarizing large contiguous regions. However…
Paper proposes GAS-ALD model for financial risk prediction.
problem Skewed distribution of financial return data.
method Generalized autoregressive score (GAS) framework with asymmetric Laplace distribution (ALD).
result GAS-ALD model predicts VaR and ES more accurately than traditional models.
Optimizes routing in decentralized exchanges with gas fees.
problem Routing in decentralized exchanges with fixed gas fees.
method General optimization framework with mixed-integer model, incorporating gas fees.
result Explicit Karush-Kuhn-Tucker system linking prices, fees, and activation.
We calculate the free energy of Coulomb gas systems on Riemann surfaces.
problem Analyzing the free energy of Coulomb gas systems on Riemann surfaces.
method Using bosonization formula and analytic torsion, we derive the asymptotic expansion of the partition function.
result We prove the geometric version of the Zabrodin-Wiegmann conjecture in the determinantal case.
Study the geometry of gas giant planets to infer their internal structure.
problem Determine the interior structure of gas giant planets using boundary data.
method Geometric analysis of Riemannian manifolds with conformal blow-up at the boundary.
result The interior structure of a gas giant is uniquely determined by different types of boundary data.
GAS-Norm improves deep learning time series forecasting in non-stationary settings.
problem Deep learning models struggle with non-stationary time series data.
method Combines GAS model for adaptive normalization with deep neural networks.
result Improves deep learning performance in 21 out of 25 settings.
Modeling gas fee competition in decentralized exchanges to optimize arbitrage profits.
problem Gas fees and transaction ordering in decentralized exchanges create arbitrage opportunities.
method Developed a first equilibrium model of gas fee competition between two arbitrageurs under three transaction reversion settings.
result Mixed equilibria exist, and their characteristics depend on inventory risk and transaction settings.
The study finds a long-term relationship between Dubai crude oil and US natural gas prices.
problem Examining the relationship between Dubai crude oil and US natural gas prices.
method Used unit root and cointegration tests, ARDL cointegration technique, and Toda-Yamamoto causality test.
result There is a long-run relationship with unidirectional causality from Dubai crude oil to US natural gas.
New algorithms allow multiple robots to search efficiently without central coordination.
problem Efficiently locating targets in an unknown environment with multiple robots.
method SPATS and LATSI algorithms for asynchronous multi-agent active search.
result Multiple robots can independently make data-collection decisions without a central coordinator.
In this work we analyse a stochastic control problem for the valuation of a natural gas power station while taking into account operating characteristics. Both electricity and gas spot price processes exhibit mean-reverting spikes and Markov regime-switches. The Levy regime-switching model incorporates the effects of d…
Paper proposes a framework to identify and obfuscate sensitive features via information density estimation.
problem Identifying and protecting sensitive attributes from leakage in obfuscation mechanisms.
method Information density estimation to identify leaking features, followed by a targeted obfuscation mechanism.
result Proven leakage guarantee in terms of Eγ-divergence for the obfuscation mechanism. We consider the ideal-gas models of trading markets, where each agent is identified with a gas molecule and each trading as an elastic or money-conserving (two-body) collision. Unlike in the ideal gas, we introduce saving propensity λ of agents, such that each agent saves a fraction λ of its money and trades with t…
We consider the ideal-gas models of trading markets, where each agent is identified with a gas molecule and each trading as an elastic or money-conserving (two-body) collision. Unlike in the ideal gas, we introduce saving propensity λ of agents, such that each agent saves a fraction λ of its money and trades with t…
Proposes a learned Bayesian Cramér-Rao bound for unknown measurement models.
problem Computing the Bayesian Cramér-Rao bound requires full knowledge of priors and measurement distributions.
method Introduces a Physics-encoded score neural network to learn priors and measurements.
result Demonstrates improved sample complexity and interpretability through domain knowledge incorporation.
Embeddings leak sensitive information about input data, which can be recovered or inferred.
problem Information leakage in embedding models.
method Developed three classes of attacks to study information leakage.
result Embeddings leak sensitive information about input data, which can be partially recovered or inferred.
Blockchain scaling reduces gas fees, allowing more frequent liquidity updates and concentration.
problem Adverse selection risk and high gas fees on decentralized exchanges.
method Instrumental variables analysis using blockchain scaling solutions (Arbitrum, Polygon) as instruments.
result Higher repositioning intensity and precision lead to greater liquidity concentration, benefiting small trades.
Model forecasts natural gas consumption with Fourier series and feedback.
problem Forecast natural gas consumption for risk minimization.
method Modulated Fourier series with temperature deviations, day-ahead feedback.
result Model outperforms time series methods for long-term projections.
Paper introduces a new volatility model for natural gas markets and discusses swing option pricing.
problem Modeling price and storage dynamics in natural gas markets with path-dependent volatility.
method Developed a novel stochastic path-dependent volatility model and used deep learning for swing option pricing.
result Proposed a deep learning method for numerical approximations of swing option pricing.
How does dynamic price information flow among Northern European electricity spot prices and prices of major electricity generation fuel sources? We use time series models combined with new advances in causal inference to answer these questions. Applying our methods to weekly Nordic and German electricity prices, and oi…
Paper uses neural networks to predict NOx emissions from gas turbines.
problem Predicting NOx emissions from degrading gas turbines.
method Applied neural network algorithm to model NOx emissions from nine process variables.
result Neural network model optimizes process variables for minimal NOx emissions.