New method uses MRI data to improve PET tomography uncertainty quantification.
problem Improving uncertainty quantification in emission tomography with multimodal data.
method Nonparametric posterior learning technique adapted for Poisson-type data.
result Sampling algorithms are scalable, parallelizable, and easy to implement.
The distribution of absorbed dose in radionuclide therapy with Lu177 can be approximated by convolving an image of the time-integrated activity distribution with a dose voxel kernel representing different tissue types. This fast but inaccurate approximation is unsuitable for personalised dosimetry because it negle…
The paper reviews machine learning methods in PET imaging.
problem Improving accuracy in PET attenuation correction and reconstruction.
method Summarizes recent machine learning-based studies in PET imaging.
result Discusses the performance and challenges of current methods.
Imaging-based early diagnosis of Alzheimer Disease (AD) has become an effective approach, especially by using nuclear medicine imaging techniques such as Positron Emission Topography (PET). In various literature it has been found that PET images can be better modeled as signals (e.g. uptake of florbetapir) defined on a…
Attenuation correction is an essential requirement of positron emission tomography (PET) image reconstruction to allow for accurate quantification. However, attenuation correction is particularly challenging for PET-MRI as neither PET nor magnetic resonance imaging (MRI) can directly image tissue attenuation properties…
Deep neural networks improve mean function estimation for functional data.
problem Estimating mean functions of functional data.
method Deep neural networks with ReLU activation, sparsely connected.
result Achieves optimal nonparametric convergence rate in empirical norm.
Study reduces emissions in portfolios with error-prone emissions data.
problem Portfolio optimization with firm-level emissions intensities measured inaccurately.
method Introduced a scope-specific penalty operator to rescale asset payoffs based on revenue-normalized emissions intensity.
result Reduces average Scope~1 emissions intensity by roughly 92% while maintaining similar Sharpe ratios.
Study examines how market dynamics affect emissions trading prices and abatement efforts.
problem Effectiveness of emissions markets depends on regulatory standards, costs, and abatement levels.
method Radner equilibrium framework that considers intertemporal decision-making and uncertainty.
result Variations in regulatory standards, costs, and abatement levels influence allowance prices and abatement efforts.
Machine learning predicts greenhouse gas emissions for undisclosed companies.
problem Lack of GHG emissions data for most companies.
method Trained machine learning model on disclosed data to estimate emissions.
result Model accurately predicts emissions for undisclosed companies.
Optimizes gradual reduction of excess carbon emissions to net-zero.
problem Achieving net-zero carbon emissions through gradual reduction of excess emissions.
method Stochastic control approach to identify optimal emission strategy under constraints.
result Identifies the emission strategy that maximizes future profit from excess emissions.
Optimal dynamic allocation of carbon allowances reduces emissions efficiently.
problem Reducing carbon emissions from firms over time with dynamic allocation and trading.
method Variational approach to solve the Stackelberg game between regulator and firms.
result Optimal policies lead to constant abatement effort and allowance price, outperforming static allocations.
In emissions trading, the initial allocation of permits is an intractable issue because it needs to be essentially fair to the participating countries. There are many ways to distribute a given total amount of emissions permits among countries, but the existing distribution methods, such as auctioning and grandfatherin…
Tackling climate change is at the top of many agendas. In this context, emission trading schemes are considered as promising tools. The regulatory framework for an emission trading scheme introduces a market for emission allowances and creates a need for risk management by appropriate financial contracts. In this work,…
For effective treatment of Alzheimer disease (AD), it is important to identify subjects who are most likely to exhibit rapid cognitive decline. Herein, we developed a novel framework based on a deep convolutional neural network which can predict future cognitive decline in mild cognitive impairment (MCI) patients using…
Model estimates non-reported GHG emissions for companies using machine learning.
problem Incomplete GHG emissions reporting by companies.
method Interpretable machine learning model tailored for non-reporting companies.
result Model accurately estimates emissions for diverse company groups.
Study examines how industrial emissions evolve over time in response to various factors.
problem Understanding how firm-level emissions change over time in response to environmental regulation, economic conditions, and organizational constraints.
method Used a time-varying mean-group estimator to link emissions data with firm characteristics and macroeconomic indicators over 1992-2023.
result Firm-level characteristics and aggregate conditions have different impacts on emissions growth at different times.
Study finds carbon emissions affect stock value, but not bought emissions.
problem Determining if carbon emissions impact stock value and whether this is due to direct or indirect emissions.
method Fixed-effects analysis with propensity score weighting to control for selection bias.
result Firms with higher Scope 1 emissions have a statistically significant positive carbon premium, but Scope 2 emissions do not.
Due to the widespread use of positron emission tomography (PET) in clinical practice, the potential risk of PET-associated radiation dose to patients needs to be minimized. However, with the reduction in the radiation dose, the resultant images may suffer from noise and artifacts that compromise diagnostic performance.…
Paper analyzes how present-bias affects carbon emissions and proposes a method to mitigate it.
problem Present-bias impacts carbon emission patterns towards a net zero target.
method Stochastic control techniques adapted from insurance risk theory.
result Higher present-bias leads to excess emissions, and carbon taxes can reduce emissions but beyond a certain point have diminishing returns.
Study travel time tomography for transversely isotropic media using modified pseudodifferential calculus.
problem Travel time tomography problem for transversely isotropic media.
method Modified scattering pseudodifferential calculus to solve the tomography problem.
result Construction and use of modified pseudodifferential calculus to solve the tomography problem.
Study uses open data to improve traffic emissions estimation.
problem Estimating accurate link level traffic emissions.
method Data-driven framework integrating MOVES, GPS, OSM, and satellite imagery.
result Neural network reduces RMSE by over 50% for key pollutants.
Study finds environmental liability insurance reduces industrial carbon emissions.
problem Reduction of industrial carbon emissions.
method Two-way fixed effect model using provincial (city) level panel data from 2010 to 2020.
result Environmental liability insurance reduces industrial carbon emissions at both direct and indirect levels, with varying effects.
This research uses reinforcement learning to find optimal emission offsets in greenhouse gas markets.
problem Finding optimal emission offsets in greenhouse gas markets to control excess emissions.
method Utilized reinforcement learning, specifically Nash-DQN, to estimate market Nash equilibria.
result Emitting firms can achieve significant financial savings by abiding by the Nash equilibria found in the market.
Summary of tensor tomography proofs on manifolds with boundaries.
problem Proving injectivity of tensor tomography on compact Riemannian manifolds with boundaries.
method Summarized proofs from previous studies.
result Summary of proofs for s-injectivity.
Paper addresses travel time tomography stability and statistical inversion.
problem Determining conformal factors of metrics from geodesic lengths.
method Established forward and inverse stability estimates; applied to Bayesian statistical inversion.
result Consistency of statistical inversion technique for travel time tomography.
A model optimizes carbon emission reduction and allowance purchasing for companies.
problem Optimizing carbon emissions and allowance purchasing for companies.
method Established an optimal control model involving two stochastic processes with two control variables, converted into an HJB equation, proved existence and uniqueness of solution.
result Proved the existence and uniqueness of the solution to the HJB equation.
Positron Emission Tomography (PET) is a functional imaging modality widely used in neuroscience studies. To obtain meaningful quantitative results from PET images, attenuation correction is necessary during image reconstruction. For PET/MR hybrid systems, PET attenuation is challenging as Magnetic Resonance (MR) images…
Study uses machine learning to analyze solar emissions.
problem Explaining the high temperature of the solar corona.
method Unsupervised machine learning to characterize impulsive emissions.
result Characterized over 34,500 features as 2D elliptical Gaussians.
Study shows reducing anthropogenic emissions significantly lowers PM2.5 levels but has little effect on O3 in Delhi.
problem Understanding and mitigating the effects of anthropogenic emissions on air pollution in Delhi.
method Predictive modeling, causal inference, Gaussian Process modeling, Granger causality analysis.
result Reductions in anthropogenic emissions lead to significant decreases in PM2.5 levels but have little effect on O3. Mandatory emission trading schemes are being established around the world. Participants of such market schemes are always exposed to risks. This leads to the creation of an accompanying market for emission-linked derivatives. To evaluate the fair prices of such financial products, one needs appropriate models for the e…
Study uses machine learning to solve photoacoustic tomography's inverse problem.
problem Solving the full inverse problem in photoacoustic tomography.
method Developed an approach using variational autoencoders for Bayesian estimation of the posterior distribution.
result Evaluated the approach with numerical simulations and compared it to a Bayesian solution.
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.
It will be difficult to gain the agreement of all the actors on any proposal for climate change management, if universality and fairness are not considered. In this work, a universal measure of emissions to be applied at the international level is proposed, based on a modification of the Greenhouse Gas Intensity (GHG-I…
Machine learning predicts CO2 emissions in power grids, reducing uncertainty.
problem Forecasting CO2 emission intensities in power grids.
method Developed a machine learning algorithm using LASSO, feature selection, and Softmax weighted average.
result Marginal emissions are independent of DK2 zone conditions, suggesting external generators.
Model predicts methane emissions from oil sands tailing ponds, suggesting significant environmental impact.
problem Estimating methane emissions from inactive oil sands tailing ponds.
method Physics constrained machine learning model using real-time weather data and laboratory experiments.
result Active oil sands tailing ponds emit between 950 to 1500 tonnes of methane per year, equivalent to 6000 gasoline vehicles.
Deep learning speeds up real-time emission monitoring.
problem Real-time greenhouse gas emission monitoring under transient conditions.
method Bayesian inference with deep learning surrogate of CFD outputs.
result Near-real-time predictions with orders-of-magnitude faster runtimes.
These are lecture notes for the course "Analysis and X-ray tomography". The course is a broad overview of various tools in analysis that can be used to study X-ray tomography. The focus is on tools and ideas, not so much on technical details and minimal assumptions. Only very basic functional analysis is assumed as bac…
Sketch Tomography improves quantum state estimation accuracy.
problem Efficiently estimating quantum states, especially MPS states.
method Hybridizes classical shadow protocol with tensor train ansatz.
result Proven convergence with quadratic sample complexity.
Investor and firm optimize sustainable investment and emission reduction through a dynamic game.
problem Optimal sustainable investment and emission reduction in a dynamic game setting.
method Formulated as a nonzero-sum dynamic game, solved via variational inequalities and verified in a diffusive setup.
result Nash equilibria show moving boundaries increasing with emission abatement, triggered by both investor and firm actions.
Deep network improves electrical tomography across multiple frequencies.
problem Nonlinear multi-frequency electrical impedance tomography (mfEIT) for tissue conductivity estimation.
method Integrates graph neural networks (GNNs) into the iterative Proximal Regularized Gauss Newton (PRGN) framework to reconstruct tissue concentrations accurately.
result Accurate reconstruction of overlapping tissue fraction concentrations across multiple frequencies.
We present a new algorithm for identifying the transition and emission probabilities of a hidden Markov model (HMM) from the emitted data. Expectation-maximization becomes computationally prohibitive for long observation records, which are often required for identification. The new algorithm is particularly suitable fo…
We present a novel approach to the pricing of financial instruments in emission markets, for example, the EU ETS. The proposed structural model is positioned between existing complex full equilibrium models and pure reduced form models. Using an exogenously specified demand for a polluting good it gives a causal explan…
Deep learning predicts road GHG emissions with speed, density, and past ERs.
problem Predicting GHG emissions from road networks to mitigate environmental impact.
method Developed a deep learning framework using LSTM networks with exogenous variables.
result LSTM with speed, density, GHG ER, and in-links speed from previous minutes performs best.
Study finds cherry-picking load shaping strategies outperforms others in reducing grid CO2 emissions.
problem Lack of detailed counterfactual data makes it hard to assess load shaping strategies' effectiveness.
method Calibrated granular ERCOT simulations for counterfactual analysis of load shaping strategies.
result LMP-based load shaping outperforms other strategies in reducing grid CO2 emissions.
Model shows how financial markets can decarbonize under climate uncertainty.
problem Decarbonization of financial markets under climate uncertainty.
method Mean-field game approach to model firm decisions and investor interactions.
result Climate uncertainty weakens the impact of green-minded investors on decarbonization.
Electricity accounts for 25% of global greenhouse gas emissions. Reducing emissions related to electricity consumption requires accurate measurements readily available to consumers, regulators and investors. In this case study, we propose a new real-time consumption-based accounting approach based on flow tracing. This…
Study assesses climate risks on supply chains and financial systems using detailed firm emissions data.
problem Lack of firm-level CO2 emissions data hinders assessment of transition risks from carbon pricing.
method Used detailed Hungarian firm emissions data and a simple economic ABM model to simulate carbon pricing impacts.
result 45% of companies are directly exposed to carbon pricing, leading to significant economic and financial losses.
Study reveals inefficiencies in EU carbon trading market.
problem Inefficiencies in carbon trading market undermine emission reduction goals.
method Analysis of granular transaction data from 2005-2020.
result 40% of firms never trade in a given year, and many trade only during high-price months.