BESS shows potential in European markets for frequency support, but not for energy arbitrage.
problem Lack of profitability for BESS in energy arbitrage in most European markets.
method Proposed a general payoff model for BESS operation and calculated utilization factors for common applications.
result BESS shows higher potential in providing frequency support services, especially in Central Western and Northern Europe.
In statistical physics, the conservation of particle number results in the equalization of the chemical potential throughout a system at equilibrium. In contrast, the homogeneity of utility in socio-economic models is usually thought to rely on the competition between individuals, leading to Nash equilibrium. We show t…
CCN estimates full potential outcome distributions without restrictive assumptions.
problem Estimating CATE is insufficient; full potential outcome distributions provide greater insights.
method Collaborating Causal Networks (CCN) learns full potential outcome distributions without restrictive assumptions.
result CCN learns distributions that asymptotically capture true potential outcome distributions.
This study found significant asymmetry between potential maximum gain and loss in asset returns, improving predictability and utility for investors.
problem Understanding the economic value of price extremes in asset returns.
method Decomposing asset returns into PMG and PML, analyzing relationships and asymmetry, and testing predictive power.
result Significant asymmetry between PMG and PML, improving asset return predictability and utility for investors.
Study examines barriers to grid-connected battery systems in Spain, finding high cycle cost remains main obstacle.
problem Barriers to grid-connected battery systems in Spain's deregulated electricity market.
method Utilization analysis and concept of 'potentially profitable utilization time' introduced.
result High cycle cost remains the main barrier for grid-connected battery systems in Spain.
Paper solves investment problem with transaction costs using spectral method.
problem Optimal investment problem with transaction costs under potential utility.
method Spectral numerical method applied to a reformulated parabolic double obstacle problem.
result Spectral method proves more efficient for high precision solutions.
Algorithmic recommendation systems can homogenize user behavior, reducing utility.
problem Algorithmic feedback loops homogenize user behavior in recommendation systems.
method Simulations of recommendation systems using confounded data.
result Using confounded data decreases utility without increasing diversity.
The paper tackles optimal policy learning with asymmetric counterfactual utilities in healthcare decisions.
problem Learning optimal policies from observed data with asymmetric counterfactual utilities.
method The approach involves identifying and minimizing the maximum expected utility loss using statistical decision theory and solving intermediate classification problems.
result One can learn minimax loss decision rules from observed data.
Develops a framework for optimal investment in assets with different liquidity constraints.
problem Optimal investment-consumption problem for a utility-maximizing investor with lower-bound constraints.
method Generalized martingale approach and decomposition of the problem into subproblems.
result Explicit formulas for optimal strategies derived for power-utility functions.
Unified framework for imbalanced data resampling improves classification performance.
problem Data imbalance negatively impacts machine learning performance.
method Unified framework combining over- and undersampling with radial basis functions optimization.
result Potential Anchoring outperforms state-of-the-art resampling algorithms.
The paper examines utility maximization in markets with hidden Gaussian drift, finding restrictions on model parameters.
problem Utility maximization problems in markets with hidden Gaussian drift mean-reverting processes.
method Derives sufficient conditions for bounded maximum expected utility of terminal wealth for models with full and partial information.
result Restrictions on model parameters for bounded maximum expected utility.
Novel approach to estimate P300 BCI efficiency using SNR.
problem Improving the accuracy of P300 BCI for severely disabled people.
method Introduced a novel approach considering P300 SNR for estimating efficiency, using a Gaussian noise model.
result P300 SNR significantly correlates with spelling accuracy, improving BCI efficiency.
Proposes LatLapMED for detecting high-utility anomalies.
problem Detecting statistically rare instances with real-world significance.
method Uses EM algorithm to combine entropy minimization and maximum entropy discrimination.
result Superior performance over existing anomaly detection methods.
Prediction markets show considerable promise for developing flexible mechanisms for machine learning. Here, machine learning markets for multivariate systems are defined, and a utility-based framework is established for their analysis. This differs from the usual approach of defining static betting functions. It is sho…
Proposes privacy-preserving sensor data transformations to prevent user re-identification and sensitive activity inference.
problem Privacy threats from shared sensor data and potential user re-identification.
method Mechanisms to transform sensor data to eliminate patterns for re-identification and sensitive activity inference, while maintaining minor utility loss.
result Reduced user re-identification accuracy to random guess level and prevented inference of sensitive activities.
This paper extends the classical consumption and portfolio rules model in continuous time (Merton 1969, 1971) to the framework of decision-makers with time-inconsistent preferences. The model is solved for different utility functions for both, naive and sophisticated agents, and the results are compared. In order to so…
The paper introduces metrics to rank potential outcomes for better decision-making.
problem Optimal action selection in uncertain situations using causal reasoning.
method Introducing two new metrics: probabilities of potential outcome ranking (PoR) and probability of achieving the best potential outcome (PoB). Establishing identification theorems and deriving bounds for these metrics, and presenting estimation methods.
result The estimators' finite-sample properties and their application to a real-world dataset are demonstrated.
Investigations have been performed into using clustering methods in data mining time-series data from smart meters. The problem is to identify patterns and trends in energy usage profiles of commercial and industrial customers over 24-hour periods, and group similar profiles. We tested our method on energy usage data p…
The paper tackles fair decision-making with imperfect labels.
problem Predictive models learn from biased data due to selective labeling.
method Proposes learning decision policies that maximize utility under fairness constraints.
result Learning to decide improves fairness and utility compared to traditional risk minimization.
Econometrics is based on the nonempiric notion of utility. Prices, dynamics, and market equilibria are supposed to be derived from utility. Utility is usually treated by economists as a price potential, other times utility rates are treated as Lagrangians. Assumptions of integrability of Lagrangians and dynamics are im…
Researchers develop variational bounds for perceptron learning from structured data.
problem Learning from structured data with concave utilities and log-concave priors.
method Variational approach combining interpolation method, log-concavity, and concentration estimates.
result Lower and upper minimax variational bounds match, identifying the solution of the model.
We utilize the Ozsvath-Szabo contact invariant to detect the action of involutions on certain homology spheres that are surgeries on symmetric links, generalizing a previous result of Akbulut and Durusoy. Potentially this may be useful to detect different smooth structures on 4-manifolds by cork twisting operation.
RUMBoost combines RUMs and deep learning for better choice modelling.
problem Creating interpretable and robust discrete choice models.
method Gradient Boosted Regression Trees for utility functions, with constraints for interpretability and monotonicity.
result RUMBoost outperforms ML and RUM benchmarks in predictive performance and interpretability.
The 1/3 Financial Rule helps prevent household bankruptcy through balanced spending, savings, and debt repayment.
problem Reducing household bankruptcy risk through effective financial planning.
method Mathematical modeling, game theory, behavioral finance, and technological analysis.
result The 1/3 Financial Rule emerges as a robust solution for supporting household financial stability.
The paper solves an insurance problem using mean-variance and rank-dependent utility theory.
problem Formulating and solving an insurance problem with rank-dependent utility and mean-variance premium principle.
method Formulated as a non-concave maximization problem, then turned into a concave quantile optimization problem, solved using calculus of variations.
result An optimal insurance contract is derived and numerically computed.
Paper proves anisotropic Minkowski inequality and related inequalities.
problem Proving anisotropic Minkowski inequality and related inequalities.
method Utilizes a nonlinear potential theoretic approach.
result Sharp anisotropic Minkowski inequality and related inequalities proved.
This paper introduces a new potential function using Tsallis entropy for neural network optimization.
problem The challenge of obtaining exponential convergence in neural network optimization.
method Utilizes a linearized potential function based on Csiszár type of Tsallis entropy.
result Derives an exponential convergence result in neural network optimization.
This paper considers mean-variance optimization under uncertainty, specifically when one desires a sparsified set of optimal portfolio weights. From the standpoint of a Bayesian investor, our approach produces a small portfolio from many potential assets while acknowledging uncertainty in asset returns and parameter es…
Cardinality potentials are a generally useful class of high order potential that affect probabilities based on how many of D binary variables are active. Maximum a posteriori (MAP) inference for cardinality potential models is well-understood, with efficient computations taking O(DlogD) time. Yet efficient marginalizat…
Proposes φ-balancing for more balanced expert utilization in MoE models.
problem Balanced expert utilization in MoE models to avoid bias.
method Directly targets population-level balance by minimizing a convex potential function.
result Consistently outperforms prior methods in stability and effectiveness.
New method reduces privacy impact on model accuracy for underrepresented groups.
problem Privacy mechanisms disproportionately affect underrepresented groups in machine learning models.
method Proposes DPSGD-F, a modified DPSGD that adjusts group contributions based on clipping bias.
result DPSGD-F removes disparate impact of differential privacy on model accuracy for protected groups.
The paper solves a thermodynamics problem about crystal shape.
problem Understanding if minimizing free energy with convex potential and mass constraint generates a convex crystal.
method Utilized a stability theorem, convexity, and a new maximum principle approach to prove a three-dimensional convexity theorem.
result Completely settled the Almgren problem in R3 under generic conditions. This paper reviews forecast combinations over 50 years, highlighting their evolution and utility.
problem Improving forecast accuracy through combining multiple forecasts.
method Evolution of forecast combination methods, from simple to sophisticated.
result Forecast combinations have become a mainstream approach in forecasting.
A novel framework combines deep metric learning and conditional random field for hyperspectral image classification.
problem Improving classification performance in hyperspectral image processing with limited labeled data.
method Combines spectrum-based deep metric learning and conditional random field. Uses center loss for spectrum-based features and Gaussian edge potentials for pixel-wise classification.
result Demonstrates advantages in classification accuracy and computation cost compared to classical methods.
Adaptive Stress Testing detects financial fraud by simulating potential failures.
problem Detecting and mitigating vulnerabilities in financial systems.
method Developed a simplified model using historical data and reinforcement learning.
result Identified the most likely path to system failure and improved fraud detection.
PDBAL targets experiments for probabilistic models to maximize insights.
problem Designing experiments to yield valuable insights efficiently.
method Combines user-specified risk function with probabilistic model to adaptively choose designs.
result PDBAL consistently outperforms standard approaches in simulations and real-world drug screen data.
Reinsurance can help life insurers maintain higher capital guarantees without losing utility.
problem Decreasing capital guarantees in life insurance products.
method Dynamic investment-reinsurance optimization problem with simultaneous Value-at-Risk and no-short-selling constraints. Introduced guarantee-equivalent utility gain for comparison.
result Optimally managed reinsurance allows insurers to offer higher capital guarantees without reducing expected utility.
New conditional risk measures called conditional generalized quantiles defined and characterized.
problem Developing new risk measures for dynamic risk assessment.
method Propose and characterize conditional generalized quantiles using expected utility model and equivalent conditions.
result Characterized conditional generalized quantiles as well-defined and equivalent to a conditional first order condition.
Bitcoin has emerged as a fascinating phenomenon of the financial markets. Without any central authority issuing the currency, it has been associated with controversy ever since its popularity and public interest reached high levels. Here, we contribute to the discussion by examining potential drivers of Bitcoin prices …
Deep learning predicts protein-small molecule binding.
problem Insufficient benchmark datasets for structure-based virtual screening.
method Learnable atom convolution, non-linear transformation, inner-product for binding potential prediction.
result New benchmark dataset improves testing of structure-based virtual screening methods.
Predict real-time crash risks during hurricane evacuations using connected vehicle data.
problem Mitigate crash risks during hurricane evacuations by predicting high-risk locations.
method Used connected vehicle data to predict crash risks in real-time, considering weather and traffic features.
result Gaussian Process Boosting and Extreme Gradient Boosting models performed best, with recall of 0.91.
Blockchain fan tokens boost sports fan engagement by 50%.
problem Low fan engagement in sports decisions.
method Analyzed 3,576 fan token polls to identify determinants of participation.
result Fan tokens engage 4,003 participants per poll, 50% of token holders.
Study finds 'happiness' search data predicts stock returns, suggesting utility needs impact firm performance.
problem Investing in firms that meet societal utility needs.
method Used Google Trends data on 'happiness' search volume to predict stock returns.
result Happiness search exposure (HSE) explains future stock returns, particularly for big and value firms.
We develop a scale-invariant truncated Lévy (STL) process to describe physical systems characterized by correlated stochastic variables. The STL process exhibits Lévy stability for the probability density, and hence shows scaling properties (as observed in empirical data); it has the advantage that all moments are fini…
A new method removes biases in data integration by using surrogate control outcomes.
problem Data integration methods can be biased due to data-dependent processes.
method Post-integrated inference method using surrogate control outcomes to account for latent heterogeneity.
result The method provides consistent and efficient estimators under minimal assumptions and potential misspecifications.
Study fixed angle inverse scattering with Riemannian metrics, proving uniqueness and stability.
problem Inverse scattering problem with Riemannian metrics.
method Direct problem via progressing wave expansion; inverse problem using symmetry assumptions.
result Uniqueness and stability results for inverse scattering with Riemannian metrics.
New PFL method addresses counterparty risk challenges.
problem Challenges with PFE for counterparty limits.
method Introducing PFL combining ES and LGD, and variants aPFL and paPFL.
result PFL and variants provide a better measure of counterparty risk.
FairJob dataset for job recommendations in advertising, preserving fairness and utility.
problem Fairness in online job recommendations for advertising.
method Anonymized dataset with proxy for sensitive attributes, fairness metric computation.
result Demonstrated potential improvements in fairness with trade-offs in utility.