The paper studies ABSDEs with jumps and various growth drivers.
problem Solving ABSDEs with specific growth conditions.
method Proves existence of unique solution for ABSDEs with jumps and various growth drivers.
result Existence of unique solution for ABSDEs with specific growth conditions.
We consider the problem of numerical approximation for forward-backward stochastic differential equations with drivers of quadratic growth (qgFBSDE). To illustrate the significance of qgFBSDE, we discuss a problem of cross hedging of an insurance related financial derivative using correlated assets. For the convergence…
Develops geometric BSDEs for modeling dynamic return risk measures.
problem Modeling continuous-time dynamic return risk measures.
method Introduces and develops Geometric Backward Stochastic Differential Equations (GBSDEs) and two-driver BSDEs.
result Establishes existence, regularity, uniqueness, and stability of solutions to GBSDEs.
This article proposes a new approximation scheme for quadratic-growth BSDEs in a Markovian setting by connecting a series of semi-analytic asymptotic expansions applied to short-time intervals. Although there remains a condition which needs to be checked a posteriori, one can avoid altogether time-consuming Monte Carlo…
We investigate a class of quadratic-exponential growth BSDEs with jumps. The quadratic structure introduced by Barrieu & El Karoui (2013) yields the universal bounds on the possible solutions. With local Lipschitz continuity and the so-called A_gamma-condition for the comparison principle to hold, we prove the existenc…
Paper classifies economic states and optimizes portfolios for stagflationary environments.
problem Economic uncertainty and stagflationary conditions.
method Mathematical techniques for analyzing multivariate time series, economic driver analysis, self-similarity identification, and portfolio optimization.
result Constructs economic state classifications and computes economic state integrals.
We study an optimal consumption and investment problem in a possibly incomplete market with general, not necessarily convex, stochastic constraints. We give explicit solutions for investors with exponential, logarithmic and power utility. Our approach is based on martingale methods which rely on recent results on the e…
Company mergers and acquisitions are often perceived to act as catalysts for corporate growth in free markets systems: it is conventional wisdom that those activities lead to better and more efficient markets. However, the broad adoption of this perception into corporate strategy is prone to result in a less diverse an…
Tech sector decouples from non-tech sectors post-2015, predicting economic growth.
problem Understanding the relationship between technology and economic growth.
method ARIMA modeling, stationarity tests, data wrangling, exploratory data analysis.
result The technology sector decouples from non-technology sectors post-2015 and predicts economic growth.
Paper predicts driver actions using deep neural networks.
problem Predicting driver actions for safer driving.
method Formulated as timeseries anomaly prediction, uses DBRNN to learn correlations.
result Achieves accurate action prediction up to 5 seconds in advance.
In that paper, we provide a new characterization of the solutions of specific reflected backward stochastic differential equations (or RBSDEs) whose driver g is convex and has quadratic growth in its second variable: this is done by introducing the extended notion of g-Snell enveloppe. Then, in a second step, we re…
This paper optimizes portfolios using path signatures, revealing trade-offs and structural results.
problem Optimizing portfolios using path signatures and estimating their expected values.
method Path Portfolio Optimization framework, using linear functionals of signature coordinates and truncated tensor algebra.
result Empirical findings show a dimensional trade-off in portfolio optimization, with gains in the symmetric block.
Method compares driver performance using behavioral advantage, removing environmental factors.
problem Challenging to analyze human driver or autonomous vehicle performance quantitatively.
method Uses driver behavioral advantage to compare performance across different environmental conditions.
result Evaluated and ranked the performance of over 100 truck drivers in terms of fuel efficiency.
New method identifies drivers from car logs without reverse-engineering CAN protocol.
problem Identifying drivers from in-vehicle network logs without access to exact signal semantics.
method Machine learning techniques applied to off-the-shelf data.
result Driver re-identification accuracy of 75-85% on a dataset of 33 drivers.
Method learns driver behavior from synthetic data.
problem Learning driver behavior from unobservable variables.
method Simultaneous policy learning and latent state inference.
result Effective driving policies learned without direct class knowledge.
Optimizes e-hailing drivers' passenger seeking to reduce congestion and pollution.
problem Reduces congestion and pollution by optimizing e-hailing drivers' passenger seeking.
method Uses Markov Decision Process (MDP) and imitation learning to model and optimize drivers' decisions.
result Achieves a 17.5% improvement in passenger return rate over a heuristic strategy.
Improved driver identification accuracy using steering wheel data.
problem Accurately identifying drivers based on naturalistic driving behavior.
method Novel approach for window length parameter design, leveraging GRUs neural network.
result Increased driver identification accuracy from under 15% to over 65%.
The paper proposes a machine learning method to detect drivers' affective states using physiological signals.
problem Detecting and assessing drivers' affective states to improve driving safety and well-being.
method Multiview multi-task machine learning approach using physiological signals.
result Accounting for drive-specific differences significantly improves model performance.
The paper identifies drivers from a single car turn using sensor data.
problem Predicting driver identity from a single car turn using sensor data.
method Time series classification of sensor readings from a single turn, focusing on unique patterns in each driver's style.
result Accurate identification of drivers from a single turn, even in varied driving conditions.
CPCMs integrate causal drivers for robust portfolio optimization.
problem Degradation of classical portfolio models under structural breaks and lack of arbitrage consistency in machine learning.
method Causal PDE-Control Models integrating structural causal drivers, nonlinear filtering, and forward-backward PDE control.
result CPCM solvers achieve higher Sharpe ratios and lower turnover than benchmarks.
Optimizes portfolios using neural network approximations of asset sensitivities to common drivers.
problem Optimizing portfolios with complex asset dynamics and common drivers.
method Model asset dynamics with PDEs, approximate sensitivities with neural networks, and use hierarchical clustering on sensitivity matrix for optimization.
result Achieves over-performance in portfolio optimization across various markets and datasets.
TNDE quantifies dynamic gene drivers from single-cell snapshots.
problem Reconstructing time-resolved regulatory effects in biological processes.
method Time-varying Network Driver Estimation (TNDE) using shared graph attention encoder and partial optimal transport.
result TNDE identifies stage-specific driver genes in mouse erythropoiesis.
Deep learning method uses asymptotic expansion to solve high-dimensional BSDEs faster.
problem Solving high-dimensional BSDEs efficiently.
method Asymptotic expansion as prior knowledge in deep learning for BSDEs.
result Significantly reduces loss function and accelerates convergence.
ProMoD models human race drivers with probabilistic movement primitives and neural networks.
problem Challenging task of modeling human driver behavior due to variability and complexity.
method Modular framework with Probabilistic Movement Primitives, clothoids, and neural networks.
result Significant advantages in imitation accuracy and robustness compared to other algorithms.
Paper proposes personalized climate control for driver comfort.
problem Limited research on in-vehicle climate control and driver preferences.
method IoT platform for data collection, machine learning for driver behavior recognition, and personalized preference recommendation.
result Prototype demonstrates effective and accurate climate control for driver comfort.
This paper optimizes driver repositioning using MARL and reward design for better service and traffic management.
problem Unserved passenger requests due to drivers' cruising behavior during passenger seeking.
method Mean field multi-agent reinforcement learning (MARL) with a reward design scheme and Bayesian optimization (BO) to solve bilevel optimization problems.
result Optimal toll charges and service charges can improve platform and city planner objectives by significant margins, leading to better traffic conditions.
Deep RL tackles fleet management and dispatching for ride-sharing platforms.
problem Optimizing dispatching and repositioning of drivers in ride-sharing platforms.
method Deep reinforcement learning approach treating drivers as a central system agent.
result Centralized decision-making improves overall fleet efficiency.
The expression "wage transition" refers to the fact that over the past two or three decades in all developed economies wage increases have levelled off. There has been a widening divergence and decoupling between wages on the one hand and GDP per capita on the other hand. Yet, in China wages and GDP per capita climbed …
Deep neural network detects driver intentions from video.
problem Detecting driver intentions for safer self-driving.
method Uses deep learning to analyze turn signals and emergency flashers.
result High per-frame accuracy in challenging scenarios.
Study uses LCRN to detect driver distraction from EEG signals.
problem Improving road safety by detecting driver distraction.
method Used a Long-term Recurrent Convolutional Network (LCRN) for EEG-based driver distraction detection.
result LCRN model outperformed state-of-the-art TSC models in detecting driver distraction.
Modeling tech transfer to explain convergence in Central and Eastern Europe.
problem Understanding mechanisms of technological diffusion in developing economies.
method Introducing a herding-based mechanism to model technological adoption and productivity growth.
result Explicit analytical solution showing nonlinear convergence to a moving frontier.
Study evaluates valuation models for UK companies using case studies.
problem Determining how accounting numbers affect business value.
method Comprehensive review of three valuation models: FCFVM, REVM, AEGM.
result Accounting numbers through valuation models can affect business value.
We use the P&L on a particular class of swaps, representing variance and higher moments for log returns, as estimators in our empirical study on the S&P500 that investigates the factors determining variance and higher-moment risk premia. This class is the discretisation invariant sub-class of swaps with Neuberger's agg…
A deep learning system detects more types of driver distractions with high accuracy.
problem Detecting various types of driver distractions to reduce road accidents.
method Genetically-weighted ensemble of convolutional neural networks.
result Achieves 90% accuracy in detecting more types of distractions than existing methods.
Paper tackles drowsy driving by learning from weakly labeled car acceleration data.
problem Lack of labeled data for estimating driver drowsiness.
method Weakly supervised learning, scalable stochastic optimization.
result Algorithm learns from weakly labeled data, outperforming baseline methods.
Model market driver impact on volatility in derivatives markets.
problem Large trader concentrations affect derivative pricing and volatility nonlinearly.
method Modified Heston's stochastic volatility model with market driver.
result Derives a new PDE for valuing derivatives products.
Financial investment returns lead to growing wealth inequality.
problem Recent rise in wealth inequality in active financial markets.
method Minimalist modelling strategy combining financial markets, wealth accumulation, and compound interest.
result Accumulated financial investment returns cause ever-increasing wealth concentration and inequality.
EBMAL improves regression for driver drowsiness estimation from EEG.
problem Optimally selecting EEG samples for offline regression models.
method Enhanced batch-mode active learning (EBMAL) for regression.
result EBMAL achieves better regression performance for driver drowsiness estimation.
Study on price formation among investors with exponential utility and liabilities.
problem Equilibrium price formation among investors with heterogeneous risk-averseness and liabilities.
method Mean-field game theory and mean-field backward stochastic differential equations (BSDE).
result Existence of equilibrium risk-premium process and market clearing in the large population limit.
Study finds color-coded DMSs improve driver behavior.
problem Improving driver behavior through dynamic message signs.
method Random forest algorithm for route diversion, choice, and compliance analyses.
result Color-coded DMSs are more effective than alphanumeric ones.
Facial landmark localization and occlusion estimation for driver safety.
problem Robust facial landmark localization and occlusion estimation under harsh lighting and occlusion.
method Occluded Stacked Hourglass approach based on Stacked Hourglass network.
result State-of-the-art results in face detection, head pose, and occlusion estimation on various datasets.
This paper evaluates financial competitiveness of Indian real estate companies using entropy method.
problem Improving financial competitiveness of Indian real estate companies in a competitive market.
method Financial competitiveness evaluation index system using key financial ratios and a scoring system.
result Companies with high scores have strong profitability and operational capacity, while those with lower scores struggle with solvency and working capital.
Study dynamic portfolio choice under rotating drivers, revealing a new geometric structure.
problem Investment under changing drivers with mutual independence.
method Analyzes geometric structure of portfolio choice, focusing on drivers and their rotation.
result Optimal policy separates into static and hedging components, reflecting the dynamic nature of drivers.
Bitcoin's price direction is better predicted without additional drivers during high volatility.
problem Predicting Bitcoin's price direction using various determinants.
method Continuous local transfer entropy for feature selection and deep learning classification model.
result Bitcoin's price direction can be better predicted without additional drivers during high volatility.
Modeling driver behavior using GPS data and HDP split-merge sampling.
problem Understanding individual driver behaviors and road network from GPS data.
method Hidden Markov Model (HMM) and Hierarchical Dirichlet Process (HDP) with split-merge sampling.
result Data-driven predictions about destinations and road conditions.
DA-RNN predicts driving maneuvers up to 3 seconds ahead.
problem Adapting driving model to new drivers and vehicles.
method Domain-Adversarial Recurrent Neural Network (DA-RNN) for robust predictions.
result DA-RNN improves performance by 30% in real drivers and 114% in simulations.
A scoring method for driving safety using trajectory data.
problem Managing traffic safety through driver behaviors and violations.
method Extract driving habits and violations from trajectories, train a model, score drivers.
result Proves the effectiveness of the scoring method using traffic simulation.
Modeling driver trajectories using inverse reinforcement learning and random utility.
problem Modeling rational driver behavior in road networks from sparse sensor data.
method Apply random utility theory to model unknown reward function, introduce extended state, and use Markov decision process.
result Maximum entropy inverse reinforcement learning is a special case of the proposed approach.