The paper reviews trajectory data applications for road transportation agencies.
problem Lack of understanding of trajectory data value for transportation agencies.
method Literature review and case study in Maryland.
result Illustrates applications of trajectory data in six areas of road transportation.
The paper improves traffic volume estimation using neural networks and vehicle probe data.
problem Accurately estimating historical traffic volumes between sparse sensors.
method Combines neural networks with existing profiling method using vehicle probe data.
result Proposed approach yields 24% more accurate estimates than volume profiles.
Framework classifies urban congestion into recurrent and non-recurrent types.
problem Classifying urban congestion into recurrent and non-recurrent types.
method Real-time distributed classification using VANET on a heterogeneous urban road network.
result Predictive accuracy of 89.17% for the boosting technique.
Managing large-scale transportation infrastructure projects is difficult due to frequent misinformation about the costs which results in large cost overruns that often threaten the overall project viability. This paper investigates the explanations for cost overruns that are given in the literature. Overall, four categ…
A new hierarchy quantifies agency in systems based on information processing.
problem Lack of a measurable, universal definition for agency in intelligent systems.
method Developed a bottom-up framework based on information processing hierarchy.
result Identified three orders of information processing (I, II, III) as necessary for agency.
The credit crisis of 2007 and 2008 has thrown much focus on the models used to price mortgage backed securities. Many institutions have relied heavily on the credit ratings provided by credit agency. The relationships between management of credit agencies and debt issuers may have resulted in conflict of interest when …
SPAC data shows premium investors get better terms, non-premium get quid pro quo deals.
problem Agency problems and informational frictions in securities issuance.
method Analysis of SPAC data to identify premium and non-premium investors.
result Non-premium investors engage in quid pro quo relationships with issuers and intermediaries.
Strategic behaviour is one of the main explanations for cost overruns. It can theoretically be supported by agency theory, in which strategic behaviour is the result of asymmetric information between the principal and agent. This paper gives a formal account of this relation by a signalling game. This is a game with in…
Central Counterparties (CCPs) are widely promoted as a requirement for safe banking with little dissent except on technical grounds (such as proliferation of CCPs). Whilst CCPs can have major operational positives, we argue that CCPs have many of the business characteristics of Rating Agencies, and face similar busines…
Study shows how machine learning can improve human performance in deception detection.
problem Improving human performance in critical tasks involving ethical and legal concerns.
method Investigated how machine learning models and their predictions can assist humans in deception detection tasks.
result Explanations and predicted labels from machine learning models can improve human performance in deception detection.
Improved DRN model predicts traffic flow more accurately.
problem Real-time traffic flow prediction to save time and improve transportation system efficiency.
method Improved Deep Residual Network (DRN) with online learning for dynamic model.
result Our model outperforms state-of-the-art models in accuracy and practical applications.
We propose a betting strategy based on Bayesian logistic regression modeling for the probability forecasting game in the framework of game-theoretic probability by Shafer and Vovk (2001). We prove some results concerning the strong law of large numbers in the probability forecasting game with side information based on …
Study analyzes costs of managing research funds, developing a model for optimal administration.
problem High variability in administration costs among research funding agencies.
method Identified standard agency activities, developed a model estimating optimum portfolio success rate and administration ratio.
result Model estimates optimum portfolio success rate and administration ratio based on input variables.
In this paper the problem of optimal derivative design, profit maximization and risk minimization under adverse selection when multiple agencies compete for the business of a continuum of heterogenous agents is studied. The presence of ties in the agents' best-response correspondences yields discontinuous payoff functi…
Fed-FEARE model extracts rules from multiple agencies' data securely.
problem Data privacy and secure rule extraction across multiple agencies.
method Federated F-score based ensemble tree model.
result Model performance measures significantly improved with federated learning.
Deep learning maps road safety features from streetview imagery.
problem Accurate mapping of road safety features for safer roads.
method Proposes a deep learning approach with LSTM to capture spatial autocorrelation.
result Model outperforms baseline methods in mapping road safety features.
New models show agents learn local interactions for collective motion.
problem Understanding how collective motion emerges from individual interactions.
method Developed a new model where agents learn local interaction rules.
result Agents can learn appropriate local interactions without prior assumptions.
AI system narrows human decision options for better outcomes.
problem Improving human decision-making in sequential tasks.
method Developed a decision support system using a pre-trained AI agent to limit human action choices.
result Participants outperformed AI and solo play in a wildfire mitigation game.
Paper tackles ESG rating disagreement in sustainable investing portfolios.
problem Lack of alignment between ESG ratings from different agencies affects investment decisions.
method Proposes a nonlinear optimization model reformulated as a convex quadratic program to address ESG rating disagreement.
result The proposed model can effectively manage ESG rating disagreement and improve investment decisions.
We present an analysis of oil prices in US$ and in other major currencies that diagnoses unsustainable faster-than-exponential behavior. This supports the hypothesis that the recent oil price run-up has been amplified by speculative behavior of the type found during a bubble-like expansion. We also attempt to unravel t…
This paper examines how ESG factors influence sovereign bond yields and credit ratings.
problem The impact of ESG factors on sovereign bond yields and credit ratings is not fully understood.
method The study identifies relevant ESG indicators and compares their importance in bond pricing and credit ratings.
result ESG factors, particularly the G and S pillars, are more important for credit ratings than the E pillar.
We propose an indicator to measure the degree to which a particular news article is novel, as well as an indicator to measure the degree to which a particular news item attracts attention from investors. The novelty measure is obtained by comparing the extent to which a particular news article is similar to earlier new…
We reformulate the Cont-Bouchaud model of financial markets in terms of classical "super-spins" where the spin value is a measure of the number of individual traders represented by a portfolio manager of an investment agency. We then extend this simplified model by switching on interactions among the super-spins to mod…
Paper predicts in-situ metro passenger density using smart card data.
problem Crowd management in metro systems.
method Statistical models and EM algorithm for time-dependent OD matrix and travel time cost estimation.
result Accurate prediction of in-situ passenger density for future time points.
Paper presents a model for identifying informative COVID-19 tweets.
problem Identifying informative COVID-19 tweets on Twitter.
method Leveraged transformers (RoBERTa, XLNet, BERTweet) trained in Semi-Supervised Learning (SSL) setting.
result Achieved F1 score of 0.9011 on test set, ranking 7th on leaderboard.
Study improves detection of accounting fraud using machine learning.
problem Global concern of accounting fraud threatening financial stability.
method Machine learning methods to differentiate between fraud and non-fraud companies.
result Out-of-sample results suggest great potential in detecting falsified financial statements.
The study proposes an audit to assess user control over recommendations in collaborative filtering systems.
problem The gap between maximizing accuracy and ensuring user control over information availability in recommender systems.
method The approach involves a computationally efficient audit for top-N linear recommender models, focusing on reachability and user agency. result The study demonstrates that model complexity affects the effort required for users to exert control over their recommendations.
The paper defines fair profit sharing ratios in Islamic PL contracts.
problem Determining fair profit sharing ratios in Islamic PL contracts.
method Introduces c-fair profit sharing ratios and uses econometrics models to compute or approximate them. result Elucidates the relation between profit sharing ratios and economic factors.
Efficiently preserves privacy in logistic regression for IoT data.
problem Balancing data privacy and utility in collaborative learning.
method Matrix encryption approach for secure multi-party computation.
result Proposes a privacy-preserving logistic regression model with fast convergence.
Study optimal contracts for pandemic risk, offering fixed shares and prevention mechanisms.
problem Optimal delegation contracts in the face of pandemic shutdown risk.
method Dynamic principal-agent model with exogenous early termination risk.
result Explicit characterization of optimal wage and action for prevention mechanisms.
Paper uses clustering to identify food assistance deserts in Ohio.
problem Food insecurity and accessibility issues in Ohio.
method Gaussian Mixture Model (GMM) clustering technique.
result Identifies food assistance deserts within Ohio.
The quality of machine translation is rapidly evolving. Today one can find several machine translation systems on the web that provide reasonable translations, although the systems are not perfect. In some specific domains, the quality may decrease. A recently proposed approach to this domain is neural machine translat…
Optimal linear contracts are possible even with memory in Gaussian settings.
problem Can optimal dynamic contracts be linear when agents control memory processes?
method Developed a methodology for non-Markovian and non-semimartingale settings, showed linear contracts are optimal for one-dimensional models.
result Linear contracts are optimal for one-dimensional models with memory, and for radial effort cost functions in higher dimensions.
PriceAggregator optimizes hotel price fetching to increase Agoda's bookings.
problem Limited QPS from suppliers causes many user searches to be ignored.
method Intelligently determines queries to suppliers for price fetching.
result PriceAggregator increases Agoda's bookings significantly.
The paper proposes a new method to assess financial risk and total risk in EU using Markov chains and copulas.
problem Assessing financial risk and total risk in European Union.
method Piece-wise homogeneous Markov chain for credit ratings and multivariate model of credit spreads.
result Financial risk inequality and total risk increase over time, with strong correlation between most European countries.
We introduce a model in which a regulator employs mechanism design to embed her human capital beta signal(s) in a firm's capital structure, in order to enhance the value of her post career change indexed executive stock option contract with the firm. We prove that the agency cost of this revolving door behavior increas…
Data science principles enhance AI interpretability for better user control.
problem Risks from opaque AI models without clear impacts.
method Synthesizes principles from interpretability literature, emphasizing audience goals.
result Illustrates basic techniques and criteria for evaluating interpretability.
This paper provides a guide to using machine learning in public administration.
problem Lack of clarity in proper use and potential pitfalls of machine learning methods.
method Provides a foundational view of machine learning and demonstrates its use in public administration research.
result Machine learning techniques can enrich public administration research and practice.
Regshock visualizes financial risks to help regulators manage systemic shocks.
problem Managing systemic risks in financial networks.
method Risk-island visualization algorithm and regshock visual exploration approach.
result Demonstrated improved risk management and control capabilities.
Generative model predicts ETA for bus routes using local data.
problem Accurate ETA prediction for public transit, especially buses, in cities with limited data.
method Generative deep learning model trained on local bus route data.
result Model updates ETA in real-time based on current trip information.
Optimal liquidation using VWAP strategies has been considered in the literature, though never in the presence of permanent market impact and only rarely with execution costs. Moreover, only VWAP strategies have been studied and the pricing of guaranteed VWAP contracts has never been addressed. In this article, we devel…
The objective of this article is to analyze the impact of capital structure on profitability. This impact can be explained by three essential theories: signaling theory, tax theory and the agency costs theory. A sample of 1846 French industrial firms are taken over the period 1999-2006, as a dynamic panel study by usin…
Stability proved for martingale and weak transport problems.
problem Stability of martingale and weak optimal transport problems.
method Established stability through unconventional topology considering temporal structure of martingales.
result Proved stability of martingale and weak transport problems.
New methods estimate transport-growth pairs in unbalanced optimal transport.
problem Statistical guarantees for Monge-type estimation in unbalanced optimal transport remain limited.
method Developed two estimators for transport-growth pairs under different setups.
result Achieved minimax optimal rate for estimation of transport-growth pairs.
Paper analyzes deep learning models for credit rating prediction using text and numerical data.
problem Improving credit rating prediction using multi-modal deep learning.
method Testing different deep learning models and fusion strategies for structured and unstructured datasets.
result CNN-based multi-modal model with two fusion strategies outperformed other models.
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.
Study improves pension scheme efficiency in Kenya through governance and risk management.
problem Limited research on efficiency of Kenyan pension schemes under governance structures.
method Quantitative panel regression analysis on 128 Kenyan pension schemes over 7 years.
result Employee board members have a significant positive effect on pension scheme efficiency.
Novel approach learns optimal transport using convex neural networks.
problem Learning optimal transport between distributions from samples.
method Solving a minimax optimization to learn two convex functions, representing the optimal transport map.
result The approach finds optimal transport mappings that are independent of initialization and can handle discontinuous distributions.