The paper uses SVAR modeling to analyze how demographic changes affect the current account and economic growth.
problem The impacts of demographic changes on the current account and economic growth.
method SVAR modeling to track dynamic impacts between population growth, current account, and economic growth.
result The long-run net impact on economic growth of the domestic working population growth and demand labor for emigrants is positive.
Max flow/min cut theorem extended to currents and topology.
problem Continuous max flow/min cut theorem for complex domains.
method Continuous analogue of max flow/min cut theorem considering topology.
result Continuous max flow/min cut theorem proven for currents and laminations.
This paper develops a theoretical model for SPPC prices and improves accounting standards.
problem Lack of theoretical price for SPPC and issues in current accounting standards.
method Developed a theoretical SPPC price model with a marginal utility-based approach, explicitly indicated stochastic processes, and proposed a convenient model.
result Improved theoretical price model and addressed problems in current accounting standards.
Paper examines LLM capability benchmarks through construct validity, favoring nomological account.
problem Linking theoretical capabilities to empirical measurements in LLMs.
method Contrasts three frameworks: nomological, inferential, and causal.
result Nomological account provides best foundation for LLM research.
This work examines the legal requirement for AI systems to provide explanations.
problem The need for accountability in AI systems that can have significant impacts on individuals.
method Review of legal contexts requiring explanation from AI systems and technical considerations for providing such explanations.
result Technical challenges in providing explanations that align with legal requirements for AI systems.
This review explores ChatGPT in accounting and finance.
problem Understanding the current state of research on ChatGPT in accounting and finance.
method A scoping review of recent publications and working papers.
result Identifies three themes: applications, research tools, and implications.
The study defines differential forms and currents on orbifolds with corners.
problem Defining differential forms and currents on orbifolds with corners.
method Using the formalism of étale proper groupoids with corners, the authors provide constructions and proofs without orbifold charts.
result The Fréchet space of differential forms and the dual space of currents are independent of the chosen groupoid representation.
Diffsurv extends differentiable sorting to handle censored time-to-event data.
problem Handling censored time-to-event data in survival analysis.
method Extending differentiable sorting methods to account for censored samples.
result Diffsurv outperforms established baselines in various risk prediction scenarios.
Policy certificates improve reinforcement learning accountability and efficiency.
problem Lack of policy quality information in reinforcement learning algorithms.
method Propose and implement algorithms with policy certificates that bound sub-optimality and return.
result First algorithm achieves minimax-optimal PAC bounds and minimax regret bounds.
Forecast future volatilities and correlations based on current trends.
problem Predict future volatilities and correlations in financial markets.
method Use cubic and quadratic polynomials of current trend strengths.
result Accurate quantification of trend effects on volatilities and correlations.
Computation of moments of transformed random variables is a problem appearing in many engineering applications. The current methods for moment transformation are mostly based on the classical quadrature rules which cannot account for the approximation errors. Our aim is to design a method for moment transformation for …
The basic financial purpose of an enterprise is maximization of its value. Trade credit management should also contribute to realization of this fundamental aim. Many of the current asset management models that are found in financial management literature assume book profit maximization as the basic financial purpose. …
Deep learning predicts traffic actors' future movements with uncertainty.
problem Predicting future states of traffic actors for autonomous vehicles.
method Deep learning models using raster images of actors' surroundings.
result Effective prediction of traffic actors' movements with uncertainty.
In an asset return series there is a conditional asymmetric dependence between current return and past volatility depending on the current return's sign. To take into account the conditional asymmetry, we introduce new models for asset return dynamics in which frequencies of the up and down movements of asset price hav…
New pension design reduces volatility without guarantees.
problem Pension volatility and guarantees issues.
method Split premium, invest in funds, redistribute to smooth volatility.
result Maximizes total accumulated capital at retirement.
Pandora hybridizes human and machine methods to explain AI system failures.
problem Understanding and explaining failures in complex AI systems.
method Hybrid human-machine methods and tools for summarizing system malfunction.
result Detailed performance views help in analysis and debugging of AI systems.
Chunking is a significant CL problem, accounting for half of performance drop, and current methods don't address it.
problem Chunking of data in continual learning.
method Analyzing and addressing the chunking sub-problem in continual learning.
result Current CL algorithms perform poorly on chunking, only as well as plain SGD training when there is no distribution shift.
A new model improves machine translation by accounting for translation ambiguity.
problem Translation ambiguity in parallel corpora leads to inconsistent models.
method A deep generative model with latent variables to handle local variation.
result The model consistently outperforms existing baselines on multiple language pairs.
Agent-based model uses SAM to create realistic economic system.
problem Lack of tools to understand and predict economic crises.
method Agent-based modeling (ABM) with Social Accounting Matrix (SAM) calibration.
result ABM can produce economic systems close to real-world data.
IDS improves exploration in deep reinforcement learning.
problem Efficient exploration in reinforcement learning, especially with heteroscedastic returns.
method Information-Directed Sampling (IDS) for deep Q-learning.
result Significant improvement in Atari game performance over alternative approaches.
In retail, there are predictable yet dramatic time-dependent patterns in customer behavior, such as periodic changes in the number of visitors, or increases in customers just before major holidays. The current paradigm of multi-armed bandit analysis does not take these known patterns into account. This means that for a…
To quantify the operational risk capital charge under the current regulatory framework for banking supervision, referred to as Basel II, many banks adopt the Loss Distribution Approach. There are many modeling issues that should be resolved to use the approach in practice. In this paper we review the quantitative metho…
WZW models are abstract conformal field theories with an infinite dimensional symmetry which accounts for their integrability, and at the same time they have a sigma model description of closed string propagation on group manifolds which, in turn, endows the models with an intuitive geometric meaning. We exploit this d…
TLMG4Eth combines language and graph models for Ethereum fraud detection.
problem Current fraud detection methods fail to consider semantic and similarity patterns in Ethereum transactions.
method TLMG4Eth uses a transaction language model and graph-based methods to capture semantic, similarity, and structural features.
result TLMG4Eth detects anomalies in Ethereum transactions more effectively than existing methods.
The Australian Government uses the means-test as a way of managing the pension budget. Changes in Age Pension policy impose difficulties in retirement modelling due to policy risk, but any major changes tend to be `grandfathered' meaning that current retirees are exempt from the new changes. In 2015, two important chan…
Improved transfer learning method considers target and source data balance.
problem Improving transfer learning performance with varying target and source data.
method Weighted Multisource Tradaboost builds on Multisource Tradaboost, weighting datapoint importance based on data availability.
result The proposed method outperforms the base method as target sample count increases.
Author discusses the Poincaré conjecture from 40 years ago.
problem Discussing the Poincaré conjecture from a historical perspective.
method Personal recollections and anecdotes.
result Memories of the Poincaré conjecture are vivid and detailed.
We analyse an issue when comparing survival curves between two subgroups. We show that there is a direct relationship between estimates of subgroups' survival at a time point and positive and negative predictive values in the binary classification settings. Our findings present a case where current methods of comparing…
New algorithms handle missing outcomes in MAB, reducing regret.
problem Missing outcomes in real-world MAB scenarios lead to biased estimates and linear regret.
method Introduced algorithms for MAR and MNAR missingness mechanisms in MAB.
result Significant improvements in decision-making by accounting for missingness.
Financial institutions are currently required to meet more stringent capital requirements than they were before the recent financial crisis; in particular, the capital requirement for a large bank's trading book under the Basel 2.5 Accord more than doubles that under the Basel II Accord. The significant increase in cap…
Paper presents a new time-series segmentation technique for mobile phone user behavior.
problem Current segmentation techniques do not accurately capture individual user behavior over time.
method Behavior-Oriented Time Segmentation (BOTS) technique that considers temporal coverage and number of incidences.
result BOTS technique better captures user behavior at various times of day and week.
Proposes a multi-resolution model for prostate cancer classification using mpMRI.
problem Improving voxel-wise classification of prostate cancer using multi-parametric MRI data.
method Multi-resolution Super Learner framework combining local base learners at multiple resolutions and spatial Gaussian kernel smoothing.
result Enhanced voxel-wise classification of prostate cancer status and clinical significance.
New bounds for unsupervised domain adaptation account for non-invertibility and support coverage.
problem Theoretical arguments for domain-invariant representations are flawed and do not account for non-invertibility and support coverage.
method Generalization bounds for any representation function acknowledging the cost of non-invertibility and penalizing distance between densities.
result Proposed bounds based on support coverage provide better generalization than current standard practice.
A combination of a priority queueing model and mean field theory shows the emergence of traders' swarm behavior, even when each has a subjective prediction of the market driven by a limit order book. Using a nonlinear Markov model, we analyze the dynamics of traders who select a favorable order price taking into accoun…
This study improves quantum classifiers by optimizing data preprocessing.
problem Quantum Machine Learning advantages are not yet clearly demonstrated.
method Used Linear Discriminant Analysis (LDA) for data preprocessing.
result Variational Quantum Algorithm (VQA) outperforms classical classifiers.
New method learns DAG structure in clustered data, accounting for local variations.
problem Learning DAG structure in clustered data with varying effects.
method Extends mixed models to structure learning, using a differentiable graph coupling mechanism.
result Asymptotically recovers true structure, detecting dependencies missed by other methods.
Two new methods improve monotonic constraint enforcement in regression and classification trees.
problem Improving monotonic constraints in regression and classification trees.
method Proposed two new methods: one yields better results than LightGBM, the other yields even better results but is slower.
result The best method consistently beats the current implementation of LightGBM, achieving up to 1% loss reduction.
CDVAE estimates treatment effects over time by accounting for unobserved variables.
problem Estimating treatment effects over time in the presence of unobserved confounders.
method Causal Dynamic Variational Autoencoder (CDVAE) that addresses unconfoundedness and unobserved heterogeneity.
result CDVAE outperforms existing methods in estimating Conditional Average Treatment Effects (CATEs).
A fast model estimates future prices from orderbook data.
problem Estimating future prices from orderbook data.
method Hyperdimensional vector Tsetlin machine framework for fast estimation.
result Demonstrated robust estimate of future prices.
This paper analyzes text in financial disclosures to improve financial analysis.
problem Insufficient analysis of unstructured text in financial disclosures.
method Reviews and explores methods in computational linguistics and NLP.
result Highlights limitations of sentiment metrics and suggests future research areas.
Generative model predicts remaining life of damaged structures.
problem Prognosis of damage and remaining useful life of structures.
method Generative adversarial networks (GANs) in a population-based SHM framework.
result Algorithm provides confident predictions about structures' remaining useful life.
We present a scalable end-to-end classifier that uses streaming physiological and medication data to accurately predict the onset of sepsis, a life-threatening complication from infections that has high mortality and morbidity. Our proposed framework models the multivariate trajectories of continuous-valued physiologic…
Gaussian DP improves reporting of ML algorithms' differential privacy guarantees.
problem Incomplete and misleading DP guarantees for ML algorithms.
method Using non-asymptotic Gaussian Differential Privacy (GDP) to provide accurate bounds on privacy profiles of ML algorithms.
result GDP captures the entire privacy profile of DP-SGD and related algorithms with virtually no error.
A neural network learns word-referent associations across various contexts.
problem Learning word-referent associations in different contexts.
method A biologically inspired multi-layered architecture that takes images and phonemes as input, builds representations, and adjusts prototypes based on current context.
result The model achieves up to 78% accuracy in ambiguous situations and mimics human learning patterns.
New PU ratio predicts long-term Bitcoin returns better than other methods.
problem Lack of convincing proxies for cryptocurrency fundamentals.
method Developed a new market-to-fundamental ratio (PU ratio) using blockchain accounting methods.
result PU ratio effectively predicts long-term Bitcoin returns compared to alternative methods.
New algorithms for efficient causal interventions with budget constraints and without constraints.
problem Efficiently learning best interventions in causal graphs with budget constraints.
method Developed algorithms for both budgeted and non-budgeted causal bandits, optimizing regret and side-information usage.
result Proposed algorithms minimize cumulative regret and perform better than standard methods.
New methods improve Bayesian inference and decision-making in online learning.
problem Current Bayesian deep learning does not fully utilize joint predictives for sequential decision-making.
method Proposes new evaluation settings for active learning and active sampling, focusing on marginal and joint cross-entropies.
result Initial experiments suggest challenges in applying current BDL inference techniques in high-dimensional spaces.
New neural network smoothness constraints improve model performance.
problem Improving model sensitivity to input changes for better generalization and robustness.
method Exploring current smoothness constraints and proposing new flexible definitions.
result Current smoothness constraints lack flexibility and understanding of data, tasks, and learning.