Study shows how socioeconomic status influences language use on Twitter.
problem Global variability of linguistic patterns due to socioeconomic factors.
method Multivariate analysis of French Twitter corpus and socioeconomic data.
result People with higher socioeconomic status use more standard language.
Study decomposes racial healthcare disparities via shifts in mediator distributions.
problem Racial disparities in healthcare expenditures and their underlying drivers.
method Framework decomposing disparities into mediator distribution shifts and residual components, using MEPS data.
result Substantial disparities persist even when mediators are equalized, suggesting unmeasured or structural factors.
Financial market created for wellbeing indices to mitigate socioeconomic risks.
problem Risk mitigation in financial indices of socioeconomic wellbeing.
method Developed new quantitative measure, created financial market, and implemented insurance instruments.
result Optimal portfolio weights and efficient frontiers for wellbeing indices.
The stability analysis of socioeconomic systems has been centered on answering whether small perturbations when a system is in a given quantitative state will push the system permanently to a different quantitative state. However, typically the quantitative state of socioeconomic systems is subject to constant change. …
This study uses causal Shapley values to analyze how socioeconomic factors cause the spread of COVID-19.
problem Understanding how socioeconomic factors cause the spread of COVID-19.
method The study employs an explanatory framework from cooperative game theory augmented with do calculus, specifically causal Shapley values, to analyze the causal connections.
result The causal Shapley values reveal distinct advantages of non-linear machine learning models over linear models in multivariate analysis.
Optimizes control interventions in real-world networks using deep-learning and network science.
problem Optimizing control over socioeconomic networks subject to constraints.
method Integrates optimization tools from deep-learning with network science.
result Characterizes vulnerability of corporate networks to takeovers.
Graph embeddings from commute networks identify socioeconomic disparities in urban areas.
problem Urban delineation and socioeconomic group identification.
method Graph Neural Network (GNN) for modeling commute networks and deriving node embeddings.
result GNNs effectively capture socioeconomic disparities between urban communities.
Deep chest X-ray classifiers show bias in predicting diagnoses.
problem Bias in deep learning classifiers predicting diagnoses from chest X-rays.
method Trained convolutional neural networks on multiple public datasets to predict 14 diagnostic labels.
result True positive rates vary significantly among different protected attributes, indicating bias.
Uncovering how college effects vary by subgroups using machine learning.
problem Exploring how college effects vary by subgroups in observational data.
method Causal trees for recursive subgrouping, honest estimation, leaf-specific matching, sensitivity analyses.
result Identifies new subgroups of individuals whose college effects on wages vary significantly.
Framework identifies causal factors of climate change using correlations and machine learning.
problem Understanding socioeconomic factors influencing carbon emissions and climate change.
method Three-step framework: correlation analysis, causal discovery, LLM interpretations.
result Adaptable solutions for data-driven policy-making and strategic decision-making.
New method evaluates multiple social disparities using machine learning.
problem Reduction of educational disparities across multiple dimensions.
method Triply-Robust Machine Learning Approach for Causal Decomposition Analysis.
result Simultaneous interventions across multiple domains reduce disparities.
Model uses Navier-Stokes equations to assess liquidity and systemic risk.
problem Traditional models fail to capture real market fluctuations and extreme events.
method Develops and validates a mathematical model based on Navier-Stokes equations, incorporating 13 macroeconomic and financial parameters.
result Model effectively describes liquidity dynamics, systemic risk, and extreme scenarios.
Nonparametric neural-network estimation of current-status data
problem Estimation of conditional cumulative distribution function with current-status data
method Neural-network sieve maximum likelihood estimator
result Explicit convergence rate for Hölder smoothness
In this model study of the commodity market, we present some evidence of competition of commodities for the status of money in the regime of parameters, where emergence of money is possible. The competition reveals itself as a rivalry of a few (typically two) dominant commodities, which take the status of money in turn…
AdaCare learns health status from biomarkers across multiple time scales.
problem Lack of explicit extraction of historical biomarker variation and adaptability to diverse patient conditions.
method Scale-adaptive feature extraction and recalibration for interpretability.
result AdaCare achieves state-of-the-art prediction accuracy and provides interpretable results.
Wind turbine status classification models are improved for cross-site applicability.
problem Standard models don't generalize across different wind turbines.
method Data normalization and convolutional neural networks with feature-space extension.
result Trained models successfully classified wind turbines from different sites.
Hybrid method reveals true currency correlations.
problem Identify currency status in foreign exchange networks.
method Combines DCCC and network deconvolution to filter indirect effects.
result Reflects currency status changes and is more stable.
NSR enables neural networks to reason with continuous numbers and extrapolate.
problem Quantitative reasoning and extrapolation in neural networks.
method Proposes Neural Status Register (NSR) for continuous number reasoning.
result NSR achieves extrapolation to numbers many orders of magnitude larger than training data.
Unsupervised learning summarizes EHR data into a patient status vector.
problem Challenges in modeling electronic health records due to irregularities and varying procedures/diagnoses.
method Two-step unsupervised representation learning scheme using auto-encoders and forecasting tasks.
result Improved generalization performance on mortality and readmission tasks.
Develops a RNN model to predict obesity status improvement using irregular activity data.
problem Predicting obesity status improvement using irregular activity data.
method Develops a RNN-based time-aware architecture to handle irregular observation times and extract relevant features from longitudinal patient records.
result Achieves an accuracy of 77-86% in predicting obesity status improvement.
Paper proposes an efficient GAN model for discrete cyber-intrusion detection.
problem Cyber-intrusion detection in systems with rare abnormal data.
method Generative adversarial network (GAN) with a specifically-designed loss function for discrete data.
result The proposed model outperforms state-of-the-art models on discrete datasets.
Study developed phenotypes for ICU patients' brain dysfunction states.
problem Underdiagnosis of acute brain dysfunction in ICU patients.
method Created algorithms to quantify and cluster brain dysfunction states.
result Developed three phenotypes of ICU patients' brain dysfunction states.
The study proposes that people prioritize status maximization over simple profit maximization in social interactions.
problem The gap between competitive and altruistic human tendencies in social interactions.
method Agent-based model of repeated dictator games in a social network, considering living costs and infractions.
result Individual strategies in the game vary from selfish to selfless, influenced by model parameters.
Graphs model human mobility patterns, reducing errors in data matching.
problem Lack of high-quality data and computational resources for graph-based mobility analysis.
method Embedding graphs into a continuous space to address matching, modeling, and visualization challenges.
result Approx 40% decrease in error on average in matched graphs vs unmatched ones.
The gender wage gap varies significantly among women based on socio-economic characteristics.
problem Understanding the extent of gender inequality in earnings among U.S. women.
method High-dimensional wage regression and double lasso analysis of 2016 American Community Survey data.
result The gender wage gap varied substantially across women and was driven by marital status, having children, race, occupation, industry, and educational attainment.
Research on long-range memory in financial and social systems using various models.
problem Understanding the nature of long-range memory in socioeconomic systems.
method Various Markov processes including point processes, stochastic differential equations, and agent-based models.
result New estimators of self-similarity and long-range memory for non-Gaussian systems are needed.
Adversarial transfer learning improves stress assessment across users.
problem Transfer learning challenges in physiological biosignals.
method Disentangled nuisance-robust representations using adversarial networks.
result Adversarial framework enhances cross-subjects stress assessment.
Unsupervised clustering reveals novel TBI phenotypes.
problem Inadequate categorization of traumatic brain injury (TBI) based on symptoms.
method Applied unsupervised learning with GLRM feature selection.
result Identified four novel TBI phenotypes with distinct feature profiles.
In survival analysis, estimating the failure time distribution is an important and difficult task, since usually the data is subject to censoring. Specifically, in this paper we consider current status data, a type of data where all of the observations are censored. The format of the data is such that the failure time …
Recurring international financial crises have adverse socioeconomic effects and demand novel regulatory instruments or strategies for risk management and market stabilization. However, the complex web of market interactions often impedes rational decisions that would absolutely minimize the risk. Here we show that, for…
Paper proposes a method to evaluate SME credit risk using meta paths.
problem Evaluate credit risk of small and medium-sized enterprises with limited data.
method Exploits the representative power of information networks and meta paths to infer SME financial status.
result Meta path feature effectively identifies SMEs with credit risks.
A framework predicts employment status for students considering unconscious biases.
problem Unconscious biases hinder college students' job hunting.
method Developed a framework MAYA using GAN, LSTM, and bias regularization.
result Framework effectively predicts employment status with bias consideration.
This paper predicts legal proceedings status using NLP and machine learning.
problem Classify Brazilian legal proceedings into archived, active, and suspended categories.
method Combined NLP techniques with machine learning to classify legal proceedings sequences.
result Achieved maximum accuracy of 93% and top average F1 Scores of 89% (macro) and 93% (weighted).
The medical research facilitates to acquire a diverse type of data from the same individual for particular cancer. Recent studies show that utilizing such diverse data results in more accurate predictions. The major challenge faced is how to utilize such diverse data sets in an effective way. In this paper, we introduc…
Lisa Piccirillo solved the mystery of the Conway knot's sliceness.
problem Determining if the Conway knot is slice.
method The main idea of the proof is given in the title.
result The Conway knot is not slice.
Neural network improves aneurysm classification accuracy in MRI images.
problem Classifying aneurysm status in MRI images with varying radiologist expertise.
method Constructed neural network trained on radiologists' annotations, compared to a control model.
result Proposed model had a higher area under the curve (0.845 vs. 0.793).
A (biased and incomplete) review of the status of the theory of symplectic connections on supermanifolds is presented. Also, some comments regarding Fedosov's technique of quantization are made.
Study on stock trading model with uncertain market status, proving free boundaries and optimal strategies.
problem Optimal trading strategies in a stock market with uncertain market status.
method Free boundary problem, variational inequality system, degenerate operator, C^∞-smoothness.
result All four switching free boundaries are no-overlapping, monotonic, and C^∞-smooth, and their relative localities are completely determined.
A new RL framework helps align RL solutions with existing practices.
problem Discrepancies between RL solutions and existing procedures.
method Student-teacher RL mechanism with a constraint on policy difference.
result Proves asymptotic optimality and effectiveness in multiple scenarios.
Study shows caste's reduced importance in predicting women's work status and type.
problem Social disadvantage and caste impact women's work status and type in India.
method Interpreted machine learning models using feature attribution.
result Caste is less influential in predicting work for younger women compared to older women.
We survey the recent results and current issues on the topological rigidity problem for closed aspherical manifolds, i.e., connected closed manifolds whose universal coverings are contractible. A number of open problems and conjectures are presented during the course of the discussion. We also review the status and app…
Study uses image analysis to predict MSI status in tumors.
problem Challenges in distinguishing MSI from its counterpart.
method Interpretable pathological image analysis strategies using Haematoxylin and eosin-stained images.
result Strategies achieve decent performance in MSI prediction.
Surveying matrix group actions on manifolds.
problem Topological Zimmer's conjecture on matrix group actions on manifolds.
method Surveying existing research and literature.
result Status update on matrix group actions on manifolds.
Survey of Baum-Connes conjecture history and methods.
problem Baum-Connes conjecture in mathematics.
method History and methods of the conjecture.
result Current status of the conjecture.
One of the key socioeconomic phenomena to explain is the distribution of wealth. Bouchaud and Mézard have proposed an interesting model of economy [Bouchaud and Mézard (2000)] based on trade and investments of agents. In the mean-field approximation, the model produces a stationary wealth distribution with a power-law …
Abstract: Unknown status of Jacobian Conjecture, proof has a gap.
problem Status of Jacobian Conjecture
method Analysis of proof of theorem 2.1
result Proof of theorem 2.1 contains a gap
Interpretable neural networks improve economic research by balancing accuracy and transparency.
problem Lack of interpretability in neural networks hinders their use in economic research.
method Proposes interpretable neural network models that balance prediction accuracy and interpretability.
result Achieved 94.5% accuracy in predicting employment status using high-dimensional data.
Model predicts community health outcomes from social media language.
problem Linking social media language to community health outcomes.
method Sentence embeddings, regression model, clustering, without additional data.
result Predicts community-level medical outcomes from social media language.