Research
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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,982 papers · 148 categories

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1223 · May 201819922001200920172026
48 results for Women

Study on women entrepreneurs' access to finance in France.

problem Inequalities in accessing external finance for women entrepreneurs in France.
method Quantitative approach using data from a representative sample of women entrepreneurs.
result Founder status affects access to external finance; increases success in fundraising but reduces bank finance.

A hybrid machine learning model predicts soccer match scores for FIFA Women's World Cups.

problem Forecasting soccer match scores for FIFA Women's World Cups.
method Combining bookmaker consensus and team strength parameters in a random forest model.
result The model favors the defending champion USA over the host France.

We analyze and develop a quantitative model describing the evolution of personal income distribution, PID, for males and females in the U.S. between 1930 and 2014. The overall microeconomic model, which we introduced ten years ago, accurately predicts the change in mean income as a function of age as well as the depend…

2015-10-09abs ↗pdf ↗

Study examines perceptions and attitudes about breast cancer on Twitter.

problem Understanding public perceptions and attitudes towards breast cancer on social media.
method Identified and collected tweets, used topic modeling and sentiment analysis.
result Identified themes and quantified users' perceptions and emotions about breast cancer.

Study measures gender bias in machine translation using multiple reference points.

problem Measuring and identifying gender bias in machine translation.
method Used an optimal non-biased translator, reference points from occupational statistics and survey.
result Found bias against both genders, but more against women, and found occupations have a greater effect than adjectives.

We investigate the Brazilian personal income distribution using data from National Household Sample Survey (PNAD), an annual research available by the Brazilian Institute of Geography and Statistics (IBGE). It provides general characteristics of the country's population. Using PNAD data background we also confirm the e…

2017-09-19abs ↗pdf ↗

FOSS is an acronym for Free and Open Source Software. The FOSS 2013 survey primarily targets FOSS contributors and relevant anonymized dataset is publicly available under CC by SA license. In this study, the dataset is analyzed from a critical perspective using statistical and clustering techniques (especially multiple…

2017-01-28abs ↗pdf ↗

New method calibrates asynchronous, error-prone covariates for longitudinal data.

problem Estimation biases and slow convergence in analyzing time-varying covariates with measurement error.
method Functional calibration approach based on functional principal component analysis.
result Asymptotically unbiased and consistent estimators for time-invariant coefficients; optimal convergence rate for time-varying coefficients.

Study on stock portfolio concentration among Finnish households and investors.

problem Understanding the concentration of stock portfolios owned by Finnish households and investors.
method Analysis of stock portfolios using Herfindahl-Hirschman index over 20 years.
result High portfolio concentration observed in Finnish retail investors, similar to institutional investors.

Studies have shown that the people depicted in image search results tend to be of majority groups with respect to socially salient attributes. This skew goes beyond that which already exists in the world - e.g., Kay et al. showed that although 28% of CEOs in US are women, only 10% of the top 100 results for CEO in Goog…

2019-01-29abs ↗pdf ↗

Deep learning improves CVD risk prediction from health records.

problem Predicting cardiovascular disease risk from administrative health data.
method Combined survival analysis and deep learning models.
result Deep learning models outperform traditional Cox models in accuracy and explained time-to-event occurrence.

We investigate whether the standard dimensionality reduction technique of PCA inadvertently produces data representations with different fidelity for two different populations. We show on several real-world data sets, PCA has higher reconstruction error on population A than on B (for example, women versus men or lower-…

2018-10-31abs ↗pdf ↗

KOOW method provides optimal covariate balance for continuous treatments.

problem Estimating effects of continuous treatments with robustness to model misspecification and extreme weights.
method Kernel Optimal Orthogonality Weighting (KOOW) using convex optimization.
result KOOW provides optimal covariate balance and controls for extreme weights.

Health professionals can use natural language processing (NLP) technologies when reviewing electronic health records (EHR). Machine learning free-text classifiers can help them identify problems and make critical decisions. We aim to develop deep learning neural network algorithms that identify EHR progress notes perta…

2018-09-16abs ↗pdf ↗

New concept of within-group fairness improves AI fairness without sacrificing accuracy.

problem Fairness issues in AI models treating individuals in the same sensitive group unfairly.
method Introducing within-group fairness, proposing mathematical definitions, and developing learning algorithms.
result Improves within-group fairness without sacrificing accuracy and between-group fairness.

A new ranking model uses nonnegative matrix factorization for tennis players.

problem Modeling latent variables influencing tennis player performance.
method Combines Bradley-Terry-Luce model with nonnegative matrix factorization.
result Model identifies surface type as key determinant of male player performance.

Deep learning models accurately recognize and estimate physical activity types and energy expenditure from wrist accelerometer data.

problem Rigorous evaluation of wrist-worn accelerometers for assessing physical activity across the lifespan.
method Built deep learning networks to extract spatial and temporal representations from time-series data, recognizing physical activity types and estimating energy expenditure.
result Deep learning models achieved high performance: F1 scores of 0.82, 0.81, and 95 for sedentary, locomotor, and lifestyle activities, respectively; root mean square error of 1.1 for EE estimation.

Although there are millions of transgender people in the world, a lack of information exists about their health issues. This issue has consequences for the medical field, which only has a nascent understanding of how to identify and meet this population's health-related needs. Social media sites like Twitter provide ne…

2018-08-18abs ↗pdf ↗

This study predicts ovarian cancer from cysts using TVUS and machine learning.

problem Early detection of ovarian cancer from cysts using TVUS screening.
method Employed Random Forest, KNN, and XGBoost machine learning techniques on PLCO dataset.
result Achieved high accuracy, recall, f1 score, and precision in predicting ovarian cancer.

The paper benchmarks OS with RCTs, accounting for right-censoring.

problem Benchmarking observational studies with experimental data under censoring.
method Two cases: independent and dependent censoring. Censoring-doubly-robust signal for CATE.
result Effectiveness of censoring-aware tests verified via experiments and real data.

A-MIL improves histopathology image classification and localization.

problem Improving diagnosis of breast cancer through better interpretation of histopathology images.
method Frame image classification as multiple instance learning, use attention-based learning for localization.
result A-MIL achieves better localization without compromising classification accuracy.

Study uses attention-based method to detect different types of online harassment.

problem Detecting different types of online harassment in social media content.
method Multi-attention based approach using Recurrent Neural Networks to address imbalanced data.
result Demonstrates effectiveness of attention-based mechanism for detecting various types of online harassment.

Study predicts academic achievement using students' support networks.

problem Predicting academic achievement in college students.
method Decision tree and random forest algorithms applied to Ties data.
result Different types of support are important for different demographics and genders.