Tri-modal model predicts personality traits from audio, visual, and text data.
problem Predicting personality traits from multimodal data.
method Stacked Convolutional Neural Networks for each modality, multimodal fusion on decision-level and by concatenation.
result Multimodal fusion outperforms individual modalities, improving personality trait prediction by 9.4%.
Study identifies personality traits from dance movements in music.
problem Predicting individual differences from music-induced movement.
method Identified Big Five personality traits and EQ/SQ scores from dance movements.
result Successfully explored unseen space for personality and EQ/SQ.
LLMs can simulate human investment attitudes based on personality traits.
problem Investigating how LLMs mimic human investment behaviors.
method Simulated investment task using LLM personas with specific Big Five personality profiles.
result LLMs can produce meaningful behavioural differences in investment tasks that align with human traits.
Study reveals LLM personas have two distinct components: frame-robust aggregated traits and frame-dependent geometric features.
problem Evaluation of LLM personas via psychometric questionnaires discards within-instance correlation structure.
method Constructed within-instance correlation matrices from IPIP-50 responses and analyzed geometry on SPD manifolds under manipulated question orderings.
result Persona expression comprises two dissociable components: aggregated features (Big Five scores) and geometric features (SPD manifold).
Many methods have been used to recognize author personality traits from text, typically combining linguistic feature engineering with shallow learning models, e.g. linear regression or Support Vector Machines. This work uses deep-learning-based models and atomic features of text, the characters, to build hierarchical, …
Study largest maize SNP dataset for heterosis traits prediction.
problem Predicting improved biological qualities in maize hybrids.
method Developed linear and non-linear models considering hybrid relationships.
result Specially designed model efficiently predicts maize traits.
Big T-Rex solves FDR-controlled sparse regression on laptops with millions of variables.
problem Scalable FDR-controlled variable selection for high-dimensional data.
method Early terminated random experiments with memory-mapping and permutation-based dummy generation.
result Solves FDR-controlled Lasso problems with 5 million variables on a laptop in 30 minutes.
Method distinguishes genetic correlations from causation in GWAS.
problem Identifying causal relationships among genetically correlated traits.
method Mixed fourth moments to quantify causal relationships.
result Identified 30 putative genetically causal relationships across 52 traits.
Many complex disease syndromes such as asthma consist of a large number of highly related, rather than independent, clinical phenotypes, raising a new technical challenge in identifying genetic variations associated simultaneously with correlated traits. In this study, we propose a new statistical framework called grap…
A new platform predicts object movements without needing specific context.
problem Predicting the next position of movable objects.
method λ-Architecture for batch and stream analytics, combining for improved accuracy.
result Combining λ-Architecture parts improves overall accuracy and performance.
Study reveals LLM personality patterns but lacks behavioral consistency.
problem Understanding and validating personality traits in LLMs.
method Characterized LLM personality across three dimensions: training dynamics, self-report validity, and intervention effects.
result Self-reported traits do not reliably predict behavior, and instructional alignment affects trait expression but not behavior.
New game design method for better trait inference.
problem Inferring latent psychological traits from human behavior.
method Formulated as a mutual information maximization problem, solved using variational lower bound optimization.
result Designed games successfully distinguish among players with different traits, outperforming traditional methods.
Big data analytics improves healthcare through early detection and quality life.
problem Limited access to healthcare data hinders evidence-based decision-making.
method Analysis of healthcare data using various tools and techniques.
result Big data analytics enhances healthcare quality and patient outcomes.
Machine learning improves predicting species interactions based on traits.
problem Variability in empirical trait-matching studies for ecological networks.
method Compared conventional GLMs with ML models (Random Forest, Boosted Regression Trees, etc.) on simulated and real data.
result ML models outperform GLMs in predicting species interactions and identifying trait-matching combinations.
New method combines simple forecasting techniques for river flow predictions.
problem Improving accuracy of long-term hydrological forecasts.
method Combines at least two forecasting methods using median combiner.
result Performs well in long-term forecasts, especially with multiple methods.
New algorithm detects interactions for better individual trait prediction.
problem Tackles the challenge of making accurate individual trait predictions using interactions.
method Extends model-based MDR (MB-MDR) to detect and utilize interactions.
result Outperforms other algorithms in detecting and utilizing interactions for better prediction.
Involutions generate mapping class groups of infinite surfaces.
problem Generating involutions for mapping class groups of infinite surfaces.
method Analyzing infinite surfaces with n ends, showing involutions generate groups for n ≥ 6 and n ≥ 3.
result Involutions generate mapping class groups for n ≥ 6 and n ≥ 3.
Software aids in estimating speciation parameters using trait-dependent models.
problem Lack of robust software to estimate numerous speciation parameters.
method Approximate Bayesian Computation approach implemented in R package pcmabc.
result Software allows estimation of parameters of trait-dependent speciation processes.
Genome-wide association studies have proven to be essential for understanding the genetic basis of disease. However, many complex traits---personality traits, facial features, disease subtyping---are inherently high-dimensional, impeding simple approaches to association mapping. We developed a nonparametric Bayesian re…
Neural network model improves leaf spectral reflectance prediction for grapevines.
problem Inaccurate modeling of grapevine leaf spectral reflectance from traits.
method Multi-head attention neural network trained on grapevine-specific data.
result Model achieved high accuracy (R^2=0.84, NRMSE=1.52%) and outperformed PROSPECT-PRO.
Big Data is one of the major challenges of statistical science and has numerous consequences from algorithmic and theoretical viewpoints. Big Data always involve massive data but they also often include online data and data heterogeneity. Recently some statistical methods have been adapted to process Big Data, like lin…
Advances of modern sensing and sequencing technologies generate a deluge of high dimensional space-temporal physiological and next-generation sequencing (NGS) data. Physiological traits are observed either as continuous random functions, or on a dense grid and referred to as function-valued traits. Both physiological a…
Interpretable neural network for plant traits and species identification.
problem Plant phenotyping and identification.
method Neural network trained on UPWINS spectral library, with visualization of weights for trait-based spectral features.
result 90% accuracy in species identification with interpretable neural network.
GPIRT uses Gaussian processes to estimate latent traits and IRFs from binary responses.
problem Nonparametric IRT models struggle to estimate flexible IRFs and latent traits simultaneously.
method GPIRT employs Gaussian process priors to relax IRF assumptions while estimating latent traits.
result GPIRT provides a flexible solution to IRT challenges, including active learning.
Develops a new method to analyze brain networks for cognitive traits.
problem Challenges in summarizing and relating brain connectomes to human traits.
method Graph Auto-Encoding (GATE) model using deep learning.
result GATE improves prediction accuracy and efficiency over existing methods.
We propose a new heavy-tailed distribution --- Gaussian-Chain (GC) distribution, which is inspirited by the hierarchical structures prevailing in social organizations. We determine the mean, variance and kurtosis of the Gaussian-Chain distribution to show its heavy-tailed property, and compute the tail distribution tab…
A genome-wide association study (GWAS) correlates marker variation with trait variation in a sample of individuals. Each study subject is genotyped at a multitude of SNPs (single nucleotide polymorphisms) spanning the genome. Here we assume that subjects are unrelated and collected at random and that trait values are n…
Background doesn't affect personality predictions in deep networks.
problem Understanding how background images influence personality attribution in deep learning models.
method Explicitly studied the effect of background images on personality prediction in deep residual networks, controlling for confounds.
result Adding background information to input decreases model performance for personality trait prediction.
This paper investigates scalable Gaussian process regression methods for big data.
problem Scalability issues in Gaussian process regression for big data.
method Investigates various scalable Gaussian process methods including sparse approximations and local aggregations.
result Most scalable GPs have linear scalability to training size, capturing different spatial and local patterns.
Two methods use BART to model missing data in leaf photosynthetic trait data.
problem Handling missing data in multivariate outcomes with non-ignorable mechanisms.
method Bayesian Additive Regression Trees (BART) for joint modeling of data and missingness indicators.
result Both methods effectively recover various missingness mechanisms and outperform existing approaches.
Model predicts personality traits from Facebook Likes.
problem Predicting personality traits from social media data.
method Mapped Facebook Likes to page categories, used machine learning.
result 83% accuracy distinguishing religious vs non-religious.
Study presents MMC model for better fitting multiple choice data.
problem Improving accuracy of latent trait estimates in IRT models.
method Fit autoencoders to MMC model, demonstrating better fit than nominal response model.
result MMC model outperforms traditional IRT models in fit.
SharedRep-RLHF learns shared traits for diverse groups, improving fairness and performance.
problem Uniform-reward RLHF fails to capture diverse preferences, leading to unfairness.
method SharedRep-RLHF learns shared traits among various groups, improving fairness and performance.
result SharedRep-RLHF outperforms MaxMin-RLHF by up to 20% in win rate.
Neural network predicts nonlinear safety behavior based on personality traits.
problem Predicting construction workers' unsafe behaviors based on personality traits.
method Developed a forecasting model using neural network algorithms.
result Nonlinear relationship exists between personality traits and safety behavior.
This paper surveys enterprise financial risk analysis from Big Data and LLMs perspectives.
problem Predicting future financial risk of enterprises.
method Systematic literature review of enterprise financial risk analysis approaches from Big Data and LLMs perspectives.
result Offers a holistic synthesis of research methods and key insights.
Many biological characteristics of evolutionary interest are not scalar variables but continuous functions. Here we use phylogenetic Gaussian process regression to model the evolution of simulated function-valued traits. Given function-valued data only from the tips of an evolutionary tree and utilising independent pri…
Deep reinforcement learning finds optimal learning policies for adaptive systems.
problem Finding individualized learning plans for learners with unknown latent traits.
method Formulated as a Markov decision process, applied deep Q-learning with a transition model estimator.
result The algorithm efficiently discovers optimal learning policies with small data sets.
PerSense assesses personality traits from text for commonsense reasoning.
problem Estimating human personality traits from text for mental health analysis.
method Aggregated Probability Density Functions (PDF) and Machine Learning (ML) models.
result PerSense algorithms achieve comparable results to ground truth data, with high accuracy for personality assessment and commonsense prediction.
Model predicts cannabis use disorder risk for adolescents and young adults.
problem Predicting cannabis use disorder progression in adolescents and young adults.
method Bayesian machine learning model trained on longitudinal data.
result Model provides personalized risk assessment with AUC of 0.68-0.75 and E/O ratio of 0.95-1.
Ensemble unsupervised anomaly detection using IRT for hidden ground truth.
problem Challenges in constructing an ensemble from unsupervised anomaly detection methods.
method Use Item Response Theory to compute latent traits and construct an ensemble that downplays noisy methods.
result Demonstrated effectiveness of IRT ensemble on extensive data repository.
New method bypasses assumptions for unbiased estimation of complex system interactions.
problem Inferring pair-wise and higher-order interactions from observational data.
method Cross-disciplinary approach using Targeted Learning for unbiased estimation.
result Universal estimator of all-order symmetric interactions without parametric assumptions.
This work explores explainability in quantum machine learning.
problem Limited understanding of quantum machine learning models.
method Identifies research avenues and proposes two explanation methods.
result Provides a clear perspective on explainability in quantum learning.
A model predicts user movie preferences based on novelty-seeking traits.
problem Accurately predicting user movie preferences for competitive websites.
method DFNSM model uses demographic, genre, and novelty-seeking data.
result DFNSM outperforms previous models in movie recommendation accuracy.
Study compares financial and gambling markets, finding similarities and potential applications.
problem Lack of comprehensive study on gambling markets compared to financial markets.
method Comprehensive comparison of five aspects: platform, product, procedure, participant, and strategy.
result Well-established financial strategies can be applied to gambling markets, particularly in peer-to-peer betting exchanges.
Study provides long-term EMG data for multi-day biometric authentication.
problem Limited long-term EMG data for multi-day biometric authentication.
method Collected EMG data from 43 participants over three days.
result Multi-day biometric authentication results in low error rates.
New method identifies rare genetic markers for Alzheimer's disease using MRI and genomics.
problem Identifying rare genetic markers for Alzheimer's disease.
method Combining MRI data with genome sequencing, using CNNs for brain traits, and kernel-based tests for rare variants.
result CNNs provide precise brain traits and novel kernels identify rare genetic markers.
New estimators improve Rasch model item parameter estimation for sparse data.
problem Estimating item parameters in sparse Rasch model data.
method Random pairing maximum likelihood estimator (RP-MLE) and its bootstrapped variant (MRP-MLE).
result RP-MLE and MRP-MLE are minimax optimal and provide precise item parameter estimates.
Consider unsupervised clustering of objects drawn from a discrete set, through the use of human intelligence available in crowdsourcing platforms. This paper defines and studies the problem of universal clustering using responses of crowd workers, without knowledge of worker reliability or task difficulty. We model sto…