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.
Develops a new method to analyze brain networks for cognitive traits.
Recent advances in the fields of machine learning and neurofinance have yielded new exciting research perspectives in practical inference of behavioural economy in financial markets and microstructure study. We here present the latest results from a recently published stock market simulator built around a multi-agent s…
PSI-KT improves KT accuracy and interpretability in learning materials.
BrainSurfCNN predicts task contrasts from resting-state fingerprints, improving accuracy over baseline.
Genome-wide association studies (GWAS) have emerged as a rich source of genetic clues into disease biology, and they have revealed strong genetic correlations among many diseases and traits. Some of these genetic correlations may reflect causal relationships. We developed a method to quantify causal relationships betwe…
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…
Study reveals LLM personality patterns but lacks behavioral consistency.
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, …
New game design method for better trait inference.
Heterosis is the improved or increased function of any biological quality in a hybrid offspring. We have studied yet the largest maize SNP dataset for traits prediction. We develop linear and non-linear models which consider relationships between different hybrids as well as other effect. Specially designed model prove…
Ecologists have long suspected that species are more likely to interact if their traits match in a particular way. For example, a pollination interaction may be more likely if the proportions of a bee's tongue fit a plant's flower shape. Empirical estimates of the importance of trait-matching for determining species in…
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.
Interactive EMA combines multiple explainability methods to improve model understanding.
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.
GPIRT uses Gaussian processes to estimate latent traits and IRFs from binary responses.
Study identifies personality traits from dance movements in music.
Availability of an explainable deep learning model that can be applied to practical real world scenarios and in turn, can consistently, rapidly and accurately identify specific and minute traits in applicable fields of biological sciences, is scarce. Here we consider one such real world example viz., accurate identific…
Model predicts cognitive health risks based on smartphone usage patterns.
The paper proposes a new framework to generate synthetic data with human-like imperfections to prevent model collapse.
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…
Phylogeny is the field of modelling the temporal discrete dynamics of speciation. Complex models can nowadays be studied using the Approximate Bayesian Computation approach which avoids likelihood calculations. The field's progression is hampered by the lack of robust software to estimate the numerous parameters of the…
Background doesn't affect personality predictions in deep networks.
Two methods use BART to model missing data in leaf photosynthetic trait data.
Study shows cognitive load impacts financial market efficiency, especially for less sophisticated investors.
A cognitive model of human learning provides information about skills a learner must acquire to perform accurately in a task domain. Cognitive models of learning are not only of scientific interest, but are also valuable in adaptive online tutoring systems. A more accurate model yields more effective tutoring through b…
Pension benefits in rural China lead to cognitive decline among the elderly.
Study presents MMC model for better fitting multiple choice data.
SharedRep-RLHF learns shared traits for diverse groups, improving fairness and performance.
Neural network predicts nonlinear safety behavior based on personality traits.
Cognitive neuroscience is enjoying rapid increase in extensive public brain-imaging datasets. It opens the door to large-scale statistical models. Finding a unified perspective for all available data calls for scalable and automated solutions to an old challenge: how to aggregate heterogeneous information on brain func…
In the eyes of a rationalist like Descartes or Spinoza, human reasoning is flawless, marching toward uncovering ultimate truth. A few centuries later, however, culminating in the work of Kahneman and Tversky, human reasoning was portrayed as anything but flawless, filled with numerous misjudgments, biases, and cognitiv…
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.
The paper investigates how ambiguous data and cognitive biases affect machine learning in humanitarian decision making.
New method identifies latent variables in cognitive models using neural networks.
PerSense assesses personality traits from text for commonsense reasoning.
Multimodal analysis assesses job interview performance and provides feedback.
While the interpretability of machine learning models is often equated with their mere syntactic comprehensibility, we think that interpretability goes beyond that, and that human interpretability should also be investigated from the point of view of cognitive science. The goal of this paper is to discuss to what exten…
Ensemble unsupervised anomaly detection using IRT for hidden ground truth.
Study links cognitive effort to thermodynamic principles, optimizing decision-making.
New filters improve radar target inference in complex scenarios.
Accurate diagnosis of Alzheimer's Disease (AD) entails clinical evaluation of multiple cognition metrics and biomarkers. Metrics such as the Alzheimer's Disease Assessment Scale - Cognitive test (ADAS-cog) comprise multiple subscores that quantify different aspects of a patient's cognitive state such as learning, memor…
For effective treatment of Alzheimer disease (AD), it is important to identify subjects who are most likely to exhibit rapid cognitive decline. Herein, we developed a novel framework based on a deep convolutional neural network which can predict future cognitive decline in mild cognitive impairment (MCI) patients using…
Study improves robustness of Bayesian inference for cognitive models.
Develops an inverse particle filter for cognitive systems.
New methods make Bayesian inference feasible for complex cognitive models.