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.
Robo-advisors estimate clients' risk aversion using interactive questionnaires.
The study analyzes how large language models form and express investor risk profiles.
MOAI evaluates indoor airflow's impact on COVID-19 transmission.
We consider the problem of identifying the most profitable product design from a finite set of candidates under unknown consumer preference. A standard approach to this problem follows a two-step strategy: First, estimate the preference of the consumer population, represented as a point in part-worth space, using an ad…
A framework uses preprocessing to improve psychiatric questionnaire predictions while maintaining interpretability.
Can humans impute missing data with similar proficiency as machines? This is the question we aim to answer in this paper. We present a novel idea of converting observations with missing data in to a survey questionnaire, which is presented to crowdworkers for completion. We replicate a multiple imputation framework by …
Early diagnosis is important for type 2 diabetes (T2D) to improve patient prognosis, prevent complications and reduce long-term treatment costs. We present a novel risk profiling approach based exclusively on health expenditure data that is available to Belgian mutual health insurers. We used expenditure data related t…
New method detects careless responding in long surveys.
GPT-4 assesses its confidence in answering USMLE questions with and without feedback.
Prediction of disease onset from patient survey and lifestyle data is quickly becoming an important tool for diagnosing a disease before it progresses. In this study, data from the National Health and Nutrition Examination Survey (NHANES) questionnaire is used to predict the onset of type II diabetes. An ensemble model…
The main aim of this paper is to inspect the properties of survey based on households inflation expectations, conducted by Reserve Bank of India. It is theorized that the respondents answers are exaggerated by extreme response bias. Latent class analysis has been hailed as a promising technique for studying measurement…
TabSODA improves imputation of surveys with skips and ordinal data.
In many countries information on expectations collected through consumer confidence surveys are used in macroeconomic policy formulation. Unfortunately, before doing so, the consistency of responses is often not taken into account, leading to biases creeping in and affecting the reliability of the indices hence created…
Ordinal data is omnipresent in almost all multiuser-generated feedback - questionnaires, preferences etc. This paper investigates modelling of ordinal data with Gaussian restricted Boltzmann machines (RBMs). In particular, we present the model architecture, learning and inference procedures for both vector-variate and …
Study explores factors influencing saving behavior among Dhaka employees.
This essay reviews human observer-based methods employed in shark spotting in Muizenberg Beach. It investigates Machine Learning methods for automated shark detection with the aim of enhancing human observation. A questionnaire and interview were used to collect information about shark spotting, the motivation of the a…
Study predicts online procrastination using machine learning.
Archetypal analysis helps understand binary data sets.
New algorithm estimates intrinsic dimension of discrete datasets.
Process discovery has seen a rise in popularity in the last decade for both researchers and businesses. Recent developments mainly focused on the power and the functionalities of the discovery algorithm. While continuous improvement of these functional aspects is very important, non-functional aspects such as visualiza…
Is it possible to predict the motivation of players just by observing their gameplay data? Even if so, how should we measure motivation in the first place? To address the above questions, on the one end, we collect a large dataset of gameplay data from players of the popular game Tom Clancy's The Division. On the other…
Survey of techniques for diagnosing pediatric sleep apnea from inexpensive data.
Method completes mixed matrix from complex surveys with heterogeneous missingness.
Researchers study how teachers' advising relationships influence their perceptions of satisfaction and students, not policy influence.
The aim of this paper is to get an overview of the online buyer profile, and also some key aspects in the way the online shopping is conducted. In this project we conducted a quantitative research, consisting of a questionnaire based survey. For data processing and interpretation we used SPSS statistical software and E…
Model predicts increased social unrest during COVID-19 using social media data.
A new personality-based recommender system tackles data sparsity without feedback.
Study reveals LLM personas have two distinct components: frame-robust aggregated traits and frame-dependent geometric features.
Client appraisal improves efficiency in microfinance banks in Adamawa State.
Sepsis is the leading cause of death in non-coronary intensive care units. Moreover, a delay of antibiotic treatment of patients with severe sepsis by only few hours is associated with increased mortality. This insight makes accurate models for early prediction of sepsis a key task in machine learning for healthcare. P…
The abstract covers various aspects of eBusiness and eGovernment, including digital currencies, m-government services, gender inclusivity, eLearning, export performance, SME digitalization, and banking customer behavior.
Study shows financial literacy, social capital, and financial tech positively impact financial inclusion of Indonesian students.
Tangles improve clustering in various datasets.
The paper proposes a test to determine the number of latent classes in ordinal categorical data.
User surveys for Quality of Experience (QoE) are a critical source of information. In addition to the common "star rating" used to estimate Mean Opinion Score (MOS), more detailed survey questions (problem tokens) about specific areas provide valuable insight into the factors impacting QoE. This paper explores two aspe…
New estimators improve Rasch model item parameter estimation for sparse data.
Simulation workflow is a top-level model for the design and control of simulation process. It connects multiple simulation components with time and interaction restrictions to form a complete simulation system. Before the construction and evaluation of the component models, the validation of upper-layer simulation work…
Prototypical examples that best summarizes and compactly represents an underlying complex data distribution communicate meaningful insights to humans in domains where simple explanations are hard to extract. In this paper we present algorithms with strong theoretical guarantees to mine these data sets and select protot…
The present research aims to highlight the main factors influencing the development of entrepreneurial innovation in a rural environment and to perform an empirical study with the purpose of assessing the main problems in rural development. The research performed is mostly of a quantitative nature, being based on the u…
New Bayesian method for sparse multidimensional item response theory.
Automatic machine learning-based detectors of various psychological and social phenomena (e.g., emotion, stress, engagement) have great potential to advance basic science. However, when a detector is trained to approximate an existing measurement tool (e.g., a questionnaire, observation protocol), then care must be…
Novel approach models life events using causal discovery and survival analysis.
Imaging fluorescent disease biomarkers in tissues and skin is a non-invasive method to screen for health conditions. We report an automated process that combines intraoral fluorescent porphyrin biomarker imaging, clinical examinations and machine learning for correlation of systemic health conditions with periodontal d…
Sparse GFA identifies disease factors in FTD subgroups.
An efficient algorithm selects the correct number of latent dimensions in multidimensional probit models.
Student performance modelling (SPM) is a critical step to assessing and improving students performances in their learning discourse. However, most existing SPM are based on statistical approaches, which on one hand are based on probability, depicting that results are based on estimation; and on the other hand, actual i…
The growth of the modern knowledge-based economy is becoming less and less dependent on tangible assets and more on intangible ones. In this context, the role of human capital in the value creation process has become central. Despite the large amount of scientific work on human capital phenomena, little research has re…
Bluetooth data predicts depression severity, showing 18.8% extra variance.