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.
Donors who defer their donations volunteer less in the future.
ClusterSC improves synthetic control by selecting relevant donor groups.
A method to detect spillover effects and select valid donors for synthetic control models.
Paper learns data-driven organ matching rules from observational data.
Donor-aware scRNA-seq benchmarks improve classification accuracy in inflammatory bowel disease.
The burgeoning need for kidney transplantation mandates immediate attention. Mismatch of deceased donor-recipient kidney leads to post-transplant death. To ensure ideal kidney donor-recipient match and minimize post-transplant deaths, the paper develops a prediction model that identifies factors that determine the prob…
Paper uses ML to classify liver diseases from clinical data.
Organic Solar Cells are a promising technology for solving the clean energy crisis in the world. However, generating candidate chemical compounds for solar cells is a time-consuming process requiring thousands of hours of laboratory analysis. For a solar cell, the most important property is the power conversion efficie…
Prediction markets can shape political behavior through persistent signals, not just forecast accuracy.
In this study, we introduce a new approach to curve pairs by using integral curves. We consider the direction curve and donor curve to study curve couples such as involute-evolute curves, Mannheim partner curves and Bertrand partner curves. We obtain new methods to construct partner curves of a unit speed curve and giv…
In this paper, we introduce a new approach to non-lightlike curve pairs by using integral curves in Minkowski 3-space. We consider direction curve and donor curve to study non-lightlike curve couples such as involute-evolute curves, Mannheim partner curves and Bertrand partner curves. We obtain new methods to construct…
Differentially private synthetic control estimates treatment effects while protecting privacy.
SFC aims to protect the Amazon with a digital currency and smart contracts.
Cooperation is a persistent behavioral pattern of entities pooling and sharing resources. Its ubiquity in nature poses a conundrum. Whenever two entities cooperate, one must willingly relinquish something of value to the other. Why is this apparent altruism favored in evolution? Classical solutions assume a net fitness…
We present a robust generalization of the synthetic control method for comparative case studies. Like the classical method, we present an algorithm to estimate the unobservable counterfactual of a treatment unit. A distinguishing feature of our algorithm is that of de-noising the data matrix via singular value threshol…
Reflective of income and wealth distributions, philanthropic gifting appears to follow an approximate power-law size distribution as measured by the size of gifts received by individual institutions. We explore the ecology of gifting by analysing data sets of individual gifts for a diverse group of institutions dedicat…
The kind of realized mission inflows the sensitivity to risk. Among other factors, the risk results from decision about liquid assets investment level and liquid assets financing. The higher the risk exposure, the higher the level of liquid assets. If the specific risk exposure is smaller, the more aggressive could be …
Charities can increase donations by targeting optimal recipients.
Machine learning improves kidney transplant outcomes prediction.
Molecular structure-property relationships are key to molecular engineering for materials and drug discovery. The rise of deep learning offers a new viable solution to elucidate the structure-property relationships directly from chemical data. Here we show that the performance of graph convolutional networks (GCNs) for…
Over the last years, huge resources of biological and medical data have become available for research. This data offers great chances for machine learning applications in health care, e.g. for precision medicine, but is also challenging to analyze. Typical challenges include a large number of possibly correlated featur…
Paper predicts IVF pregnancy rates from basic patient info.
IGSD separates task-specific content channels in transformer components by comparing activation replacement with zero ablation.
Generative Distribution Embeddings learn multiscale representations of distributions.
Motivation: Thanks to digitization, we often have access to large databases, consisting of various fields of information, ranging from numbers to texts and even boolean values. Such databases lend themselves especially well to machine learning, classification and big data analysis tasks. We are able to train classifier…
Improved logistic regression for multi-omics data improves prediction and variable selection.
Model captures neural activity related to behavior while separating internal computations.
Behavior modification improves prediction accuracy by nudging user behavior.
LISBET automates social behavior analysis using machine learning.
The paper introduces new metrics for evaluating generative models of behavior.
Mobile phones can record individual's daily behavioral data as a time-series. In this paper, we present an effective time-series segmentation technique that extracts optimal time segments of individual's similar behavioral characteristics utilizing their mobile phone data. One of the determinants of an individual's beh…
Study reveals LLM personality patterns but lacks behavioral consistency.
Herd behavior is an important economic phenomenon, especially in the context of the recent financial crises. In this paper, herd behavior in global stock markets is investigated with a focus on intercontinental comparison. Since most existing herd behavior indices do not provide a comparative method, we propose a new h…
Method learns behavioral states from wearable sensor data.
Interactive news recommendation has been launched and attracted much attention recently. In this scenario, user's behavior evolves from single click behavior to multiple behaviors including like, comment, share etc. However, most of the existing methods still use single click behavior as the unique criterion of judging…
Study examines if LLMs' trading styles match real market behavior.
A test measures artificial agents' human-like behavior in video games.
Method uses DNNs to approximate functions with specific asymptotic behavior.
Study models Ricci flow on complex surfaces, showing mixed behavior.
Model predicts human food choices based on demographics.
We consider the problem of off-policy evaluation in Markov decision processes. Off-policy evaluation is the task of evaluating the expected return of one policy with data generated by a different, behavior policy. Importance sampling is a technique for off-policy evaluation that re-weights off-policy returns to account…
Paper develops a framework for learning interpretable representations of sequential decision behavior.
Study uses contrastive learning to analyze market order behavior.
Collective behavior of the complex socio-economic systems is heavily influenced by the herding, group, behavior of individuals. The importance of the herding behavior may enable the control of the collective behavior of the individuals. In this contribution we consider a simple agent-based herding model modified to inc…
Study asymptotic behaviors of solutions near singular boundaries for the Yamabe problem.
We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. …
Study uses ML to analyze financial behavior in big data.
Revisits behavioral finance option pricing model to align with rational asset pricing theory.