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.
Study examines cryptoasset service providers in Austria, revealing global integration and distinct responses to market shocks.
Deep learning for integrating diverse clinical measurements.
The size distribution of land plots is a result of land allocation processes in the past. In the absence of regulation this is a Markov process leading an equilibrium described by a probabilistic equation used commonly in the insurance and financial mathematics. We support this claim by analyzing the distribution of tw…
DRUM transfers cardiac arrest models across registries with missing covariates.
The public package registry npm is one of the biggest software registry. With its 216 911 software packages, it forms a big network of software dependencies. In this paper we evaluate various methods for finding similar packages in the npm network, using only the structure of the graph. Namely, we want to find a way of…
Objectives: Most cancer data sources lack information on metastatic recurrence. Electronic medical records (EMRs) and population-based cancer registries contain complementary information on cancer treatment and outcomes, yet are rarely used synergistically. To enable detection of metastatic breast cancer (MBC), we appl…
This paper presents the first deep reinforcement learning (DRL) framework to estimate the optimal Dynamic Treatment Regimes from observational medical data. This framework is more flexible and adaptive for high dimensional action and state spaces than existing reinforcement learning methods to model real-life complexit…
Over the last 23 years, the U.S. Securities and Exchange Commission has required over 34,000 companies to file over 165,000 annual reports. These reports, the so-called "Form 10-Ks," contain a characterization of a company's financial performance and its risks, including the regulatory environment in which a company op…
AdaptiveNet tackles disease progression prediction in rheumatoid arthritis using deep neural networks.
This paper examines market misconduct in DeFi and proposes regulatory solutions.
Paper develops a framework to discover bioprocessing regulatory mechanisms using symbolic and statistical learning.
In order to investigate the breast cancer prediction problem on the aging population with the grades of DCIS, we conduct a tree augmented naive Bayesian network experiment trained and tested on a large clinical dataset including consecutive diagnostic mammography examinations, consequent biopsy outcomes and related can…
regvis.net offers a visual survey of regulatory visualization.
Optimizes insurance profits under regulatory constraints.
Agent-to-agent finance aims to manage payments and trust for AI agents.
We present in this paper experiments on Table Recognition in hand-written registry books. We first explain how the problem of row and column detection is modeled, and then compare two Machine Learning approaches (Conditional Random Field and Graph Convolutional Network) for detecting these table elements. Evaluation wa…
TNDE quantifies dynamic gene drivers from single-cell snapshots.
In a market system, regulations are designed to prevent or rectify market failures that inhibit fair exchange, such as monopoly or transactions with hidden costs. Because regulations reduce profits to those possessing unfair advantage, these advantaged corporations (whether individuals, companies, or other collective o…
SYNTHONY selects tabular synthesizers based on stress profiling and user intent.
Paper constructs a CRRIX index to assess cryptocurrency market risks from regulatory changes.
InfoSEM infers gene regulatory networks without GT labels, improving performance.
New method constructs confidence bands for ODE models with unknown regulatory effects.
Proposes a new method for determining LGD discount rates based on cost of capital.
Gene regulatory networks play a crucial role in controlling an organism's biological processes, which is why there is significant interest in developing computational methods that are able to extract their structure from high-throughput genetic data. Many of these computational methods are designed to infer individual …
Funding is a cost to trading desks that they see as an input. Current FVA-related literature reflects this by also taking funding costs as an input, usually constant, and always risk-neutral. However, this funding curve is the output from a Treasury point of view. Treasury must consider Regulatory-required liquidity bu…
Simple method calculates WWR for regulatory and accounting purposes.
DASH simplifies neural networks for gene regulatory dynamics using domain knowledge.
Gene regulatory networks play a crucial role in controlling an organism's biological processes, which is why there is significant interest in developing computational methods that are able to extract their structure from high-throughput genetic data. A typical approach consists of a series of conditional independence t…
Cryptocurrency markets show similar returns but different volatility responses to infrastructure and regulatory shocks.
New framework for adaptive clinical trials to address real-world challenges.
Efficiently infers gene regulatory networks from spatial data.
SHARC explains machine learning risk models for regulatory capital, linking outputs to scenarios.
Robust machine learning models improve DNA regulatory sequence prediction under various shifts.
Develops probabilistic models for gene regulatory network inference.
Study examines how business units can benefit from group cohesion under regulatory constraints.
Study analyzes FIT schemes under market and regulatory uncertainty.
The DAO Report led to a significant shift of ICO activity to Europe.
Machine learning identifies key metabolic control circuits in bacterial pathways.
Constructing gene regulatory networks is a critical step in revealing disease mechanisms from transcriptomic data. In this work, we present NO-BEARS, a novel algorithm for estimating gene regulatory networks. The NO-BEARS algorithm is built on the basis of the NOTEARS algorithm with two improvements. First, we propose …
Reconstructing transcriptional regulatory networks is an important task in functional genomics. Data obtained from experiments that perturb genes by knockouts or RNA interference contain useful information for addressing this reconstruction problem. However, such data can be limited in size and/or are expensive to acqu…
Model predicts trade volume changes from financial filings.
Regulations impose idiosyncratic capital and funding costs for holding derivatives. Capital requirements are costly because derivatives desks are risky businesses; funding is costly in part because regulations increase the minimum funding tenor. Idiosyncratic costs mean no single measure makes derivatives martingales f…
We present a Bayesian hierarchical multi-view mixture model termed Symphony that simultaneously learns clusters of cells representing cell types and their underlying gene regulatory networks by integrating data from two views: single-cell gene expression data and paired epigenetic data, which is informative of gene-gen…
FinDiff generates synthetic financial data for regulatory tasks.
New framework makes ML methods compliant with regulations.
In 1999 Robert Fernholz observed an inconsistency between the normative assumption of existence of an equivalent martingale measure (EMM) and the empirical reality of diversity in equity markets. We explore a method of imposing diversity on market models by a type of antitrust regulation that is compatible with EMMs. T…
Regulatory compliance is an organization's adherence to laws, regulations, guidelines and specifications relevant to its business. Compliance officers responsible for maintaining adherence constantly struggle to keep up with the large amount of changes in regulatory requirements. Keeping up with the changes entail two …
RSI uses Bayesian inference to monitor compliance in rule-governed domains.