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arXiv research

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.

168,695 papers · 148 categories

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12253749 · Jun 202019922001200920172026
48 results for digital platforms

A monitoring procedure improves machine learning forecasts for digital platforms.

problem Maintaining accurate and stable forecasts for data streams at digital platforms.
method Developed a monitoring procedure to determine when to retrain machine learning algorithms.
result Monitor-based retraining produces accurate forecasts compared to benchmarks.

A method for fast, accurate cross-temporal forecasts using machine learning.

problem Inconsistent forecasts across different levels of platform data.
method Non-linear hierarchical forecast reconciliation using machine learning.
result Automated direct production of reconciled forecasts for high-frequency decision making.

Study shows small groups can influence machine learning algorithms.

problem How small groups can influence machine learning algorithms deployed on digital platforms.
method Proposed a theoretical model and conducted experiments on a large-scale language model.
result Small groups can exert significant control over machine learning algorithms.

A new method streamlines digital payment programming using smart contracts.

problem High costs and security challenges in programming smart contracts for digital payments.
method Transforming digital currencies into token streams and using configurable templates to generate specialized smart contracts.
result Reduces payment programming costs and enhances security, self-enforcement, adaptability, and controllability.

Digital tools may hinder or facilitate multidisciplinary collaboration in occupational health.

problem Challenges in multidisciplinary collaboration in occupational health.
method Study of a precursor SPSTI's digital tools and their impacts.
result Digital tools can act as both brakes and levers for multidisciplinary dynamics.

Applying traditional collaborative filtering to digital publishing is challenging because user data is very sparse due to the high volume of documents relative to the number of users. Content based approaches, on the other hand, is attractive because textual content is often very informative. In this paper we describe …

2015-01-18abs ↗pdf ↗

This study evaluates Algorand and Ethereum 2.0 for blockchain trilemma challenges.

problem Balancing decentralization, security, and scalability in blockchain systems.
method Comparative analysis of Algorand and Ethereum 2.0 using metrics for decentralization, scalability, and security.
result Each system has strengths in addressing the blockchain trilemma, providing insights for other blockchain technologies.

(ABRIDGED) In previous work, two platforms have been developed for testing computer-vision algorithms for robotic planetary exploration (McGuire et al. 2004b,2005; Bartolo et al. 2007). The wearable-computer platform has been tested at geological and astrobiological field sites in Spain (Rivas Vaciamadrid and Riba de S…

2009-10-28abs ↗pdf ↗

Research identifies four motivational groups for crypto-metaverse landowners.

problem Understanding motivations of retail investors in the crypto-metaverse.
method Detailed financial behavior survey and principal components analysis.
result Four distinct motivational groups identified: Aesthetics, Social, Speculation, Innovation.

Paper develops security model and pricing for stable digital currency in quantum blockchain network.

problem Securing and pricing stable digital currency in a quantum blockchain network.
method Developed a block-based quantum channel networking technology and a FinTech platform model with dynamic pricing.
result Established a generalized IoB security model using quantum channel networking and QKD.

The paper analyzes RfQ processes on MD2C platforms using probabilistic models.

problem Analyzing the negotiation process on MD2C platforms to ensure dealers' profitability.
method Probabilistic graphical models and causal inference.
result Generative models can match predictive accuracy of discriminative models while enforcing business requirements.

Interest surrounding cryptocurrencies, digital or virtual currencies that are used as a medium for financial transactions, has grown tremendously in recent years. The anonymity surrounding these currencies makes investors particularly susceptible to fraud---such as "pump and dump" scams---where the goal is to artificia…

2019-02-04abs ↗pdf ↗

New method identifies how platforms can influence consumer behavior.

problem Estimating the causal effect of digital platforms on consumption.
method General causal inference problem, focusing on observational designs, and explicitly modeling consumption dynamics.
result Exogenous variation in consumption and responsive algorithmic control actions are sufficient for identifying steerability of consumption.

In this note we describe the application of existing smart contract technologies with the aim to construct a new digital representation of a financial derivative contract. We compare several existing DLT based technologies. We provide a detailed description of two separate prototypes which are able to be executed on a …

2019-02-26abs ↗pdf ↗

It is not clear how to target patients who are most likely to benefit from digital care management programs ex-ante, a shortcoming of current risk score based approaches. This study focuses on defining impactability by identifying those patients most likely to benefit from technology enabled care management, delivered …

2019-04-19abs ↗pdf ↗

An average adult is exposed to hundreds of digital advertisements daily (https://www.mediadynamicsinc.com/uploads/files/PR092214-Note-only-150-Ads-2mk.pdf), making the digital advertisement industry a classic example of a big-data-driven platform. As such, the ad-tech industry relies on historical engagement logs (clic…

2020-02-07abs ↗pdf ↗

Financial fraud detection in digital banking requires reasoning over multiple heterogeneous event streams.

problem Financial fraud detection in digital banking requires reasoning over multiple heterogeneous event streams.
method Multi-Stream Fraud Transformer (MSFT) architecture that encodes each event stream with independent Transformer encoders and fuses their representations through configurable mechanisms.
result Sequence models significantly outperform gradient-boosted trees operating on aggregated features.

It takes skill to build a meaningful predictive model even with the abundance of implementations of modern machine learning algorithms and readily available computing resources. Building a model becomes challenging if hundreds of terabytes of data need to be processed to produce the training data set. In a digital adve…

2014-02-25abs ↗pdf ↗

The paper optimizes A/B tests by balancing lift and cost in large-scale settings.

problem Balancing lift and cost in A/B tests for large-scale experimentation.
method Empirical Bayes approach using a greedy knapsack algorithm to rank experiments based on lift-to-cost ratio, incorporating local false discovery rate (lfdr).
result The proposed method maximizes expected profit while controlling false discovery rate, demonstrating superior performance in large-scale settings.

The paper introduces reservoir computing models for complex systems.

problem Modeling complex engineering systems using nonlinear autoregression.
method Introduces reservoir computing with output feedback as stationary and ergodic infinite-order nonlinear autoregressive models.
result Demonstrates versatility of classical and quantum reservoir computers in modeling synthetic and real data.

Study analyzes factors influencing healthcare providers' engagement with SMS campaigns.

problem Understanding what drives healthcare providers to engage with SMS campaigns.
method Used logistic regression, random forest, and neural network models to analyze data.
result Identified key factors influencing engagement with SMS campaigns.

A dataset for detecting online hate speech from YouTube and Reddit comments.

problem Detecting and preventing hate speech on social media platforms.
method Created a dataset with two variants: binary and multi-label, based on YouTube and Reddit comments, using Figure-Eight crowdsourcing platform.
result Demonstrated that even a small amount of labelled data can help detect hate speech occurrences.

This paper uses LLMs to streamline industrial data-centric R&D cycles.

problem High costs in human, computational, and time resources in data-centric R&D.
method Explores how large language models can understand domain-specific requirements and automate R&D tasks.
result Promising results show potential for automating industrial data-centric R&D cycles.

This paper highlights new opportunities for designing large-scale machine learning systems as a consequence of blurring traditional boundaries that have allowed algorithm designers and application-level practitioners to stay -- for the most part -- oblivious to the details of the underlying hardware-level implementatio…

2014-09-09abs ↗pdf ↗

This paper examines how wash traders exploit market conditions in Bitcoin, finding strategic timing and spillover effects.

problem Wash trading in cryptocurrency markets to inflate volume and manipulate market conditions.
method Analysis of 18 million Mt. Gox transactions, exogenous demand shock study.
result Wash trading intensifies in low legitimate trading volume and responds to demand shocks, indicating strategic behavior.

A generative model based on training deep architectures is proposed. The model consists of K networks that are trained together to learn the underlying distribution of a given data set. The process starts with dividing the input data into K clusters and feeding each of them into a separate network. After few iterations…

2017-02-10abs ↗pdf ↗