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This https://arxiv.org/abs/2412.03970 has been replaced.
initial toot: https://mastoxiv.page/@ar…

A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems
In complex physical systems, conventional differential equations often fall short in capturing non-local and memory effects, as they are limited to local dynamics and integer-order interactions. This study introduces a stepwise data-driven framework for discovering fractional differential equations (FDEs) directly from data. FDEs, known for their capacity to model non-local dynamics with fewer parameters than integer-order derivatives, can represent complex systems with long-range interactions.…

Modern search demands scalable personalisation. Join Piotr Kobziakowski
at this year's Berlin Buzzwords to discover how Vespa's multi-stage ranking and tensor framework can be used for hybrid queries, multimodal retrieval, and real-time machine learning. Learn how to deploy low-latency, high-relevance search systems at petabyte scale.
Learn more:

Vespa.ai’s Personalized Search: Advanced Ranking & Tensor framework
Today’s applications require search engines to unify text, vectors, and business logic with millisecond latency at petabyte scale. It’s not easy to balance speed, relevance, and personalization for a large user population and a billion scale item base. Vespa.ai, the open-source engine powering Yahoo, Perplexity, Qwant, Vinted, Spotify addresses this through multi-stage ranking with close to data tensor operations and easy to understand custom functions. Vespa’s phased architecture enables…

StarBASE-GP: Biologically-Guided Automated Machine Learning for Genotype-to-Phenotype Association Analysis
Jose Guadalupe Hernandez, Attri Ghosh, Philip J. Freda, Yufei Meng, Nicholas Matsumoto, Jason H. Moore
https://arxiv.org/abs/2505.22746

StarBASE-GP: Biologically-Guided Automated Machine Learning for Genotype-to-Phenotype Association Analysis
We present the Star-Based Automated Single-locus and Epistasis analysis tool - Genetic Programming (StarBASE-GP), an automated framework for discovering meaningful genetic variants associated with phenotypic variation in large-scale genomic datasets. StarBASE-GP uses a genetic programming-based multi-objective optimization strategy to evolve machine learning pipelines that simultaneously maximize explanatory power (r2) and minimize pipeline complexity. Biological domain knowledge is integrated …

Split the Yield, Share the Risk: Pricing, Hedging and Fixed rates in DeFi
Viraj Nadkarni, Pramod Viswanath
https://arxiv.org/abs/2505.22784 https://…

Split the Yield, Share the Risk: Pricing, Hedging and Fixed rates in DeFi
We present the first formal treatment of \emph{yield tokenization}, a mechanism that decomposes yield-bearing assets into principal and yield components to facilitate risk transfer and price discovery in decentralized finance (DeFi). We propose a model that characterizes yield token dynamics using stochastic differential equations. We derive a no-arbitrage pricing framework for yield tokens, enabling their use in hedging future yield volatility and managing interest rate risk in decentralized l…

Modeling the Path of Structural Strategic Deterrence: A Sand Table Simulation and Research Report on China's Military-Industrial Capability System against the United States Based on Rare Earth Supply Disconnection
Wei Meng
https://arxiv.org/abs/2505.21579

Modeling the Path of Structural Strategic Deterrence: A Sand Table Simulation and Research Report on China's Military-Industrial Capability System against the United States Based on Rare Earth Supply Disconnection
This study proposes a systematic non-kinetic deterrence path modeling framework based on strategic rare earth supply cut-off, aiming to assess the strategic effects of China's export control policy against the United States at the military system level. The model adopts a four-layer structure of "policy input -- resource node -- equipment system -- capability output" and integrates path dependency modeling, degradation function design, and capability lag prediction mechanisms to form a strategi…

StarBASE-GP: Biologically-Guided Automated Machine Learning for Genotype-to-Phenotype Association Analysis
Jose Guadalupe Hernandez, Attri Ghosh, Philip J. Freda, Yufei Meng, Nicholas Matsumoto, Jason H. Moore
https://arxiv.org/abs/2505.22746

StarBASE-GP: Biologically-Guided Automated Machine Learning for Genotype-to-Phenotype Association Analysis
We present the Star-Based Automated Single-locus and Epistasis analysis tool - Genetic Programming (StarBASE-GP), an automated framework for discovering meaningful genetic variants associated with phenotypic variation in large-scale genomic datasets. StarBASE-GP uses a genetic programming-based multi-objective optimization strategy to evolve machine learning pipelines that simultaneously maximize explanatory power (r2) and minimize pipeline complexity. Biological domain knowledge is integrated …

This https://arxiv.org/abs/2412.04265 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

On Extrapolation of Treatment Effects in Multiple-Cutoff Regression Discontinuity Designs
Regression discontinuity (RD) designs typically identify the treatment effect at a single cutoff point, which may not always be the primary interest. When and how, then, can we learn about treatment effects away from the cutoff? This paper addresses this question within a multiple-cutoff RD framework. We begin by examining the plausibility of the constant bias assumption proposed by Cattaneo, Keele, Titiunik, and Vazquez-Bare (2021) through the lens of rational decision-making behavior. We argu…

This https://arxiv.org/abs/2504.16152 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Heterogeneous networks in drug-target interaction prediction
Drug discovery requires a tremendous amount of time and cost. Computational drug-target interaction prediction, a significant part of this process, can reduce these requirements by narrowing the search space for wet lab experiments. In this survey, we provide comprehensive details of graph machine learning-based methods in predicting drug-target interaction, as they have shown promising results in this field. These details include the overall framework, main contribution, datasets, and their so…

NewsNet-SDF: Stochastic Discount Factor Estimation with Pretrained Language Model News Embeddings via Adversarial Networks
Shunyao Wang, Ming Cheng, Christina Dan Wang
https://arxiv.org/abs/2505.06864

NewsNet-SDF: Stochastic Discount Factor Estimation with Pretrained Language Model News Embeddings via Adversarial Networks
Stochastic Discount Factor (SDF) models provide a unified framework for asset pricing and risk assessment, yet traditional formulations struggle to incorporate unstructured textual information. We introduce NewsNet-SDF, a novel deep learning framework that seamlessly integrates pretrained language model embeddings with financial time series through adversarial networks. Our multimodal architecture processes financial news using GTE-multilingual models, extracts temporal patterns from macroecono…