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Noise-Tolerant Hybrid Approach for Data-Driven Predictive Control
Mahmood Mazare, Hossein Ramezani
https://arxiv.org/abs/2506.20780 https://

Noise-Tolerant Hybrid Approach for Data-Driven Predictive Control
This paper focuses on a key challenge in hybrid data-driven predictive control: the effect of measurement noise on Hankel matrices. While noise is handled in direct and indirect methods, hybrid approaches often overlook its impact during trajectory estimation. We propose a Noise-Tolerant Data-Driven Predictive Control (NTDPC) framework that integrates singular value decomposition to separate system dynamics from noise within reduced-order Hankel matrices. This enables accurate prediction with s…

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection
Songsoo Kim, Seungtae Lee, See Young Lee, Joonho Kim, Keechan Kan, Dukyong Yoon
https://arxiv.org/abs/2506.20112

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection
Background: The positive predictive value (PPV) of large language model (LLM)-based proofreading for radiology reports is limited due to the low error prevalence. Purpose: To assess whether a three-pass LLM framework enhances PPV and reduces operational costs compared with baseline approaches. Materials and Methods: A retrospective analysis was performed on 1,000 consecutive radiology reports (250 each: radiography, ultrasonography, CT, MRI) from the MIMIC-III database. Two external datasets (C…

Improving elliptic curve rank classification using multi-value and learned Mestre-Nagao sums
Zvonimir Bujanovi\'c, Matija Kazalicki, Domagoj Vlah
https://arxiv.org/abs/2506.07967

Improving elliptic curve rank classification using multi-value and learned Mestre-Nagao sums
Determining the rank of an elliptic curve E/Q remains a central challenge in number theory. Heuristics such as Mestre--Nagao sums are widely used to estimate ranks, but there is considerable room for improving their predictive power. This paper introduces two novel methods for enhancing rank classification using Mestre--Nagao sums. First, we propose a ``multi-value'' approach that simultaneously uses two distinct sums, S_0 and S_5, evaluated over multiple ranges. This multi-sum perspective sign…

A Unified Theory of Compositionality, Modularity, and Interpretability in Markov Decision Processes
Thomas J. Ringstrom, Paul R. Schrater
https://arxiv.org/abs/2506.09499

A Unified Theory of Compositionality, Modularity, and Interpretability in Markov Decision Processes
We introduce Option Kernel Bellman Equations (OKBEs) for a new reward-free Markov Decision Process. Rather than a value function, OKBEs directly construct and optimize a predictive map called a state-time option kernel (STOK) to maximize the probability of completing a goal while avoiding constraint violations. STOKs are compositional, modular, and interpretable initiation-to-termination transition kernels for policies in the Options Framework of Reinforcement Learning. This means: 1) STOKs can…

This https://arxiv.org/abs/2410.06307 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Model Predictive Control is Almost Optimal for Restless Bandit
We consider the discrete time infinite horizon average reward restless markovian bandit (RMAB) problem. We propose a \emph{model predictive control} based non-stationary policy with a rolling computational horizon $τ$. At each time-slot, this policy solves a $τ$ horizon linear program whose first control value is kept as a control for the RMAB. Our solution requires minimal assumptions and quantifies the loss in optimality in terms of $τ$ and the number of arms, $N$. We show that its sub-opt…

This https://arxiv.org/abs/2411.13983 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

Learning Two-agent Motion Planning Strategies from Generalized Nash Equilibrium for Model Predictive Control
We introduce an Implicit Game-Theoretic MPC (IGT-MPC), a decentralized algorithm for two-agent motion planning that uses a learned value function that predicts the game-theoretic interaction outcomes as the terminal cost-to-go function in a model predictive control (MPC) framework, guiding agents to implicitly account for interactions with other agents and maximize their reward. This approach applies to competitive and cooperative multi-agent motion planning problems which we formulate as const…

Evaluating financial tail risk forecasts: Testing Equal Predictive Ability
Lukas Bauer
https://arxiv.org/abs/2505.23333 https://arxiv…

Evaluating financial tail risk forecasts: Testing Equal Predictive Ability
This paper provides comprehensive simulation results on the finite sample properties of the Diebold-Mariano (DM) test by Diebold and Mariano (1995) and the model confidence set (MCS) testing procedure by Hansen et al. (2011) applied to the asymmetric loss functions specific to financial tail risk forecasts, such as Value-at-Risk (VaR) and Expected Shortfall (ES). We focus on statistical loss functions that are strictly consistent in the sense of Gneiting (2011a). We find that the tests show lit…

GenDMR: A dynamic multimodal role-swapping network for identifying risk gene phenotypes
Lina Qin, Cheng Zhu, Chuqi Zhou, Yukun Huang, Jiayi Zhu, Ping Liang, Jinju Wang, Yixing Huang, Cheng Luo, Dezhong Yao, Ying Tan
https://arxiv.org/abs/2506.01456

GenDMR: A dynamic multimodal role-swapping network for identifying risk gene phenotypes
Recent studies have shown that integrating multimodal data fusion techniques for imaging and genetic features is beneficial for the etiological analysis and predictive diagnosis of Alzheimer's disease (AD). However, there are several critical flaws in current deep learning methods. Firstly, there has been insufficient discussion and exploration regarding the selection and encoding of genetic information. Secondly, due to the significantly superior classification value of AD imaging features com…