Tootfinder

Robust Real-Time Coordination of CAVs: A Distributed Optimization Framework under Uncertainty
Haojie Bai, Yang Wang, Cong Guo, Xiongwei Zhao, Hai Zhu
https://arxiv.org/abs/2508.21322

Robust Real-Time Coordination of CAVs: A Distributed Optimization Framework under Uncertainty
Achieving both safety guarantees and real-time performance in cooperative vehicle coordination remains a fundamental challenge, particularly in dynamic and uncertain environments. This paper presents a novel coordination framework that resolves this challenge through three key innovations: 1) direct control of vehicles' trajectory distributions during coordination, formulated as a robust cooperative planning problem with adaptive enhanced safety constraints, ensuring a specified level of safety…

Unsupervised Learning in a General Semiparametric Clusterwise Index Distribution Model
Jen-Chieh Teng, Chin-Tsang Chiang
https://arxiv.org/abs/2509.24987 https://

Unsupervised Learning in a General Semiparametric Clusterwise Index Distribution Model
This study introduces a general semiparametric clusterwise index distribution model to analyze how latent clusters affect the covariate-response relationships. By employing sufficient dimension reduction to account for the effects of covariates on the cluster variable, we develop a distinct method for estimating model parameters. Building on a subjectwise representation of the underlying model, the proposed separation penalty estimation method partitions individuals and estimates cluster index …

Conditional Risk Minimization with Side Information: A Tractable, Universal Optimal Transport Framework
Xinqiao Xie, Jonathan Yu-Meng Li
https://arxiv.org/abs/2509.23128 https:/…

Conditional Risk Minimization with Side Information: A Tractable, Universal Optimal Transport Framework
Conditional risk minimization arises in high-stakes decisions where risk must be assessed in light of side information, such as stressed economic conditions, specific customer profiles, or other contextual covariates. Constructing reliable conditional distributions from limited data is notoriously difficult, motivating a series of optimal-transport-based proposals that address this uncertainty in a distributionally robust manner. Yet these approaches remain fragmented, each constrained by its o…

Robust Semiparametric Inference for Bayesian Additive Regression Trees
Christoph Breunig, Ruixuan Liu, Zhengfei Yu
https://arxiv.org/abs/2509.24634 https://

Robust Semiparametric Inference for Bayesian Additive Regression Trees
We develop a semiparametric framework for inference on the mean response in missing-data settings using a corrected posterior distribution. Our approach is tailored to Bayesian Additive Regression Trees (BART), which is a powerful predictive method but whose nonsmoothness complicate asymptotic theory with multi-dimensional covariates. When using BART combined with Bayesian bootstrap weights, we establish a new Bernstein-von Mises theorem and show that the limit distribution generally contains a…

My first shot with the Laowa 9mm/F5.6 W-Dreamer which is a rectilinear fisheye lens that leaves straight lines more or less straight -- here's a Cayuga Medical Center facility that has a gym, sports med doc, physical therapist and weight loss clinic right by the inlet
#photo #photography…

A judge ruled late Friday
the Trump administration cannot deny funding to Boston, Chicago, Denver, Los Angeles and 30 other cities and counties
because of policies that limit cooperation with federal immigration efforts.
U.S. District Judge William Orrick in San Francisco extended a preliminary injunction
blocking the administration from cutting off or conditioning the use of federal funds
for so-called “sanctuary” jurisdictions.
His earlier order protected…

Judge blocks Trump from cutting funding over ‘sanctuary’ policies
A judge has ruled the Trump administration cannot deny funding to Boston, Chicago, Denver, Los Angeles and 30 other cities and counties because of policies that limit cooperation with federal immigration efforts. U.S. District Judge William Orrick on Friday extended a preliminary injunction blocking the administration from cutting off or conditioning the use of federal funds for so-called “sanctuary” jurisdictions. His earlier order protected more than a dozen other cities and counties, inc…

CoPAD : Multi-source Trajectory Fusion and Cooperative Trajectory Prediction with Anchor-oriented Decoder in V2X Scenarios
Kangyu Wu, Jiaqi Qiao, Ya Zhang
https://arxiv.org/abs/2509.15984

CoPAD : Multi-source Trajectory Fusion and Cooperative Trajectory Prediction with Anchor-oriented Decoder in V2X Scenarios
Recently, data-driven trajectory prediction methods have achieved remarkable results, significantly advancing the development of autonomous driving. However, the instability of single-vehicle perception introduces certain limitations to trajectory prediction. In this paper, a novel lightweight framework for cooperative trajectory prediction, CoPAD, is proposed. This framework incorporates a fusion module based on the Hungarian algorithm and Kalman filtering, along with the Past Time Attention (…

Cooperative missile guidance design using Distributed Nonlinear Dynamic Inversion
Sabyasachi Mondal, Bhaskar Biswas, Venkatraman Renganathan
https://arxiv.org/abs/2509.18022 htt…

Cooperative missile guidance design using Distributed Nonlinear Dynamic Inversion
This paper presents a new cooperative guidance algorithm based on Distributed Nonlinear Dynamic Inversion (DNDI) to demonstrate a coordinated missile attack.

SpeedCP: Fast Kernel-based Conditional Conformal Prediction
Yeo Jin Jung, Yating Liu, Zixuan Wu, So Won Jeong, Claire Donnat
https://arxiv.org/abs/2509.24100 https://

SpeedCP: Fast Kernel-based Conditional Conformal Prediction
Conformal prediction provides distribution-free prediction sets with finite-sample conditional guarantees. We build upon the RKHS-based framework of Gibbs et al. (2023), which leverages families of covariate shifts to provide approximate conditional conformal prediction intervals, an approach with strong theoretical promise, but with prohibitive computational cost. To bridge this gap, we develop a stable and efficient algorithm that computes the full solution path of the regularized RKHS confor…

Transfer Learning under Group-Label Shift: A Semiparametric Exponential Tilting Approach
Manli Cheng, Subha Maity, Qinglong Tian, Pengfei Li
https://arxiv.org/abs/2509.22268 htt…

Transfer Learning under Group-Label Shift: A Semiparametric Exponential Tilting Approach
We propose a new framework for binary classification in transfer learning settings where both covariate and label distributions may shift between source and target domains. Unlike traditional covariate shift or label shift assumptions, we introduce a group-label shift assumption that accommodates subpopulation imbalance and mitigates spurious correlations, thereby improving robustness to real-world distributional changes. To model the joint distribution difference, we adopt a flexible exponenti…