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Mississippi State University

Academic institutionnorthamerica · us
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Research library141linked papers
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Selected work

Representative Papers

Benchmarking Affordance Generalization with BusyBox

Feb 05, 2026

This work investigates the affordance generalization capabilities of vision–language–action (VLA) models when encountering novel objects that possess familiar physical properties but have never been seen before. To this end, we introduce BusyBox—a physical evaluation benchmark built upon six interchangeable modules, which enables the systematic and semi-automated assessment of model performance by generating visually diverse yet affordance-consistent object variants through module rotation and substitution. BusyBox provides, for the first time, a reproducible, low-cost, and easily constructible real-world testbed, accompanied by open-sourced 3D printing schematics, an electronic bill of materials, and a dual-arm robot demonstration dataset. Experiments reveal that current state-of-the-art open-source VLA models, such as π₀.₅ and GR00T-N1.6, exhibit limited generalization on this benchmark, thereby validating its effectiveness and necessity.

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Model-assisted inference for dynamic causal effects in staggered rollout cluster randomized experiments

Feb 16, 2025

This paper addresses model-free inference for dynamic causal effects in staggered-rollout cluster-randomized experiments (SR-CREs), accounting for both anticipated treatment effects and non-negligible cluster–period-level heterogeneity in size. We propose a design-based regression estimation framework that unifies three input types: individual-level data, cluster–period means, and scaled cluster–period totals. For the first time under a finite-population setting, we establish rigorous consistency, asymptotic normality, and conservativeness of the variance estimator. Theory shows that the scaled-total estimator achieves superior asymptotic efficiency. The method imposes no outcome modeling assumptions and guarantees variance conservatism in the Löwner order. By extending parallel-arm cluster-randomized experiment theory to dynamic staggered-rollout designs, our approach delivers a robust, efficient, and model-agnostic tool for causal inference in real-world policy evaluation.

1 citationsRead paper

Bankrupting DoS Attackers

May 17, 2022

This paper addresses the severe cost asymmetry between attackers and defenders in Denial-of-Service (DoS) attacks by proposing an economic deterrence mechanism based on dynamic pricing. The server employs a lightweight probabilistic estimator to continuously characterize legitimate traffic features and dynamically prices each incoming request, thereby ensuring that the attacker’s per-unit cost asymptotically exceeds the combined cost incurred by the server and honest users. Theoretical contributions include: (i) the first asymptotically optimal guarantee wherein the attacker’s cost strictly dominates the defender’s; (ii) provably optimal online pricing algorithms for both synchronous and asynchronous adversarial models; and (iii) tight lower bounds linking estimation error to cost growth. Experimental and competitive analysis demonstrates that, under constant-factor estimation error, the server’s total cost grows strictly slower than the attacker’s—significantly enhancing the economic sustainability and deterrent efficacy of DoS defense.

1 citationsRead paper
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