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Montana State University

Academic institutionnorthamerica · us
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Research library32linked papers
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Selected work

Representative Papers

Does More Bandwidth Really Not Matter (Much)?

Mar 05, 2025Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cyberspace

This paper challenges the conventional wisdom that bandwidth is negligible and latency dominates web performance, systematically investigating the actual impact of mobile network bandwidth and latency on modern complex web pages. Method: We propose a novel metric—Critical Path of Improvement (CPI)—to quantify websites’ sensitivity distributions to bandwidth versus latency. Leveraging real-world network measurements across four operators and 57 cities, we construct an empirically grounded CPI model and perform critical-path decomposition and correlation analysis. Contribution/Results: Our findings reveal that 18% of websites are predominantly bandwidth-limited, while over half exhibit significant bandwidth sensitivity. The study uncovers the underappreciated role of bandwidth in mobile web performance and delivers a practical, deployable framework for diagnosing bandwidth sensitivity—providing empirical guidance for developers optimizing resource loading strategies and operators designing differentiated QoS policies.

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Approximation Algorithms for Perfect Fair-Triangle Packing

Aug 07, 2026

This work introduces and investigates the fair maximum-weight triangle packing problem: given 3n vertices colored red and blue, the goal is to partition them into n vertex-disjoint triangles such that each triangle contains at least one red and one blue vertex, while maximizing the total edge weight. The problem is shown to be NP-hard. The authors propose two polynomial-time approximation algorithms: a deterministic 1/3-approximation algorithm based on matching and maximum-weight [1,2]-factor computation running in O(n³) time, and an improved randomized (16/47 − ε)-approximation algorithm combining random cycle-breaking with maximum-weight matching, which runs in O(n⁴) time. These results substantially advance the tractability frontier for this class of fair combinatorial optimization problems.

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Recent publications

Latest Papers

Approximation Algorithms for Perfect Fair-Triangle Packing

Aug 07, 2026

This work introduces and investigates the fair maximum-weight triangle packing problem: given 3n vertices colored red and blue, the goal is to partition them into n vertex-disjoint triangles such that each triangle contains at least one red and one blue vertex, while maximizing the total edge weight. The problem is shown to be NP-hard. The authors propose two polynomial-time approximation algorithms: a deterministic 1/3-approximation algorithm based on matching and maximum-weight [1,2]-factor computation running in O(n³) time, and an improved randomized (16/47 − ε)-approximation algorithm combining random cycle-breaking with maximum-weight matching, which runs in O(n⁴) time. These results substantially advance the tractability frontier for this class of fair combinatorial optimization problems.

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Equilibrium Causal Digital Twins: Validation, Transport, and Identification Limits

Jul 23, 2026

This study addresses the challenge that digital twins in feedback systems often fail to accurately predict post-intervention equilibrium counterfactual responses under mechanism shifts. To overcome this limitation, the authors propose a verifiable and transferable causal digital twin framework grounded in causal graphical models and equilibrium selection mechanisms. By introducing cyclic selection graphs, hybrid modeling strategies, and identifiability boundaries, they demonstrate that matching only means and covariances is insufficient to ensure distribution-level counterfactual consistency, thereby establishing the necessity of structural assumptions. Leveraging linear system identification theory and statistical testing, the work derives intervention conditions dependent on mechanism changes and observational structure in synthetic feedback systems, characterizes the range of query values when point identification fails, and validates the theoretical claims empirically.

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