Institution profile

Eötvös Loránd University

Academic institutioneurope · hu
Official website
Research library136linked papers
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Selected work

Representative Papers

Weighted Equitability and Matroid-Constrained Discrepancy

Aug 14, 2026

This study addresses weighted fair division and discrepancy theory under matroid constraints, proposing strongly polynomial-time algorithms grounded in local exchange theorems and constructive proofs. Key contributions include establishing weighted matroid partitionability and extending the Beck-Fiala framework to matroid settings, yielding logarithmic discrepancy bounds. The work achieves EF1 and additive approximation guarantees for fair allocation and scheduling optimization while refuting the weighted carpool conjecture. By systematically resolving fairness and discrepancy control challenges in constrained environments, this research provides novel theoretical foundations and efficient algorithmic tools for related combinatorial optimization problems.

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Recent publications

Latest Papers

Weighted Equitability and Matroid-Constrained Discrepancy

Aug 14, 2026

This study addresses weighted fair division and discrepancy theory under matroid constraints, proposing strongly polynomial-time algorithms grounded in local exchange theorems and constructive proofs. Key contributions include establishing weighted matroid partitionability and extending the Beck-Fiala framework to matroid settings, yielding logarithmic discrepancy bounds. The work achieves EF1 and additive approximation guarantees for fair allocation and scheduling optimization while refuting the weighted carpool conjecture. By systematically resolving fairness and discrepancy control challenges in constrained environments, this research provides novel theoretical foundations and efficient algorithmic tools for related combinatorial optimization problems.

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Removing Infrastructure Barriers in Human-Robot Collaboration Through Wireless Reconfigurable Cells

Aug 10, 2026

This work addresses the limitations of conventional human-robot collaboration systems, which are constrained by tethered connections, lack modularity and rapid reconfigurability, and fail to meet real-time perception and safety requirements with existing commercial wireless solutions. The authors propose an infrastructure-free, 5G-enabled wireless reconfigurable collaborative unit architecture integrating a battery-powered multi-sensor platform and an edge vision module, enabling cross-scenario deployment. By training a highly robust hand and grasp pose estimation model using a fusion of synthetic and real-world data, and leveraging 5G edge computing to optimize the bandwidth–latency trade-off, the system achieves a round-trip latency of 12 ms across multiple international 5G networks, a mean average precision (mAP@50–95) of 97.74% ± 0.10% for pose detection, and an average inference time of 12.5 ms, thereby demonstrating the feasibility of safe and adaptive human-robot collaboration.

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