Selected work

Representative Papers

Parameterized Spanning Tree Congestion

Oct 10, 2024International Symposium on Mathematical Foundations of Computer Science

This paper studies the Tree Congestion Minimization problem: given a graph (G = (V,E)), compute a spanning tree (T) minimizing the maximum number of vertex-pair unique paths in (T) traversing any single edge—i.e., the edge congestion. While known to be NP-hard, its parameterized complexity remained open for years. We resolve this by proving, under the Exponential Time Hypothesis (ETH), that the problem is not fixed-parameter tractable (FPT) with respect to treewidth. Using a novel generic reduction framework, we establish W[1]-hardness with respect to stronger or incomparable structural parameters—including tree-depth plus feedback vertex set, and twin cover. Furthermore, we show NP-completeness even on graphs with maximum degree (Delta = 8) and modular width (mathrm{mw} = 4). These results comprehensively settle multiple long-standing open questions and significantly advance the theoretical boundaries of structural parameterized algorithms.

2 citationsRead paper

Finding a Shortest Curve that Separates Few Objects from Many

Apr 04, 2025

This paper addresses the NP-hard problem of computing a shortest closed curve in the plane that separates $k$ required polygons from numerous optional polygons: the curve must enclose all required polygons, avoid their interiors and any obstacles, and minimize the sum of its length and the weighted penalties of included optional polygons. We present the first fixed-parameter tractable (FPT) algorithm with time complexity $O(3^k n^3)$, unifying classical variants—including positive enclosure, negative isolation, and geometric knapsack—under a single framework. Our approach integrates planar embedding theory, computational geometry, dynamic programming, and topological separation analysis, supporting both geometric instances and abstract planar graph representations. Notably, we generalize the problem to optimal closed walks on weighted planar graphs, establishing the first FPT solution for this setting. The method bridges deep theoretical insights with practical algorithmic design, offering both rigorous guarantees and implementable efficiency.

1 citationsRead paper

Matching random colored points with rectangles (Corrigendum)

Mar 31, 2025

This paper studies the problem of matching $n$ points sampled uniformly at random in the unit square and colored red or blue, using pairwise disjoint axis-aligned rectangles to match points of the same color, with the goal of maximizing the number $M(n)$ of covered points. Prior work erroneously modeled the matching process as a Markov chain, overlooking its inherent non-Markovian nature. We correct this by formulating it as a first-order homogeneous stochastic process and integrate tools from stochastic geometry, probabilistic methods, and combinatorial matching theory. Rigorously, we prove that for sufficiently large $n$, there exists, with high probability, a monochromatic rectangle matching covering at least $0.83n$ points—i.e., $M(n) geq 0.83n$. This result rectifies the fundamental modeling flaw in prior approaches and establishes, for the first time, an asymptotic lower bound for this problem, thereby significantly advancing the theoretical understanding of random geometric matching.

1 citationsRead paper

Fused-Planes: Improving Planar Representations for Learning Large Sets of 3D Scenes

Oct 31, 2024

To address the high memory footprint and inefficiency in sharing Tri-Planes representations across multiple inverse graphics scenes, this paper proposes Fused-Planes. Our method introduces: (1) a novel two-stage scene-grouping joint compression paradigm that enables cross-scene sharing of 3D-aware latent spaces; and (2) resolution-adaptive rendering combined with latent-space compression—preserving both Tri-Planes’ architectural compatibility and rendering fidelity while substantially reducing representational complexity. Experiments demonstrate that Fused-Planes achieves rendering accuracy on par with Tri-Planes on multi-scene tasks, while reducing GPU memory consumption by 37% and inference FLOPs by 42%. The code and project page are publicly available.

1 citationsRead paper

BOP-Distrib: Revisiting 6D Pose Estimation Benchmarks for Better Evaluation under Visual Ambiguities

Aug 30, 2024

Current 6D pose estimation benchmarks oversimplify visual ambiguities—such as symmetry and occlusion—as global object symmetries, neglecting image-level, viewpoint-dependent visibility variations, thereby misrepresenting real-world pose uncertainty. To address this, we propose a novel image-level pose distribution evaluation paradigm: (1) the first automatic pose distribution annotation method grounded in single-image surface visibility; (2) BOP-Dist, the first pose distribution benchmark tailored to realistic images; and (3) a symmetry-aware sampling strategy coupled with a distribution-aware accuracy/recall evaluation framework. After re-annotating all BOP datasets with pose distributions, we observe substantial corrections to the performance ranking of state-of-the-art single-solution methods—revealing their rankings to be highly sensitive to annotation granularity. This work establishes a physically interpretable, reproducible, and quantitative evaluation standard for multi-solution pose estimation.

1 citationsRead paper

Recent publications

Latest Papers

Resolving Envy by Adding Goods with Bounded Supply: A Type-Count Dichotomy and Two-Agent Hardness

Aug 10, 2026

This study investigates the computational complexity of achieving envy-free allocations by adding items when item supplies are limited and agents face no individual budget constraints. Focusing on binary additive valuations, the work establishes a sharp dichotomy based on the number of added item types: a polynomial-time algorithm exists for a single item type, whereas the problem becomes NP-complete with just two item types—even weakly NP-complete for only two agents. The approach models envy relations via a system of difference constraints and employs the Bellman-Ford algorithm to compute a minimal feasible extension. Complexity reductions further delineate the hardness across multiple scenarios. This work fully resolves the open two-type case posed by Bentert et al. and establishes the first precise computational complexity boundary for this setting.

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Capacity-Aware Deep Learning for Generalizable Traffic Volume Estimation Across Links and Cities

Jul 27, 2026

This study addresses the poor generalization of traffic flow estimation in sparsely monitored road networks caused by reliance on fixed sensors. To overcome this limitation, the authors propose a link-level deep learning approach that leverages readily available data—including probe vehicle speeds, road topology, and weather conditions. The method introduces capacity-aware modeling to decouple traffic flow into structural capacity and time-varying utilization, incorporating fundamental traffic flow theory as hard constraints. By integrating supervised local mapping learning with out-of-distribution spatial generalization training strategies, the model achieves significantly enhanced transferability across road segments and cities. Experimental results demonstrate that the proposed approach consistently outperforms state-of-the-art models under diverse cross-domain settings, confirming the effectiveness of structural constraints in mitigating spatial distribution shifts.

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Entropic analogues of Grünbaum's inequality

Jul 25, 2026

This work investigates an entropy-analogue of Grünbaum’s inequality, focusing on sharp upper and lower bounds for the differential and Rényi entropies of log-concave random variables conditioned to lie on the left side of their mean. By integrating degree-of-freedom analysis with a KKT-type optimization lemma and leveraging structural properties of entropy functionals, the study establishes the first entropy counterpart to Grünbaum’s classical volume inequality. The main contributions include proving tight bounds for both conditional differential entropy and minimal Rényi entropy, fully characterizing all distributions attaining equality, and extending these results to general Rényi orders. The paper also examines the feasibility of higher-dimensional extensions and presents counterexamples demonstrating inherent limitations in such settings.

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Twin-Fidelity-Aware Resolution of Direct xApp Conflicts in Open RAN

Jul 24, 2026

This work addresses the conflict between energy-saving and coverage/throughput-oriented xApps in Open RAN regarding downlink power configuration by proposing a lightweight, training-free arbitration mechanism that requires no prior knowledge of optimal policies. The approach employs a dual-fidelity-aware hard-switching strategy that online fuses power recommendations from both xApp types. It leverages a network digital twin to predict the utility of candidate actions and dynamically selects the optimal action based on real-time utility feedback and exponential weighted moving average error monitoring. System-level 5G evaluations demonstrate a normalized utility regret as low as 0.017 ± 0.006. Under severe digital twin drift (10 dB), the utility regret drops significantly from 11.19 ± 3.58 to 0.55 ± 0.25, substantially outperforming baseline methods.

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Complexity Classification of Colouring Problems with Parity Constraints

Jul 18, 2026

This work investigates graph coloring problems under parity constraints, providing a unified framework that captures the computational complexity of various classical coloring variants. It introduces a general model that encompasses existing formulations by imposing combinatorial constraints—such as parity (odd/even), positivity, or zero conditions—on the number of same-colored or differently colored neighbors in vertex colorings. Employing techniques from computational complexity theory, reductions, and graph theory, the study systematically delineates the complexity landscape of all possible constraint combinations. The paper fully characterizes each case as either polynomial-time solvable (in P) or NP-complete, thereby establishing a complete complexity classification within the proposed framework.

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