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Kasetsart University

Academic institutionasia · th
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Research library9linked papers
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Selected work

Representative Papers

Robust Bichromatic Classification in 3D Using Planes and Slices

Jul 22, 2026

This study addresses the problem of robust binary classification of red and blue point sets in three-dimensional space, with a focus on resilience against various types of outliers. Leveraging the duality between points and planes in 3D, the work extends existing two-dimensional robust bichromatic classification techniques to the three-dimensional setting and formulates a classifier centered on linear constraints. The authors develop several efficient geometric algorithms tailored to distinct outlier models, including spatial outliers and label noise. Experimental results demonstrate that the proposed approach consistently and accurately separates red and blue point sets across diverse contamination scenarios, significantly enhancing the practicality and adaptability of robust classification in three dimensions.

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Covering Planar Lattices with Interior-Disjoint Unit Disks

Jul 22, 2026

This study investigates the feasibility of covering periodic point sets in the plane—specifically triangular, square, and honeycomb lattices—with non-overlapping unit disks, determining the ranges of inter-point distances that permit complete coverage. By constructing single- and multi-family periodic covering patterns centered at lattice vertices, face centers, and off-lattice points, and combining symmetry analysis with computer-assisted verification, the work systematically characterizes the conditions for full coverage. The main contributions include identifying several new intervals of coverable spacing for the triangular lattice, establishing the first effective covering scheme for honeycomb point sets, and correcting an overlap error in prior results for the square lattice—thereby recovering known coverable intervals and discovering additional ones.

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An Improved Linear Extractable Sketch Data Structure for Flow Count Statistics

Jul 22, 2026

This work addresses a key limitation in traditional linear sketching methods for streaming data statistics, which rely on the strong assumption that each hash bucket contains only a single key—thereby constraining space efficiency. To overcome this, the authors propose a novel approach that stores randomized linear combinations of multiple keys within each bucket and reconstructs key-value pairs during recovery by solving a sparse linear system. This design effectively relaxes the single-key-per-bucket constraint, achieving substantially improved space efficiency with only a modest increase in computational overhead. Experimental results demonstrate that the proposed method significantly reduces memory consumption while markedly enhancing space utilization for streaming data statistics.

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Recent publications

Latest Papers

Robust Bichromatic Classification in 3D Using Planes and Slices

Jul 22, 2026

This study addresses the problem of robust binary classification of red and blue point sets in three-dimensional space, with a focus on resilience against various types of outliers. Leveraging the duality between points and planes in 3D, the work extends existing two-dimensional robust bichromatic classification techniques to the three-dimensional setting and formulates a classifier centered on linear constraints. The authors develop several efficient geometric algorithms tailored to distinct outlier models, including spatial outliers and label noise. Experimental results demonstrate that the proposed approach consistently and accurately separates red and blue point sets across diverse contamination scenarios, significantly enhancing the practicality and adaptability of robust classification in three dimensions.

0 citationsRead paper

Covering Planar Lattices with Interior-Disjoint Unit Disks

Jul 22, 2026

This study investigates the feasibility of covering periodic point sets in the plane—specifically triangular, square, and honeycomb lattices—with non-overlapping unit disks, determining the ranges of inter-point distances that permit complete coverage. By constructing single- and multi-family periodic covering patterns centered at lattice vertices, face centers, and off-lattice points, and combining symmetry analysis with computer-assisted verification, the work systematically characterizes the conditions for full coverage. The main contributions include identifying several new intervals of coverable spacing for the triangular lattice, establishing the first effective covering scheme for honeycomb point sets, and correcting an overlap error in prior results for the square lattice—thereby recovering known coverable intervals and discovering additional ones.

0 citationsRead paper

An Improved Linear Extractable Sketch Data Structure for Flow Count Statistics

Jul 22, 2026

This work addresses a key limitation in traditional linear sketching methods for streaming data statistics, which rely on the strong assumption that each hash bucket contains only a single key—thereby constraining space efficiency. To overcome this, the authors propose a novel approach that stores randomized linear combinations of multiple keys within each bucket and reconstructs key-value pairs during recovery by solving a sparse linear system. This design effectively relaxes the single-key-per-bucket constraint, achieving substantially improved space efficiency with only a modest increase in computational overhead. Experimental results demonstrate that the proposed method significantly reduces memory consumption while markedly enhancing space utilization for streaming data statistics.

0 citationsRead paper