Selected work

Representative Papers

A Graph Theoretic Approach to Spatial Modeling of Post Disaster Shelter Camps Using Rainbow and Roman Domination Parameters

Sep 30, 2026

This study addresses the challenge of post-disaster shelter layout planning, which requires balancing multi-type facility coverage with tiered capacity allocation—a task difficult for traditional methods to optimize jointly. This work proposes a graph-theoretic framework based on rainbow k-domination and Roman domination parameters to model simultaneous facility accessibility and service capacity hierarchies. Theoretically, we design a linear-time algorithm for computing minimum rainbow k-dominating sets on trees, establish novel bounds for both parameters in general graphs, characterize the structural properties under which their extremal values coincide, and prove the NP-completeness of the associated recognition problem. Experimentally, large-scale instances validate the scalability of the proposed model. Overall, this research provides a rigorous mathematical foundation and efficient solution strategies for post-disaster facility location.

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Distance-Residual Physics-Informed Neural Networks: A Deep Learning Framework for Differential and Partial Differential Inclusions

Sep 25, 2026

This study addresses the challenge of solving differential inclusions where differential operators are constrained by set-valued mappings, a task for which conventional pointwise residual methods prove inadequate. To this end, we propose the DR-PINNs framework, which replaces traditional point residuals with differentiable distance residuals and computes the loss function via metric projections. By integrating convex quadratic programming with the chain rule, the approach effectively handles state-dependent constraints, supported by a theoretical consistency proof under continuous functionals. The proposed method achieves efficient approximation of solutions for both ordinary and partial differential inclusions. Benchmark evaluations demonstrate its superior accuracy and numerical stability, establishing DR-PINNs as a novel paradigm that combines rigorous theoretical guarantees with computational efficiency for solving differential inclusion problems.

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Packing chromatic critical graphs with radius at most 2

May 05, 2026

This study investigates the structure of packing chromatic critical graphs with radius at most two. By partitioning the vertex set into i-packing subsets—subsets in which any two vertices are at distance greater than i—and leveraging properties related to graph radius, diameter, and the structural characteristics of cactus graphs, the authors provide the first complete characterization of all packing chromatic critical graphs of radius one. Furthermore, they fully determine the class of packing chromatic critical cactus graphs of radius two whose diameter is either two or three. This work offers a systematic classification of critical graph structures under specified constraints on radius and diameter, significantly advancing the theoretical understanding of packing chromatic criticality.

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Sample Selection Using Multi-Task Autoencoders in Federated Learning with Non-IID Data

Apr 28, 2026

This work addresses the vulnerability of federated learning to redundant or anomalous client samples under non-IID and high-noise conditions, which often degrades model performance. The authors propose a federated sample selection method based on a multi-task autoencoder, coordinated by the central server to perform unsupervised anomaly detection using One-Class SVM, Isolation Forest, and an adaptive loss threshold. Notably, they introduce for the first time a federated multi-class Deep SVDD loss to enhance sample filtering in the feature space. Experimental results demonstrate that the proposed approach improves accuracy by up to 7.02% on CIFAR-10 and 1.83% on MNIST, with the federated SVDD component contributing an additional 0.99% gain, significantly boosting model robustness across varying client scales and noise levels.

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Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential Privacy

Apr 25, 2026

This work addresses the challenges of high communication overhead and privacy leakage in non-IID federated learning by proposing a novel approach that integrates differential privacy with adaptive quantization. The method employs the Laplace mechanism to provide rigorous privacy guarantees and introduces a global bit-width scheduler based on cosine annealing across communication rounds, coupled with a client-aware quantization strategy informed by dataset entropy, to dynamically optimize the volume of uploaded data. Experimental results demonstrate significant communication reductions—up to 52.64% on MNIST, 45.06% on CIFAR-10, and 31%–37% on medical imaging datasets—while preserving model accuracy, thereby achieving a synergistic optimization of communication efficiency and strong privacy protection.

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Recent publications

Latest Papers

A Graph Theoretic Approach to Spatial Modeling of Post Disaster Shelter Camps Using Rainbow and Roman Domination Parameters

Sep 30, 2026

This study addresses the challenge of post-disaster shelter layout planning, which requires balancing multi-type facility coverage with tiered capacity allocation—a task difficult for traditional methods to optimize jointly. This work proposes a graph-theoretic framework based on rainbow k-domination and Roman domination parameters to model simultaneous facility accessibility and service capacity hierarchies. Theoretically, we design a linear-time algorithm for computing minimum rainbow k-dominating sets on trees, establish novel bounds for both parameters in general graphs, characterize the structural properties under which their extremal values coincide, and prove the NP-completeness of the associated recognition problem. Experimentally, large-scale instances validate the scalability of the proposed model. Overall, this research provides a rigorous mathematical foundation and efficient solution strategies for post-disaster facility location.

0 citationsRead paper

Distance-Residual Physics-Informed Neural Networks: A Deep Learning Framework for Differential and Partial Differential Inclusions

Sep 25, 2026

This study addresses the challenge of solving differential inclusions where differential operators are constrained by set-valued mappings, a task for which conventional pointwise residual methods prove inadequate. To this end, we propose the DR-PINNs framework, which replaces traditional point residuals with differentiable distance residuals and computes the loss function via metric projections. By integrating convex quadratic programming with the chain rule, the approach effectively handles state-dependent constraints, supported by a theoretical consistency proof under continuous functionals. The proposed method achieves efficient approximation of solutions for both ordinary and partial differential inclusions. Benchmark evaluations demonstrate its superior accuracy and numerical stability, establishing DR-PINNs as a novel paradigm that combines rigorous theoretical guarantees with computational efficiency for solving differential inclusion problems.

0 citationsRead paper

Packing chromatic critical graphs with radius at most 2

May 05, 2026

This study investigates the structure of packing chromatic critical graphs with radius at most two. By partitioning the vertex set into i-packing subsets—subsets in which any two vertices are at distance greater than i—and leveraging properties related to graph radius, diameter, and the structural characteristics of cactus graphs, the authors provide the first complete characterization of all packing chromatic critical graphs of radius one. Furthermore, they fully determine the class of packing chromatic critical cactus graphs of radius two whose diameter is either two or three. This work offers a systematic classification of critical graph structures under specified constraints on radius and diameter, significantly advancing the theoretical understanding of packing chromatic criticality.

0 citationsRead paper

Sample Selection Using Multi-Task Autoencoders in Federated Learning with Non-IID Data

Apr 28, 2026

This work addresses the vulnerability of federated learning to redundant or anomalous client samples under non-IID and high-noise conditions, which often degrades model performance. The authors propose a federated sample selection method based on a multi-task autoencoder, coordinated by the central server to perform unsupervised anomaly detection using One-Class SVM, Isolation Forest, and an adaptive loss threshold. Notably, they introduce for the first time a federated multi-class Deep SVDD loss to enhance sample filtering in the feature space. Experimental results demonstrate that the proposed approach improves accuracy by up to 7.02% on CIFAR-10 and 1.83% on MNIST, with the federated SVDD component contributing an additional 0.99% gain, significantly boosting model robustness across varying client scales and noise levels.

0 citationsRead paper

Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential Privacy

Apr 25, 2026

This work addresses the challenges of high communication overhead and privacy leakage in non-IID federated learning by proposing a novel approach that integrates differential privacy with adaptive quantization. The method employs the Laplace mechanism to provide rigorous privacy guarantees and introduces a global bit-width scheduler based on cosine annealing across communication rounds, coupled with a client-aware quantization strategy informed by dataset entropy, to dynamically optimize the volume of uploaded data. Experimental results demonstrate significant communication reductions—up to 52.64% on MNIST, 45.06% on CIFAR-10, and 31%–37% on medical imaging datasets—while preserving model accuracy, thereby achieving a synergistic optimization of communication efficiency and strong privacy protection.

0 citationsRead paper