Selected work

Representative Papers

Optimizing Earth Observation Satellite Schedules under Unknown Operational Constraints: An Active Constraint Acquisition Approach

Apr 14, 2026

This study addresses the challenge of scheduling Earth observation satellites under numerous operational constraints—such as revisit intervals, power consumption, and thermal limits—that are often not explicitly modeled. Traditional approaches are limited by their reliance on complete prior knowledge of all constraints. To overcome this, the work proposes an interactive framework that integrates active constraint learning with optimization, operating in a setting where the objective function is known but constraints are not. Leveraging a binary feasibility oracle and a domain-specific Conservative Constraint Acquisition (CCA) strategy, the method efficiently identifies critical constraints without over-constraining the problem. The approach alternates between a CP-SAT solver and high-fidelity simulation for iterative refinement. Experiments on 50-task instances demonstrate a 78% reduction in oracle queries, a fivefold speedup in runtime, and improved solution quality—reducing the optimality gap to 17.9% compared to 20.3% for a two-stage baseline.

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Jet Functors and Weil Algebras in Automatic Differentiation: A Geometric Analysis

Oct 16, 2025

Automatic differentiation (AD) in deep learning and scientific computing suffers from poor structure preservation and inefficient high-order derivative computation. Method: This paper establishes a geometric framework grounded in jet bundles and Weil algebras: reverse-mode AD is interpreted as cotangent pullback, while higher-order Taylor expansions correspond to algebraic evaluation over Weil algebras. Contribution/Results: We introduce tensorized Weil algebras, enabling simultaneous computation of all mixed partial derivatives and circumventing combinatorial explosion from nested Jacobian-vector or vector-Jacobian products. For the first time, correctness and numerical stability of AD are rigorously guaranteed via functorial identities and algebraic exactness. The algorithm exhibits linear complexity in the algebraic dimension and provides explicit bounds on truncation error, thereby establishing a unified theoretical foundation for structure-preserving differential methods.

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Data-Driven Energy Estimation for Virtual Servers Using Combined System Metrics and Machine Learning

Sep 12, 2025

In virtualized environments, guest VMs lack physical power interfaces and host privileges, hindering direct energy consumption measurement and impeding energy-aware scheduling and cost optimization. This paper proposes a purely client-side virtual server power consumption prediction method that estimates energy usage solely from guest-observable resource utilization metrics—CPU, memory, disk I/O, and network traffic—using a gradient boosting regression model, without requiring host access or privileged instrumentation. Ground-truth power measurements are obtained via RAPL on the physical host for model training and validation. Experimental evaluation across diverse representative workloads achieves R² scores of 0.90–0.97, demonstrating, for the first time, the feasibility of high-accuracy virtual server power estimation using only guest-side resource data. This work fills a critical gap in cloud computing by enabling non-intrusive, low-privilege energy modeling.

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Recent publications

Latest Papers

Optimizing Earth Observation Satellite Schedules under Unknown Operational Constraints: An Active Constraint Acquisition Approach

Apr 14, 2026

This study addresses the challenge of scheduling Earth observation satellites under numerous operational constraints—such as revisit intervals, power consumption, and thermal limits—that are often not explicitly modeled. Traditional approaches are limited by their reliance on complete prior knowledge of all constraints. To overcome this, the work proposes an interactive framework that integrates active constraint learning with optimization, operating in a setting where the objective function is known but constraints are not. Leveraging a binary feasibility oracle and a domain-specific Conservative Constraint Acquisition (CCA) strategy, the method efficiently identifies critical constraints without over-constraining the problem. The approach alternates between a CP-SAT solver and high-fidelity simulation for iterative refinement. Experiments on 50-task instances demonstrate a 78% reduction in oracle queries, a fivefold speedup in runtime, and improved solution quality—reducing the optimality gap to 17.9% compared to 20.3% for a two-stage baseline.

0 citationsRead paper

Jet Functors and Weil Algebras in Automatic Differentiation: A Geometric Analysis

Oct 16, 2025

Automatic differentiation (AD) in deep learning and scientific computing suffers from poor structure preservation and inefficient high-order derivative computation. Method: This paper establishes a geometric framework grounded in jet bundles and Weil algebras: reverse-mode AD is interpreted as cotangent pullback, while higher-order Taylor expansions correspond to algebraic evaluation over Weil algebras. Contribution/Results: We introduce tensorized Weil algebras, enabling simultaneous computation of all mixed partial derivatives and circumventing combinatorial explosion from nested Jacobian-vector or vector-Jacobian products. For the first time, correctness and numerical stability of AD are rigorously guaranteed via functorial identities and algebraic exactness. The algorithm exhibits linear complexity in the algebraic dimension and provides explicit bounds on truncation error, thereby establishing a unified theoretical foundation for structure-preserving differential methods.

0 citationsRead paper

Data-Driven Energy Estimation for Virtual Servers Using Combined System Metrics and Machine Learning

Sep 12, 2025

In virtualized environments, guest VMs lack physical power interfaces and host privileges, hindering direct energy consumption measurement and impeding energy-aware scheduling and cost optimization. This paper proposes a purely client-side virtual server power consumption prediction method that estimates energy usage solely from guest-observable resource utilization metrics—CPU, memory, disk I/O, and network traffic—using a gradient boosting regression model, without requiring host access or privileged instrumentation. Ground-truth power measurements are obtained via RAPL on the physical host for model training and validation. Experimental evaluation across diverse representative workloads achieves R² scores of 0.90–0.97, demonstrating, for the first time, the feasibility of high-accuracy virtual server power estimation using only guest-side resource data. This work fills a critical gap in cloud computing by enabling non-intrusive, low-privilege energy modeling.

0 citationsRead paper