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Researcher profile

Jun Chen

Jun Chen contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial Intelligencecond-mat.mtrl-sciComputer Visioncond-mat.mes-hallcond-mat.str-eleess.SYInformation Theorymath.ITmath.OCphysics.opticsSystems and Control

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Published work

6 published item(s)

preprint2026arXiv

Clipped Affine Policy: Low-Complexity Near-Optimal Online Power Control for Energy Harvesting Communications over Fading Channels

This paper investigates online power control for point-to-point energy harvesting communications over wireless fading channels. A linear-policy-based approximation is derived for the relative-value function in the Bellman equation of the power control problem. This approximation leads to two fundamental power control policies: optimistic and robust clipped affine policies, both taking the form of a clipped affine function of the battery level and the reciprocal of channel signal-to-noise ratio coefficient. They are essentially battery-limited weighted directional waterfilling policies operating between adjacent time slots. By leveraging the relative-value approximation and derived policies, a domain-knowledge-enhanced reinforcement learning (RL) algorithm is proposed for online power control. The proposed approach is further extended to scenarios with energy and/or channel lookahead. Comprehensive simulation results demonstrate that the proposed methods achieve a good balance between computational complexity and optimality. In particular, the robust clipped affine policy (combined with RL, using at most five parameters) outperforms all existing approaches across various scenarios, with less than 2\% performance loss relative to the optimal policy.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Enhancing Multimodal In-Context Learning via Inductive-Deductive Reasoning

In-context learning (ICL) allows large models to adapt to tasks using a few examples, yet its extension to vision-language models (VLMs) remains fragile. Our analysis reveals that the fundamental limitation lies in an inductive gap, models often produce correct answers from flawed reasoning, while struggling to extract consistent rules across demonstrations. This gap is further exacerbated by two visual-level obstacles: an overwhelming proportion of redundant visual tokens that obscure textual cues, and a skewed attention distribution that favors the initial image at the expense of subsequent context. To address these issues, we introduce a framework that restructures multimodal ICL as a principled inductive-deductive process. The framework incorporates a similarity-based visual token compression module to filter out redundant patches, a dynamic attention rebalancing mechanism to distribute focus equitably across all images, and a chain-of-thought paradigm that explicitly guides the model to analyze individual examples, derive a generalizable rule, and then apply it to the query. An auxiliary learning pipeline combines supervised fine-tuning with reinforcement learning using verifiable rewards to reinforce faithful citation and noise filtering. Evaluations across eight benchmarks covering visual perception, logical reasoning, STEM problems, and sarcasm detection demonstrate consistent and significant improvements over standard ICL baselines for multiple open-source VLMs, highlighting the potential of equipping models with genuine inductive capabilities in multimodal settings.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Instance-Aware Parameter Configuration in Bilevel Late Acceptance Hill Climbing for the Electric Capacitated Vehicle Routing Problem

Algorithm performance in combinatorial optimization is highly sensitive to parameter settings, while a single globally tuned configuration often fails to exploit the heterogeneity of instances. This limitation is particularly evident in the Electric Capacitated Vehicle Routing Problem, where instances differ in structure, demand patterns, and energy constraints. This paper investigates instance-aware parameter configuration for Bilevel Late Acceptance Hill Climbing, a state-of-the-art metaheuristic for the Electric Capacitated Vehicle Routing Problem. An offline tuning procedure is used to obtain instance-specific parameter labels, which are then mapped from instance features via a regression model to enable parameter prediction for unseen instances prior to execution. Experimental results on the IEEE WCCI 2020 benchmark and its extensions show that the proposed approach achieves an average objective value reduction of $0.28\%$ across eight held-out test instances relative to a globally tuned configuration. This corresponds to a significant cost reduction in multimillion-dollar transportation operations.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Magnetoelectric torque in polar magnetic bilayers

Energy-efficient fast switching of spin orientations or textures is a core issue of spintronics, which is highly demanded but remains challenging. Different from the mainstream routes based on spin-transfer torque or spin-orbit torque, here we propose another mechanism coined as magnetoelectric torque to switch the magnetization in polar magnetic bilayers via pure electric field. In some magnetic van der Waals bilayers, when the electrostatic energy of polarization can compensate the interlayer magnetic coupling, a magnetoelectric torque is generated to fastly flip spins within a few picoseconds, which is demonstrated by combining the first-principles calculations, analytic model, as well as atomistic simulations. Such a magnetoelectric torque doesn't rely on the spin-orbit coupling and is generally active in polar magnetic homostructures and heterostructures. Our work provides an alternative route to switch magnetization in nanoscale, which may benefit the energy-saving and fast-response spintronic devices.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Modulating near-field radiative energy and momentum transfer via rotating Weyl semimetals

We study near-field radiative transfer of energy, angular momentum, and linear momentum between a nanoparticle and a plate consisting of magnetic Weyl semimetals, and demonstrate that these can be efficiently tuned by a relative angle between the Weyl node separations. This tunability originates from the coupling between the particle-induced rotational Poynting vector and the nonreciprocal surface plasmon polaritons supported by the plate. Remarkably, we uncover a counterintuitive regime in which both energy and angular momentum transfer are maximized when the Weyl node separations are antiparallel rather than parallel. This arises from optimal mode matching between the rotation direction of the particle's circular heat flux and the propagation direction of the surface plasmon polaritons in the antiparallel configuration.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Unexpected type-II multiferroic phase in GdMnO3 under high magnetic fields

Perovskite manganites with small A-site ions, as the first and canonical branch of type-II multiferroics, are ideal systems to exhibit magnetism-induced ferroelectricity. Despite their established magnetoelectric phase diagrams under low magnetic fields, here an unidentified phase with a large magnetism-induced polarization (up to 1500 μC/m2) is revealed in GdMnO3 under high magnetic fields up to 60 T. Based on multiprobe experiments, a complete phase diagram is constructed with successive polar-nonpolar-polar-nonpolar transitions. Such a nonmonotonic evolution is well mimicked by model simulation, while the spin-lattice coupling is the key ingredient for the reentrant ferroelectric phase.

0 citations0 reviewsNo code linkedOpen access