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Zhuolin Li

Publications and source records attributed to Zhuolin Li.

8 recordsLinked to original sources
arXiv2026

Visualizing Microwave-Driven Dynamics of Antiskyrmions and Surface Skyrmions

Microwaves provide coherent access to low-energy excitations and serve as effective probes of high-frequency spin dynamics in quantum and magnetic systems. For topological spin textures, microwave excitation is expected to generate rich collective responses, yet direct real-space observation of ultrafast dynamics remains limited. Here we use time-resolved Lorentz transmission electron microscopy to visualize microwave-driven dynamics in a hybrid antiskyrmion structure composed of a central antiskyrmion and surface skyrmions. We resolve the picosecond evolution of antiskyrmion area and second-harmonic signals, evidencing nonlinear responses of spin textures under microwave excitations. We track the core motions of the antiskyrmion and surface skyrmions, which follow distinct trajectories while sharing the same rotational sense. Micromagnetic simulations reproduce the key observations and associate the dynamic modes with the spatial modulation of the core profile along the thickness. These achievements establish ultrafast electron microscopy as a powerful real-space approach for probing high-frequency microwave-driven dynamics of topological magnetic solitons.

cond-mat.mtrl-sci↗
arXiv2026

Partial regularity for $\mathscr{A}$-quasiconvex variational problems of linear growth

We prove that minimizers of variational integrals $$ \mathcal E(v)=\int_\Omega f(v)\quad\text{for }v\in\mathcal M(\Omega)\text{ such that } \mathscr{A} v=0, $$ are partially continuous provided that the integrands $f$ are strongly $\mathscr{A}$-quasiconvex in a suitable sense. We consider linear growth problems, linear PDE operators $\mathscr{A}$ of constant rank, and variations of the form $v+\varphi$ with $\mathscr{A}$-free $\varphi\in \mathrm{C}_{\mathrm{c}}^\infty(\Omega)$. Our analysis also covers the ``potentials case'' $$ \mathcal F(u)=\int_\Omega f( \mathscr{B} u)\quad\text{for } u\in\mathscr D'(\Omega)\text{ such that }\mathscr B u\in \mathcal M(\Omega), $$ where $\mathscr{B}$ is a different linear pde operator of constant rank. Both our main results extend to $x$-dependent integrands.

math.AP↗
arXiv2025

ChipSeek: Optimizing Verilog Generation via EDA-Integrated Reinforcement Learning

Large Language Models have emerged as powerful tools for automating Register-Transfer Level (RTL) code generation, yet they face critical limitations: existing approaches typically fail to simultaneously optimize functional correctness and hardware efficiency metrics such as Power, Performance, and Area (PPA). Methods relying on supervised fine-tuning commonly produce functionally correct but suboptimal designs due to the lack of inherent mechanisms for learning hardware optimization principles. Conversely, external post-processing techniques aiming to refine PPA performance after generation often suffer from inefficiency and do not improve the LLMs' intrinsic capabilities. To overcome these challenges, we propose ChipSeek, a novel hierarchical reward based reinforcement learning framework designed to encourage LLMs to generate RTL code that is both functionally correct and optimized for PPA metrics. Our approach integrates direct feedback from EDA simulators and synthesis tools into a hierarchical reward mechanism, facilitating a nuanced understanding of hardware design trade-offs. Through Curriculum-Guided Dynamic Policy Optimization (CDPO), ChipSeek enhances the LLM's ability to generate high-quality, optimized RTL code. Evaluations on standard benchmarks demonstrate ChipSeek's superior performance, achieving state-of-the-art functional correctness and PPA performance. Furthermore, it excels in specific optimization tasks, consistently yielding highly efficient designs when individually targeting fine-grained optimization goals such as power, delay, and area. The artifact is open-source in https://github.com/rong-hash/chipseek.

cs.AI↗
arXiv2024

Partial regularity and higher integrability for A-quasiconvex variational problems

We prove that minimizers of variational problems on open sets $\Omega \subset \mathbb{R}^n$ $$ \mbox{minimize}\quad \mathcal E(v)=\int_\Omega f(v(x))\mathrm{d} x\quad\text{for } \mathscr{A} v=0, $$ are partially continuous provided that the integrands $f$ are strongly $\mathscr{A}$-quasiconvex in a suitable sense. We consider $p$-growth problems with $1<p<\infty$, linear constant rank PDE operators $\mathscr{A}$ on $\mathbb{R}^n$ between vector spaces $V$ and $W$, and Dirichlet boundary conditions, in the sense that admissible fields are of the form $v=v_0+\varphi$, with $\mathscr{A}$-free $\varphi\in C_c^\infty(\Omega,V)$. Our analysis also covers the ``potentials case'' $$ \mbox{minimize}\quad \mathcal F(u)=\int_\Omega f(\mathscr{B} u(x))\mathrm{d} x\quad\text{for } u\in u_0+ C_c^\infty(\Omega,U), $$ where $\mathscr{B}$ is another linear constant rank PDE operator on $\mathbb{R}^n$ between vector spaces $U,V$. We also prove appropriate higher integrability of minimizers for both types of problems. In addition, our approach covers non-autonomous integrands $f(x,v(x))$ or $f(x,\mathscr{B} u(x))$.

math.AP↗
arXiv2024

An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting

This paper introduces a novel incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting (MCS) problems, enabling decision makers to progressively provide assignment example preference information. Specifically, we first construct a max-margin optimization-based model to model potentially non-monotonic preferences and inconsistent assignment example preference information in each iteration of the incremental preference elicitation process. Using the optimal objective function value of the max-margin optimization-based model, we devise information amount measurement methods and question selection strategies to pinpoint the most informative alternative in each iteration within the framework of uncertainty sampling in active learning. Once the termination criterion is satisfied, the sorting result for non-reference alternatives can be determined through the use of two optimization models, i.e., the max-margin optimization-based model and the complexity controlling optimization model. Subsequently, two incremental preference elicitation-based algorithms are developed to learn potentially non-monotonic preferences, considering different termination criteria. Ultimately, we apply the proposed approach to a credit rating problem to elucidate the detailed implementation steps, and perform computational experiments on both artificial and real-world data sets to compare the proposed question selection strategies with several benchmark strategies.

cs.AI↗
arXiv2024

Lexicographic optimization-based approaches to learning a representative model for multi-criteria sorting with non-monotonic criteria

Deriving a representative model using value function-based methods from the perspective of preference disaggregation has emerged as a prominent and growing topic in multi-criteria sorting (MCS) problems. A noteworthy observation is that many existing approaches to learning a representative model for MCS problems traditionally assume the monotonicity of criteria, which may not always align with the complexities found in real-world MCS scenarios. Consequently, this paper proposes some approaches to learning a representative model for MCS problems with non-monotonic criteria through the integration of the threshold-based value-driven sorting procedure. To do so, we first define some transformation functions to map the marginal values and category thresholds into a UTA-like functional space. Subsequently, we construct constraint sets to model non-monotonic criteria in MCS problems and develop optimization models to check and rectify the inconsistency of the decision maker's assignment example preference information. By simultaneously considering the complexity and discriminative power of the models, two distinct lexicographic optimization-based approaches are developed to derive a representative model for MCS problems with non-monotonic criteria. Eventually, we offer an illustrative example and conduct comprehensive simulation experiments to elaborate the feasibility and validity of the proposed approaches.

cs.AI↗
arXiv2022

Partial regularity for $ω$-minimizers of quasiconvex functionals

We establish partial regularity for the $ω$-minimizers of quasiconvex functionals of power growth. A first-order partial regularity result of $BV$ $ω$-minimizers is obtained in the linear growth case under a Dini-type condition on $ω$. Only assuming the smallness of $ω$ near the origin, we show partial Hölder continuity in the subquadratic case by considering a normalised excess.

math.AP↗