arXiv subjects

Huimin Zhu

Publications and source records attributed to Huimin Zhu.

9 recordsLinked to original sources

arXiv2026

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.

physics.optics↗

arXiv2025

ActivityDiff: A diffusion model with Positive and Negative Activity Guidance for De Novo Drug Design

Achieving precise control over a molecule's biological activity-encompassing targeted activation/inhibition, cooperative multi-target modulation, and off-target toxicity mitigation-remains a critical challenge in de novo drug design. However, existing generative methods primarily focus on producing molecules with a single desired activity, lacking integrated mechanisms for the simultaneous management of multiple intended and unintended molecular interactions. Here, we propose ActivityDiff, a generative approach based on the classifier-guidance technique of diffusion models. It leverages separately trained drug-target classifiers for both positive and negative guidance, enabling the model to enhance desired activities while minimizing harmful off-target effects. Experimental results show that ActivityDiff effectively handles essential drug design tasks, including single-/dual-target generation, fragment-constrained dual-target design, selective generation to enhance target specificity, and reduction of off-target effects. These results demonstrate the effectiveness of classifier-guided diffusion in balancing efficacy and safety in molecular design. Overall, our work introduces a novel paradigm for achieving integrated control over molecular activity, and provides ActivityDiff as a versatile and extensible framework.

cs.LG↗

arXiv2025

Enhancing far-field thermal radiation by Floquet engineering

Time modulation introduces a dynamic degree of freedom for tailoring thermal radiation beyond the limits of static materials. Here we investigate far-field thermal radiation from a periodically time-modulated SiC film under the Floquet nonequilibrium Green's function framework. We show that time modulation enables radiative energy transfer into the far field that surpasses the limit imposed by the equilibrium thermal fluctuations. This enhancement originates from the modulation-induced coupling between evanescent surface phonon polaritons and propagating modes, effectively bridging the energy and momentum mismatch through frequency conversion. Notably, even at zero temperature, the film emits a finite radiative heat flux due to nonequilibrium photon occupation generated by the modulation. The radiative output grows with increasing modulation strength, highlighting the role of external work in driving far-field emission. These results establish time modulation as an effective mechanism for bridging near-field and far-field regimes, opening new pathways for active thermal radiation control.

cond-mat.mes-hall↗

arXiv2024

Vector Approximate Survey Propagation

Approximate Message Passing (AMP), originally designed to solve high-dimensional linear inverse problems, has found broad applications in signal processing and statistical inference. Among its key variants, Vector Approximate Message Passing (VAMP) and Generalized Approximate Survey Propagation (GASP) have demonstrated effectiveness even in scenarios where the assumed generative models differ from the true models. However, the maximum a posteriori (MAP) versions of VAMP and GASP have limitations: VAMP is restricted to differentiable priors and likelihoods, while GASP requires the measurement matrix to have independent identically distributed (i.i.d.) elements. To overcome these limitations, this paper introduces a new algorithm, Vector Approximate Survey Propagation (VASP). VASP utilizes survey propagation to handle non-differentiable priors and likelihoods, along with employs vector-form messages to account for correlations in the measurement matrix. Simulations reveal that VASP significantly surpasses VAMP and GASP in estimation accuracy, particularly when the assumed prior is discrete-supported and the measurement matrix is non-i.i.d.. Additionally, the state evolution (SE) of VASP, derived heuristically, accurately reflects the per-iteration mean squared error (MSE). A comparison between the SE and the free energy computed by Takahashi and Kabashima under the one-step replica symmetry breaking (1RSB) ansatz shows that the SE's fixed-point equations align with the free energy's saddle point equations. This suggests that VASP efficiently implements the postulated MAP estimator (which is NP-hard in the worst case) with cubic computational complexity, assuming the 1RSB ansatz is valid.

cs.IT↗

arXiv2024

K-step Vector Approximate Survey Propagation

Approximate Message Passing (AMP), originally developed to address high-dimensional linear inverse problems, has found widespread applications in signal processing and statistical inference. Among its notable variants, Vector Approximate Message Passing (VAMP), Generalized Approximate Survey Propagation (GASP), and Vector Approximate Survey Propagation (VASP) have demonstrated effectiveness even when the assumed generative models differ from the true models. However, many fundamental questions regarding model mismatch remain unanswered. For instance, it is still unclear what level of model mismatch is required for the postulated posterior estimate (PPE) to exhibit a replica symmetry breaking (RSB) structure in the extremum conditions of its free energy, and what order of RSB is necessary. In this paper, we introduce a novel approximate message passing algorithm that incorporates K-step RSB (KRSB) and naturally reduces to VAMP and VASP with specific parameter selections. We refer to this as the K-step VASP (KVASP) algorithm. Simulations show that KVASP significantly outperforms VAMP and GASP in estimation accuracy, particularly when the assumed prior has discrete support and the measurement matrix is non-i.i.d.. Additionally, the state evolution (SE) of KVASP, derived heuristically, accurately tracks the per-iteration mean squared error (MSE). A comparison between the SE and the free energy under the KRSB ansatz reveals that the fixed-point equations of SE align with the saddle-point equations of the free energy. This suggests that, once the KRSB ansatz holds and the SE fixed point is reached, KVASP can accurately compute the PPE in the large system limit (LSL).

cs.IT↗

arXiv2024

Quadratic Message Passing for Generalized Quadratic Equations Model

For approximate inference in the generalized quadratic equations model, many state-of-the-art algorithms lack any prior knowledge of the target signal structure, exhibits slow convergence, and can not handle any analytic prior knowledge of the target signal structure. So, this paper proposes a new algorithm, Quadratic Message passing (QMP). QMP has a complexity as low as $O(N^{3})$. The SE derived for QMP can capture precisely the per-iteration behavior of the simulated algorithm. Simulation results confirm QMP outperforms many state-of-the-art algorithms.

cs.IT↗

arXiv2024

Current-induced near-field radiative energy, linear-momentum, and angular-momentum transfer

In this paper, we study the near-field radiative energy, linear-momentum, and angular-momentum transfer from a current-biased graphene to nanoparticles. The electric current through the graphene sheet induces nonequilibrium fluctuations, causing energy and momentum transfer even in the absence of a temperature difference. The inherent spin-momentum locking of graphene surface plasmons leads to an in-plane torque perpendicular to the direction of the electric current. In the presence of a temperature difference, the energy transfer is greatly enhanced while the lateral force and torque remain within the same order. Our work explores the potential of utilizing current-biased graphene to manipulate nanoparticles.

physics.optics↗

arXiv2024

Symbol Detection for Coarsely Quantized OTFS

This paper explicitly models a coarse and noisy quantization in a communication system empowered by orthogonal time frequency space (OTFS) for cost and power efficiency. We first point out, with coarse quantization, the effective channel is imbalanced and thus no longer able to circularly shift the transmitted symbols along the delay-Doppler domain. Meanwhile, the effective channel is non-isotropic, which imposes a significant loss to symbol detection algorithms like the original approximate message passing (AMP). Although the algorithm of generalized expectation consistent for signal recovery (GEC-SR) can mitigate this loss, the complexity in computation is prohibitively high, mainly due to an dramatic increase in the matrix size of OTFS. In this context, we propose a low-complexity algorithm that incorporates into the GEC-SR a quick inversion of quasi-banded matrices, reducing the complexity from a cubic order to a linear order while keeping the performance at the same level.

cs.IT↗

arXiv2022

PGMG: A Pharmacophore-Guided Deep Learning Approach for Bioactive Molecular Generation

The rational design of novel molecules with desired bioactivity is a critical but challenging task in drug discovery, especially when treating a novel target family or understudied targets. Here, we propose PGMG, a pharmacophore-guided deep learning approach for bioactivate molecule generation. Through the guidance of pharmacophore, PGMG provides a flexible strategy to generate bioactive molecules with structural diversity in various scenarios using a trained variational autoencoder. We show that PGMG can generate molecules matching given pharmacophore models while maintaining a high level of validity, uniqueness, and novelty. In the case studies, we demonstrate the application of PGMG to generate bioactive molecules in ligand-based and structure-based drug de novo design, as well as in lead optimization scenarios. Overall, the flexibility and effectiveness of PGMG make it a useful tool for accelerating the drug discovery process.

q-bio.BM↗