arXiv subjects

Wu Wang

Publications and source records attributed to Wu Wang.

At least 19 recordsLinked to original sources

arXiv2026

Extreme mixing enables broadband high-temperature electromagnetic absorption in high-entropy perovskites

Developing oxide absorbers with broadband high-temperature electromagnetic absorption performance has long been desirable in the electromagnetic absorption field, yet their absorption performance has remained unsatisfactory. Here, through an extreme mixing strategy, we successfully develop an ultrabroad effective absorption bandwidth (EAB) of 9.3 GHz in high-entropy perovskites up to 600 degree celsius, surpassing the previously reported oxide absorbers. Specifically, we achieve the extreme mixing in high-entropy perovskites by the successful incorporation of up to 13 cation elements, with 8 and 5 cations occupying the A- and B-sites, respectively, using a laser-driven controllable synthesis technique. Such extreme mixing maximally promotes the formation of atomic-scale interfaces to markedly amplify atomic-level interfacial polarization loss for the effective dissipation of electromagnetic wave energy, thereby giving rise to a remarkably broad EAB of 9.3 GHz in high-entropy perovskites up to 600 degree celsius. Owing to the profoundly increased electrical conductivity that causes impedance mismatch at elevated temperatures, however, their EAB deteriorates to 4.6 GHz at 700 degree celsius. This work establishes a new extreme mixing design paradigm for enabling high-entropy oxide absorbers with unprecedented high-temperature EM absorption performance.

cond-mat.mtrl-sci↗

arXiv2026

Shortcut to Adiabatic Isomeric Population Transfer of the $^{229}\mathrm{Th}$ Nucleus via Hyperfine Electronic Bridge

The $^{229}$Th nucleus is well known for its exceptionally low-lying nuclear isomeric level, which provides a unique platform for exploring electron-nucleus interactions and gives rise to a variety of rich physical phenomena. One such phenomenon is the hyperfine electronic bridge, which has recently been shown to enable efficient and precise manipulation of the nuclear isomeric levels of $^{229}$Th [W. Wang $et~ al.$, Phys. Rev. Lett. \textbf{133}, 223001 (2024)]. However, that study used the stimulated Raman adiabatic passage method, which requires relatively long operation times. In this work, we employ the stimulated Raman shortcut-to-adiabatic passage method, which dramatically shortens the operation time from the order of hundreds of milliseconds to hundreds of microseconds while maintaining a transfer efficiency of about $79.38\%$.

quant-ph↗

arXiv2026

Local Spatiotemporal Convolutional Network for Robust Gait Recognition

Gait recognition, as a promising biometric technology, identifies individuals through their unique walking patterns and offers distinctive advantages including non-invasiveness, long-range applicability, and resistance to deliberate disguise. Despite these merits, capturing the intrinsic motion patterns concealed within consecutive video frames remains challenging due to the complexity of video data and the interference of external covariates such as viewpoint changes, clothing variations, and carrying conditions. Existing approaches predominantly rely on either static appearance features extracted from individual silhouette frames or employ complex sequential models (\eg, LSTM, 3D convolutions) that demand substantial computational resources and sophisticated training strategies. To address these limitations, we propose a Local Spatiotemporal Convolutional Network (LSTCN), a structurally simple yet highly effective dual-branch architecture that endows standard two-dimensional convolutional networks with the capacity to extract temporal information. Specifically, we introduce a Global Bidirectional Spatial Pooling (GBSP) mechanism that reduces the dimensionality of gait tensors by decomposing spatial features into horizontal and vertical strip-based local representations, enabling the temporal dimension to participate in standard 2D convolution operations. Building upon this, we design a Local Spatiotemporal Convolutional (LSTC) layer that jointly processes temporal and spatial dimensions, allowing the network to adaptively learn strip-based gait motion patterns. We further extend this formulation with asymmetric convolution kernels that independently attend to the temporal, spatial, and joint spatiotemporal domains, thereby enriching the extracted feature representations.

cs.CV↗

arXiv2026

Hyperfine-resolved laser excitation and detection of nuclear isomer in trapped $^{229}$Th$^{3+}$ ions

We present a comprehensive theoretical investigation of hyperfine-resolved excitation and detection of the low-energy isomeric state of $^{229}$Th in trapped $^{229}\mathrm{Th}^{3+}$ ions. Using a quantum master equation approach, we quantitatively analyze the dependence of the isomeric population on laser linewidth, detuning, and irradiation time, showing that their proper matching is essential for efficient excitation. Going beyond earlier conceptual discussions of electronic-fluorescence-based nuclear-state detection, we propose two concrete nuclear-state detection schemes based on three hyperfine-resolved electronic fluorescence channels at 690, 984, and 1088 nm. Our quantitative analysis shows that, for 50 ions, the 690- and 984-nm scheme yields detectable photon count rates on the order of $10^3~\mathrm{s}^{-1}$ at 690 nm and $10^4~\mathrm{s}^{-1}$ at 984 nm, whereas the 1088-nm scheme achieves a detectable photon rate on the order of $10^3~\mathrm{s}^{-1}$. By quantifying the trade-off between irradiation time and scan-step size, we show that the nuclear transition can be located within one month for a 100-MHz uncertainty using currently available vacuum-ultraviolet laser technology. These results provide practical guidance for trapped-ion $^{229}\mathrm{Th}$ spectroscopy and the development of nuclear clocks.

physics.atom-ph↗

arXiv2026

Efficient Handwriting-Based Alzheimer,s Disease Diagnosis Using a Low-Rank Mixture of Experts Deep Learning Framework

Early and reliable detection of Alzheimer's disease (AD) is crucial for timely clinical intervention and improved patient management. It also supports the evaluation of emerging therapeutic strategies. In this paper, we propose a Low-Rank Mixture of Experts (LoRA-MoE) deep learning framework for Alzheimer's disease diagnosis based on handwriting analysis. Handwriting signals provide a non-invasive and scalable digital biomarker that captures subtle cognitive-motor impairments associated with early AD progression. The proposed architecture allows multiple experts to specialize in different handwriting patterns while sharing a common base network. This design enables efficient learning of general representations while reducing interference between experts. Each expert is equipped with lightweight low-rank adapters. This mechanism significantly reduces the number of trainable parameters compared with standard Mixture of Experts (MoE) models and improves training stability. The proposed framework is evaluated on the Diagnosis AlzheimeR WIth haNdwriting (DARWIN) dataset. Extensive experiments are conducted, including ablation studies on key architectural parameters such as hidden dimension size, number of experts, and LoRA rank. The method is compared with multilayer perceptron (MLP) and conventional MoE architectures. In addition, stacking ensemble strategies (StackMean and StackMax) are investigated to improve robustness and predictive performance. Experimental results show that the LoRA-MoE framework achieves powerful diagnostic performance while activating significantly fewer parameters during inference. These results highlight the potential of the proposed approach as an accurate and computationally efficient solution for handwriting-based Alzheimer's disease screening and digital health applications.

cs.LG↗

arXiv2026

Heavy-quark transport across the QCD crossover driven by a lattice-constrained in-medium potential

We present a self-consistent framework for heavy-quark transport in the quark-gluon plasma across the QCD crossover region. By synthesizing perturbative and nonperturbative interactions into a unified interaction kernel, we circumvent the traditional reliance on arbitrary soft-hard momentum separation scales. The interaction is governed by an in-medium effective potential, incorporating short-range Yukawa screening and long-range confining string contributions, both rigorously constrained by the latest lattice QCD data. Our results reveal that the nonperturbative string tension is indispensable for capturing the extreme opacity of the medium near the critical temperature $T_c$. Specifically, our model predicts a spatial diffusion coefficient of $2\pi T D_s \approx 0.5 \sim 1.7$, demonstrating a striking quantitative agreement with the recent lattice QCD extractions. Ultimately, our results provide a robust dynamical interpretation of the strong heavy-quark coupling near the QCD crossover and offer a unified framework for describing heavy-flavor transport in hot and dense QCD matter.

hep-ph↗

arXiv2025

Soft-hard factorization of heavy-quark transport in QCD matter at finite chemical potential

We calculate the collisional energy loss and momentum diffusion coefficients of heavy quarks traversing a hot and dense QCD medium at finite quark chemical potential, $\mu\neq0$. The analysis is performed within an extended soft-hard factorization model (SHFM) that consistently incorporates the $\mu$-dependence of the Debye screening mass $M_D(\mu)$ and of the fermionic thermal distribution functions. Both the energy loss and the diffusion coefficients are found to increase with $\mu$, with the enhancement being most pronounced at low temperatures where the chemical potential effects dominate the medium response. To elucidate the origin of this dependence, we derive analytic high-energy approximations in which the leading $\mu$-corrections appear as logarithmic terms: a soft logarithm $\sim\mu^{2}\ln(|t^{*}|/M_{D}^{2})$ from $t$-channel scattering off thermal gluonic excitations, and a hard logarithm $\sim\mu^{2}\ln(E_{1}T/|t^{*}|)$ from scattering off thermal quarks. In the complete result the dependence on the intermediate separation scale $t^{\ast}$ cancels, as required. We also confirm the expected mass hierarchy $-dE/dz(charm)<-dE/dz(bottom)$ at fixed velocity. Our findings demonstrate that finite chemical potential plays a significant role in heavy-quark transport and must be included in theoretical descriptions of heavy-flavor dynamics in baryon-rich environments, such as those probed in the RHIC Beam Energy Scan, and at FAIR and NICA.

hep-ph↗

arXiv2025

Label-shift robust federated feature screening for high-dimensional classification

Distributed and federated learning are important tools for high-dimensional classification of large datasets. To reduce computational costs and overcome the curse of dimensionality, feature screening plays a pivotal role in eliminating irrelevant features during data preprocessing. However, data heterogeneity, particularly label shifting across different clients, presents significant challenges for feature screening. This paper introduces a general framework that unifies existing screening methods and proposes a novel utility, label-shift robust federated feature screening (LR-FFS), along with its federated estimation procedure. The framework facilitates a uniform analysis of methods and systematically characterizes their behaviors under label shift conditions. Building upon this framework, LR-FFS leverages conditional distribution functions and expectations to address label shift without adding computational burdens and remains robust against model misspecification and outliers. Additionally, the federated procedure ensures computational efficiency and privacy protection while maintaining screening effectiveness comparable to centralized processing. We also provide a false discovery rate (FDR) control method for federated feature screening. Experimental results and theoretical analyses demonstrate LR-FFS's superior performance across diverse client environments, including those with varying class distributions, sample sizes, and missing categorical data.

stat.ML↗

arXiv2024

Local excitation of kagome spin ice magnetism in HoAgGe seen by scanning tunneling microscopy

The kagome spin ice can host frustrated magnetic excitations by flipping its local spin. Under an inelastic tunneling condition, the tip in a scanning tunneling microscope can flip the local spin, and we apply this technique to kagome metal HoAgGe with a long-range ordered spin ice ground state. Away from defects, we discover a pair of pronounced dips in the local tunneling spectrum at symmetrical bias voltages with negative intensity values, serving as a striking inelastic tunneling signal. This signal disappears above the spin ice formation temperature and has a dependence on the magnetic fields, demonstrating its intimate relation with the spin ice magnetism. We provide a two-level spin-flip model to explain the tunneling dips considering the spin ice magnetism under spin-orbit coupling. Our results uncover a local emergent excitation of spin ice magnetism in a kagome metal, suggesting that local electrical field induced spin flip climbs over a barrier caused by spin-orbital locking.

cond-mat.str-el↗

arXiv2023

Clustered Federated Learning based on Nonconvex Pairwise Fusion

This study investigates clustered federated learning (FL), one of the formulations of FL with non-i.i.d. data, where the devices are partitioned into clusters and each cluster optimally fits its data with a localized model. We propose a clustered FL framework that incorporates a nonconvex penalty to pairwise differences of parameters. Without a priori knowledge of the set of devices in each cluster and the number of clusters, this framework can autonomously estimate cluster structures. To implement the proposed framework, we introduce a novel clustered FL method called Fusion Penalized Federated Clustering (FPFC). Building upon the standard alternating direction method of multipliers (ADMM), FPFC can perform partial updates at each communication round and allows parallel computation with variable workload. These strategies significantly reduce the communication cost while ensuring privacy, making it practical for FL. We also propose a new warmup strategy for hyperparameter tuning in FL settings and explore the asynchronous variant of FPFC (asyncFPFC). Theoretical analysis provides convergence guarantees for FPFC with general losses and establishes the statistical convergence rate under a linear model with squared loss. Extensive experiments have demonstrated the superiority of FPFC compared to current methods, including robustness and generalization capability.

cs.LG↗

arXiv2023

Quantum theory of isomeric excitation of $^{229}$Th in strong laser fields

A general quantum mechanical theory is developed for the isomeric excitation of $^{229}$Th in strong femtosecond laser pulses. The theory describes the tripartite interaction between the nucleus, the atomic electrons, and the laser field. The nucleus can be excited both by the laser field and by laser-driven electronic transitions. Numerical results show that strong femtosecond laser pulses are very efficient in exciting the $^{229}$Th nucleus, yielding nuclear excitation probabilities on the order of $10^{-11}$ per nucleus per pulse. Laser-driven electronic excitations are found to be more efficient than direct optical excitations.

nucl-th↗

arXiv2022

One-step Closure, Ideal Convergence and Monotone Determined Space

Monotone determined spaces are natural topological extensions of dcpo. Its main purpose is to build an extended framework for domain theory. In this paper, we study the one-step closure and ideal convergence on monotone determined space. Then we also introduce the equivalent characterizations of c-spaces and locally hypercompact space. The main results are:1.Every c-space has one-step closure and every locally hypercompact space has weak one-step closure;2.A monotone determined space has one-step closure if and only if it is d-meet continuous and has weak one-step closure. 3.IS-convergence(resp. IGS-convergence) is topological iff X is a c-space (resp. locally hypercompact space); 4.If X is a d-meet continuous space, then the following three conditions are equivalent to each other: (i) X is c-space; (ii) The net (xj ) ISL-converges to x iff (xj ) I-converges to x with respect to Lawson topology; (iii) The net (xj ) IGSL-converges to x iff (xj ) I-converges to x with respect to Lawson topology.

math.GN↗

arXiv2022

Subgroup analysis for the functional linear model

Classical functional linear regression models the relationship between a scalar response and a functional covariate, where the coefficient function is assumed to be identical for all subjects. In this paper, the classical model is extended to allow heterogeneous coefficient functions across different subgroups of subjects. The greatest challenge is that the subgroup structure is usually unknown to us. To this end, we develop a penalization-based approach which innovatively applies the penalized fusion technique to simultaneously determine the number and structure of subgroups and coefficient functions within each subgroup. An effective computational algorithm is derived. We also establish the oracle properties and estimation consistency. Extensive numerical simulations demonstrate its superiority compared to several competing methods. The analysis of an air quality dataset leads to interesting findings and improved predictions.

stat.ME↗

arXiv2022

Continuity of Powerspaces Structures in Directed Spaces

Powerspaces of directed spaces play an important role in modeling the semantics of nondeterministic functional programming languages. The notions of upper,lower and convex powerspace of a directed space are defined by the way of free algebras[25]. In this paper, We study the continuity of power structures of directed spaces and show that the directed lower powerspaces, directed upper powerspaces and directed convex powerspaces of continuous spaces are continuous spaces.

math.GN↗

arXiv2022

QFS-space and its properties

In this paper, the concept of quasi-finitely separating map and quasiapproximate identity are introduced. Based on these concepts, QFS-spaces and quasicontinuous maps are defined. Properties and characterizations of QFS-spaces are explored. Main results are: (1) Each QFS-space is quasicontinuous space; (2) Closed subspaces, quasicontinuous projection spaces of QFS-spaces are QFS-spaces; (3) Continuous retracts of QFS-spaces are QFS-spaces and a kind of retracts of QFS-spaces are constructed; (4) Upper powerspaces of continuous QFS-spaces are FS-spaces.

math.GN↗

arXiv2022

Generalized ideal convergence on quasi-continuous domains

In this paper,the concepts of generalized ideal inf-limit and generalized ideal final lower bound limit are introduced in the directed complete poset,and their relations with Scott topology and Lawson topology are studied. The main results are as follows: (1) On directed complete posets,generalized ideal inf-limit topology is consistent with Scott topology; (2) Generalized ideal inf-limiti convergence is topological if and only if directed complete posets are quasi-continuous domains; (3) In quasi-continuous domain,generalized ideal final lower bound limit topology is consistent with Lawson topology;(4) In meet continuous directed complete posets,the generalized ideal final lower bound limit convergence is topological if and only if the directed complete poset is continuous.

math.GN↗

arXiv2022

Uncertainty Inspired Underwater Image Enhancement

A main challenge faced in the deep learning-based Underwater Image Enhancement (UIE) is that the ground truth high-quality image is unavailable. Most of the existing methods first generate approximate reference maps and then train an enhancement network with certainty. This kind of method fails to handle the ambiguity of the reference map. In this paper, we resolve UIE into distribution estimation and consensus process. We present a novel probabilistic network to learn the enhancement distribution of degraded underwater images. Specifically, we combine conditional variational autoencoder with adaptive instance normalization to construct the enhancement distribution. After that, we adopt a consensus process to predict a deterministic result based on a set of samples from the distribution. By learning the enhancement distribution, our method can cope with the bias introduced in the reference map labeling to some extent. Additionally, the consensus process is useful to capture a robust and stable result. We examined the proposed method on two widely used real-world underwater image enhancement datasets. Experimental results demonstrate that our approach enables sampling possible enhancement predictions. Meanwhile, the consensus estimate yields competitive performance compared with state-of-the-art UIE methods. Code available at https://github.com/zhenqifu/PUIE-Net.

cs.CV↗

arXiv2022

Nuclear excitation cross section of $^{229}$Th via inelastic electron scattering

Nuclear excitation cross section of $^{229}$Th from the ground state to the low-lying isomeric state via inelastic electron scattering is calculated, on the level of Dirac distorted wave Born approximation. With electron energies below 100 eV, inelastic scattering is very efficient in the isomeric excitation, yielding excitation cross sections on the order of 10$^{-27}$ to 10$^{-26}$ cm$^2$. Systematic analyses are presented on elements affecting the excitation cross section, including the ion-core potential, the relativistic effect, the knowledge of the reduced nuclear transition probabilities, etc.

nucl-th↗