arXiv subjects

Xiangrong Wang

Publications and source records attributed to Xiangrong Wang.

At least 19 recordsLinked to original sources

arXiv2026

Room-temperature magnetic p-n junctions for charge-and-spin diodes

Non-magnetic p-n junctions have been fundamental components in the silicon era, serving as the backbone for nearly all Si-based semiconductor devices, including transistors. To tackle challenges such as scaling limitations, excessive latency, and high-power consumption in Si-based electronics, we develop magnetic p-n junctions composed of a p-type amorphous magnetic semiconductor (p-AMS) and n-type Si. These charge-and-spin junctions exhibit typical diode characteristics for charge current, along with distinctive spin diode features. By manipulating spin-polarized space charges, we observed a giant magnetic enhancement of approximately 24.36% at a breakdown current of 5 mA, and an impressive 29-fold increase in magnetic moments for p-AMS. The observed spin behavior is attributed to space charge effects or carrier depletion in the p-AMS with extended hole states.

cond-mat.mtrl-sci↗

arXiv2026

RKKY-like interactions between two magnetic skyrmions

Understanding skyrmion-skyrmion interactions is crucial for effectively manipulating the motion of multiple skyrmions in racetrack and logic devices. However, the fundamental nature and microscopic origins of these interactions remain poorly understood. In this study, we investigate skyrmion-skyrmion interactions in chiral magnetic films and reveal that they possess intrinsic, anisotropic, and oscillatory characteristics. Specifically, we demonstrate that the attractive and repulsive forces between skyrmions oscillate with a well-defined period, akin to the Ruderman-Kittel-Kasuya-Yosida (RKKY) coupling observed between two magnetic moments in metals. Our analysis uncovers the essential physics behind a previously unrecognized universal wavy tail in the skyrmion spin texture. Notably, the resulting RKKY-like interaction between skyrmions is universal for all tilted skyrmions, irrespective of whether the titled easy-axis is from an external field or a crystalline magnetic anisotropy. These findings introduce a novel physical principle for the design of skyrmion molecules or skyrmion superstructures, which hold significant potential for applications in skyrmion-based spintronics and neuromorphic computing.

cond-mat.mes-hall↗

arXiv2026

Open quantum theory of magnetoresistance in mesoscopic magnetic materials

Magnetoresistance (MR) in magnetic materials arises from spin-exchange coupling between local moments and itinerant electrons, representing a challenging many-body open-quantum problem. Here we develop a comprehensive microscopic theory of MR within an open-quantum-system framework by solving the Liouville-von Neumann equation for a hybrid system of free electrons and local moments using the time-convolutionless projection operator method. Our approach reveals both ferromagnetic and antiferromagnetic MR as consequences of temperature- and field-dependent spin decoherence, encompassing spin relaxation and dephasing. In particular, the resistance associated with spin decoherence is governed by the order parameters of magnetic materials, such as the magnetization in ferromagnets and the Néel vector in antiferromagnets. This theory deepens the fundamental understanding of MR and offers guidance for interpreting and designing experiments on magnetic materials.

cond-mat.mes-hall↗

arXiv2026

Effective Graph Resistance as Cumulative Heat Dissipation

Effective graph resistance is a fundamental structural metric in network science, widely used to quantify global connectivity, compare network architectures, and assess robustness in flow-based systems. Despite its importance, current formulations rely mainly on spectral or pseudo-inverse Laplacian representations, offering limited physical insight into how structural features shape this quantity or how it can be efficiently optimized. Here, we establish an exact and physically transparent relationship between effective graph resistance and the cumulative heat dissipation generated by Laplacian diffusion dynamics. We show that the total heat dissipated during relaxation to equilibrium precisely equals the effective graph resistance. This dynamical viewpoint uncovers a natural multi-scale decomposition of the Laplacian spectrum: early-time dissipation is governed by degree-based local structure, intermediate times isolate eigenvalues below the spectral mean, and long times are dominated by the algebraic connectivity. These multi-scale properties yield continuous and interpretable strategies for modifying network structure and constructing optimized ensembles, enabling improvements that are otherwise NP-hard to achieve via combinatorial methods. Our results unify structural and dynamical perspectives on network connectivity and provide new tools for analyzing, comparing, and optimizing complex networks across domains.

physics.soc-ph↗

arXiv2025

Directionality-induced jamming in multiplex networks

We study diffusion on multiplex networks with directed interlayer couplings. We demonstrate both numerically and analytically that even with undirected layers, interlayer directionality alone reproduces superdiffusion and the prime regime. We further reveal a new phenomenon, the directionality-induced jamming, whereby directed interlayer links hinder diffusion, fragmenting the system into dynamically disconnected components and preventing convergence to the steady state of the diffusion process. Via an optimization process, we show that this new regime is attainable in both toy models and real-world topologies. These findings underscore the crucial role of interlayer link directionality in shaping the emergent behavior of multiplex systems, with potential implications for the design and control of such systems.

physics.soc-ph↗

arXiv2025

A Physics Prior-Guided Dual-Stream Attention Network for Motion Prediction of Elastic Bragg Breakwaters

Accurate motion response prediction for elastic Bragg breakwaters is critical for their structural safety and operational integrity in marine environments. However, conventional deep learning models often exhibit limited generalization capabilities when presented with unseen sea states. These deficiencies stem from the neglect of natural decay observed in marine systems and inadequate modeling of wave-structure interaction (WSI). To overcome these challenges, this study proposes a novel Physics Prior-Guided Dual-Stream Attention Network (PhysAttnNet). First, the decay bidirectional self-attention (DBSA) module incorporates a learnable temporal decay to assign higher weights to recent states, aiming to emulate the natural decay phenomenon. Meanwhile, the phase differences guided bidirectional cross-attention (PDG-BCA) module explicitly captures the bidirectional interaction and phase relationship between waves and the structure using a cosine-based bias within a bidirectional cross-computation paradigm. These streams are synergistically integrated through a global context fusion (GCF) module. Finally, PhysAttnNet is trained with a hybrid time-frequency loss that jointly minimizes time-domain prediction errors and frequency-domain spectral discrepancies. Comprehensive experiments on wave flume datasets demonstrate that PhysAttnNet significantly outperforms mainstream models. Furthermore,cross-scenario generalization tests validate the model's robustness and adaptability to unseen environments, highlighting its potential as a framework to develop predictive models for complex systems in ocean engineering.

cs.LG↗

arXiv2025

A Causality- and Frequency-Aware Deep Learning Framework for Wave Elevation Prediction Behind Floating Breakwaters

Predicting the elevations of nonlinear wave fields behind floating breakwaters (FBs) is crucial for optimizing coastal engineering structures, enhancing safety, and improving design efficiency. Existing deep learning approaches exhibit limited generalization capability under unseen operating conditions. To address this challenge, this study proposes the Exogenous-to-Endogenous Frequency-Aware Network (E2E-FANet), a novel end-to-end neural network designed to model relationships between waves and structures. First, the Dual-Basis Frequency Mapping (DBFM) module leverages orthogonal cosine and sine bases to generate an adaptive time-frequency representation, enabling the model to effectively disentangle the evolving spectral components of wave signals. Second, the Exogenous-to-Endogenous Cross-Attention (E2ECA) module employs cross attention to explicitly model the unidirectional causal influence of floating breakwater motion on wave elevations. Additionally, a Temporal-wise Attention (TA) mechanism is incorporated that adaptively captures complex dependencies in endogenous variables. Extensive experiments, including generalization tests across diverse wave conditions and adaptability tests under varying relative water density (RW) conditions, demonstrate that E2E-FANet achieves superior predictive accuracy and robust generalization compared to mainstream models. This work emphasizes the importance of integrating causality and frequency-aware modeling in deep learning architectures for modeling nonlinear dynamics systems.

cs.LG↗

arXiv2025

Heterogeneous Influence Maximization in User Recommendation

User recommendation systems enhance user engagement by encouraging users to act as inviters to interact with other users (invitees), potentially fostering information propagation. Conventional recommendation methods typically focus on modeling interaction willingness. Influence-Maximization (IM) methods focus on identifying a set of users to maximize the information propagation. However, existing methods face two significant challenges. First, recommendation methods fail to unleash the candidates' spread capability. Second, IM methods fail to account for the willingness to interact. To solve these issues, we propose two models named HeteroIR and HeteroIM. HeteroIR provides an intuitive solution to unleash the dissemination potential of user recommendation systems. HeteroIM fills the gap between the IM method and the recommendation task, improving interaction willingness and maximizing spread coverage. The HeteroIR introduces a two-stage framework to estimate the spread profits. The HeteroIM incrementally selects the most influential invitee to recommend and rerank based on the number of reverse reachable (RR) sets containing inviters and invitees. RR set denotes a set of nodes that can reach a target via propagation. Extensive experiments show that HeteroIR and HeteroIM significantly outperform the state-of-the-art baselines with the p-value < 0.05. Furthermore, we have deployed HeteroIR and HeteroIM in Tencent's online gaming platforms and gained an 8.5\% and 10\% improvement in the online A/B test, respectively. Implementation codes are available at https://github.com/socialalgo/HIM.

cs.IR↗

arXiv2025

A Theory of Anisotropic Magnetoresistance in Altermagnets and Its Applications

Altermagnets, a newly discovered class of magnets, integrate the advantages of both ferromagnets and antiferromagnets, such as enabling anomalous transport without stray fields and supporting ultrafast spin dynamics, offering exciting opportunities for spintronics. A key challenge in altermagnetic spintronics is the efficient reading and writing of information by switching the Neel vector orientations to represent binary 0 and 1. Here, we develop a microscopic theory of the magnetoresistance effect in altermagnets and propose that magnetoresistance anisotropy can serve as an effective mechanism for the electrical readout of the Neel vector. Our theory describes a two-step charge-spin-charge conversion process governed by the interplay between spin splitting and spin Hall effects: a longitudinal electric field induces transverse drift spin currents, which induce significant spin accumulation at the boundaries, generating a diffusive spin current that is converted back into a longitudinal charge current. By switching the Neel vector, a substantial change in magnetoresistance, akin to giant magnetoresistance in ferromagnets, is realized, enabling an electrically readable altermagnetic memory. Our microscopic theory provides deeper insights into the fundamental physics of the magnetoresistance effect in altermagnets and offers valuable guidance for designing next-generation ultradense and ultrafast spintronic devices based on altermagnetism.

cond-mat.mes-hall↗

arXiv2025

A Novel Framework for Significant Wave Height Prediction based on Adaptive Feature Extraction Time-Frequency Network

Precise forecasting of significant wave height (Hs) is essential for the development and utilization of wave energy. The challenges in predicting Hs arise from its non-linear and non-stationary characteristics. The combination of decomposition preprocessing and machine learning models have demonstrated significant effectiveness in Hs prediction by extracting data features. However, decomposing the unknown data in the test set can lead to data leakage issues. To simultaneously achieve data feature extraction and prevent data leakage, a novel Adaptive Feature Extraction Time-Frequency Network (AFE-TFNet) is proposed to improve prediction accuracy and stability. It is encoder-decoder rolling framework. The encoder consists of two stages: feature extraction and feature fusion. In the feature extraction stage, global and local frequency domain features are extracted by combining Wavelet Transform (WT) and Fourier Transform (FT), and multi-scale frequency analysis is performed using Inception blocks. In the feature fusion stage, time-domain and frequency-domain features are integrated through dominant harmonic sequence energy weighting (DHSEW). The decoder employed an advanced long short-term memory (LSTM) model. Hourly measured wind speed (Ws), dominant wave period (DPD), average wave period (APD) and Hs from three stations are used as the dataset, and the four metrics are employed to evaluate the forecasting performance. Results show that AFE-TFNet significantly outperforms benchmark methods in terms of prediction accuracy. Feature extraction can significantly improve the prediction accuracy. DHSEW has substantially increased the accuracy of medium-term to long-term forecasting. The prediction accuracy of AFE-TFNet does not demonstrate significant variability with changes of rolling time window size. Overall, AFE-TFNet shows strong potential for handling complex signal forecasting.

cs.LG↗

arXiv2025

Predicting the critical behavior of complex dynamic systems via learning the governing mechanisms

Critical points separate distinct dynamical regimes of complex systems, often delimiting functional or macroscopic phases in which the system operates. However, the long-term prediction of critical regimes and behaviors is challenging given the narrow set of parameters from which they emerge. Here, we propose a framework to learn the rules that govern the dynamic processes of a system. The learned governing rules further refine and guide the representative learning of neural networks from a series of dynamic graphs. This combination enables knowledge-based prediction for the critical behaviors of dynamical networked systems. We evaluate the performance of our framework in predicting two typical critical behaviors in spreading dynamics on various synthetic and real-world networks. Our results show that governing rules can be learned effectively and significantly improve prediction accuracy. Our framework demonstrates a scenario for facilitating the representability of deep neural networks through learning the underlying mechanism, which aims to steer applications for predicting complex behavior that learnable physical rules can drive.

physics.soc-ph↗

arXiv2025

Spreading dynamics of information on online social networks

Social media is profoundly changing our society with its unprecedented spreading power. Due to the complexity of human behaviors and the diversity of massive messages, the information spreading dynamics are complicated, and the reported mechanisms are different and even controversial. Based on data from mainstream social media platforms, including WeChat, Weibo, and Twitter, cumulatively encompassing a total of 7.45 billion users, we uncover a ubiquitous mechanism that the information spreading dynamics are basically driven by the interplay of social reinforcement and social weakening effects. Accordingly, we propose a concise equation, which, surprisingly, can well describe all the empirical large-scale spreading trajectories. Our theory resolves a number of controversial claims and satisfactorily explains many phenomena previously observed. It also reveals that the highly clustered nature of social networks can lead to rapid and high-frequency information bursts with relatively small coverage per burst. This vital feature enables social media to have a high capacity and diversity for information dissemination, beneficial for its ecological development.

physics.soc-ph↗

arXiv2024

Physics Origin of Universal Unusual Magnetoresistance

The discovery of the unusual magnetoresistance (UMR) during the rotation of magnetization in the plane perpendicular to the electric current, which has been typically attributed to magnetization-dependent interfacial reflection of spin current, has brought remarkable impacts on the understanding and application of a variety of spintronic phenomena. Here, we report that giant UMR occurs also in single-layer magnetic metals and exhibits high-order contributions and a universal sum rule, which agree well with the physics origin of the recently proposed two-vector magnetoresistance that simply considers electron scattering by the magnetization vector and interfacial electric field, without the need for any relevance to spin current. Revisiting of the literature data reveals that the most representative data that were used to claim spin Hall magnetoresistance or other magnetoresistances related or unrelated to spin current can be understood unifiedly by the two-vector MR theory. Experimental and theoretical results against spin-current-related magnetoresistances, but not the two-vector magnetoresistance, are discussed.

cond-mat.mes-hall↗

arXiv2024

Wideband Coherent Microwave Conversion via Magnon Nonlinearity in Hybrid Quantum System

Frequency conversion is a widely realized physical process in nonlinear systems of optics and electronics. As an emerging nonlinear platform, spintronic devices have the potential to achieve stronger frequency conversion. Here, we demonstrated a microwave frequency conversion method in a hybrid quantum system, integrating nitrogen-vacancy centers in diamond with magnetic thin film CoFeB. We achieve a conversion bandwidth ranging from 0.1 to 12GHz, presenting an up to $\mathrm{25^{th}}$ order frequency conversion and further display the application of this method for frequency detection and qubits coherent control. Distinct from traditional frequency conversion techniques based on nonlinear electric response, our approach employs nonlinear magnetic response in spintronic devices. The nonlinearity, originating from the symmetry breaking such as domain walls in magnetic films, presents that our method can be adapted to hybrid systems of other spintronic devices and spin qubits, expanding the application scope of spintronic devices and providing a promising on-chip platform for coupling quantum systems.

quant-ph↗

arXiv2024

Saliency-Aware Regularized Graph Neural Network

The crux of graph classification lies in the effective representation learning for the entire graph. Typical graph neural networks focus on modeling the local dependencies when aggregating features of neighboring nodes, and obtain the representation for the entire graph by aggregating node features. Such methods have two potential limitations: 1) the global node saliency w.r.t. graph classification is not explicitly modeled, which is crucial since different nodes may have different semantic relevance to graph classification; 2) the graph representation directly aggregated from node features may have limited effectiveness to reflect graph-level information. In this work, we propose the Saliency-Aware Regularized Graph Neural Network (SAR-GNN) for graph classification, which consists of two core modules: 1) a traditional graph neural network serving as the backbone for learning node features and 2) the Graph Neural Memory designed to distill a compact graph representation from node features of the backbone. We first estimate the global node saliency by measuring the semantic similarity between the compact graph representation and node features. Then the learned saliency distribution is leveraged to regularize the neighborhood aggregation of the backbone, which facilitates the message passing of features for salient nodes and suppresses the less relevant nodes. Thus, our model can learn more effective graph representation. We demonstrate the merits of SAR-GNN by extensive experiments on seven datasets across various types of graph data. Code will be released.

cs.LG↗

arXiv2021

Nonreciprocal Emergence of Hybridized Magnons in magnetic thin Films

We investigate the transfer and control of nonreciprocity through magnons themselves in permalloy thin films deposited on surface oxide silicon substrate. Evidences of nonreciprocal emergence of hybridized dipole exchange magnons (spin waves) at two permalloy surfaces are provided by studying magnon transmission and asymmetry, via Brillouin light scattering measurements. The dipole dominated spin wave and exchange dominated spin wave are found to be localized near the top and bottom surfaces, respectively, and traveling along opposite directions. The nonreciprocity and the localization are intertwined and ca n be tuned by an in plane magnetic field. The effects are well explained by the magnetostatic theory and can be quantitatively reproduced by the micromagnetic simulations. Our findings provide a simple and flexible approach to nonreciprocal all magnon logi c devices with highly compatible with silicon based integrated circuit technology.

cond-mat.mes-hall↗

arXiv2021

Cognitive-Driven Optimization of Sparse Array Transceiver for MIMO Radar Beamforming

Cognitive multiple-input multiple-output (MIMO) radar is capable of adjusting system parameters adaptively by sensing and learning in complex dynamic environment. Beamforming performance of MIMO radar is guided by both beamforming weight coefficients and the transceiver configuration. We propose a cognitive-driven MIMO array design where both the beamforming weights and the transceiver configuration are adaptively and concurrently optimized under different environmental conditions. The perception-action cycle involves data collection of full virtual array, covariance reconstruction and joint design of the transmit and receive arrays by antenna selection.The optimal transceiver array design is realized by promoting two-dimensional group sparsity via iteratively minimizing reweighted mixed L21-norm, with constraints imposed on transceiver antenna spacing for proper transmit/receive isolation. Simulations are provided to demonstrate the "perception-action" capability of the proposed cognitive sparse MIMO array in achieving enhanced beamforming and anti-jamming in dynamic target and interference environment.

eess.SP↗

arXiv2021

Adaptive Sparse Array Beamformer Design by Regularized Complementary Antenna Switching

In this work, we propose a novel strategy of adaptive sparse array beamformer design, referred to as regularized complementary antenna switching (RCAS), to swiftly adapt both array configuration and excitation weights in accordance to the dynamic environment for enhancing interference suppression. In order to achieve an implementable design of array reconfiguration, the RCAS is conducted in the framework of regularized antenna switching, whereby the full array aperture is collectively divided into separate groups and only one antenna in each group is switched on to connect with the processing channel. A set of deterministic complementary sparse arrays with good quiescent beampatterns is first designed by RCAS and full array data is collected by switching among them while maintaining resilient interference suppression. Subsequently, adaptive sparse array tailored for the specific environment is calculated and reconfigured based on the information extracted from the full array data. The RCAS is devised as an exclusive cardinality-constrained optimization, which is reformulated by introducing an auxiliary variable combined with a piece-wise linear function to approximate the $l_0$-norm function. A regularization formulation is proposed to solve the problem iteratively and eliminate the requirement of feasible initial search point. A rigorous theoretical analysis is conducted, which proves that the proposed algorithm is essentially an equivalent transformation of the original cardinality-constrained optimization. Simulation results validate the effectiveness of the proposed RCAS strategy.

eess.SP↗