arXiv subjects

Zi-An Wang

Publications and source records attributed to Zi-An Wang.

11 recordsLinked to original sources

arXiv2026

Interfacial Noncollinear Filtering of Spin Hall Currents

Spin Hall currents generated in nonmagnetic materials are conventionally regarded as bulk responses whose polarization is fixed by crystal symmetry. This view has motivated the search for intrinsically low-symmetry spin sources when unconventional spin polarizations are required. Here we point out that, in realistic heterostructures, the device-relevant quantity is not the fully symmetry-averaged bulk spin Hall current, but the emitted spin current transmitted across the interface. We therefore establish emitted spin currents as bulk-interface hybrid responses and propose interfacial noncollinear filtering as a mechanism to bypass the bulk-symmetry constraint. A low-symmetry interfacial spin-orbit field, generally noncollinear with the momentum-resolved spin polarization of the incident spin Hall current, imposes spin-dependent transmission and converts hidden momentum-resolved spin-polarization components into an observable unconventional emitted spin current. Using both a rotationally symmetric minimal model and a realistic high-symmetry Dirac-semimetal model, we show that conventional spin Hall sources can emit sizable out-of-plane spin currents when their hidden bulk spin Hall textures are selectively transmitted by the interfacial spin-orbit field. Our results reveal that spin-current polarization emerges from the cooperative action of bulk and interfacial responses, providing a strategy for reprogramming spin-current polarization in high-efficiency, CMOS-compatible spin Hall materials without relying on intrinsically low-symmetry bulk crystals or external symmetry-breaking schemes.

cond-mat.mes-hall↗

arXiv2026

Deterministic Switching of the Néel Vector by Asymmetric Spin Torque

Néel vector, the order parameter of collinear antiferromagnets, serves as a state variable in associated antiferromagnetic (AFM) spintronic devices to encode information. A deterministic switching of Néel vector is crucial for the write-in operation, which, however, remains a challenging problem in AFM spintronics. Here we demonstrate, based on analytical derivation and macro-spin simulations, that Néel vector switching can be generally achieved via a current-induced spin torque, provided the spin accumulations responsible for this torque are non-identical between opposite sublattices. This condition occurs widely in AFM films, as symmetry equivalence between sublattice-dependent spin accumulations is usually absent, allowing unequal spin accumulations induced by Edelstein effect or a spin current. Unlike previously studied spin torques induced by uniform or staggered spin accumulations -- where either the field-like or damping-like component dominates exclusively -- the asymmetric spin torque features cooperative contributions from both components, leading to Néel vector dynamics that are fundamentally distinct from previous expectations. Crucially, the static states stabilized by the asymmetric spin torque enable versatile Néel vector switching strategies -- field-free spin-transfer torque switching during current application, as well as field-free or field-assisted spin-orbit torque switching after the current pulse -- demonstrating that established spin torque techniques from ferromagnetic spintronics can be directly adapted to AFM systems, a capability absent in previous theoretical frameworks. Our work establishes a general mechanism for current-induced Néel vector switching, which is in principle feasible for all collinear antiferromagnets, and thus paves the route to realize efficient writing in AFM spintronics.

cond-mat.mtrl-sci↗

arXiv2025

Multi-Modal Motion Retrieval by Learning a Fine-Grained Joint Embedding Space

Motion retrieval is crucial for motion acquisition, offering superior precision, realism, controllability, and editability compared to motion generation. Existing approaches leverage contrastive learning to construct a unified embedding space for motion retrieval from text or visual modality. However, these methods lack a more intuitive and user-friendly interaction mode and often overlook the sequential representation of most modalities for improved retrieval performance. To address these limitations, we propose a framework that aligns four modalities -- text, audio, video, and motion -- within a fine-grained joint embedding space, incorporating audio for the first time in motion retrieval to enhance user immersion and convenience. This fine-grained space is achieved through a sequence-level contrastive learning approach, which captures critical details across modalities for better alignment. To evaluate our framework, we augment existing text-motion datasets with synthetic but diverse audio recordings, creating two multi-modal motion retrieval datasets. Experimental results demonstrate superior performance over state-of-the-art methods across multiple sub-tasks, including an 10.16% improvement in R@10 for text-to-motion retrieval and a 25.43% improvement in R@1 for video-to-motion retrieval on the HumanML3D dataset. Furthermore, our results show that our 4-modal framework significantly outperforms its 3-modal counterpart, underscoring the potential of multi-modal motion retrieval for advancing motion acquisition.

cs.CV↗

arXiv2025

Semantics-Aware Human Motion Generation from Audio Instructions

Recent advances in interactive technologies have highlighted the prominence of audio signals for semantic encoding. This paper explores a new task, where audio signals are used as conditioning inputs to generate motions that align with the semantics of the audio. Unlike text-based interactions, audio provides a more natural and intuitive communication method. However, existing methods typically focus on matching motions with music or speech rhythms, which often results in a weak connection between the semantics of the audio and generated motions. We propose an end-to-end framework using a masked generative transformer, enhanced by a memory-retrieval attention module to handle sparse and lengthy audio inputs. Additionally, we enrich existing datasets by converting descriptions into conversational style and generating corresponding audio with varied speaker identities. Experiments demonstrate the effectiveness and efficiency of the proposed framework, demonstrating that audio instructions can convey semantics similar to text while providing more practical and user-friendly interactions.

cs.SD↗

arXiv2025

Highly Efficient 3D Human Pose Tracking from Events with Spiking Spatiotemporal Transformer

Event camera, as an asynchronous vision sensor capturing scene dynamics, presents new opportunities for highly efficient 3D human pose tracking. Existing approaches typically adopt modern-day Artificial Neural Networks (ANNs), such as CNNs or Transformer, where sparse events are converted into dense images or paired with additional gray-scale images as input. Such practices, however, ignore the inherent sparsity of events, resulting in redundant computations, increased energy consumption, and potentially degraded performance. Motivated by these observations, we introduce the first sparse Spiking Neural Networks (SNNs) framework for 3D human pose tracking based solely on events. Our approach eliminates the need to convert sparse data to dense formats or incorporate additional images, thereby fully exploiting the innate sparsity of input events. Central to our framework is a novel Spiking Spatiotemporal Transformer, which enables bi-directional spatiotemporal fusion of spike pose features and provides a guaranteed similarity measurement between binary spike features in spiking attention. Moreover, we have constructed a large-scale synthetic dataset, SynEventHPD, that features a broad and diverse set of 3D human motions, as well as much longer hours of event streams. Empirical experiments demonstrate the superiority of our approach over existing state-of-the-art (SOTA) ANN-based methods, requiring only 19.1% FLOPs and 3.6% energy cost. Furthermore, our approach outperforms existing SNN-based benchmarks in this task, highlighting the effectiveness of our proposed SNN framework. The dataset will be released upon acceptance, and code can be found at https://github.com/JimmyZou/HumanPoseTracking_SNN.

cs.CV↗

arXiv2025

X-type stacking in cross-chain antiferromagnets

Physical phenomena in condensed matter normally arise from the collective effect of all atoms, while selectively addressing a lone atomic sublattice by external stimulus is elusive. The later functionality may, however, benefit various applications, as the response may differ when the external stimulus affects only a specific sublattice rather than the entire solid. Here, we introduce cross-chain antiferromagnets, where the stacking of two magnetic sublattices forms a pattern of intersecting atomic chains, allowing for the sublattice selectivity. We dub this antiferromagnetic (AFM) stacking X-type and demonstrate that it exhibits unique spin-dependent transport properties not present in conventional magnets. Through high-throughput analyses and computations, we unveil three prototypes of X-type AFM stacking and identify 15 candidate candidates. Using $β$-Fe$_{2}$PO$_{5}$ as a representative X-type antiferromagnet, we predict sublattice-selective spin-polarized transport driven by the X-type stacking, where one magnetic sublattice conducts, while the other does not. Consequently, a spin torque can be exerted solely on a single sublattice, leading to unconventional ultrafast dynamics of the Nèel vector capable of deterministic switching of the AFM domains. Our work uncovers a previously overlooked type of magnetic moment stacking and reveals sublattice-selective physical properties promising for high-performance spintronic applications.

cond-mat.mtrl-sci↗

arXiv2025

Giant Uncompensated Magnon Spin Currents in X-type Magnets

Magnon spin currents in insulating magnets are useful for low-power spintronics. However, in magnets stacked by antiferromagnetic (AFM) exchange coupling, which have recently aroused significant interest for potential applications in spintronics, these currents are largely counteracted by opposite magnetic sublattices, thus suppressing their net effect. Contrary to this common observation, here, we show that magnets with X-type AFM stacking, where opposite magnetic sublattices form orthogonal intersecting chains, support giant magnon spin currents with minimal compensation. Our model Hamiltonian calculations predict magnetic chain locking of magnon spin currents in these X-type magnets, significantly reducing their compensation ratio. In addition, the one-dimensional nature of the chain-like magnetic sublattices enhances magnon spin conductivities surpassing those of two-dimensional ferromagnets and canonical altermagnets. Notably, uncompensated X-type magnets, such as odd-layer antiferromagnets and ferrimagnets, can exhibit magnon spin currents polarized opposite to those expected by their net magnetization. These unprecedented properties of X-type magnets, combined with their inherent advantages resulting from AFM coupling, offer a promising new path for low-power high-performance spintronics.

cond-mat.mtrl-sci↗

arXiv2023

Prediction of Giant Tunneling Magnetoresistance in RuO$_{2}$/TiO$_{2}$/RuO$_{2}$ (110) Antiferromagnetic Tunnel Junctions

Using first-principles quantum-transport calculations, we investigate spin-dependent electronic and transport properties of antiferromagnetic tunnel junctions (AFMTJs) that consist of (110)-oriented antiferromagnetic (AFM) metal RuO$_{2}$ electrodes and an insulating TiO$_{2}$ tunneling barrier. We predict the emergence of a giant tunneling magnetoresistance (TMR) effect in a wide energy window, a series of barrier layer thicknesses, and different interface terminations, indicating the robustness of this effect. We show that the predicted TMR cannot be explained in terms of the global transport spin-polarization of RuO$_{2}$ (110) but is well understood based on matching the momentum-dependent spin-polarized conduction channels of the two RuO$_{2}$ (110) electrodes. We predict oscillations of TMR with increasing barrier thickness, indicating a non-negligible contribution from the perfectly epitaxial interfaces. Our work helps the understanding of the physics of TMR in AFMTJs and aids in realizing efficient AFM spintronic devices.

cond-mat.mtrl-sci↗

arXiv2023

Tunneling valley Hall effect driven by tilted Dirac fermions

Valleytronics is a research field utilizing a valley degree of freedom of electrons for information processing and storage. A strong valley polarization is critical for realistic valleytronic applications. Here, we predict a tunneling valley Hall effect (TVHE) driven by tilted Dirac fermions in all-in-one tunnel junctions based on a two-dimensional (2D) valley material. Different doping of the electrode and spacer regions in these tunnel junctions results in momentum filtering of the tunneling Dirac fermions, generating a strong transverse valley Hall current dependent on the Dirac-cone tilting. Using the parameters of an existing 2D valley material, we demonstrate that such a TVHE is much stronger than that induced by the intrinsic Berry curvature mechanism reported previously. Finally, we predict that resonant tunneling can occur in a tunnel junction with properly engineered device parameters such as the spacer width and transport direction, providing significant enhancement of the valley Hall angle. Our work opens a new approach to generate valley polarization in realistic valleytronic systems.

cond-mat.mes-hall↗

arXiv2022

Néel Spin Currents in Antiferromagnets

Ferromagnets are known to support spin-polarized currents that control various spin-dependent transport phenomena useful for spintronics. On the contrary, fully compensated antiferromagnets are expected to support only globally spin-neutral currents. Here, we demonstrate that these globally spin-neutral currents can represent the Néel spin currents, i.e. staggered spin currents flowing through different magnetic sublattices. The Néel spin currents emerge in antiferromagnets with strong intra-sublattice coupling (hopping) and drive the spin-dependent transport phenomena such as tunneling magnetoresistance (TMR) and spin-transfer torque (STT) in antiferromagnetic tunnel junctions (AFMTJs). Using RuO$_{2}$ and Fe$_{4}$GeTe$_{2}$ as representative antiferromagnets, we predict that the Néel spin currents with a strong staggered spin-polarization produce a sizable field-like STT capable of the deterministic switching of the Néel vector in the associated AFMTJs. Our work uncovers the previously unexplored potential of fully compensated antiferromagnets and paves a new route to realize the efficient writing and reading of information for antiferromagnetic spintronics.

cond-mat.mes-hall↗

arXiv2022

Spin-Neutral Tunneling Anomalous Hall Effect

Anomalous Hall effect (AHE) is a fundamental spin-dependent transport property that is widely used in spintronics. It is generally expected that currents carrying net spin polarization are required to drive the AHE. Here we demonstrate that, in contrast to this common expectation, a spin-neutral tunneling AHE (TAHE), i.e. a TAHE driven by spin-neutral currents, can be realized in an antiferromagnetic (AFM) tunnel junction where an AFM electrode with a non-spin-degenerate Fermi surface and a normal metal electrode are separated by a non-magnetic barrier with strong spin-orbit coupling (SOC). The symmetry mismatch between the AFM electrode and the SOC barrier results in an asymmetric spin-dependent momentum filtering of the spin-neutral longitudinal current generating the transverse Hall current in each electrode. We predict a sizable spin-neutral TAHE in an AFM tunnel junction with a RuO$_{2}$-type AFM electrode and a SnTe-type SOC barrier and show that the Hall currents are reversible by the Néel vector switching. With the Hall angle being comparable to that in conventional AHE bulk materials, the predicted spin-neutral TAHE can be used for the Néel vector detection in antiferromagnetic spintronics.

cond-mat.mes-hall↗