arXiv subjects

Jia Zhang

Publications and source records attributed to Jia Zhang.

At least 37 records · Page 2Linked to original sources

arXiv2026

Giant orbital-magnon conversion driven perpendicular magnetization switching

The pursuit of beyond-Moore information technologies has stimulated the exploration of novel information carriers, such as electron spin, orbital, and magnon, beyond electron charge. Efficient interconversion among these degrees of freedom and precise control over the information states are crucial for advancing nanoelectronic devices. However, a direct coupling between orbital angular momentum (L) and magnons (M) has remained elusive, and magnetization switching through orbital-to-magnon (L-M) conversion has not yet been achieved. Here, we report the experimental demonstration of L-M conversion in an orbital metal/antiferromagnetic insulator bilayer at room temperature, with an efficiency over an order of magnitude higher than that in traditional orbital systems lacking the L-M process. Consequently, we achieved efficient room-temperature perpendicular magnetization switching in a CoFeB ferromagnetic layer mediated by this new mechanism. Our findings establish a direct link between orbitronics and magnonics, providing a new platform for the development of advanced nano-devices based on orbital-driven magnonic phenomena.

cond-mat.mes-hall↗

arXiv2026

UBio-MolFM: A Universal Molecular Foundation Model for Bio-Systems

All-atom molecular simulation serves as a quintessential ``computational microscope'' for understanding the machinery of life, yet it remains fundamentally limited by the trade-off between quantum-mechanical (QM) accuracy and biological scale. We present UBio-MolFM, a universal foundation model framework specifically engineered to bridge this gap. UBio-MolFM introduces three synergistic innovations: (1) UBio-Mol26, a large bio-specific dataset constructed via a multi-fidelity ``Two-Pronged Strategy'' that combines systematic bottom-up enumeration with top-down sampling of native protein environments (up to 1,200 atoms); (2) E2Former-V2, a linear-scaling equivariant transformer that integrates Equivariant Axis-Aligned Sparsification (EAAS) and Long-Short Range (LSR) modeling to capture non-local physics with up to ~4x higher inference throughput in our large-system benchmarks; and (3) a Three-Stage Curriculum Learning protocol that transitions from energy initialization to energy-force consistency, with force-focused supervision to mitigate energy offsets. Rigorous benchmarking across microscopic forces and macroscopic observables -- including liquid water structure, ionic solvation, and peptide folding -- demonstrates that UBio-MolFM achieves ab initio-level fidelity on large, out-of-distribution biomolecular systems (up to ~1,500 atoms) and realistic MD observables. By reconciling scalability with quantum precision, UBio-MolFM provides a robust, ready-to-use tool for the next generation of computational biology.

physics.chem-ph↗

arXiv2026

CausalVAD: De-confounding End-to-End Autonomous Driving via Causal Intervention

Planning-oriented end-to-end driving models show great promise, yet they fundamentally learn statistical correlations instead of true causal relationships. This vulnerability leads to causal confusion, where models exploit dataset biases as shortcuts, critically harming their reliability and safety in complex scenarios. To address this, we introduce CausalVAD, a de-confounding training framework that leverages causal intervention. At its core, we design the sparse causal intervention scheme (SCIS), a lightweight, plug-and-play module to instantiate the backdoor adjustment theory in neural networks. SCIS constructs a dictionary of prototypes representing latent driving contexts. It then uses this dictionary to intervene on the model's sparse vectorized queries. This step actively eliminates spurious associations induced by confounders, thereby eliminating spurious factors from the representations for downstream tasks. Extensive experiments on benchmarks like nuScenes show CausalVAD achieves state-of-the-art planning accuracy and safety. Furthermore, our method demonstrates superior robustness against both data bias and noisy scenarios configured to induce causal confusion.

cs.CV↗

arXiv2026

Agents on a Tree: Pathwise Coordination for Multi-Objective Molecular Optimization

Multi-objective molecular optimization requires searching vast chemical spaces under conflicting objectives, where early design decisions strongly constrain downstream outcomes. Existing methods typically rely on a single policy or fixed scalarization, which limits their ability to represent diverse trade-offs and to explore multiple promising design trajectories. We propose ATOM, a multi-agent framework that formulates molecular optimization as a tree-structured search. Each node corresponds to an atomic operation and hosts an agent specialized for a particular objective or decision context. Agents coordinate along different paths of the tree rather than enforcing a global consensus, enabling the method to maintain and compare alternative molecular evolution trajectories. A global memory of past optimization behaviors further supports balanced exploration and exploitation across objectives. This tree-structured interaction enables reasoning over long-horizon dependencies inherent in molecular design. Experiments on challenging multi-objective benchmarks involving activity, synthesizability, and ADMET-related properties show that ATOM consistently achieves improved Pareto coverage and hypervolume over strong baselines. These results demonstrate the effectiveness of pathwise multi-agent coordination for molecular optimization. Code is available at https://anonymous.4open.science/r/ATOM-41CE.

cs.AI↗

arXiv2026

Data Selection for LLM Alignment Using Fine-Grained Preferences

Large language models (LLMs) alignment aims to ensure that the behavior of LLMs meets human preferences. While collecting data from multiple fine-grained, aspect-specific preferences becomes more and more feasible, existing alignment methods typically work on a single preference and thus struggle with conflicts inherent in such aggregated datasets. As one early attempt, in this paper, we propose a data-centric approach to align LLMs through the effective use of fine-grained preferences. Specifically, we formulate the problem as a direct fine-grained preference optimization and introduce preference divergence (PD) that quantifies inter-aspect preference conflicts. Instead of directly tackling the consequent complicated optimization, we recast it as a data selection problem and propose a simple yet effective strategy, which identifies a subset of data corresponding to the most negative PD values, for efficient training. We theoretically analyze the loss-bound optimality of our selection strategy and conduct extensive empirical studies on varied settings and datasets to demonstrate that our practical selection method could achieve consistent improvement against standard full-data alignment, using even just 30% of the data. Our work shares a line that LLM alignment using fine-grained preferences is highly feasible.

cs.LG↗

arXiv2026

The reason for the occurrence of W-type contact binaries

For more than half a century, the puzzling W-type phenomenon in contact binaries has challenged astrophysicists. In these systems, the less massive component exhibits a higher surface temperature than its more massive companion, which is a reversal of the typical A-type configuration, where the more massive star is hotter. This counterintuitive temperature inversion defies the basic stellar physics and still lacks a widely accepted explanation. In this study, we assembled a sample of over 3,000 extensively observed contact binaries and derived their complete set of physical parameters. Our statistical analysis revealed a strong positive correlation between the occurrence of W-type contact binaries and the intensity and frequency of magnetic activities. This result strongly supports the hypothesis that magnetic activities are the primary driver of the W-type phenomenon and offers a compelling explanation for the observed transitions between the W-type and A-type.

astro-ph.SR↗

arXiv2026

HD 26172: an active solar-type subgiant in a close binary system

We present the first comprehensive photometric and spectroscopic analysis of the RS CVn system HD 26172, robustly determining the previously debated evolutionary state of its primary star. Since this system is a single-lined spectroscopic binary with spot-induced light curve modulations, we derived its physical parameters by combining the TESS light curves, the radial velocity curve from our observations, and the primary-star mass estimates based on three complementary methods.Our results reveal that HD 26172 is a detached binary system composed of a $1.25 \pm 0.32 M_{\odot}$ subgiant and a $0.63 \pm 0.11 M_{\odot}$ main-sequence star. The conclusion of subgiant primary is also supported by the absence of lithium absorption and no observed infrared excess. Using long-term photometry from the KWS survey, we detected a tentative stellar activity cycle of 5635 days with an amplitude of 0.04 mag in HD 26172. Additionally, we identified ten optical flare events exhibiting temporally clustered outburst behavior. The presence of a long-term activity cycle, pronounced starspot activity, and frequent optical flares makes HD 26172 a valuable laboratory for studying magnetic activity in subgiants within close binary systems.

astro-ph.SR↗

arXiv2026

Electric field switching of altermagnetic spin-splitting in multiferroic skyrmions

Magnetic skyrmions are localized magnetic structures that retain their shape and stability over time, thanks to their topological nature. Recent theoretical and experimental progress has laid the groundwork for understanding magnetic skyrmions characterized by negligible net magnetization and ultrafast dynamics. Notably, skyrmions emerging in materials with altermagnetism, a novel magnetic phase featuring lifted Kramers degeneracy-have remained unreported until now. In this study, we demonstrate that BiFeO3, a multiferroic renowned for its strong coupling between ferroelectricity and magnetism, can transit from a spin cycloid to a Neel-type skyrmion under antidamping spin-orbit torque at room temperature. Strikingly, the altermagnetic spin splitting within BiFeO3 skyrmion can be reversed through the application of an electric field, revealed via the Circular photogalvanic effect. This quasiparticle, which possesses a neutral topological charge, holds substantial promise for diverse applications-most notably, enabling the development of unconventional computing systems with low power consumption and magnetoelectric controllability.

cond-mat.mtrl-sci↗

arXiv2026

Scalable Machine Learning Force Fields for Macromolecular Systems Through Long-Range Aware Message Passing

Machine learning force fields (MLFFs) have revolutionized molecular simulations by providing quantum mechanical accuracy at the speed of molecular mechanical computations. However, a fundamental reliance of these models on fixed-cutoff architectures limits their applicability to macromolecular systems where long-range interactions dominate. We demonstrate that this locality constraint causes force prediction errors to scale monotonically with system size, revealing a critical architectural bottleneck. To overcome this, we establish the systematically designed MolLR25 ({Mol}ecules with {L}ong-{R}ange effect) benchmark up to 1200 atoms, generated using high-fidelity DFT, and introduce E2Former-LSR, an equivariant transformer that explicitly integrates long-range attention blocks. E2Former-LSR exhibits stable error scaling, achieves superior fidelity in capturing non-covalent decay, and maintains precision on complex protein conformations. Crucially, its efficient design provides up to 30% speedup compared to purely local models. This work validates the necessity of non-local architectures for generalizable MLFFs, enabling high-fidelity molecular dynamics for large-scale chemical and biological systems.

physics.chem-ph↗

arXiv2026

Arca: A Lightweight Confidential Container Architecture for Cloud-Native Environments

Confidential containers protect cloud-native workloads using trusted execution environments (TEEs). However, existing Container-in-TEE designs (e.g., Confidential Containers (CoCo)) encapsulate the entire runtime within the TEE, inflating the trusted computing base (TCB) and introducing redundant components and cross-layer overhead. We present Arca, a lightweight confidential container framework based on a TEE-in-Container architecture that isolates each workload in an independent, hardware-enforced trust domain while keeping orchestration logic outside the TEE. This design minimizes inter-layer dependencies, confines compromise to per-container boundaries, and restores the TEE's minimal trust principle. We implemented Arca on Intel SGX, Intel TDX, and AMD SEV. Experimental results show that Arca achieves near-native performance and outperforms CoCo in most benchmarks, while the reduced TCB significantly improves verifiability and resilience against host-level compromise. Arca emonstrates that efficient container management and strong runtime confidentiality can be achieved without sacrificing security assurance.

cs.CR↗

arXiv2025

Tidally Trapped Two-pole Pulsations Discovered in a Close Binary with a Massive $β$ Cephei Star

Tidally tilted pulsators (TTPs), whose pulsation axis aligns with the binary's semi-major axis, represent a newly established class of oscillators in binary systems. While all previously known TTPs are either $δ$ Scuti or subdwarf B-type stars, their existence has remained unidentified in more massive $β$ Cephei variables. Here, we report the discovery of tidally trapped pulsations in the massive ellipsoidal variable HD~329379, based on photometry from the Transiting Exoplanet Survey Satellite (TESS). Our analysis reveals a $β$ Cephei pulsator in a 2.25-day orbit whose pulsation mode amplitude is strongly modulated with the orbital frequency. Based on our analysis, we concluded that this modulation can be explained by pulsations with significantly larger amplitude near the star's two tidal poles (apsides). We interpret this as a tidally distorted quadrupole pulsation chariacteristiced by trapped two-pole pulsations, with a pulsation axis aligned with the tidal axis. This represents the first identification of such a pulsation mode in a $β$ Cephei star, which differs from single-sided pulsations observed in previous works, marking a rare and important discovery. Our work extends the family of TTPs beyond $δ$ Scuti and subdwarf B-type stars to include more massive $β$ Cephei variables. In particular, the two-pole pulsator HD~329379 stands out as the prototype of a new class of TTPs in massive stars. These results not only provide a new insight to probe the interior structure and evolutionary state for massive stars but also offer a unique opportunity to study the interaction between pulsations and strong tidal distortions.

astro-ph.SR↗

arXiv2025

Asteroseismology and Dynamics Reveal Interior Structure and Coeval Evolution in the Triply Post-Main-Sequence system DG Leo

$δ$ Scuti stars in binary or multiple systems serve as crucial probes for studying stellar pulsation and evolution. However, many such systems are not ideal for asteroseismology due to uncertainties in mass transfer with close companions and the challenges of dynamically measuring all components' physical properties. The triple system DG~Leo, comprising an inner binary and a distant $δ$ Scuti star, is an ideal target due to its well-separated pulsator. By combining new \textit{TESS} photometry with archival spectroscopy, our dynamical analysis shows that the system's three components share similar masses, radii, and luminosities within errors, occupying coincident Hertzsprung--Russell diagram positions, indicative of coeval evolution. By fitting seven observed $δ$ Scuti frequencies through asteroseismic modeling with dynamically constrained theoretical grids, we simultaneously trace the pulsating star's evolution and constrain the triple system's evolutionary stage, with the derived fundamental parameters showing consistency with the dynamical solutions. Our analysis reveals that all three components of DG~Leo are in the post-main-sequence phase, with a system age of $0.7664^{+0.1402}_{-0.1258}$~Gyr. Additionally, the $δ$ Scuti component shows multiple non-radial modes with significant mixed-character frequencies, providing precise constraints on its convective core extent ($R_{\mathrm{cz}}/R = 0.0562^{+0.0137}_{-0.0021}$).

astro-ph.SR↗

arXiv2025

Ultrahigh Charge-to-Spin Conversion and Tunneling Magnetoresistance in Quasi-Two-Dimensional d-wave Altermagnet

The emergence of altermagnets has driven groundbreaking advances in spintronics. Notably, d-wave altermagnets support non-relativistic spin transport, efficient charge-to-spin conversion, and T-odd spin currents. In addition, their integration as electrodes in antiferromagnetic tunnel junctions (AFMTJs) enables a tunneling magnetoresistance (TMR) effect, allowing electrical detection of Néel vectors for next-generation memory devices. In this work, we investigate the non-relativistic spin transport properties of the quasi-two-dimensional (quasi-2D) d-wave altermagnet KV\textsubscript{2}Se\textsubscript{2}O and the TMR effect in KV\textsubscript{2}Se\textsubscript{2}O-based AFMTJs via first-principles calculations. Our results reveal that KV\textsubscript{2}Se\textsubscript{2}O exhibits a non-relativistic longitudinal spin polarization and a spin Hall angle both exceeding 60\% at room temperature, while KV\textsubscript{2}Se\textsubscript{2}O-based AFMTJs achieve a giant TMR reaching approximately $10^{12}$\%, which remains robust against Fermi level shifts. These findings highlight the anisotropic spin polarization inherent to d-wave staggered magnetism and underscore the critical role of Fermi surface topology in enhancing T-odd spin transport and the TMR effect in AFMTJs.

cond-mat.mtrl-sci↗

arXiv2025

Spin-orbit torques in bulk collinear antiferromagnets:complete classifications and the induced spin dynamics

Electric field induced spin-orbit torques are the crucial mechanism for electric regulations of antiferromagnetic order. However, the spin-orbit torques in antiferromagnets and the induced spin dynamics remain largely unexplored. In this work, the full classifications of SOTs in bulk collinear AFMs have been achieved based on magnetic point group. Dependent on the symmetries connecting the opposite spin sublattices, the SOTs are classified into six distinct types. Among them, the SOTs and the induced Neel vector dynamics in three representative AFMs have been investigated, where the spin sublattices are connected by fractional translation, spatial inversion, and neither by translation nor inversion symmetry respectively. The SOTs on spin sublattices have been calculated by first-principles calculations based on Kubo linear response theory, and then the induced spin dynamics are simulated by LLG equations. In typical PT symmetric AFM and the inversion symmetry breaking altermagnet, the simulations indicate that the deterministic switching of Neel vectors can be driven by field like torques. What's more, the fully electric writing of multiple antiferromagnetic domains into single domain state with preset Neel vector direction and 180 deterministic switching may also be realized. Our work may shed light on the current control of antiferromagnetic orders in collinear AFMs. Especially, for inversion symmetry breaking altermagnet, the electric writing and reading of Neel vector are highly desirable for antiferromagnetic memory applications.

cond-mat.mtrl-sci↗

arXiv2025

Diffuse Laser Cooling Based on the $6\mathrm{P}_{3/2}$ Excited State of Rubidium Atoms via 420 nm Blue Light

To date, the laser cooling of rubidium atoms has inevitably relied on 780 nm cooling light corresponding to the first excited state $5\mathrm{P}_{3/2}$. Surprisingly, we demonstrate laser cooling directly utilizing 420 nm blue light for active optical clock, which corresponds to the high excited state $6\mathrm{P}_{3/2}$ of Rb atom. Experimentally, we successfully apply the 420 nm diffuse laser cooling technique to prepare a cold $^{87}\mathrm{Rb}$ atomic cloud with a length of up to one meter, and measure the cold-atom absorption spectroscopy. The cold atom number is approximately $4.4\times10^{7}$. We systematically compare the cooling effects of 420 nm and 780 nm diffuse laser cooling, and verify the feasibility of blue light cooling using high excited state. This work directly employs blue light to cool and manipulate ground-state Rb atoms to the 6P excited state, providing a new and efficient approach for the cold-atom active optical clock. It is also expected to open up research directions and application prospects in frontier fields such as Rydberg atoms, ultracold quantum gases Bose-Einstein condensation, quantum information, and so on.

physics.atom-ph↗

arXiv2025

E2Former: An Efficient and Equivariant Transformer with Linear-Scaling Tensor Products

Equivariant Graph Neural Networks (EGNNs) have demonstrated significant success in modeling microscale systems, including those in chemistry, biology and materials science. However, EGNNs face substantial computational challenges due to the high cost of constructing edge features via spherical tensor products, making them impractical for large-scale systems. To address this limitation, we introduce E2Former, an equivariant and efficient transformer architecture that incorporates the Wigner $6j$ convolution (Wigner $6j$ Conv). By shifting the computational burden from edges to nodes, the Wigner $6j$ Conv reduces the complexity from $O(|\mathcal{E}|)$ to $ O(| \mathcal{V}|)$ while preserving both the model's expressive power and rotational equivariance. We show that this approach achieves a 7x-30x speedup compared to conventional $\mathrm{SO}(3)$ convolutions. Furthermore, our empirical results demonstrate that the derived E2Former mitigates the computational challenges of existing approaches without compromising the ability to capture detailed geometric information. This development could suggest a promising direction for scalable and efficient molecular modeling.

cs.LG↗

arXiv2025

Accretion rates of stellar-mass compact objects embedded in AGN discs

Stellar-mass compact objects (COs) embedded in active galactic nucleus (AGN) discs are commonly assumed to accrete via Bondi or Bondi-Hoyle-Lyttleton (BHL) prescriptions, neglecting gas angular momentum. We show that differential rotation in AGN discs can impart non-negligible angular momentum, in which case accretion proceeds through a viscous disc rather than Bondi/BHL flow. Our model provides a new framework estimating the CO accretion rate as $\dot{M}_\mathrm{CO} = \min\{\dot{M}_\mathrm{vis}, \dot{M}_\mathrm{BHL}\}$, where the viscous rate $\dot{M}_\mathrm{vis}$ accounts for gas--CO relative motion decomposed into a local gradient term (due to differential rotation) and bulk motion (from differing orbital parameters). This rate can be expressed as $\dot{M}_\mathrm{vis} = αξ(r_\mathrm{H}/r_\mathrm{BHL})^3\dot{M}_\mathrm{BHL}$, where $ξ$ is a coefficient of order unity. It can also be approximately scaled to the global AGN accretion rate as $\dot{M}_\mathrm{vis} \propto \dot{M}_1$, with the scaling coefficients in both forms determined by the specific dynamical configuration. The accretion is viscosity-limited when $q > [αξ(1+\mathcal{M}^2)^{3}/3]^{1/2} h^3$, where $q$ is the mass ratio between the CO and the supermassive black hole, $α$ the viscosity parameter, $\mathcal{M}$ the Mach number of the bulk relative motion, and $h$ the aspect ratio of the AGN disc. In thin AGN discs this condition is satisfied for most stellar-mass or more massive COs. Our framework also naturally allows for the inclusion of established outflow corrections, thereby enabling a more realistic treatment of super-Eddington flows. Our formulation thus improves upon Bondi/BHL prescriptions and offers a more physically motivated basis for studying CO evolution in AGN environments.

astro-ph.HE↗

arXiv2025

Tibetan Language and AI: A Comprehensive Survey of Resources, Methods and Challenges

Tibetan, one of the major low-resource languages in Asia, presents unique linguistic and sociocultural characteristics that pose both challenges and opportunities for AI research. Despite increasing interest in developing AI systems for underrepresented languages, Tibetan has received limited attention due to a lack of accessible data resources, standardized benchmarks, and dedicated tools. This paper provides a comprehensive survey of the current state of Tibetan AI in the AI domain, covering textual and speech data resources, NLP tasks, machine translation, speech recognition, and recent developments in LLMs. We systematically categorize existing datasets and tools, evaluate methods used across different tasks, and compare performance where possible. We also identify persistent bottlenecks such as data sparsity, orthographic variation, and the lack of unified evaluation metrics. Additionally, we discuss the potential of cross-lingual transfer, multi-modal learning, and community-driven resource creation. This survey aims to serve as a foundational reference for future work on Tibetan AI research and encourages collaborative efforts to build an inclusive and sustainable AI ecosystem for low-resource languages.

cs.CL↗