arXiv subjects

Liu Yang

Publications and source records attributed to Liu Yang.

At least 55 records · Page 3Linked to original sources

arXiv2026

Extreme Terahertz Nonlinear Phononics by Coherence-Imprinted Control of Hybrid Order

Coherent control of quantum materials has progressed along two major fronts: nonlinear phononics, which reshapes lattices to induce emergent states, and Floquet engineering, which tailors electronic band reconstruction via time-periodic driving. Both mechanisms face fundamental limitations at terahertz (THz) frequencies: phononic nonlinearities are intrinsically weak in standard lattices, while electronic Floquet states are often constrained by rapid decoherence upon light-off and by a scarcity of coherence-resolved, multi-correlation probes beyond (quasi-)stationary band structures. Here we report an extreme THz nonlinear-phononics mechanism in $\text{Ta}_\text{2}\text{NiSe}_\text{5}$, where a highly susceptible non-equilibrium electronic correlation bath dramatically amplifies lattice nonlinearities under coherent driving. Utilizing THz two-dimensional spectroscopy as a coherence-tomography tool, we resolve an exceptionally rich landscape of approximately 30 distinct multi-order quantum pathways, including high-harmonic phonon generation, multi-quantum coherences, and multi-wave anharmonic cross-mode mixing. The density and complexity of this extreme manifold establishes a new benchmark for THz nonlinear phononics, as the multi-order quantum pathways surpass the limits of conventional lattice responses. These high-order signals collapse above ~100~K, defining an electronic correlation scale of a coherence-imprinted hybrid electronic-phonon order that governs the sustainability of high-order quantum correlations and nonlinear pathways beyond linear and equilibrium responses. Our results establish a route for correlation-boosted, phonon-anchored periodic Hamiltonian engineering and for certifying such periodically-driven states via multi-correlation coherence tomography.

cond-mat.str-el↗

arXiv2026

Pressure-Induced Superconducting-like Transition in the $\it d$-wave Altermagnet Candidate CsV$_2$Se$_2$O

Altermagnetism generates exchange-type spin splitting without net magnetization and, in its $\it d$-wave form, resembles the angular symmetry of unconventional $\it d$-wave superconductivity. Whether this correspondence bears directly on superconducting instabilities in real correlated materials remains open. Here we study the quasi-two-dimensional vanadium oxychalcogenide CsV$_2$Se$_2$O (CVSO), a square-net $\it d$-wave altermagnet candidate, through combined experimental and theoretical investigation of its lattice structure, electronic structure and transport properties. At ambient pressure, CVSO is a weakly insulating parent state with a density-wave-like anomaly near 100 K, and its bulk properties are most consistent with a G-type compensated antiferromagnetic background. Under compression, the density-wave-like feature is suppressed, the magnetoresistance evolves from predominantly negative to positive, and a superconducting-like resistive downturn emerges below about 3 K. This low-temperature anomaly is reproducible across samples and pressure media, and is suppressed by magnetic field. Room-temperature X-ray diffraction reveals no symmetry lowering, whereas does show a pronounced compressibility anomaly over the same pressure range. CVSO thus reveals a pressure-tuned phase diagram in which a reconstructed weakly insulating parent state gives way to strange-metal-like transport and superconducting-like behavior, echoing broader phenomenology associated with unconventional superconductors, including cuprates and nickelates.

cond-mat.supr-con↗

arXiv2026

Magnetoelastic Transport-Path Reconstruction and Giant Magnetotransport Responses in a Two-Dimensional Antiferromagnet

Nonvolatile magnetotransport responses in a single magnetic material have generally not been expected to exhibit a large ON/OFF ratio, because they are usually tied to spin-orbit coupling and therefore remain relatively weak. Here we show, contrary to this expectation, that giant nonvolatile magnetotransport can arise in a single magnetic material through magnetoelastic reconstruction of nonrelativistic real-space transport paths. Using the two-dimensional antiferromagnet FePS$_{3}$ as a representative system, first-principles quantum transport calculations reveal that charge transport is strongly tied to its quasi-one-dimensional zigzag sublattice chains and, under suitable doping, can even become confined to them. Moreover, strain lifts the degeneracy among symmetry-related zigzag variants and thus reorients these transport paths through magnetoelastic coupling. As a result, both the longitudinal and transverse conductivities change dramatically, yielding a giant magnetoelastic magnetoresistance of up to $10^{4}$% and an energy-independent Hall ratio that far exceeds the spontaneous Hall ratios found in conventional magnets. These results establish a route to exploiting symmetry-related magnetic variants and their associated transport paths for reconfigurable, high-performance spintronic devices with large nonvolatile readout contrast.

cond-mat.mtrl-sci↗

arXiv2026

T-800: An 800 Hz Data Glove for Precise Hand Gesture Tracking

Human dexterity relies on rapid, sub-second motor adjustments, yet capturing these high-frequency dynamics remains an enduring challenge in biomechanics and robotics. Existing motion capture paradigms are compromised by a trade-off between temporal resolution and visual occlusion, failing to record the fine-grained hand motion of fast, contact-rich manipulation. Here we introduce T-800, a high-bandwidth data glove system that achieves synchronized, full-hand motion tracking at 800 Hz. By integrating a novel broadcast-based synchronization mechanism with a mechanical stress isolation architecture, our system maintains sub-frame temporal alignment across 18 distributed inertial measurement units (IMUs) during extended, vigorous movements. We demonstrate that T-800 recovers fine-grained manipulation details previously lost to temporal undersampling. Our analysis reveals that human dexterity exhibits significantly high-frequency motion energy (>100 Hz) that was fundamentally inaccessible due to the Nyquist sampling limit imposed by previous hardware constraints. To validate the system's utility for robotic manipulation, we implement a kinematic retargeting algorithm that maps T-800's high-fidelity human gestures onto dexterous robotic hand models. This demonstrates that the high-frequency motion data can be accurately translated while respecting the kinematic constraints of robotic hands, providing the rich behavioral data necessary for training robust control policies in the future.

cs.RO↗

arXiv2026

Complete transparency with three active-passive-coupled optical resonators

The phenomena of induced transparency, with the typical examples of electromagnetically induced transparency (EIT) in atomic media and those based on coupled optical resonators, have attracted tremendous interest since their discoveries. Owing to the limitations of the involved physical elements, however, near-100\% transmissions were reported under highly demanding experimental conditions. With a structure of three linearly coupled optical resonators, an active one carrying optical gain and two passive ones simply with dissipation, we demonstrate that a transmitted light field can become completely transparent through the structure, which displays all properties similar to those of EIT. It is due to a destructive interference mechanism that totally eliminates the intracavity field in the dissipative resonator directly coupled to the transmitted field of any feasible power, when the coupling strength of two other resonators is tuned across a point determined by their associated gain and loss rates. This mechanism works for all possible coupling strengths of the dark resonator with the input field and its neighboring resonator, as well as for any available quality factor from its fabrication. The transparency window size and output field intensity can be freely adjusted by tuning two inter-cavity couplings of wide ranges, without modifying the built-in optical gain which can be just slightly stronger than the dissipation of the active resonator.

quant-ph↗

arXiv2026

ODI-Bench: Can MLLMs Understand Immersive Omnidirectional Environments?

Omnidirectional images (ODIs) provide full 360x180 view which are widely adopted in VR, AR and embodied intelligence applications. While multi-modal large language models (MLLMs) have demonstrated remarkable performance on conventional 2D image and video understanding benchmarks, their ability to comprehend the immersive environments captured by ODIs remains largely unexplored. To address this gap, we first present ODI-Bench, a novel comprehensive benchmark specifically designed for omnidirectional image understanding. ODI-Bench contains 2,000 high-quality omnidirectional images and over 4,000 manually annotated question-answering (QA) pairs across 10 fine-grained tasks, covering both general-level and spatial-level ODI understanding. Extensive experiments are conducted to benchmark 20 representative MLLMs, including proprietary and open-source models, under both close-ended and open-ended settings. Experimental results reveal that current MLLMs still struggle to capture the immersive context provided by ODIs. To this end, we further introduce Omni-CoT, a training-free method which significantly enhances MLLMs' comprehension ability in the omnidirectional environment through chain-of-thought reasoning across both textual information and visual cues. Both the benchmark and the code will be released at https://github.com/ylylyl-sjtu/ODI-Bench.

cs.CV↗

arXiv2026

Anomalous cosmic rays within the inner heliosphere: Observations of helium by the High Energy Telescope onboard Solar Orbiter

Radial gradients of cosmic rays are key parameters for understanding the transport of particles in space. Solar Orbiter, launched on 2020 February 10, approaches the Sun approximately every half year, with a closest perihelion distance of 0.29 au after the end of 2022 during the nominal mission phase. The two double-ended high energy telescopes(HET)onboard the Solar Orbiter measure energetic particles in the energy range between a few MeV/nuc and a few hundred MeV/nuc, which are dominated by anomalous cosmic rays (ACRs) and galactic cosmic rays (GCRs) during solar quiet times. By obtaining the radial gradient of the ACR helium in the inner heliosphere, we advance our understanding of how the transport of the cosmic rays is affected by the particle drift effect and the large-scale magnetic field. The helium observations at Solar Orbiter/HET between 11.1 and 49 MeV/nuc are analyzed. Since we focus on quiet time measurements, we remove the periods of solar energetic particle (SEP) events. The intensities are averaged over the Carrington rotation period. The helium observations from the Proton and Helium Instrument(EPHIN)onboard SOHO were utilized as the baseline to correct the long-term variation caused by the solar modulations. We present the first observation of ACR helium at Solar Orbiter/HET between 2020 February and 2022 July in the inner heliosphere before the sun became fully active. We derive the radial gradient of the ACR helium between 0.3 and 1 au. The averaged radial gradient between 11.1 and 49MeV/nuc is about 22$\pm$4%/au and the averaged value between 11.1 and 41.2MeV/nuc is raised to 32$\pm$8%/au after removing the GCR contribution, which is estimated by a GCR model. In addition, the temporal variation of radial gradients indicates that the gradients are increasing with the enhancement of the solar modulation and the increased tilt angle of the heliospheric current sheet.

physics.space-ph↗

arXiv2026

Gaussian Ensemble Topology (GET): A New Explicit and Inherently Smooth Framework for Manufacture-Ready Topology Optimization

We introduce the Gaussian Ensemble Topology (GET) method, a new explicit and manufacture-ready framework for topology optimization in which design geometries are represented as superpositions of anisotropic Gaussian functions. By combining explicit Gaussian descriptions with a level-set-like Heaviside projection, GET inherently generates smooth, curvature-continuous designs without requiring post-processing steps such as mesh or corner smoothing and feature extraction. The method is validated on standard compliance-minimization and compliant mechanism benchmarks in two and three dimensions. The optimized designs achieve objective values comparable to those obtained with classical Moving Morphable Component (MMC) approaches, but with geometrically consistent, refined boundaries. Numerical examples demonstrate additional advantages of the GET framework, including mesh independence inherent to explicit parameterizations, strong geometric expressiveness, and effective control over smoothness, discreteness, and structural complexity through parameter tuning. As a robust and manufacture-ready approach to explicit topology optimization, GET opens avenues for tackling advanced and complex design problems.

cs.CE↗

arXiv2026

Reinforced Attention Learning

Post-training with Reinforcement Learning (RL) has substantially improved reasoning in Large Language Models (LLMs) via test-time scaling. However, extending this paradigm to Multimodal LLMs (MLLMs) through verbose rationales yields limited gains for perception and can even degrade performance. We propose Reinforced Attention Learning (RAL), a policy-gradient framework that directly optimizes internal attention distributions rather than output token sequences. By shifting optimization from what to generate to where to attend, RAL promotes effective information allocation and improved grounding in complex multimodal inputs. Experiments across diverse image and video benchmarks show consistent gains over GRPO and other baselines. We further introduce On-Policy Attention Distillation, demonstrating that transferring latent attention behaviors yields stronger cross-modal alignment than standard knowledge distillation. Our results position attention policies as a principled and general alternative for multimodal post-training.

cs.CL↗

arXiv2026

Curriculum Approximate Unlearning for Session-based Recommendation

Approximate unlearning for session-based recommendation refers to eliminating the influence of specific training samples from the recommender without retraining of (sub-)models. Gradient ascent (GA) is a representative method to conduct approximate unlearning. However, there still exist dual challenges to apply GA for session-based recommendation. On the one hand, naive applying of GA could lead to degradation of recommendation performance. On the other hand, existing studies fail to consider the ordering of unlearning samples when simultaneously processing multiple unlearning requests, leading to sub-optimal recommendation performance and unlearning effect. To address the above challenges, we introduce CAU, a curriculum approximate unlearning framework tailored to session-based recommendation. CAU handles the unlearning task with a GA term on unlearning samples. Specifically, to address the first challenge, CAU formulates the overall optimization task as a multi-objective optimization problem, where the GA term for unlearning samples is combined with retaining terms for preserving performance. The multi-objective optimization problem is solved through seeking the Pareto-Optimal solution, which achieves effective unlearning with trivial sacrifice on recommendation performance. To tackle the second challenge, CAU adopts a curriculum-based sequence to conduct unlearning on batches of unlearning samples. The key motivation is to perform unlearning from easy samples to harder ones. To this end, CAU first introduces two metrics to measure the unlearning difficulty, including gradient unlearning difficulty and embedding unlearning difficulty. Then, two strategies, hard-sampling and soft-sampling, are proposed to select unlearning samples according to difficulty scores.

cs.IR↗

arXiv2026

VICON: Vision In-Context Operator Networks for Multi-Physics Fluid Dynamics Prediction

In-Context Operator Networks (ICONs) have demonstrated the ability to learn operators across diverse partial differential equations using few-shot, in-context learning. However, existing ICONs process each spatial point as an individual token, severely limiting computational efficiency when handling dense data in higher spatial dimensions. We propose Vision In-Context Operator Networks (VICON), which integrates vision transformer architectures to efficiently process 2D data through patch-wise operations while preserving ICON's adaptability to multiphysics systems and varying timesteps. Evaluated across three fluid dynamics benchmarks, VICON significantly outperforms state-of-the-art baselines: DPOT and MPP, reducing the averaged last-step rollout error by 37.9% compared to DPOT and 44.7% compared to MPP, while requiring only 72.5% and 34.8% of their respective inference times. VICON naturally supports flexible rollout strategies with varying timestep strides, enabling immediate deployment in imperfect measurement systems where sampling frequencies may differ or frames might be dropped - common challenges in real-world settings - without requiring retraining or interpolation. In these realistic scenarios, VICON exhibits remarkable robustness, experiencing only 24.41% relative performance degradation compared to 71.37%-74.49% degradation in baseline methods, demonstrating its versatility for deploying in realistic applications. Our scripts for processing datasets and code are publicly available at https://github.com/Eydcao/VICON.

cs.LG↗

arXiv2025

Interface-Controlled Antiferromagnetic Tunnel Junctions based on a metallic van der Waals A-type Antiferromagnet

Magnetic tunnel junctions (MTJs) are crucial components in high-performance spintronic devices. Traditional MTJs rely on ferromagnetic (FM) materials but significant improvements in speed and packing density could be enabled by exploiting antiferromagnetic (AFM) compounds instead. Here, we report all-collinear AFM tunnel junctions (AFMTJs) fabricated with van der Waals A-type AFM metal (Fe0.6Co0.4)5GeTe2 (FCGT) electrodes and nonmagnetic semiconducting WSe2 tunnel barriers. The AFMTJ heterostructure device achieves a tunneling magnetoresistance (TMR) ratio of up to 75% in response to magnetic field switching. Our results demonstrate that the TMR exclusively emerges in the AFM state of FCGT, rather than during the AFM-to-FM transition. By engineering FCGT electrodes with either even- or odd-layer configurations, volatile or non-volatile TMR could be selected, consistent with an entirely interfacial effect. TMR in the even-layer devices arose by Néel vector switching. In the odd-layer devices, TMR stemmed from interfacial spin-flipping. Experimental and theoretical analyses reveal a new TMR mechanism associated with interface-driven spin-polarized transport, despite the spin-independent nature of bulk FCGT. Our work demonstrates that collinear AFMTJs can provide comparable performance to conventional MTJs and introduces a new paradigm for AFM spintronics, in which the spin-dependent properties of AFM interfaces are harnessed.

cond-mat.mes-hall↗

arXiv2025

Detecting Axion Dark Matter with an Organic Molecular Maser

We present a novel quantum sensing approach to search for axion-electron interactions around the axion mass of 6 \mueV. In this region, laboratory searches are relatively scarce, and our direct experiment measuring the axion-electron coupling constant reaches the sensitivity of 8 \times 10^{-6} GeV^{-1}. The method, based on an organic molecular maser establishes a proof-of-principle for quantum-enhanced detection, with a corresponding magnetic field sensitivity of 0.85 fT/\sqrt{\rm{Hz}}. The methodology is generic and can be readily extended to other physical systems, further broadening its applicability in quantum sensing and dark matter searches.

hep-ph↗

arXiv2025

Exploiting Radio Frequency Fingerprints for Device Identification: Tackling Cross-receiver Challenges in the Source-data-free Scenario

With the rapid proliferation of edge computing, Radio Frequency Fingerprint Identification (RFFI) has become increasingly important for secure device authentication. However, practical deployment of deep learning-based RFFI models is hindered by a critical challenge: their performance often degrades significantly when applied across receivers with different hardware characteristics due to distribution shifts introduced by receiver variation. To address this, we investigate the source-data-free cross-receiver RFFI (SCRFFI) problem, where a model pretrained on labeled signals from a source receiver must adapt to unlabeled signals from a target receiver, without access to any source-domain data during adaptation. We first formulate a novel constrained pseudo-labeling-based SCRFFI adaptation framework, and provide a theoretical analysis of its generalization performance. Our analysis highlights a key insight: the target-domain performance is highly sensitive to the quality of the pseudo-labels generated during adaptation. Motivated by this, we propose Momentum Soft pseudo-label Source Hypothesis Transfer (MS-SHOT), a new method for SCRFFI that incorporates momentum-center-guided soft pseudo-labeling and enforces global structural constraints to encourage confident and diverse predictions. Notably, MS-SHOT effectively addresses scenarios involving label shift or unknown, non-uniform class distributions in the target domain -- a significant limitation of prior methods. Extensive experiments on real-world datasets demonstrate that MS-SHOT consistently outperforms existing approaches in both accuracy and robustness, offering a practical and scalable solution for source-data-free cross-receiver adaptation in RFFI.

cs.LG↗

arXiv2025

Cavity-Vacuum-Induced Chiral Spin Liquids in Kagome Lattices: Tuning and Probing Topological Quantum Phases via Cavity Quantum Electrodynamics

Topological phases in frustrated quantum magnetic systems have captivated researchers for decades, with the chiral spin liquid (CSL) standing out as one of the most compelling examples. Featured by long-range entanglement, topological order, and exotic fractional excitations, the CSL has inspired extensive exploration for practical realizations. In this work, we demonstrate that CSLs can emerge in a kagome lattice driven by vacuum quantum fluctuations over the non-interacting vacuum within a single-mode gyrotropic cavity. The gyrotropic cavity imprints quantum fluctuations with time-reversal symmetry breaking and stabilizes a robust CSL phase without external laser excitation. Moreover, we identify experimentally accessible observables -- such as average photon number and transport properties -- that reveal connections between photon dynamics and the emergent chiral order. Our findings establish a novel pathway for creating, controlling, and probing topological and symmetry-breaking quantum phases in strongly correlated systems.

cond-mat.str-el↗

arXiv2025

Secure Analog Beamforming for Multi-user MISO Systems with Movable Antennas

Movable antennas (MAs) represent a novel approach that enables flexible adjustments to antenna positions, effectively altering the channel environment and thereby enhancing the performance of wireless communication systems. However, conventional MA implementations often adopt fully digital beamforming (FDB), which requires a dedicated RF chain for each antenna. This requirement significantly increase hardware costs, making such systems impractical for multi-antenna deployments. To address this, hardware-efficient analog beamforming (AB) offers a cost-effective alternative. This paper investigates the physical layer security (PLS) in an MA-enabled multiple-input single-output (MISO) communication system with an emphasis on AB. In this scenario, an MA-enabled transmitter with AB broadcasts common confidential information to a group of legitimate receivers, while a number of eavesdroppers overhear the transmission and attempt to intercept the information. Our objective is to maximize the multicast secrecy rate (MSR) by jointly optimizing the phase shifts of the AB and the positions of the MAs, subject to constraints on the movement area of the MAs and the constant modulus (CM) property of the analog phase shifters. This MSR maximization problem is highly challenging, as we have formally proven it to be NP-hard. To solve it efficiently, we propose a penalty constrained product manifold (PCPM) framework. Specifically, we first reformulate the position constraints as a penalty function, enabling unconstrained optimization on a product manifold space (PMS), and then propose a parallel conjugate gradient descent algorithm to efficiently update the variables. Simulation results demonstrate that MA-enabled systems with AB can achieve a well-balanced performance in terms of MSR and hardware costs.

eess.SP↗

arXiv2025

Systematic dispersion engineering of crystalline microresonators for broadband and coherent frequency comb generation

Ultraprecision machining offers a powerful route to dispersion control in crystalline microresonators, allowing the design of waveguide geometries for tailoring the spectrum of microresonator frequency combs. By precisely designing the geometry, both group-velocity and higher-order dispersions can be engineered across a broad wavelength range. However, despite their promising features, such advantages have remained largely unexplored due to fabrication challenges. Here, we demonstrate that resonators shaped by ultrapecision machining exhibit high precision and strongly suppressed spatial mode interactions, facilitating the generation of smooth dissipative Kerr soliton combs and broadband frequency combs beyond the telecommunication C-band. These results underscore the effectiveness of precision geometry control for realizing coherent and broadband microcombs on crystalline photonic platforms.

physics.optics↗

arXiv2025

Early GVHD Prediction in Liver Transplantation via Multi-Modal Deep Learning on Imbalanced EHR Data

Graft-versus-host disease (GVHD) is a rare but often fatal complication in liver transplantation, with a very high mortality rate. By harnessing multi-modal deep learning methods to integrate heterogeneous and imbalanced electronic health records (EHR), we aim to advance early prediction of GVHD, paving the way for timely intervention and improved patient outcomes. In this study, we analyzed pre-transplant electronic health records (EHR) spanning the period before surgery for 2,100 liver transplantation patients, including 42 cases of graft-versus-host disease (GVHD), from a cohort treated at Mayo Clinic between 1992 and 2025. The dataset comprised four major modalities: patient demographics, laboratory tests, diagnoses, and medications. We developed a multi-modal deep learning framework that dynamically fuses these modalities, handles irregular records with missing values, and addresses extreme class imbalance through AUC-based optimization. The developed framework outperforms all single-modal and multi-modal machine learning baselines, achieving an AUC of 0.836, an AUPRC of 0.157, a recall of 0.768, and a specificity of 0.803. It also demonstrates the effectiveness of our approach in capturing complementary information from different modalities, leading to improved performance. Our multi-modal deep learning framework substantially improves existing approaches for early GVHD prediction. By effectively addressing the challenges of heterogeneity and extreme class imbalance in real-world EHR, it achieves accurate early prediction. Our proposed multi-modal deep learning method demonstrates promising results for early prediction of a GVHD in liver transplantation, despite the challenge of extremely imbalanced EHR data.

cs.LG↗