arXiv subjects

Yanan Zhang

Publications and source records attributed to Yanan Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Magnetic phases of Kondo lattice materials Ce$_5$RhGe$_2$ and Ce$_5$IrGe$_2$

Single crystals of Ce$_5$RhGe$_2$ and Ce$_5$IrGe$_2$ have been systematically investigated by electrical resistivity, specific heat, and magnetization measurements. Together with Ce$_5$CoGe$_2$, all three compounds crystallize in the orthorhombic \emph{Pnma} structure, with the lattice parameters increasing monotonically from Co to Rh to Ir, consistent with the effect of negative chemical pressure. Magnetization measurements along the three principal crystallographic axes identify the \emph{a} axis as the easy magnetization direction throughout the series. Ce$_5$RhGe$_2$ exhibits ferromagnetic ordering with a Curie temperature of approximately 11.5 K and shows magnetic behavior closely resembling that of Ce$_5$CoGe$_2$. In contrast, Ce$_5$IrGe$_2$ undergoes two successive magnetic transitions at $T_{\rm M1}=12.7$ K and $T_{\rm M2}=11.8$ K, and there are multiple metamagnetic transitions under magnetic fields, giving rise to magnetization plateaus at fractions of the saturation magnetization $M_{\rm s}$ of approximately $M_{\rm s}/5$ and $M_{\rm s}/3$. The low-field metamagnetic transition along the easy axis shifts to lower field with decreasing temperature, and eventually a pronounced hysteresis loop is observed about zero-field, establishing that Ce$_5$IrGe$_2$ exhibits a ferrimagnetic ground state at the lowest measured temperatures.

cond-mat.str-el↗

arXiv2026

LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments

World Action Models (WAMs) have emerged as a powerful paradigm for embodied intelligence, yet the prevailing reliance on pixel-level video generation creates a fundamental bottleneck. Forcing models to reconstruct task-irrelevant visual details dissipates representational capacity and renders policies vulnerable to visual distractors. In this paper, we propose LeapBot-WA, which establishes a novel Predictive-Latent paradigm for WAMs by operationalizing the Joint-Embedding Predictive Architecture (JEPA) as a World-Anchor. Departing from the traditional reliance on visual synthesis, LeapBot-WA shifts the core of world modeling to Predictive Semantic Alignment, extracting abstract physical dynamics directly within a latent foundation space. To bridge the modality gap between non-Gaussian predictive features and diffusion priors, we introduce the Isotropic Semantic Autoencoder (ISAE), which reshapes the anchor's latent space into a diffusion-friendly manifold to prevent off-manifold drift. Furthermore, we design an Asymmetric Mixture-of-Transformers (MoT) architecture. During training, an Anchor Diffusion Transformer acts as a privileged dynamics expert to guide the Action Diffusion Transformer; at inference, this heavy dynamics branch is pruned, enabling zero-overhead execution. LeapBot-WA achieves state-of-the-art performance among predictive models on LIBERO and matches top-tier generative WAMs on RoboTwin 2.0 without requiring large-scale trajectory pre-training. It further demonstrates superior zero-shot robustness to unseen environments and successful real-world transfer, establishing a highly efficient and robust latent-centric paradigm for scalable robotic control. Code: https://github.com/LeapWM/leapbot-wa.

cs.RO↗

arXiv2026

DroneFINE: Domain-Aware Parameter-Efficient Fine-Tuning of Vision-Language Detectors for Drone Images

Object detection for Unmanned Aerial Vehicles (UAVs) working in open and dynamic environments is a highly challenging task. While Vision-Language Models (VLMs) have offered a powerful solution for universal object detection, adapting them to UAV scenarios remains non-trivial due to a substantial domain gap between VLM pre-training data and aerial imagery. The prevailing Parameter-Efficient Fine-Tuning (PEFT) methods prove ineffective in bridging this gap, as VLMs' "natural-scene, foreground-dominant" visual priors misalign with the "bird's-eye-view, background-dominant, small-object" characteristics of UAV data. To address this issue, we propose DroneFINE, a novel PEFT paradigm comprising two domain-aware complementary modules tailored for VLM-based drone image detectors. Specifically, a data-dependent, foreground-aware, and multi-path adaptation mechanism named HyperAdapter is designed, which overcomes the static structural constraints of PEFT. In addition, a background suppression algorithm named SemanticGate is developed. It is a text-conditioned guidance strategy that employs background vocabulary to actively guide the model in suppressing responses from irrelevant regions. Extensive experiments on VisDrone and UAVDT demonstrate that DroneFINE significantly outperforms existing PEFT methods and achieves performance comparable to full fine-tuning while substantially reducing the number of trainable parameters.

cs.CV↗

arXiv2026

An Electromagnetic Particle-Particle Method for Relativistic Electron Bunch Dynamics from Early Expansion to Long-Range Transport

Particle-mesh methods, such as the particle-in-cell (PIC) method, cannot retain exact pairwise interaction at sub-cell scales. For dense nonneutral relativistic electron bunches, this makes it difficult to accurately capture the inter-particle electromagnetic interaction and the associated bunch divergence. In this work, the previously developed electromagnetic particle-particle (EM-PP) model for relativistic two-particle interaction is extended to many-particle electron bunch transport in the Earth's magnetosphere. The method combines the Li\'enard--Wiechert fields, an improved retarded-time evaluation procedure, and a relativistic particle pusher, and adopts a two-stage strategy to couple the dense early self-field-dominated evolution to the later long-range geomagnetic-field-controlled transport. The method provides a practical mesh-free approach for accurately simulating long-range transport of relativistic electron bunches when short-range electromagnetic interaction is important.

physics.plasm-ph↗

arXiv2026

Antiferromagnetic Dimers in the Parent Phase of a Correlated Kagome Superconductor

Kagome metals are prone to charge-density wave (CDW), magnetic, and superconducting phases, with their flat electronic band conducive for correlated physics. In contrast to the weakly correlated $A$V$_3$Sb$_5$ ($A$ = K, Rb, Cs) kagome metals with a $2\times2$ CDW, CsCr$_3$Sb$_5$ is a correlated metal with a flat band close to the Fermi level, and exhibits a $4\times1$ CDW intertwined with magnetic order. Under pressure, the intertwined orders are suppressed and give way to a dome of superconductivity that emerges from a non-Fermi liquid normal state. Here, we solve the crystal structure of the $4\times 1$ CDW state in CsCr$_3$Sb$_5$, and show it consists of Cr dimers separated by Cr chains. First-principles calculations show the dominant exchange interaction is antiferromagnetic within the dimers, while the intra-chain and dimer-chain couplings are much weaker. The CDW transition of CsCr$_3$Sb$_5$ is found to be more strongly first-order than those in $A$V$_3$Sb$_5$, without significant soft phonons or diffuse scattering above the CDW transition temperature. These findings suggest that fluctuating antiferromagnetic dimers may play a major role in the electron pairing of superconducting CsCr$_3$Sb$_5$.

cond-mat.str-el↗

arXiv2026

Dimensionality tuning of heavy-fermion states in ultrathin CeSi2 films

Dimensionality tuning is an important method to modify the electronic states of quantum materials. However, the mechanism of such tuning in heavy fermion systems and its connection with transport properties remain largely unexplored. Here by combining molecular beam epitaxy (MBE), in-situ angle-resolved photoemission spectroscopy (ARPES) and transport measurements, we study the electronic states of the heavy-fermion compound CeSi2 as a function of film thickness. In three dimensional thick films, our measurements reveal a dispersive Kondo peak at the Fermi level (EF) and satellite peaks originating from crystal electric field (CEF) excitations, characteristic of heavy fermion systems. For two-dimensional ultrathin films, the CEF satellites are largely suppressed while the ground-state Kondo peak at EF remains strong, although it develops at lower temperatures. Simultaneously, the maximum temperature Tmax of the magnetic resistivity, \r{ho}m(T), changes from ~100 K in thick films to ~35 K in ultrathin films. This can be attributed to the dimensionality driven reduction of CEF excitations during the Kondo process, in good agreement with spectroscopic results. Our work provides direct insight to understand the quantum confinement effects on strongly correlated 4f-electron systems and opens up new opportunities to explore emergent phenomena in two-dimensional heavy-fermion materials.

cond-mat.str-el↗

arXiv2026

Pressure-induced superconductivity beyond magnetic quantum criticality in a Kondo ferromagnet

Quantum phase transitions are an established setting for emergent phenomena driven by strong electronic correlations, including strange metals and unconventional superconductivity. These have been explored extensively in Kondo lattice materials tuned to an antiferromagnetic quantum critical point (QCP), but superconductivity emerging near ferromagnetic quantum criticality is not yet observed, and the conditions under which it occurs in proximity to ferromagnetism are undetermined. Here, we report a new setting for superconductivity in the ferromagnetic Kondo-lattice material Ce5CoGe2, where there is a ferromagnetic ground state at ambient pressure, which evolves to antiferromagnetism under applied pressures. The antiferromagnetic transition is suppressed to a zero-temperature QCP, which is accompanied by strange-metal behavior. Superconductivity does not occur at the QCP, but instead appears at pressures beyond the magnetic instability. These findings suggest that Ce5CoGe2 represents a distinct class of correlated materials exhibiting a unique scenario for the emergence of superconductivity, likely associated with unconventional pairing mechanisms beyond spin-fluctuations.

cond-mat.supr-con↗

arXiv2026

Infinite Magnetoresistance and Vortex Coupling in the Pb/BSCCO Heterostructure

Combining superconductivity with spintronics provides exciting opportunities to realize low-dissipation quantum devices. Here we report the synthesis, characterization and magnetotransport measurements of the Pb/Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$ (BSCCO) superconducting heterostructures, where an insulating PbO$_{x}$ layer spontaneously forms at the interface. Non-volatile switching between superconducting (logical "0") and normal ("1") states in Pb films by an external field, i.e., infinite magnetoresistance (IMR), can be realized and are attributed to the strong trapping and pinning of vortices in BSCCO. Furthermore, butterfly-shaped hysteresis loops in magnetoresistance, pronounced resistance dips/jumps and thermal reset to superconducting states can be observed and are direct manifestations of the peculiar vortex dynamics in BSCCO and vortex coupling across the Pb/BSCCO interface. Our work demonstrates a simple and effective way to realize IMR through superconducting vortices and opens up new opportunities to study the vortex interactions across the superconducting interfaces.

cond-mat.supr-con↗

arXiv2026

Magnetic states of the Kondo lattice Ce$_2$PdSi$_3$ and their pressure evolution

Frustrated Kondo lattices are ideal platforms for exploring unconventional forms of quantum criticality, as well as magnetism and other emergent phases. Here we report the magnetic properties of the candidate frustrated heavy fermion compound Ce$_2$PdSi$_3$, and map their evolution upon applying magnetic fields and hydrostatic pressure. We find that at ambient pressure Ce$_2$PdSi$_3$ exhibits two distinct magnetic phase transitions, a ferromagnetic-like transition at $T_{\mathrm{M1}}=3.8$ K and an incommensurate antiferromagnetic transition at $T_{\mathrm{M2}}=2.9$ K. Upon applying pressure, $T_{\mathrm{M1}}$ is continuously suppressed and becomes undetectable above 4.2 GPa, whereas $T_{\mathrm{M2}}$ increases and remains robust up to at least 7.5 GPa. The observed pressure evolution of magnetic order in Ce$_2$PdSi$_3$ suggests the presence of competing magnetic orders, and cannot be simply encapsulated by the Doniach phase diagram, motivating further investigations for its origin, including discerning the role of geometric frustration.

cond-mat.str-el↗

arXiv2026

Towards Unbiased Source-Free Object Detection via Vision Foundation Models

Source-Free Object Detection (SFOD) has garnered much attention in recent years by eliminating the need of source-domain data in cross-domain tasks, but existing SFOD methods suffer from the Source Bias problem, i.e. the adapted model remains skewed towards the source domain, leading to poor generalization and error accumulation during self-training. To overcome this challenge, we propose Debiased Source-free Object Detection (DSOD), a novel VFM-assisted SFOD framework that can effectively mitigate source bias with the help of powerful VFMs. Specifically, we propose Unified Feature Injection (UFI) module that integrates VFM features into the CNN backbone through Simple-Scale Extension (SSE) and Domain-aware Adaptive Weighting (DAAW). Then, we propose Semantic-aware Feature Regularization (SAFR) that constrains feature learning to prevent overfitting to source domain characteristics. Furthermore, we propose a VFM-free variant, termed DSOD-distill for computation-restricted scenarios through a novel Dual-Teacher distillation scheme. Extensive experiments on multiple benchmarks demonstrate that DSOD outperforms state-of-the-art SFOD methods, achieving 48.1% AP on Normal-to-Foggy weather adaptation, 39.3% AP on Cross-scene adaptation, and 61.4% AP on Synthetic-to-Real adaptation.

cs.CV↗

arXiv2026

Multiple superconducting phases and order-parameter evolution in pressurized UTe$_2$

The recently discovered heavy-fermion spin-triplet superconductor candidate UTe$_2$ provides a rich platform for unconventional pairing and topological phenomena. However, limited has been known about its superconducting order parameters and their evolution with control parameters, largely due to the lack of appropriate symmetry-sensitive detections. Here, we report comprehensive point-contact spectroscopy measurements of pressurized UTe$_2$ on the (0~0~1) surface. The observation of Andreev bound states strongly suggests the presence of a $p_z$ component in the superconducting order parameters. Quantitative analysis based on an extended Blonder-Tinkham-Klapwijk model unveils the superconducting order parameters with a finite odd-$k_z$ component (e.g. $B_{2u}$ or $B_{3u}$) for both ambient and pressurized UTe$_2$. Remarkably, the multiple superconducting phases can be distinguished by a single parameter $\langle \Delta_{z}\rangle/\langle\Delta_{x(y)}\rangle$, the relative weight between the $p_z$-wave and $p_{x(y)}$-wave pairings. These findings place stringent constraints on the pairing symmetry and provide essential spectroscopic signatures for distinguishing pressure-induced multiple superconducting phases in UTe$_2$.

cond-mat.str-el↗

arXiv2025

Test-Time Adaptive Object Detection with Foundation Model

In recent years, test-time adaptive object detection has attracted increasing attention due to its unique advantages in online domain adaptation, which aligns more closely with real-world application scenarios. However, existing approaches heavily rely on source-derived statistical characteristics while making the strong assumption that the source and target domains share an identical category space. In this paper, we propose the first foundation model-powered test-time adaptive object detection method that eliminates the need for source data entirely and overcomes traditional closed-set limitations. Specifically, we design a Multi-modal Prompt-based Mean-Teacher framework for vision-language detector-driven test-time adaptation, which incorporates text and visual prompt tuning to adapt both language and vision representation spaces on the test data in a parameter-efficient manner. Correspondingly, we propose a Test-time Warm-start strategy tailored for the visual prompts to effectively preserve the representation capability of the vision branch. Furthermore, to guarantee high-quality pseudo-labels in every test batch, we maintain an Instance Dynamic Memory (IDM) module that stores high-quality pseudo-labels from previous test samples, and propose two novel strategies-Memory Enhancement and Memory Hallucination-to leverage IDM's high-quality instances for enhancing original predictions and hallucinating images without available pseudo-labels, respectively. Extensive experiments on cross-corruption and cross-dataset benchmarks demonstrate that our method consistently outperforms previous state-of-the-art methods, and can adapt to arbitrary cross-domain and cross-category target data. Code is available at https://github.com/gaoyingjay/ttaod_foundation.

cs.CV↗

arXiv2025

OBJVanish: Physically Realizable Text-to-3D Adv. Generation of LiDAR-Invisible Objects

LiDAR-based 3D object detectors are fundamental to autonomous driving, where failing to detect objects poses severe safety risks. Developing effective 3D adversarial attacks is essential for thoroughly testing these detection systems and exposing their vulnerabilities before real-world deployment. However, existing adversarial attacks that add optimized perturbations to 3D points have two critical limitations: they rarely cause complete object disappearance and prove difficult to implement in physical environments. We introduce the text-to-3D adversarial generation method, a novel approach enabling physically realizable attacks that can generate 3D models of objects truly invisible to LiDAR detectors and be easily realized in the real world. Specifically, we present the first empirical study that systematically investigates the factors influencing detection vulnerability by manipulating the topology, connectivity, and intensity of individual pedestrian 3D models and combining pedestrians with multiple objects within the CARLA simulation environment. Building on the insights, we propose the physically-informed text-to-3D adversarial generation (Phy3DAdvGen) that systematically optimizes text prompts by iteratively refining verbs, objects, and poses to produce LiDAR-invisible pedestrians. To ensure physical realizability, we construct a comprehensive object pool containing 13 3D models of real objects and constrain Phy3DAdvGen to generate 3D objects based on combinations of objects in this set. Extensive experiments demonstrate that our approach can generate 3D pedestrians that evade six state-of-the-art (SOTA) LiDAR 3D detectors in both CARLA simulation and physical environments, thereby highlighting vulnerabilities in safety-critical applications.

cs.CV↗

arXiv2025

Arcturus: A Cloud Overlay Network for Global Accelerator with Enhanced Performance and Stability

Global Accelerator (GA) services play a vital role in ensuring low-latency, high-reliability communication for real-time interactive applications. However, existing GA offerings are tightly bound to specific cloud providers, resulting in high costs, rigid deployment, and limited flexibility, especially for large-scale or budget-sensitive deployments. Arcturus is a cloud-native GA framework that revisits the design of GA systems by leveraging low-cost, heterogeneous cloud resources across multiple providers. Rather than relying on fixed, high-end infrastructure, Arcturus dynamically constructs its acceleration network and balances performance, stability, and resource efficiency. To achieve this, Arcturus introduces a two-plane design: a forwarding plane that builds a proxy network with adaptive control, and a scheduling plane that coordinates load and routing through lightweight, quantitative optimization. Evaluations under millions of RPS show that Arcturus outperforms commercial GA services by up to 1.7X in acceleration performance, reduces cost by 71%, and maintains over 80% resource efficiency--demonstrating efficient use of cloud resources at scale.

cs.NI↗

arXiv2025

Emergent ferromagnetic ladder excitations in heavy fermion superconductor CeSb$_{2}$

Low-dimensional spin fluctuations play a crucial role in unconventional superconductors, with quasi-one-dimensional spin excitations potentially linked with spin-triplet superconductivity. The heavy fermion superconductor CeSb$_2$ exhibits an unusual large inverted S-shaped upper critical field that suggests a possible triplet pairing state within its pressure-induced superconducting dome. Using inelastic neutron scattering, we discover quasi-one-dimensional magnetic excitations in CeSb$_2$ emerging from nearly square Ce layers with minor orthorhombic deformation. We show that the data are well described by a ferromagnetic spin ladder model, where the "rungs" of the ladder straddle Ce bilayers. Moreover, we find that diffuse excitations akin to those in the ordered phase persist well above $T_{\rm N}$, suggesting that quasi-one-dimensional ferromagnetic paramagnons may significantly contribute to the unusual superconductivity that appears under pressure once magnetic order is suppressed.

cond-mat.supr-con↗

arXiv2025

CoSDH: Communication-Efficient Collaborative Perception via Supply-Demand Awareness and Intermediate-Late Hybridization

Multi-agent collaborative perception enhances perceptual capabilities by utilizing information from multiple agents and is considered a fundamental solution to the problem of weak single-vehicle perception in autonomous driving. However, existing collaborative perception methods face a dilemma between communication efficiency and perception accuracy. To address this issue, we propose a novel communication-efficient collaborative perception framework based on supply-demand awareness and intermediate-late hybridization, dubbed as \mymethodname. By modeling the supply-demand relationship between agents, the framework refines the selection of collaboration regions, reducing unnecessary communication cost while maintaining accuracy. In addition, we innovatively introduce the intermediate-late hybrid collaboration mode, where late-stage collaboration compensates for the performance degradation in collaborative perception under low communication bandwidth. Extensive experiments on multiple datasets, including both simulated and real-world scenarios, demonstrate that \mymethodname~ achieves state-of-the-art detection accuracy and optimal bandwidth trade-offs, delivering superior detection precision under real communication bandwidths, thus proving its effectiveness and practical applicability. The code will be released at https://github.com/Xu2729/CoSDH.

cs.CV↗

arXiv2025

An Electromagnetic Particle-Particle Model on Solving Relativistic Binary Collision

With the significant advancements in parallel computing techniques, the particle-particle (PP) model has been effectively utilized in various plasma-related applications. However, PP has been limited for solving only electrostatic problems under Coulomb's law, by analogy to the particle-in-cell (PIC) model solving Poisson's equation. While electromagnetic PIC is common with coupled solutions of Maxwell's equations, we propose an electromagnetic (EM) PP model taking advantage of Lienard-Wiechert potentials for point charge in this paper. In addition, this EM-PP model can contribute to simulate relativistic binary collisions with high accuracy, thus its results are used as a baseline to compare with the classical Frankel's relativistic scattering angle, and the accuracy and applicable scope of Frankel's formula are discussed.

physics.plasm-ph↗

arXiv2025

Comparison between electrostatic PP and PIC simulations on electron bunch expansion

With the great development of parallel computing techniques, the particle-particle (PP) model has been successfully applied in a number of plasma applications. Comparing to particle-mesh (PM) models, for example the widely used particle-in-cell (PIC) method, PP has the advantages of high accuracy in solving Coulomb interactions. In this paper, it is shown that PP is also advantageous to simulate non-neutral plasmas, such as electron bunch expansion in vacuum. The numerical effects of the macro-particle weight and the time step length are investigated for a PP model, accurate and convergent results can be obtained with less effort. On the contrary, PIC needs to simulate the same problem with extremely large effort. It is found that the simulation accuracy does not grow with reduced cell size monotonously, thus no convergence can be easily obtained. In the long run, PIC must apply large enough domain to cover all the expanding particles and avoid non-physical effects caused by imperfect infinite boundary condition, which may result in too heavy computation and make PIC infeasible.

physics.plasm-ph↗