arXiv subjects

Wenjie Song

Publications and source records attributed to Wenjie Song.

16 recordsLinked to original sources

arXiv2026

Direct Observation of Antimagnons with Inverted Dispersion

We report direct spectroscopic evidence of antimagnons, i.e., negative-energy spin waves identified by their signature inverted dispersion and left-handed precession with Brillouin light scattering (BLS) spectroscopy. We investigate an ultrathin BiYIG film with a perpendicular magnetized anisotropy that compensates the demagnetizing field. By injecting a spin-orbit torque, the magnetization is driven into auto-oscillation and eventually into a non-equilibrium reversed state above a secondary current threshold ($\sim$1.2$\times$10$^7$~A/cm$^2$). The dispersion is measured by wavevector-resolved BLS and exhibits a sharp change from an upward dispersion to a downward one, in agreement with theoretical predictions and micromagnetic simulations. Around the threshold current, we observe the coexistence of conventional magnons and antimagnons. Our work establishes antimagnons with inverted dispersion and is a first step towards exploring novel phenomena and applications due to magnon-antimagnon coupling, such as magnon amplification and magnon-antimagnon entanglement, which are part of the emerging field of antimagnonics.

cond-mat.mes-hall↗

arXiv2025

Decentralized Multi-Robot Relative Navigation in Unknown, Structurally Constrained Environments under Limited Communication

Multi-robot navigation in unknown, structurally constrained, and GPS-denied environments presents a fundamental trade-off between global strategic foresight and local tactical agility, particularly under limited communication. Centralized methods achieve global optimality but suffer from high communication overhead, while distributed methods are efficient but lack the broader awareness to avoid deadlocks and topological traps. To address this, we propose a fully decentralized, hierarchical relative navigation framework that achieves both strategic foresight and tactical agility without a unified coordinate system. At the strategic layer, robots build and exchange lightweight topological maps upon opportunistic encounters. This process fosters an emergent global awareness, enabling the planning of efficient, trap-avoiding routes at an abstract level. This high-level plan then inspires the tactical layer, which operates on local metric information. Here, a sampling-based escape point strategy resolves dense spatio-temporal conflicts by generating dynamically feasible trajectories in real time, concurrently satisfying tight environmental and kinodynamic constraints. Extensive simulations and real-world experiments demonstrate that our system significantly outperforms in success rate and efficiency, especially in communication-limited environments with complex topological structures.

cs.RO↗

arXiv2025

MobRT: A Digital Twin-Based Framework for Scalable Learning in Mobile Manipulation

Recent advances in robotics have been largely driven by imitation learning, which depends critically on large-scale, high-quality demonstration data. However, collecting such data remains a significant challenge-particularly for mobile manipulators, which must coordinate base locomotion and arm manipulation in high-dimensional, dynamic, and partially observable environments. Consequently, most existing research remains focused on simpler tabletop scenarios, leaving mobile manipulation relatively underexplored. To bridge this gap, we present \textit{MobRT}, a digital twin-based framework designed to simulate two primary categories of complex, whole-body tasks: interaction with articulated objects (e.g., opening doors and drawers) and mobile-base pick-and-place operations. \textit{MobRT} autonomously generates diverse and realistic demonstrations through the integration of virtual kinematic control and whole-body motion planning, enabling coherent and physically consistent execution. We evaluate the quality of \textit{MobRT}-generated data across multiple baseline algorithms, establishing a comprehensive benchmark and demonstrating a strong correlation between task success and the number of generated trajectories. Experiments integrating both simulated and real-world demonstrations confirm that our approach markedly improves policy generalization and performance, achieving robust results in both simulated and real-world environments.

cs.RO↗

arXiv2025

SSR-ZSON: Zero-Shot Object Navigation via Spatial-Semantic Relations within a Hierarchical Exploration Framework

Zero-shot object navigation in unknown environments presents significant challenges, mainly due to two key limitations: insufficient semantic guidance leads to inefficient exploration, while limited spatial memory resulting from environmental structure causes entrapment in local regions. To address these issues, we propose SSR-ZSON, a spatial-semantic relative zero-shot object navigation method based on the TARE hierarchical exploration framework, integrating a viewpoint generation strategy balancing spatial coverage and semantic density with an LLM-based global guidance mechanism. The performance improvement of the proposed method is due to two key innovations. First, the viewpoint generation strategy prioritizes areas of high semantic density within traversable sub-regions to maximize spatial coverage and minimize invalid exploration. Second, coupled with an LLM-based global guidance mechanism, it assesses semantic associations to direct navigation toward high-value spaces, preventing local entrapment and ensuring efficient exploration. Deployed on hybrid Habitat-Gazebo simulations and physical platforms, SSR-ZSON achieves real-time operation and superior performance. On Matterport3D and Habitat-Matterport3D datasets, it improves the Success Rate(SR) by 18.5\% and 11.2\%, and the Success weighted by Path Length(SPL) by 0.181 and 0.140, respectively, over state-of-the-art methods.

cs.RO↗

arXiv2025

SemSight: Probabilistic Bird's-Eye-View Prediction of Multi-Level Scene Semantics for Navigation

In target-driven navigation and autonomous exploration, reasonable prediction of unknown regions is crucial for efficient navigation and environment understanding. Existing methods mostly focus on single objects or geometric occupancy maps, lacking the ability to model room-level semantic structures. We propose SemSight, a probabilistic bird's-eye-view prediction model for multi-level scene semantics. The model jointly infers structural layouts, global scene context, and target area distributions, completing semantic maps of unexplored areas while estimating probability maps for target categories. To train SemSight, we simulate frontier-driven exploration on 2,000 indoor layout graphs, constructing a diverse dataset of 40,000 sequential egocentric observations paired with complete semantic maps. We adopt an encoder-decoder network as the core architecture and introduce a mask-constrained supervision strategy. This strategy applies a binary mask of unexplored areas so that supervision focuses only on unknown regions, forcing the model to infer semantic structures from the observed context. Experimental results show that SemSight improves prediction performance for key functional categories in unexplored regions and outperforms non-mask-supervised approaches on metrics such as Structural Consistency (SC) and Region Recognition Accuracy (PA). It also enhances navigation efficiency in closed-loop simulations, reducing the number of search steps when guiding robots toward target areas.

cs.RO↗

arXiv2025

Observation and Control of Chiral Spin Frustration in BiYIG Thin Films

Chiral interactions within magnetic layers stabilize the formation of noncollinear spin textures, which can be leveraged to design devices with tailored magnetization dynamics. Here, we introduce chiral spin frustration in which energetically degenerate magnetic states frustrate the Dzyaloshinskii-Moriya interaction. We demonstrate magnon-driven switching of the chirally frustrated spin states in Bi-substituted yttrium iron garnet thin films. These states are defined by an in-plane macrospin neighboring two out-ofplane spins on either side with opposing chirality. Using scanning nitrogen-vacancy magnetometry and spin pumping, we identified four degenerate frustrated states and achieved their controllable switching via magnon spin torque. Crucially, the switching is unidirectional, with selectivity determined by the incoming magnon direction. This mechanism provides a powerful approach to manipulate frustrated spin states with magnons. Chiral spin frustration unlocks the geometry constraints of conventional frustration, and therefore opens new horizons for frustrated magnetism, paving the way for energy-efficient spintronic devices based on frustratio

cond-mat.mes-hall↗

arXiv2025

TASeg: Text-aware RGB-T Semantic Segmentation based on Fine-tuning Vision Foundation Models

Reliable semantic segmentation of open environments is essential for intelligent systems, yet significant problems remain: 1) Existing RGB-T semantic segmentation models mainly rely on low-level visual features and lack high-level textual information, which struggle with accurate segmentation when categories share similar visual characteristics. 2) While SAM excels in instance-level segmentation, integrating it with thermal images and text is hindered by modality heterogeneity and computational inefficiency. To address these, we propose TASeg, a text-aware RGB-T segmentation framework by using Low-Rank Adaptation (LoRA) fine-tuning technology to adapt vision foundation models. Specifically, we propose a Dynamic Feature Fusion Module (DFFM) in the image encoder, which effectively merges features from multiple visual modalities while freezing SAM's original transformer blocks. Additionally, we incorporate CLIP-generated text embeddings in the mask decoder to enable semantic alignment, which further rectifies the classification error and improves the semantic understanding accuracy. Experimental results across diverse datasets demonstrate that our method achieves superior performance in challenging scenarios with fewer trainable parameters.

cs.CV↗

arXiv2025

Object Navigation with Structure-Semantic Reasoning-Based Multi-level Map and Multimodal Decision-Making LLM

The zero-shot object navigation (ZSON) in unknown open-ended environments coupled with semantically novel target often suffers from the significant decline in performance due to the neglect of high-dimensional implicit scene information and the long-range target searching task. To address this, we proposed an active object navigation framework with Environmental Attributes Map (EAM) and MLLM Hierarchical Reasoning module (MHR) to improve its success rate and efficiency. EAM is constructed by reasoning observed environments with SBERT and predicting unobserved ones with Diffusion, utilizing human space regularities that underlie object-room correlations and area adjacencies. MHR is inspired by EAM to perform frontier exploration decision-making, avoiding the circuitous trajectories in long-range scenarios to improve path efficiency. Experimental results demonstrate that the EAM module achieves 64.5\% scene mapping accuracy on MP3D dataset, while the navigation task attains SPLs of 28.4\% and 26.3\% on HM3D and MP3D benchmarks respectively - representing absolute improvements of 21.4\% and 46.0\% over baseline methods.

cs.RO↗

arXiv2024

Residual Feature-Reutilization Inception Network for Image Classification

Capturing feature information effectively is of great importance in the field of computer vision. With the development of convolutional neural networks (CNNs), concepts like residual connection and multiple scales promote continual performance gains in diverse deep learning vision tasks. In this paper, we propose a novel CNN architecture that it consists of residual feature-reutilization inceptions (ResFRI) or split-residual feature-reutilization inceptions (Split-ResFRI). And it is composed of four convolutional combinations of different structures connected by specially designed information interaction passages, which are utilized to extract multi-scale feature information and effectively increase the receptive field of the model. Moreover, according to the network structure designed above, Split-ResFRI can adjust the segmentation ratio of the input information, thereby reducing the number of parameters and guaranteeing the model performance. Specifically, in experiments based on popular vision datasets, such as CIFAR10 ($97.94$\%), CIFAR100 ($85.91$\%) and Tiny Imagenet ($70.54$\%), we obtain state-of-the-art results compared with other modern models under the premise that the model size is approximate and no additional data is used.

cs.CV↗

arXiv2024

Contrastive Loss Based Frame-wise Feature disentanglement for Polyphonic Sound Event Detection

Overlapping sound events are ubiquitous in real-world environments, but existing end-to-end sound event detection (SED) methods still struggle to detect them effectively. A critical reason is that these methods represent overlapping events using shared and entangled frame-wise features, which degrades the feature discrimination. To solve the problem, we propose a disentangled feature learning framework to learn a category-specific representation. Specifically, we employ different projectors to learn the frame-wise features for each category. To ensure that these feature does not contain information of other categories, we maximize the common information between frame-wise features within the same category and propose a frame-wise contrastive loss. In addition, considering that the labeled data used by the proposed method is limited, we propose a semi-supervised frame-wise contrastive loss that can leverage large amounts of unlabeled data to achieve feature disentanglement. The experimental results demonstrate the effectiveness of our method.

cs.SD↗

arXiv2022

The Number of Possible CETIs within Our Galaxy and the Communication Probability among These CETIs

As the only known intelligent civilization, human beings are always curious about the existence of other communicating extraterrestrial intelligent civilizations (CETIs). Based on the latest astrophysical information, we carry out Monte Carlo simulations to estimate the number of possible CETIs within our Galaxy and the communication probability among them. Two poorly known parameters have a great impact on the results. One is the probability of life appearing on terrestrial planets and eventually evolving a into CETI ($f_c$), and the other determines at what stage of their host star's evolution CETIs would be born ($F$). In order to ensure the completeness of the simulation, we consider a variety of combinations of $f_c$ and $F$. Our results indicate that for optimistic situations (e.g. $F=25\%$ and $f_c=0.1\%$), there could be $42777_{-369}^{+267}$ CETIs and they need to survive for $3_{-2}^{+17}$ yr ($2000_{-1400}^{+2000}$ yr) to achieve one-way communication (two-way communication). In this case, human beings need to survive $0.3_{-0.298}^{+0.6}$ Myr to receive one alien signal. For pessimistic situations (e.g. $F=75\%$ and $f_c=0.001\%$), only $111_{-17}^{+28}$ CETIs could be born and they need to survive for $0.8_{-0.796}^{+1.2}$ Myr ($0.9_{-0.88}^{+4.1}$ Myr) to achieve one-way communication (two-way communication). In this case, human beings need to survive $50_{-49.6}^{+250}$ Myr to receive one signal from other CETIs. Our results may quantitatively explain why we have not detected any alien signals so far. The uncertainty of the results has been discussed in detail and would be alleviated with the further improvement of our astronomical observation ability in the future.

astro-ph.EP↗

arXiv2022

Quantifying the Dzyaloshinskii-Moriya Interaction Induced by the Bulk Magnetic Asymmetry

A broken interfacial inversion symmetry in ultrathin ferromagnet/heavy metal (FM/HM) bilayers is generally believed to be a prerequisite for accommodating the Dzyaloshinskii-Moriya interaction (DMI) and for stabilizing chiral spin textures. In these bilayers, the strength of the DMI decays as the thickness of the FM layer increases and vanishes around a few nanometers. In the present study, through synthesizing relatively thick films of compositions CoPt or FePt, CoCu or FeCu, FeGd and FeNi, contributions to DMI from the composition gradient induced bulk magnetic asymmetry (BMA) and spin-orbit coupling (SOC) are systematically examined. Using Brillouin light scattering spectroscopy, both the sign and amplitude of DMI in films with controllable direction and strength of BMA, in the presence and absence of SOC are experimentally studied. In particular, we show that a sizable amplitude of DMI (0.15 mJ/m^2) can be realized in CoPt or FePt films with BMA and strong SOC, whereas negligible DMI strengths are observed in other thick films with BMA but without significant SOC. The pivotal roles of BMA and SOC are further examined based on the three-site Fert-Levy model and first-principles calculations. It is expected that our findings may help to further understand the origin of chiral magnetism and to design novel non-collinear spin textures.

cond-mat.mtrl-sci↗

arXiv2021

Nonreciprocal Emergence of Hybridized Magnons in magnetic thin Films

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

cond-mat.mes-hall↗

arXiv2020

Backward magnetostatic surface spin waves in exchange coupled Co/FeNi bilayers

Propagation of backward magnetostatic surface spin waves (SWs) in exchange coupled Co/FeNi bilayers are studied by using Brillouin light scattering (BLS) technique. Two types of SWs modes were identified in our BLS measurements. They are magnetostatic surface waves (MSSWs) mode and perpendicular standing spin waves (PSSWs) mode. The dispersion relations of MSSWs obtained from the Stokes and Anti-Stokes measurements display respectively positive and negative group velocities. The Anti-Stokes branch with positive phase velocities and negative group velocities, known as backward magnetostatic surface mode originates from the magnetostatic interaction of the bilayer. The experimental data are in good agreement with the theoretical calculations. Our results are useful for understanding the SWs propagation and miniaturizing SWs storage devices.

cond-mat.mes-hall↗

arXiv2018

Anomalous increasing of the intensity of field dependence optical mode ferromagnetic resonance in the exchange coupled bilayer system

Acoustic and optical ferromagnetic resonance (FMR) in the interlayer exchange coupled Fe$_{20}$Ni$_{80}$/Co bilayer have been investigated. In the optical mode, unexpected increasing tendencies of peak value at the resonance frequency has been observed under an increasing magnetic field. We presented analytical calculations with which the exchange coupling between Co and Fe$_{20}$Ni$_{80}$ layers, the magnetization and the in-plane uniaxial anisotropy are taken into account, to interpret the increasing of the maximum values of the optical permeability. Both experimental measurements and theoretical calculation show that such tendencies are dependent on the layer thickness t, and that there is a critical field above which the optical peak value begins to decrease. These results might help us to understand the mechanism of interlayer exchange coupling induced optical FMR and might enlighten us to find new possibility of high frequency applications of magnetic materials.

cond-mat.mes-hall↗

arXiv2018

Robust Estimation of Similarity Transformation for Visual Object Tracking

Most of existing correlation filter-based tracking approaches only estimate simple axis-aligned bounding boxes, and very few of them is capable of recovering the underlying similarity transformation. To tackle this challenging problem, in this paper, we propose a new correlation filter-based tracker with a novel robust estimation of similarity transformation on the large displacements. In order to efficiently search in such a large 4-DoF space in real-time, we formulate the problem into two 2-DoF sub-problems and apply an efficient Block Coordinates Descent solver to optimize the estimation result. Specifically, we employ an efficient phase correlation scheme to deal with both scale and rotation changes simultaneously in log-polar coordinates. Moreover, a variant of correlation filter is used to predict the translational motion individually. Our experimental results demonstrate that the proposed tracker achieves very promising prediction performance compared with the state-of-the-art visual object tracking methods while still retaining the advantages of high efficiency and simplicity in conventional correlation filter-based tracking methods.

cs.CV↗