arXiv subjects

Wei Ji

Publications and source records attributed to Wei Ji.

At least 91 records · Page 5Linked to original sources

arXiv2025

Real-space titration and manipulation of particle-like correlated electrons in doped Mott insulator

The localized (particle-like) correlated electrons deserve particular attention as they govern various exotic quantum phenomena, such as quantum spin liquids, Wigner crystals, and Mott insulators in correlated systems. However, direct observation and manipulation of these particle-like electrons at the atomic or single-electron scale remain highly challenging. Here, we successfully realize and directly visualize particle-like correlated electrons in 1T-TaS2 through hole doping. The potential-dependent local electronic structure of single particle-like electron is revealed by angle-resolved photoemission spectroscopy (ARPES), scanning tunneling spectroscopy (STS) combined with theoretical calculations. The complex correlated interactions including nearest-neighbor attractive interactions and many-body repulsive interactions are further demonstrated and discussed based on the spatial distribution of particle-like electrons. Furthermore, the tentative manipulation of the particle-like electrons is successfully achieved by the energy-excitation mechanism. Our results not only provide profound insights into particle-like electrons in correlated systems, but also establish a versatile platform for designing and controlling quantum states at the atomic scale.

cond-mat.str-el↗

arXiv2025

Tunable altermagnetism via inter-chain engineering in parallelassembled atomic chains

Altermagnetism has recently drawn considerable attention in three- and twodimensional materials. Here, we extend this concept to quasi-one-dimensional (Q1D) monolayers assembled from single-atomic magnetic chains. Through systematically examining nine types of structures, two stacking orders, and intra-/inter-chain magnetic couplings, we identify four out of thirty promising structural prototypes for hosting altermagnetism, which yields 192 potential monolayer materials. We further confirm eight thermodynamically stable Q1D monolayers via high-throughput calculations. Using symmetry analysis and first-principles calculations, we find that the existence of altermagnetism is determined by the type of inter-chain magnetic coupling and predict three intrinsic altermagnets,$CrBr_3$,$VBr_3$,$MnBr_3$,due to their ferromagnetic inter-chain couplings and five extrinsic ones,$CrF_3$,$CrCl_3$,$CrI_3$,$FeCl_3$and $CoTe_3$, ascribed to their neglectable or antiferromagnetic inter-chain couplings. Moreover, the inter-chain magnetic coupling here is highly tunable by manipulating the inter-chain spacing, leading to experimentally feasible transitions between altermagnetic and nodal-line semiconducting states. In addition, applying external electric fields can further modulate the spin splitting. Our findings establish a highly tunable family of Q1D altermagnets, offering fundamental insights into the intricate relationship between geometry, electronic structure, and magnetism.These discoveries hold significant promises for experimental realization and future spintronic applications.

cond-mat.mtrl-sci↗

arXiv2025

Cross-Level Multi-Instance Distillation for Self-Supervised Fine-Grained Visual Categorization

High-quality annotation of fine-grained visual categories demands great expert knowledge, which is taxing and time consuming. Alternatively, learning fine-grained visual representation from enormous unlabeled images (e.g., species, brands) by self-supervised learning becomes a feasible solution. However, recent researches find that existing self-supervised learning methods are less qualified to represent fine-grained categories. The bottleneck lies in that the pre-text representation is built from every patch-wise embedding, while fine-grained categories are only determined by several key patches of an image. In this paper, we propose a Cross-level Multi-instance Distillation (CMD) framework to tackle the challenge. Our key idea is to consider the importance of each image patch in determining the fine-grained pre-text representation by multiple instance learning. To comprehensively learn the relation between informative patches and fine-grained semantics, the multi-instance knowledge distillation is implemented on both the region/image crop pairs from the teacher and student net, and the region-image crops inside the teacher / student net, which we term as intra-level multi-instance distillation and inter-level multi-instance distillation. Extensive experiments on CUB-200-2011, Stanford Cars and FGVC Aircraft show that the proposed method outperforms the contemporary method by upto 10.14% and existing state-of-the-art self-supervised learning approaches by upto 19.78% on both top-1 accuracy and Rank-1 retrieval metric.

cs.CV↗

arXiv2025

Existence and Multiplicity of Normalized Solutions to Schrödinger Equations with General Nonlinearities in Bounded Domains

This paper focuses on the existence of multiple normalized solutions to Schrödinger equations with general nonlinearities in bounded domains via variational methods. We first obtain two positive normalized solutions, one is a normalized ground state by searching for a local minimizer, and the other one is a mountain pass solution. Secondly, using a version of Linking theorems for normalized solutions, we prove the multiplicity of solutions to Schrödinger equations in a star-shaped bounded domain. Moreover, we arrive at the existence of nonradial normalized solutions to Schrödinger equations in a ball.

math.AP↗

arXiv2025

Dose-aware Diffusion Model for 3D PET Image Denoising: Multi-institutional Validation with Reader Study and Real Low-dose Data

Reducing scan times, radiation dose, and enhancing image quality for lower-performance scanners, are critical in low-dose PET imaging. Deep learning techniques have been investigated for PET image denoising. However, existing models have often resulted in compromised image quality when achieving low-count/low-dose PET and have limited generalizability to different image noise-levels, acquisition protocols, and patient populations. Recently, diffusion models have emerged as the new state-of-the-art generative model to generate high-quality samples and have demonstrated strong potential for medical imaging tasks. However, for low-dose PET imaging, existing diffusion models failed to generate consistent 3D reconstructions, unable to generalize across varying noise-levels, often produced visually-appealing but distorted image details, and produced images with biased tracer uptake. Here, we develop DDPET-3D, a dose-aware diffusion model for 3D low-dose PET imaging to address these challenges. Collected from 4 medical centers globally with different scanners and clinical protocols, we evaluated the proposed model using a total of 9,783 18F-FDG studies with low-dose levels ranging from 1% to 50%. With a cross-center, cross-scanner validation, the proposed DDPET-3D demonstrated its potential to generalize to different low-dose levels, different scanners, and different clinical protocols. As confirmed with reader studies performed by board-certified nuclear medicine physicians, experienced readers judged the images to be similar or superior to the full-dose images and previous DL baselines based on qualitative visual impression. Lesion-level quantitative accuracy was evaluated using a Monte Carlo simulation study and a lesion segmentation network. The presented results show the potential to achieve low-dose PET while maintaining image quality. Real low-dose scans was also included for evaluation.

eess.IV↗

arXiv2025

Evidence of Ferroelectricity in an Antiferromagnetic Vanadium Trichloride Monolayer

A reduced dimensionality of multiferroic materials is highly desired for device miniaturization, but the coexistence of ferroelectricity and magnetism at the two-dimensional limit is yet to be conclusively demonstrated. Here, we used a NbSe2 substrate to break both the C3 rotational and inversion symmetries in monolayer VCl3 and thus introduced exceptional in-plane ferroelectricity into a two-dimensional magnet. Scanning tunneling spectroscopy directly visualized ferroelectric domains and manipulated their domain boundaries in monolayer VCl3, where coexisting antiferromagnetic order with canted magnetic moments was verified by vibrating sample magnetometer measurements. Our density functional theory calculations highlight the crucial role that highly directional interfacial Cl-Se interactions play in breaking the symmetries and thus in introducing in-plane ferroelectricity, which was further verified by examining an ML-VCl3/graphene sample. Our work demonstrates an approach to manipulate the ferroelectric states in monolayered magnets through van der Waals interfacial interactions.

cond-mat.mtrl-sci↗

arXiv2025

Atomic to mesoscale hierarchical structures and magnetic states in an anisotropic layered ferromagnet FePd2Te2

Two-dimensional (2D) magnetic materials have predominantly exhibited easy-axis or easy-plane anisotropy and display a high sensitivity to the underlying crystal structure and lattice symmetry. Recently, an in-plane anisotropic 2D ferromagnet of FePd2Te2 has been discovered with intriguing structure and quasi-one-dimensional spin system. Here, we report a real-space investigation of its twinning structure and magnetic states using atomic/magnetic force microscopy (AFM/MFM) combined with scanning tunneling microscopy (STM). The atomic to mesoscale hierarchical structures with the orthogonal and corrugated compressive /tensile(C/T) regions are directly observed due to the intrinsic twinning-domain characteristic. The structure-related intact ferromagnetic (FM), field-induced polarized-FM states and their transitions are comparatively discussed at the mesoscale with the corresponding macroscopic magnetic measurements. Temperature- and field-dependent evolution of magnetic phase are further investigated at the FM and PM states, and summarized to obtain a unique H-T phase diagram of FePd2Te2. Our work provides key results for understanding the complicated magnetic properties of FePd2Te2, and suggests new directions for manipulating magnetic states through the atomic and mesoscale structure engineering.

cond-mat.mtrl-sci↗

arXiv2025

What Limits Virtual Agent Application? OmniBench: A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities

As multimodal large language models (MLLMs) advance, MLLM-based virtual agents have demonstrated remarkable performance. However, existing benchmarks face significant limitations, including uncontrollable task complexity, extensive manual annotation with limited scenarios, and a lack of multidimensional evaluation. In response to these challenges, we introduce OmniBench, a self-generating, cross-platform, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity through subtask composition. To evaluate the diverse capabilities of virtual agents on the graph, we further present OmniEval, a multidimensional evaluation framework that includes subtask-level evaluation, graph-based metrics, and comprehensive tests across 10 capabilities. Our synthesized dataset contains 36k graph-structured tasks across 20 scenarios, achieving a 91\% human acceptance rate. Training on our graph-structured data shows that it can more efficiently guide agents compared to manually annotated data. We conduct multidimensional evaluations for various open-source and closed-source models, revealing their performance across various capabilities and paving the way for future advancements. Our project is available at https://omni-bench.github.io/.

cs.CV↗

arXiv2025

FormFactory: An Interactive Benchmarking Suite for Multimodal Form-Filling Agents

Online form filling is a common yet labor-intensive task involving extensive keyboard and mouse interactions. Despite the long-standing vision of automating this process with "one click", existing tools remain largely rule-based and lack generalizable, generative capabilities. Recent advances in Multimodal Large Language Models (MLLMs) have enabled promising agents for GUI-related tasks in general-purpose scenarios. However, they struggle with the unique challenges of form filling, such as flexible layouts and the difficulty of aligning textual instructions with on-screen fields. To bridge this gap, we formally define the form-filling task and propose FormFactory, an interactive benchmarking suite comprising a web-based interface, backend evaluation module, and carefully constructed dataset. Our benchmark covers diverse real-world scenarios, incorporates various field formats, and simulates high-fidelity form interactions. We conduct a comprehensive evaluation of state-of-the-art MLLMs and observe that no model surpasses 5% accuracy, underscoring the inherent difficulty of the task. These findings also reveal significant limitations in current models' visual layout reasoning and field-value alignment abilities. We hope our benchmark can serve as a stepping stone for further research into robust, practical form-filling agents.

cs.CL↗

arXiv2025

Semiregular tessellation of electronic lattices in untwisted bilayer graphene under anisotropic strain gradients

Two-dimensional (2D) moiré superlattices have emerged as a versatile platform for uncovering exotic quantum phases, many of which arise in bilayer systems exhibiting Archimedean tessellation patterns such as triangular, hexagonal, and kagome lattices. Here, we propose a strategy to engineer semiregular tessellation patterns in untwisted bilayer graphene by applying anisotropic epitaxial tensile strain (AETS) along crystallographic directions. Through force-field and first-principles calculations, we demonstrate that AETS can induce a rich variety of semiregular tessellation geometries, including truncated hextille, prismatic pentagon, and brick-phase arrangements. The characteristic electronic bands (Dirac and flat bands) of the lattice models associated with these semiregular tessellations are observed near the Fermi level, arising from interlayer interactions generated by the redistribution of specific stacking registries (AB, BA, and SP). Furthermore, the electronic kagome, distorted Lieb, brick-like, and one-dimensional stripe lattices captured in real-space confirm the tunable nature of the semiregular tessellation lattices enabled by AETS. Our study identifies AETS as a promising new degree of freedom in moiré engineering, offering a reproducible and scalable platform for exploring exotic electronic lattices in moiré systems.

cond-mat.mes-hall↗

arXiv2025

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

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

cs.CV↗

arXiv2025

SpikeVideoFormer: An Efficient Spike-Driven Video Transformer with Hamming Attention and $\mathcal{O}(T)$ Complexity

Spiking Neural Networks (SNNs) have shown competitive performance to Artificial Neural Networks (ANNs) in various vision tasks, while offering superior energy efficiency. However, existing SNN-based Transformers primarily focus on single-image tasks, emphasizing spatial features while not effectively leveraging SNNs' efficiency in video-based vision tasks. In this paper, we introduce SpikeVideoFormer, an efficient spike-driven video Transformer, featuring linear temporal complexity $\mathcal{O}(T)$. Specifically, we design a spike-driven Hamming attention (SDHA) which provides a theoretically guided adaptation from traditional real-valued attention to spike-driven attention. Building on SDHA, we further analyze various spike-driven space-time attention designs and identify an optimal scheme that delivers appealing performance for video tasks, while maintaining only linear temporal complexity. The generalization ability and efficiency of our model are demonstrated across diverse downstream video tasks, including classification, human pose tracking, and semantic segmentation. Empirical results show our method achieves state-of-the-art (SOTA) performance compared to existing SNN approaches, with over 15\% improvement on the latter two tasks. Additionally, it matches the performance of recent ANN-based methods while offering significant efficiency gains, achieving $\times 16$, $\times 10$ and $\times 5$ improvements on the three tasks. https://github.com/JimmyZou/SpikeVideoFormer

cs.CV↗

arXiv2025

Tailoring composite skyrmionic spin textures in an above-room-temperature ferromagnet Fe3-xGaTe2

Realizing room-temperature tunable skyrmionic objects in van der Waals ferromagnet offers unparalleled prospects for future spintronics. Here, we report an experimental investigation on the emergence and evolution of skyrmionic spin textures in the non-stoichiometric Fe3-xGaTe2 using magnetic force microscopy. The iron-deficiency-specific magnetic states of stripe, striped skyrmionium and striped skyrmion sack are observed. Through zero-field-cooling and field-cooling measurements, we observed distinct topological transitions and trivial transitions (distinguished by changes in topological charge) emerging during the stepwise evolution of topological spin textures, which enabled us to develop an evolution pathway model. Leveraging this model, the room-temperature stable composite topological spin textures of skyrmionium, skyrmion bag and sack states are further controllably realized via the exclusive topological-transition path (regulated by magnetic field and DMI intensity). Our work provides valuable insights into the room-temperature realization of topological spin textures in Fe3-xGaTe2, and inspires further exploration of their potential applications in heterostructure spintronics.

cond-mat.mtrl-sci↗

arXiv2025

Search for a parity-violating long-range spin-dependent interaction

High-sensitivity quantum sensors are a promising tool for experimental searches for beyond-Standard-Model interactions. Here, we demonstrate an atomic comagnetometer operating under a resonantly-coupled hybrid spin-resonance (HSR) regime to probe P-odd, T-even interactions. The HSR regime enables robust nuclear-electron spin coupling, enhancing measurement bandwidth and stability without compromising the high sensitivity of spin-exchange relaxation-free magnetometers. To minimize vibration noise from velocity-modulated sources, we implement a multistage vibration isolation system, achieving a vibration noise reduction exceeding 700-fold. We establish new constraints on vector-boson-mediated parity-violating interactions, improving experimental sensitivity by three orders of magnitude compared to previous limits. The new constraints complement existing astrophysical and laboratory studies of potential extensions to the Standard Model.

quant-ph↗

arXiv2025

Levitated Sensor for Magnetometry in Ambient Environment

Levitated particle systems have gained significant attention as a rapidly advancing platform for precision sensing, offering low-loss, highly isolated environments by eliminating mechanical contact and associated noise. Current room-temperature levitation techniques are primarily sensitive to acceleration, with magnetic sensing often relying on the Meissner effect, which is impractical under ambient conditions. Here, we demonstrate a diamagnetically stabilized magnetically levitated magnet magnetometer (LeMaMa), where the motion of the magnet is detected optically. Leveraging strong spin-lattice coupling in the ferromagnet to suppress spin-projection noise and minimizing dissipation through levitation, we achieve a sensitivity of 32 fT $/Hz^{1/2}$. This sensitivity is adequate for a wide range of applications in biology, chemistry, and fundamental physics, matching the performance of leading technologies like SQUIDs and atomic magnetometers, while offering the distinct advantage of operating at room temperature and under Earth's magnetic field.

physics.ins-det↗

arXiv2025

A Data-Driven Based Concurrent Coupling Approach for Cryogenic Propellant Tank Long-term Pressure Control Predictions

The design and optimization of cryogenic propellant storage tanks for NASA's future space missions require fast and accurate predictions of long-term fluid behaviors. Computational fluid dynamics (CFD) techniques are high-fidelity but computationally prohibitive. Coarse mesh nodal techniques are fast but heavily rely on empirical correlations to capture prominent three-dimensional phenomena. A data-driven based concurrent coupling (DCC) approach has been developed to integrate CFD with nodal techniques for efficient and accurate analysis. This concurrent coupling scheme generates case-specific correlations on the fly through a short period of co-solving CFD and nodal simulations, followed by a long-period nodal simulation with CFD-corrected solutions. This paper presents the approach development, stability analysis, and efficiency demonstration, specifically for modeling two-phase cryogenic propellant tank self-pressurization and periodic mixing phenomena. Linear regression is employed to derive the data-driven correlations. The self-pressurization experiments of Multipurpose Hydrogen Test Bed experiments and K-Site tank are used to validate the approach. The DCC approach accurately predicts temperature stratifications while reducing computational time by as much as 70% compared to pure CFD simulations. Additionally, the DCC approach mitigates the risks of numerical instability and correlation loss inherent in current domain decomposition or overlapping-based coupling methods, making it a flexible and user-friendly approach for integrated CFD and nodal analysis of cryogenic tank behaviors.

physics.flu-dyn↗

arXiv2025

DGFamba: Learning Flow Factorized State Space for Visual Domain Generalization

Domain generalization aims to learn a representation from the source domain, which can be generalized to arbitrary unseen target domains. A fundamental challenge for visual domain generalization is the domain gap caused by the dramatic style variation whereas the image content is stable. The realm of selective state space, exemplified by VMamba, demonstrates its global receptive field in representing the content. However, the way exploiting the domain-invariant property for selective state space is rarely explored. In this paper, we propose a novel Flow Factorized State Space model, dubbed as DG-Famba, for visual domain generalization. To maintain domain consistency, we innovatively map the style-augmented and the original state embeddings by flow factorization. In this latent flow space, each state embedding from a certain style is specified by a latent probability path. By aligning these probability paths in the latent space, the state embeddings are able to represent the same content distribution regardless of the style differences. Extensive experiments conducted on various visual domain generalization settings show its state-of-the-art performance.

cs.CV↗

arXiv2025

Learning Fine-grained Domain Generalization via Hyperbolic State Space Hallucination

Fine-grained domain generalization (FGDG) aims to learn a fine-grained representation that can be well generalized to unseen target domains when only trained on the source domain data. Compared with generic domain generalization, FGDG is particularly challenging in that the fine-grained category can be only discerned by some subtle and tiny patterns. Such patterns are particularly fragile under the cross-domain style shifts caused by illumination, color and etc. To push this frontier, this paper presents a novel Hyperbolic State Space Hallucination (HSSH) method. It consists of two key components, namely, state space hallucination (SSH) and hyperbolic manifold consistency (HMC). SSH enriches the style diversity for the state embeddings by firstly extrapolating and then hallucinating the source images. Then, the pre- and post- style hallucinate state embeddings are projected into the hyperbolic manifold. The hyperbolic state space models the high-order statistics, and allows a better discernment of the fine-grained patterns. Finally, the hyperbolic distance is minimized, so that the impact of style variation on fine-grained patterns can be eliminated. Experiments on three FGDG benchmarks demonstrate its state-of-the-art performance.

cs.CV↗