arXiv subjects

Junjie He

Publications and source records attributed to Junjie He.

At least 37 records · Page 2Linked to original sources

arXiv2026

Magnetizing altermagnets by ultrafast asymmetric spin dynamics

Laser pulses are known to induce symmetric demagnetization: equal loss of magnetic moments in the identical sublattices of antiferromagnets and ferromagnets at ultrashort timescales. Using time-dependent density functional theory, we show that linearly polarized laser pulses can drive asymmetric demagnetization between otherwise identical sublattices in the $d$-wave compensated altermagnet (AM) RuO$_2$, resulting in a \textit{photo-induced ferrimagnetic state} with a strong net magnetization of $\sim$0.2 $μ_B$ per unit cell. The sign and magnitude of this metastable magnetization are highly controllable by laser polarization. We identify polarization-selective asymmetric optical intersite spin transfer (a-OISTR) as the primary mechanism generating the net moment, followed by asymmetric spin flips (a-SF) that further amplifies it. Both effects originate from the characteristic nodal spin band topology of \textit{d}-wave AMs. Moreover, we demonstrate that this laser-induced magnetization is universal across various $d$-wave AMs, including experimentally confirmed KV$_2$Se$_2$O and RbV$_2$Te$_2$O. We uncover a robust route to light-controlled magnetization in AMs on ultrafast timescales.

cond-mat.mtrl-sci↗

arXiv2026

VPGS-SLAM: Voxel-based Progressive 3D Gaussian SLAM in Large-Scale Scenes

3D Gaussian Splatting has recently shown promising results in dense visual SLAM. However, existing 3DGS-based SLAM methods are all constrained to small-room scenarios and struggle with memory explosion in large-scale scenes and long sequences. To this end, we propose VPGS-SLAM, the first 3DGS-based large-scale RGBD SLAM framework for both indoor and outdoor scenarios. We design a novel voxel-based progressive 3D Gaussian mapping method with multiple submaps for compact and accurate scene representation in large-scale and long-sequence scenes. This allows us to scale up to arbitrary scenes and improves robustness (even under pose drifts). In addition, we propose a 2D-3D fusion camera tracking method to achieve robust and accurate camera tracking in both indoor and outdoor large-scale scenes. Furthermore, we design a 2D-3D Gaussian loop closure method to eliminate pose drift. We further propose a submap fusion method with online distillation to achieve global consistency in large-scale scenes when detecting a loop. Experiments on various indoor and outdoor datasets demonstrate the superiority and generalizability of the proposed framework. The code will be open source on https://github.com/dtc111111/vpgs-slam.

cs.CV↗

arXiv2025

When Energy and Information Revolutions Meet 2D Janus

The depletion of energy sources, worsening environmental issues, and the quantum limitations of integrated circuits for information storage in the post-Moore era, are pressing global concerns. Fortunately, two-dimensional (2D) Janus materials, possessing broken spatial symmetry, with emerging pressure-dependent and non-linear optical response, piezoelectricity, valley polarization, Rashba spin splitting and more, have established a substantial platform for exploring and applying modifiable physical, chemical and biological properties in material science and offered a promising solution for these energy and information issues. To furnish researchers with a comprehensive repository of 2D Janus family, this review systematically summarizes their theoretical predictions, experimental preparations, and modulation strategies. It also retrospectively outlines the recent advances in modifiable properties, applications, and inherent mechanisms in optics, catalysis, piezoelectricity, electrochemistry, thermoelectricity, magnetism, and electronics, with a focus on experimentally realized hexagonal and trigonal Janus structures. Additionally, their current research state is summarized, and potential opportunities and challenges that may arise are highlighted. Overall, this review aims to serve as a valuable resource for designing, fabricating, regulating, and applying 2D Janus systems, both theoretically and experimentally. This review will strongly promote the advanced academic investigations and industrial applications of 2D Janus materials in energy and information fields.

physics.app-ph↗

arXiv2025

Above room temperature multiferroic tunnel junction with the altermagnetic metal CrSb

Altermagnets with nonrelativistic momentum-dependent spin splitting and compensated net magnetic moments have recently garnered significant interest in spintronics, particularly as pinning layers in magnetic tunnel junctions (MTJs). However, room temperature (RT) altermagnet-based MTJs with tunable tunneling magnetoresistance (TMR) or electroresistance (TER) modulated by multiferroicity remain largely unexplored. Here, we propose an experimentally fabricable above-RT multiferroic MTJ, comprising an altermagnetic metal, ferroelectric barrier, and ferromagnetic metal-epitomized by a CrSb/In2Se3/Fe3GaTe2 heterostructure. Our calculations with first-principles and nonequilibrium Green function method indicate that the architecture enables magnetically switchable TER, electrically tunable TMR, and dual-mode controllable spin filtering. To disentangle the roles of ferroelectricity and the tunnel barrier, nonferroelectric Sb2Se3 and a vacuum gap are exploited as control cases. Remarkably, the system achieves TMR up to 2308%, TER of 707%, and near-perfect spin filtering efficiency. Both TMR and TER are considerable for CrSb/In2Se3/Fe3GaTe2 with either Cr or Sb interface. The transport performance is robust under bias voltage. These findings demonstrate the above-RT multiferroic altermagnet-based MTJs and highlight their exciting potential as a versatile platform for next-generation spin dynamics, magnetic sensing, and quantum logic nanodevices.

cond-mat.mtrl-sci↗

arXiv2025

Ultrafast controlling net magnetization in g-wave altermagnets via laser fields

The diverse nodal spin structures in d/g/i-wave altermagnets (AM) may cause distinct light-induced spin responses yet remain poorly understood. Using time-dependent density functional theory (TDDFT), we reveal that laser induced ultrafast demagnetization dynamics in the g-wave AM CrSb are strongly governed by the laser incidence direction. Under normal incidence along the [0001] axis, two Cr sublattices exhibit symmetric temporal demagnetization but with different amplitudes, preserving the net-zero magnetization, unlike the behavior in d-wave AM. Off-normal incidence, however, induces pronounced asymmetric demagnetization between sublattices, transiently driving the system into a ferrimagnetic-like state with a sizable net magnetization. This direction-dependent response arises from the characteristic nodal structures in bulk g-wave AM electronic structure, which enable anisotropic optical intersite spin transfer (OISTR). By comparing g-wave and d-wave AMs, we propose that light-induced magnetization arises when laser polarization aligns with spin-uncompensated regions in electronic structures. This can be readily determined from the local spin density of states along specific band paths. Our results provide a fundamental understanding for laser-induced ultrafast dynamics in AM.

cond-mat.mtrl-sci↗

arXiv2025

Achieving fully-compensated ferrimagnetism through two-dimensional heterojunctions

In addition to altermagnets, fully-compensated ferrimagnets are another category of collinear magnetic materials that possess zero-net total magnetic moment and exhibit spin-splitting, making them promising for low-energy spintronics, high-density data storage and high-sensitivity sensors. Although many methods, such as alloying, external electric field, Janus engineering, ferroelectric field and spin ordering, have been proposed to achieve fully-compensated ferrimagnetism, these approaches either face experimental difficulties or produce a small spin-splitting or are volatile. Here, we propose to form vertical heterostructures by stacking two different but equally magnetized two-dimensional ferromagnetic materials. If an A-type antiferromagnetic ordering is satisfied, a fully compensated ferrimagnet can be formed. This vertical heterostructure approach is insensitive to lattice matching and stacking manner, thus being more conducive to experimental realization. Through first-principles calculations, we verify our proposal with several examples, focusing in particular on $\mathrm{CrI_3}$/$\mathrm{CrGeTe_3}$ heterojunction composed of experimentally synthesized $\mathrm{CrI_3}$ and $\mathrm{CrGeTe_3}$ monolayers. The calculations show that $\mathrm{CrI_3}$/$\mathrm{CrGeTe_3}$ is a fully-compensated ferrimagnet, with pronounced spin-splitting, and that tensile strain is more favorable for achieving fully-compensated ferrimagnetism. Our work provides an experimentally feasible strategy for realizing fully-compensated ferrimagnetism, thereby further advancing the development of this field.

cond-mat.mtrl-sci↗

arXiv2025

Sensing-Assisted Channel Prediction in Complex Wireless Environments: An LLM-Based Approach

This letter studies the sensing-assisted channel prediction for a multi-antenna orthogonal frequency division multiplexing (OFDM) system operating in realistic and complex wireless environments. In this system,an integrated sensing and communication (ISAC) transmitter leverages the mono-static sensing capability to facilitate the prediction of its bi-static communication channel, by exploiting the fact that the sensing and communication channels share the same physical environment involving shared scatterers. Specifically, we propose a novel large language model (LLM)-based channel prediction approach,which adapts pre-trained text-based LLM to handle the complex-matrix-form channel state information (CSI) data. This approach utilizes the LLM's strong ability to capture the intricate spatiotemporal relationships between the multi-path sensing and communication channels, and thus efficiently predicts upcoming communication CSI based on historical communication and sensing CSI data. Experimental results show that the proposed LLM-based approach significantly outperforms conventional deep learning-based methods and the benchmark scheme without sensing assistance.

eess.SP↗

arXiv2025

AnyStory: Towards Unified Single and Multiple Subject Personalization in Text-to-Image Generation

Recently, large-scale generative models have demonstrated outstanding text-to-image generation capabilities. However, generating high-fidelity personalized images with specific subjects still presents challenges, especially in cases involving multiple subjects. In this paper, we propose AnyStory, a unified approach for personalized subject generation. AnyStory not only achieves high-fidelity personalization for single subjects, but also for multiple subjects, without sacrificing subject fidelity. Specifically, AnyStory models the subject personalization problem in an "encode-then-route" manner. In the encoding step, AnyStory utilizes a universal and powerful image encoder, i.e., ReferenceNet, in conjunction with CLIP vision encoder to achieve high-fidelity encoding of subject features. In the routing step, AnyStory utilizes a decoupled instance-aware subject router to accurately perceive and predict the potential location of the corresponding subject in the latent space, and guide the injection of subject conditions. Detailed experimental results demonstrate the excellent performance of our method in retaining subject details, aligning text descriptions, and personalizing for multiple subjects. The project page is at https://aigcdesigngroup.github.io/AnyStory/ .

cs.CV↗

arXiv2025

From Prompts to Templates: A Systematic Prompt Template Analysis for Real-world LLMapps

Large Language Models (LLMs) have revolutionized human-AI interaction by enabling intuitive task execution through natural language prompts. Despite their potential, designing effective prompts remains a significant challenge, as small variations in structure or wording can result in substantial differences in output. To address these challenges, LLM-powered applications (LLMapps) rely on prompt templates to simplify interactions, enhance usability, and support specialized tasks such as document analysis, creative content generation, and code synthesis. However, current practices heavily depend on individual expertise and iterative trial-and-error processes, underscoring the need for systematic methods to optimize prompt template design in LLMapps. This paper presents a comprehensive analysis of prompt templates in practical LLMapps. We construct a dataset of real-world templates from open-source LLMapps, including those from leading companies like Uber and Microsoft. Through a combination of LLM-driven analysis and human review, we categorize template components and placeholders, analyze their distributions, and identify frequent co-occurrence patterns. Additionally, we evaluate the impact of identified patterns on LLMs' instruction-following performance through sample testing. Our findings provide practical insights on prompt template design for developers, supporting the broader adoption and optimization of LLMapps in industrial settings.

cs.SE↗

arXiv2025

Valley resolved dynamics of phonon bottleneck in semiconductor molybdenum ditelluride

Semiconductor molybdenum ditelluride (2H-MoTe2) possess multiple valleys in the band structure, enriching its physical properties and potentials in applications. The understanding of its multivalley nature of fundamental processes involving population and relaxation of carriers and phonons is still evolving; particularly, the possible phonon bottleneck has not yet been addressed. Here, we investigate the carrier intra- and intervalley scattering and the phonon dynamics in different valleys in photoexcited few-layer 2H-MoTe2, by using the time resolved measurements of optical absorption and electron diffraction, together with the density functional theory calculation and molecular dynamics simulation. The pathways and timescales of carrier relaxation, accompanied with the emissions of optical phonons at the Brillouin zone center and acoustic phonons at the zone border are revealed. We present a couple of approaches to estimate the population of different phonon modes based on the results of optical and electron diffraction measurements, hence quantitatively identify the occurrences of phonon bottleneck located in different valleys. Our findings make possible to construct a comprehensive picture of the complex interactions between carriers and phonons in 2H-MoTe2 with the valley degree of freedom resolved.

cond-mat.mtrl-sci↗

arXiv2024

UniPortrait: A Unified Framework for Identity-Preserving Single- and Multi-Human Image Personalization

This paper presents UniPortrait, an innovative human image personalization framework that unifies single- and multi-ID customization with high face fidelity, extensive facial editability, free-form input description, and diverse layout generation. UniPortrait consists of only two plug-and-play modules: an ID embedding module and an ID routing module. The ID embedding module extracts versatile editable facial features with a decoupling strategy for each ID and embeds them into the context space of diffusion models. The ID routing module then combines and distributes these embeddings adaptively to their respective regions within the synthesized image, achieving the customization of single and multiple IDs. With a carefully designed two-stage training scheme, UniPortrait achieves superior performance in both single- and multi-ID customization. Quantitative and qualitative experiments demonstrate the advantages of our method over existing approaches as well as its good scalability, e.g., the universal compatibility with existing generative control tools. The project page is at https://aigcdesigngroup.github.io/UniPortrait-Page/ .

cs.CV↗

arXiv2024

LIV-GaussMap: LiDAR-Inertial-Visual Fusion for Real-time 3D Radiance Field Map Rendering

We introduce an integrated precise LiDAR, Inertial, and Visual (LIV) multimodal sensor fused mapping system that builds on the differentiable \pre{surface splatting }\now{Gaussians} to improve the mapping fidelity, quality, and structural accuracy. Notably, this is also a novel form of tightly coupled map for LiDAR-visual-inertial sensor fusion. This system leverages the complementary characteristics of LiDAR and visual data to capture the geometric structures of large-scale 3D scenes and restore their visual surface information with high fidelity. The initialization for the scene's surface Gaussians and the sensor's poses of each frame are obtained using a LiDAR-inertial system with the feature of size-adaptive voxels. Then, we optimized and refined the Gaussians using visual-derived photometric gradients to optimize their quality and density. Our method is compatible with various types of LiDAR, including solid-state and mechanical LiDAR, supporting both repetitive and non-repetitive scanning modes. Bolstering structure construction through LiDAR and facilitating real-time generation of photorealistic renderings across diverse LIV datasets. It showcases notable resilience and versatility in generating real-time photorealistic scenes potentially for digital twins and virtual reality, while also holding potential applicability in real-time SLAM and robotics domains. We release our software and hardware and self-collected datasets to benefit the community.

cs.RO↗

arXiv2024

Adaptive deep density approximation for stochastic dynamical systems

In this paper we consider adaptive deep neural network approximation for stochastic dynamical systems. Based on the Liouville equation associated with the stochastic dynamical systems, a new temporal KRnet (tKRnet) is proposed to approximate the probability density functions (PDFs) of the state variables. The tKRnet gives an explicit density model for the solution of the Liouville equation, which alleviates the curse of dimensionality issue that limits the application of traditional grid based numerical methods. To efficiently train the tKRnet, an adaptive procedure is developed to generate collocation points for the corresponding residual loss function, where samples are generated iteratively using the approximate density function at each iteration. A temporal decomposition technique is also employed to improve the long-time integration. Theoretical analysis of our proposed method is provided, and numerical examples are presented to demonstrate its performance.

math.NA↗

arXiv2024

Coherent Phonon Control of Ultrafast Magnetization Dynamics in Fe$_\text{3}$GeTe$_\text{2}$ from Time-Dependent Ab Initio Theory

Exploring ultrafast magnetization control in two-dimensional (2D) magnets through optically driven coherent phonons has been well-established. Yet, the microscopic interplay between spin dynamics and lattice degrees of freedom remains less explored. Employing real-time time-dependent density functional theory (rt-TDDFT) coupled with Ehrenfest dynamics, we systematically investigate laser-induced spin-nuclei dynamics with coherent phonon excitation in the 2D ferromagnet Fe3GeTe2. We found that selectively pre-exciting three typical coherent phonon modes results in up to a 53% additional spin moment loss in an out-of-plane A2 1g mode within ~50 fs. Coherent phonon control of spin dynamics is closely linked to laser pulse parameters. The underlying microscopic mechanism of this phenomenon is primarily governed by coherent phonon-induced asymmetric spin-resolved charge transfer following the disappearance of the laser pulse, thereby enabling effective control of the spin moment loss. Our findings offer a novel insight into the coupling of coherent phonons with spin systems in 2D limits on femtosecond timescales.

physics.comp-ph↗

arXiv2024

Ultrafast chiral precession of spin and orbital angular momentum induced by circularly polarized laser pulse

Despite spin (SAM) and orbital (OAM) angular momentum dynamics are well-studied in demagnetization processes, their components receive less focus. Here, we utilize the non-collinear spin version of real-time time-dependent density functional theory (rt-TDDFT) to unveil significant x and y components of SAM and OAM induced by circularly left and right polarized laser pulse in Ferromagnetic Fe, Co and Ni. Our results show that the magnitude of OAM is an order of magnitude larger than that of SAM, highlighting a stronger optical response from the orbital degrees of freedom of electrons compared to spin. Additionally, we observe a marked dependency of the oscillations of the x and y components on the helicity of the pulse. Intriguingly, left and right polarized pulses induce chirality in the precession of SAM and OAM, respectively, with clear associations with laser frequency and duration. Finally, we demonstrate that the time scale of OAM and SAM precession occurs even earlier than that of the demagnetization process and OISTR effect. Our investigation provides detailed insight into the dynamics of SAM and OAM of electrons during and shortly after a circularly polarized laser excitation.

cond-mat.mes-hall↗

arXiv2024

Optically Helicity-Dependent Orbital and Spin Dynamics in Two-Dimensional Ferromagnets

Disentangling orbital (OAM) and spin (SAM) angular momenta in the ultrafast spin dynamics of two-dimensional (2D) ferromagnets on subfemtoseconds is a challenge in the field of ultrafast magnetism. Herein, we employed non-collinear spin version of real-time time-dependent density functional theory to investigate the orbital and spin dynamics of 2D ferromagnets Fe3GeTe2 (FGT) induced by circularly polarized light. Our results show the demagnetization of Fe sublattice in FGT is accompanied by helicity-dependent precession of OAM and SAM excited by circularly polarized lasers. We further identify that precession of OAM and SAM in FGT is faster than the demagnetization within a few femtoseconds. Remarkably, circularly polarized lasers can significantly induce a periodically transverse response of OAM and SAM on very ultrafast timescales of ~250 attoseconds. Our finding suggests a powerful new route for attosecond regimes of the angular momentum manipulation to coherently control helicity-dependent orbital and spin dynamics in 2D limits.

cond-mat.mtrl-sci↗

arXiv2023

FastInst: A Simple Query-Based Model for Real-Time Instance Segmentation

Recent attention in instance segmentation has focused on query-based models. Despite being non-maximum suppression (NMS)-free and end-to-end, the superiority of these models on high-accuracy real-time benchmarks has not been well demonstrated. In this paper, we show the strong potential of query-based models on efficient instance segmentation algorithm designs. We present FastInst, a simple, effective query-based framework for real-time instance segmentation. FastInst can execute at a real-time speed (i.e., 32.5 FPS) while yielding an AP of more than 40 (i.e., 40.5 AP) on COCO test-dev without bells and whistles. Specifically, FastInst follows the meta-architecture of recently introduced Mask2Former. Its key designs include instance activation-guided queries, dual-path update strategy, and ground truth mask-guided learning, which enable us to use lighter pixel decoders, fewer Transformer decoder layers, while achieving better performance. The experiments show that FastInst outperforms most state-of-the-art real-time counterparts, including strong fully convolutional baselines, in both speed and accuracy. Code can be found at https://github.com/junjiehe96/FastInst .

cs.CV↗

arXiv2022

Deep neural network based adaptive learning for switched systems

In this paper, we present a deep neural network based adaptive learning (DNN-AL) approach for switched systems. Currently, deep neural network based methods are actively developed for learning governing equations in unknown dynamic systems, but their efficiency can degenerate for switching systems, where structural changes exist at discrete time instants. In this new DNN-AL strategy, observed datasets are adaptively decomposed into subsets, such that no structural changes within each subset. During the adaptive procedures, DNNs are hierarchically constructed, and unknown switching time instants are gradually identified. Especially, network parameters at previous iteration steps are reused to initialize networks for the later iteration steps, which gives efficient training procedures for the DNNs. For the DNNs obtained through our DNN-AL, bounds of the prediction error are established. Numerical studies are conducted to demonstrate the efficiency of DNN-AL.

cs.LG↗