arXiv subjects

Li Xiang

Publications and source records attributed to Li Xiang.

At least 19 recordsLinked to original sources

arXiv2026

Classifying the Groups of Order $p^3$ in Lean

This note discusses our formalisation in Lean 4 of the classification of groups of order $p^3$ for a prime number $p$, using mathlib4. We present the five isomorphism classes and give a detailed account of the formalisation, with particular emphasis on the non-abelian case, which requiring the most substantial formal development. For odd~$p$, the non-abelian groups are the Heisenberg group $\Heis(\Z/p\Z)$ and the semidirect product $\Z/p^2\Z\rtimes\Z/p\Z$; for $p=2$, they are $D_4$ and $Q_8$. We describe the construction of these concrete groups, the structural lemmas about centers, commutators, and exponents, and the explicit isomorphism constructions that classify an arbitrary non-abelian $p^3$-group.

math.HO↗

arXiv2026

Flat-Band Generation in InAs/GaSb Quantum Wells through Vertically Engineered Heterostructures

Quantum materials constitute a novel category of substances wherein quantum effects and electron-electron (e-e) interactions give rise to unforeseen phenomena on a macroscopic scale. Of particular interest within the realm of quantum materials are flat bands, which promote heavy conduction electrons and enhance e-e correlation effects. While the engineering of such flat bands has been demonstrated in graphene and two-dimensional transition metal dichalcogenides moir\'e superlattices and in lithography defined semiconductor moir\'e superlattices, conventional tear-and-stack fabrication methods face challenges due to inevitable twist-angle disorder, strain, and relaxation effects, leading to issues with reproducibility and scalability. Here, we explore the creation and modification of flat bands through vertically engineered III-V semiconductor heterostructures, without the need for twisting. These artificial quantum materials offer a reproducible and scalable means for producing high-quality flat-band materials via molecular beam epitaxy growth. Our investigation includes magnetotransport and infrared magneto-spectroscopy studies of quad-layer InAs/GaSb quantum wells, accompanied by k*p band structure calculations, which illustrate the flattening of bands in vertically designed heterostructures.

cond-mat.mes-hall↗

arXiv2025

Field-induced magnetic phase transition driven by a ground state level-crossing in CsErSe$_2$

We report a comprehensive study of the low-temperature magnetic properties of the insulating rare-earth triangular magnet CsErSe$_2$. We uncover a field-induced level crossing at the crystal electric field (CEF) ground state, which gives rise to a first order phase transition as well as to distinctive magnetic properties. This crossing is identified by the accurate determination of the single-ion Hamiltonian, the reliability of which is substantially enhanced by field-dependent optical spectroscopy that directly tracks the Zeeman splittings of Kramers doublets. We also observe spontaneous antiferromagnetic ordering in CsErSe$_2$ below $T_N \approx 110$ mK, and resolve the corresponding magnetic structure using elastic neutron scattering. We discuss how the rich magnetic behavior of CsErSe$_2$ arises from the interplay of non-trivial field-dependent single-ion physics and spontaneous ordering, and highlight the implications of these results for understanding magnetic phenomena across a wide range of insulating rare-earth magnets.

cond-mat.str-el↗

arXiv2025

Lithography defined semiconductor moires with anomalous in-gap quantum Hall states

Quantum materials and phenomena have attracted great interest for their potential applications in next-generation microelectronics and quantum-information technologies. In one especially interesting class of quantum materials, moire superlattices (MSL) formed by twisted bilayers of 2D materials, a wide range of novel phenomena are observed. However, there exist daunting challenges such as reproducibility and scalability of utilizing 2D MSLs for microelectronics and quantum technologies due to their exfoliate-tear-stack method. Here, we propose lithography defined semiconductor moires superlattices, in which three fundamental parameters, electron-electron interaction, spin-orbit coupling, and band topology, are designable. We experimentally investigate quantum transport properties in a moire specimen made in an InAs quantum well. Strong anomalous in-gap states are observed within the same integer quantum Hall state. Our work opens up new horizons for studying 2D quantum-materials phenomena in semiconductors featuring superior industry-level quality and state-of-the-art technologies, and they may potentially enable new quantum information and microelectronics technologies.

cond-mat.mes-hall↗

arXiv2025

Mechanical resonant sensing of spin texture dynamics in a two-dimensional antiferromagnet

The coupling between the spin degrees of freedom and macroscopic mechanical motions, including striction, shearing, and rotation, has attracted wide interest with applications in actuation, transduction, and information processing. Experiments so far have established the mechanical responses to the long-range ordered or isolated single spin states. However, it remains elusive whether mechanical motions can couple to a different type of magnetic structure, the non-collinear spin textures, which exhibit nanoscale spatial variations of spin (domain walls, skyrmions, etc.) and are promising candidates to realize high-speed computing devices. Here, we report the detection of collective spin texture dynamics with nanoelectromechanical resonators made of two-dimensional antiferromagnetic (AFM) MnPS3 with $10^{-9}$ strain sensitivity. By examining radio frequency mechanical oscillations under magnetic fields, new magnetic transitions were identified with sharp dips in resonant frequency. They are attributed to the collective AFM domain wall motions as supported by the analytical modeling of magnetostriction and large-scale spin-dynamics simulations. Additionally, an abnormally large modulation in the mechanical nonlinearity at the transition field infers a fluid-like response due to the ultrafast domain motion. Our work establishes a strong coupling between spin texture and mechanical dynamics, laying the foundation for electromechanical manipulation of spin texture and developing quantum hybrid devices.

cond-mat.mes-hall↗

arXiv2024

Light-Induced Electron Pairing in a Bilayer Structure

Previous experimental and theoretical work has given evidence of the existence of doubly charged exciton states in strongly screened bilayers of transition metal dichalcogenide (TMD) layers. These complexes are important because they are performed electron pairs that can, in principle, undergo Bose-Einstein condensation (BEC), in which case they would also form a new type of superconductor, consisting of stable bosons with net charges. In this paper, we present key electrostatic and magnetic measurements that definitively confirm the existence of these charged bosons. These measurements include 1) continuous control of the doping density with both positive and negative carriers, showing the expected population dependencies on the free carrier density, and 2) measurement of the dependence on the magnetic field, showing that this new bound state is a spin triplet. These results imply that it is promising to look for BEC and superconductivity in this system.

cond-mat.supr-con↗

arXiv2024

DiffDet4SAR: Diffusion-based Aircraft Target Detection Network for SAR Images

Aircraft target detection in SAR images is a challenging task due to the discrete scattering points and severe background clutter interference. Currently, methods with convolution-based or transformer-based paradigms cannot adequately address these issues. In this letter, we explore diffusion models for SAR image aircraft target detection for the first time and propose a novel \underline{Diff}usion-based aircraft target \underline{Det}ection network \underline{for} \underline{SAR} images (DiffDet4SAR). Specifically, the proposed DiffDet4SAR yields two main advantages for SAR aircraft target detection: 1) DiffDet4SAR maps the SAR aircraft target detection task to a denoising diffusion process of bounding boxes without heuristic anchor size selection, effectively enabling large variations in aircraft sizes to be accommodated; and 2) the dedicatedly designed Scattering Feature Enhancement (SFE) module further reduces the clutter intensity and enhances the target saliency during inference. Extensive experimental results on the SAR-AIRcraft-1.0 dataset show that the proposed DiffDet4SAR achieves 88.4\% mAP$_{50}$, outperforming the state-of-the-art methods by 6\%. Code is availabel at \href{https://github.com/JoyeZLearning/DiffDet4SAR}.

eess.IV↗

arXiv2024

Exciton-activated effective phonon magnetic moment in monolayer MoS2

Optical excitation of chiral phonons plays a vital role in studying the phonon-driven magnetic phenomena in solids. Transition metal dichalcogenides host chiral phonons at high symmetry points of the Brillouin zone, providing an ideal platform to explore the interplay between chiral phonons and valley degree of freedom. Here, we investigate the helicity-resolved magneto-Raman response of monolayer MoS2 and identify a doubly degenerate Brillouin-zone-center chiral phonon mode at ~270 cm-1. Our wavelength- and temperature-dependent measurements show that this chiral phonon is activated through the resonant excitation of A exciton. Under an out-of-plane magnetic field, the chiral phonon exhibits giant Zeeman splitting, which corresponds to an effective magnetic moment of ~2.5mu_B. Moreover, we carry out theoretical calculations based on the morphic effects in nonmagnetic crystals, which reproduce the linear Zeeman splitting and Raman cross-section of the chiral phonon. Our study provides important insights into lifting the chiral phonon degeneracy in an achiral covalent material, paving a new route to excite and control chiral phonons.

cond-mat.mtrl-sci↗

arXiv2024

Comparative Raman Scattering Study of Crystal Field Excitations in Co-based Quantum Magnets

Co-based materials have recently been explored due to potential to realise complex bond-dependent anisotropic magnetism. Prominent examples include Na$_2$Co$_2$TeO$_6$, BaCo$_2$(AsO$_4$)$_2$, Na$_2$BaCo(PO$_4$)$_2$, and CoX$_2$ (X = Cl, Br, I). In order to provide insight into the magnetic interactions in these compounds, we make a comparative analysis of their local crystal electric field excitations spectra via Raman scattering measurements. Combining these measurements with theoretical analysis confirms the validity of $j_{\rm eff} = 1/2$ single-ion ground states for all compounds, and provides accurate experimental estimates of the local crystal distortions, which play a prominent role in the magnetic couplings between spin-orbital coupled Co moments.

cond-mat.str-el↗

arXiv2023

DI-V2X: Learning Domain-Invariant Representation for Vehicle-Infrastructure Collaborative 3D Object Detection

Vehicle-to-Everything (V2X) collaborative perception has recently gained significant attention due to its capability to enhance scene understanding by integrating information from various agents, e.g., vehicles, and infrastructure. However, current works often treat the information from each agent equally, ignoring the inherent domain gap caused by the utilization of different LiDAR sensors of each agent, thus leading to suboptimal performance. In this paper, we propose DI-V2X, that aims to learn Domain-Invariant representations through a new distillation framework to mitigate the domain discrepancy in the context of V2X 3D object detection. DI-V2X comprises three essential components: a domain-mixing instance augmentation (DMA) module, a progressive domain-invariant distillation (PDD) module, and a domain-adaptive fusion (DAF) module. Specifically, DMA builds a domain-mixing 3D instance bank for the teacher and student models during training, resulting in aligned data representation. Next, PDD encourages the student models from different domains to gradually learn a domain-invariant feature representation towards the teacher, where the overlapping regions between agents are employed as guidance to facilitate the distillation process. Furthermore, DAF closes the domain gap between the students by incorporating calibration-aware domain-adaptive attention. Extensive experiments on the challenging DAIR-V2X and V2XSet benchmark datasets demonstrate DI-V2X achieves remarkable performance, outperforming all the previous V2X models. Code is available at https://github.com/Serenos/DI-V2X

cs.CV↗

arXiv2023

Secure and Fast Asynchronous Vertical Federated Learning via Cascaded Hybrid Optimization

Vertical Federated Learning (VFL) attracts increasing attention because it empowers multiple parties to jointly train a privacy-preserving model over vertically partitioned data. Recent research has shown that applying zeroth-order optimization (ZOO) has many advantages in building a practical VFL algorithm. However, a vital problem with the ZOO-based VFL is its slow convergence rate, which limits its application in handling modern large models. To address this problem, we propose a cascaded hybrid optimization method in VFL. In this method, the downstream models (clients) are trained with ZOO to protect privacy and ensure that no internal information is shared. Meanwhile, the upstream model (server) is updated with first-order optimization (FOO) locally, which significantly improves the convergence rate, making it feasible to train the large models without compromising privacy and security. We theoretically prove that our VFL framework converges faster than the ZOO-based VFL, as the convergence of our framework is not limited by the size of the server model, making it effective for training large models with the major part on the server. Extensive experiments demonstrate that our method achieves faster convergence than the ZOO-based VFL framework, while maintaining an equivalent level of privacy protection. Moreover, we show that the convergence of our VFL is comparable to the unsafe FOO-based VFL baseline. Additionally, we demonstrate that our method makes the training of a large model feasible.

cs.LG↗

arXiv2023

Disorder-enriched magnetic excitations in the Kitaev quantum spin liquid candidate Na$_2$Co$_2$TeO$_6$

Using optical magneto-spectroscopy, we investigate the magnetic excitations of Na$_2$Co$_2$TeO$_6$ in a broad magnetic field range ($0\ \rm{T}\leq B\leq 17.5\ \rm{T}$) at low temperature. Our measurements reveal rich spectra of in-plane magnetic excitations with a surprisingly large number of modes, even in the high-field spin-polarized state. Theoretical calculations find that the Na-occupation disorder in \NCTO plays a crucial role in generating these modes. Our work demonstrates the necessity to consider disorder in the spin environment in the search for Kitaev quantum spin liquid states in practicable materials.

cond-mat.str-el↗

arXiv2023

Infrared-induced ultrafast melting of nanostructured platinum films probed by an X-ray free-electron laser

Understanding melting in metals is a hot topic of present research. This may be accomplished by pumping the system with infrared (IR) laser radiation, and probing it with hard X-rays produced by an X-ray Free-Electron Laser (XFEL). In this work we studied nanostructured polycrystalline thin films of platinum that were illuminated by IR radiation of increasing fluences. We characterized the structural response as well as the nucleation and propagation of the liquid phase as a function of time delay between the IR pump and X-ray probe. We observed partial melting of the samples for IR fluences higher than 200 mJ$\cdot$cm$^{-2}$. To fit the contribution of the liquid phase to the scattering pattern in platinum we applied a model of liquid metal. The two-temperature model simulations were performed to understand the solid-state fraction of the sample heating process as a function of time delay and fluence.

cond-mat.mtrl-sci↗

arXiv2023

DINF: Dynamic Instance Noise Filter for Occluded Pedestrian Detection

Occlusion issue is the biggest challenge in pedestrian detection. RCNN-based detectors extract instance features by cropping rectangle regions of interest in the feature maps. However, the visible pixels of the occluded objects are limited, making the rectangle instance feature mixed with a lot of instance-irrelevant noise information. Besides, by counting the number of instances with different degrees of overlap of CrowdHuman dataset, we find that the number of severely overlapping objects and the number of slightly overlapping objects are unbalanced, which may exacerbate the challenges posed by occlusion issues. Regarding to the noise issue, from the perspective of denoising, an iterable dynamic instance noise filter (DINF) is proposed for the RCNN-based pedestrian detectors to improve the signal-noise ratio of the instance feature. Simulating the wavelet denoising process, we use the instance feature vector to generate dynamic convolutional kernels to transform the RoIs features to a domain in which the near-zero values represent the noise information. Then, soft thresholding with channel-wise adaptive thresholds is applied to convert the near-zero values to zero to filter out noise information. For the imbalance issue, we propose an IoU-Focal factor (IFF) to modulate the contributions of the well-regressed boxes and the bad-regressed boxes to the loss in the training process, paying more attention to the minority severely overlapping objects. Extensive experiments conducted on CrowdHuman and CityPersons demonstrate that our methods can help RCNN-based pedestrian detectors achieve state-of-the-art performance.

cs.CV↗

arXiv2022

X-PuDu at SemEval-2022 Task 7: A Replaced Token Detection Task Pre-trained Model with Pattern-aware Ensembling for Identifying Plausible Clarifications

This paper describes our winning system on SemEval 2022 Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts. A replaced token detection pre-trained model is utilized with minorly different task-specific heads for SubTask-A: Multi-class Classification and SubTask-B: Ranking. Incorporating a pattern-aware ensemble method, our system achieves a 68.90% accuracy score and 0.8070 spearman's rank correlation score surpassing the 2nd place with a large margin by 2.7 and 2.2 percent points for SubTask-A and SubTask-B, respectively. Our approach is simple and easy to implement, and we conducted ablation studies and qualitative and quantitative analyses for the working strategies used in our system.

cs.CL↗

arXiv2022

Effects of physical and chemical pressure on charge density wave transitions in LaAg1-xAuxSb2 single crystals

The structural characterization and electrical transport measurements at ambient and applied pressures of the compounds of the La(Ag1-xAux)Sb2 family are presented. Up to two charge density wave (CDW) transitions could be detected upon cooling from room temperature and an equivalence of the effects of chemical and physical pressure on the CDW ordering temperatures was observed with the unit cell volume being a salient structural parameter. As such La(Ag1-xAux)Sb2 is a rare example of a non-cubic system that exhibits good agreement between the effects of applied, physical, pressure and changes in unit cell volume from steric changes induced by isovalent substitution. Additionally, for La(Ag0.54Au0.46)Sb2 anomalies in low temperature electrical transport were observed in the pressure range where the lower charge density wave is completely suppressed.

cond-mat.mtrl-sci↗

arXiv2022

Magnetism and $T-x$ phase diagrams of Na and Ag substituted EuCd$_2$As$_2$

EuCd$_2$As$_2$ is an antiferromagnetic semimetal, that can host non-trivial topological properties, depending upon its magnetic state and excitations. Here, we report the synthesis and characterization of Eu(Cd$_{1-x}$Ag$_x$)$_2$As$_2$ and Eu$_{1-y}$Na$_y$Cd$_2$As$_2$, and study the evolution and nature of magnetic order with doping. Temperature-substitution phase diagrams are constructed from the electrical resistance and magnetic susceptibility data. We observe a splitting of the magnetic transition into two different transitions, and the gradual increase in one of the transition temperatures with Ag- and Na-substitution. The other transition remains more or less independent of doping. We further show that a magnetic state with a net ferromagnetic moment is stabilized by both Ag and Na doping and this can be explained by considering the changes in band filling due to substitution as suggested by density functional theory (DFT) calculations. We thus show that chemical substitution and the subsequent changes in band filling could be a pathway to tune the magnetic ground state and to stabilize a ferromagnetic phase in EuCd$_2$As$_2$.

cond-mat.mtrl-sci↗

arXiv2022

TSAA: A Two-Stage Anchor Assignment Method towards Anchor Drift in Crowded Object Detection

Among current anchor-based detectors, a positive anchor box will be intuitively assigned to the object that overlaps it the most. The assigned label to each anchor will directly determine the optimization direction of the corresponding prediction box, including the direction of box regression and category prediction. In our practice of crowded object detection, however, the results show that a positive anchor does not always regress toward the object that overlaps it the most when multiple objects overlap. We name it anchor drift. The anchor drift reflects that the anchor-object matching relation, which is determined by the degree of overlap between anchors and objects, is not always optimal. Conflicts between the fixed matching relation and learned experience in the past training process may cause ambiguous predictions and thus raise the false-positive rate. In this paper, a simple but efficient adaptive two-stage anchor assignment (TSAA) method is proposed. It utilizes the final prediction boxes rather than the fixed anchors to calculate the overlap degree with objects to determine which object to regress for each anchor. The participation of the prediction box makes the anchor-object assignment mechanism adaptive. Extensive experiments are conducted on three classic detectors RetinaNet, Faster-RCNN and YOLOv3 on CrowdHuman and COCO to evaluate the effectiveness of TSAA. The results show that TSAA can significantly improve the detectors' performance without additional computational costs or network structure changes.

cs.CV↗