arXiv subjects

Lina Liu

Publications and source records attributed to Lina Liu.

At least 19 recordsLinked to original sources

arXiv2026

Variable Charge State, Magnetic Excitations, and Kondo Effect of Sm/g/Ir(111)

Using low-temperature scanning tunneling microscopy we investigate the charge state, magnetic excitations, and Kondo features of individual Sm adatoms on graphene/Ir(111). Depending on the number and distance of their neighbors, Sm atoms can be in two discrete charge states. At certain distances, a reversible transition between these two states is induced by the electric field of the STM tip leading to concentric charge rings in the images. Only atoms in one of the two charge states exhibit magnetic excitations in d$I$/d$V$ spectra. Two such excitations are located at 35~meV and 54~meV and related to transitions from the $J = 1/2$ ground state doublet to the first crystal field split $J = 3/2$ multiplet. Together with the intra-atomic exchange excitations at higher energy, these observations indicate that Sm transfers one $6s$ electron to the substrate while it retains its gas-phase $4f$ filling. New for lanthanide adatoms, we observe a Kondo resonance. The Zeeman splitting of the Kondo peak reveals that Sm retains its large gas-phase $g$-factor. Comparison of d$I$/d$V$ spectra to cotunneling theory yields the crystal field acting on the $4f$ shell and, consequently, on the $J = 3/2$ quadruplet, confirms Sm$^+$ as the ground state, and identifies Sm$^{2+}$ as being energetically close, thereby rationalizing our observation of variable charge states.

cond-mat.mes-hall↗

arXiv2026

A Colorful Way to Park: An Introduction to Exact $k$-Typed Parking Functions

Parking functions are tuples that describe the parking of $M$ cars on a street with $M$ parking spots. In this paper, we define exact $k$-typed parking functions ($k$-TPFs) to be a variant of classical parking functions. We then establish that every exact $k$-TPF $\alpha$ of length $M$, corresponds to a unique parking configuration $C$. We observe that the collection of all exact $k$-TPFs which result in the same configuration form a disjoint subset of all exact $k$-TPFs. Lastly, we conclude by showing how parking permutations of an exact $k$-TPF can be related to other combinatorial objects.

math.CO↗

arXiv2026

Riemannian Momentum Tracking: Distributed Optimization with Momentum on Compact Submanifolds

Gradient descent with momentum has been widely applied in various signal processing and machine learning tasks, demonstrating a notable empirical advantage over standard gradient descent. However, momentum-based distributed Riemannian algorithms have been only scarcely explored. In this paper, we propose Riemannian Momentum Tracking (RMTracking), a decentralized optimization algorithm with momentum over a compact submanifold. Given the non-convex nature of compact submanifolds, the objective function, composed of a finite sum of smooth (possibly non-convex) local functions, is minimized across agents in an undirected and connected network graph. With a constant step-size, we establish an $\mathcal{O}(\frac{1-\beta}{K})$ convergence rate of the Riemannian gradient average for any momentum weight $\beta \in [0,1)$. Especially, RMTracking can achieve a convergence rate of $\mathcal{O}(\frac{1-\beta}{K})$ to a stationary point when the step-size is sufficiently small. To best of our knowledge, RMTracking is the first decentralized algorithm to achieve exact convergence that is $\frac{1}{1-\beta}$ times faster than other related algorithms. Finally, we verify these theoretical claims through numerical experiments on eigenvalue problems.

math.OC↗

arXiv2025

Invariant and Coinvariant Morse Homologies for Orbifolds

In this note, we construct invariant and coinvariant Morse chain complexes with integer coefficients for any compact effective orbifold. We show that the homologies of these two chain complexes are invariants of the orbifold. We conjecture that the homology of the coinvariant chain complex computes the singular homology of the underlying topological space with $\mathbb{Z}$-coefficients, thereby refining the construction by Cho-Hong, which recovers the homology over $\mathbb{Q}$. In contrast, the homology of the invariant Morse chain complex is sensitive to the orbifold structure.

math.GT↗

arXiv2025

Gaussian-LIC2: LiDAR-Inertial-Camera Gaussian Splatting SLAM

This paper presents the first photo-realistic LiDAR-Inertial-Camera Gaussian Splatting SLAM system that simultaneously addresses visual quality, geometric accuracy, and real-time performance. The proposed method performs robust and accurate pose estimation within a continuous-time trajectory optimization framework, while incrementally reconstructing a 3D Gaussian map using camera and LiDAR data, all in real time. The resulting map enables high-quality, real-time novel view rendering of both RGB images and depth maps. To effectively address under-reconstruction in regions not covered by the LiDAR, we employ a lightweight zero-shot depth model that synergistically combines RGB appearance cues with sparse LiDAR measurements to generate dense depth maps. The depth completion enables reliable Gaussian initialization in LiDAR-blind areas, significantly improving system applicability for sparse LiDAR sensors. To enhance geometric accuracy, we use sparse but precise LiDAR depths to supervise Gaussian map optimization and accelerate it with carefully designed CUDA-accelerated strategies. Furthermore, we explore how the incrementally reconstructed Gaussian map can improve the robustness of odometry. By tightly incorporating photometric constraints from the Gaussian map into the continuous-time factor graph optimization, we demonstrate improved pose estimation under LiDAR degradation scenarios. We also showcase downstream applications via extending our elaborate system, including video frame interpolation and fast 3D mesh extraction. To support rigorous evaluation, we construct a dedicated LiDAR-Inertial-Camera dataset featuring ground-truth poses, depth maps, and extrapolated trajectories for assessing out-of-sequence novel view synthesis. Both the dataset and code will be made publicly available on project page https://xingxingzuo.github.io/gaussian_lic2.

cs.RO↗

arXiv2025

Decentralized Optimization on Compact Submanifolds by Quantized Riemannian Gradient Tracking

This paper considers the problem of decentralized optimization on compact submanifolds, where a finite sum of smooth (possibly non-convex) local functions is minimized by $n$ agents forming an undirected and connected graph. However, the efficiency of distributed optimization is often hindered by communication bottlenecks. To mitigate this, we propose the Quantized Riemannian Gradient Tracking (Q-RGT) algorithm, where agents update their local variables using quantized gradients. The introduction of quantization noise allows our algorithm to bypass the constraints of the accurate Riemannian projection operator (such as retraction), further improving iterative efficiency. To the best of our knowledge, this is the first algorithm to achieve an $\mathcal{O}(1/K)$ convergence rate in the presence of quantization, matching the convergence rate of methods without quantization. Additionally, we explicitly derive lower bounds on decentralized consensus associated with a function of quantization levels. Numerical experiments demonstrate that Q-RGT performs comparably to non-quantized methods while reducing communication bottlenecks and computational overhead.

math.OC↗

arXiv2025

CMD: Constraining Multimodal Distribution for Domain Adaptation in Stereo Matching

Recently, learning-based stereo matching methods have achieved great improvement in public benchmarks, where soft argmin and smooth L1 loss play a core contribution to their success. However, in unsupervised domain adaptation scenarios, we observe that these two operations often yield multimodal disparity probability distributions in target domains, resulting in degraded generalization. In this paper, we propose a novel approach, Constrain Multi-modal Distribution (CMD), to address this issue. Specifically, we introduce \textit{uncertainty-regularized minimization} and \textit{anisotropic soft argmin} to encourage the network to produce predominantly unimodal disparity distributions in the target domain, thereby improving prediction accuracy. Experimentally, we apply the proposed method to multiple representative stereo-matching networks and conduct domain adaptation from synthetic data to unlabeled real-world scenes. Results consistently demonstrate improved generalization in both top-performing and domain-adaptable stereo-matching models. The code for CMD will be available at: \href{https://github.com/gallenszl/CMD}{https://github.com/gallenszl/CMD}.

cs.CV↗

arXiv2025

MAG: Multi-Modal Aligned Autoregressive Co-Speech Gesture Generation without Vector Quantization

This work focuses on full-body co-speech gesture generation. Existing methods typically employ an autoregressive model accompanied by vector-quantized tokens for gesture generation, which results in information loss and compromises the realism of the generated gestures. To address this, inspired by the natural continuity of real-world human motion, we propose MAG, a novel multi-modal aligned framework for high-quality and diverse co-speech gesture synthesis without relying on discrete tokenization. Specifically, (1) we introduce a motion-text-audio-aligned variational autoencoder (MTA-VAE), which leverages pre-trained WavCaps' text and audio embeddings to enhance both semantic and rhythmic alignment with motion, ultimately producing more realistic gestures. (2) Building on this, we propose a multimodal masked autoregressive model (MMAG) that enables autoregressive modeling in continuous motion embeddings through diffusion without vector quantization. To further ensure multi-modal consistency, MMAG incorporates a hybrid granularity audio-text fusion block, which serves as conditioning for diffusion process. Extensive experiments on two benchmark datasets demonstrate that MAG achieves stateof-the-art performance both quantitatively and qualitatively, producing highly realistic and diverse co-speech gestures.The code will be released to facilitate future research.

cs.GR↗

arXiv2025

Dual-branch Graph Feature Learning for NLOS Imaging

The domain of non-line-of-sight (NLOS) imaging is advancing rapidly, offering the capability to reveal occluded scenes that are not directly visible. However, contemporary NLOS systems face several significant challenges: (1) The computational and storage requirements are profound due to the inherent three-dimensional grid data structure, which restricts practical application. (2) The simultaneous reconstruction of albedo and depth information requires a delicate balance using hyperparameters in the loss function, rendering the concurrent reconstruction of texture and depth information difficult. This paper introduces the innovative methodology, \xnet, which integrates an albedo-focused reconstruction branch dedicated to albedo information recovery and a depth-focused reconstruction branch that extracts geometrical structure, to overcome these obstacles. The dual-branch framework segregates content delivery to the respective reconstructions, thereby enhancing the quality of the retrieved data. To our knowledge, we are the first to employ the GNN as a fundamental component to transform dense NLOS grid data into sparse structural features for efficient reconstruction. Comprehensive experiments demonstrate that our method attains the highest level of performance among existing methods across synthetic and real data. https://github.com/Nicholassu/DG-NLOS.

eess.IV↗

arXiv2024

Equivariant Morse Homology for Reflection Actions via Broken Trajectories

We consider a finite group $G$ acting on a manifold $M$. For any equivariant Morse function, which is a generic condition, there does not always exist an equivariant metric $g$ on $M$ such that the pair $(f,g)$ is Morse-Smale. Here, the pair $(f,g)$ is called Morse-Smale if the descending and ascending manifolds intersect transversely. The best possible metrics $g$ are those that make the pair $(f,g)$ stably Morse-Smale. A diffeomorphism $\phi: M \to M$ is a reflection, if $\phi^2 = \operatorname{id}$ and the fixed point set of $\phi$ forms a codimension-one submanifold (with $M \setminus M^{\operatorname{fix}}$ not necessarily disconnected). In this note, we focus on the special case where the group $G = \{\operatorname{id}, \phi\}$. We show that the condition of being stably Morse-Smale is generic for metrics $g$. Given a stably Morse-Smale pair, we introduce a canonical equivariant Thom-Smale-Witten complex by counting certain broken trajectories. This has applications to the case when we have a manifold with boundary and when the Morse function has critical points on the boundary. We provide an alternative definition of the Thom-Smale-Witten complexes, which are quasi-isomorphic to those defined by Kronheimer and Mrowka. We also explore the case when $G$ is generated by multiple reflections. As an example, we compute the Thom-Smale-Witten complex of an upright higher-genus surface by counting broken trajectories.

math.GT↗

arXiv2024

Analog Beamforming Aided by Full-Dimension One-Bit Chains

This paper investigates the design of analog beamforming at the receiver in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems, aided by full digital chains featuring 1-bit ADCs. We advocate utilizing these full digital chains to facilitate rapid channel estimation and beam acquisition for subsequent communication, even without prior knowledge of the training pilots. To balance energy consumption and implementation costs, we opt for 1-bit ADCs. We propose a two-stage maximum likelihood (ML)-based algorithm to estimate angles of arrival (AoAs) and facilitate the design of analog beamforming to maximize the received signal-to-noise ratio (SNR). We validate our proposed beamforming schemes in narrowband coherent channels through synthetic testing and in wideband coherent channels, particularly under the 3GPP clustered-delay-line (CDL)-C channel model.

eess.SP↗

arXiv2024

A Metastable Pentagonal 2D Material Synthesized by Symmetry-Driven Epitaxy

Most two-dimensional (2D) materials experimentally studied so far have hexagons as their building blocks. Only a few exceptions, such as PdSe2, are lower in energy in pentagonal phases and exhibit pentagons as building blocks. While theory has predicted a large number of pentagonal 2D materials, many of them are metastable and their experimental realization is difficult. Here we report the successful synthesis of a metastable pentagonal 2D material, the monolayer pentagonal PdTe2, by symmetry-driven epitaxy. Scanning tunneling microscopy and complementary spectroscopy measurements are used to characterize the monolayer pentagonal PdTe2, which demonstrates well-ordered low-symmetry atomic arrangements and is stabilized by lattice matching with the underlying Pd(100) substrate. Theoretical calculations, along with angle-resolved photoemission spectroscopy, reveal monolayer pentagonal PdTe2 is a semiconductor with an indirect bandgap of 1.05 eV. Our work opens an avenue for the synthesis of pentagon-based 2D materials and gives opportunities to explore their applications such as multifunctional nanoelectronics.

cond-mat.mtrl-sci↗

arXiv2024

Gaussian-LIC: Real-Time Photo-Realistic SLAM with Gaussian Splatting and LiDAR-Inertial-Camera Fusion

In this paper, we present a real-time photo-realistic SLAM method based on marrying Gaussian Splatting with LiDAR-Inertial-Camera SLAM. Most existing radiance-field-based SLAM systems mainly focus on bounded indoor environments, equipped with RGB-D or RGB sensors. However, they are prone to decline when expanding to unbounded scenes or encountering adverse conditions, such as violent motions and changing illumination. In contrast, oriented to general scenarios, our approach additionally tightly fuses LiDAR, IMU, and camera for robust pose estimation and photo-realistic online mapping. To compensate for regions unobserved by the LiDAR, we propose to integrate both the triangulated visual points from images and LiDAR points for initializing 3D Gaussians. In addition, the modeling of the sky and varying camera exposure have been realized for high-quality rendering. Notably, we implement our system purely with C++ and CUDA, and meticulously design a series of strategies to accelerate the online optimization of the Gaussian-based scene representation. Extensive experiments demonstrate that our method outperforms its counterparts while maintaining real-time capability. Impressively, regarding photo-realistic mapping, our method with our estimated poses even surpasses all the compared approaches that utilize privileged ground-truth poses for mapping. Our code has been released on https://github.com/APRIL-ZJU/Gaussian-LIC.

cs.RO↗

arXiv2024

GaussianGrasper: 3D Language Gaussian Splatting for Open-vocabulary Robotic Grasping

Constructing a 3D scene capable of accommodating open-ended language queries, is a pivotal pursuit, particularly within the domain of robotics. Such technology facilitates robots in executing object manipulations based on human language directives. To tackle this challenge, some research efforts have been dedicated to the development of language-embedded implicit fields. However, implicit fields (e.g. NeRF) encounter limitations due to the necessity of processing a large number of input views for reconstruction, coupled with their inherent inefficiencies in inference. Thus, we present the GaussianGrasper, which utilizes 3D Gaussian Splatting to explicitly represent the scene as a collection of Gaussian primitives. Our approach takes a limited set of RGB-D views and employs a tile-based splatting technique to create a feature field. In particular, we propose an Efficient Feature Distillation (EFD) module that employs contrastive learning to efficiently and accurately distill language embeddings derived from foundational models. With the reconstructed geometry of the Gaussian field, our method enables the pre-trained grasping model to generate collision-free grasp pose candidates. Furthermore, we propose a normal-guided grasp module to select the best grasp pose. Through comprehensive real-world experiments, we demonstrate that GaussianGrasper enables robots to accurately query and grasp objects with language instructions, providing a new solution for language-guided manipulation tasks. Data and codes can be available at https://github.com/MrSecant/GaussianGrasper.

cs.RO↗

arXiv2024

Semi-Supervised Learning for Visual Bird's Eye View Semantic Segmentation

Visual bird's eye view (BEV) semantic segmentation helps autonomous vehicles understand the surrounding environment only from images, including static elements (e.g., roads) and dynamic elements (e.g., vehicles, pedestrians). However, the high cost of annotation procedures of full-supervised methods limits the capability of the visual BEV semantic segmentation, which usually needs HD maps, 3D object bounding boxes, and camera extrinsic matrixes. In this paper, we present a novel semi-supervised framework for visual BEV semantic segmentation to boost performance by exploiting unlabeled images during the training. A consistency loss that makes full use of unlabeled data is then proposed to constrain the model on not only semantic prediction but also the BEV feature. Furthermore, we propose a novel and effective data augmentation method named conjoint rotation which reasonably augments the dataset while maintaining the geometric relationship between the front-view images and the BEV semantic segmentation. Extensive experiments on the nuScenes and Argoverse datasets show that our semi-supervised framework can effectively improve prediction accuracy. To the best of our knowledge, this is the first work that explores improving visual BEV semantic segmentation performance using unlabeled data. The code is available at https://github.com/Junyu-Z/Semi-BEVseg

cs.CV↗

arXiv2024

Self-supervised Event-based Monocular Depth Estimation using Cross-modal Consistency

An event camera is a novel vision sensor that can capture per-pixel brightness changes and output a stream of asynchronous ``events''. It has advantages over conventional cameras in those scenes with high-speed motions and challenging lighting conditions because of the high temporal resolution, high dynamic range, low bandwidth, low power consumption, and no motion blur. Therefore, several supervised monocular depth estimation from events is proposed to address scenes difficult for conventional cameras. However, depth annotation is costly and time-consuming. In this paper, to lower the annotation cost, we propose a self-supervised event-based monocular depth estimation framework named EMoDepth. EMoDepth constrains the training process using the cross-modal consistency from intensity frames that are aligned with events in the pixel coordinate. Moreover, in inference, only events are used for monocular depth prediction. Additionally, we design a multi-scale skip-connection architecture to effectively fuse features for depth estimation while maintaining high inference speed. Experiments on MVSEC and DSEC datasets demonstrate that our contributions are effective and that the accuracy can outperform existing supervised event-based and unsupervised frame-based methods.

cs.CV↗

arXiv2023

Towards Semi-Markov Model-based Dependability Evaluation of VM-based Multi-Domain Service Function Chain

In NFV networks, service functions (SFs) can be deployed on virtual machines (VMs) across multiple domains and then form a service function chain (MSFC) for end-to-end network service provision. However, any software component in a VM-based MSFC must experience software aging issue after a long period of operation. This paper quantitatively investigates the capability of proactive rejuvenation techniques in reducing the damage of software aging on a VM-based MSFC. We develop a semi-Markov model to capture the behaviors of SFs, VMs and virtual machine monitors (VMMs) from software aging to recovery under the condition that failure times and recovery times follow general distributions. We derive the formulas for calculating the steady-state availability and reliability of the VM-based MSFC composed of multiple SFs running on VMs hosted by VMMs. Sensitivity analysis is also conducted to identify potential dependability bottlenecks.

cs.SE↗

arXiv2023

Chiral Quantum well Rashba splitting in Sb monolayer on Au(111)

We present a comprehensive investigation into the atomic and electronic structures of a single-layer Sb(110) rhombohedral crystal formed on an Au(111) substrate. Low-energy electron diffraction (LEED) and scanning tunneling microscopy (STM) reveal a pure two-dimensional (2D) Sb stripe structure, composed of a pair of Sb(110) unit cells located in a chiral configuration with mirror symmetry breaking perpendicular to the direction of the bright stripe. Based on angle-resolved photoemission spectroscopy (ARPES) measurements and Sb-weighted band structure from density functional theory calculations, we report the unambiguous determination of Rashba spin-orbit coupled bands from the 2D Sb film, exhibiting a chiral symmetry in the electronic structure with the crossing points located at the ${\Gamma}$ point and the X point, respectively. Moreover, From dI/dV spectra and density of states (DOS) calculations, the quantum well (QW) Rashba-type states induced by the in-plane mirror symmetry breaking in the Sb stripe structure have been identified. Orbital decomposition of the projected band structure reveals that hybridization between Sb py states and Au states modifies the spin splitting of the QW states, attributed to the intrinsic strong SOC of Au states introduced into the QW states.

cond-mat.mtrl-sci↗