arXiv subjects

Liang Liu

Publications and source records attributed to Liang Liu.

At least 127 records · Page 7Linked to original sources

arXiv2024

Tunable topological edge states in black phosphorus-like Bi(110)

We have investigated the structures and electronic properties of ultra-thin Bi(110) films grown on an s-wave superconductor substrate using low-temperature scanning tunneling microscopy and spectroscopy. Remarkably, our experimental results validate the theoretical predictions that the manipulation of Bi(110) surface atom buckling can control the topological phase transition. Notably, we have observed robust unreconstructed edge states at the edges of both 3-bilayer (BL) and 4-BL Bi(110) films, with the 4-BL film displaying stronger edge state intensity and a smaller degree of atomic buckling. First-principle calculations further substantiate these findings, demonstrating a gradual reduction in buckling as the film thickness increases, with average height differences between two Bi atoms of approximately 0.19 Å, 0.10 Å, 0.05 Å, and 0.00 Å for the 1-BL, 2-BL, 3-BL, and 4-BL Bi(110) films, respectively. When Bi films are larger than 2 layers, the system changes from a trivial to a non-trivial phase. This research sets the stage for the controlled realization of topological superconductors through the superconducting proximity effect, providing a significant platform for investigating Majorana zero modes and fabricating quantum devices.

cond-mat.mtrl-sci↗

arXiv2024

High-temperature ferromagnetism and ferroelasticity in ultraflexible atomically thin square-shaped lattices

The coexistence of high-temperature intrinsic ferromagnetic ordering, large magnetic anisotropy, along with novel mechanical properties such as ferroelasticity and flexibility, in experimental feasible two-dimensional (2D) crystals is greatly appealing for nanoscale spintronics. However, the progress in identifying such materials is limited. Here, by first-principles calculations, we report the findings of an extraordinary combination of the above qualities for the first time in a new 2D exfoliated FeSi nanosheet in the P4/nmm space group. Due to the strong anion-mediated superexchange interaction, the monolayer FeSi (ML-FeSi) exhibits a Curie temperature Tc as high as 830 K, surpassing the current experimental record (344 K for ML-Cr3Te4). Furthermore, including FeSi, such isostructural lattices all demonstrate exceptional softness, as evidenced by their ultra-low in-plane stiffness. Remarkably, the transition metal atom and square-shaped crystal form work together to give this family of ML materials unique properties that can transition from Ising-like 2D ferromagnets in FeSi, MnP, MnAs, CrP, FeI, and VAs to 2D-XY ones in CrAs, VP, and multiferroic MnGe and TiTe. Overall, our work highlights such 2D lattices as promising candidates in emerging multifunctional device applications and nontrivial topological spintronics.

cond-mat.mes-hall↗

arXiv2024

A Visual Cooperative Localization Method for Airborne Magnetic Surveying Based on a Manifold Sensor Fusion Algorithm Using Lie Groups

Recent advancements in UAV technology have spurred interest in developing multi-UAV aerial surveying systems for use in confined environments where GNSS signals are blocked or jammed. This paper focuses airborne magnetic surveying scenarios. To obtain clean magnetic measurements reflecting the Earth's magnetic field, the magnetic sensor must be isolated from other electronic devices, creating a significant localization challenge. We propose a visual cooperative localization solution. The solution incorporates a visual processing module and an improved manifold-based sensor fusion algorithm, delivering reliable and accurate positioning information. Real flight experiments validate the approach, demonstrating single-axis centimeter-level accuracy and decimeter-level overall 3D positioning accuracy.

eess.SP↗

arXiv2024

Understanding Reasoning in Chain-of-Thought from the Hopfieldian View

Large Language Models have demonstrated remarkable abilities across various tasks, with Chain-of-Thought (CoT) prompting emerging as a key technique to enhance reasoning capabilities. However, existing research primarily focuses on improving performance, lacking a comprehensive framework to explain and understand the fundamental factors behind CoT's success. To bridge this gap, we introduce a novel perspective grounded in the Hopfieldian view of cognition in cognitive neuroscience. We establish a connection between CoT reasoning and key cognitive elements such as stimuli, actions, neural populations, and representation spaces. From our view, we can understand the reasoning process as the movement between these representation spaces. Building on this insight, we develop a method for localizing reasoning errors in the response of CoTs. Moreover, we propose the Representation-of-Thought (RoT) framework, which leverages the robustness of low-dimensional representation spaces to enhance the robustness of the reasoning process in CoTs. Experimental results demonstrate that RoT improves the robustness and interpretability of CoT reasoning while offering fine-grained control over the reasoning process.

cs.AI↗

arXiv2024

Controllable magnetic anisotropy and ferroelasticity in superconducting FeSe monolayer with surface fluorine adsorption

Controllable magnetization in atomically thin two-dimensional magnets is highly desirable for developing spintronics. For FeSe monolayer, its magnetic ground state is not yet fully understood, and the potential in constructing high-speed and advanced devices remains unknown. Using density functional theory calculations, we confirm the spin ordering of monolayer FeSe to be dimer texture. With Fluorine (F) adsorption (F/FeSe), the system exhibits a coverage dependent magnetic anisotropy and multiferroicity which can be attributable to the Jahn-Teller effect, being the benefit to potential spintronic applications. Intriguingly, an inherent coupling between magnetism and ferroelasticity in the most energetically favorable F/FeSe system is proposed. Our study thus not only provides a promising way to control the spintronic properties and construct multiferroics, but also renders F/FeSe an ideal platform for magnetism studies and practical high-performance multifunctional devices.

cond-mat.mtrl-sci↗

arXiv2024

Bayesian estimation of transmission networks for infectious diseases

Reconstructing transmission networks is essential for identifying key factors like superspreaders and high-risk locations, which are critical for developing effective pandemic prevention strategies. In this study, we developed a Bayesian framework that integrates genomic and temporal data to reconstruct transmission networks for infectious diseases. The Bayesian transmission model accounts for the latent period and differentiates between symptom onset and actual infection time, enhancing the accuracy of transmission dynamics and epidemiological models. Additionally, the model allows for the transmission of multiple pathogen lineages, reflecting the complexity of real-world transmission events more accurately than models that assume a single lineage transmission. Simulation results show that the Bayesian model reliably estimates both the model parameters and the transmission network. Moreover, hypothesis testing effectively identifies direct transmission events. This approach highlights the crucial role of genetic data in reconstructing transmission networks and understanding the origins and transmission dynamics of infectious diseases.

q-bio.QM↗

arXiv2024

Joint Transmission and Compression Optimization for Networked Sensing with Limited-Capacity Fronthaul Links

This paper considers networked sensing in cellular network, where multiple base stations (BSs) first compress their received echo signals from multiple targets and then forward the quantized signals to the central unit (CU) via limited-capacity fronthaul links, such that the CU can leverage all useful echo signals to perform high-resolution localization. Under this setup, we manage to characterize the posterior Cramer-Rao Bound (PCRB) for localizing all the targets with random positions as a function of the transmit covariance matrix and the compression noise covariance matrix of each BS. Then, a PCRB minimization problem subject to the transmit power constraints and the fronthaul capacity constraints is formulated to jointly design the BSs' transmission and compression strategies. We propose an efficient algorithm to solve this problem based on the alternating optimization technique. Specifically, it is shown that when either the transmit covariance matrices or the compression noise covariance matrices are fixed, the successive convex approximation (SCA) technique can be leveraged to optimize the other type of covariance matrices locally optimally. Moreover, we also propose a novel estimate-then-beamform-then-compress strategy for the massive receive antenna scenario, under which each BS first estimates targets' angle-of-arrivals (AOAs) locally, then beamforms its high-dimension received signals into low-dimension ones based on the estimated AOAs, and last compresses the beamformed signals for fronthaul transmission. An efficient beamforming and compression design method is devised under this strategy. Numerical results are provided to verify the effectiveness of our proposed algorithms.

eess.SP↗

arXiv2024

Joint LOS Identification and Data Association for 6G-Enabled Networked Device-Free Sensing

This paper considers networked device-free sensing in an orthogonal frequency division multiplexing (OFDM) cellular system with multipath environment, where the passive targets reflect the downlink signals to the base stations (BSs) via non-line-of-sight (NLOS) paths and/or line-of-sight (LOS) paths, and the BSs share the sensing information extracted from their received echoes to jointly localize the targets. A two-phase localization protocol is considered. In Phase I, we design an efficient method that is able to accurately estimate the range of any path from a transmitting BS to a receiving BS via a target, even if the transmitting and receiving BSs are separated and not perfectly synchronized. In Phase II, we propose an effective method that is able to jointly identify the ranges of the LOS paths between the targets and the BSs as well as associate the ranges of LOS paths with the right targets, such that the number and the locations of the targets both can be accurately estimated. Numerical results verify that our proposed two-phase protocol can achieve high performance of networked sensing in the multipath environment.

eess.SP↗

arXiv2024

DQFormer: Towards Unified LiDAR Panoptic Segmentation with Decoupled Queries

LiDAR panoptic segmentation, which jointly performs instance and semantic segmentation for things and stuff classes, plays a fundamental role in LiDAR perception tasks. While most existing methods explicitly separate these two segmentation tasks and utilize different branches (i.e., semantic and instance branches), some recent methods have embraced the query-based paradigm to unify LiDAR panoptic segmentation. However, the distinct spatial distribution and inherent characteristics of objects(things) and their surroundings(stuff) in 3D scenes lead to challenges, including the mutual competition of things/stuff and the ambiguity of classification/segmentation. In this paper, we propose decoupling things/stuff queries according to their intrinsic properties for individual decoding and disentangling classification/segmentation to mitigate ambiguity. To this end, we propose a novel framework dubbed DQFormer to implement semantic and instance segmentation in a unified workflow. Specifically, we design a decoupled query generator to propose informative queries with semantics by localizing things/stuff positions and fusing multi-level BEV embeddings. Moreover, a query-oriented mask decoder is introduced to decode corresponding segmentation masks by performing masked cross-attention between queries and mask embeddings. Finally, the decoded masks are combined with the semantics of the queries to produce panoptic results. Extensive experiments on nuScenes and SemanticKITTI datasets demonstrate the superiority of our DQFormer framework.

cs.CV↗

arXiv2024

Atomic-Scale Imaging of Fractional Spinon Quasiparticles in Open-Shell Triangulene Spin-$\frac{1}{2}$ Chains

The emergence of spinon quasiparticles, which carry spin but lack charge, is a hallmark of collective quantum phenomena in low-dimensional quantum spin systems. While the existence of spinons has been demonstrated through scattering spectroscopy in ensemble samples, real-space imaging of these quasiparticles within individual spin chains has remained elusive. In this study, we construct individual Heisenberg antiferromagnetic spin-$\frac{1}{2}$ chains using open-shell [2]triangulene molecules as building blocks. Each [2]triangulene unit, owing to its sublattice imbalance, hosts a net spin-$\frac{1}{2}$ in accordance with Lieb's theorem, and these spins are antiferromagnetically coupled within covalent chains with a coupling strength of $J = 45$ meV. Through scanning tunneling microscopy and spectroscopy, we probe the spin states, excitation gaps, and their spatial excitation weights within covalent spin chains of varying lengths with atomic precision. Our investigation reveals that the excitation gap decreases as the chain length increases, extrapolating to zero for long chains, consistent with Haldane's gapless prediction. Moreover, inelastic tunneling spectroscopy reveals an m-shaped energy dispersion characteristic of confined spinon quasiparticles in a one-dimensional quantum box. These findings establish a promising strategy for exploring the unique properties of excitation quasiparticles and their broad implications for quantum information.

cond-mat.mtrl-sci↗

arXiv2024

Hybrid Near-Far Field Channel Estimation for Holographic MIMO Communications

Holographic MIMO communications, enabled by large-scale antenna arrays with quasi-continuous apertures, is a potential technology for spectrum efficiency improvement. However, the increased antenna aperture size extends the range of the Fresnel region, leading to a hybrid near-far field communication mode. The users and scatterers randomly lie in near-field and far-field zones, and thus, conventional far-field-only and near-field-only channel estimation methods may not work. To tackle this challenge, we demonstrate the existence of the power diffusion (PD) effect, which leads to a mismatch between the hybrid-field channel and existing channel estimation methods. Specifically, in far-field and near-field transform domains, the power gain of one channel path may diffuse to other positions, thus generating fake paths. This renders the conventional techniques unable to detect those real paths. We propose a PD-aware orthogonal matching pursuit algorithm to eliminate the influence of the PD effect by identifying the PD range within which paths diffuse to other positions. PD-OMP fits a general case without prior knowledge of near-field and far-field path numbers and the user's location. The computational complexity of PD-OMP and the Cramer-Rao Lower Bound for the sparse-signal-recovery-based channel estimation are also derived. Simulation results show that PD-OMP outperforms state-of-the-art hybrid-field channel estimation methods.

cs.IT↗

arXiv2024

Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection

In the field of multi-class anomaly detection, reconstruction-based methods derived from single-class anomaly detection face the well-known challenge of "learning shortcuts", wherein the model fails to learn the patterns of normal samples as it should, opting instead for shortcuts such as identity mapping or artificial noise elimination. Consequently, the model becomes unable to reconstruct genuine anomalies as normal instances, resulting in a failure of anomaly detection. To counter this issue, we present a novel unified feature reconstruction-based anomaly detection framework termed RLR (Reconstruct features from a Learnable Reference representation). Unlike previous methods, RLR utilizes learnable reference representations to compel the model to learn normal feature patterns explicitly, thereby prevents the model from succumbing to the "learning shortcuts" issue. Additionally, RLR incorporates locality constraints into the learnable reference to facilitate more effective normal pattern capture and utilizes a masked learnable key attention mechanism to enhance robustness. Evaluation of RLR on the 15-category MVTec-AD dataset and the 12-category VisA dataset shows superior performance compared to state-of-the-art methods under the unified setting. The code of RLR will be publicly available.

cs.CV↗

arXiv2024

LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Views

Fine-tuning is becoming widely used for leveraging the power of pre-trained foundation models in new downstream tasks. While there are many successes of fine-tuning on various tasks, recent studies have observed challenges in the generalization of fine-tuned models to unseen distributions (i.e., out-of-distribution; OOD). To improve OOD generalization, some previous studies identify the limitations of fine-tuning data and regulate fine-tuning to preserve the general representation learned from pre-training data. However, potential limitations in the pre-training data and models are often ignored. In this paper, we contend that overly relying on the pre-trained representation may hinder fine-tuning from learning essential representations for downstream tasks and thus hurt its OOD generalization. It can be especially catastrophic when new tasks are from different (sub)domains compared to pre-training data. To address the issues in both pre-training and fine-tuning data, we propose a novel generalizable fine-tuning method LEVI (Layer-wise Ensemble of different VIews), where the pre-trained model is adaptively ensembled layer-wise with a small task-specific model, while preserving its efficiencies. By combining two complementing models, LEVI effectively suppresses problematic features in both the fine-tuning data and pre-trained model and preserves useful features for new tasks. Broad experiments with large language and vision models show that LEVI greatly improves fine-tuning generalization via emphasizing different views from fine-tuning data and pre-trained features.

cs.LG↗

arXiv2024

A Hopfieldian View-based Interpretation for Chain-of-Thought Reasoning

Chain-of-Thought (CoT) holds a significant place in augmenting the reasoning performance for large language models (LLMs). While some studies focus on improving CoT accuracy through methods like retrieval enhancement, yet a rigorous explanation for why CoT achieves such success remains unclear. In this paper, we analyze CoT methods under two different settings by asking the following questions: (1) For zero-shot CoT, why does prompting the model with "let's think step by step" significantly impact its outputs? (2) For few-shot CoT, why does providing examples before questioning the model could substantially improve its reasoning ability? To answer these questions, we conduct a top-down explainable analysis from the Hopfieldian view and propose a Read-and-Control approach for controlling the accuracy of CoT. Through extensive experiments on seven datasets for three different tasks, we demonstrate that our framework can decipher the inner workings of CoT, provide reasoning error localization, and control to come up with the correct reasoning path.

cs.CL↗

arXiv2024

Magnetic ground state and strain-mediated chiral-like atomic distortions behavior in two-dimensional rectangular spin lattice

Due to the large perpendicular magnetic anisotropy originating from spin-orbit coupling, magnetoelastic coupling is generally reported in easy-plane magnets with rectangular lattice where the easy magnetization is coupled with the lattice direction, while the acquisition of a novel coupling, beyond the easy-plane ferromagnets, in two-dimensional (2D) materials remains unknown. Here, by employing the density functional theory calculations, we demonstrate this feasibility with the discovery of long-range ferromagnetic ordering and elastic strain-mediated chiral-like atomic distortions behavior in a newly tetragonal As-Fe-As trilayer (t-FeAs monolayer), which shows large perpendicular magnetic anisotropy, robust ferromagnetic ordering, and in-plane ferroelasticity. We firstly point out that obvious limits exist when using the four magnetic configurations to determine the magnetic ground state for a rectangular spin lattice even if more exchange interaction parameters are included. A four-state mapping analysis is carefully examined for t-FeAs, where the calculated Curie temperature, Tc, is 435 K, which is higher than most reported 2D magnets, and can be further tuned by appropriate strains. Intriguingly, the chiral-like atomic distortion behavior of the Fe sub-layer is scanning tunneling microscopy characterizable, which can switch the magnetization axis between the out-of-plane and in-plane direction. This unusual finding of ferroelastic manipulation of both the atomic displacement and spin properties makes t-FeAs a promising candidate for future spintronics and also provides the possibility for exploring unprecedented coupling physics.

cond-mat.mtrl-sci↗

arXiv2024

The LuViRA Dataset: Synchronized Vision, Radio, and Audio Sensors for Indoor Localization

We present a synchronized multisensory dataset for accurate and robust indoor localization: the Lund University Vision, Radio, and Audio (LuViRA) Dataset. The dataset includes color images, corresponding depth maps, inertial measurement unit (IMU) readings, channel response between a 5G massive multiple-input and multiple-output (MIMO) testbed and user equipment, audio recorded by 12 microphones, and accurate six degrees of freedom (6DOF) pose ground truth of 0.5 mm. We synchronize these sensors to ensure that all data is recorded simultaneously. A camera, speaker, and transmit antenna are placed on top of a slowly moving service robot, and 89 trajectories are recorded. Each trajectory includes 20 to 50 seconds of recorded sensor data and ground truth labels. Data from different sensors can be used separately or jointly to perform localization tasks, and data from the motion capture (mocap) system is used to verify the results obtained by the localization algorithms. The main aim of this dataset is to enable research on sensor fusion with the most commonly used sensors for localization tasks. Moreover, the full dataset or some parts of it can also be used for other research areas such as channel estimation, image classification, etc. Our dataset is available at: https://github.com/ilaydayaman/LuViRA_Dataset

eess.SP↗

arXiv2024

LuViRA Dataset Validation and Discussion: Comparing Vision, Radio, and Audio Sensors for Indoor Localization

We present a unique comparative analysis, and evaluation of vision, radio, and audio based localization algorithms. We create the first baseline for the aforementioned sensors using the recently published Lund University Vision, Radio, and Audio (LuViRA) dataset, where all the sensors are synchronized and measured in the same environment. Some of the challenges of using each specific sensor for indoor localization tasks are highlighted. Each sensor is paired with a current state-of-the-art localization algorithm and evaluated for different aspects: localization accuracy, reliability and sensitivity to environment changes, calibration requirements, and potential system complexity. Specifically, the evaluation covers the ORB-SLAM3 algorithm for vision-based localization with an RGB-D camera, a machine-learning algorithm for radio-based localization with massive MIMO technology, and the SFS2 algorithm for audio-based localization with distributed microphones. The results can serve as a guideline and basis for further development of robust and high-precision multi-sensory localization systems, e.g., through sensor fusion, context, and environment-aware adaptation.

eess.SP↗

arXiv2024

UAV Trajectory Optimization for Sensing Exploiting Target Location Distribution Map

In this paper, we study the trajectory optimization of a cellular-connected unmanned aerial vehicle (UAV) which aims to sense the location of a target while maintaining satisfactory communication quality with the ground base stations (GBSs). In contrast to most existing works which assumed the target's location is known, we focus on a more challenging scenario where the exact location of the target to be sensed is unknown and random, while its distribution is known a priori and stored in a novel target location distribution map. Based on this map, the probability for the UAV to successfully sense the target can be expressed as a function of the UAV's trajectory. We aim to optimize the UAV's trajectory between two pre-determined locations to maximize the overall sensing probability during its flight, subject to a GBS-UAV communication quality constraint at each time instant and a maximum mission completion time constraint. Despite the non-convexity and NP-hardness of this problem, we devise three high-quality suboptimal solutions tailored for it with polynomial complexity. Numerical results show that our proposed designs outperform various benchmark schemes.

cs.IT↗