arXiv subjects

Tong Liu

Publications and source records attributed to Tong Liu.

At least 145 records · Page 8Linked to original sources

arXiv2025

Oblique rotational axis detection using elliptical optical vortex based on rotational Doppler effect

The rotational Doppler effect (RDE) of structured light carrying orbital angular momentum (OAM) has attracted widespread attention for applications in optical sensors and OAM spectrum detection. These studies, however, based on RDE, are mostly focused on the motion parameters of rotating objects; other equally important attitude characteristics, e.g., the tilt angle of the axis of rotation, have rarely been considered. We observed an interesting phenomenon in the experiments: the rotational Doppler spectral distribution varies with the ellipticity of the elliptical optical vortex (EOV) and the tilt angle between the rotational axis and optical axis, which inspired us to wonder if it is possible to detect oblique rotational axis or compensate the rotational Doppler broadening effect induced by oblique incidence by utilizing the EOV. Here, we reveal the RDE quantitative relationship with tilt angle and ellipticity for the first time and report a novel approach for tilt angle measurement. By employing a series of EOV with periodically varying ellipticity to illuminate a rotating object and analyze the time-frequency spectral distribution of scattered light associated with ellipticity and tilt angle, the tilt angle can be acquired accurately based on the specific relationship between the tilt angle and ellipticity of the EOV. Furthermore, the spectrum broadening effect arising from oblique incidence in the actual scenario may be addressed through our scheme. The method may find applications in industrial manufacturing and target attitude measurement, and our results provide new insights for obtaining more information about objects.

physics.optics↗

arXiv2024

Diffusion Actor-Critic with Entropy Regulator

Reinforcement learning (RL) has proven highly effective in addressing complex decision-making and control tasks. However, in most traditional RL algorithms, the policy is typically parameterized as a diagonal Gaussian distribution with learned mean and variance, which constrains their capability to acquire complex policies. In response to this problem, we propose an online RL algorithm termed diffusion actor-critic with entropy regulator (DACER). This algorithm conceptualizes the reverse process of the diffusion model as a novel policy function and leverages the capability of the diffusion model to fit multimodal distributions, thereby enhancing the representational capacity of the policy. Since the distribution of the diffusion policy lacks an analytical expression, its entropy cannot be determined analytically. To mitigate this, we propose a method to estimate the entropy of the diffusion policy utilizing Gaussian mixture model. Building on the estimated entropy, we can learn a parameter $α$ that modulates the degree of exploration and exploitation. Parameter $α$ will be employed to adaptively regulate the variance of the added noise, which is applied to the action output by the diffusion model. Experimental trials on MuJoCo benchmarks and a multimodal task demonstrate that the DACER algorithm achieves state-of-the-art (SOTA) performance in most MuJoCo control tasks while exhibiting a stronger representational capacity of the diffusion policy.

cs.LG↗

arXiv2024

Topological eigenvalues braiding and quantum state transfer near a third-order exceptional point

Non-Hermitian systems exhibit a variety of unique features rooted in the presence of exceptional points (EP). The distinct topological structure in the proximity of an EP gives rise to counterintuitive behaviors absent in Hermitian systems, which emerge after encircling the EP either quasistatically or dynamically. However, experimental exploration of EP encirclement in quantum systems, particularly those involving high-order EPs, remains challenging due to the difficulty of coherently controlling more degrees of freedom. In this work, we experimentally investigate the eigenvalues braiding and state transfer arising from the encirclement of EP in a three-dimensional non-Hermitian quantum system using superconducting circuits. We characterize the second- and third-order EPs through the coalescence of eigenvalues. Then we reveal the topological structure near the EP3 by quasistatically encircling it along various paths with three independent parameters, which yields the eigenvalues braiding described by the braid group $B_3$. Additionally, we observe chiral state transfer between three eigenstates under a fast driving scheme when no EPs are enclosed, while time-symmetric behavior occurs when at least one EP is encircled. Our findings offer insights into understanding non-Hermitian topological structures and the manipulation of quantum states through dynamic operations.

quant-ph↗

arXiv2024

Depolarization by jet precession in early optical afterglows of gamma-ray bursts

Polarization observations provide a unique way to probe the nature of jet magnetic fields in gamma-ray bursts (GRBs). Currently, some GRBs have been detected to be polarized in their early optical afterglows. However, the measured polarization degrees (PDs) of these GRBs are much lower than those predicted by theoretical models. In this work, we investigate the depolarization induced by jet precession in combination with the measured PDs of the GRB early optical afterglows in the reverse shock (RS) dominated phase ($\sim 10^2-10^3 \,{\rm s}$). We calculate the PDs of RS emission with and without jet precession in both magnetic field configurations, i.e., aligned and toroidal magnetic fields, and meanwhile explore the effect of different parameters on the PDs. We find that the PDs are slightly affected by the configurations of the ordered magnetic fields and are positively related to the precession period. Moreover, the PDs are sensitive to the observed angle and the measured low PDs favor a small one. Thus, as one of the plausible origins of the structured jets, jet precession could be considered as an alternative mechanism for the low PDs observed in GRB early optical afterglows.

astro-ph.HE↗

arXiv2024

Beyond Isolation: Multi-Agent Synergy for Improving Knowledge Graph Construction

This paper introduces CooperKGC, a novel framework challenging the conventional solitary approach of large language models (LLMs) in knowledge graph construction (KGC). CooperKGC establishes a collaborative processing network, assembling a team capable of concurrently addressing entity, relation, and event extraction tasks. Experimentation demonstrates that fostering collaboration within CooperKGC enhances knowledge selection, correction, and aggregation capabilities across multiple rounds of interactions.

cs.AI↗

arXiv2024

Bound states in one-dimensional systems with colored noise

We investigate the phase transitions in a one-dimensional system with colored noise. Previous studies indicated that the phase diagram of this system included extended and disorder-induced localized phases. However, by studying the properties of wave functions, we find that this phase diagram can be further refined, revealing the existence of a bound phase for the large potential amplitude $W$ and noise control parameter $α$. In the bound phase, the wave function cannot extend throughout the entire chain, tails decay faster than exponentially and its distribution expands as the system size increases. By adjusting the potential amplitude to induce a transition from the extended phase to the bound phase, we find that bound states coexist with extended states in the spectrum. In contrast, when the system transitions from the Anderson localized phase to the bound phase, we do not observe the obvious coexistence of Anderson localized and bound states. Finally, by performing the time evolution, we find that the dynamic transition point of the bound phase is inconsistent with the static one for large $α$.

cond-mat.dis-nn↗

arXiv2024

Defects in graphite engineered by ion implantation for the self-assembly of gold nanoparticles

Defect engineering in two-dimensional (2D) materials is essential for advancing applications such as gas sensing, single-atom catalysis, and guided nanoparticle self-assembly, enabling the creation of materials with tailored functionalities. This study investigates ion implantation effects on highly ordered pyrolytic graphite (HOPG) surfaces, using scanning tunneling microscopy (STM) and density functional theory (DFT) simulations to identify distinct defect structures. High-energy heavy ions cause inelastic scattering, increasing surface damage, while gold atoms deposited onto defect sites preferentially form atomic clusters. Through focused ion beam techniques, spatially distributed defects were engineered, guiding the self-assembly of nanoparticles. This research highlights the precision of ion irradiation for modifying HOPG surfaces, with significant implications for catalysis, nanotechnology, and the development of functional materials with controlled nanoscale properties.

cond-mat.mtrl-sci↗

arXiv2024

Simultaneous Measurement of Polarization and Excitation-Emission Spectrum of Suspended Particles in Water

The detection of suspended single particles (SSPs) in water is a crucial element for monitoring water ecosystems. Although there are a variety of sensory methods, it is still an unrealized goal to combine multi-wavelength polarized light scattering and fluorescence excitation-emission matrix(EEM) to characterize and classify SSP in water,which can deeply characterize and classify SSPs with different properties. In this paper, we propose an efficient optical method that can simultaneously obtain the SSP's Stokes parameters of four wavelengths and the fluorescence excitation-emission matrix (EEM) within 125 ns at 120 degree scattering angle in water. We use Mie scattering simulation to validate our method and obtain the optimal parameters for the experiments. We demonstrate the high performance of our method in terms of sensitivity and accuracy through experiments involving microalgae, microplastics and sediments, as well as control experiments under different illumination conditions and classification experiments of harmful microalgae by using a multi-layer perceptron (MLP) to classify underwater suspended particles and harmful microalgae based on our method, and show that our method achieves better results than using single-wavelength light. Our method paves the way for the detection of SSPs and polarization research in marine science.

physics.optics↗

arXiv2024

Classifying extended, localized and critical states in quasiperiodic lattices via unsupervised learning

Classification of quantum phases is one of the most important areas of research in condensed matter physics. In this work, we obtain the phase diagram of one-dimensional quasiperiodic models via unsupervised learning. Firstly, we choose two advanced unsupervised learning algorithms, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ordering Points To Identify the Clustering Structure (OPTICS), to explore the distinct phases of Aubry-André-Harper model and quasiperiodic p-wave model. The unsupervised learning results match well with traditional numerical diagonalization. Finally, we compare the similarity of different algorithms and find that the highest similarity between the results of unsupervised learning algorithms and those of traditional algorithms has exceeded 98\%. Our work sheds light on applications of unsupervised learning for phase classification.

cond-mat.dis-nn↗

arXiv2024

End-to-End Heterogeneous Graph Neural Networks for Traffic Assignment

The traffic assignment problem is one of the significant components of traffic flow analysis for which various solution approaches have been proposed. However, deploying these approaches for large-scale networks poses significant challenges. In this paper, we leverage the power of heterogeneous graph neural networks to propose a novel end-to-end surrogate model for traffic assignment, specifically user equilibrium traffic assignment problems. Our model integrates an adaptive graph attention mechanism with auxiliary "virtual" links connecting origin-destination node pairs, This integration enables the model to capture spatial traffic patterns across different links, By incorporating the node-based flow conservation law into the overall loss function, the model ensures the prediction results in compliance with flow conservation principles, resulting in highly accurate predictions for both link flow and flow-capacity ratios. We present numerical experiments on urban transportation networks and show that the proposed heterogeneous graph neural network model outperforms other conventional neural network models in terms of convergence rate and prediction accuracy. Notably, by introducing two different training strategies, the proposed heterogeneous graph neural network model can also be generalized to different network topologies. This approach offers a promising solution for complex traffic flow analysis and prediction, enhancing our understanding and management of a wide range of transportation systems.

cs.LG↗

arXiv2024

Supply Chain Network Extraction and Entity Classification Leveraging Large Language Models

Supply chain networks are critical to the operational efficiency of industries, yet their increasing complexity presents significant challenges in mapping relationships and identifying the roles of various entities. Traditional methods for constructing supply chain networks rely heavily on structured datasets and manual data collection, limiting their scope and efficiency. In contrast, recent advancements in Natural Language Processing (NLP) and large language models (LLMs) offer new opportunities for discovering and analyzing supply chain networks using unstructured text data. This paper proposes a novel approach that leverages LLMs to extract and process raw textual information from publicly available sources to construct a comprehensive supply chain graph. We focus on the civil engineering sector as a case study, demonstrating how LLMs can uncover hidden relationships among companies, projects, and other entities. Additionally, we fine-tune an LLM to classify entities within the supply chain graph, providing detailed insights into their roles and relationships. The results show that domain-specific fine-tuning improves classification accuracy, highlighting the potential of LLMs for industry-specific supply chain analysis. Our contributions include the development of a supply chain graph for the civil engineering sector, as well as a fine-tuned LLM model that enhances entity classification and understanding of supply chain networks.

cs.LG↗

arXiv2024

Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence

The rapid rise in popularity of Large Language Models (LLMs) with emerging capabilities has spurred public curiosity to evaluate and compare different LLMs, leading many researchers to propose their own LLM benchmarks. Noticing preliminary inadequacies in those benchmarks, we embarked on a study to critically assess 23 state-of-the-art LLM benchmarks, using our novel unified evaluation framework through the lenses of people, process, and technology, under the pillars of benchmark functionality and integrity. Our research uncovered significant limitations, including biases, difficulties in measuring genuine reasoning, adaptability, implementation inconsistencies, prompt engineering complexity, evaluator diversity, and the overlooking of cultural and ideological norms in one comprehensive assessment. Our discussions emphasized the urgent need for standardized methodologies, regulatory certainties, and ethical guidelines in light of Artificial Intelligence (AI) advancements, including advocating for an evolution from static benchmarks to dynamic behavioral profiling to accurately capture LLMs' complex behaviors and potential risks. Our study highlighted the necessity for a paradigm shift in LLM evaluation methodologies, underlining the importance of collaborative efforts for the development of universally accepted benchmarks and the enhancement of AI systems' integration into society.

cs.AI↗

arXiv2024

Probing blackbody components in gamma-ray bursts from black hole neutrino-dominated accretion flows

A stellar-mass black hole (BH) surrounded by a neutrino-dominated accretion flow (NDAF) is generally considered to be the central engine of gamma-ray bursts (GRBs). Neutrinos escaping from the disk will annihilate out of the disk to produce the fireball that could power GRBs with blackbody (BB) components. The initial GRB jet power and fireball launch radius are related to the annihilation luminosity and annihilation height of the NDAFs, respectively. In this paper, we collect 7 GRBs with known redshifts and identified BB components to test whether the NDAF model works. We find that, in most cases, the values of the accretion rates and the central BH properties are all in the reasonable range, suggesting that these BB components indeed originate from the neutrino annihilation process.

astro-ph.HE↗

arXiv2024

Smart Navigation System for Parking Assignment at Large Events: Incorporating Heterogeneous Driver Characteristics

Parking challenges escalate significantly during large events such as concerts and sports games, yet few studies address dynamic parking lot assignments in these occasions. This paper introduces a smart navigation system designed to optimize parking assignments efficiently during major events, employing a mixed search algorithm that considers diverse drivers characteristics. We validated our system through simulations conducted in Berkeley, CA during the "Big Game" showcasing the advantages of our novel parking assignment approach.

cs.HC↗

arXiv2024

Airport Delay Prediction with Temporal Fusion Transformers

Since flight delay hurts passengers, airlines, and airports, its prediction becomes crucial for the decision-making of all stakeholders in the aviation industry and thus has been attempted by various previous research. However, previous delay predictions are often categorical and at a highly aggregated level. To improve that, this study proposes to apply the novel Temporal Fusion Transformer model and predict numerical airport arrival delays at quarter hour level for U.S. top 30 airports. Inputs to our model include airport demand and capacity forecasts, historic airport operation efficiency information, airport wind and visibility conditions, as well as enroute weather and traffic conditions. The results show that our model achieves satisfactory performance measured by small prediction errors on the test set. In addition, the interpretability analysis of the model outputs identifies the important input factors for delay prediction.

cs.LG↗

arXiv2024

Fast unconditional reset and leakage reduction in fixed-frequency transmon qubits

The realization of fault-tolerant quantum computing requires the execution of quantum error-correction (QEC) schemes, to mitigate the fragile nature of qubits. In this context, to ensure the success of QEC, a protocol capable of implementing both qubit reset and leakage reduction is highly desirable. We demonstrate such a protocol in an architecture consisting of fixed-frequency transmon qubits pair-wise coupled via tunable couplers -- an architecture that is compatible with the surface code. We use tunable couplers to transfer any undesired qubit excitation to the readout resonator of the qubit, from which this excitation decays into the feedline. In total, the combination of qubit reset, leakage reduction, and coupler reset takes only 83ns to complete. Our reset scheme is fast, unconditional, and achieves fidelities well above 99%, thus enabling fixed-frequency qubit architectures as future implementations of fault-tolerant quantum computers. Our protocol also provides a means to both reduce QEC cycle runtime and improve algorithmic fidelity on quantum computers.

quant-ph↗

arXiv2024

Multi-Channel Masked Autoencoder and Comprehensive Evaluations for Reconstructing 12-Lead ECG from Arbitrary Single-Lead ECG

Electrocardiogram (ECG) has emerged as a widely accepted diagnostic instrument for cardiovascular diseases (CVD). The standard clinical 12-lead ECG configuration causes considerable inconvenience and discomfort, while wearable devices offers a more practical alternative. To reduce information gap between 12-lead ECG and single-lead ECG, this study proposes a multi-channel masked autoencoder (MCMA) for reconstructing 12-Lead ECG from arbitrary single-lead ECG, and a comprehensive evaluation benchmark, ECGGenEval, encompass the signal-level, feature-level, and diagnostic-level evaluations. MCMA can achieve the state-of-the-art performance. In the signal-level evaluation, the mean square errors of 0.0317 and 0.1034, Pearson correlation coefficients of 0.7885 and 0.7420. In the feature-level evaluation, the average standard deviation of the mean heart rate across the generated 12-lead ECG is 1.0481, the coefficient of variation is 1.58%, and the range is 3.2874. In the diagnostic-level evaluation, the average F1-score with two generated 12-lead ECG from different single-lead ECG are 0.8233 and 0.8410.

cs.LG↗

arXiv2024

Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector

This paper studies the challenging cross-domain few-shot object detection (CD-FSOD), aiming to develop an accurate object detector for novel domains with minimal labeled examples. While transformer-based open-set detectors, such as DE-ViT, show promise in traditional few-shot object detection, their generalization to CD-FSOD remains unclear: 1) can such open-set detection methods easily generalize to CD-FSOD? 2) If not, how can models be enhanced when facing huge domain gaps? To answer the first question, we employ measures including style, inter-class variance (ICV), and indefinable boundaries (IB) to understand the domain gap. Based on these measures, we establish a new benchmark named CD-FSOD to evaluate object detection methods, revealing that most of the current approaches fail to generalize across domains. Technically, we observe that the performance decline is associated with our proposed measures: style, ICV, and IB. Consequently, we propose several novel modules to address these issues. First, the learnable instance features align initial fixed instances with target categories, enhancing feature distinctiveness. Second, the instance reweighting module assigns higher importance to high-quality instances with slight IB. Third, the domain prompter encourages features resilient to different styles by synthesizing imaginary domains without altering semantic contents. These techniques collectively contribute to the development of the Cross-Domain Vision Transformer for CD-FSOD (CD-ViTO), significantly improving upon the base DE-ViT. Experimental results validate the efficacy of our model.

cs.CV↗