arXiv subjects

Lei Wang

Publications and source records attributed to Lei Wang.

At least 577 records · Page 32Linked to original sources

arXiv2024

Distributed Adaptive Time-Varying Optimization with Global Asymptotic Convergence

In this note, we study distributed time-varying optimization for a multi-agent system. We first focus on a class of time-varying quadratic cost functions, and develop a new distributed algorithm that integrates an average estimator and an adaptive optimizer, with both bridged by a Dead Zone Algorithm. Based on a composite Lyapunov function and finite escape-time analysis, we prove the closed-loop global asymptotic convergence to the optimal solution under mild assumptions. Particularly, the introduction of the estimator relaxes the requirement for the Hessians of cost functions, and the integrated design eliminates the waiting time required in the relevant literature for estimating global parameter during algorithm implementation. We then extend this result to a more general class of time-varying cost functions. Two examples are used to verify the proposed designs.

eess.SY↗

arXiv2024

RoCo:Robust Collaborative Perception By Iterative Object Matching and Pose Adjustment

Collaborative autonomous driving with multiple vehicles usually requires the data fusion from multiple modalities. To ensure effective fusion, the data from each individual modality shall maintain a reasonably high quality. However, in collaborative perception, the quality of object detection based on a modality is highly sensitive to the relative pose errors among the agents. It leads to feature misalignment and significantly reduces collaborative performance. To address this issue, we propose RoCo, a novel unsupervised framework to conduct iterative object matching and agent pose adjustment. To the best of our knowledge, our work is the first to model the pose correction problem in collaborative perception as an object matching task, which reliably associates common objects detected by different agents. On top of this, we propose a graph optimization process to adjust the agent poses by minimizing the alignment errors of the associated objects, and the object matching is re-done based on the adjusted agent poses. This process is carried out iteratively until convergence. Experimental study on both simulated and real-world datasets demonstrates that the proposed framework RoCo consistently outperforms existing relevant methods in terms of the collaborative object detection performance, and exhibits highly desired robustness when the pose information of agents is with high-level noise. Ablation studies are also provided to show the impact of its key parameters and components. The code is released at https://github.com/HuangZhe885/RoCo.

cs.AI↗

arXiv2024

Long-distance distribution of telecom time-energy entanglement generated on a silicon chip

Entanglement distribution is a critical technique that enables numerous quantum applications. Most fiber-based long-distance experiments reported to date have utilized photon pair sources generated in bulk optical crystals, with the entanglement encoded in the polarization degree of freedom. Here, we create time-energy entanglement for photon pairs generated from an on-chip silicon ring resonator via SFWM process and report the distribution of the entanglement over standard optical fiber with distance >81 km. Our work paves the way for future large-scale quantum networks with connect of distant quantum nodes.

quant-ph↗

arXiv2024

SAPI: Surroundings-Aware Vehicle Trajectory Prediction at Intersections

In this work we propose a deep learning model, i.e., SAPI, to predict vehicle trajectories at intersections. SAPI uses an abstract way to represent and encode surrounding environment by utilizing information from real-time map, right-of-way, and surrounding traffic. The proposed model consists of two convolutional network (CNN) and recurrent neural network (RNN)-based encoders and one decoder. A refiner is proposed to conduct a look-back operation inside the model, in order to make full use of raw history trajectory information. We evaluate SAPI on a proprietary dataset collected in real-world intersections through autonomous vehicles. It is demonstrated that SAPI shows promising performance when predicting vehicle trajectories at intersection, and outperforms benchmark methods. The average displacement error(ADE) and final displacement error(FDE) for 6-second prediction are 1.84m and 4.32m respectively. We also show that the proposed model can accurately predict vehicle trajectories in different scenarios.

cs.LG↗

arXiv2024

Distributed Continuous-Time Optimization with Uncertain Time-Varying Quadratic Cost Functions

This paper studies distributed continuous-time optimization for time-varying quadratic cost functions with uncertain parameters. We first propose a centralized adaptive optimization algorithm using partial information of the cost function. It can be seen that even if there are uncertain parameters in the cost function, exact optimization can still be achieved. To solve this problem in a distributed manner when different local cost functions have identical Hessians, we propose a novel distributed algorithm that cascades the fixed-time average estimator and the distributed optimizer. We remove the requirement for the upper bounds of certain complex functions by integrating state-based gains in the proposed design. We further extend this result to address the distributed optimization where the time-varying cost functions have nonidentical Hessians. We prove the convergence of all the proposed algorithms in the global sense. Numerical examples verify the proposed algorithms.

eess.SY↗

arXiv2024

Thermocapillary migration of a self-rewetting droplet on an inclined surface: A phase-field simulation

In this paper, we investigated the thermocapillary migration of a self-rewetting droplet on an inclined surface using a phase field based lattice Boltzmann method. Unlike the normal fluid whose surface tension decreases linearly with temperature, the self-rewetting fluid consider in the current work has a quadratic temperature dependence of surface tension with a well-defined minimum. we first explored the influence of the Marangoni number on droplet migration, and found that the droplet hardly deforms and migrates slowly when the Marangoni number is small. However, as the Marangoni number increases, the droplet begins to deform and elongate, and its migration speed increases. Subsequently, we studied the effect of surface wettability on droplet migration. The results show that the droplet migrate towards regions of higher surface energy on hydrophilic surfaces and in the opposite direction on hydrophobic surfaces. Furthermore, by varying the viscosity ratio and the inclination angle of the plate, we found that the droplet's migration speed decreases with an increase in the viscosity ratio. In particular, two vortices appear inside the droplet at a high viscosity ratio, whereas only one vortex is present at a low viscosity ratio.

physics.flu-dyn↗

arXiv2024

Automated high-resolution backscattered-electron imaging at macroscopic scale

Scanning electron microscopy (SEM) has been widely utilized in the field of materials science due to its significant advantages, such as large depth of field, wide field of view, and excellent stereoscopic imaging. However, at high magnification, the limited imaging range in SEM cannot cover all the possible inhomogeneous microstructures. In this research, we propose a novel approach for generating high-resolution SEM images across multiple scales, enabling a single image to capture physical dimensions at the centimeter level while preserving submicron-level details. We adopted the SEM imaging on the AlCoCrFeNi2.1 eutectic high entropy alloy (EHEA) as an example. SEM videos and image stitching are combined to fulfill this goal, and the video-extracted low-definition (LD) images are clarified by a well-trained denoising model. Furthermore, we segment the macroscopic image of the EHEA, and area of various microstructures are distinguished. Combining the segmentation results and hardness experiments, we found that the hardness is positively correlated with the content of body-centered cubic (BCC) phase, negatively correlated with the lamella width, and the relationship with the proportion of lamellar structures was not significant. Our work provides a feasible solution to generate macroscopic images based on SEMs for further analysis of the correlations between the microstructures and spatial distribution, and can be widely applied to other types of microscope.

cond-mat.mtrl-sci↗

arXiv2024

Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Iterative Refinement Labeling

Price movement forecasting, aimed at predicting financial asset trends based on current market information, has achieved promising advancements through machine learning (ML) methods. Most existing ML methods, however, struggle with the extremely low signal-to-noise ratio and stochastic nature of financial data, often mistaking noises for real trading signals without careful selection of potentially profitable samples. To address this issue, we propose LARA, a novel price movement forecasting framework with two main components: Locality-Aware Attention (LA-Attention) and Iterative Refinement Labeling (RA-Labeling). (1) LA-Attention, enhanced by metric learning techniques, automatically extracts the potentially profitable samples through masked attention scheme and task-specific distance metrics. (2) RA-Labeling further iteratively refines the noisy labels of potentially profitable samples, and combines the learned predictors robust to the unseen and noisy samples. In a set of experiments on three real-world financial markets: stocks, cryptocurrencies, and ETFs, LARA significantly outperforms several machine learning based methods on the Qlib quantitative investment platform. Extensive ablation studies confirm LARA's superior ability in capturing more reliable trading opportunities.

cs.LG↗

arXiv2024

On the Adversarial Robustness of Learning-based Image Compression Against Rate-Distortion Attacks

Despite demonstrating superior rate-distortion (RD) performance, learning-based image compression (LIC) algorithms have been found to be vulnerable to malicious perturbations in recent studies. However, the adversarial attacks considered in existing literature remain divergent from real-world scenarios, both in terms of the attack direction and bitrate. Additionally, existing methods focus solely on empirical observations of the model vulnerability, neglecting to identify the origin of it. These limitations hinder the comprehensive investigation and in-depth understanding of the adversarial robustness of LIC algorithms. To address the aforementioned issues, this paper considers the arbitrary nature of the attack direction and the uncontrollable compression ratio faced by adversaries, and presents two practical rate-distortion attack paradigms, i.e., Specific-ratio Rate-Distortion Attack (SRDA) and Agnostic-ratio Rate-Distortion Attack (ARDA). Using the performance variations as indicators, we evaluate the adversarial robustness of eight predominant LIC algorithms against diverse attacks. Furthermore, we propose two novel analytical tools for in-depth analysis, i.e., Entropy Causal Intervention and Layer-wise Distance Magnify Ratio, and reveal that hyperprior significantly increases the bitrate and Inverse Generalized Divisive Normalization (IGDN) significantly amplifies input perturbations when under attack. Lastly, we examine the efficacy of adversarial training and introduce the use of online updating for defense. By comparing their advantages and disadvantages, we provide a reference for constructing more robust LIC algorithms against the rate-distortion attacks.

eess.IV↗

arXiv2024

A thermodynamically consistent phase-field lattice Boltzmann method for two-phase electrohydrodynamic flows

In this work, we aim to develop a phase-field based lattice Boltzmann (LB) method for simulating two-phase electrohydrodynamics (EHD) flows, which allows for different properties (densities, viscosities, conductivity and permittivity) of each phase while maintaining thermodynamic consistency. To this end, we first present a theoretical analysis on the two-phase EHD flows by using the Onsager's variational principle, which is an extension of Rayleigh's principle of least energy dissipation and, naturally, guarantees thermodynamic consistency. It shows that the governing equations of the model include the hydrodynamic equations, Cahn-Hilliard equation coupled with additional electrical effect, and the full Poisson-Nernst-Planck electrokinetic equations. After that, a coupled lattice Boltzmann (LB) scheme is constructed for simulating two-phase EHD flows. In particular, in order to handle two-phase EHD flows with a relatively larger electric permittivity ratio, we also introduce a delicately designed discrete forcing term into the LB equation for electrostatic field. Moreover, some numerical examples including two-phase EHD flows in planar layers and charge diffusion of a Gaussian bell are simulated with the developed LB method. It is shown that our numerical scheme shares a second-order convergence rate in space in predicting electric potential and charge density. Finally, we used the current model to simulate the deformation of a droplet under an electric field and the dynamics of droplet detachment in reversed electrowetting. Our numerical results align well with the theoretic solutions, and the available experimental/numerical data, demonstrating that the proposed method is feasible for simulating two-phase EHD flows.

physics.flu-dyn↗

arXiv2024

Machine-Type Communication Waveforms: An Exploration of New Dimensions

This paper derives a generalized class of waveforms with an application to machine-type communication (MTC) while studying its underlying structural characteristics in relation to conventional modulation waveforms. First, a canonical waveform of frequency-error tolerance is identified for a unified preamble and traffic signal design, ideal for MTC use as a composite waveform, commonly known as a transmission burst. It is shown that the most widely used modulation schemes for mIoT traffic signals, e.g., FSK and LoRa modulation, are simply subsets of the canonical waveform. The intrinsic characteristics and degrees of freedom the waveform offers are then explored. Most significantly, a new waveform dimension is uncovered and exploited as additional degrees of freedom for satisfying the MTC requirements, i.e., energy and resource efficiency and robustness. The corresponding benefits are evaluated analytically and numerically in AWGN, frequency-flat, and selective channels. We demonstrate that neither FSK nor LoRa can fully address the mIoT requirements since neither fully exploits the degrees of freedom from the perspective of the generalized waveform class. Finally, a solution is devised to optimize energy and resource efficiency under various deployment environments and practical constraints while maintaining the low-complexity property.

eess.SP↗

arXiv2024

YuLan: An Open-source Large Language Model

Large language models (LLMs) have become the foundation of many applications, leveraging their extensive capabilities in processing and understanding natural language. While many open-source LLMs have been released with technical reports, the lack of training details hinders further research and development. This paper presents the development of YuLan, a series of open-source LLMs with $12$ billion parameters. The base model of YuLan is pre-trained on approximately $1.7$T tokens derived from a diverse corpus, including massive English, Chinese, and multilingual texts. We design a three-stage pre-training method to enhance YuLan's overall capabilities. Subsequent phases of training incorporate instruction-tuning and human alignment, employing a substantial volume of high-quality synthesized data. To facilitate the learning of complex and long-tail knowledge, we devise a curriculum-learning framework throughout across these stages, which helps LLMs learn knowledge in an easy-to-hard manner. YuLan's training is finished on Jan, 2024 and has achieved performance on par with state-of-the-art LLMs across various English and Chinese benchmarks. This paper outlines a comprehensive technical roadmap for developing LLMs from scratch. Our model and codes are available at https://github.com/RUC-GSAI/YuLan-Chat.

cs.CL↗

arXiv2024

Compensate Quantization Errors: Make Weights Hierarchical to Compensate Each Other

Emergent Large Language Models (LLMs) use their extraordinary performance and powerful deduction capacity to discern from traditional language models. However, the expenses of computational resources and storage for these LLMs are stunning, quantization then arises as a trending conversation. To address accuracy decay caused by quantization, two streams of works in post-training quantization methods stand out. One uses other weights to compensate existing quantization error, while the other transfers the quantization difficulty to other parts in the model. Combining both merits, we introduce Learnable Singular value Increment (LSI) as an advanced solution. LSI uses Singular Value Decomposition to extract singular values of the weights and make them learnable to help weights compensate each other conditioned on activation. Incorporating LSI with existing techniques, we achieve state-of-the-art performance in diverse quantization settings, no matter in weight-only, weight-activation or extremely low bit scenarios. By unleashing the potential of LSI, efficient finetuning on quantized model is no longer a prohibitive problem.

cs.CL↗

arXiv2024

Direct Production of Light Scalar in the Type-I Two-Higgs-Doublet Model at the Lifetime Frontier of LHC

A light pseudoscalar $A$ in the sufficient large $\tanβ$ region of type-I two-Higgs-doublet model (2HDM) can be naturally a long-lived particle (LLP). We focus on $H^{\pm}A$, $HA$ and $AA$ pair productions via the electroweak processes mediated by the bosons at the LHC, including $pp \rightarrow W^\pm/Z \rightarrow H^{\pm}/H A$ and $pp \rightarrow h \rightarrow AA$ at the 14 TeV LHC. The possibility of probing $A$ as a LLP at the FASER-2, FACET, MoEDAL-MAPP-2, MATHUSLA is discussed. We find that FASER-2 fails to probe any parameter space within 0.2 GeV $< m_A <$ 10 GeV for all the considered processes. For 130 $< m_{H\pm} = m_H <$ 400 GeV, FACET, MoEDAL-MAPP-2 and MATHUSLA can probe $\tan β\lesssim 10^{4-6}$ for $m_A \lesssim 3$ GeV, and $\tan β\lesssim 10^{6-8}$ for 3 GeV $\lesssim m_A <$ 10 GeV from $pp \rightarrow W^\pm/Z \rightarrow H^{\pm}/Z A$ processes. And $pp \rightarrow h \rightarrow AA$ process covers similar parameter space. All processes can surpass the current limits.

hep-ph↗

arXiv2024

STEMO: Early Spatio-temporal Forecasting with Multi-Objective Reinforcement Learning

Accuracy and timeliness are indeed often conflicting goals in prediction tasks. Premature predictions may yield a higher rate of false alarms, whereas delaying predictions to gather more information can render them too late to be useful. In applications such as wildfires, crimes, and traffic jams, timely forecasting are vital for safeguarding human life and property. Consequently, finding a balance between accuracy and timeliness is crucial. In this paper, we propose an early spatio-temporal forecasting model based on Multi-Objective reinforcement learning that can either implement an optimal policy given a preference or infer the preference based on a small number of samples. The model addresses two primary challenges: 1) enhancing the accuracy of early forecasting and 2) providing the optimal policy for determining the most suitable prediction time for each area. Our method demonstrates superior performance on three large-scale real-world datasets, surpassing existing methods in early spatio-temporal forecasting tasks.

cs.LG↗

arXiv2024

Analysis and Evaluation of Kinect-based Action Recognition Algorithms

Human action recognition still exists many challenging problems such as different viewpoints, occlusion, lighting conditions, human body size and the speed of action execution, although it has been widely used in different areas. To tackle these challenges, the Kinect depth sensor has been developed to record real time depth sequences, which are insensitive to the color of human clothes and illumination conditions. Many methods on recognizing human action have been reported in the literature such as HON4D, HOPC, RBD and HDG, which use the 4D surface normals, pointclouds, skeleton-based model and depth gradients respectively to capture discriminative information from depth videos or skeleton data. In this research project, the performance of four aforementioned algorithms will be analyzed and evaluated using five benchmark datasets, which cover challenging issues such as noise, change of viewpoints, background clutters and occlusions. We also implemented and improved the HDG algorithm, and applied it in cross-view action recognition using the UWA3D Multiview Activity dataset. Moreover, we used different combinations of individual feature vectors in HDG for performance evaluation. The experimental results show that our improvement of HDG outperforms other three state-of-the-art algorithms for cross-view action recognition.

cs.CV↗

arXiv2024

Neural canonical transformations for vibrational spectra of molecules

The behavior of polyatomic molecules around their equilibrium positions can be regarded as quantum coupled anharmonic oscillators. Solving the corresponding Schrödinger equations can interpret or predict experimental spectra of molecules. In this study, we develop a novel approach to solve excited states of anharmonic vibrational systems. The normal coordinates of molecules are transformed into new coordinates through a normalizing flow parameterized by a neural network, facilitating the construction of a set of orthogonal many-body variational wavefunctions. Our methodology has been validated on an exactly solvable $64$-dimensional coupled harmonic oscillator, yielding numerical results with a relative error on the order of $10^{-6}$. Furthermore, the neural canonical transformations are also applied to calculate the energy levels of two specific molecules, acetonitrile ($\text{C}\text{H}_3\text{CN}$) and ethylene oxide ($\text{C}_2\text{H}_4\text{O}$) involving $12$ and $15$ vibrational modes respectively, which are in agreement with experimental findings. One of the key advantages of our approach is its flexibility concerning the potential energy surface, requiring no specific form for its application. Moreover, this method can be easily implemented on large-scale distributed computing platforms, making it particularly suitable for investigating more complex vibrational structures.

physics.chem-ph↗

arXiv2024

NUMCoT: Numerals and Units of Measurement in Chain-of-Thought Reasoning using Large Language Models

Numeral systems and units of measurement are two conjoined topics in activities of human beings and have mutual effects with the languages expressing them. Currently, the evaluation of Large Language Models (LLMs) often involves mathematical reasoning, yet little attention is given to how minor changes in numbers or units can drastically alter the complexity of problems and the performance of LLMs. In this paper, we scrutinize existing LLMs on processing of numerals and units of measurement by constructing datasets with perturbations. We first anatomize the reasoning of math word problems to different sub-procedures like numeral conversions from language to numbers and measurement conversions based on units. Then we further annotate math word problems from ancient Chinese arithmetic works which are challenging in numerals and units of measurement. Experiments on perturbed datasets demonstrate that LLMs still encounter difficulties in handling numeral and measurement conversions.

cs.CL↗