arXiv subjects

Jie Wang

Publications and source records attributed to Jie Wang.

At least 343 records · Page 19Linked to original sources

arXiv2024

Learning to Cut via Hierarchical Sequence/Set Model for Efficient Mixed-Integer Programming

Cutting planes (cuts) play an important role in solving mixed-integer linear programs (MILPs), which formulate many important real-world applications. Cut selection heavily depends on (P1) which cuts to prefer and (P2) how many cuts to select. Although modern MILP solvers tackle (P1)-(P2) by human-designed heuristics, machine learning carries the potential to learn more effective heuristics. However, many existing learning-based methods learn which cuts to prefer, neglecting the importance of learning how many cuts to select. Moreover, we observe that (P3) what order of selected cuts to prefer significantly impacts the efficiency of MILP solvers as well. To address these challenges, we propose a novel hierarchical sequence/set model (HEM) to learn cut selection policies. Specifically, HEM is a bi-level model: (1) a higher-level module that learns how many cuts to select, (2) and a lower-level module -- that formulates the cut selection as a sequence/set to sequence learning problem -- to learn policies selecting an ordered subset with the cardinality determined by the higher-level module. To the best of our knowledge, HEM is the first data-driven methodology that well tackles (P1)-(P3) simultaneously. Experiments demonstrate that HEM significantly improves the efficiency of solving MILPs on eleven challenging MILP benchmarks, including two Huawei's real problems.

cs.AI↗

arXiv2024

A Survey of Neural Network Robustness Assessment in Image Recognition

In recent years, there has been significant attention given to the robustness assessment of neural networks. Robustness plays a critical role in ensuring reliable operation of artificial intelligence (AI) systems in complex and uncertain environments. Deep learning's robustness problem is particularly significant, highlighted by the discovery of adversarial attacks on image classification models. Researchers have dedicated efforts to evaluate robustness in diverse perturbation conditions for image recognition tasks. Robustness assessment encompasses two main techniques: robustness verification/ certification for deliberate adversarial attacks and robustness testing for random data corruptions. In this survey, we present a detailed examination of both adversarial robustness (AR) and corruption robustness (CR) in neural network assessment. Analyzing current research papers and standards, we provide an extensive overview of robustness assessment in image recognition. Three essential aspects are analyzed: concepts, metrics, and assessment methods. We investigate the perturbation metrics and range representations used to measure the degree of perturbations on images, as well as the robustness metrics specifically for the robustness conditions of classification models. The strengths and limitations of the existing methods are also discussed, and some potential directions for future research are provided.

cs.CV↗

arXiv2024

FEASTS Combined with Interferometry (I): Overall Properties of Diffuse HI and Implications for Gas Accretion in Nearby Galaxies

We present a statistical study of the properties of diffuse HI in ten nearby galaxies, comparing the HI detected by the single-dish telescope FAST (FEASTS program) and the interferometer VLA (THINGS program), respectively. The THINGS' observation missed HI with a median of 23% due to the short-spacing problem of interferometry and limited sensitivity. We extract the diffuse HI by subtracting the dense HI, which is obtained from the THINGS data with a uniform flux-density threshold, from the total HI detected by FAST. Among the sample, the median diffuse-HI fraction is 34%, and more diffuse HI is found in galaxies exhibiting more prominent tidal-interaction signatures. The diffuse HI we detected seems to be distributed in disk-like layers within a typical thickness of $1\,\text{kpc}$, different from the more halo-like diffuse HI detected around NGC 4631 in a previous study. Most of the diffuse HI is cospatial with the dense HI and has a typical column density of $10^{17.7}$-$10^{20.1}\,\text{cm}^{-2}$. The diffuse and dense HI exhibits a similar rotational motion, but the former lags by a median of 25% in at least the inner disks, and its velocity dispersions are typically twice as high. Based on a simplified estimation of circum-galactic medium properties and assuming pressure equilibrium, the volume density of diffuse HI appears to be constant within each individual galaxy, implying its role as a cooling interface. Comparing with existing models, these results are consistent with a possible link between tidal interactions, the formation of diffuse HI, and gas accretion.

astro-ph.GA↗

arXiv2024

Trustworthy Multimodal Fusion for Sentiment Analysis in Ordinal Sentiment Space

Multimodal video sentiment analysis aims to integrate multiple modal information to analyze the opinions and attitudes of speakers. Most previous work focuses on exploring the semantic interactions of intra- and inter-modality. However, these works ignore the reliability of multimodality, i.e., modalities tend to contain noise, semantic ambiguity, missing modalities, etc. In addition, previous multimodal approaches treat different modalities equally, largely ignoring their different contributions. Furthermore, existing multimodal sentiment analysis methods directly regress sentiment scores without considering ordinal relationships within sentiment categories, with limited performance. To address the aforementioned problems, we propose a trustworthy multimodal sentiment ordinal network (TMSON) to improve performance in sentiment analysis. Specifically, we first devise a unimodal feature extractor for each modality to obtain modality-specific features. Then, an uncertainty distribution estimation network is customized, which estimates the unimodal uncertainty distributions. Next, Bayesian fusion is performed on the learned unimodal distributions to obtain multimodal distributions for sentiment prediction. Finally, an ordinal-aware sentiment space is constructed, where ordinal regression is used to constrain the multimodal distributions. Our proposed TMSON outperforms baselines on multimodal sentiment analysis tasks, and empirical results demonstrate that TMSON is capable of reducing uncertainty to obtain more robust predictions.

cs.CV↗

arXiv2024

Chaos in Motion: Unveiling Robustness in Remote Heart Rate Measurement through Brain-Inspired Skin Tracking

Heart rate is an important physiological indicator of human health status. Existing remote heart rate measurement methods typically involve facial detection followed by signal extraction from the region of interest (ROI). These SOTA methods have three serious problems: (a) inaccuracies even failures in detection caused by environmental influences or subject movement; (b) failures for special patients such as infants and burn victims; (c) privacy leakage issues resulting from collecting face video. To address these issues, we regard the remote heart rate measurement as the process of analyzing the spatiotemporal characteristics of the optical flow signal in the video. We apply chaos theory to computer vision tasks for the first time, thus designing a brain-inspired framework. Firstly, using an artificial primary visual cortex model to extract the skin in the videos, and then calculate heart rate by time-frequency analysis on all pixels. Our method achieves Robust Skin Tracking for Heart Rate measurement, called HR-RST. The experimental results show that HR-RST overcomes the difficulty of environmental influences and effectively tracks the subject movement. Moreover, the method could extend to other body parts. Consequently, the method can be applied to special patients and effectively protect individual privacy, offering an innovative solution.

cs.CV↗

arXiv2024

Beyond Gait: Learning Knee Angle for Seamless Prosthesis Control in Multiple Scenarios

Deep learning models have become a powerful tool in knee angle estimation for lower limb prostheses, owing to their adaptability across various gait phases and locomotion modes. Current methods utilize Multi-Layer Perceptrons (MLP), Long-Short Term Memory Networks (LSTM), and Convolutional Neural Networks (CNN), predominantly analyzing motion information from the thigh. Contrary to these approaches, our study introduces a holistic perspective by integrating whole-body movements as inputs. We propose a transformer-based probabilistic framework, termed the Angle Estimation Probabilistic Model (AEPM), that offers precise angle estimations across extensive scenarios beyond walking. AEPM achieves an overall RMSE of 6.70 degrees, with an RMSE of 3.45 degrees in walking scenarios. Compared to the state of the art, AEPM has improved the prediction accuracy for walking by 11.31%. Our method can achieve seamless adaptation between different locomotion modes. Also, this model can be utilized to analyze the synergy between the knee and other joints. We reveal that the whole body movement has valuable information for knee movement, which can provide insights into designing sensors for prostheses. The code is available at https://github.com/penway/Beyond-Gait-AEPM.

cs.RO↗

arXiv2024

The abundance of dark matter haloes down to Earth mass

We use the Voids-within-Voids-within-Voids (VVV) simulations, a suite of successive nested N-body simulations with extremely high resolution (denoted, from low to high resolution, by L0 to L7), to test the Press-Schechter (PS), Sheth-Tormen (ST), and extended Press-Schechter (EPS) formulae for the halo abundance over the entire mass range, from mini-haloes of $10^{-6}\ \mathrm{M_\odot}$, to cluster haloes of $10^{15}\ \mathrm{M_\odot}$, at different redshifts, from $z=30$ to the present. We find that at $z=0$ and $z=2$, ST best reproduces the results of L0, which has the mean cosmic density (overdensity $δ=0$), at $10^{11-15} ~\mathrm{M_\odot}$. The higher resolution levels (L1-L7) are biased underdense regions ($δ<-0.6$). The EPS formalism takes this into account since it gives the mass function of a region conditioned, in this case, on having a given underdensity. EPS provides good matches to these higher levels, with deviations $\lesssim 20\%$, at $10^{-6-12.5} ~\mathrm{M_\odot}$. At $z \sim 7-15$, the ST predictions for L0 and the EPS for L1-L7 show somewhat larger deviations from the simulation results. However, at even higher redshifts, $z \sim 30$, EPS fits the simulations well again. We confirm our results by picking more subvolumes from the L0 simulation, finding that our conclusions depend only weakly on the size and overdensity of the region. The good agreement of EPS with the higher-level simulations implies that PS (or ST) gives an accurate description of the total halo mass function in representative regions of the universe.

astro-ph.GA↗

arXiv2024

Enhancing Safety in Mixed Traffic: Learning-Based Modeling and Efficient Control of Autonomous and Human-Driven Vehicles

With the increasing presence of autonomous vehicles (AVs) on public roads, developing robust control strategies to navigate the uncertainty of human-driven vehicles (HVs) is crucial. This paper introduces an advanced method for modeling HV behavior, combining a first-principles model with Gaussian process (GP) learning to enhance velocity prediction accuracy and provide a measurable uncertainty. We validated this innovative HV model using real-world data from field experiments and applied it to develop a GP-enhanced model predictive control (GP-MPC) strategy. This strategy aims to improve safety in mixed vehicle platoons by integrating uncertainty assessment into distance constraints. Comparative simulation studies with a conventional model predictive control (MPC) approach demonstrated that our GP-MPC strategy ensures more reliable safe distancing and fosters efficient vehicular dynamics, achieving notably higher speeds within the platoon. By incorporating a sparse GP technique in HV modeling and adopting a dynamic GP prediction within the MPC framework, we significantly reduced the computation time of GP-MPC, marking it only 4.6% higher than that of the conventional MPC. This represents a substantial improvement, making the process about 100 times faster than our preliminary work without these approximations. Our findings underscore the effectiveness of learning-based HV modeling in enhancing both safety and operational efficiency in mixed-traffic environments, paving the way for more harmonious AV-HV interactions.

cs.RO↗

arXiv2024

Ultrafast dynamic beam steering with optical frequency comb arrays

Efficient spatiotemporal control of optical beams is of paramount importance in diverse technological domains. Conventional systems focusing on quasi-static beam control demand precise phase or wavelength tuning for steering. This work presents a time-efficient solution for dynamic beam steering, emphasizing high-duty-cycle operation with fast scan rates, and eliminating the need for active tuning of the beam direction. We achieve 100%-duty-cycle scans at a rate of $\sim$9.8 GHz within an angular range of $\sim$1$^\circ$. Furthermore, leveraging the dispersion characteristics of a virtually imaged phased array (VIPA), we devise a broadband source array that seamlessly transitions from continuous-angular steering to pulsed discrete-angular operation, unlocking possibilities for high-sensitivity angle-, range-, and time-resolved imaging. We also elucidate the adaptability of integrated photonic designs incorporating wavelength-selective switches and spectral dispersers, for enabling a versatile on-chip realization of the proposed beam steering schemes.

physics.optics↗

arXiv2024

Learning-Based Modeling of Human-Autonomous Vehicle Interaction for Improved Safety in Mixed-Vehicle Platooning Control

The rising presence of autonomous vehicles (AVs) on public roads necessitates the development of advanced control strategies that account for the unpredictable nature of human-driven vehicles (HVs). This study introduces a learning-based method for modeling HV behavior, combining a traditional first-principles approach with a Gaussian process (GP) learning component. This hybrid model enhances the accuracy of velocity predictions and provides measurable uncertainty estimates. We leverage this model to develop a GP-based model predictive control (GP-MPC) strategy to improve safety in mixed vehicle platoons by integrating uncertainty assessments into distance constraints. Comparative simulations between our GP-MPC approach and a conventional model predictive control (MPC) strategy reveal that the GP-MPC ensures safer distancing and more efficient travel within the mixed platoon. By incorporating sparse GP modeling for HVs and a dynamic GP prediction in MPC, we significantly reduce the computation time of GP-MPC, making it only marginally longer than standard MPC and approximately 100 times faster than previous models not employing these techniques. Our findings underscore the effectiveness of learning-based HV modeling in enhancing safety and efficiency in mixed-traffic environments involving AV and HV interactions.

cs.RO↗

arXiv2024

A Tutorial on Gaussian Process Learning-based Model Predictive Control

This tutorial provides a systematic introduction to Gaussian process learning-based model predictive control (GP-MPC), an advanced approach integrating Gaussian process (GP) with model predictive control (MPC) for enhanced control in complex systems. It begins with GP regression fundamentals, illustrating how it enriches MPC with enhanced predictive accuracy and robust handling of uncertainties. A central contribution of this tutorial is the first detailed, systematic mathematical formulation of GP-MPC in literature, focusing on deriving the approximation of means and variances propagation for GP multi-step predictions. Practical applications in robotics control, such as path-following for mobile robots in challenging terrains and mixed-vehicle platooning, are discussed to demonstrate the real-world effectiveness and adaptability of GP-MPC. This tutorial aims to make GP-MPC accessible to researchers and practitioners, enriching the learning-based control field with in-depth theoretical and practical insights and fostering further innovations in complex system control.

cs.RO↗

arXiv2024

Improving safety in mixed traffic: A learning-based model predictive control for autonomous and human-driven vehicle platooning

As autonomous vehicles (AVs) become more common on public roads, their interaction with human-driven vehicles (HVs) in mixed traffic is inevitable. This requires new control strategies for AVs to handle the unpredictable nature of HVs. This study focused on safe control in mixed-vehicle platoons consisting of both AVs and HVs, particularly during longitudinal car-following scenarios. We introduce a novel model that combines a conventional first-principles model with a Gaussian process (GP) machine learning-based model to better predict HV behavior. Our results showed a significant improvement in predicting HV speed, with a 35.64% reduction in the root mean square error compared with the use of the first-principles model alone. We developed a new control strategy called GP-MPC, which uses the proposed HV model for safer distance management between vehicles in the mixed platoon. The GP-MPC strategy effectively utilizes the capacity of the GP model to assess uncertainties, thereby significantly enhancing safety in challenging traffic scenarios, such as emergency braking scenarios. In simulations, the GP-MPC strategy outperformed the baseline MPC method, offering better safety and more efficient vehicle movement in mixed traffic.

cs.RO↗

arXiv2024

Autonomous Locomotion Mode Transition in Quadruped Track-Legged Robots: A Simulation-Based Analysis for Step Negotiation

Hybrid track/wheel-legged robots combine the advantages of wheel-based and leg-based locomotion, granting adaptability across varied terrains through efficient transitions between rolling and walking modes. However, automating these transitions remains a significant challenge. In this paper, we introduce a method designed for autonomous mode transition in a quadruped hybrid robot with a track/wheel-legged configuration, especially during step negotiation. Our approach hinges on a decision-making mechanism that evaluates the energy efficiency of both locomotion modes using a proposed energy-based criterion. To guarantee a smooth negotiation of steps, we incorporate two climbing gaits designated for the assessment of energy usage in walking locomotion. Simulation results validate the method's effectiveness, showing successful autonomous transitions across steps of diverse heights. Our suggested approach has universal applicability and can be modified to suit other hybrid robots of similar mechanical configuration, provided their locomotion energy performance is studied beforehand.

cs.RO↗

arXiv2024

Strong interactions and isospin symmetry breaking in a supermoiré lattice

In multilayer moiré heterostructures, the interference of multiple twist angles ubiquitously leads to tunable ultra-long-wavelength patterns known as supermoiré lattices. However, their impact on the system's many-body electronic phase diagram remains largely unexplored. We present local compressibility measurements revealing numerous incompressible states resulting from supermoiré-lattice-scale isospin symmetry breaking driven by strong interactions. By using the supermoiré lattice occupancy as a probe of isospin symmetry, we observe an unexpected doubling of the miniband filling near $ν=-2$, possibly indicating a hidden phase transition or normal-state pairing proximal to the superconducting phase. Our work establishes supermoiré lattices as a tunable parameter for designing novel quantum phases and an effective tool for unraveling correlated phenomena in moiré materials.

cond-mat.mes-hall↗

arXiv2024

Quantum Weak Force Sensing with Squeezed Magnomechanics

Cavity magnomechanics, exhibiting remarkable experimental tunability, rich magnonic nonlinearities, and compatibility with various quantum systems, has witnessed considerable advances in recent years. However, the potential benefits of using cavity magnomechanical (CMM) systems in further improving the performance of quantum-enhanced sensing for weak forces remain largely unexplored. Here we show that the performance of a quantum CMM sensor can be significantly enhanced beyond the standard quantum limit (SQL), by squeezing the magnons. We find that, for comparable parameters, two orders of enhancement in force sensitivity can be achieved in comparison with the case without the magnon squeezing. Moreover, we show optimal parameter regimes of homodyne angle for minimizing added quantum noise. Our findings provide a promising approach for highly tunable and compatible quantum force sensing using hybrid CMM devices, with potential applications ranging from quantum precision measurements to quantum information processing.

quant-ph↗

arXiv2024

Simulating emission line galaxies for the next generation of large-scale structure surveys

We investigate emission line galaxies across cosmic time by combining the modified L-Galaxies semi-analytical galaxy formation model with the JiuTian cosmological simulation. We improve the tidal disruption model of satellite galaxies in L-Galaxies to address the time dependence problem. We utilise the public code CLOUDY to compute emission line ratios for a grid of HII region models. The emission line models assume the same initial mass function as that used to generate the spectral energy distribution of semi-analytical galaxies, ensuring a coherent treatment for modelling the full galaxy spectrum. By incorporating these emission line ratios with galaxy properties, we reproduce observed luminosity functions for H$α$, H$β$, [OII], and [OIII] in the local Universe and at high redshifts. We also find good agreement between model predictions and observations for auto-correlation and cross-correlation functions of [OII]-selected galaxies, as well as their luminosity dependence. The bias of emission line galaxies depends on both luminosity and redshift. At lower redshifts, it remains constant with increasing luminosity up to around $\sim 10^{42.5}\rm \, erg\,s^{-1}$ and then rises steeply for higher luminosities. The transition luminosity increases with redshift and becomes insignificant above $z$=1.5. Generally, galaxy bias shows an increasing trend with redshift. However, for luminous galaxies, the bias is higher at low redshifts, as the strong luminosity dependence observed at low redshifts diminishes at higher redshifts. We provide a fitting formula for the bias of emission line galaxies as a function of luminosity and redshift, which can be utilised for large-scale structure studies with future galaxy surveys.

astro-ph.GA↗

arXiv2024

Two-sample Test using Projected Wasserstein Distance

We develop a projected Wasserstein distance for the two-sample test, a fundamental problem in statistics and machine learning: given two sets of samples, to determine whether they are from the same distribution. In particular, we aim to circumvent the curse of dimensionality in Wasserstein distance: when the dimension is high, it has diminishing testing power, which is inherently due to the slow concentration property of Wasserstein metrics in the high dimension space. A key contribution is to couple optimal projection to find the low dimensional linear mapping to maximize the Wasserstein distance between projected probability distributions. We characterize the theoretical property of the finite-sample convergence rate on IPMs and present practical algorithms for computing this metric. Numerical examples validate our theoretical results.

stat.ML↗

arXiv2024

Reinforcement Learning-based Recommender Systems with Large Language Models for State Reward and Action Modeling

Reinforcement Learning (RL)-based recommender systems have demonstrated promising performance in meeting user expectations by learning to make accurate next-item recommendations from historical user-item interactions. However, existing offline RL-based sequential recommendation methods face the challenge of obtaining effective user feedback from the environment. Effectively modeling the user state and shaping an appropriate reward for recommendation remains a challenge. In this paper, we leverage language understanding capabilities and adapt large language models (LLMs) as an environment (LE) to enhance RL-based recommenders. The LE is learned from a subset of user-item interaction data, thus reducing the need for large training data, and can synthesise user feedback for offline data by: (i) acting as a state model that produces high quality states that enrich the user representation, and (ii) functioning as a reward model to accurately capture nuanced user preferences on actions. Moreover, the LE allows to generate positive actions that augment the limited offline training data. We propose a LE Augmentation (LEA) method to further improve recommendation performance by optimising jointly the supervised component and the RL policy, using the augmented actions and historical user signals. We use LEA, the state and reward models in conjunction with state-of-the-art RL recommenders and report experimental results on two publicly available datasets.

cs.IR↗