arXiv subjects

Yao Meng

Publications and source records attributed to Yao Meng.

At least 19 recordsLinked to original sources

arXiv2026

Detecting Quantum Stochastic Effects in Radiation Reaction via Laser-Produced Surface QED Plasmas

We propose a method to detect quantum stochastic effects in radiation reaction by irradiating a V-shaped plasma cavity with an ultra-intense laser pulse. The pulse accelerates GeV electrons along the inner surface and simultaneously drives strong-field surface wave near the cavity apex. The accelerated electron bunches then collide with the surface wave, the latter acts as an effective counter-propagating ultra-intense electromagnetic wave, triggering significant radiation reaction. Importantly, because the surface wave is confined to an ultra-thin QED plasma layer (on the scale of the skin depth) where the expected number of hard photon emissions per electron is of order unity, stochastic effects are expected. Three-dimensional particle-in-cell simulations with different QED models show that radiation reaction strongly reshapes the angular distribution of high-energy electrons. In particular, electrons deflected by the surface wave experience strong radiation loss. However, compared with the semi-classical model, the stochastic QED model preserves a higher-energy component in the deflected beam, producing a clear angular-spectral signature, which potentially opens a pathway for experimental study of quantum stochastic effects in radiation reaction.

physics.plasm-ph↗

arXiv2025

Relativistic Oscillating Window Driven by an Intense Laguerre Gaussian Laser Pulse

High-order harmonic generation by the diffraction of an intense Laguerre-Gaussian (LG) laser beam through a small aperture is studied. It is found that the 2D peripheral electron dynamics on the rim can facilitate complex interplay between the spin and orbital angular momentum interaction, which leads to distinct selection rules for LG pulses with different polarization states. In particular, when the driver is linearly polarized, the harmonic beams no longer follow a simple orbital angular momentum conservation rule. Instead, multiple LG modes with different topological charges are produced in each harmonic beam, and the number of modes equals to the harmonic order. A theory is derived and validated by simulations, which can predict the harmonic topological charges as well as their relative intensities for LG drivers with different polarization states. Our work provides fundamental insight into the behavior of light in nonlinear optics, and paves the way towards high-intensity UV or X-ray pulses carrying controllable OAM, that can serve as versatile tools at frontiers of various scientific fields.

physics.plasm-ph↗

arXiv2025

Personalised strategy of allocating social goods in structured populations

Cooperation underlies many aspects of the evolution of human and animal societies, where cooperators produce social goods to benefit others. Explaining the emergence of cooperation among selfish individuals has become a major research interest in evolutionary dynamics. Previous studies typically use complex networks to capture the interactions between individuals, and assume that cooperators distribute benefits equally to their neighbors. In practice, the distribution of social goods is often non-uniform, and individuals may selectively provide benefits to those they interact with based on their personal preferences. Here, we develop an efficient algorithm to optimize the placement of donation structure in any given network to minimize the threshold for the emergence of cooperation. We find when cooperators allocate the benefits preferentially compared to the traditional settings of donating to all neighbors, cooperation tends to be maximally promoted. Furthermore, the optimal donation structure is strongly disassortative -- the low-degree nodes tend to donate to high-degree ones preferentially and vice versa. Based on this finding, we offer a local heuristic strategy based on degree thresholds for personalizing the allocation of social goods and choosing each cooperator's recipient, which we use to prove its effectiveness in empirical datasets. Our findings advance the understanding of mechanisms for promoting cooperation with strategic allocations of social goods.

physics.soc-ph↗

arXiv2025

Evolutionary game dynamics for higher-order interactions

Cooperative behaviors are deeply embedded in structured biological and social systems. Networks are often employed to portray pairwise interactions among individuals, where network nodes represent individuals and links indicate who interacts with whom. However, it is increasingly recognized that many empirical interactions often involve triple or more individuals instead of the massively oversimplified lower-order pairwise interactions, highlighting the fundamental gap in understanding the evolution of collective cooperation for higher-order interactions with diverse scales of the number of individuals. Here, we develop a theoretical framework of evolutionary game dynamics for systematically analyzing how cooperation evolves and fixates under higher-order interactions. Specifically, we offer a simple condition under which cooperation is favored under arbitrary combinations of different orders of interactions. Compared to pairwise interactions, our findings suggest that higher-order interactions enable lower thresholds for the emergence of cooperation. Surprisingly, we show that higher-order interactions favor the evolution of cooperation in large-scale systems, which is the opposite for lower-order scenarios. Our results offer a new avenue for understanding the evolution of collective cooperation in empirical systems with higher-order interactions.

physics.soc-ph↗

arXiv2024

Promoting collective intelligence: The advantage of temporal star-structures

System structures play an essential role in the emergence of collective intelligence in many natural and engineering systems. In empirical systems, interactions among multiple agents may change over time, forming a temporal network structure, where nodes represent the system's components and links capture who interacts with whom. Recent studies report that temporal networks are more conducive to the emergence of collective cooperation compared to their aggregated static structures. However, the question of which kind of structural characteristics of temporal networks promote collective cooperation still remains elusive. Here we systematically investigate the evolution of cooperation on temporal networks with diverse structural characteristics, such as random, star, and cluster structures. We uncover that temporal networks with single-star structures which lack network clusters are more conducive to collective cooperation than other structures. This counterintuitive result cautions against the common belief that network clusters normally facilitate collective cooperation, revealing the unique advantages of temporal networks over static networks. We further propose an index to quantify the capacity of structural characteristics of temporal networks in promoting collective cooperation. Our findings pave the way for designing the optimal structure of temporal networks to favour collective cooperation.

physics.soc-ph↗

arXiv2024

Promoting collective cooperation through temporal interactions

Collective cooperation drives the dynamics of many natural, social, and economic phenomena, making understanding the evolution of cooperation with evolutionary game theory a central question of modern science. Although human interactions are best described as complex networks, current explorations are limited to static networks where interactions represented by network links are permanent and do not change over time. In reality, human activities often involve temporal interactions, where links are impermanent, and understanding the evolution of cooperation on such ubiquitous temporal networks is an open question. Here, we present a general framework for systematically analyzing how collective cooperation evolves on any temporal network, which unifies the study of evolutionary game dynamics with dynamic and static interactions. We show that the emergence of cooperation is facilitated by a simple rule of thumb: hubs (individuals with many social ties) should be temporally deprioritized in interactions. We further provide a quantitative metric capturing the priority of hubs, which we utilize to orchestrate the ordering of interactions to best promote cooperation on empirical temporal networks. Our findings unveil the fundamental advantages conferred by temporal interactions for promoting collective cooperation, which transcends the specific insights gleaned from studying traditional static snapshots.

physics.soc-ph↗

arXiv2023

Temporal networks provide a unifying understanding of the evolution of cooperation

Understanding the evolution of cooperation in structured populations represented by networks is a problem of long research interest, and a most fundamental and widespread property of social networks related to cooperation phenomena is that the node's degree (i.e., number of edges connected to the node) is heterogeneously distributed. Previous results indicate that static heterogeneous (i.e., degree-heterogeneous) networks promote cooperation in stationarity compared to static regular (i.e., degree-homogeneous) networks if equilibrium dynamics starting from many cooperators and defectors is employed. However, the above conclusion reverses if we employ non-equilibrium stochastic processes to measure the fixation probability for cooperation, i.e., the probability that a single cooperator successfully invades a population. Here we resolve this conundrum by analyzing the fixation of cooperation on temporal (i.e., time-varying) networks. We theoretically prove and numerically confirm that on both synthetic and empirical networks, contrary to the case of static networks, temporal heterogeneous networks can promote cooperation more than temporal regular networks in terms of the fixation probability of cooperation. Given that the same conclusion is known for the equilibrium fraction of cooperators on temporal networks, the present results provide a unified understanding of the effect of temporal degree heterogeneity on promoting cooperation across two main analytical frameworks, i.e., equilibrium and non-equilibrium ones.

physics.soc-ph↗

arXiv2023

Doubly heterogeneous networks facilitate the emergence of collective cooperation

There is growing recognition that the network structures arising from interactions between different entities in physical, social and biological systems fundamentally alter the evolutionary outcomes. Previous paradigm exploring evolutionary game dynamics has assumed that individuals update their strategies at an identical rate, reporting that structurally heterogeneous networks -- despite their ubiquity in real systems -- generally hinder the emergence of collective cooperation compared to their homogeneous counterparts. Here we solve this paradox by creating a new paradigm where individuals on arbitrary networks are allowed to update strategies at arbitrary, personalized rates, and provide the precise condition under which universal collective cooperation is favored. We find that when individuals' update rates vary inversely with their number of connections, heterogeneous networks actually outperform homogeneous ones in promoting cooperation. This surprising property of such "doubly heterogeneous" networks cautions against the conventional wisdom that heterogeneous networks are antagonistic to cooperation. We further develop an efficient protocol for optimizing the promotion of cooperation by tuning individuals' update rates in any structure. Our findings highlight that personalized interaction dynamics, beyond structure, in complex networks are fundamental to understanding and promoting collective cooperation.

physics.soc-ph↗

arXiv2023

TaxoKnow: Taxonomy as Prior Knowledge in the Loss Function of Multi-class Classification

In this paper, we investigate the effectiveness of integrating a hierarchical taxonomy of labels as prior knowledge into the learning algorithm of a flat classifier. We introduce two methods to integrate the hierarchical taxonomy as an explicit regularizer into the loss function of learning algorithms. By reasoning on a hierarchical taxonomy, a neural network alleviates its output distributions over the classes, allowing conditioning on upper concepts for a minority class. We limit ourselves to the flat classification task and provide our experimental results on two industrial in-house datasets and two public benchmarks, RCV1 and Amazon product reviews. Our obtained results show the significant effect of a taxonomy in increasing the performance of a learner in semisupervised multi-class classification and the considerable results obtained in a fully supervised fashion.

cs.LG↗

arXiv2023

Embrace Limited and Imperfect Training Datasets: Opportunities and Challenges in Plant Disease Recognition Using Deep Learning

Recent advancements in deep learning have brought significant improvements to plant disease recognition. However, achieving satisfactory performance often requires high-quality training datasets, which are challenging and expensive to collect. Consequently, the practical application of current deep learning-based methods in real-world scenarios is hindered by the scarcity of high-quality datasets. In this paper, we argue that embracing poor datasets is viable and aim to explicitly define the challenges associated with using these datasets. To delve into this topic, we analyze the characteristics of high-quality datasets, namely large-scale images and desired annotation, and contrast them with the \emph{limited} and \emph{imperfect} nature of poor datasets. Challenges arise when the training datasets deviate from these characteristics. To provide a comprehensive understanding, we propose a novel and informative taxonomy that categorizes these challenges. Furthermore, we offer a brief overview of existing studies and approaches that address these challenges. We believe that our paper sheds light on the importance of embracing poor datasets, enhances the understanding of the associated challenges, and contributes to the ambitious objective of deploying deep learning in real-world applications. To facilitate the progress, we finally describe several outstanding questions and point out potential future directions. Although our primary focus is on plant disease recognition, we emphasize that the principles of embracing and analyzing poor datasets are applicable to a wider range of domains, including agriculture.

cs.CV↗

arXiv2023

Impact of misinformation in the evolution of collective cooperation

Human societies are organized and developed through collective cooperative behaviors, in which interactions between individuals are governed by the underlying social connections. It is well known that, based on the information in their environment, individuals can form collective cooperation by strategically imitating superior behaviors and changing unfavorable surroundings in self-organizing ways. However, facing the tough situation that some humans and social bots keep spreading misinformation, we still lack the systematic investigation on the impact of such proliferation of misinformation on the evolution of social cooperation. Here we study this problem by virtue of classical evolutionary game theory. We find that misinformation generally impedes the emergence of collective cooperation compared to scenarios with completely true information, although the level of cooperation is slightly higher when the benefits provided by cooperators are reduced below a proven threshold. We further show that this possible advantage shrinks as social connections become denser, suggesting that misinformation is more detrimental to the formation of collective cooperation when 'social viscosity' is low. Our results uncover the quantitative effect of misinformation on the social cooperative behavior in the complex networked society, and pave the way for designing possible interventions to improve collective cooperation.

physics.soc-ph↗

arXiv2020

FeederGAN: Synthetic Feeder Generation via Deep Graph Adversarial Nets

This paper presents a novel, automated, generative adversarial networks (GAN) based synthetic feeder generation mechanism, abbreviated as FeederGAN. FeederGAN digests real feeder models represented by directed graphs via a deep learning framework powered by GAN and graph convolutional networks (GCN). Information of a distribution feeder circuit is extracted from its model input files so that the device connectivity is mapped onto the adjacency matrix and the device characteristics, such as circuit types (i.e., 3-phase, 2-phase, and 1-phase) and component attributes (e.g., length and current ratings), are mapped onto the attribute matrix. Then, Wasserstein distance is used to optimize the GAN and GCN is used to discriminate the generated graphs from the actual ones. A greedy method based on graph theory is developed to reconstruct the feeder using the generated adjacency and attribute matrices. Our results show that the GAN generated feeders resemble the actual feeder in both topology and attributes verified by visual inspection and by empirical statistics obtained from actual distribution feeders.

eess.SY↗

arXiv2020

A Lifting Wing Fixed on Multirotor UAVs for Long Flight Ranges

This paper presents a lifting-wing multirotor UAV that allows long-range flight. The UAV features a lifting wing in a special mounting angle that works together with rotors to supply lift when it flies forward, achieving a reduction in energy consumption and improvement of flight range compared to traditional multirotor UAVs. Its dynamic model is built according to the classical multirotor theory and the fixed-wing theory, as the aerodynamics of its multiple propellers and that of its lifting wing are almost decoupled. Its design takes into consideration aerodynamics, airframe configuration and the mounting angle. The performance of the UAV is verified by experiments, which show that the lifting wing saves 50.14% of the power when the UAV flies at the cruise speed (15m/s).

eess.SY↗

arXiv2020

Bootstrapping Named Entity Recognition in E-Commerce with Positive Unlabeled Learning

Named Entity Recognition (NER) in domains like e-commerce is an understudied problem due to the lack of annotated datasets. Recognizing novel entity types in this domain, such as products, components, and attributes, is challenging because of their linguistic complexity and the low coverage of existing knowledge resources. To address this problem, we present a bootstrapped positive-unlabeled learning algorithm that integrates domain-specific linguistic features to quickly and efficiently expand the seed dictionary. The model achieves an average F1 score of 72.02% on a novel dataset of product descriptions, an improvement of 3.63% over a baseline BiLSTM classifier, and in particular exhibits better recall (4.96% on average).

cs.CL↗

arXiv2020

Time Series Classification for Locating Forced Oscillation Sources

Forced oscillations are caused by sustained cyclic disturbances. This paper presents a machine learning (ML) based time-series classification method that uses the synchrophasor measurements to locate the sources of forced oscillations for fast disturbance removal. Sequential feature selection is used to identify the most informative measurements of each power plant so that multivariate time series (MTS) can be constructed. By training the Mahalanobis matrix, we measure and compare the distance between the MTSs. Templates for representing each class is constructed to reduce the size of training datasets and improve the online matching efficiency. Dynamic time warping (DTW) algorithm is used to align the out-of-sync MTSs to account for oscillation detection errors. The algorithm is validated on two test systems: the IEEE 39-bus system and the WECC 179-bus system. When a forced oscillation occurs, MTSs will be constructed by designated PMU measurements. Then, the MTSs will be classified by the trained classifiers, the class membership of which corresponds to the location of each oscillation source. Simulation results show that the proposed method can be used online to identify the forced oscillation sources with high accuracy. The robustness of the proposed algorithm in the presence of oscillation detection errors is also quantified.

eess.SY↗

arXiv2020

Estimating red noise in quasi-periodic signals with MCMC-based Bayesian

Multi-parameter Bayesian inferences based on Markov chain Monte Carlo (MCMC) samples have been widely used to estimate red noise in solar period-periodic signals. To MCMC, proper priors and sufficient iterations are prerequisites ensuring the accuracy of red noise estimation. We used MCMC-based Bayesian inferences to estimate 100 groups of red noise synthesized randomly for evaluating its accuracy. At the same time, the Brooks-Gelman algorithm was employed to precisely diagnose the convergence of the Markov chains generated by MCMC. The root-mean-square error of parameter inferences to the synthetic data is only 1.14. Furthermore, we applied the algorithm to analyze the oscillation modes in a sunspot and a flare. A 70 s period is detected in the sunspot umbra in addition to 3- and 5-minute periods, and a 40 s period is detected in the flare. The results prove that estimating red noise with MCMC-based Bayesian has more high accuracy in the case of proper priors and convergence. We also find that the number of iterations increases dramatically to achieve convergence as the number of parameters grows. Therefore, we strongly recommend that when estimating red noise with MCMC-based Bayesian, different initial values must be selected to ensure that the entire posterior distribution is covered.

astro-ph.SR↗

arXiv2019

Spatial Distributions of Sunspot Oscillation Modes at Different Temperatures

Three- and five-minute oscillations of sunspots have different spatial distributions in the solar atmospheric layers. The spatial distributions are crucial to reveal the physical origin of sunspot oscillations and to investigate their propagation. In this study, six sunspots observed by Solar Dynamics Observatory/Atmospheric Imaging Assembly were used to obtain the spatial distributions of three- and five-minute oscillations. The fast Fourier transform method is applied to represent the power spectra of oscillation modes. We find that, from the temperature minimum to the lower corona, the powers of the five-minute oscillation exhibit a circle-shape distribution around its umbra, and the shapes gradually expand with temperature increase. However, the circle-shape is disappeared and the powers of the oscillations appear to be very disordered in the higher corona. This indicates that the five-minute oscillation can be suppressed in the high-temperature region. For the three-minute oscillations, from the temperature minimum to the high corona, their powers mostly distribute within an umbra, and part of them locate at the coronal fan loop structures. Moreover, those relative higher powers are mostly concentrated in the position of coronal loop footpoints.

astro-ph.SR↗

arXiv2017

Forced Oscillation Source Location via Multivariate Time Series Classification

Precisely locating low-frequency oscillation sources is the prerequisite of suppressing sustained oscillation, which is an essential guarantee for the secure and stable operation of power grids. Using synchrophasor measurements, a machine learning method is proposed to locate the source of forced oscillation in power systems. Rotor angle and active power of each power plant are utilized to construct multivariate time series (MTS). Applying Mahalanobis distance metric and dynamic time warping, the distance between MTS with different phases or lengths can be appropriately measured. The obtained distance metric, representing characteristics during the transient phase of forced oscillation under different disturbance sources, is used for offline classifier training and online matching to locate the disturbance source. Simulation results using the four-machine two-area system and IEEE 39-bus system indicate that the proposed location method can identify the power system forced oscillation source online with high accuracy.

eess.SP↗