Searcharxiv⌕ Search

arXiv subjects

Ge Chen

Publications and source records attributed to Ge Chen.

At least 73 records · Page 4Linked to original sources
arXiv2022

A comprehensive observational study of the FRB 121102 persistent radio source

FRB 121102 is the first fast radio burst source to be spatially associated with a persistent radio source (QRS121102), the nature of which remains unknown. We present a detailed observational study of QRS121102 and its host galaxy. We constrain the physical size of QRS121102 by measuring its flux-density variability with the VLA in the Ku-band (12 to 18 GHz) and the K-band (18 to 26 GHz). Any such variability would likely be due to Galactic refractive scintillation and would require the source radius to be <10^17 cm at the host-galaxy redshift. We found the radio variability to be lower than the scintillation theory predictions for such a small source, leaving open the possibility for non-AGN models for QRS121102. In addition, we roughly estimated the mass of any potential supermassive black hole (SMBH) associated with QRS121102 from the width of the Hαemission line using a medium-resolution optical spectrum from the Keck Observatory. The line width gives a velocity dispersion of <30 km/s, indicating a SMBH mass of <10^{4~5} M_sun. We find the SMBH mass too low for the observed radio luminosity, and X-ray luminosity constraints, if QRS121102 were an AGN. Finally, some dwarf galaxies that host SMBH may be the stripped cores of massive galaxies during the tidal interactions with companion systems. We find no nearby galaxy at the same redshift as the QRS121102 host from low-resolution Keck spectra, or from the PanSTARRS catalog. In conclusion, we find no evidence supporting the hypothesis that the persistent radio source associated with FRB 121102 is an AGN. We instead argue that the inferred size, and the flat radio spectrum, favors a plerion interpretation. We urge continued broadband radio monitoring of QRS121102 to search for long-term evolution, and the detailed evaluation of potential analogs that may provide greater insight into the nature of this class of object.

astro-ph.HE↗
arXiv2021

District Cooling System Control for Providing Operating Reserve based on Safe Deep Reinforcement Learning

Heating, ventilation, and air conditioning (HVAC) systems are well proved to be capable to provide operating reserve for power systems. As a type of large-capacity and energy-efficient HVAC system (up to 100 MW), district cooling system (DCS) is emerging in modern cities and has huge potential to be regulated as a flexible load. However, strategically controlling a DCS to provide flexibility is challenging, because one DCS services multiple buildings with complex thermal dynamics and uncertain cooling demands. Improper control may lead to significant thermal discomfort and even deteriorate the power system's operation security. To address the above issues, we propose a model-free control strategy based on the deep reinforcement learning (DRL) without the requirement of accurate system model and uncertainty distribution. To avoid damaging "trial & error" actions that may violate the system's operation security during the training process, we further propose a safe layer combined to the DRL to guarantee the satisfaction of critical constraints, forming a safe-DRL scheme. Moreover, after providing operating reserve, DCS increases power and tries to recover all the buildings' temperature back to set values, which may probably cause an instantaneous peak-power rebound and bring a secondary impact on power systems. Therefore, we design a self-adaption reward function within the proposed safe-DRL scheme to constrain the peak-power effectively. Numerical studies based on a realistic DCS demonstrate the effectiveness of the proposed methods.

eess.SY↗
arXiv2021

P4COM: In-Network Computation with Programmable Switches

Traditionally, switches only provide forwarding services and have no credits on computation in distributed computing frameworks. The emerging programmable switches make in-network computing (INC) possible, i.e., offloading some computation to the switch data plane. While some proposals have attempted to offload computation onto special hardwares (e.g., NetFPGA), many practical issues have not been addressed. Therefore, we propose P4COM - a user-friendly, memory-efficient, and fault-tolerant framework realizing in-network computation (e.g., MapReduce) with programmable switches. P4COM consists of three modules. First, P4COM automatically translates application logic to switch data plane programs with a lightweight interpreter. Second, P4COM adopts a memory management policy to efficiently utilize the limited switch on-chip memory. Third, P4COM provides a cutting-payload mechanism to handle packet losses. We have built a P4COM prototype with a Barefoot Tofino switch and multiple commodity servers. Through a combination of testbed experiments and large-scale simulations, we show that P4COM is able to achieve line-rate processing at 10Gbps links, and can increase the data shuffling throughput by 2-5 times for the MapReduce-style applications.

cs.NI↗
arXiv2020

Structural Balance and Interpersonal Appraisals Dynamics: Beyond All-to-All and Two-Faction Networks

Structural balance theory describes stable configurations of topologies of signed interpersonal appraisal networks. Existing models explaining the convergence of appraisal networks to structural balance either diverge in finite time, or could get stuck in jammed states, or converge to only complete graphs. In this paper, we study the open problem how steady non-all-to-all structural balance emerges via local dynamics of interpersonal appraisals. We first compare two well-justified definitions of structural balance for general non-all-to-all graphs, i.e., the triad-wise structural balance and the two-faction structural balance, and thoroughly study their relations. Secondly, based on three widely adopted sociological mechanisms: the symmetry mechanism, the influence mechanism, and the homophily mechanism, we propose two simple models of gossip-like appraisal dynamics, the symmetry-influence-homophily (SIH) dynamics and the symmetry-influence-opinion-homophily (SIOH) dynamics. In these models, the appraisal network starting from any initial condition almost surely achieves non-all-to-all triad-wise and two-faction structural balance in finite time respectively. Moreover, the SIOH dynamics capture the co-evolution of interpersonal appraisals and individuals' opinions. Regarding the theoretical contributions, we show that the equilibrium set of the SIH (SIOH resp.) dynamics corresponds to the set of all the possible triad-wise (two-faction resp.) structural balance configurations of the appraisal networks. Moreover, we prove that, for any initial condition, the appraisal networks in the SIH (SIOH resp.) dynamics almost surely achieve triad-wise (two-faction resp.) structural balance in finite time. Numerical studies of the SIH dynamics also imply some insightful take-home messages on whether multilateral relations reduce or exacerbate conflicts.

cs.SI↗
arXiv2020

Learning Heatmap-Style Jigsaw Puzzles Provides Good Pretraining for 2D Human Pose Estimation

The target of 2D human pose estimation is to locate the keypoints of body parts from input 2D images. State-of-the-art methods for pose estimation usually construct pixel-wise heatmaps from keypoints as labels for learning convolution neural networks, which are usually initialized randomly or using classification models on ImageNet as their backbones. We note that 2D pose estimation task is highly dependent on the contextual relationship between image patches, thus we introduce a self-supervised method for pretraining 2D pose estimation networks. Specifically, we propose Heatmap-Style Jigsaw Puzzles (HSJP) problem as our pretext-task, whose target is to learn the location of each patch from an image composed of shuffled patches. During our pretraining process, we only use images of person instances in MS-COCO, rather than introducing extra and much larger ImageNet dataset. A heatmap-style label for patch location is designed and our learning process is in a non-contrastive way. The weights learned by HSJP pretext task are utilised as backbones of 2D human pose estimator, which are then finetuned on MS-COCO human keypoints dataset. With two popular and strong 2D human pose estimators, HRNet and SimpleBaseline, we evaluate mAP score on both MS-COCO validation and test-dev datasets. Our experiments show that downstream pose estimators with our self-supervised pretraining obtain much better performance than those trained from scratch, and are comparable to those using ImageNet classification models as their initial backbones.

cs.CV↗
arXiv2020

Learning Enhanced Resolution-wise features for Human Pose Estimation

Recently, multi-resolution networks (such as Hourglass, CPN, HRNet, etc.) have achieved significant performance on pose estimation by combining feature maps of various resolutions. In this paper, we propose a Resolution-wise Attention Module (RAM) and Gradual Pyramid Refinement (GPR), to learn enhanced resolution-wise feature maps for precise pose estimation. Specifically, RAM learns a group of weights to represent the different importance of feature maps across resolutions, and the GPR gradually merges every two feature maps from low to high resolutions to regress final human keypoint heatmaps. With the enhanced resolution-wise features learnt by CNN, we obtain more accurate human keypoint locations. The efficacies of our proposed methods are demonstrated on MS-COCO dataset, achieving state-of-the-art performance with average precision of 77.7 on COCO val2017 set and 77.0 on test-dev2017 set without using extra human keypoint training dataset.

cs.CV↗
arXiv2020

Topology-free optimal power dispatch for distribution network considering security constraints and flexible building thermal inertia

With the increasing integration of distributed PV generation, the distribution network requires more and more flexibility to achieve the security-constrained optimal power dispatch. However, the conventional flexibility sources usually require additional investment cost for equipment. Moreover, involving the security constraints is very challenging due to the requirements of accurate network model that may be unavailable in practice. This paper addresses the aforementioned challenge by proposing a topology-free optimal power dispatch framework for distribution networks. It utilizes building thermal inertia to provide flexibility to avoid additional investment. To guarantee the operation safety, a multi-layer perception (MLP) is trained based on historical operational data and then reformulated as mixed-integer constraints to replace the inexplicit original security constraints. Numerical results confirm that the proposed framework can derive a feasible and optimal strategy without any topology information.

math.OC↗
arXiv2020

Quasi-synchronization of bounded confidence opinion dynamics with stochastic asynchronous rule

Recently the theory of noise-induced synchronization of Hegselmann-Krause (HK) dynamics has been well developed. As a typical opinion dynamics of bounded confidence, the HK model obeys a synchronous updating rule, i.e., \emph{all} agents check and update their opinions at each time point. However, whether asynchronous bounded confidence models, including the famous Deffuant-Weisbuch (DW) model, can be synchronized by noise have not been theoretically proved. In this paper, we propose a generalized bounded confidence model which possesses a stochastic asynchronous rule. The model takes the DW model and the HK model as special cases and can significantly generalize the bounded confidence models to practical application. We discover that the asynchronous model possesses a different noise-based synchronization behavior compared to the synchronous HK model. Generally, the HK dynamics can achieve quasi-synchronization \emph{almost surely} under the drive of noise. For the asynchronous dynamics, we prove that the model can achieve quasi-synchronization \emph{in mean}, which is a new type of quasi-synchronization weaker than the "almost surely" sense. The results unify the theory of noise-induced synchronization of bounded confidence opinion dynamics and hence proves the noise-induced synchronization of DW model theoretically for the first time. Moreover, the results provide a theoretical foundation for developing noise-based control strategy of more complex social opinion systems with stochastic asynchronous rules.

cs.MA↗
arXiv2020

The multiwavelength counterparts of fast radio bursts

The engines that produce extragalactic fast radio bursts (FRBs), and the mechanism by which the emission is generated, remain unknown. Many FRB models predict prompt multi-wavelength counterparts, which can be used to refine our knowledge of these fundamentals of the FRB phenomenon. However, several previous targeted searches for prompt FRB counterparts have yielded no detections, and have additionally not reached sufficient sensitivity with respect to the predictions. In this work, we demonstrate a technique to estimate the ratio, $η$, between the energy outputs of FRB counterparts at various wavelengths and the radio-wavelength emission. Our technique combines the fluence distribution of the FRB population with results from several wide-field blind surveys for fast transients from the optical to the TeV bands. We present constraints on $η$ that improve upon previous observations even in the case that all unclassified transient events in existing surveys are FRB counterparts. In some scenarios for the FRB engine and emission mechanism, we find that FRB counterparts should have already been detected, thus demonstrating that our technique can successfully test predictions for $η$. However, it is possible that FRB counterparts are lurking amongst catalogs of unclassified transient events. Although our technique is robust to the present uncertainty in the FRB fluence distribution, its ultimate application to accurately estimate or bound $η$ will require the careful analysis of all candidate fast-transient events in multi-wavelength survey data sets.

astro-ph.HE↗
arXiv2020

Convergence Properties of the Heterogeneous Deffuant-Weisbuch Model

The Deffuant-Weisbuch (DW) model is a bounded-confidence opinion dynamics model that has attracted much recent interest. Despite its simplicity and appeal, the DW model has proved technically hard to analyze and its most basic convergence properties, easy to observe numerically, are only conjectures. This paper solves the convergence problem for the heterogeneous DW model with the weighting factor not less than $1/2$. We establish that, for any positive confidence bounds and initial values, the opinion of each agent will converge to a limit value almost surely, and the convergence rate is exponential in mean square. Moreover, we show that the limiting opinions of any two agents either are the same or have a distance larger than the confidence bounds of the two agents. Finally, we provide some sufficient conditions for the heterogeneous DW model to reach consensus.

eess.SY↗
arXiv2019

Heterogeneous Hegselmann-Krause Dynamics with Environment and Communication Noise

The Hegselmann-Krause (HK) model is a wellknown opinion dynamics, attracting a significant amount of interest from a number of fields. However, the heterogeneous HK model is difficult to analyze - even the most basic property of convergence is still open to prove. For the first time, this paper takes into consideration heterogeneous HK models with environment or communication noise. Under environment noise, it has been revealed that the heterogeneous HK model with or without global information has a phase transition for the upper limit of the maximum opinion difference, and has a critical noise amplitude depending on the minimal confidence threshold for quasi-synchronization. In addition, the convergence time to the quasi-synchronization is bounded by a negative exponential distribution. The heterogeneous HK model with global information and communication noise is also analyzed. Finally, for the basic HK model with communication noise, we show that the heterogeneous case exhibits a different behavior regarding quasi-synchronization from the homogenous case. Interestingly, raising the confidence thresholds of constituent agents may break quasi-synchronization. Our results reveal that the heterogeneity of individuals is harmful to synchronization, which may be the reason why the synchronization of opinions is hard to reach in reality, even within that of a small group.

math.OC↗
arXiv2019

Dynamic Social Balance and Convergent Appraisals via Homophily and Influence Mechanisms

Social balance theory describes allowable and forbidden configurations of the topologies of signed directed social appraisal networks. In this paper, we propose two discrete-time dynamical systems that explain how an appraisal network \textcolor{blue}{converges to} social balance from an initially unbalanced configuration. These two models are based on two different socio-psychological mechanisms respectively: the homophily mechanism and the influence mechanism. Our main theoretical contribution is a comprehensive analysis for both models in three steps. First, we establish the well-posedness and bounded evolution of the interpersonal appraisals. Second, we fully characterize the set of equilibrium points; for both models, each equilibrium network is composed by an arbitrary number of complete subgraphs satisfying structural balance. Third, we establish the equivalence among three distinct properties: non-vanishing appraisals, convergence to all-to-all appraisal networks, and finite-time achievement of social balance. In addition to theoretical analysis, Monte Carlo validations illustrates how the non-vanishing appraisal condition holds for generic initial conditions in both models. Moreover, numerical comparison between the two models indicate that the homophily-based model might be a more universal explanation for the formation of social balance. Finally, adopting the homophily-based model, we present numerical results on the mediation and globalization of local conflicts, the competition for allies, and the asymptotic formation of a single versus two factions.

cs.SI↗
arXiv2019

Noise-induced synchronization of Hegselmann-Krause dynamics in full space

The Hegselmann-Krause (HK) model is a typical self-organizing system with local rule dynamics. In spite of its widespread use and numerous extensions, the underlying theory of its synchronization induced by noise still needs to be developed. In its original formulation, as a model first proposed to address opinion dynamics, its state-space was assumed to be bounded, and the theoretical analysis of noise-induced synchronization for this particular situation has been well established. However, when system states are allowed to exist in an unbounded space, mathematical difficulties arise whose theoretical analysis becomes non-trivial and is as such still lacking. In this paper, we completely resolve this problem by exploring the topological properties of HK dynamics and by employing the theory of independent stopping time. The associated result in full statespace provides a solid interpretation of the randomness-induced synchronization of self-organizing systems

nlin.AO↗
arXiv2019

Noise-based control of opinion dynamics

Designing feasible control strategies for opinion dynamics in complex social systems has never been an easy task. It requires a control protocol which 1) is not enforced on all individuals in the society, and 2) does not exclusively rely on specific opinion values shared by the social system. Thanks to the recent studies on noise-induced consensus in opinion dynamics, the noise-based intervention strategy has emerged as the only one meeting both of the above requirements, yet its underlying general theory is still lacking. In this paper, we perform rigorous theoretical analysis and simulations of a noise-based control strategy for opinion formation in which only a fraction of individuals is affected by randomly generated noise. We found that irrespective of the number of noise-driven individuals, including the case of only one single noise-affected individual, the system can attain a quasi-consensus in finite time, and the critical noise strength can be obtained. Our results highlight the efficiency of noise-driven mechanisms for the control of complex social dynamics.

physics.soc-ph↗
arXiv2019

Interaction-aware Factorization Machines for Recommender Systems

Factorization Machine (FM) is a widely used supervised learning approach by effectively modeling of feature interactions. Despite the successful application of FM and its many deep learning variants, treating every feature interaction fairly may degrade the performance. For example, the interactions of a useless feature may introduce noises; the importance of a feature may also differ when interacting with different features. In this work, we propose a novel model named \emph{Interaction-aware Factorization Machine} (IFM) by introducing Interaction-Aware Mechanism (IAM), which comprises the \emph{feature aspect} and the \emph{field aspect}, to learn flexible interactions on two levels. The feature aspect learns feature interaction importance via an attention network while the field aspect learns the feature interaction effect as a parametric similarity of the feature interaction vector and the corresponding field interaction prototype. IFM introduces more structured control and learns feature interaction importance in a stratified manner, which allows for more leverage in tweaking the interactions on both feature-wise and field-wise levels. Besides, we give a more generalized architecture and propose Interaction-aware Neural Network (INN) and DeepIFM to capture higher-order interactions. To further improve both the performance and efficiency of IFM, a sampling scheme is developed to select interactions based on the field aspect importance. The experimental results from two well-known datasets show the superiority of the proposed models over the state-of-the-art methods.

cs.LG↗
arXiv2018

Noise-synchronizability of opinion dynamics

With the analysis of noise-induced synchronization of opinion dynamics with bounded confidence (BC), a natural and fundamental question is what opinion structures can be synchronized by noise. In the traditional Hegselmann-Krause (HK) model, each agent examines the opinion values of all the other ones and then choose neighbors to update its own opinion according to the BC scheme. In reality, people are more likely to interchange opinions with only some individuals, resulting in a predetermined local discourse relationship as introduced by the DeGroot model. In this paper, we consider an opinion dynamics that combines the schemes of BC and local discourse topology and investigate its synchronization induced by noise. The new model endows the heterogeneous HK model with a time-varying discourse topology. With the proposed definition of noise-synchronizability, it is shown that the compound noisy model is almost surely noise-synchronizable if and only if the time-varying discourse graph is uniformly jointly connected, taking the noise-induced synchronization of the classical heterogeneous HK model as a special case. As a natural implication, the result for the first time builds the equivalence between the connectivity of discourse graph and the beneficial effect of noise for opinion consensus.

eess.SY↗
arXiv2018

Novel Multi Agent Models for Chemical Self-assembly

The chemical self-assembly has been considered as one of most important scientific problems in the 21th Century; however, since the process of self-assembly is very complex, there is few mathematic theory for it currently. This paper provides a novel multi-agent model for chemical self-assembly, where the interaction between agents adopts the classic Lennard-Jones potential. Under this model, we propose an optimal problem by taking the temperature as the control input, and choosing the internal energy as the optimal object. A numerical solution for our optimal problem is also developed. Simulations show that our control scheme can improve the product of self-assembly. Further more, we give a strict analysis for the self-assembly model without noise, which corresponds to an attraction-repulsion multi-agent system, and prove it converges to a stable configuration eventually.

math.OC↗