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Jun Yan

Publications and source records attributed to Jun Yan.

At least 199 records · Page 11Linked to original sources
arXiv2021

Hamilton-Jacobi equations with their Hamiltonians depending Lipschitz continuously on the unknown

We study the Hamilton-Jacobi equations $H(x,Du,u)=0$ in $M$ and $\partial u/\partial t +H(x,D_xu,u)=0$ in $M\times(0,\infty)$, where the Hamiltonian $H=H(x,p,u)$ depends Lipschitz continuously on the variable $u$. In the framework of the semicontinuous viscosity solutions due to Barron-Jensen, we establish the comparison principle, existence theorem, and representation formula as value functions for extended real-valued, lower semicontinuous solutions for the Cauchy problem. We also establish some results on the long-time behavior of solutions for the Cauchy problem and classification of solutions for the stationary problem.

math.AP↗
arXiv2021

Aubry-Mather theory for contact Hamiltonian systems II

In this paper, we continue to develop Aubry-Mather and weak KAM theories for contact Hamiltonian systems $H(x,u,p)$ with certain dependence on the contact variable $u$. For the Lipschitz dependence case, we obtain some properties of the Mañé set. For the non-decreasing case, we provide some information on the Aubry set, such as the comparison property, graph property and a partially ordered relation for the collection of all projected Aubry sets with respect to backward weak KAM solutions. Moreover, we find a new flow-invariant set $\tilde{\mathcal{S}}_s$ consists of strongly static orbits, which coincides with the Aubry set $\tilde{\mathcal{A}}$ in classical Hamiltonian systems. Nevertheless, a class of examples are constructed to show $\tilde{\mathcal{S}}_s\subsetneqq\tilde{\mathcal{A}}$ in the contact case. As their applications, we find some new phenomena appear even if the strictly increasing dependence of $H$ on $u$ fails at only one point, and we show that there is a difference for the vanishing discount problem from the negative direction between the minimal viscosity solution and non-minimal ones.

math.DS↗
arXiv2021

Probing the bright exciton state in twisted bilayer graphene via resonant Raman scattering

The band structure of bilayer graphene is tunable by introducing a relative twist angle between the two layers, unlocking exotic phases, such as superconductor and Mott insulator, and providing a fertile ground for new physics. At intermediate twist angles around 10°, highly degenerate electronic transitions hybridize to form excitonic states, a quite unusual phenomenon in a metallic system. We probe the bright exciton mode using resonant Raman scattering measurements to track the evolution of the intensity of the graphene Raman G peak, corresponding to the E2g phonon. By cryogenically cooling the sample, we are able to resolve both the incoming and outgoing resonance in the G peak intensity evolution as a function of excitation energy, a prominent manifestation of the bright exciton serving as the intermediate state in the Raman process. For a sample with twist angle 8.6°, we report a weakly temperature dependent resonance broadening $γ$ ${\approx}$ 0.07 eV. In the limit of small inhomogeneous broadening, the observed $γ$ places a lower bound for the bright exciton scattering lifetime at 10 fs in the presence of charges and excitons excited by the light pulse for Raman measurement, limited by the rapid exciton-exciton and exciton-charge scattering in graphene.

cond-mat.mes-hall↗
arXiv2021

Learning Contextualized Knowledge Structures for Commonsense Reasoning

Recently, knowledge graph (KG) augmented models have achieved noteworthy success on various commonsense reasoning tasks. However, KG edge (fact) sparsity and noisy edge extraction/generation often hinder models from obtaining useful knowledge to reason over. To address these issues, we propose a new KG-augmented model: Hybrid Graph Network (HGN). Unlike prior methods, HGN learns to jointly contextualize extracted and generated knowledge by reasoning over both within a unified graph structure. Given the task input context and an extracted KG subgraph, HGN is trained to generate embeddings for the subgraph's missing edges to form a "hybrid" graph, then reason over the hybrid graph while filtering out context-irrelevant edges. We demonstrate HGN's effectiveness through considerable performance gains across four commonsense reasoning benchmarks, plus a user study on edge validness and helpfulness.

cs.CL↗
arXiv2021

AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding

Automatic extraction of product attribute values is an important enabling technology in e-Commerce platforms. This task is usually modeled using sequence labeling architectures, with several extensions to handle multi-attribute extraction. One line of previous work constructs attribute-specific models, through separate decoders or entirely separate models. However, this approach constrains knowledge sharing across different attributes. Other contributions use a single multi-attribute model, with different techniques to embed attribute information. But sharing the entire network parameters across all attributes can limit the model's capacity to capture attribute-specific characteristics. In this paper we present AdaTag, which uses adaptive decoding to handle extraction. We parameterize the decoder with pretrained attribute embeddings, through a hypernetwork and a Mixture-of-Experts (MoE) module. This allows for separate, but semantically correlated, decoders to be generated on the fly for different attributes. This approach facilitates knowledge sharing, while maintaining the specificity of each attribute. Our experiments on a real-world e-Commerce dataset show marked improvements over previous methods.

cs.CL↗
arXiv2021

Parameterized viscosity solutions of convex Hamiltonian systems with time periodic damping

In this article we develop an analogue of Aubry Mather theory for time periodic dissipative equation \[ \left\{ \begin{aligned} \dot x&=\partial_p H(x,p,t),\\ \dot p&=-\partial_x H(x,p,t)-f(t)p \end{aligned} \right. \] with $(x,p,t)\in T^*M\times\mathbb T$ (compact manifold $M$ without boundary). We discuss the asymptotic behaviors of viscosity solutions of associated Hamilton-Jacobi equation \[ \partial_t u+f(t)u+H(x,\partial_x u,t)=0,\quad(x,t)\in M\times\mathbb T \] w.r.t. certain parameters, and analyze the meanings in controlling the global dynamics. We also discuss the prospect of applying our conclusions to many physical models.

math.DS↗
arXiv2021

Viscosity subsoltions of Hamilton-Jacobi equations and Invariant sets of contact Hamilton systems

The objective of this paper is to present some results about viscosity subsolutions of the contact Hamiltonian-Jacobi equations on connected, closed manifold $M$ $$ H(x,\partial_x u,u)= 0, \quad x\in M. $$ Based on implicit variational principles introduced in [12,14], we focus on the monotonicity of the solution semigroups on viscosity subsolutions and the positive invariance of the epigraph for viscosity subsolutions. Besides, we show a similar consequence for strict viscosity subsolutions on $M$.

math.DS↗
arXiv2021

The FAST Galactic Plane Pulsar Snapshot survey: I. Project design and pulsar discoveries

Discovery of pulsars is one of the main goals for large radio telescopes. The Five-hundred-meter Aperture Spherical radio Telescope (FAST), that incorporates an L-band 19-beam receiver with a system temperature of about 20~K, is the most sensitive radio telescope utilized for discovering pulsars. We designed the {\it snapshot} observation mode for a FAST key science project, the Galactic Plane Pulsar Snapshot (GPPS) survey, in which every four nearby pointings can observe {\it a cover} of a sky patch of 0.1575 square degrees through beam-switching of the L-band 19-beam receiver. The integration time for each pointing is 300 seconds so that the GPPS observations for a cover can be made in 21 minutes. The goal of the GPPS survey is to discover pulsars within the Galactic latitude of $\pm10^{\circ}$ from the Galactic plane, and the highest priority is given to the inner Galaxy within $\pm5^{\circ}$. Up to now, the GPPS survey has discovered 201 pulsars, including currently the faintest pulsars which cannot be detected by other telescopes, pulsars with extremely high dispersion measures (DMs) which challenge the currently widely used models for the Galactic electron density distribution, pulsars coincident with supernova remnants, 40 millisecond pulsars, 16 binary pulsars, some nulling and mode-changing pulsars and rotating radio transients (RRATs). The follow-up observations for confirmation of new pulsars have polarization-signals recorded for polarization profiles of the pulsars. Re-detection of previously known pulsars in the survey data also leads to significant improvements in parameters for 64 pulsars. The GPPS survey discoveries are published and will be updated at http://zmtt.bao.ac.cn/GPPS/ .

astro-ph.HE↗
arXiv2021

PageRank centrality and algorithms for weighted, directed networks with applications to World Input-Output Tables

PageRank (PR) is a fundamental tool for assessing the relative importance of the nodes in a network. In this paper, we propose a measure, weighted PageRank (WPR), extended from the classical PR for weighted, directed networks with possible non-uniform node-specific information that is dependent or independent of network structure. A tuning parameter leveraging node degree and strength is introduced. An efficient algorithm based on R program has been developed for computing WPR in large-scale networks. We have tested the proposed WPR on widely used simulated network models, and found it outperformed other competing measures in the literature. By applying the proposed WPR to the real network data generated from World Input-Output Tables, we have seen the results that are consistent with the global economic trends, which renders it a preferred measure in the analysis.

physics.soc-ph↗
arXiv2021

Medium modifications of girth distributions for inclusive jets and $Z^0+{\rm jet}$ in relativistic heavy-ion collisions at the LHC

In this paper we investigate the medium modifications of girth distributions for inclusive jets and $Z^0$ tagged jets with small radius ($R=0.2$) in Pb+Pb collisions with $\sqrt{s}=2.76$ TeV at the LHC. The partonic spectrum in the initial hard scattering of elementary collisions are obtained by a event generator POWHEG+PYTHIA, which matches the next-to-leading (NLO) matrix elements with parton showering, and energy loss of faster parton traversing in hot/dense QCD medium is calculated by Monte Carlo simulation within Higher-Twist formalism of jet quenching in heavy-ion collisions. We present the model calculations of event normalized girth distributions for inclusive jets in p+p and Pb+Pb collisions at $\sqrt{s}=2.76$ TeV, which give nice descriptions of ALICE measurements. It is shown that the girth distributions of inclusive jets in Pb+Pb are shifted to lower girth region relative to that in p+p. Thus the nuclear modification factor of girth distributions for inclusive jets is larger than unity at small girth region, while smaller than one at large girth region. This behavior results from more soft fragments inside a jet as well as the fraction alteration of gluon/quark initiated jets in heavy-ion collisions. We further predict the girth distributions for $Z^0$ boson tagged jets in Pb+Pb collisions at $\sqrt{s}=2.76$ TeV, and demonstrate that the medium modification on girth distributions for $Z^0$ tagged jets is less pronounced as compared to that for inclusive jets because the dominant components of $Z^0$ tagged jets are quark-initiated jets.

hep-ph↗
arXiv2021

A Comprehensive Framework for Analyzing IoT Platforms: A Smart City Industrial Experience

The compliance of IoT platforms to quality is paramount to achieve users satisfaction. Currently, we do not have a comprehensive set of guidelines to appraise and select the most suitable IoT platform architectures that meet relevant criteria. This paper is a tentative response to this critical knowledge gap where we adopted the design science research approach to develop a novel evaluation framework. Our research, on the one hand, stimulates an unbiased competition among IoT platform providers and, on the other hand, establishes a solid foundation for IoT platform consumers to make informed decisions in this multiplicity. The application of the framework is illustrated in example scenarios. Moreover, lessons learned from applying design science research are shared.

cs.SE↗
arXiv2021

Monitoring Object Detection Abnormalities via Data-Label and Post-Algorithm Abstractions

While object detection modules are essential functionalities for any autonomous vehicle, the performance of such modules that are implemented using deep neural networks can be, in many cases, unreliable. In this paper, we develop abstraction-based monitoring as a logical framework for filtering potentially erroneous detection results. Concretely, we consider two types of abstraction, namely data-label abstraction and post-algorithm abstraction. Operated on the training dataset, the construction of data-label abstraction iterates each input, aggregates region-wise information over its associated labels, and stores the vector under a finite history length. Post-algorithm abstraction builds an abstract transformer for the tracking algorithm. Elements being associated together by the abstract transformer can be checked against consistency over their original values. We have implemented the overall framework to a research prototype and validated it using publicly available object detection datasets.

cs.AI↗
arXiv2021

Electric Vehicle Charging Scheduling in Green Logistics: Challenges, Approaches and Opportunities

Replacing a fossil fuel-powered car with an electric model can halve greenhouse gas emissions over the course of the vehicle's lifetime and reduce the noise pollution in urban areas. In green logistics, a well-scheduled charging ensures an efficient operation of transportation and power systems and, at the same time, provides economical and satisfactory charging services for drivers. This paper presents a taxonomy of current electric vehicle charging scheduling problems in green logistics by analyzing its unique features with some typical use cases, such as space assignment, routing and energy management; discusses the challenges, i.e., the information availability and stakeholders' strategic behaviors that arise in stochastic and decentralized environments; and classifies the existing approaches, as centralized, distributed and decentralized ones, that apply to these challenges. Moreover, we discuss research opportunities in applying market-based mechanisms, which shall be coordinated with stochastic optimization and machine learning, to the decentralized, dynamic and data-driven charging scheduling problems for the management of the future green logistics.

cs.MA↗
arXiv2021

The Effects of the NBA COVID Bubble on the NBA Playoffs: A Case Study for Home-Court Advantage

The 2020 NBA playoffs were played inside of a bubble in Disney World because of the COVID-19 pandemic. This meant that there were no fans in attendance, games played on neutral courts and no traveling for teams, which in theory removes home-court advantage from the games. This setting has attracted much discussion as analysts and fans debated the possible effects it may have on the outcome of games. Home-court advantage has historically played an influential role in NBA playoff series outcomes. The 2020 playoff provided a unique opportunity to study the effects of the bubble and home-court advantage by comparing the 2020 season with the seasons in the past. While many factors contribute to the outcome of games, points scored is the deciding factor of who wins games, so scoring is the primary focus of this study. The specific measures of interest are team scoring totals and team shooting percentage on two-pointers, three-pointers, and free throws. Comparing these measures for home teams and away teams in 2020 vs. 2017-2019 shows that the 2020 playoffs favored away teams more than usual, particularly with two point shooting and total scoring.

stat.AP↗
arXiv2021

Regional and Sectoral Structures and Their Dynamics of Chinese Economy: A Network Perspective from Multi-Regional Input-Output Tables

A multi-regional input-output table (MRIOT) containing the transactions among the region-sectors in an economy defines a weighted and directed network. Using network analysis tools, we analyze the regional and sectoral structure of the Chinese economy and their temporal dynamics from 2007 to 2012 via the MRIOTs of China. Global analyses are done with network topology measures. Growth-driving province-sector clusters are identified with community detection methods. Influential province-sectors are ranked by weighted PageRank scores. The results revealed a few interesting and telling insights. The level of inter-province-sector activities increased with the rapid growth of the national economy, but not as fast as that of intra-province economic activities. Regional community structures were deeply associated with geographical factors. The community heterogeneity across the regions was high and the regional fragmentation increased during the study period. Quantified metrics assessing the relative importance of the province-sectors in the national economy echo the national and regional economic development policies to a certain extent.

physics.soc-ph↗
arXiv2021

Clustering US States by Time Series of COVID-19 New Case Counts with Non-negative Matrix Factorization

The spreading pattern of COVID-19 differ a lot across the US states under different quarantine measures and reopening policies. We proposed to cluster the US states into distinct communities based on the daily new confirmed case counts via a nonnegative matrix factorization (NMF) followed by a k-means clustering procedure on the coefficients of the NMF basis. A cross-validation method was employed to select the rank of the NMF. Applying the method to the entire study period from March 22 to July 25, we clustered the 49 continental states (including District of Columbia) into 7 groups, two of which contained a single state. To investigate the dynamics of the clustering results over time, the same method was successively applied to the time periods with increment of one week, starting from the period of March 22 to March 28. The results suggested a change point in the clustering in the week starting on May 30, which might be explained by a combined impact of both quarantine measures and reopening policies.

stat.AP↗
arXiv2021

Atomic Norm Based Localization of Far-Field and Near-Field Signals with Generalized Symmetric Arrays

Most localization methods for mixed far-field (FF) and near-field (NF) sources are based on uniform linear array (ULA) rather than sparse linear array (SLA). In this paper, we propose a localization method for mixed FF and NF sources based on the generalized symmetric linear arrays, which include ULAs, Cantor array, Fractal array and many other SLAs. Our method consists of two steps. In the first step, the high-order statistics of the array output is exploited to increase the degree of freedom. Then the direction-of-arrivals (DOAs) of the FF and NF sources are jointly estimated by using the recently proposed atomic norm minimization (ANM), which belongs to the gridless super-resolution method since the discretization of the parameter space is not required. In the second step, the ranges are given by MUSIC-like one-dimensional searching. Simulations results are provided to demonstrate the advantages of our method.

cs.IT↗