arXiv subjects

Jun Li

Publications and source records attributed to Jun Li.

At least 595 records · Page 33Linked to original sources

arXiv2022

Measuring Changes in Regional Network Traffic Due to COVID-19 Stay-at-Home Measures

During the 2020 pandemic caused by the COVID-19 virus, many countries implemented stay-at-home measures, which led to many businesses and schools moving from in-person to online mode of operation. We analyze sampled Netflow records at a medium-sized US Regional Optical Network to quantify the changes in the network traffic due to stay-at-home measures in that region. We find that human-driven traffic in the network decreases to around 70%, and mostly shifts to local ISPs, while VPN and online meeting traffic increases up to 5 times. We also find that networks adopt a variety of online meeting solutions and favor one but continue using a few others. We find that educational and government institutions experience large traffic changes, but aim to keep their productivity via increased online meetings. Some scientific traffic also reduces possibly leading to loss of research productivity. Businesses mostly lose their traffic and few show VPN or online meeting activity. Most network prefixes experience large loss of live addresses but a handful increase their liveness. We also find increased incidence of network attacks. Our findings can help plan network provisioning and management to prepare for future possible infection outbreaks and natural disasters.

cs.NI↗

arXiv2022

Generalized Donaldson-Thomas Invariants via Kirwan Blowups

We develop a virtual cycle approach towards generalized Donaldson-Thomas theory of Calabi-Yau threefolds. Let $\mathcal{M}$ be the moduli stack of Gieseker semistable sheaves of fixed topological type on a Calabi-Yau threefold $W$. We construct an associated Deligne-Mumford stack $\widetilde{\mathcal{M}}$ with an induced semi-perfect obstruction theory of virtual dimension zero and define the generalized Donaldson-Thomas invariant of $W$ via Kirwan blowups to be the degree of the virtual cycle $[\widetilde{\mathcal{M}}]^{\mathrm{vir}}$. We show that it is invariant under deformations of the complex structure of $W$.

math.AG↗

arXiv2022

Bulk and edge dynamics of a 2D Affleck-Kennedy-Lieb-Tasaki model

We study the dynamical properties of both bulk and edge spins of a two-dimensional Affleck-Kennedy-Lieb-Tasaki (AKLT) model mainly by using the stochastic series expansion quantum Monte Carlo method with stochastic analytic continuation. In the deep AKLT phase, we obtain a spin spectrum with flat band, which is a strong evidence for a localized state. Through the spectrum analysis, we see a clear continuous phase transition from the AKLT phase to the Néel phase in the model, and the energy gap becomes closed at the corresponding momentum point. In comparison with linear spin-wave theory, the differences show that there are strong interactions among magnons at high energies. With open boundary condition, the gap of edge spins in the AKLT phase closes at both the $Γ$ point and the $π$ point interestingly to emerge into a flat-band-like Luttinger liquid phase, which can be explained by symmetry and perturbation approximation. This paper helps us to better understand the completely different dynamical behaviors of bulk and edge spins in the symmetry protected topological phase.

cond-mat.str-el↗

arXiv2022

A Deep Learning Approach to Predicting Ventilator Parameters for Mechanically Ventilated Septic Patients

We develop a deep learning approach to predicting a set of ventilator parameters for a mechanically ventilated septic patient using a long and short term memory (LSTM) recurrent neural network (RNN) model. We focus on short-term predictions of a set of ventilator parameters for the septic patient in emergency intensive care unit (EICU). The short-term predictability of the model provides attending physicians with early warnings to make timely adjustment to the treatment of the patient in the EICU. The patient specific deep learning model can be trained on any given critically ill patient, making it an intelligent aide for physicians to use in emergent medical situations.

q-bio.QM↗

arXiv2022

RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images

Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs. Despite some saliency models were proposed to solve the intrinsic problem of optical RSIs (such as complex background and scale-variant objects), the accuracy and completeness are still unsatisfactory. To this end, we propose a relational reasoning network with parallel multi-scale attention for SOD in optical RSIs in this paper. The relational reasoning module that integrates the spatial and the channel dimensions is designed to infer the semantic relationship by utilizing high-level encoder features, thereby promoting the generation of more complete detection results. The parallel multi-scale attention module is proposed to effectively restore the detail information and address the scale variation of salient objects by using the low-level features refined by multi-scale attention. Extensive experiments on two datasets demonstrate that our proposed RRNet outperforms the existing state-of-the-art SOD competitors both qualitatively and quantitatively.

cs.CV↗

arXiv2022

Light-induced dimension crossover in 1T-TiSe$_2$ dictated by excitonic correlations

In low-dimensional systems with strong electronic correlations, the application of an ultrashort laser pulse often yields novel phases that are otherwise inaccessible. The central challenge in understanding such phenomena is to determine how dimensionality and many-body correlations together govern the pathway of a non-adiabatic transition. To this end, we examine a layered compound, 1T-TiSe$_2$, whose three-dimensional charge-density-wave (3D CDW) state also features exciton condensation due to strong electron-hole interactions. We find that photoexcitation suppresses the equilibrium 3D CDW while creating a nonequilibrium 2D CDW. Remarkably, the dimension reduction does not occur unless bound electron-hole pairs are broken. This relation suggests that excitonic correlations maintain the out-of-plane CDW coherence, settling a long-standing debate over their role in the CDW transition. Our findings demonstrate how optical manipulation of electronic interaction enables one to control the dimensionality of a broken-symmetry order, paving the way for realizing other emergent states in strongly correlated systems.

cond-mat.str-el↗

arXiv2022

Entanglement-assisted concatenated quantum codes

Entanglement-assisted concatenated quantum codes (EACQCs), constructed by concatenating two quantum codes, are proposed. These EACQCs show several advantages over the standard concatenated quantum codes (CQCs). Several families of EACQCs that, unlike standard CQCs, can beat the nondegenerate Hamming bound for entanglement-assisted quantum error correction codes (EAQECCs) are derived. Further, a number of EACQCs with better parameters than the best known standard quantum error correction codes (QECCs) and EAQECCs are also derived. In particular, several catalytic EACQCs with better parameters than the best known QECCs of the same length and net transmission are constructed. Furthermore, each catalytic EACQC consumes only one or two ebits. It is also shown that EACQCs make entanglement-assisted quantum communication possible even if the ebits are noisy. Finally, it is shown that EACQCs can outperform CQCs in entanglement fidelity over depolarizing channels if the ebits are less noisy than the qubits. Moreover, the threshold error probability of EACQCs is larger than that of CQCs when the error probability of ebits is sufficiently lower than that of qubits. Therefore EACQCs are not only competitive in quantum communication but also applicable in fault-tolerant quantum computation.

quant-ph↗

arXiv2022

Chambers in the symplectic cone and stability of symplectomorphism group for ruled surface

We continue our previous work to prove that for any non-minimal ruled surface $(M,ω)$, the stability under symplectic deformations of $π_0, π_1$ of $Symp(M,ω)$ is guided by embedded $J$-holomorphic curves. Further, we prove that for any fixed sizes blowups, when the area ratio $μ$ between the section and fiber goes to infinity, there is a topological colimit of $Symp(M,ω_μ).$ Moreover, when the blowup sizes are all equal to half the area of the fiber class, we give a topological model of the colimit which induces non-trivial symplectic mapping classes in $Symp(M,ω) \cap \rm Diff_0(M),$ where $\rm Diff_0(M)$ is the identity component of the diffeomorphism group. These mapping classes are not Dehn twists along Lagrangian spheres.

math.SG↗

arXiv2022

Dust Mass Associated with the Supernova Remnant IC 443 when Emission Meets Extinction

The dust mass of the well-known supernova remnant (SNR) IC 443 is estimated from both the infrared emission and the visual extinction. With photometry to the images taken by \emph{Spitzer}, \emph{WISE}, \emph{IRAS}, \emph{AKARI} and \emph{Planck}, the spectral energy distribution (SED) of the dust is obtained after subtracting the synchrotron radiation and considering the spectral line emission. The dust mass is derived from fitting the SED by a two-component model, which results in a warm component of the temperature of $\sim$ 53 K and the mass of 0.1 $M_\odot$, and a cold component of the temperature of $\sim 17$ K and the mass of 46 $M_\odot$. On the other hand, the dust mass is derived to be $\sim$ 66 $M_\odot$ from the visual extinction of IC 443 which is identified from the 3D Bayestar extinction map and its coincidence with the infrared emission morphology. Roughly the dust mass derived from the infrared emission and the extinction agree mutually. However, the dust mass derived from the infrared emission can be adjusted to be more consistent with that from the extinction by using different dust opacity property or considering optically thick radiation. In addition, the distribution of dust temperature and mass is analyzed by fitting the SED pixel by pixel.

astro-ph.HE↗

arXiv2022

Regularized Covariance Estimation for Polarization Radar Detection in Compound Gaussian Sea Clutter

This paper investigates regularized estimation of Kronecker-structured covariance matrices (CM) for polarization radar in sea clutter scenarios where the data are assumed to follow the complex, elliptically symmetric (CES) distributions with a Kronecker-structured CM. To obtain a well-conditioned estimate of the CM, we add penalty terms of Kullback-Leibler divergence to the negative log-likelihood function of the associated complex angular Gaussian (CAG) distribution. This is shown to be equivalent to regularizing Tyler's fixed-point equations by shrinkage. A sufficient condition that the solution exists is discussed. An iterative algorithm is applied to solve the resulting fixed-point iterations and its convergence is proved. In order to solve the critical problem of tuning the shrinkage factors, we then introduce two methods by exploiting oracle approximating shrinkage (OAS) and cross-validation (CV). The proposed estimator, referred to as the robust shrinkage Kronecker estimator (RSKE), is shown to achieve better performance compared with several existing methods when the training samples are limited. Simulations are conducted for validating the RSKE and demonstrating its high performance by using the IPIX 1998 real sea data.

eess.SP↗

arXiv2022

Entangling metropolitan-distance separated quantum memories

Quantum internet gives the promise of getting all quantum resources connected, and it will enable applications far beyond a localized scenario. A prototype is a network of quantum memories that are entangled and well separated. Previous realizations are limited in the distance. In this paper, we report the establishment of remote entanglement between two atomic quantum memories physically separated by 12.5 km directly in a metropolitan area. We create atom-photon entanglement in one node and send the photon to a second node for storage. We harness low-loss transmission through a field-deployed fiber of 20.5 km by making use of frequency down-conversion and up-conversion. The final memory-memory entanglement is verified to have a fidelity of 90% via retrieving to photons. Our experiment paves the way to study quantum network applications in a practical scenario.

quant-ph↗

arXiv2022

Dust Models for the Extinction of Type IIn Supernova SN 2010jl

The unusual extinction curves of SN 2010jl provide an excellent opportunity to investigate the properties of dust formed by core-collapse supernovae. By using a series of dust models with different compositions and grain size distributions, we fit the extinction curves of SN 2010jl and find that a silicate-graphite mixture dust model characterized by exponentially cutoff power-law size distributions can well reproduce its unusual extinction curves. The best-fit results show that the extinctions derived from the dust models are consistent with the observed values at all epochs. However, the total-to-selective extinction ratio $R_V$ is about 2.8 - 3.1, which is significantly smaller than the value of $R_V \approx 6.4$ derived by Gall et al. The best-fit models indicate that the dust grains around SN 2010jl are possibly composed of small-size astronomical silicate grains and micron-size graphite grains. In addition, by fitting the optical to mid-infrared spectral energy distribution, we find that the dust mass around SN 2010jl increases with time, up to $0.005\,M_{\odot}$ around 1300 days after peak brightness, which is consistent with previous estimates.

astro-ph.SR↗

arXiv2022

On Nonlocal Cohesive Continuum Mechanics and Cohesive Peridynamic Modeling (CPDM) of Inelastic Fracture

In this work, we developed a bond-based cohesive peridynamics model (CPDM) and apply it to simulate inelastic fracture by using the meso-scale Xu-Needleman cohesive potential . By doing so, we have successfully developed a bond-based cohesive continuum mechanics model with intrinsic stress/strain measures as well as consistent and built-in macro-scale constitutive relations. The main novelties of this work are: (1) We have shown that the cohesive stress of the proposed nonlocal cohesive continuum mechanics model is exactly the same as the nonlocal peridynamic stress; (2) For the first time, we have applied an irreversible built-in cohesive stress-strain relation in a bond-based cohesive peridynamics to model inelastic material behaviors without prescribing phenomenological plasticity stress-strain relations; (3) The cohesive bond force possesses both axial and tangential components, and they contribute a nonlinear constitutive relation with variable Poisson's ratios; (4) The bond-based cohesive constitutive model is consistent with the cohesive fracture criterion, and (5) We have shown that the proposed method is able to model inelastic fracture and simulate ductile fracture of small scale yielding in the nonlocal cohesive continua. Several numerical examples have been presented to be compared with the finite element based continuum cohesive zone model, which shows that the proposed approach is a simple, efficient and effective method to model inelastic fracture in the nonlocal cohesive media.

cond-mat.mtrl-sci↗

arXiv2022

Peridynamic stress is the static first Piola-Kirchhoff Virial stress

The peridynamic stress formula proposed by Lehoucq and Silling [1, 2] is cumbersome to be implemented in numerical computations. Here, we show that the peridynamic stress tensor has the exact mathematical expression as that of the first Piola-Kirchhoff static Virial stress originated from Irving-Kirkwood-Noll formalism [3, 4] through the Hardy-Murdoch procedure [5, 6], which offers a simple and clear expression for numerical calculations of peridynamic stress. Several numerical verifications have been carried out to validate the accuracy of proposed peridynamic stress formula in predicting the stress states in the vicinity of the crack tip and other sources of stress concentration. The peridynamic stress is evaluated within the bond-based peridynamics with prototype microelastic brittle (PMB) material model. It is found that the PMB material model may exhibit nonlinear constitutive behaviors at large deformations. The stress fields calculated through the proposed peridynamic stress formula show good agreements with finite element analysis results, analytical solutions, and experimental data, demonstrating the promising potential of derived peridynamic stress formula in simulating the stress states of problems with discontinuities, especially in the bond-based peridynamics.

physics.class-ph↗

arXiv2022

Path Planning for the Dynamic UAV-Aided Wireless Systems using Monte Carlo Tree Search

For UAV-aided wireless systems, online path planning attracts much attention recently. To better adapt to the real-time dynamic environment, we, for the first time, propose a Monte Carlo Tree Search (MCTS)-based path planning scheme. In details, we consider a single UAV acts as a mobile server to provide computation tasks offloading services for a set of mobile users on the ground, where the movement of ground users follows a Random Way Point model. Our model aims at maximizing the average throughput under energy consumption and user fairness constraints, and the proposed timesaving MCTS algorithm can further improve the performance. Simulation results show that the proposed algorithm achieves a larger average throughput and a faster convergence performance compared with the baseline algorithms of Q-learning and Deep Q-Network.

cs.NI↗

arXiv2022

Fractional and composite excitations of antiferromagnetic quantum spin trimer chains

Using quantum Monte Carlo, exact diagonalization and perturbation theory, we study the spectrum of the $S=1/2$ antiferromagnetic Heisenberg trimer chain by varying the ratio $g=J_2/J_1$ of the intertrimer and intratrimer coupling strengths. The doublet ground states of trimers form effective interacting $S=1/2$ degrees of freedom described by a Heisenberg chain. Therefore, the conventional two-spinon continuum of width $\propto J_1$ when $g=1$ evolves into to a similar continuum of width $\propto J_2$ when $g\to 0$. The intermediate-energy and high-energy modes are termed \emph{doublons} and \emph{quartons} which fractionalize with increasing $g$ to form the conventional spinon continuum. In particular, at $g \approx 0.716$, the gap between the low-energy spinon branch and the high-energy band with mixed doublons, quartons, and spinons closes. These features should be observable in inelastic neutron scattering experiments if a quasi-one-dimensional quantum magnet with the linear trimer structure and $J_2<J_1$ can be identified. Our results may open a window for exploring the high-energy fractional excitations.

cond-mat.str-el↗

arXiv2022

Nonlinear Optimal Guidance for Fixed-Time Impact on a Stationary Target

This paper is concerned with devising the nonlinear optimal guidance for intercepting a stationary target with a fixed impact time. According to Pontryagin's Maximum Principle (PMP), some optimality conditions for the solutions of the nonlinear optimal interception problem are established, and the structure of the corresponding optimal control is presented. By employing the optimality conditions, we formulate a parameterized system so that its solution space is the same as that of the nonlinear optimal interception problem. As a consequence, a simple propagation of the parameterized system, without using any optimization method, is sufficient to generate enough sampled data for the mapping from current state and time-to-go to the optimal guidance command. By virtue of the universal approximation theorem, a feedforward neural network, trained by the generated data, is able to represent the mapping from current state and time-to-go to the optimal guidance command. Therefore, the trained network eventually can generate fixed-impact-time nonlinear optimal guidance within a constant time. Finally, the developed nonlinear optimal guidance is exemplified and studied through simulations, showing that the nonlinear optimal guidance law performs better than existing interception guidance laws.

math.OC↗

arXiv2021

Temporal Transformer Networks with Self-Supervision for Action Recognition

In recent years, 2D Convolutional Networks-based video action recognition has encouragingly gained wide popularity; However, constrained by the lack of long-range non-linear temporal relation modeling and reverse motion information modeling, the performance of existing models is, therefore, undercut seriously. To address this urgent problem, we introduce a startling Temporal Transformer Network with Self-supervision (TTSN). Our high-performance TTSN mainly consists of a temporal transformer module and a temporal sequence self-supervision module. Concisely speaking, we utilize the efficient temporal transformer module to model the non-linear temporal dependencies among non-local frames, which significantly enhances complex motion feature representations. The temporal sequence self-supervision module we employ unprecedentedly adopts the streamlined strategy of "random batch random channel" to reverse the sequence of video frames, allowing robust extractions of motion information representation from inversed temporal dimensions and improving the generalization capability of the model. Extensive experiments on three widely used datasets (HMDB51, UCF101, and Something-something V1) have conclusively demonstrated that our proposed TTSN is promising as it successfully achieves state-of-the-art performance for action recognition.

cs.CV↗