arXiv subjects

Jing Li

Publications and source records attributed to Jing Li.

At least 613 records · Page 34Linked to original sources

arXiv2021

Global Existence of Strong and Weak Solutions to 2D Compressible Navier-Stokes System in Bounded Domains with Large Data and Vacuum

We study the barotropic compressible Navier-Stokes system where the shear viscosity is a positive constant and the bulk one proportional to a power of the density with the power bigger than one and a third. The system is subject to the Navier-slip boundary conditions in a general two-dimensional bounded simply connected domain. For initial density allowed to vanish, we establish the global existence of strong and weak solutions without any restrictions on the size of initial value. To get over the difficulties brought by boundary, on the one hand, we apply Riemann mapping theorem and the pull back Green's function method to get a pointwise representation of the effective viscous flux. On the other hand, observing that the orthogonality is preserved under conformal mapping due to its preservation on the angle, we use the slip boundary conditions to reduce the integral representation to the desired commutator form whose singularities can be cancelled out by using the estimates on the spatial gradient of the velocity.

math.AP↗

arXiv2021

Accurate predictions of chaotic motion of a free fall disk

It is important to know the accurate trajectory of a free fall object in fluid (such as a spacecraft), whose motion might be chaotic in many cases. However, it is impossible to accurately predict its chaotic trajectory in a long enough duration by traditional numerical algorithms in double precision. In this paper, we give the accurate predictions of the same problem by a new strategy, namely the Clean Numerical Simulation (CNS). Without loss of generality, a free fall disk in water is considered, whose motion is governed by the Andersen-Pesavento-Wang model. We illustrate that convergent and reliable trajectories of a chaotic free fall disk in a long enough interval of time can be obtained by means of the CNS, but different traditional algorithms in double precision give disparate trajectories. Besides, unlike the traditional algorithms in double precision, the CNS can predict the accurate posture of the free fall disk near the vicinity of the bifurcation point of some physical parameters in a long duration. Therefore, the CNS can provide reliable prediction of chaotic systems in a long enough interval of time.

nlin.CD↗

arXiv2021

Near octave-spanning perfect soliton crystals in AlN microresonators

The perfect soliton crystal (PSC) was recently discovered as an extraordinary Kerr soliton state with regularly distributed soliton pulses and enhanced comb line power spaced by multiples of the cavity free spectral ranges (FSRs). The modulation of continuous-wave excitation in optical microresonators and the tunable repetition rate characteristic will significantly enhance and extend the application potential of soliton microcombs for self-referencing comb source, terahertz wave generation, and arbitrary waveform generation. However, the reported PSC spectrum is generally narrow. Here, we demonstrate the deterministic accessing of versatile perfect soliton crystals in the AlN microresonators (FSR ~374 GHz), featuring a broad spectral range up to 0.96 of an octave-span (1170-2300 nm) and terahertz repetition rates (up to ~1.87 THz). The measured 60-fs short pulses and low-noise characteristics confirms the high coherence of the PSCs

physics.optics↗

arXiv2021

Electron Spectrum for the Prompt Emission of Gamma-ray Bursts in the Synchrotron Radiation Scenario

Growing evidence indicates that the synchrotron radiation mechanism may be responsible for the prompt emission of gamma-ray bursts (GRBs). In the synchrotron radiation scenario, the electron energy spectrum of the prompt emission is diverse in theoretical works and has not been estimated from observations in a general way (i.e., without specifying a certain physical model for the electron spectrum). In this paper, we creatively propose a method to directly estimate the electron spectrum for the prompt emission, without specifying a certain physical model for the electron spectrum in the synchrotron radiation scenario. In this method, an empirical function (i.e., a four-order Bezier curve jointed with a linear function at high-energy) is applied to describe the electron spectrum in log-log coordinate. It is found that our empirical function can well mimic the electron spectra obtained in many numerical calculations or simulations. Then, our method can figure out the electron spectrum for the prompt emission without specifying a model. By employing our method on observations, taking GRB 180720B and GRB 160509A as examples, it is found that the obtained electron spectra are generally different from that in the standard fast-cooling scenario and even a broken power law. Moreover, the morphology of electron spectra in its low-energy regime varies with time in a burst and even in a pulse. Our proposed method provides a valuable way to confront the synchrotron radiation mechanism with observations.

astro-ph.HE↗

arXiv2021

Cometary Activity Begins at Kuiper Belt Distances: Evidence from C/2017 K2

We study the development of activity in the incoming long-period comet C/2017 K2 over the heliocentric distance range 9 < r_H < 16 AU. The comet continues to be characterized by a coma of sub-millimeter and larger particles ejected at low velocity. In a fixed co-moving volume around the nucleus we find that the scattering cross-section of the coma is related to the heliocentric distance by a power law with heliocentric index $s = 1.14\pm0.05$. This dependence is significantly weaker than the inverse square variation of the insolation as a result of two effects. These are, first, the heliocentric dependence of the dust velocity and, second, a lag effect due to very slow-moving particles ejected long before the observations were taken. A Monte Carlo model of the photometry shows that dust production beginning at r_H ~ 35 AU is needed to match the measured heliocentric index, with only a slight dependence on the particle size distribution. Mass loss rates in dust at 10 AU are of order 1000 kg/s, while loss rates in gas may be much smaller, depending on the unknown dust to gas ratio. Consequently, the ratio of the non-gravitational acceleration to the local solar gravity may, depending on the nucleus size, attain values comparable to values found in short-period comets at much smaller distances. Non-gravitational acceleration in C/2017 K2 and similarly distant comets, while presently unmeasured, may limit the accuracy with which we can infer the properties of the Oort cloud from the orbits of long-period comets.

astro-ph.EP↗

arXiv2021

Mimicing the Kane-Mele type spin orbit interaction by spin-flexual phonon coupling in graphene devices

On the efforts of enhancing the spin orbit interaction (SOI) of graphene for seeking the dissipationless quantum spin Hall devices, unique Kane-Mele type SOI and high mobility samples are desired. However, common external decoration often introduces extrinsic Rashba-type SOI and simultaneous impurity scattering. Here we show, by the EDTA-Dy molecule decorating, the Kane-Mele type SOI is mimicked with even improved carrier mobility. It is evidenced by the suppressed weak localization at equal carrier densities and simultaneous Elliot-Yafet spin relaxation. The extracted spin scattering time is monotonically dependent on the carrier elastic scattering time, where the Elliot-Yafet plot gives the interaction strength of 3.3 meV. Improved quantum Hall plateaus can be even seen after the external operation. This is attributed to the spin-flexural phonon coupling induced by the enhanced graphene ripples, as revealed by the in-plane magnetotransport measurement.

cond-mat.mes-hall↗

arXiv2021

Dispersively probed microwave spectroscopy of a silicon hole double quantum dot

Owing to ever increasing gate fidelities and to a potential transferability to industrial CMOS technology, silicon spin qubits have become a compelling option in the strive for quantum computation. In a scalable architecture, each spin qubit will have to be finely tuned and its operating conditions accurately determined. In this prospect, spectroscopic tools compatible with a scalable device layout are of primary importance. Here we report a two-tone spectroscopy technique providing access to the spin-dependent energy-level spectrum of a hole double quantum dot defined in a split-gate silicon device. A first GHz-frequency tone drives electric-dipole spin resonance enabled by the valence-band spin-orbit coupling. A second lower-frequency tone (approximately 500 MHz) allows for dispersive readout via rf-gate reflectometry. We compare the measured dispersive response to the linear response calculated in an extended Jaynes-Cummings model and we obtain characteristic parameters such as g-factors and tunnel/spin-orbit couplings for both even and odd occupation.

cond-mat.mes-hall↗

arXiv2021

Hydrogen molecule spectrum by many-body GW and Bethe-Salpeter equation

We check the ab initio GW approximation and Bethe-Salpeter equation (BSE) many-body methodology against the exact solution benchmark of the hydrogen molecule H$_2$ ground state and excitation spectrum, and in comparison with the configuration interaction (CI) and time-dependent Hartree-Fock methods. The comparison is made on all the states we could unambiguously identify from the excitonic wave functions' symmetry. At the equilibrium distance $R = 1.4 \, a_0$, the GW+BSE energy levels are in good agreement with the exact results, with an accuracy of 0.1~0.2 eV. GW+BSE potential-energy curves are also in good agreement with the CI and the exact result up to $2.3 \, a_0$. The solution no longer exists beyond $3.0 \, a_0$ for triplets ($4.3 \, a_0$ for singlets) due to instability of the ground state. We tried to improve the GW reference ground state by a renormalized random-phase approximation (r-RPA), but this did not solve the problem.

physics.chem-ph↗

arXiv2021

Exploring the Galactic Anticenter substructure with LAMOST & Gaia DR2

We characterize the kinematic and chemical properties of 589 Galactic Anticenter Substructure Stars (GASS) with K-/M- giants in Integrals-of-Motion space. These stars likely include members of previously identified substructures such as Monoceros, A13, and the Triangulum-Andromeda cloud (TriAnd). We show that these stars are on nearly circular orbits on both sides of the Galactic plane. We can see velocity($V_{Z}$) gradient along Y-axis especially for the south GASS members. Our GASS members have similar energy and angular momentum distributions to thin disk stars. Their location in [$α$/M] vs. [M/H] space is more metal poor than typical thin disk stars, with [$α$/M] \textbf{lower} than the thick disk. We infer that our GASS members are part of the outer metal-poor disk stars, and the outer-disk extends to 30 kpc. Considering the distance range and $α$-abundance features, GASS could be formed after the thick disk was formed due to the molecular cloud density decreased in the outer disk where the SFR might be less efficient than the inner disk.

astro-ph.GA↗

arXiv2020

Constructive and Destructive Interference of Kerker-type Scattering in an Ultra-thin Silicon Huygens Metasurface

High refractive index dielectric nanoparticles have provided a new platform for exotic light manipulation through the interference of multipole modes. The Kerker effect is one example of a Huygens source design. Rather than exploiting interference between the electric dipole and magnetic dipole, as in many conventional Huygens source designs, we explore Kerker-type suppressed backward scattering mediated by the dominant electric dipole, toroidal dipole and magnetic quadrupole. These modes are provided by a designed and fabricated CMOS compatible ultra-thin Silicon nanodisk metasurface with a suppressed magnetic dipole contribution, and verified through multipole decomposition. The non-trivial substrate effect is considered using a semi-analytical transfer matrix model. The model successfully predicts the observed reflection dip. By applying a general criterion for constructive and destructive interference, it is shown that while constructive interference occurs between the electric and toroidal dipole contributions, the experimentally observed suppressed backward Kerker-type scattering arises from the destructive interference between backward scattered contributions due to the total electric dipole and the magnetic quadrupole. Our study paves the way towards new types of Huygens sources or metasurface design, such as for peculiar transverse Kerker scattering.

physics.optics↗

arXiv2020

Directly accessing octave-spanning dissipative Kerr soliton frequency combs in an AlN microring resonator

Self-referenced dissipative Kerr solitons (DKSs) based on optical microresonators offer prominent characteristics including miniaturization, low power consumption, broad spectral range and inherent coherence for various applications such as precision measurement, communications, microwave photonics, and astronomical spectrometer calibration. To date, octave-spanning DKSs with a free spectral range (FSR) of ~1 THz have been achieved only in ultrahigh-Q silicon nitride microresonators, with elaborate wavelength control required. Here we demonstrate an octave-spanning DKS in an aluminium nitride (AlN) microresonator with moderate loaded Q (500,000) and FSR of 374 GHz. In the design, a TE00 mode and a TE10 mode are nearly degenerate and act as pump and auxiliary modes. The presence of the auxiliary resonance balances the thermal dragging effect in dissipative soliton comb formation, crucially simplifying the DKS generation with a single pump and leading to a wide single soliton access window. We experimentally demonstrate stable DKS operation with a record single soliton step (~80 pm) and octave-spanning bandwidth (1100-2300 nm) through adiabatic pump tuning and on-chip power of 340 mW. Our scheme also allows for direct creation of the DKS state with high probability and without elaborate wavelength or power schemes being required to stabilize the soliton behavior.

physics.optics↗

arXiv2020

Robust Facial Landmark Detection by Multi-order Multi-constraint Deep Networks

Recently, heatmap regression has been widely explored in facial landmark detection and obtained remarkable performance. However, most of the existing heatmap regression-based facial landmark detection methods neglect to explore the high-order feature correlations, which is very important to learn more representative features and enhance shape constraints. Moreover, no explicit global shape constraints have been added to the final predicted landmarks, which leads to a reduction in accuracy. To address these issues, in this paper, we propose a Multi-order Multi-constraint Deep Network (MMDN) for more powerful feature correlations and shape constraints learning. Specifically, an Implicit Multi-order Correlating Geometry-aware (IMCG) model is proposed to introduce the multi-order spatial correlations and multi-order channel correlations for more discriminative representations. Furthermore, an Explicit Probability-based Boundary-adaptive Regression (EPBR) method is developed to enhance the global shape constraints and further search the semantically consistent landmarks in the predicted boundary for robust facial landmark detection. It's interesting to show that the proposed MMDN can generate more accurate boundary-adaptive landmark heatmaps and effectively enhance shape constraints to the predicted landmarks for faces with large pose variations and heavy occlusions. Experimental results on challenging benchmark datasets demonstrate the superiority of our MMDN over state-of-the-art facial landmark detection methods. The code has been publicly available at https://github.com/junwan2014/MMDN-master.

cs.CV↗

arXiv2020

The influence of impurities on the charge carrier mobility of small molecule organic semiconductors

Amorphous organic semiconductors based on small molecules and polymers are used in many applications, most prominently organic light emitting diodes (OLEDs) and organic solar cells. Impurities and charge traps are omnipresent in most currently available organic semiconductors and limit charge transport and thus device efficiency. The microscopic cause as well as the chemical nature of these traps are presently not well understood. Using a multiscale model we characterize the influence of impurities on the density of states and charge transport in small-molecule amorphous organic semiconductors. We use the model to quantitatively describe the influence of water molecules and water-oxygen complexes on the electron and hole mobilities. These species are seen to impact the shape of the density of states and to act as explicit charge traps within the energy gap. Our results show that trap states introduced by molecular oxygen can be deep enough to limit the electron mobility in widely used materials.

cond-mat.soft↗

arXiv2020

Entanglement of Two Distinguishable Atoms in a Rectangular Waveguide: Linear Approximation with Single Excitation

We consider two two-level systems (TLSs) coupled to the vacuum of guided modes confined in a rectangular waveguide. Two TLSs are fixed at different points in the waveguide and initially share an excitation. For the energy separation of the TLSs far away from the cutoff frequencies of transverse modes, two coupled delay-differential equations are obtained for the probability amplitudes of the TLSs. The effects of the difference of TLSs' energy separations and the inter-TLS distance on the time evolution of the concurrence of the TLSs are examined.

quant-ph↗

arXiv2020

Cross-Media Keyphrase Prediction: A Unified Framework with Multi-Modality Multi-Head Attention and Image Wordings

Social media produces large amounts of contents every day. To help users quickly capture what they need, keyphrase prediction is receiving a growing attention. Nevertheless, most prior efforts focus on text modeling, largely ignoring the rich features embedded in the matching images. In this work, we explore the joint effects of texts and images in predicting the keyphrases for a multimedia post. To better align social media style texts and images, we propose: (1) a novel Multi-Modality Multi-Head Attention (M3H-Att) to capture the intricate cross-media interactions; (2) image wordings, in forms of optical characters and image attributes, to bridge the two modalities. Moreover, we design a unified framework to leverage the outputs of keyphrase classification and generation and couple their advantages. Extensive experiments on a large-scale dataset newly collected from Twitter show that our model significantly outperforms the previous state of the art based on traditional attention networks. Further analyses show that our multi-head attention is able to attend information from various aspects and boost classification or generation in diverse scenarios.

cs.CV↗

arXiv2020

Strategy for Boosting Pair Comparison and Improving Quality Assessment Accuracy

The development of rigorous quality assessment model relies on the collection of reliable subjective data, where the perceived quality of visual multimedia is rated by the human observers. Different subjective assessment protocols can be used according to the objectives, which determine the discriminability and accuracy of the subjective data. Single stimulus methodology, e.g., the Absolute Category Rating (ACR) has been widely adopted due to its simplicity and efficiency. However, Pair Comparison (PC) is of significant advantage over ACR in terms of discriminability. In addition, PC avoids the influence of observers' bias regarding their understanding of the quality scale. Nevertheless, full pair comparison is much more time-consuming. In this study, we therefore 1) employ a generic model to bridge the pair comparison data and ACR data, where the variance term could be recovered and the obtained information is more complete; 2) propose a fusion strategy to boost pair comparisons by utilizing the ACR results as initialization information; 3) develop a novel active batch sampling strategy based on Minimum Spanning Tree (MST) for PC. In such a way, the proposed methodology could achieve the same accuracy of pair comparison but with the compelxity as low as ACR. Extensive experimental results demonstrate the efficiency and accuracy of the proposed approach, which outperforms the state of the art approaches.

cs.AI↗

arXiv2020

Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification

Mammogram benign or malignant classification with only image-level labels is challenging due to the absence of lesion annotations. Motivated by the symmetric prior that the lesions on one side of breasts rarely appear in the corresponding areas on the other side, given a diseased image, we can explore a counterfactual problem that how would the features have behaved if there were no lesions in the image, so as to identify the lesion areas. We derive a new theoretical result for counterfactual generation based on the symmetric prior. By building a causal model that entails such a prior for bilateral images, we obtain two optimization goals for counterfactual generation, which can be accomplished via our newly proposed counterfactual generative network. Our proposed model is mainly composed of Generator Adversarial Network and a \emph{prediction feedback mechanism}, they are optimized jointly and prompt each other. Specifically, the former can further improve the classification performance by generating counterfactual features to calculate lesion areas. On the other hand, the latter helps counterfactual generation by the supervision of classification loss. The utility of our method and the effectiveness of each module in our model can be verified by state-of-the-art performance on INBreast and an in-house dataset and ablation studies.

cs.CV↗

arXiv2020

Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination

A growing specter in the rise of machine learning is whether the decisions made by machine learning models are fair. While research is already underway to formalize a machine-learning concept of fairness and to design frameworks for building fair models with sacrifice in accuracy, most are geared toward either supervised or unsupervised learning. Yet two observations inspired us to wonder whether semi-supervised learning might be useful to solve discrimination problems. First, previous study showed that increasing the size of the training set may lead to a better trade-off between fairness and accuracy. Second, the most powerful models today require an enormous of data to train which, in practical terms, is likely possible from a combination of labeled and unlabeled data. Hence, in this paper, we present a framework of fair semi-supervised learning in the pre-processing phase, including pseudo labeling to predict labels for unlabeled data, a re-sampling method to obtain multiple fair datasets and lastly, ensemble learning to improve accuracy and decrease discrimination. A theoretical decomposition analysis of bias, variance and noise highlights the different sources of discrimination and the impact they have on fairness in semi-supervised learning. A set of experiments on real-world and synthetic datasets show that our method is able to use unlabeled data to achieve a better trade-off between accuracy and discrimination.

cs.LG↗