arXiv subjects

Jing Li

Publications and source records attributed to Jing Li.

At least 703 records · Page 39Linked to original sources

arXiv2019

High Resolution Thermal Infrared Imaging of 3200 Phaethon

We present thermal infrared observations of the active asteroid (and Geminid meteoroid stream parent) 3200 Phaethon using the Very Large Telescope. The images, at 10.7 micron wavelength, were taken with Phaethon at its closest approach to Earth (separation 0.07 AU) in 2017 December, at a linear resolution of about 14 km. We probe the Hill sphere (of radius 66 km) for trapped dust and macroscopic bodies, finding neither, and we set limits to the presence of unbound dust. The derived limits to the optical depth of dust near Phaethon depend somewhat on the assumed geometry, but are of order 1e-5. The upper limit to the rate of loss of mass in dust is 14 kg/s. This is about 50 times smaller than the rate needed to sustain the Geminid meteoroid stream in steady state. The observations thus show that the production of the Geminids does not proceed in steady state.

astro-ph.EP↗

arXiv2019

Metallic Phase and Temperature Dependence of the $ν= 0$ Quantum Hall State in Bilayer Graphene

The $ν= 0$ quantum Hall state of bilayer graphene is a fertile playground to realize many-body ground states with various broken symmetries. Here we report the experimental observations of a previously unreported metallic phase. The metallic phase resides in the phase space between the previously identified layer polarized state at large transverse electric field and the canted antiferromagnetic state at small transverse electric field. We also report temperature dependence studies of the quantum spin Hall state of $ν= 0$. Complex nonmonotonic behavior reveals concomitant bulk and edge conductions and excitations. These results provide a timely experimental update to understand the rich physics of the $ν= 0$ state.

cond-mat.mes-hall↗

arXiv2019

DLocRL: A Deep Learning Pipeline for Fine-Grained Location Recognition and Linking in Tweets

In recent years, with the prevalence of social media and smart devices, people causally reveal their locations such as shops, hotels, and restaurants in their tweets. Recognizing and linking such fine-grained location mentions to well-defined location profiles are beneficial for retrieval and recommendation systems. In this paper, we propose DLocRL, a new deep learning pipeline for fine-grained location recognition and linking in tweets, and verify its effectiveness on a real-world Twitter dataset.

cs.CL↗

arXiv2019

Comparing many-body approaches against the helium atom exact solution

Over time, many different theories and approaches have been developed to tackle the many-body problem in quantum chemistry, condensed-matter physics, and nuclear physics. Here we use the helium atom, a real system rather than a model, and we use the exact solution of its Schrödinger equation as a benchmark for comparison between methods. We present new results beyond the random-phase approximation (RPA) from a renormalized RPA (r-RPA) in the framework of the self-consistent RPA (SCRPA) originally developed in nuclear physics, and compare them with various other approaches like configuration interaction (CI), quantum Monte Carlo (QMC), time-dependent density-functional theory (TDDFT), and the Bethe-Salpeter equation on top of the GW approximation. Most of the calculations are consistently done on the same footing, e.g. using the same basis set, in an effort for a most faithful comparison between methods.

physics.atom-ph↗

arXiv2019

Detecting the Sagittarius stream with LAMOST DR4 M giants \& Gaia DR2

We use LAMOST DR4 M giants combined with Gaia DR2 proper motions and ALLWISE photometry to obtain an extremely pure sample of Sagittarius (Sgr) stream stars. Using TiO5 and CaH spectral indices as an indicator, we selected out a large sample of M giant stars from M dwarf stars in LAMOST DR4 spectra. Considering the position, distance, proper motion and the angular momentum distribution, we obtained 164 pure Sgr stream stars. We find that the trailing arm has higher energy than the leading arm in same angular momentum. The trailing arm we detected extends to a heliocentric distance of $\sim 130$ kpc at $\tildeΛ_{\odot}\sim 170^{\circ}$, which is consistent with the feature found in RR Lyrae in \citet{sgr2017}. Both of these detections of Sgr, in M giants and in RR Lyrae, imply that the Sgr stream may contain multiple stellar populations with a broad metallicity range.

astro-ph.GA↗

arXiv2019

Robust Cubature Kalman Filter for Dynamic State Estimation of Synchronous Machines under Unknown Measurement Noise Statistics

Kalman-type filtering techniques including cubature Kalman filter (CKF) does not work well in non-Gaussian environments, especially in the presence of outliers. To solve this problem, Huber's M-estimation based robust CKF (RCKF) is proposed for synchronous machines by combining the Huber's M-estimation theory with the classical CKF, which is capable of coping with the deterioration in performance and discretization of tracking curves when measurement noise statistics deviatefrom the prior noise statistics. The proposed RCKF algorithm has good adaptability to unknown measurement noise statistics characteristics including non-Gaussian measurement noise and outliers. The simulation results on the WSCC 3-machine 9-bus system and New England 16-machine 68-bus system verify the effectiveness of the proposed method and its advantage over the classical CKF.

eess.SY↗

arXiv2019

Polarization fraction measurement in same-sign WW scattering using deep learning

Studying the longitudinally polarized fraction of $W^\pm W^\pm$ scattering at the LHC is crucial to examine the unitarization mechanism of the vector boson scattering amplitude through Higgs and possible new physics. We apply here for the first time a Deep Neural Network classification to extract the longitudinal fraction. Based on fast simulation implemented with the Delphes framework, significant improvement from a deep neural network is found to be achievable and robust over all dijet mass region. A conservative estimation shows that a high significance of four standard deviations can be reached with the High-Luminosity LHC designed luminosity of 3000 $fb^{-1}$

hep-ph↗

arXiv2018

Evaluating Row Buffer Locality in Future Non-Volatile Main Memories

DRAM-based main memories have read operations that destroy the read data, and as a result, must buffer large amounts of data on each array access to keep chip costs low. Unfortunately, system-level trends such as increased memory contention in multi-core architectures and data mapping schemes that improve memory parallelism may cause only a small amount of the buffered data to be accessed. This makes buffering large amounts of data on every memory array access energy-inefficient. Emerging non-volatile memories (NVMs) such as PCM, STT-RAM, and RRAM, however, do not have destructive read operations, opening up opportunities for employing small row buffers without incurring additional area penalty and/or design complexity. In this work, we discuss architectural changes to enable small row buffers at a low cost in NVMs. We provide a memory access protocol, energy model, and timing model to enable further system-level evaluation. We evaluate the system-level tradeoffs of employing different row buffer sizes in NVM main memories in terms of energy, performance, and endurance, with different data mapping schemes. We find that on a multi-core CMP system, reducing the row buffer size can greatly reduce main memory dynamic energy compared to a DRAM baseline with large row sizes, without greatly affecting endurance, and for some memories, leads to improved performance.

cs.AR↗

arXiv2018

A valley valve and electron beam splitter

Developing alternative paradigms of electronics beyond silicon technology requires the exploration of fundamentally new physical mechanisms, such as the valley specific phenomena in hexagonal two-dimensional materials. We realize ballistic valley Hall kink states in bilayer graphene and demonstrate gate-controlled current transmission in a four-kink router device. The operations of a waveguide, a valve and a tunable electron beam splitter are demonstrated. The valley valve exploits the valley-momentum locking of the kink states and reaches an on/off ratio of 8 at zero magnetic field. A magnetic field enables a full-ranged tunable coherent beam splitter. These results pave the path to building a scalable, coherent quantum transportation network based on the kink states.

cond-mat.mes-hall↗

arXiv2018

When Bifidelity Meets CoKriging: An Efficient Physics-Informed Multifidelity Method

In this work, we propose a framework that combines the approximation-theory-based multifidelity method and Gaussian-process-regression-based multifidelity method to achieve data-model convergence when stochastic simulation models and sparse accurate observation data are available. Specifically, the two types of multifidelity methods we use are the bifidelity and CoKriging methods. The new approach uses the bifidelity method to efficiently estimate the empirical mean and covariance of the stochastic simulation outputs, then it uses these statistics to construct a Gaussian process (GP) representing low-fidelity in CoKriging. We also combine the bifidelity method with Kriging, where the approximated empirical statistics are used to construct the GP as well. We prove that the resulting posterior mean by the new physics-informed approach preserves linear physical constraints up to an error bound. By using this method, we can obtain an accurate construction of a state of interest based on a partially correct physical model and a few accurate observations. We present numerical examples to demonstrate performance of the method.

stat.ML↗

arXiv2018

RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation

Recurrent neural networks for session-based recommendation have attracted a lot of attention recently because of their promising performance. repeat consumption is a common phenomenon in many recommendation scenarios (e.g., e-commerce, music, and TV program recommendations), where the same item is re-consumed repeatedly over time. However, no previous studies have emphasized repeat consumption with neural networks. An effective neural approach is needed to decide when to perform repeat recommendation. In this paper, we incorporate a repeat-explore mechanism into neural networks and propose a new model, called RepeatNet, with an encoder-decoder structure. RepeatNet integrates a regular neural recommendation approach in the decoder with a new repeat recommendation mechanism that can choose items from a user's history and recommends them at the right time. We report on extensive experiments on three benchmark datasets. RepeatNet outperforms state-of-the-art baselines on all three datasets in terms of MRR and Recall. Furthermore, as the dataset size and the repeat ratio increase, the improvements of RepeatNet over the baselines also increase, which demonstrates its advantage in handling repeat recommendation scenarios.

cs.IR↗

arXiv2018

Active Asteroid P/2017 S5 (ATLAS)

Observations of active asteroid P/2017 S5 when near perihelion reveal the ejection of large (0.1 to 10 mm) particles at 0.2 to 2 m/s speeds, with estimated mass-loss rates of a few kg/s. The protracted nature of the mass loss (continuous over 150 days) is compatible with a sublimation origin, meaning that this object is likely an ice-bearing main-belt comet. Equilibrium sublimation of exposed water ice covering as little as 0.1 sq. km can match the data. Observations a year after perihelion show the object in an inactive state from which we deduce a nucleus effective radius 450(+100/-60) m (albedo 0.06+/-0.02 assumed). The gravitational escape speed from a body of this size is just 0.3 m/s, comparable to the inferred ejection speed of the dust. Time-series photometry provides tentative evidence for rapid rotation (lightcurve period 1.4 hour) that may also play a role in the loss of mass and which, if real, is a likely consequence of spin-up by sublimation torques. P/2017 S5 shares both physical and orbital similarities with the split active asteroid pair P/2016 J1-A and J1-B, and all three objects are likely members of the 7 Myr old, collisionally produced, Theobalda family.

astro-ph.EP↗

arXiv2018

Global boundedness and decay property of a three-dimensional Keller--Segel--Stokes system modeling coral fertilization

This paper is concerned with the four-component Keller--Segel--Stokes system modelling the fertilization process of corals: \begin{equation*} \left\{ \begin{array}{ll} ρ_t+u\cdot\nablaρ=Δρ-\nabla\cdot(ρ\mathcal{S}(x,ρ,c)\nabla c)-ρm, & \quad (x,t)\in Ω\times (0,T), \\ m_t+u\cdot\nabla m=Δm-ρm, & \quad (x,t)\in Ω\times (0,T), \\ c_t+u\cdot\nabla c=Δc-c+m, & \quad (x,t)\in Ω\times (0,T), \\ u_t=Δu-\nabla P+(ρ+m)\nablaϕ,\quad \nabla\cdot u=0, & \quad (x,t)\in Ω\times (0,T) \end{array}\right. \end{equation*} subject to the boundary conditions $\nabla c\cdot ν=\nabla m\cdot ν=(\nablaρ-ρ\mathcal{S}(x,ρ,c)\nabla c)\cdot ν=0$ and $u=0$, and suitably regular initial data $(ρ_0(x),m_0(x), c_0(x),u_0(x))$, where $T\in (0,\infty]$, $Ω\subset\mathbb R^3$ is a bounded domain with smooth boundary $\partialΩ$. This system describes the spatio-temporal dynamics of the population densities of sperm $ρ$ and egg $m$ under a chemotactic process facilitated by a chemical signal released by the egg with concentration $c$ in a fluid-flow environment $u$ modeled by the incompressible Stokes equation. In this model, the chemotactic sensitivity tensor $\mathcal{S}\in C^2(\overlineΩ\times [0,\infty)^2)^{3\times 3}$ satisfies $|\mathcal{S}(x,ρ,c)|\leq C_S(1+ρ)^{-α}$ with some $C_S>0$ and $α\geq 0$. We will show that for $α\geq \frac 13$, the solutions to the system are globally bounded and decay to a spatially homogeneous equilibrium exponentially as time goes to infinity. In addition, we will also show that, for any $α\geq 0$, a similar result is valid when the initial data satisfy a certain smallness condition.

math.AP↗

arXiv2018

Fast control of interactions in an ultracold two atom system: Managing correlations and irreversibility

We design and explore a shortcut to adiabaticity (STA) for changing the interaction strength between two ultracold, harmonically trapped bosons. Starting from initially uncorrelated, non-interacting particles, we assume a time-dependent tuning of the inter-particle interaction through a Feshbach resonance, such that the two particles are strongly interacting at the end of the driving. The efficiency of the STA is then quantified by examining the thermodynamic properties of the system, such as the irreversible work, which is related to the out-of-equilibrium excitations in the system. We also quantify the entanglement of the two-particle state through the von Neumann entropy and show that the entanglement produced in the STA process matches that of the desired target state. Given the fundamental nature of the two-atom problem in ultracold atomic physics, the presented shortcut can be expected to have significant impact on many processes that rely on inter-particle interactions.

quant-ph↗

arXiv2018

Distant Comet C/2017 K2 and the Cohesion Bottleneck

Distant long-period comet C/2017 K2 has been outside the planetary region of the solar system for 3 Myr, negating the possibility that heat retained from the previous perihelion could be responsible for its activity. This inbound comet is also too cold for water ice to sublimate and too cold for amorphous water ice, if present, to crystallize. C/2017 K2 thus presents an ideal target in which to investigate the mechanisms responsible for activity in distant comets. We have used Hubble Space Telescope to study the comet in the pre-perihelion distance range 13.8 to 15.9 AU. The coma maintains a logarithmic surface brightness gradient $m = -1.010\pm$0.004, consistent with steady-state mass loss. The absence of a radiation pressure swept tail indicates that the effective particle size is large (0.1 mm) and the mass loss rate is $\sim$200 kg s$^{-1}$, remarkable for a comet still beyond the orbit of Saturn. Extrapolation of the photometry indicates that activity began in 2012.1, at 25.9$\pm$0.9 AU, where the blackbody temperature is only 55 K. This large distance and low temperature suggest that cometary activity is driven by the sublimation of a super-volatile ice (e.g.~CO), presumably preserved by K2's long-term residence in the Oort cloud. The mass loss rate can be sustained by CO sublimation from an area $\lesssim 2$ km$^2$, if located near the hot sub-solar point on the nucleus. However, while the drag force from sublimated CO is sufficient to lift millimeter sized particles against the gravity of the cometary nucleus, it is 10$^2$ to 10$^3$ times too small to eject these particles against inter-particle cohesion. Our observations thus require either a new understanding of the physics of inter-particle cohesion or the introduction of another mechanism to drive distant cometary mass loss. We suggest thermal fracture and electrostatic supercharging in this context.

astro-ph.EP↗

arXiv2018

Theoretical investigation of the HgF radical towards laser cooling and eEDM measurement

In order to realize more sensitive eEDM measurement, it would be worthwhile to find some new laser-cooled molecules with larger internal effective electric field (E$_{eff}$), higher electric polarizability and longer lifetime of the eEDM measurement state. Here we explore the merits of mercuric monofluoride ($^{202}Hg^{19}F$, X$^ 2 Σ_{1/2}$) for its potential of laser cooling and eEDM measurement. We theoretically investigated the electronic, rovibrational and hyperfine structures and verified the highly diagonal Franck-Condon factors (FCFs) of the main transitions by the Rydberg-Klein-Rees inversion (RKR) method and the Morse approximation. Hyperfine manifolds of the X$^ 2 Σ_{1/2} (ν=0$) rotational states were examined with the effective Hamiltonian approach and a feasible sideband modulation scheme was proposed. In order to enhance optical cycling, the microwave remixing method was employed to address all the necessary levels. The Zeeman effect and the hyperfine structure magnetic g factors of the X$^ 2 Σ_{1/2} (ν=0$,$\mathit{ N } $=1) state were studied subsequently. Finally, its statistical sensitivities for the eEDM measurement were estimated respectively to be about $9\times 10^{-31} e\bullet cm $ (the laser cooled transverse beam experiment), $2\times 10^{-31} e\bullet cm$ (the fountain experiment) and $1\times 10^{-32}$ e\bullet cm (experiment with trapped cold molecules), indicating that $^{202}Hg^{19}F$ might be another promising eEDM candidate when compared with the most recent ThO result of $d_{ e } = (4.3 \pm 3.1_{ stat } \pm 2.6_{ syst })\times 10^{-30} e\bullet cm$ (Nature, 562, 355 (2018)). In addition, the possibility of direct Stark decelerating of the HgF radical was also discussed.

physics.atom-ph↗

arXiv2018

SD-CNN: a Shallow-Deep CNN for Improved Breast Cancer Diagnosis

Breast cancer is the second leading cause of cancer death among women worldwide. Nevertheless, it is also one of the most treatable malignances if detected early. Screening for breast cancer with digital mammography (DM) has been widely used. However it demonstrates limited sensitivity for women with dense breasts. An emerging technology in the field is contrast-enhanced digital mammography (CEDM), which includes a low energy (LE) image similar to DM, and a recombined image leveraging tumor neoangiogenesis similar to breast magnetic resonance imaging (MRI). CEDM has shown better diagnostic accuracy than DM. While promising, CEDM is not yet widely available across medical centers. In this research, we propose a Shallow-Deep Convolutional Neural Network (SD-CNN) where a shallow CNN is developed to derive "virtual" recombined images from LE images, and a deep CNN is employed to extract novel features from LE, recombined or "virtual" recombined images for ensemble models to classify the cases as benign vs. cancer. To evaluate the validity of our approach, we first develop a deep-CNN using 49 CEDM cases collected from Mayo Clinic to prove the contributions from recombined images for improved breast cancer diagnosis (0.86 in accuracy using LE imaging vs. 0.90 in accuracy using both LE and recombined imaging). We then develop a shallow-CNN using the same 49 CEDM cases to learn the nonlinear mapping from LE to recombined images. Next, we use 69 DM cases collected from the hospital located at Zhejiang University, China to generate "virtual" recombined images. Using DM alone provides 0.91 in accuracy, whereas SD-CNN improves the diagnostic accuracy to 0.95.

cs.CV↗

arXiv2018

Achieving Efficient and Secure Data Acquisition for Cloud-supported Internet of Things in Smart Grid

Cloud-supported Internet of Things (Cloud-IoT) has been broadly deployed in smart grid systems. The IoT front-ends are responsible for data acquisition and status supervision, while the substantial amount of data is stored and managed in the cloud server. Achieving data security and system efficiency in the data acquisition and transmission process are of great significance and challenging, because the power grid-related data is sensitive and in huge amount. In this paper, we present an efficient and secure data acquisition scheme based on CP-ABE (Ciphertext Policy Attribute Based Encryption). Data acquired from the terminals will be partitioned into blocks and encrypted with its corresponding access sub-tree in sequence, thereby the data encryption and data transmission can be processed in parallel. Furthermore, we protect the information about the access tree with threshold secret sharing method, which can preserve the data privacy and integrity from users with the unauthorized sets of attributes. The formal analysis demonstrates that the proposed scheme can fulfill the security requirements of the Cloud-supported IoT in smart grid. The numerical analysis and experimental results indicate that our scheme can effectively reduce the time cost compared with other popular approaches.

cs.CR↗