arXiv subjects

Jie Li

Publications and source records attributed to Jie Li.

At least 649 records · Page 36Linked to original sources

arXiv2019

Single full gap with mixed type-I and type-II superconductivity on surface of the type-II Dirac semimetal PdTe2 by point-contact spectroscopy

We report our point-contact spectroscopy (PCS) study on the superconducting state of the type-II Dirac semimetal PdTe$_2$ with a superconducting transition temperature $T\rm_c \sim$ 1.65 K. Both mechanical- and soft- PCS differential conductance curves at 0.3 K show a consistent double-peak structure and they can be perfectly fitted by a single s-wave gap based on the Blonder-Tinkham-Klapwijk model. The gap follows a typical Bardeen-Cooper-Schrieffer temperature behavior, yielding $Δ_0 \sim$ 0.29 meV and 2$Δ_0$/$k\rm_{B}$$T\rm_{c}$ = 4.15 in the strong coupling regime. A sudden suppression of the superconducting gap in magnetic field around $H{\rm_{c1}}$ $\sim$ 130 Oe is observed for most point-contacts on PdTe$_2$, characteristic of a first-order transition for type-I superconductor in field. However, for other contacts, a smooth evolution of the PCS conductance persists up to $H{\rm_{c2}}$ $\sim$ 600 Oe, signaling a local type-II superconductivity. The observed admixture of type-I and type-II superconductivity can possibly arise from an inhomogeneous electron mean free path on the surface of PdTe$_2$ due to its topological surface states.

cond-mat.supr-con↗

arXiv2019

A Multi-Resolution 3D-DenseNet for Chemical Shift Prediction in NMR Crystallography

We have developed a deep learning algorithm for chemical shift prediction for atoms in molecular crystals that utilizes an atom-centered Gaussian density model for the 3D data representation of a molecule. We define multiple channels that describe different spatial resolutions for each atom type that utilizes cropping, pooling, and concatenation to create a multi-resolution 3D-DenseNet architecture (MR-3D-DenseNet). Because the training and testing time scale linearly with the number of samples, the MR-3D-DenseNet can exploit data augmentation that takes into account the property of rotational invariance of the chemical shifts, thereby also increasing the size of the training dataset by an order of magnitude without additional cost. We obtain very good agreement for 13C, 15N, and 17O chemical shifts, with the highest accuracy found for 1H chemical shifts that is equivalent to the best predictions using ab initio quantum chemistry methods.

physics.chem-ph↗

arXiv2019

A Bayesian analysis of sneutrino DM in the NMSSM with Type-I seesaw mechanism

In the Next-to-Minimal Supersymmetric Standard Model (NMSSM) with extra heavy neutrino superfields, neutrino may acquire its mass via a seesaw mechanism and sneutrino may act as a viable dark matter (DM) candidate. Given the strong tension between the naturalness for $Z$ boson mass and the DM direct detection experiments for customary neutralino DM candidate, we augment the NMSSM with Type-I seesaw mechanism, which is the simplest extension of the theory to predict neutrino mass, and study the scenarios of sneutrino DM. We construct likelihood function with LHC Higgs data, B-physics measurements, DM relic density and its direct and indirect search limits, and perform a comprehensive scan over the parameter space of the theory by Nested Sampling method. We adopt both Bayesian and frequentist statistical quantities to illustrate the favored parameter space of the scenarios, the DM annihilation mechanism as well as the features of DM-nucleon scattering. We find that the scenarios are viable over broad parameter regions, especially the Higgsino mass $μ$ can be below about $250 {\rm GeV}$ for a significant part of the region, which predicts $Z$ boson mass in a natural way. We also find that the DM usually co-annihilated with the Higgsinos to get the measured relic density, and consequently the DM-nucleon scattering rate is naturally suppressed to coincide with the recent XENON-1T results even for light Higgsinos. Other issues, such as the LHC search for the Higgsinos, are also addressed.

hep-ph↗

arXiv2019

Evidence for nematic superconductivity of topological surface states in PbTaSe2

Spontaneous symmetry breaking has been a paradigm to describe the phase transitions in condensed matter physics. In addition to the continuous electromagnetic gauge symmetry, an unconventional superconductor can break discrete symmetries simultaneously, such as time reversal and lattice rotational symmetry. In this work we report a characteristic in-plane 2-fold behaviour of the resistive upper critical field and point-contact spectra on the superconducting semimetal PbTaSe2 with topological nodal-rings, despite its hexagonal lattice symmetry (or D_3h in bulk while C_3v on surface, to be precise). However, we do not observe any lattice rotational symmetry breaking signal from field-angle-dependent specific heat. It is worth noting that such surface-only electronic nematicity is in sharp contrast to the observation in the topological superconductor candidate, CuxBi2Se3, where the nematicity occurs in various bulk measurements. In combination with theory, superconducting nematicity is likely to emerge from the topological surface states of PbTaSe2, rather than the proximity effect. The issue of time reversal symmetry breaking is also addressed. Thus, our results on PbTaSe2 shed new light on possible routes to realize nematic superconductivity with nontrivial topology.

cond-mat.supr-con↗

arXiv2019

Learning to Fuse Things and Stuff

We propose an end-to-end learning approach for panoptic segmentation, a novel task unifying instance (things) and semantic (stuff) segmentation. Our model, TASCNet, uses feature maps from a shared backbone network to predict in a single feed-forward pass both things and stuff segmentations. We explicitly constrain these two output distributions through a global things and stuff binary mask to enforce cross-task consistency. Our proposed unified network is competitive with the state of the art on several benchmarks for panoptic segmentation as well as on the individual semantic and instance segmentation tasks.

cs.CV↗

arXiv2019

Accuracy of the semiclassical picture of photoionization in intense laser fields

In the semiclassical picture of photoionization process in intense laser fields, the ionization rate solely depends on the amplitude of the electric field and the final photoelectron momentum corresponds to the instant of ionization of the photoelectron, however, this picture has never been checked rigorously. Recently an attosecond angular streaking technique based on this semiclassical perspective has been widely applied to temporal measurement of the atomic and molecular dynamics in intense laser fields. We use a Wigner-distribution-like function to calculate the time-emission angle distribution, angular distribution and ionization time distribution for atomic ionization process in elliptically polarized few-cycle laser fields. By comparing with semiclassical calculations, we find that the two methods always show discrepancies except in some specific cases and the offset angles are generally not consistent with the offset times of the ionization time distributions obtained by the two methods even when the non-adiabatic effect is taken into account, indicating that the "attoclock" technique is in principle inaccurate. Moreover, calculations for linearly polarized laser fields also show similar discrepancies between two methods in the ionization time distribution. Our analysis indicates that the discrepancy between the semiclassical and quantum calculations can be attributed to correlation, i. e., temporal nonlocalization effect.

physics.atom-ph↗

arXiv2019

The Transiting Multi-planet System HD15337: Two Nearly Equal-mass Planets Straddling the Radius Gap

We report the discovery of a super-Earth and a sub-Neptune transiting the star HD 15337 (TOI-402, TIC 120896927), a bright (V=9) K1 dwarf observed by the Transiting Exoplanet Survey Satellite (TESS) in Sectors 3 and 4. We combine the TESS photometry with archival HARPS spectra to confirm the planetary nature of the transit signals and derive the masses of the two transiting planets. With an orbital period of 4.8 days, a mass of 7.51(+1.09)(-1.01) M_Earth, and a radius of 1.64+/-0.06 R_Earth, HD 15337b joins the growing group of short-period super-Earths known to have a rocky terrestrial composition. The sub-Neptune HD 15337c has an orbital period of 17.2 days, a mass of 8.11(+1.82)(-1.69) M_Earth, and a radius of 2.39+/-0.12 R_Earth, suggesting that the planet might be surrounded by a thick atmospheric envelope. The two planets have similar masses and lie on opposite sides of the radius gap, and are thus an excellent testbed for planet formation and evolution theories. Assuming that HD 15337c hosts a hydrogen-dominated envelope, we employ a recently developed planet atmospheric evolution algorithm in a Bayesian framework to estimate the history of the high-energy (extreme ultraviolet and X-ray) emission of the host star. We find that at an age of 150 Myr, the star possessed on average between 3.7 and 127 times the high-energy luminosity of the current Sun.

astro-ph.EP↗

arXiv2019

RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion

RGB images differentiate from depth images as they carry more details about the color and texture information, which can be utilized as a vital complementary to depth for boosting the performance of 3D semantic scene completion (SSC). SSC is composed of 3D shape completion (SC) and semantic scene labeling while most of the existing methods use depth as the sole input which causes the performance bottleneck. Moreover, the state-of-the-art methods employ 3D CNNs which have cumbersome networks and tremendous parameters. We introduce a light-weight Dimensional Decomposition Residual network (DDR) for 3D dense prediction tasks. The novel factorized convolution layer is effective for reducing the network parameters, and the proposed multi-scale fusion mechanism for depth and color image can improve the completion and segmentation accuracy simultaneously. Our method demonstrates excellent performance on two public datasets. Compared with the latest method SSCNet, we achieve 5.9% gains in SC-IoU and 5.7% gains in SSC-IOU, albeit with only 21% network parameters and 16.6% FLOPs employed compared with that of SSCNet.

cs.CV↗

arXiv2019

Enhancement of Power Equipment Management Using Knowledge Graph

Accurate retrieval of the power equipment information plays an important role in guiding the full-lifecycle management of power system assets. Because of data duplication, database decentralization, weak data relations, and sluggish data updates, the power asset management system eager to adopt a new strategy to avoid the information losses, bias, and improve the data storage efficiency and extraction process. Knowledge graph has been widely developed in large part owing to its schema-less nature. It enables the knowledge graph to grow seamlessly and allows new relations addition and entities insertion when needed. This study proposes an approach for constructing power equipment knowledge graph by merging existing multi-source heterogeneous power equipment related data. A graph-search method to illustrate exhaustive results to the desired information based on the constructed knowledge graph is proposed. A case of a 500 kV station example is then demonstrated to show relevant search results and to explain that the knowledge graph can improve the efficiency of power equipment management.

cs.AI↗

arXiv2019

No-Reference Quality Assessment of Contrast-Distorted Images using Contrast Enhancement

No-reference image quality assessment (NR-IQA) aims to measure the image quality without reference image. However, contrast distortion has been overlooked in the current research of NR-IQA. In this paper, we propose a very simple but effective metric for predicting quality of contrast-altered images based on the fact that a high-contrast image is often more similar to its contrast enhanced image. Specifically, we first generate an enhanced image through histogram equalization. We then calculate the similarity of the original image and the enhanced one by using structural-similarity index (SSIM) as the first feature. Further, we calculate the histogram based entropy and cross entropy between the original image and the enhanced one respectively, to gain a sum of 4 features. Finally, we learn a regression module to fuse the aforementioned 5 features for inferring the quality score. Experiments on four publicly available databases validate the superiority and efficiency of the proposed technique.

cs.CV↗

arXiv2019

Multifunctional 2D CuSe monolayer nanodevice

In a very recent experimental work [Gao et al., 2018 Adv. Mater. 30, 1707055], a graphene-like CuSe monolayer was realized. Motivated by this success, we performed first-principles calculations to investigate its electronic transport and photoelectronic properties. We find that the CuSe ML shows a strong electrical anisotropy, and its current-voltage (I-V) curves along the zigzag and armchair directions are noticeably different. The CuSe ML also displays a useful negative differential resistance (NDR) effect along the both directions when the bias is beyond 1.0 V. Moreover, it has a large photon absorption to orange light. Our study suggests that CuSe ML is a multifunctional material and has various potential applications in electrical-anisotropy-based, NDR-based, and even optical nanodevices.

cond-mat.mes-hall↗

arXiv2019

Shared Predictive Cross-Modal Deep Quantization

With explosive growth of data volume and ever-increasing diversity of data modalities, cross-modal similarity search, which conducts nearest neighbor search across different modalities, has been attracting increasing interest. This paper presents a deep compact code learning solution for efficient cross-modal similarity search. Many recent studies have proven that quantization-based approaches perform generally better than hashing-based approaches on single-modal similarity search. In this paper, we propose a deep quantization approach, which is among the early attempts of leveraging deep neural networks into quantization-based cross-modal similarity search. Our approach, dubbed shared predictive deep quantization (SPDQ), explicitly formulates a shared subspace across different modalities and two private subspaces for individual modalities, and representations in the shared subspace and the private subspaces are learned simultaneously by embedding them to a reproducing kernel Hilbert space, where the mean embedding of different modality distributions can be explicitly compared. In addition, in the shared subspace, a quantizer is learned to produce the semantics preserving compact codes with the help of label alignment. Thanks to this novel network architecture in cooperation with supervised quantization training, SPDQ can preserve intramodal and intermodal similarities as much as possible and greatly reduce quantization error. Experiments on two popular benchmarks corroborate that our approach outperforms state-of-the-art methods.

cs.CV↗

arXiv2019

Hybrid Noise Removal in Hyperspectral Imagery With a Spatial-Spectral Gradient Network

The existence of hybrid noise in hyperspectral images (HSIs) severely degrades the data quality, reduces the interpretation accuracy of HSIs, and restricts the subsequent HSIs applications. In this paper, the spatial-spectral gradient network (SSGN) is presented for mixed noise removal in HSIs. The proposed method employs a spatial-spectral gradient learning strategy, in consideration of the unique spatial structure directionality of sparse noise and spectral differences with additional complementary information for better extracting intrinsic and deep features of HSIs. Based on a fully cascaded multi-scale convolutional network, SSGN can simultaneously deal with the different types of noise in different HSIs or spectra by the use of the same model. The simulated and real-data experiments undertaken in this study confirmed that the proposed SSGN performs better at mixed noise removal than the other state-of-the-art HSI denoising algorithms, in evaluation indices, visual assessments, and time consumption.

cs.CV↗

arXiv2019

Short-term Load Forecasting at Different Aggregation Levels with Predictability Analysis

Short-term load forecasting (STLF) is essential for the reliable and economic operation of power systems. Though many STLF methods were proposed over the past decades, most of them focused on loads at high aggregation levels only. Thus, low-aggregation load forecast still requires further research and development. Compared with the substation or city level loads, individual loads are typically more volatile and much more challenging to forecast. To further address this issue, this paper first discusses the characteristics of small-and-medium enterprise (SME) and residential loads at different aggregation levels and quantifies their predictability with approximate entropy. Various STLF techniques, from the conventional linear regression to state-of-the-art deep learning, are implemented for a detailed comparative analysis to verify the forecasting performances as well as the predictability using an Irish smart meter dataset. In addition, the paper also investigates how using data processing improves individual-level residential load forecasting with low predictability. Effectiveness of the discussed method is validated with numerical results.

cs.LG↗

arXiv2019

Bayesian Estimation Based Parameter Estimation for Composite Load

Accurate identification of parameters of load models is essential in power system computations, including simulation, prediction, and stability and reliability analysis. Conventional point estimation based composite load modeling approaches suffer from disturbances and noises and provide limited information of the system dynamics. In this work, a statistic (Bayesian Estimation) based distribution estimation approach is proposed for both static (ZIP) and dynamic (Induction Motor) load modeling. When dealing with multiple parameters, Gibbs sampling method is employed. In each iteration, the proposal samples each parameter while keeps others fixed. The proposed method provides a distribution estimation of load models coefficients and is robust to measurement errors.

eess.SY↗

arXiv2019

TESS full orbital phase curve of the WASP-18b system

We present a visible-light full orbital phase curve of the transiting planet WASP-18b measured by the TESS Mission. The phase curve includes the transit, secondary eclipse, and sinusoidal modulations across the orbital phase shaped by the planet's atmospheric characteristics and the star-planet gravitational interaction. We measure the beaming (Doppler boosting) and tidal ellipsoidal distortion phase modulations and show that the amplitudes of both agree with theoretical expectations. We find that the light from the planet's day-side hemisphere occulted during secondary eclipse, with a relative brightness of $341_{-18}^{+17}$ ppm, is dominated by thermal emission, leading to an upper limit on the geometric albedo in the TESS band of 0.048 (2$σ$). We also detect the phase modulation due to the planet's atmosphere longitudinal brightness distribution. We find that its maximum is well-aligned with the sub-stellar point, to within 2.9 deg (2$σ$). We do not detect light from the planet's night-side hemisphere, with an upper limit of 43 ppm (2$σ$), which is 13 % of the day-side brightness. The low albedo, lack of atmospheric phase shift, and inefficient heat distribution from the day to night hemispheres that we deduce from our analysis are consistent with theoretical expectations and similar findings for other strongly irradiated gas giant planets. This work demonstrates the potential of TESS data for studying full orbital phase curves of transiting systems. Finally, we complement our study by looking for transit timing variations (TTVs) in the TESS data and combined with previously published transit times, although we do not find a statistically significant TTV signal.

astro-ph.EP↗

arXiv2019

SPIGAN: Privileged Adversarial Learning from Simulation

Deep Learning for Computer Vision depends mainly on the source of supervision.Photo-realistic simulators can generate large-scale automatically labeled syntheticdata, but introduce a domain gap negatively impacting performance. We propose anew unsupervised domain adaptation algorithm, called SPIGAN, relying on Sim-ulator Privileged Information (PI) and Generative Adversarial Networks (GAN).We use internal data from the simulator as PI during the training of a target tasknetwork. We experimentally evaluate our approach on semantic segmentation. Wetrain the networks on real-world Cityscapes and Vistas datasets, using only unla-beled real-world images and synthetic labeled data with z-buffer (depth) PI fromthe SYNTHIA dataset. Our method improves over no adaptation and state-of-the-art unsupervised domain adaptation techniques.

cs.CV↗

arXiv2019

Squeezed states of magnons and phonons in cavity magnomechanics

We show how to create quantum squeezed states of magnons and phonons in a cavity magnomechanical system. The magnons are embodied by a collective motion of a large number of spins in a macroscopic ferrimagnet, and couple to cavity microwave photons and phonons (vibrational modes of the ferrimagnet) via the magnetic dipole interaction and magnetostrictive interaction, respectively. The cavity is driven by a weak squeezed vacuum field generated by a flux-driven Josephson parametric amplifier, which is essential to get squeezed states of the magnons and phonons. We show that the magnons can be prepared in a squeezed state via the cavity-magnon beamsplitter interaction, and by further driving the magnon mode with a strong red-detuned microwave field, the phonons are squeezed. We show optimal parameter regimes for obtaining large squeezing of the magnons and phonons, which are robust against temperature and could be realized with experimentally reachable parameters.

quant-ph↗