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Yan Liu

Publications and source records attributed to Yan Liu.

At least 397 records · Page 22Linked to original sources
arXiv2020

Anisotropic gapping of topological Weyl rings in the charge-density-wave superconductor InxTaSe2

Topological materials and topological phases have recently become a hot topic in condensed matter physics. In this work, we report a topological nodal-line semimetal InxTaSe2, in the presence of both charge density wave (CDW) and superconductivity. In the x = 0.58 samples, the 2 * /3 commensurate CDW (CCDW) and the 2 * 2 CCDW are observed below 116 K and 77 K, respectively. Consistent with theoretical calculations, the spin-orbital coupling gives rise to two two-fold-degenerate nodal rings (Weyl rings) connected by drumhead surface states, confirmed by angle-resolved photoemission spectroscopy. Our results suggest that the 2 * 2 CCDW ordering gaps out one Weyl ring in accordance with the CDW band folding, while the other Weyl ring remains gapless with intact surface states. In addition, superconductivity emerges at 0.91 K, with the upper critical field deviating from the s-wave behavior at low temperature, implying possibly unconventional superconductivity. Therefore, InxTaSe2 represents an interesting material system to study the interplay between CDW, nontrivial band topology and superconductivity.

cond-mat.supr-con↗
arXiv2020

Scalar Self-force for High Spin Black Holes

We semianalytically investigate the scalar self-force experienced in the final stages of extreme mass ratio inspirals of nonspinning scalar particles into supermassive nearly extremal Kerr black holes. We exploit the near-horizon conformal symmetry to find the self-force for general corotating equatorial geodesics. The angular component of the self-force is shown to be universal at leading order in the high spin limit. We verify that the energy and angular momentum losses of the scalar particle match with the asymptotic fluxes of scalar radiation. In particular, we relate the previously described persistent oscillations in the asymptotic energy and angular momentum fluxes with the local self-force. Such oscillations arise from traveling waves that prevent the near-horizon and the asymptotic region to fully decouple in the extremal limit. Conformal invariance is therefore reduced to discrete scale invariance with associated logarithmic periodicity.

gr-qc↗
arXiv2020

Real-time cosmology with SKA

In this work, we investigate what role the redshift drift data of Square Kilometre Array (SKA) will play in the cosmological parameter estimation in the future. To test the constraint capability of the redshift drift data of SKA-only, the $Λ$CDM model is chosen as a reference model. We find that using the SKA1 mock data, the $Λ$CDM model can be loosely constrained, while the model can be well constrained when the SKA2 mock data are used. When the mock data of SKA are combined with the data of the European Extremely Large Telescope (E-ELT), the constraints can be significantly improved almost as good as the data combination of the type Ia supernovae observation (SN), the cosmic microwave background observation (CMB), and the baryon acoustic oscillations observation (BAO). Furthermore, we explore the impact of the redshift drift data of SKA on the basis of SN+CMB+BAO+E-ELT in the $Λ$CDM model, the $w$CDM model, the CPL model, and the HDE model. We find that the redshift drift measurement of SKA could help to significantly improve the constraints on dark energy and could break the degeneracy between the cosmological parameters. Therefore, we conclude that redshift-drift observation of SKA would provide a good improvement in the cosmological parameter estimation in the future and has the enormous potential to be one of the most competitive cosmological probes in constraining dark energy.

astro-ph.CO↗
arXiv2020

NoiseRank: Unsupervised Label Noise Reduction with Dependence Models

Label noise is increasingly prevalent in datasets acquired from noisy channels. Existing approaches that detect and remove label noise generally rely on some form of supervision, which is not scalable and error-prone. In this paper, we propose NoiseRank, for unsupervised label noise reduction using Markov Random Fields (MRF). We construct a dependence model to estimate the posterior probability of an instance being incorrectly labeled given the dataset, and rank instances based on their estimated probabilities. Our method 1) Does not require supervision from ground-truth labels, or priors on label or noise distribution. 2) It is interpretable by design, enabling transparency in label noise removal. 3) It is agnostic to classifier architecture/optimization framework and content modality. These advantages enable wide applicability in real noise settings, unlike prior works constrained by one or more conditions. NoiseRank improves state-of-the-art classification on Food101-N (~20% noise), and is effective on high noise Clothing-1M (~40% noise).

cs.CV↗
arXiv2020

Towards Using Count-level Weak Supervision for Crowd Counting

Most existing crowd counting methods require object location-level annotation, i.e., placing a dot at the center of an object. While being simpler than the bounding-box or pixel-level annotation, obtaining this annotation is still labor-intensive and time-consuming especially for images with highly crowded scenes. On the other hand, weaker annotations that only know the total count of objects can be almost effortless in many practical scenarios. Thus, it is desirable to develop a learning method that can effectively train models from count-level annotations. To this end, this paper studies the problem of weakly-supervised crowd counting which learns a model from only a small amount of location-level annotations (fully-supervised) but a large amount of count-level annotations (weakly-supervised). To perform effective training in this scenario, we observe that the direct solution of regressing the integral of density map to the object count is not sufficient and it is beneficial to introduce stronger regularizations on the predicted density map of weakly-annotated images. We devise a simple-yet-effective training strategy, namely Multiple Auxiliary Tasks Training (MATT), to construct regularizes for restricting the freedom of the generated density maps. Through extensive experiments on existing datasets and a newly proposed dataset, we validate the effectiveness of the proposed weakly-supervised method and demonstrate its superior performance over existing solutions.

cs.CV↗
arXiv2020

Deep Reinforcement Learning-Based Beam Tracking for Low-Latency Services in Vehicular Networks

Ultra-Reliable and Low-Latency Communications (URLLC) services in vehicular networks on millimeter-wave bands present a significant challenge, considering the necessity of constantly adjusting the beam directions. Conventional methods are mostly based on classical control theory, e.g., Kalman filter and its variations, which mainly deal with stationary scenarios. Therefore, severe application limitations exist, especially with complicated, dynamic Vehicle-to-Everything (V2X) channels. This paper gives a thorough study of this subject, by first modifying the classical approaches, e.g., Extended Kalman Filter (EKF) and Particle Filter (PF), for non-stationary scenarios, and then proposing a Reinforcement Learning (RL)-based approach that can achieve the URLLC requirements in a typical intersection scenario. Simulation results based on a commercial ray-tracing simulator show that enhanced EKF and PF methods achieve packet delay more than $10$ ms, whereas the proposed deep RL-based method can reduce the latency to about $6$ ms, by extracting context information from the training data.

cs.IT↗
arXiv2020

Higher derivatives driven symmetry breaking in holographic superconductors

In this paper, we construct a novel holographic superconductor from higher derivative (HD) gravity involving a coupling between the complex scalar field and the Weyl tensor. This HD coupling term provides a near horizon effective mass squared, which can violates IR Breitenlohner-Freedman (BF) bound by tuning the HD coupling and induces the instability of black brane such that the superconducting phase transition happens. We also study the properties of the condensation and the conductivity in the probe limit. We find that a wider extension of the superconducting energy gap ranging from 4.6 to 10.5 may provide a novel platform to model and interpret the phenomena in the real materials of high temperature superconductor.

hep-th↗
arXiv2019

An efficient penalized estimation approach for a semi-parametric linear transformation model with interval-censored data

We consider efficient estimation of flexible transformation models with interval-censored data. To reduce the dimension of semi-parametric models, the unknown monotone transformation function is approximated via monotone splines. A penalization technique is used to provide more computationally efficient estimation of all parameters. To accomplish model fitting, a computationally efficient nested iterative expectation-maximization (EM) based algorithm is developed for estimation, and an easily implemented variance-covariance approach is proposed for inference on regression parameters. Theoretically, we show that the estimator of the transformation function achieves the optimal rate of convergence and the estimators of regression parameters are asymptotically normal and efficient. The penalized procedure is assessed through extensive numerical experiments and further illustrated via two real applications.

stat.ME↗
arXiv2019

CIS-Net: A Novel CNN Model for Spatial Image Steganalysis via Cover Image Suppression

Image steganalysis is a special binary classification problem that aims to classify natural cover images and suspected stego images which are the results of embedding very weak secret message signals into covers. How to effectively suppress cover image content and thus make the classification of cover images and stego images easier is the key of this task. Recent researches show that Convolutional Neural Networks (CNN) are very effective to detect steganography by learning discriminative features between cover images and their stegos. Several deep CNN models have been proposed via incorporating domain knowledge of image steganography/steganalysis into the design of the network and achieve state of the art performance on standard database. Following such direction, we propose a novel model called Cover Image Suppression Network (CIS-Net), which improves the performance of spatial image steganalysis by suppressing cover image content as much as possible in model learning. Two novel layers, the Single-value Truncation Layer (STL) and Sub-linear Pooling Layer (SPL), are proposed in this work. Specifically, STL truncates input values into a same threshold when they are out of a predefined interval. Theoretically, we have proved that STL can reduce the variance of input feature map without deteriorating useful information. For SPL, it utilizes sub-linear power function to suppress large valued elements introduced by cover image contents and aggregates weak embedded signals via average pooling. Extensive experiments demonstrate the proposed network equipped with STL and SPL achieves better performance than rich model classifiers and existing CNN models on challenging steganographic algorithms.

cs.MM↗
arXiv2019

Chiral vortical conductivity across a topological phase transition from holography

We study the chiral vortical conductivity in a holographic Weyl semimetal model, which describes a topological phase transition from the strongly coupled topologically nontrivial phase to a trivial phase. We focus on the temperature dependence of the chiral vortical conductivity where the mixed gauge-gravitational anomaly plays a crucial role. After a proper renormalization of the chiral vortical conductivity by the anomalous Hall conductivity and temperature squared, we find that at low temperature in both the Weyl semimetal phase and the quantum critical region this renormalized ratio stays as universal constants. More intriguingly, this ratio in the quantum critical region depends only on the emergent Lifshitz scaling exponent at the quantum critical point.

hep-th↗
arXiv2019

Holographic Topological Semimetals

The holographic duality allows to construct and study models of strongly coupled quantum matter via dual gravitational theories. In general such models are characterized by the absence of quasiparticles, hydrodynamic behavior and Planckian dissipation times. One particular interesting class of quantum materials are ungapped topological semimetals which have many interesting properties from Hall transport to topologically protected edge states. We review the application of the holographic duality to this type of quantum matter including the construction of holographic Weyl semimetals, nodal line semimetals, quantum phase transition to trivial states (ungapped and gapped), the holographic dual of Fermi arcs and how new unexpected transport properties, such as Hall viscosities arise. The holographic models promise to lead to new insights into the properties of this type of quantum matter.

hep-th↗
arXiv2019

Spin and Quadrupole Couplings for High Spin Equatorial Intermediate Mass-ratio Coalescences

Intermediate mass-ratio coalescences are potential signals of ground-based and space-based gravitational observatories. Accurate modeling of their waveforms within general relativity can be achieved within black hole perturbation theory including self-force and finite size effects. In this paper, we present analytic results to the Teukolsky perturbation of equatorial orbits in the near-horizon region of an extremely high spin black hole including spin coupling and finite size effects at leading order in the high spin limit while neglecting the self-force. We detail the critical behavior occuring close to the smallest specific angular momentum, and we discuss features of spin and quadrupole couplings.

gr-qc↗
arXiv2019

Robust reconstruction of fluorescence molecular tomography with an optimized illumination pattern

Fluorescence molecular tomography (FMT) is an emerging powerful tool for biomedical research. There are two factors that influence FMT reconstruction most effectively. The first one is the regularization techniques. Traditional methods such as Tikhonov regularization suffer from low resolution and poor signal to noise ratio. Therefore sparse regularization techniques have been introduced to improve the reconstruction quality. The second factor is the illumination pattern. A better illumination pattern ensures the quantity and quality of the information content of the data set thus leading to better reconstructions. In this work, we take advantage of the discrete formulation of the forward problem to give a rigorous definition of an illumination pattern as well as the admissible set of patterns. We add restrictions in the admissible set as different types of regularizers to a discrepancy functional, generating another inverse problem with the illumination pattern as unknown. Both inverse problems of reconstructing the fluorescence distribution and finding the optimal illumination pattern are solved by fast efficient iterative algorithms. Numerical experiments have shown that with a suitable choice of the regularization parameters the two-step approach converges to an optimal illumination pattern quickly and the reconstruction result is improved significantly, regardless of the initial illumination setting.

eess.IV↗
arXiv2019

GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection

In this paper we present GumDrop, Georgetown University's entry at the DISRPT 2019 Shared Task on automatic discourse unit segmentation and connective detection. Our approach relies on model stacking, creating a heterogeneous ensemble of classifiers, which feed into a metalearner for each final task. The system encompasses three trainable component stacks: one for sentence splitting, one for discourse unit segmentation and one for connective detection. The flexibility of each ensemble allows the system to generalize well to datasets of different sizes and with varying levels of homogeneity.

cs.CL↗
arXiv2019

Optimal p-ary cyclic codes with two zeros

As a subclass of linear codes, cyclic codes have efficient encoding and decoding algorithms, so they are widely used in many areas such as consumer electronics, data storage systems and communication systems. In this paper, we give a general construction of optimal p-ary cyclic codes which leads to three explicit constructions. In addition, another class of p-ary optimal cyclic codes are presented.

cs.IT↗
arXiv2019

From Crowdsourcing to Crowdmining: Using Implicit Human Intelligence for Better Understanding of Crowdsourced Data

With the development of mobile social networks, more and more crowdsourced data are generated on the Web or collected from real-world sensing. The fragment, heterogeneous, and noisy nature of online/offline crowdsourced data, however, makes it difficult to be understood. Traditional content-based analyzing methods suffer from potential issues such as computational intensiveness and poor performance. To address them, this paper presents CrowdMining. In particular, we observe that the knowledge hidden in the process of data generation, regarding individual/crowd behavior patterns (e.g., mobility patterns, community contexts such as social ties and structure) and crowd-object interaction patterns (flickering or tweeting patterns) are neglected in crowdsourced data mining. Therefore, a novel approach that leverages implicit human intelligence (implicit HI) for crowdsourced data mining and understanding is proposed. Two studies titled CrowdEvent and CrowdRoute are presented to showcase its usage, where implicit HIs are extracted either from online or offline crowdsourced data. A generic model for CrowdMining is further proposed based on a set of existing studies. Experiments based on real-world datasets demonstrate the effectiveness of CrowdMining.

cs.HC↗
arXiv2019

Hybrid Low-order and Higher-order Graph Convolutional Networks

With higher-order neighborhood information of graph network, the accuracy of graph representation learning classification can be significantly improved. However, the current higher order graph convolutional network has a large number of parameters and high computational complexity. Therefore, we propose a Hybrid Lower order and Higher order Graph convolutional networks (HLHG) learning model, which uses weight sharing mechanism to reduce the number of network parameters. To reduce computational complexity, we propose a novel fusion pooling layer to combine the neighborhood information of high order and low order. Theoretically, we compare the model complexity of the proposed model with the other state-of-the-art model. Experimentally, we verify the proposed model on the large-scale text network datasets by supervised learning, and on the citation network datasets by semi-supervised learning. The experimental results show that the proposed model achieves highest classification accuracy with a small set of trainable weight parameters.

cs.LG↗