arXiv subjects

Feng Xu

Publications and source records attributed to Feng Xu.

At least 199 records · Page 11Linked to original sources

arXiv2019

Von Neumann Entropy in QFT

In the framework of Quantum Field Theory, we provide a rigorous, operator algebraic notion of entanglement entropy associated with a pair of open double cones $O \subset \tilde O$ of the spacetime, where the closure of $O$ is contained in $\tilde O$. Given a QFT net $A$ of local von Neumann algebras $A(O)$, we consider the von Neumann entropy $S_A(O, \tilde O)$ of the restriction of the vacuum state to the canonical intermediate type $I$ factor for the inclusion of von Neumann algebras $A(O)\subset A(\tilde O)$ (split property). We show that this canonical entanglement entropy $S_A(O, \tilde O)$ is finite for the chiral conformal net on the circle generated by finitely many free Fermions (here double cones are intervals). To this end, we first study the notion of von Neumann entropy of a closed real linear subspace of a complex Hilbert space, that we then estimate for the local free fermion subspaces. We further consider the lower entanglement entropy $\underline S_A(O, \tilde O)$, the infimum of the vacuum von Neumann entropy of $F$, where $F$ here runs over all the intermediate, discrete type $I$ von Neumann algebras. We prove that $\underline S_A(O, \tilde O)$ is finite for the local chiral conformal net generated by finitely many commuting $U(1)$-currents.

math-ph↗

arXiv2019

Data-driven Discovery of Partial Differential Equations for Multiple-Physics Electromagnetic Problem

Deriving governing equations in Electromagnetic (EM) environment based on first principles can be quite tough when there are some unknown sources of noise and other uncertainties in the system. For nonlinear multiple-physics electromagnetic systems, deep learning to solve these problems can achieve high efficiency and accuracy. In this paper, we propose a deep learning neutral network in combination with sparse regression to solve the hidden governing equations in multiple-physics EM problem. Pareto analysis is also adopted to preserve inversion as precise and simple as possible. This proposed network architecture can discover a set of governing partial differential equations (PDEs) based on few temporalspatial samples. The data-driven discovery method for partial differential equations (PDEs) in electromagnetic field may also contribute to solve more sophisticated problem which may not be solved by first principles.

physics.comp-ph↗

arXiv2019

Tell-the-difference: Fine-grained Visual Descriptor via a Discriminating Referee

In this paper, we investigate a novel problem of telling the difference between image pairs in natural language. Compared to previous approaches for single image captioning, it is challenging to fetch linguistic representation from two independent visual information. To this end, we have proposed an effective encoder-decoder caption framework based on Hyper Convolution Net. In addition, a series of novel feature fusing techniques for pairwise visual information fusing are introduced and a discriminating referee is proposed to evaluate the pipeline. Because of the lack of appropriate datasets to support this task, we have collected and annotated a large new dataset with Amazon Mechanical Turk (AMT) for generating captions in a pairwise manner (with 14764 images and 26710 image pairs in total). The dataset is the first one on the relative difference caption task that provides descriptions in free language. We evaluate the effectiveness of our model on two datasets in the field and it outperforms the state-of-the-art approach by a large margin.

cs.CV↗

arXiv2019

Point cloud ridge-valley feature enhancement based on position and normal guidance

Ridge-valley features are important elements of point clouds, as they contain rich surface information. To recognize these features from point clouds, this paper introduces an extreme point distance (EPD) criterion with scale independence. Compared with traditional methods, the EPD greatly reduces the number of potential feature points and improves the robustness of multiscale feature point recognition. On this basis, a feature enhancement algorithm based on user priori guidance is proposed that adjusts the coordinates of the feature area by solving an objective equation containing the expected position and normal constraints. Since the expected normal can be expressed as a function of neighborhood point coordinates, the above objective equation can be converted into linear sparse equations with enhanced feature positions as variables, and thus, the closed solution can be obtained. In addition, a parameterization method for scattered point clouds based on feature line guidance is proposed, which reduces the number of unknowns by 2/3 and eliminates lateral sliding in the direction perpendicular to feature lines. Finally, the application of the algorithm in multiscale ridge-valley feature recognition, freeform surface feature enhancement and computer-aided design (CAD) workpiece sharp feature restoration verifies its effectiveness.

cs.GR↗

arXiv2019

Reciprocal Translation between SAR and Optical Remote Sensing Images with Cascaded-Residual Adversarial Networks

Despite the advantages of all-weather and all-day high-resolution imaging, synthetic aperture radar (SAR) images are much less viewed and used by general people because human vision is not adapted to microwave scattering phenomenon. However, expert interpreters can be trained by comparing side-by-side SAR and optical images to learn the mapping rules from SAR to optical. This paper attempts to develop machine intelligence that are trainable with large-volume co-registered SAR and optical images to translate SAR image to optical version for assisted SAR image interpretation. Reciprocal SAR-Optical image translation is a challenging task because it is raw data translation between two physically very different sensing modalities. This paper proposes a novel reciprocal adversarial network scheme where cascaded residual connections and hybrid L1-GAN loss are employed. It is trained and tested on both spaceborne GF-3 and airborne UAVSAR images. Results are presented for datasets of different resolutions and polarizations and compared with other state-of-the-art methods. The FID is used to quantitatively evaluate the translation performance. The possibility of unsupervised learning with unpaired SAR and optical images is also explored. Results show that the proposed translation network works well under many scenarios and it could potentially be used for assisted SAR interpretation.

cs.CV↗

arXiv2019

Design of reversible low-field magnetocaloric effect at room temperature in hexagonal MnMX ferromagnets

Giant magnetocaloric effect is widely achieved in hexagonal MnMX-based (M = Co or Ni, X = Si or Ge) ferromagnets at their first-order magnetostructural transition. However, the thermal hysteresis and the low sensitivity of the magnetostructural transition to the magnetic field inevitably lead to a sizeable irreversibility of the low-field magnetocaloric effect. In this work, we show an alternative way to realize a reversible low-field magnetocaloric effect in MnMX-based alloys by taking advantage of the second-order phase transition. With introducing Cu into Co in MnCoGe alloy, the martensitic transition is stabilized at high temperature, while the Curie temperature of the orthorhombic phase is reduced to room temperature. As a result, a second-order magnetic transition with negligible thermal hysteresis and a large magnetization change can be observed, enabling a large reversible magnetocaloric effect. By both calorimetric and direct measurements, a reversible adiabatic temperature change of about 1 K is obtained under a field change of 0-1 T at 304 K, which is larger than that obtained in a first-order magnetostructural transition. To get a better insight into the origin of these experimental results, first-principles calculations are carried out to characterize the chemical bonds and the magnetic exchange interaction. Our work provides a new understanding of the MnCoGe alloy and indicates a feasible route to improve the reversibility of the low-field magnetocaloric effect in the MnMX system.

cond-mat.mtrl-sci↗

arXiv2019

Demonstration of 3D ISAR Security Imaging at 24GHz with a Sparse MIMO Array

A 3D ISAR security imaging experiment at 24GHz is demonstrated with a sparse MIMO array. The MIMO array is an 8Tx/16Rx linear array to achieve real-aperture imaging along the vertical dimension. It is time-switching multiplexed with a low-cost FMCW transceiver working at 22GHz-26GHz. A calibration procedure is proposed to calibrate the channel imbalance across the MIMO array. The experiment is conducted on human moving on a cart, where we take advantage of the linear motion of human to form inverse synthetic aperture along the horizontal dimension. To track the motion of human, a 3D depth camera is used as an auxiliary sensor to capture the rough position of target to aid ISAR imaging. The back projection imaging algorithm is implemented on GPU for quasi-real-time operation. Finally, experiments are conducted with real human with concealed objects and a preliminary automatic object recognition algorithm based on convolutional neural networks are developed and evaluated on real data.

eess.SP↗

arXiv2019

Constraining the vertical distribution of coastal dust aerosol using OCO-2 O2 A-band measurements

Quantifying the vertical distribution of atmospheric aerosols is crucial for estimating their impact on the Earth energy budget and climate, improving forecast of air pollution in cities, and reducing biases in the retrieval of greenhouse gases (GHGs) from space. However, to date, passive remote sensing measurements have provided limited information about aerosol extinction profiles. In this study, we propose the use of a spectral sorting approach to constrain the aerosol vertical structure using spectra of reflected sunlight absorption within the molecular oxygen (O2) A-band collected by the Orbiting Carbon Observatory-2 (OCO-2). The effectiveness of the approach is evaluated using spectra acquired over the western Sahara coast by comparing the aerosol profile retrievals with lidar measurements from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Using a radiative transfer model to simulate OCO-2 measurements, we found that high-resolution O2 A-band measurements have high sensitivity to aerosol optical depth (AOD) and aerosol layer height (ALH). Retrieved estimates of AOD and ALH based on a look up table technique show good agreement with CALIPSO measurements, with correlation coefficients of 0.65 and 0.53, respectively. The strength of the proposed spectral sorting technique lies in its ability to identify spectral channels with high sensitivity to AOD and ALH and extract the associated information from the observed radiance in a straightforward manner. The proposed approach has the potential to enable future passive remote sensing missions to map the aerosol vertical distribution on a global scale.

physics.ao-ph↗

arXiv2019

A Closed-form Solution to Universal Style Transfer

Universal style transfer tries to explicitly minimize the losses in feature space, thus it does not require training on any pre-defined styles. It usually uses different layers of VGG network as the encoders and trains several decoders to invert the features into images. Therefore, the effect of style transfer is achieved by feature transform. Although plenty of methods have been proposed, a theoretical analysis of feature transform is still missing. In this paper, we first propose a novel interpretation by treating it as the optimal transport problem. Then, we demonstrate the relations of our formulation with former works like Adaptive Instance Normalization (AdaIN) and Whitening and Coloring Transform (WCT). Finally, we derive a closed-form solution named Optimal Style Transfer (OST) under our formulation by additionally considering the content loss of Gatys. Comparatively, our solution can preserve better structure and achieve visually pleasing results. It is simple yet effective and we demonstrate its advantages both quantitatively and qualitatively. Besides, we hope our theoretical analysis can inspire future works in neural style transfer. Code is available at https://github.com/lu-m13/OptimalStyleTransfer.

cs.CV↗

arXiv2019

Planar topological Hall effect in a uniaxial van der Waals ferromagnet Fe3GeTe2

In this work, we reported the observation of a novel planar topological Hall effect (PTHE) in single crystal of Fe3GeTe2, a paradigmatic two-dimensional ferromagnet with strong uniaxial anisotropy. The Hall effect and magnetoresistance varied periodically when the external magnetic field rotated in the ac (or bc) plane, while the PTHE emerged and maintained robust with field swept across the hard-magnetized ab plane. The PTHE covers the whole temperature region below Tc (~150 K) and a comparatively large value is observed at 100 K. Emergence of an internal gauge field was proposed to explain the origin of this large PTHE, which is either generated by the possible topological domain structure of uniaxial Fe3GeTe2 or the non-coplanar spin structure formed during the in-plane magnetization. Our results promisingly provide an alternative detection method to the in-plane skyrmion formation and may bring brand-new prospective to magneto-transport studies in condensed matter physics.

cond-mat.mtrl-sci↗

arXiv2019

Remote sensing of angular scattering effect of aerosols in a North American megacity

The angle-dependent scattering effect of aerosols in the atmosphere can be used to infer their compositions, which in turn is important to understand their impacts of human health and Earth climate. The aerosol phase function, which characterizes the angular signature of scattering, has been continuously monitored from ground-based and space-borne observations. However, the range of scattering angles these instruments can sample is very limited. There is a dearth of research on the remote sensing of aerosol angular scattering effect at a city scale that analyzes diurnal variability and includes a wide range of scattering angles. Here, we quantify the aerosol angular scattering effect using measurements from a mountain-top remote sensing instrument: the California Laboratory for Atmospheric Remote Sensing Fourier Transform Spectrometer (CLARS-FTS). CLARS-FTS is located on top of the Mt. Wilson (1.67km above sea level) overlooking the Los Angeles (LA) megacity and receives reflected sunlight from targeted surface reflection points. The observational geometries of CLARS-FTS provide a wide range of scattering angles, from about 20 degrees (forward) to about 140 degrees (backward). The O2 ratio, which is the ratio of retrieved O2 Slant Column Density (SCD) to geometric O2 SCD, quantifies the aerosol transmission with a value of 1.0 represent aerosol-free and with a value closer to 0.0 represents stronger aerosol loadings. The aerosol transmission quantified by the O2 ratio from CLARS measurements provides an effective indicator of the aerosol scattering effect.

physics.ao-ph↗

arXiv2019

DenseAttentionSeg: Segment Hands from Interacted Objects Using Depth Input

We propose a real-time DNN-based technique to segment hand and object of interacting motions from depth inputs. Our model is called DenseAttentionSeg, which contains a dense attention mechanism to fuse information in different scales and improves the results quality with skip-connections. Besides, we introduce a contour loss in model training, which helps to generate accurate hand and object boundaries. Finally, we propose and release our InterSegHands dataset, a fine-scale hand segmentation dataset containing about 52k depth maps of hand-object interactions. Our experiments evaluate the effectiveness of our techniques and datasets, and indicate that our method outperforms the current state-of-the-art deep segmentation methods on interaction segmentation.

cs.CV↗

arXiv2019

Hazard assessment of potential storm tide inundation at Southeast China coast

Hazard assessment of storm tide is addressed for Southeast China coast in this study. In particular, we pay attention to the scarcely discussed issue of storm tide inundation. The main procedures of hazard assessment are: 1) non-stationary tropical cyclone intensification (TCI) and sea level rise (SLR) for the study area are analyzed based on the long-term historical database; 2) four typical scenarios of storm tide are examined using the surge-tide-wave coupled hydrodynamic model; 3) the potential inundated regions are identified on the GIS platform. The distributions of water elevation show that high water elevation tends to occur in the bays and around the estuaries. Without considering the impacts of TCI and SLR, the maximal water elevations caused by the typhoon wind of 100-year recurrence period can reach as high as 6.06 m, 5.82 m and 5.67 m around Aojiang, Feiyunjiang and Oujiang river estuaries, respectively. In this circumstance, about 533 km2 area is under the threat of storm tide inundation. TCI and SLR due to climate change can further deteriorate the situation and enhance the risk of inundation there. The maximal water elevations at the aforementioned three estuaries considering the potential non-stationary TCI and SLR in 2100s can be as high as 7.02m, 6.67 m, 6.44m, respectively. The corresponding potential inundation area expands by 50% compared with the situation without considering TCI and SLR. In addition, the remotely sensed maps and inundation durations of the hardest hit regions are provided as well.

physics.geo-ph↗

arXiv2019

On Relative Entropy and Global Index

Certain duality of relative entropy can fail for chiral conformal net with nontrivial representations. In this paper we quantify such statement by defining a quantity which measures the failure of such duality, and identify this quantity with relative entropy and global index associated with multi-interval subfactors for a large class of conformal nets. In particular we show that the duality holds for a large class of conformal nets if and only if they are holomorphic. The same argument also applies to CFT in two dimensions. In particular we show that the duality holds for a large class of CFT in two dimensions if and only if they are modular invariant. We also obtain various limiting properties of relative entropies which naturally follow from our formula.

math-ph↗

arXiv2019

Translating SAR to Optical Images for Assisted Interpretation

Despite the advantages of all-weather and all-day high-resolution imaging, SAR remote sensing images are much less viewed and used by general people because human vision is not adapted to microwave scattering phenomenon. However, expert interpreters can be trained by compare side-by-side SAR and optical images to learn the translation rules from SAR to optical. This paper attempts to develop machine intelligence that are trainable with large-volume co-registered SAR and optical images to translate SAR image to optical version for assisted SAR interpretation. A novel reciprocal GAN scheme is proposed for this translation task. It is trained and tested on both spaceborne GF-3 and airborne UAVSAR images. Comparisons and analyses are presented for datasets of different resolutions and polarizations. Results show that the proposed translation network works well under many scenarios and it could potentially be used for assisted SAR interpretation.

cs.CV↗

arXiv2018

Real-time Indoor Scene Reconstruction with RGBD and Inertia Input

Camera motion estimation is a key technique for 3D scene reconstruction and Simultaneous localization and mapping (SLAM). To make it be feasibly achieved, previous works usually assume slow camera motions, which limits its usage in many real cases. We propose an end-to-end 3D reconstruction system which combines color, depth and inertial measurements to achieve robust reconstruction with fast sensor motions. Our framework extends Kalman filter to fuse the three kinds of information and involve an iterative method to jointly optimize feature correspondences, camera poses and scene geometry. We also propose a novel geometry-aware patch deformation technique to adapt the feature appearance in image domain, leading to a more accurate feature matching under fast camera motions. Experiments show that our patch deformation method improves the accuracy of feature tracking, and our 3D reconstruction outperforms the state-of-the-art solutions under fast camera motions.

cs.CV↗

arXiv2018

Some Results On Relative Entropy in Quantum Field Theory

We prove that the mutual information for vacuum state as defined by Araki is finite for quantum field theory of free fermions on a Minkowski spacetime of any dimension. In the case of two dimensional chiral conformal field theory (CFT) we use our previous results for the free fermions to show that for a large class of chiral CFT the mutual information is finite. We also provide a direct relation between relative entropy and the index of a representation of conformal net.

math-ph↗

arXiv2018

Strain induced superconducting pair-density-wave states in graphene

Graphene is known to be non-superconducting. However, surprising superconductivity is recently discovered in a flat-band in a twisted bi-layer graphene. Here we show that superconductivity can be more easily realized in topological flat-bands induced by strain in graphene through periodic ripples. Specifically, it is shown that by including correlation effects, the chiral d-wave superconductivity can be stabilized under strain even for slightly doped graphene. The chiral d-wave superconductivity generally coexists with charge density waves (CDW) and pair density waves (PDW) of the same period. Remarkably, a pure PDW state with doubled period that coexists with the CDW state is found to emerge at a finite temperature region under reasonable strain strength. The emergent PDW state is shown to be superconducting with non-vanishing superfluid density, and it realizes the long searched superconducting states with non-vanishing center of mass momentum for Cooper pairs.

cond-mat.supr-con↗