arXiv subjects

Hao Chen

Publications and source records attributed to Hao Chen.

At least 1,243 records · Page 69Linked to original sources

arXiv2021

Symmetry-Enforced Nodal Chain Phonons

Topological phonons in crystalline materials have been attracting great interest. However, most cases studied so far are direct generalizations of the topological states from electronic systems. Here, we reveal a novel class of topological phonons -- the symmetry-enforced nodal-chain phonons, which manifest features unique for phononic systems. We show that with $D_{2d}$ little co-group at a non-time-reversal-invariant-momentum point, the phononic nodal chain is guaranteed to exist owing to the vector basis symmetry of phonons, which is a unique character distinct from electronic and other systems. Combined with the spinless character, this makes the proposed nodal-chain phonons enforced by symmorphic crystal symmetries. We further screen all 230 space groups, and find five candidate groups. Interestingly, the nodal chains in these five groups exhibit two different patterns: for tetragonal systems, they are one-dimensional along the fourfold axis; for cubic systems, they form a three-dimensional network structure. Based on first-principles calculations, we identify K$_{2}$O as a realistic material hosting almost ideal nodal-chain phonons. We show that the effect of LO-TO splitting, another unique feature for phonons, helps to expose the nodal-chain phonons in K$_{2}$O in a large energy window. In addition, all the five candidate groups have spacetime inversion symmetry, so the nodal chains also feature a quantized $π$ Berry phase. This leads to drumhead surface phonon modes that must exist on multiple surfaces of a sample.

cond-mat.mtrl-sci↗

arXiv2021

The Lottery Ticket Hypothesis for Object Recognition

Recognition tasks, such as object recognition and keypoint estimation, have seen widespread adoption in recent years. Most state-of-the-art methods for these tasks use deep networks that are computationally expensive and have huge memory footprints. This makes it exceedingly difficult to deploy these systems on low power embedded devices. Hence, the importance of decreasing the storage requirements and the amount of computation in such models is paramount. The recently proposed Lottery Ticket Hypothesis (LTH) states that deep neural networks trained on large datasets contain smaller subnetworks that achieve on par performance as the dense networks. In this work, we perform the first empirical study investigating LTH for model pruning in the context of object detection, instance segmentation, and keypoint estimation. Our studies reveal that lottery tickets obtained from ImageNet pretraining do not transfer well to the downstream tasks. We provide guidance on how to find lottery tickets with up to 80% overall sparsity on different sub-tasks without incurring any drop in the performance. Finally, we analyse the behavior of trained tickets with respect to various task attributes such as object size, frequency, and difficulty of detection.

cs.CV↗

arXiv2021

A turbulence model based on deep neural network considering the near-wall effect

There exists continuous demand of improved turbulence models for the closure of Reynolds Averaged Navier-Stokes (RANS) simulations. Machine Learning (ML) offers effective tools for establishing advanced empirical Reynolds stress closures on the basis of high fidelity simulation data. This paper presents a turbulence model based on the Deep Neural Network(DNN) which takes into account the non-linear relationship between the Reynolds stress anisotropy tensor and the local mean velocity gradient as well as the near-wall effect. The construction and the tuning of the DNN-turbulence model are detailed. We show that the DNN-turbulence model trained on data from direct numerical simulations yields an accurate prediction of the Reynolds stresses for plane channel flow. In particular, we propose including the local turbulence Reynolds number in the model input.

physics.flu-dyn↗

arXiv2021

Spinning Gluons from the QCD Light-Ray OPE

We study the transverse spin structure of the squeezed limit of the three-point energy correlator, $\langle \mathcal{E}(\vec n_1) \mathcal{E}(\vec n_2) \mathcal{E}(\vec n_3) \rangle$. To describe its all orders perturbative behavior, we develop the light-ray operator product expansion (OPE) in QCD. At leading twist the iterated OPE of $\mathcal{E}(\vec n_i)$ operators closes onto light-ray operators $\mathbb{O}^{[J]}(\vec n)$ with spin $J$, and transverse spin $j=0,2$. We compute the $\mathcal{E}(\vec n_1) \mathcal{E}(\vec n_2)$, $\mathcal{E}(\vec n_1) \mathbb{O}^{[J]}(\vec n_2) $ and $\mathbb{O}^{[J_1]}(\vec n_1) \mathbb{O}^{[J_2]}(\vec n_2) $ OPEs as analytic functions of $J$, which allows for the description of arbitrary squeezed limits of $N$-point correlators in QCD. We use these results with $J=3$ to reproduce the perturbative expansion in the squeezed limit of the three-point correlator, as well as to resum the leading twist singular structure for both quark and gluon jets, including transverse spin contributions, as required for phenomenological applications. Finally, we briefly comment on the transverse spin structure at higher twists, and show that to all orders in the twist expansion the highest transverse spin contributions are universal between quark and gluon jets, and are descendants of the leading twist transverse spin-2 operator, allowing their resummation into a simple two-dimensional Euclidean conformal block. Due to the general applicability of our results to arbitrary correlation functions of energy flow operators, we anticipate that they can be widely applied to improving our understanding of jet substructure at the LHC.

hep-ph↗

arXiv2021

Quantum Interference in Jet Substructure from Spinning Gluons

Collimated sprays of hadrons, called jets, are an emergent phenomenon of Quantum Chromodynamics (QCD) at collider experiments, whose detailed internal structure encodes valuable information about the interactions of high energy quarks and gluons, and their confinement into color-neutral hadrons. The flow of energy within jets is characterized by correlation functions of energy flow operators, with the three-point correlator, being the first correlator with non-trivial shape dependence, playing a special role in unravelling the dynamics of QCD. In this Letter we initiate a study of the three-point energy correlator to all orders in the strong coupling constant, in the limit where two of the detectors are squeezed together. We show that by rotating the two squeezed detectors with respect to the third by an angle $ϕ$, a $\cos (2ϕ)$ dependence arising from the quantum interference between intermediate virtual gluons with $+/-$ helicity is imprinted on the detector. This can be regarded as a double slit experiment performed with jet substructure, and it provides a direct probe of the ultimately quantum nature of the substructure of jets, and of transverse spin physics in QCD. To facilitate our all-orders analysis, we adopt the Operator Product Expansion (OPE) for light-ray operators in conformal field theory and develop it in QCD. Our application of the light-ray OPE in real world QCD establishes it as a powerful theoretical tool with broad applications for the study of jet substructure.

hep-ph↗

arXiv2021

Joint Generative and Contrastive Learning for Unsupervised Person Re-identification

Recent self-supervised contrastive learning provides an effective approach for unsupervised person re-identification (ReID) by learning invariance from different views (transformed versions) of an input. In this paper, we incorporate a Generative Adversarial Network (GAN) and a contrastive learning module into one joint training framework. While the GAN provides online data augmentation for contrastive learning, the contrastive module learns view-invariant features for generation. In this context, we propose a mesh-based view generator. Specifically, mesh projections serve as references towards generating novel views of a person. In addition, we propose a view-invariant loss to facilitate contrastive learning between original and generated views. Deviating from previous GAN-based unsupervised ReID methods involving domain adaptation, we do not rely on a labeled source dataset, which makes our method more flexible. Extensive experimental results show that our method significantly outperforms state-of-the-art methods under both, fully unsupervised and unsupervised domain adaptive settings on several large scale ReID datsets.

cs.CV↗

arXiv2021

Flexibility Management for Space Logistics via Decision Rules

This paper develops a flexibility management framework for space logistics mission planning under uncertainty through decision rules and multi-stage stochastic programming. It aims to add built-in flexibility to space architectures in the phase of early-stage mission planning. The proposed framework integrates the decision rule formulation into a network-based space logistics optimization formulation model. It can output a series of decision rules and generate a Pareto front between the expected mission cost (i.e., initial mass in low-Earth orbit) and the expected mission performance (i.e., effective crew operating time) considering the uncertainty in the environment and mission demands. The generated decision rules and the Pareto front plot can help decision-makers create implementable policies immediately when uncertainty events occur during space missions. An example mission case study about space station resupply under rocket launch delay uncertainty is established to demonstrate the value of the proposed framework.

math.OC↗

arXiv2021

TPPI-Net: Towards Efficient and Practical Hyperspectral Image Classification

Hyperspectral Image(HSI) classification is the most vibrant field of research in the hyperspectral community, which aims to assign each pixel in the image to one certain category based on its spectral-spatial characteristics. Recently, some spectral-spatial-feature based DCNNs have been proposed and demonstrated remarkable classification performance. When facing a real HSI, however, these Networks have to deal with the pixels in the image one by one. The pixel-wise processing strategy is inefficient since there are numerous repeated calculations between adjacent pixels. In this paper, firstly, a brand new Network design mechanism TPPI (training based on pixel and prediction based on image) is proposed for HSI classification, which makes it possible to provide efficient and practical HSI classification with the restrictive conditions attached to the hyperspectral dataset. And then, according to the TPPI mechanism, TPPI-Net is derived based on the state of the art networks for HSI classification. Experimental results show that the proposed TPPI-Net can not only obtain high classification accuracy equivalent to the state of the art networks for HSI classification, but also greatly reduce the computational complexity of hyperspectral image prediction.

cs.CV↗

arXiv2021

Generic Perceptual Loss for Modeling Structured Output Dependencies

The perceptual loss has been widely used as an effective loss term in image synthesis tasks including image super-resolution, and style transfer. It was believed that the success lies in the high-level perceptual feature representations extracted from CNNs pretrained with a large set of images. Here we reveal that, what matters is the network structure instead of the trained weights. Without any learning, the structure of a deep network is sufficient to capture the dependencies between multiple levels of variable statistics using multiple layers of CNNs. This insight removes the requirements of pre-training and a particular network structure (commonly, VGG) that are previously assumed for the perceptual loss, thus enabling a significantly wider range of applications. To this end, we demonstrate that a randomly-weighted deep CNN can be used to model the structured dependencies of outputs. On a few dense per-pixel prediction tasks such as semantic segmentation, depth estimation and instance segmentation, we show improved results of using the extended randomized perceptual loss, compared to the baselines using pixel-wise loss alone. We hope that this simple, extended perceptual loss may serve as a generic structured-output loss that is applicable to most structured output learning tasks.

cs.CV↗

arXiv2021

3D Human Pose, Shape and Texture from Low-Resolution Images and Videos

3D human pose and shape estimation from monocular images has been an active research area in computer vision. Existing deep learning methods for this task rely on high-resolution input, which however, is not always available in many scenarios such as video surveillance and sports broadcasting. Two common approaches to deal with low-resolution images are applying super-resolution techniques to the input, which may result in unpleasant artifacts, or simply training one model for each resolution, which is impractical in many realistic applications. To address the above issues, this paper proposes a novel algorithm called RSC-Net, which consists of a Resolution-aware network, a Self-supervision loss, and a Contrastive learning scheme. The proposed method is able to learn 3D body pose and shape across different resolutions with one single model. The self-supervision loss enforces scale-consistency of the output, and the contrastive learning scheme enforces scale-consistency of the deep features. We show that both these new losses provide robustness when learning in a weakly-supervised manner. Moreover, we extend the RSC-Net to handle low-resolution videos and apply it to reconstruct textured 3D pedestrians from low-resolution input. Extensive experiments demonstrate that the RSC-Net can achieve consistently better results than the state-of-the-art methods for challenging low-resolution images.

cs.CV↗

arXiv2021

Multi-Fidelity Space Mission Planning and Infrastructure Design Framework for Space Resource Logistics

To build a sustainable and affordable space transportation system for human space exploration, the design and deployment of space infrastructures are critical; one attractive and promising infrastructure system is the in-situ resource utilization (ISRU) system. The design analysis and trade studies for ISRU systems require the consideration of not only the design of the ISRU plant itself but also other infrastructure systems (e.g., storage, power) and various ISRU architecture options (e.g., resource, location, technology). This paper proposes a system-level space infrastructure and its logistics design optimization framework to perform architecture trade studies. A new space infrastructure logistics optimization problem formulation is proposed that considers infrastructure subsystems' internal interactions and their external synergistic effects with space logistics simultaneously. Since the full-size version of this proposed problem formulation can be computationally prohibitive, a new multi-fidelity optimization formulation is developed by varying the granularity of the commodity type definition over the network graph; this multi-fidelity formulation can find an approximation solution to the full-size problem computationally efficiently with little sacrifice in the solution quality. The proposed problem formulation and method are applied to a multi-mission lunar exploration campaign to demonstrate their values.

math.OC↗

arXiv2021

Momentum-Resolved Visualization of Electronic Evolution in Doping a Mott Insulator

High temperature superconductivity in cuprates arises from doping a parent Mott insulator by electrons or holes. A central issue is how the Mott gap evolves and the low-energy states emerge with doping. Here we report angle-resolved photoemission spectroscopy measurements on a cuprate parent compound by sequential in situ electron doping. The chemical potential jumps to the bottom of the upper Hubbard band upon a slight electron doping, making it possible to directly visualize the charge transfer band and the full Mott gap region. With increasing doping, the Mott gap rapidly collapses due to the spectral weight transfer from the charge transfer band to the gapped region and the induced low-energy states emerge in a wide energy range inside the Mott gap. These results provide key information on the electronic evolution in doping a Mott insulator and establish a basis for developing microscopic theories for cuprate superconductivity.

cond-mat.str-el↗

arXiv2021

Analysis of Emission Dynamics of a Long Lifetime in Single InAs/GaAs Quantum Dots

A very long lifetime emission with non-single exponential decay characteristic has been reported for single InAs/GaAs quantum dot (QD) samples, in which there exists a long-lived metastable state in the wetting layer (WL) [ACS Photonics 2020,7,3228-3235]. In this article we have proposed a new three-level model to simulate the emission decay curve. In this model, assuming that the excitons in metastable state will diffuse and be trapped by QDs, and then emit fluorescence in QDs, a stretched-like exponential decay formula is derived as I(t)=At^(β-1)e^(-(rt)^β), which can well describe the long lifetime decay curve with an analytical expression of average lifetime <τ>=1/rΓ(1/β+1), where Γ is the Gamma function. Furthermore, based on the proposed three-level model, an expression of the second-order auto-correlation function g^2 (t) which can well fit the measured g^2 (t) curve is also obtained.

quant-ph↗

arXiv2021

Analysis of the Dirac equation with the Killingbeck potential in non-commutative space

In this paper, we investigate the Dirac equation with the Killingbeck potential under the external magnetic field in non-commutative space. Corresponding to the expressions of the energy level and wave functions in spin symmetry limit and pseudo-spin symmetry limit are derived by using the Bethe ansatz method. The parameter B associated with the external magnetic field and non-commutative parameter θ make to modify the energy level for considered systems.

hep-th↗

arXiv2021

Interaction between optical pulse and tumor using finite element analysis

Photoacoustic imaging is an emerging technology based on the photoacoustic effect that has developed rapidly in recent years. It combines the high contrast of optical imaging and the high penetration and high resolution of acoustic imaging. As a non-destructive biological tissue imaging technology, photoacoustic imaging has important application value in the field of biomedicine. With its high efficiency bi-oimaging capabilities and excellent biosafety performance, it has been favored by researchers. The visualization of photoacoustic imaging has great research signifi-cance in the early diagnosis of some diseases, especially tumors. In photoacoustic imaging, light transmission and thermal effects are important processes. This article is based on COMSOL software and uses finite element analysis to construct a physi-cal model for simulation. Through laser pulses into the stomach tissue containing tumor, the physical process of light transmission and biological heat transfer was studied, and a photothermal model composed of two physical fields was built, and finally a series of visualization graphics were obtained. This work has certain theo-retical guiding significance for further promoting the application of photoacoustic imaging in the field of biomedicine.

physics.bio-ph↗

arXiv2021

Exploring the Capacity of an Orderless Box Discretization Network for Multi-orientation Scene Text Detection

Multi-orientation scene text detection has recently gained significant research attention. Previous methods directly predict words or text lines, typically by using quadrilateral shapes. However, many of these methods neglect the significance of consistent labeling, which is important for maintaining a stable training process, especially when it comprises a large amount of data. Here we solve this problem by proposing a new method, Orderless Box Discretization (OBD), which first discretizes the quadrilateral box into several key edges containing all potential horizontal and vertical positions. To decode accurate vertex positions, a simple yet effective matching procedure is proposed for reconstructing the quadrilateral bounding boxes. Our method solves the ambiguity issue, which has a significant impact on the learning process. Extensive ablation studies are conducted to validate the effectiveness of our proposed method quantitatively. More importantly, based on OBD, we provide a detailed analysis of the impact of a collection of refinements, which may inspire others to build state-of-the-art text detectors. Combining both OBD and these useful refinements, we achieve state-of-the-art performance on various benchmarks, including ICDAR 2015 and MLT. Our method also won the first place in the text detection task at the recent ICDAR2019 Robust Reading Challenge for Reading Chinese Text on Signboards, further demonstrating its superior performance. The code is available at https://git.io/TextDet.

cs.CV↗

arXiv2021

The study of the generalized Klein-Gordon oscillator in the context of the Som-Raychaudhuri space-time

In this paper we study the relativistic scalar particle described by the Klein-Gordon interacts with the uniform magnetic field in the context of the Som-Raychaudhuri space-time. Based on the property of the biconfluent Heun function equation, the corresponding Klein-Gordon oscillator and generalized Klein-Gordon oscillator under considering the Coulomb potential are separately investigated, and the analogue of the Aharonov-Bohm effect is analyzed in this scenario. On this basis, we also give the influence of different parameters including the parameter α and oscillator frequency ω, and the potential parameter ξ2 on the energy eigenvalues of the considered systems.

quant-ph↗

arXiv2021

Gate-tunable chiral phonons in low-buckled group-IVA monolayers

We investigate the electric response of chiral phonons on the low-buckled group-IVA monolayers by performing first-principles calculations. The vertical electric field breaks the degeneracy of phonon modes at high-symmetry $\pm K$ points of the phonon Brillouin zone, and the size of the phononic gap is proportional to the strength of the electric field. The gapped phonon modes at $\pm K$ possess chiralities with considerable phonon circular polarizations and discrete phonon pseudoangular momenta. The chiralities of phonons are robust against the variation of the field strength, but reversed by changing the field direction. Electric control of chiral phonons adds a new dimension to the study of chiral phonons, which has potential use in the design of phononic and valley devices.

cond-mat.mtrl-sci↗