arXiv subjects

Ying Chen

Publications and source records attributed to Ying Chen.

At least 343 records · Page 19Linked to original sources

arXiv2020

DEAL: Difficulty-aware Active Learning for Semantic Segmentation

Active learning aims to address the paucity of labeled data by finding the most informative samples. However, when applying to semantic segmentation, existing methods ignore the segmentation difficulty of different semantic areas, which leads to poor performance on those hard semantic areas such as tiny or slender objects. To deal with this problem, we propose a semantic Difficulty-awarE Active Learning (DEAL) network composed of two branches: the common segmentation branch and the semantic difficulty branch. For the latter branch, with the supervision of segmentation error between the segmentation result and GT, a pixel-wise probability attention module is introduced to learn the semantic difficulty scores for different semantic areas. Finally, two acquisition functions are devised to select the most valuable samples with semantic difficulty. Competitive results on semantic segmentation benchmarks demonstrate that DEAL achieves state-of-the-art active learning performance and improves the performance of the hard semantic areas in particular.

cs.CV↗

arXiv2020

The Origin of Hadron Masses

The color confinement can be decently explained by assuming the global $SU(3)$ color symmetry. A hadron is viewed as a bag of a finite size, whose energy is contributed by the color fields within the hadron. In the large momentum frame, the mass of a hadron can be quantized and can be expressed as the sum of the current masses of quarks involved plus a flavor independent term which depends on a universal parameter and can be identified to be the contribution from the QCD trace anomaly. This term depicts the Regge's trajectories of light hadron masses, from which the parameter can be determined and gives the minimal vector meson mass and baryon mass to be 761 MeV and 935 MeV, respectively. The mass formula can explain many features of the hadron spectroscopy.

hep-ph↗

arXiv2020

Confinement and the Global $SU(3)$ Color Symmetry

The global $SU(3)$ color symmetry and its physical consequences are discussed. The Nöther current is actually governed by the conserved matter current of color charges if the color field generated by this charge is properly polarized. The color field strength of a charge can have a uniform part due to the nontrivial QCD vacuum field and the nonzero gluon condensate, which implies that the self-energy of a system with a net color charge is infinite and thereby cannot exist as a free state. This is precisely what the color confinement means. Accordingly, the Cornell type potential with the feature of the Casimir scaling is derived for a color singlet system composed of a static color charge and an anti-charge. The uniform color field also implies that a hadron has a minimal size and a minimal energy. Furthermore, the global $SU(3)$ color symmetry requires that the minimal irreducible color singlet systems can only be $q\bar{q}$, $qqq$, $gg$, $ggg$, $q\bar{q}g$, $qqqg$ and $\bar{q}\bar{q}\bar{q}g$, etc., as such a multi-quark systems can only exist as a molecular configurations if there are no other binding mechanisms.

hep-ph↗

arXiv2020

Towards Accurate Predictions of Carrier Mobilities and Thermoelectric Performances in 2D Materials

The interactions between electrons and lattice vibrational modes play the key role in determining the carrier transport properties, thermoelectric performance and other physical quantities related to phonons in semiconductors. However, for two-dimensional (2D) materials, the widely-used models for carrier transport only consider the interactions between electrons and some specific phonon modes, which usually leads to inaccruate predictions of electrons/phonons transport properties. In this work, comprehensive investigations on full electron-phonon couplings and their influences on carrier mobility and thermoelectric performances of 2D group-IV and V elemental monolayers were performed, and we also analyzed in details the selection rules on electron-phonon couplings using group-theory arguments. Our calculations revealed that, for the cases of shallow dopings where only intravalley scatterings are allowed, the contributions from optical phonon modes are significantly larger than those from acoustic phonon modes in group-IV elemental monolayers, and LA and some specific optical phonon modes contribute significantly to the total intravalley scatterings. When the doping increases and intervalley scatterings are allowed, the intervalley scatterings are much stronger than intravalley scatterings, and ZA/TA/LO phonon modes dominate the intervalley scatterings in monolayer Si, Ge and Sn. The dominant contributions to the total intervalley scatterings are ZA/TO in monolayer P, ZA/TO in monolayer As and TO/LO in monolayer Sb. Based on the thorough investigations on the full electron-phonon couplings, we predict accurately the carrier mobilities and thermoelectric figure of merits in these two elemental crystals, and reveal significant reductions when compared with the calculations based on the widely-used simplified model.

cond-mat.mtrl-sci↗

arXiv2020

Effects of intervalley scatterings in thermoelectric performance of band-convergent antimonene

The strategy of band convergence of multi-valley conduction bands or multi-peak valence bands has been widely used to search or improve thermoelectric materials. However, the phonon-assisted intervalley scatterings due to multiple band degeneracy are usually neglected in the thermoelectric community. In this work, we investigate the (thermo)electric properties of non-polar monolayer $β$- and $α$-antimonene considering full mode- and momentum-resolved electron-phonon interactions. We also analyze thoroughly the selection rules on electron-phonon matrix-elements using group-theory arguments. Our calculations reveal strong intervalley scattering between the nearly degenerate valley states in both $β$- and $α$-antimonene, and the commonly-used deformation potential approximation neglecting the dominant intervalley scattering gives inaccurate estimations of the electron-phonon scattering and thermoelectric transport properties. By considering full electron-phonon interactions based on the rigid-band approximation, we find that, the maximum value of the thermoelectric figure of merits $zT$ at room temperature reduces to 0.37 in $β$-antimonene, by a factor of 5.7 comparing to the value predicted based on the constant relaxation-time approximation method. Our work not only provides an accurate prediction of the thermoelectric performances of antimonenes that reveals the key role of intervalley scatterings in determining the electronic part of zT, but also showcases a computational framework for thermoelectric materials.

cond-mat.mtrl-sci↗

arXiv2020

Limousine Service Management: Capacity Planning with Predictive Analytics and Optimization

The limousine service in luxury hotels is an integral component of the whole customer journey in the hospitality industry. One of the largest hotels in Singapore manages a fleet of both in-house and outsourced vehicles around the clock, serving 9000 trips per month on average. The need for vehicles may scale up rapidly, especially during special events and festive periods in the country. The excess demand is met by having additional outsourced vehicles on standby, incurring millions of dollars of additional expenses per year for the hotel. Determining the required number of limousines by hour of the day is a challenging service capacity planning problem. In this paper, a recent transformational journey to manage this problem in the hotel is introduced, driving up to S\$3.2 million of savings per year with improved service level. The approach builds on widely available open-source statistical and spreadsheet optimization tools, along with robotic process automation, to optimize the schedule of its fleet of limousines and drivers, and to support decision-making for planners/controllers to drive sustained business value.

stat.AP↗

arXiv2020

Regularised Text Logistic Regression: Key Word Detection and Sentiment Classification for Online Reviews

Online customer reviews have become important for managers and executives in the hospitality and catering industry who wish to obtain a comprehensive understanding of their customers' demands and expectations. We propose a Regularized Text Logistic (RTL) regression model to perform text analytics and sentiment classification on unstructured text data, which automatically identifies a set of statistically significant and operationally insightful word features, and achieves satisfactory predictive classification accuracy. We apply the RTL model to two online review datasets, Restaurant and Hotel, from TripAdvisor. Our results demonstrate satisfactory classification performance compared with alternative classifiers with a highest true positive rate of 94.9%. Moreover, RTL identifies a small set of word features, corresponding to 3% for Restaurant and 20% for Hotel, which boosts working efficiency by allowing managers to drill down into a much smaller set of important customer reviews. We also develop the consistency, sparsity and oracle property of the estimator.

stat.ML↗

arXiv2020

Two-dimensional van der Waals Heterostructures for Synergistically Improved Surface Enhanced Raman Spectroscopy

Surface enhanced Raman spectroscopy (SERS) is a precise and non-invasive analytical technique that is widely used in chemical analysis, environmental protection, food processing, pharmaceutics, and diagnostic biology. However, it is still a challenge to produce highly sensitive and reusable SERS substrates with minimum fluorescence background. In this work, we propose the use of van der Waals heterostructures of two-dimensional materials (2D materials) to cover plasmonic metal nanoparticles to solve this challenge. The heterostructures of atomically thin boron nitride (BN) and graphene provide synergistic effects: (1) electrons could tunnel through the atomically thin BN, allowing the charge transfer between graphene and probe molecules to suppress fluorescence background; (2) the SERS sensitivity is enhanced by graphene via chemical enhancement mechanism (CM) in addition to electromagnetic field mechanism (EM); (3) the atomically thin BN protects the underlying graphene and Ag nanoparticles from oxidation during heating for regeneration at 360 °C in the air so that the SERS substrates could be reused. These advances will facilitate wider applications of SERS, especially on the detection of fluorescent molecules with higher sensitivity.

physics.app-ph↗

arXiv2020

Atomically Thin Boron Nitride as an Ideal Spacer for Metal-Enhanced Fluorescence

The metal-enhanced fluorescence (MEF) considerably enhances the luminescence for various applications, but its performance largely depends on the dielectric spacer between the fluorophore and plasmonic system. It is still challenging to produce a defect-free spacer having an optimized thickness with a subnanometer accuracy that enables reusability without affecting the enhancement. In this study, we demonstrate the use of atomically thin hexagonal boron nitride (BN) as an ideal MEF spacer owing to its multifold advantages over the traditional dielectric thin films. With rhodamine 6G as a representative fluorophore, it largely improves the enhancement factor (up to ~95+-5), sensitivity (10^-8 M), reproducibility, and reusability (~90% of the plasmonic activity is retained after 30 cycles of heating at 350 °C in air) of MEF. This can be attributed to its two-dimensional structure, thickness control at the atomic level, defect-free quality, high affinities to aromatic fluorophores, good thermal stability, and excellent impermeability. The atomically thin BN spacers could increase the use of MEF in different fields and industries.

physics.app-ph↗

arXiv2020

Mechanical Properties of Atomically Thin Boron Nitride and the Role of Interlayer Interactions

Atomically thin boron nitride (BN) nanosheets are important two-dimensional nanomaterials with many unique properties distinct from those of graphene, but the investigation of their mechanical properties still greatly lacks. Here we report that high-quality single-crystalline mono- and few-layer BN nanosheets are one of the strongest electrically insulating materials. More intriguingly, few-layer BN shows mechanical behaviors quite different from those of few-layer graphene under indentation. In striking contrast to graphene, whose strength decreases by more than 30% when the number of layers increases from 1 to 8, the mechanical strength of BN nanosheets is not sensitive to increasing thickness. We attribute this difference to the distinct interlayer interactions and hence sliding tendencies in these two materials under indentation. The significantly better mechanical integrity of BN nanosheets makes them a more attractive candidate than graphene for several applications, e.g. as mechanical reinforcements.

cond-mat.mtrl-sci↗

arXiv2020

Raman Signature and Phonon Dispersion of Atomically Thin Boron Nitride

Raman spectroscopy has become an essential technique to characterize and investigate graphene and many other two-dimensional materials. However, there still lacks consensus on the Raman signature and phonon dispersion of atomically thin boron nitride (BN), which has many unique properties distinct from graphene. Such a knowledge gap greatly affects the understanding of basic physical and chemical properties of atomically thin BN as well as the use of Raman spectroscopy to study these nanomaterials. Here, we use both experiment and simulation to reveal the intrinsic Raman signature of monolayer and few-layer BN. We find experimentally that atomically thin BN without interaction with substrate has a G band frequency similar to that of bulk hexagonal BN, but strain induced by substrate can cause pronounced Raman shifts. This is in excellent agreement with our first-principles density functional theory (DFT) calculations at two levels of theory, including van der Waals dispersion forces (opt-vdW) and a fractional of the exact exchange from Hartree-Fock (HF) theory through hybrid HSE06 functional. Both calculations demonstrate that the intrinsic E2g mode of BN does not depend sensibly on the number of layers. Our simulations also suggest the importance of the exact exchange mixing parameter in calculating the vibrational modes in BN, as it determines the fraction of HF exchange included in the DFT calculations.

cond-mat.mtrl-sci↗

arXiv2020

Strangeonium-like hybrids on the lattice

The strangeonium-like $s\bar{s}g$ hybrids are investigated from lattice QCD in the quenched approximation. In the Coulomb gauge, spatially extended operators are constructed for $1^{--}$ and $(0,1,2)^{-+}$ states with the color octet $s\bar{s}$ component being separated from the chromomagnetic field strength by spatial distances $r$, whose matrix elements between the vacuum and the corresponding states are interpreted as Bethe-Salpeter (BS) wave functions. In each of the $(1,2)^{-+}$ channels, the masses and the BS wave functions are reliably derived. The $1^{-+}$ ground state mass is around 2.1-2.2 GeV, and that of $2^{-+}$ is around 2.3-2.4 GeV, while the masses of the first excited states are roughly 1.4 GeV higher. This mass splitting is much larger than the expectation of the phenomenological flux-tube model or constituent gluon model for hybrids, which is usually a few hundred MeV. The BS wave functions with respect to $r$ show clear radial nodal structures of non-relativistic two-body system, which imply that $r$ is a meaningful dynamical variable for these hybrids and motivate a color halo picture of hybrids that the color octet $s\bar{s}$ is surrounded by gluonic degrees of freedom. In the $1^{--}$ channel, the properties of the lowest two states comply with those of $ϕ(1020)$ and $ϕ(1680)$. We have not obtained convincing information relevant to $ϕ(2170)$ yet, however, we argue that whether $ϕ(2170)$ is a conventional $s\bar{s}$ meson or a $s\bar{s}g$ hybrid within the color halo scenario, the ratio of partial decay widths $Γ(ϕη)$ and $Γ(ϕη')$ observed by BESIII can be understood by the mechanism of hadronic transition of a strangeonium-like meson along with the $η-η'$ mixing.

hep-lat↗

arXiv2020

Automatic Crack Detection on Road Pavements Using Encoder Decoder Architecture

Inspired by the development of deep learning in computer vision and object detection, the proposed algorithm considers an encoder-decoder architecture with hierarchical feature learning and dilated convolution, named U-Hierarchical Dilated Network (U-HDN), to perform crack detection in an end-to-end method. Crack characteristics with multiple context information are automatically able to learn and perform end-to-end crack detection. Then, a multi-dilation module embedded in an encoder-decoder architecture is proposed. The crack features of multiple context sizes can be integrated into the multi-dilation module by dilation convolution with different dilatation rates, which can obtain much more cracks information. Finally, the hierarchical feature learning module is designed to obtain a multi-scale features from the high to low-level convolutional layers, which are integrated to predict pixel-wise crack detection. Some experiments on public crack databases using 118 images were performed and the results were compared with those obtained with other methods on the same images. The results show that the proposed U-HDN method achieves high performance because it can extract and fuse different context sizes and different levels of feature maps than other algorithms.

cs.CV↗

arXiv2020

GSTO: Gated Scale-Transfer Operation for Multi-Scale Feature Learning in Pixel Labeling

Existing CNN-based methods for pixel labeling heavily depend on multi-scale features to meet the requirements of both semantic comprehension and detail preservation. State-of-the-art pixel labeling neural networks widely exploit conventional scale-transfer operations, i.e., up-sampling and down-sampling to learn multi-scale features. In this work, we find that these operations lead to scale-confused features and suboptimal performance because they are spatial-invariant and directly transit all feature information cross scales without spatial selection. To address this issue, we propose the Gated Scale-Transfer Operation (GSTO) to properly transit spatial-filtered features to another scale. Specifically, GSTO can work either with or without extra supervision. Unsupervised GSTO is learned from the feature itself while the supervised one is guided by the supervised probability matrix. Both forms of GSTO are lightweight and plug-and-play, which can be flexibly integrated into networks or modules for learning better multi-scale features. In particular, by plugging GSTO into HRNet, we get a more powerful backbone (namely GSTO-HRNet) for pixel labeling, and it achieves new state-of-the-art results on the COCO benchmark for human pose estimation and other benchmarks for semantic segmentation including Cityscapes, LIP and Pascal Context, with negligible extra computational cost. Moreover, experiment results demonstrate that GSTO can also significantly boost the performance of multi-scale feature aggregation modules like PPM and ASPP. Code will be made available at https://github.com/VDIGPKU/GSTO.

cs.CV↗

arXiv2020

DO-Conv: Depthwise Over-parameterized Convolutional Layer

Convolutional layers are the core building blocks of Convolutional Neural Networks (CNNs). In this paper, we propose to augment a convolutional layer with an additional depthwise convolution, where each input channel is convolved with a different 2D kernel. The composition of the two convolutions constitutes an over-parameterization, since it adds learnable parameters, while the resulting linear operation can be expressed by a single convolution layer. We refer to this depthwise over-parameterized convolutional layer as DO-Conv. We show with extensive experiments that the mere replacement of conventional convolutional layers with DO-Conv layers boosts the performance of CNNs on many classical vision tasks, such as image classification, detection, and segmentation. Moreover, in the inference phase, the depthwise convolution is folded into the conventional convolution, reducing the computation to be exactly equivalent to that of a convolutional layer without over-parameterization. As DO-Conv introduces performance gains without incurring any computational complexity increase for inference, we advocate it as an alternative to the conventional convolutional layer. We open-source a reference implementation of DO-Conv in Tensorflow, PyTorch and GluonCV at https://github.com/yangyanli/DO-Conv.

cs.CV↗

arXiv2020

Correlators in the supereigenvalue model in the Ramond sector

We investigate the supereigenvalue model in the Ramond sector. We prove that its partition function can be obtained by acting on elementary functions with exponents of the given operators. The Virasoro constraints for this supereigenvalue model are presented. The remarkable property of these bosonic constraint operators is that they obey the Witt algebra and null 3-algebra. The compact expression of correlators can be derived from these Virasoro constraints.

hep-th↗

arXiv2020

A Lattice Study of the Two-photon Decay Widths for Scalar and Pseudo-scalar Charmonium

In this exploratory study, two photon decay widths of pseudo-scalar ($η_c$) and scalar ($χ_{c0}$) charmonium are computed using two ensembles of $N_f=2$ twisted mass lattice QCD gauge configurations. The simulation is performed two lattice ensembles with lattice spacings $a=0.067$ fm with size $32^3\times{64}$ and $a=0.085$ fm with size $24^3\times{48}$, respectively. The results for the decay widths for the two charmonia are obtained which are in the right ballpark however smaller than the experimental ones. Possible reasons for these discrepancies are discussed.

hep-lat↗

arXiv2020

Stochastic Sparse Subspace Clustering

State-of-the-art subspace clustering methods are based on self-expressive model, which represents each data point as a linear combination of other data points. By enforcing such representation to be sparse, sparse subspace clustering is guaranteed to produce a subspace-preserving data affinity where two points are connected only if they are from the same subspace. On the other hand, however, data points from the same subspace may not be well-connected, leading to the issue of over-segmentation. We introduce dropout to address the issue of over-segmentation, which is based on randomly dropping out data points in self-expressive model. In particular, we show that dropout is equivalent to adding a squared $\ell_2$ norm regularization on the representation coefficients, therefore induces denser solutions. Then, we reformulate the optimization problem as a consensus problem over a set of small-scale subproblems. This leads to a scalable and flexible sparse subspace clustering approach, termed Stochastic Sparse Subspace Clustering, which can effectively handle large scale datasets. Extensive experiments on synthetic data and real world datasets validate the efficiency and effectiveness of our proposal.

cs.LG↗