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Yang Li

Publications and source records attributed to Yang Li.

At least 1,297 records · Page 72Linked to original sources
arXiv2020

RGB-IR Cross-modality Person ReID based on Teacher-Student GAN Model

RGB-Infrared (RGB-IR) person re-identification (ReID) is a technology where the system can automatically identify the same person appearing at different parts of a video when light is unavailable. The critical challenge of this task is the cross-modality gap of features under different modalities. To solve this challenge, we proposed a Teacher-Student GAN model (TS-GAN) to adopt different domains and guide the ReID backbone to learn better ReID information. (1) In order to get corresponding RGB-IR image pairs, the RGB-IR Generative Adversarial Network (GAN) was used to generate IR images. (2) To kick-start the training of identities, a ReID Teacher module was trained under IR modality person images, which is then used to guide its Student counterpart in training. (3) Likewise, to better adapt different domain features and enhance model ReID performance, three Teacher-Student loss functions were used. Unlike other GAN based models, the proposed model only needs the backbone module at the test stage, making it more efficient and resource-saving. To showcase our model's capability, we did extensive experiments on the newly-released SYSU-MM01 RGB-IR Re-ID benchmark and achieved superior performance to the state-of-the-art with 49.8% Rank-1 and 47.4% mAP.

cs.CV↗
arXiv2020

Parton Distribution Functions of Heavy Mesons on the Light Front

The parton distribution functions (PDFs) of heavy mesons are evaluated from their light-front wave functions, which are obtained from a basis light-front quantization in the leading Fock sector representation. We consider the mass eigenstates from an effective Hamiltonian consisting of the confining potential adopted from light-front holography in the transverse direction, a longitudinal confinement, and a one-gluon exchange interaction with running coupling. We present the gluon and the sea quark PDFs which we generate dynamically from the QCD evolution of the valence quark distributions.

nucl-th↗
arXiv2020

SplitFusion: Simultaneous Tracking and Mapping for Non-Rigid Scenes

We present SplitFusion, a novel dense RGB-D SLAM framework that simultaneously performs tracking and dense reconstruction for both rigid and non-rigid components of the scene. SplitFusion first adopts deep learning based semantic instant segmentation technique to split the scene into rigid or non-rigid surfaces. The split surfaces are independently tracked via rigid or non-rigid ICP and reconstructed through incremental depth map fusion. Experimental results show that the proposed approach can provide not only accurate environment maps but also well-reconstructed non-rigid targets, e.g. the moving humans.

cs.CV↗
arXiv2020

Probabilistic Classification Vector Machine for Multi-Class Classification

The probabilistic classification vector machine (PCVM) synthesizes the advantages of both the support vector machine and the relevant vector machine, delivering a sparse Bayesian solution to classification problems. However, the PCVM is currently only applicable to binary cases. Extending the PCVM to multi-class cases via heuristic voting strategies such as one-vs-rest or one-vs-one often results in a dilemma where classifiers make contradictory predictions, and those strategies might lose the benefits of probabilistic outputs. To overcome this problem, we extend the PCVM and propose a multi-class probabilistic classification vector machine (mPCVM). Two learning algorithms, i.e., one top-down algorithm and one bottom-up algorithm, have been implemented in the mPCVM. The top-down algorithm obtains the maximum a posteriori (MAP) point estimates of the parameters based on an expectation-maximization algorithm, and the bottom-up algorithm is an incremental paradigm by maximizing the marginal likelihood. The superior performance of the mPCVMs, especially when the investigated problem has a large number of classes, is extensively evaluated on synthetic and benchmark data sets.

cs.LG↗
arXiv2020

Light mesons within the basis light-front quantization framework

We study the light-unflavored mesons as relativistic bound states in the nonperturbative Hamiltonian formalism of the basis light-front quantization (BLFQ) approach. The dynamics for the valence quarks of these mesons is specified by an effective Hamiltonian containing the one-gluon exchange interaction and the confining potentials both introduced in our previous work on heavy quarkonia, supplemented additionally by a pseudoscalar contact interaction. We diagonalize this Hamiltonian in our basis function representation to obtain the mass spectrum and the light-front wave functions (LFWFs). Based on these LFWFs, we then study the structure of these mesons by computing the electromagnetic form factors, the decay constants, the parton distribution amplitudes (PDAs), and the parton distribution functions (PDFs). Our results are comparable to those from experiments and other theoretical models.

nucl-th↗
arXiv2020

Self-force on moving electric and magnetic dipoles: dipole radiation, Vavilov-Čerenkov radiation, friction with a conducting surface, and the Einstein-Hopf effect

The classical electromagnetic self-force on an arbitrary time-dependent electric or magnetic dipole moving with constant velocity in vacuum, and in a medium, is considered. Of course, in vacuum there is no net force on such a particle. Rather, because of loss of mass by the particle due to radiation, the self-force precisely cancels this inertial effect, and thus the spectral distribution of the energy radiated by dipole radiation is deduced without any consideration of radiation fields or of radiation reaction, in both the nonrelativistic and relativistic regimes. If the particle is moving in a homogeneous medium faster than the speed of light in the medium, Vavilov-Čerenkov radiation results. This is derived for the different polarization states, in agreement with the earlier results of Frank. The friction experienced by a point (time-independent) dipole moving parallel to an imperfectly conducting surface is examined. Finally, the quantum/thermal Einstein-Hopf effect is rederived. We obtain a closed form for the spectral distribution of the force, and demonstrate that, even if the atom and the blackbody background have independent temperatures, the force is indeed a drag in the case that the imaginary part of the polarizability is proportional to a power of the frequency.

hep-th↗
arXiv2020

Integrating Tensor Similarity to Enhance Clustering Performance

The performance of most the clustering methods hinges on the used pairwise affinity, which is usually denoted by a similarity matrix. However, the pairwise similarity is notoriously known for its vulnerability of noise contamination or the imbalance in samples or features, and thus hinders accurate clustering. To tackle this issue, we propose to use information among samples to boost the clustering performance. We proved that a simplified similarity for pairs, denoted by a fourth order tensor, equals to the Kronecker product of pairwise similarity matrices under decomposable assumption, or provide complementary information for which the pairwise similarity missed under indecomposable assumption. Then a high order similarity matrix is obtained from the tensor similarity via eigenvalue decomposition. The high order similarity capturing spatial information serves as a robust complement for the pairwise similarity. It is further integrated with the popular pairwise similarity, named by IPS2, to boost the clustering performance. Extensive experiments demonstrated that the proposed IPS2 significantly outperformed previous similarity-based methods on real-world datasets and it was capable of handling the clustering task over under-sampled and noisy datasets.

cs.LG↗
arXiv2020

Attention-based Graph ResNet for Motor Intent Detection from Raw EEG signals

In previous studies, decoding electroencephalography (EEG) signals has not considered the topological relationship of EEG electrodes. However, the latest neuroscience has suggested brain network connectivity. Thus, the exhibited interaction between EEG channels might not be appropriately measured via Euclidean distance. To fill the gap, an attention-based graph residual network, a novel structure of Graph Convolutional Neural Network (GCN), was presented to detect human motor intents from raw EEG signals, where the topological structure of EEG electrodes was built as a graph. Meanwhile, deep residual learning with a full-attention architecture was introduced to address the degradation problem concerning deeper networks in raw EEG motor imagery (MI) data. Individual variability, the critical and longstanding challenge underlying EEG signals, has been successfully handled with the state-of-the-art performance, 98.08% accuracy at the subject level, 94.28% for 20 subjects. Numerical results were promising that the implementation of the graph-structured topology was superior to decode raw EEG data. The innovative deep learning approach was expected to entail a universal method towards both neuroscience research and real-world EEG-based practical applications, e.g., seizure prediction.

eess.SP↗
arXiv2020

Extracting Non-Gaussian Governing Laws from Data on Mean Exit Time

Motivated by the existing difficulties in establishing mathematical models and in observing the system state time series for some complex systems, especially for those driven by non-Gaussian Levy motion, we devise a method for extracting non-Gaussian governing laws with observations only on mean exit time. It is feasible to observe mean exit time for certain complex systems. With the observations, a sparse regression technique in the least squares sense is utilized to obtain the approximated function expression of mean exit time. Then, we learn the generator and further identify the stochastic differential equations through solving an inverse problem for a nonlocal partial differential equation and minimizing an error objective function. Finally, we verify the efficacy of the proposed method by three examples with the aid of the simulated data from the original systems. Results show that the method can apply to not only the stochastic dynamical systems driven by Gaussian Brownian motion but also those driven by non-Gaussian Levy motion, including those systems with complex rational drift.

cs.CE↗
arXiv2020

Highly Efficient Second Harmonic Generation of Thin Film Lithium Niobate Nanograting near Bound States in the Continuum

Bound states in the continuum (BICs), a concept from quantum mechanics, are ubiquitous physical phenomena where waves will be completely locked inside physical systems without energy leaky. Such a physical phenomenon in optics will provide a platform for optical mode confinement to strengthen local field enhancement in nonlinear optics. Here we utilize an optical system consisting of asymmetric nanogratings and waveguide of thin film lithium niobate (LiNbO3) material to enhance second harmonic response near BICs. By breaking the symmetry of grating periodicity, we realize strong local field confined inside waveguide up to 25 times normalized to incident field (with dissymmetric factor of 0.2), allowing strong light-matter interaction in nonlinear material. From the numerical simulation, we theoretically demonstrate that such an optical system can greatly enhance second harmonic intensity enhancement of about 104 compared with undersigned LiNbO3 film and conversion efficiency reaching 1.53e-5 for dissymmetric factor=0.2 under illumination of 1.33 GW/(suqare cm). Surprisingly, we can predict that a giant enhancement of second harmonic conversion efficiency will exceed 8.13e-5 for dissymmetric factor=0.1 when the optical system is extremely close to BICs. We believe that such an optical system to trap local field inside is also accessible to promote the application of thin film lithium niobate in the field of integrated nonlinear optics.

physics.optics↗
arXiv2020

Grafted network for person re-identification

Convolutional neural networks have shown outstanding effectiveness in person re-identification (re-ID). However, the models always have large number of parameters and much computation for mobile application. In order to relieve this problem, we propose a novel grafted network (GraftedNet), which is designed by grafting a high-accuracy rootstock and a light-weighted scion. The rootstock is based on the former parts of ResNet-50 to provide a strong baseline, while the scion is a new designed module, composed of the latter parts of SqueezeNet, to compress the parameters. To extract more discriminative feature representation, a joint multi-level and part-based feature is proposed. In addition, to train GraftedNet efficiently, we propose an accompanying learning method, by adding an accompanying branch to train the model in training and removing it in testing for saving parameters and computation. On three public person re-ID benchmarks (Market1501, DukeMTMC-reID and CUHK03), the effectiveness of GraftedNet are evaluated and its components are analyzed. Experimental results show that the proposed GraftedNet achieves 93.02%, 85.3% and 76.2% in Rank-1 and 81.6%, 74.7% and 71.6% in mAP, with only 4.6M parameters.

cs.CV↗
arXiv2020

Mapping Natural Language Instructions to Mobile UI Action Sequences

We present a new problem: grounding natural language instructions to mobile user interface actions, and create three new datasets for it. For full task evaluation, we create PIXELHELP, a corpus that pairs English instructions with actions performed by people on a mobile UI emulator. To scale training, we decouple the language and action data by (a) annotating action phrase spans in HowTo instructions and (b) synthesizing grounded descriptions of actions for mobile user interfaces. We use a Transformer to extract action phrase tuples from long-range natural language instructions. A grounding Transformer then contextually represents UI objects using both their content and screen position and connects them to object descriptions. Given a starting screen and instruction, our model achieves 70.59% accuracy on predicting complete ground-truth action sequences in PIXELHELP.

cs.CL↗
arXiv2020

A heterogeneous branch and multi-level classification network for person re-identification

Convolutional neural networks with multiple branches have recently been proved highly effective in person re-identification (re-ID). Researchers design multi-branch networks using part models, yet they always attribute the effectiveness to multiple parts. In addition, existing multi-branch networks always have isomorphic branches, which lack structural diversity. In order to improve this problem, we propose a novel Heterogeneous Branch and Multi-level Classification Network (HBMCN), which is designed based on the pre-trained ResNet-50 model. A new heterogeneous branch, SE-Res-Branch, is proposed based on the SE-Res module, which consists of the Squeeze-and-Excitation block and the residual block. Furthermore, a new multi-level classification joint objective function is proposed for the supervised learning of HBMCN, whereby multi-level features are extracted from multiple high-level layers and concatenated to represent a person. Based on three public person re-ID benchmarks (Market1501, DukeMTMC-reID and CUHK03), experimental results show that the proposed HBMCN reaches 94.4%, 85.7% and 73.8% in Rank-1, and 85.7%, 74.6% and 69.0% in mAP, achieving a state-of-the-art performance. Further analysis demonstrates that the specially designed heterogeneous branch performs better than an isomorphic branch, and multi-level classification provides more discriminative features compared to single-level classification. As a result, HBMCN provides substantial further improvements in person re-ID tasks.

cs.CV↗
arXiv2020

Low-loss Zero-Index Materials

Materials with a zero refractive index support electromagnetic modes that exhibit stationary phase profiles. While such materials have been realized across the visible and near-infrared spectral range, radiative and dissipative optical losses have hindered their development. We reduce losses in zero-index, on-chip photonic crystals by introducing high-Q resonances via resonance-trapped and symmetry-protected states. Using these approaches, we experimentally obtain quality factors of 2.6*10^3 and 7.8*10^3 at near-infrared wavelengths, corresponding to an order-of-magnitude reduction in propagation loss over previous designs. Our work presents a viable approach to fabricate zero-index on-chip nanophotonic devices with low-loss.

physics.optics↗
arXiv2020

Illumination adaptive person reid based on teacher-student model and adversarial training

Most existing works in Person Re-identification (ReID) focus on settings where illumination either is kept the same or has very little fluctuation. However, the changes in the illumination degree may affect the robustness of a ReID algorithm significantly. To address this problem, we proposed a Two-Stream Network that can separate ReID features from lighting features to enhance ReID performance. Its innovations are threefold: (1) A discriminative entropy loss to ensure the ReID features contain no lighting information. (2) A ReID Teacher model trained by images under "neutral" lighting conditions to guide ReID classification. (3) An illumination Teacher model trained by the differences between the illumination-adjusted and original images to guide illumination classification. We construct two augmented datasets by synthetically changing a set of predefined lighting conditions in two of the most popular ReID benchmarks: Market1501 and DukeMTMC-ReID. Experiments demonstrate that our algorithm outperforms other state-of-the-art works and particularly potent in handling images under extremely low light.

cs.CV↗
arXiv2020

Heavy-Light Mesons on the Light Front

We study the heavy-light mesons within basis light-front quantization. The resulting mass spectra of $D$, $D_s$, $B$, and $B_s$ agree reasonably well with experiments. We also predict states which could be measured in the near future. In the light-front formalism, we calculate the light-front wave functions and additional experimental observables, such as parton distribution functions, distribution amplitudes, and decay constants by means of integrations over light-front wave functions. We also provide ratios of decay constants for selected pseudoscalar meson decays ($D_s$ to $D$ and $B_s$ to $B$) as they may prove to be theoretically more robust and more reliably determined in experiments. We find that our ratios are systematically smaller than existing experiment and other approaches by $5-18\%$.

nucl-th↗