Searcharxiv⌕ Search

arXiv subjects

Liang Li

Publications and source records attributed to Liang Li.

At least 289 records · Page 16Linked to original sources
arXiv2021

Calibrated Feature Decomposition for Generalizable Person Re-Identification

Existing disentangled-based methods for generalizable person re-identification aim at directly disentangling person representations into domain-relevant interference and identity-relevant feature. However, they ignore that some crucial characteristics are stubbornly entwined in both the domain-relevant interference and identity-relevant feature, which are intractable to decompose in an unsupervised manner. In this paper, we propose a simple yet effective Calibrated Feature Decomposition (CFD) module that focuses on improving the generalization capacity for person re-identification through a more judicious feature decomposition and reinforcement strategy. Specifically, a calibrated-and-standardized Batch normalization (CSBN) is designed to learn calibrated person representation by jointly exploring intra-domain calibration and inter-domain standardization of multi-source domain features. CSBN restricts instance-level inconsistency of feature distribution for each domain and captures intrinsic domain-level specific statistics. The calibrated person representation is subtly decomposed into the identity-relevant feature, domain feature, and the remaining entangled one. For enhancing the generalization ability and ensuring high discrimination of the identity-relevant feature, a calibrated instance normalization (CIN) is introduced to enforce discriminative id-relevant information, and filter out id-irrelevant information, and meanwhile the rich complementary clues from the remaining entangled feature are further employed to strengthen it. Extensive experiments demonstrate the strong generalization capability of our framework. Our models empowered by CFD modules significantly outperform the state-of-the-art domain generalization approaches on multiple widely-used benchmarks. Code will be made public: https://github.com/zkcys001/CFD.

cs.CV↗
arXiv2021

Is the Birth of PSR J0538+2817 Accompanied by a Gamma-ray Burst?

Recently, the Five-hundred-meter Aperture Spherical radio Telescope (FAST) measured the three-dimensional velocity of PSR J0538+2817 in its associated supernova remnant S147 and found a possible spin-velocity alignment in this pulsar. Here we show that the high velocity and the spin-velocity alignment in this pulsar can be explained by the so-called electromagnetic rocket mechanism. In this framework, the pulsar is kicked in the direction of the spin axis, which naturally explains the spin-velocity alignment. We scrutinize the evolution of this pulsar and show that the pulsar kick can create a highly relativistic jet at the opposite direction of the kick velocity. The lifetime and energetics of the jet is estimated. It is argued that the jet can generate a Gamma-ray Burst (GRB). The long term dynamical evolution of the jet is calculated. It is found that the shock radius of the jet should expand to about 32 pc at present, which is well consistent with the observed radius of the supernova remnant S147 ($32.1\pm4.8$ pc). Additionally, our calculations indicate that the current velocity of the GRB remnant should be about 440 km s$^{-1}$, which is also consistent with the observed blast wave velocity of the remnant of S147 (500 km s$^{-1}$).

astro-ph.HE↗
arXiv2021

Dynamical transition of hydromagnetic convection in a rotating fluid layer

In this article, we aim to study the stability and dynamic transition of an electrically conducting fluid in the presence of an external uniform horizontal magnetic field and rotation based on a Boussinesq approximation model. By analyzing the spectrum of the linear part of the model and verifying the validity of the principle of exchange of stability, we take a hybrid approach combining theoretical analysis with numerical computation to study the transition from a simple real eigenvalue, a pair of complex conjugate eigenvalues and a real eigenvalue of multiplicity two, respectively. The center manifold reduction theory is applied to reduce the infinite dimensional system to the corresponding finite dimensional one together with one or several non-dimensional transition numbers that determine the dynamic transition types. Careful numerical computations are performed to determine these transition numbers as well as related temporal and flow patterns etc. Our results indicate that both continuous and jump transitions can occur at certain parameter region.

math.DS↗
arXiv2021

Nature of the ultrarelativistic prompt emission phase of GRB 190114C

We address the physical origin of the ultrarelativistic prompt emission (UPE) phase of GRB 190114C observed in the interval 1.9-3.99 s, by the Fermi-GBM in 10 keV-10 MeV . Thanks to high S/N ratio of Fermi-GBM data, a time resolved spectral analysis has evidenced a sequence of similar blackbody plus cutoff power-law spectra, on ever decreasing time intervals during the entire UPE phase. We assume that during the UPE phase, the inner engine of the GRB, composed of a Kerr black hole and a uniform test magnetic field B0, aligned with the BH rotation axis, operates in an overcritical field. We infer an $e^+e^-$ pair electromagnetic plasma in presence of a baryon load, a PEMB pulse, originating from a vacuum polarization quantum process in the inner engine. This initially optically thick plasma self-accelerates, giving rise at the transparency radius to the MeV radiation observed by Ferm-GBM. At trf > 3.99 s, the electric field becomes undercritical, and the inner engine operates in the classical electrodynamics regime and generate the GeV emission. During both the quantum and the classical electrodynamics processes, we determine the time varying mass and spin of the Kerr BH in the inner engine, fulfilling the Christodoulou-Hawking-Ruffini mass-energy formula. For the first time, we quantitatively show how the inner engine, by extracting the rotational energy of the Kerr BH, produces a series of PEMB pulses. We follow the quantum vacuum polarization process in sequences with decreasing time bins. We compute the Lorentz factors, the baryon loads and the radii at transparency, as well as the value of the magnetic field, assumed to be constant in each sequence. The fundamental hierarchical structure, linking the quantum electrodynamics regime to the classical electrodynamics regime, is characterized by the emission of blackholic quanta with a timescale $t=10^{-9}$s, and energy $E=10^{45}$ erg.

astro-ph.HE↗
arXiv2021

R$^3$Net:Relation-embedded Representation Reconstruction Network for Change Captioning

Change captioning is to use a natural language sentence to describe the fine-grained disagreement between two similar images. Viewpoint change is the most typical distractor in this task, because it changes the scale and location of the objects and overwhelms the representation of real change. In this paper, we propose a Relation-embedded Representation Reconstruction Network (R$^3$Net) to explicitly distinguish the real change from the large amount of clutter and irrelevant changes. Specifically, a relation-embedded module is first devised to explore potential changed objects in the large amount of clutter. Then, based on the semantic similarities of corresponding locations in the two images, a representation reconstruction module (RRM) is designed to learn the reconstruction representation and further model the difference representation. Besides, we introduce a syntactic skeleton predictor (SSP) to enhance the semantic interaction between change localization and caption generation. Extensive experiments show that the proposed method achieves the state-of-the-art results on two public datasets.

cs.CL↗
arXiv2021

The well-posedness and the regularity of global attractor for a couple stress fluid through porous layer with the local thermal non-equilibrium effect

In the article, we aim to investigate the well-posedness of solution and the regularity of the global attractor for the couple stress fluid in saturated porous media with the local thermal non-equilibrium effect. To be more specific, we firstly show the existence and uniqueness of global weak solution to the model by making use of the standard Galerkin method. Second, relying on verifying the uniformly compact condition required, we prove the existence of the global attractor of the model in the space where the weak solution resides. Finally, we improve the regularity of global attractor by uniformly compact condition and obtain the $C^{\infty}$ attractor for the model.

math.AP↗
arXiv2021

Manipulating the anisotropic phase separation in strained VO2 epitaxial films by nanoscale ion-implantation

Manipulating the strain induced poly-domains and phase transition in correlated oxide material are important for high performance devices fabrication. Though the electronic transport in the strained oxide film at macroscopic scales can be directly measured, the anisotropic electronic state and the controllable phase separation cross the insulator-to-metal transition within nanoscale size are still elusive. Here, we selected VO2 crystal film as a prototypical oxide and achieved the manipulation of anisotropy electronic phase separation via injecting He+ nanobeam into VO2 film at room temperature. In addition, this nanoscale phase separation was directly visualized by infrared near-field imaging measurements, showing the pronounced and unique cR-axis dependent anisotropy on VO2 surface. Our results offered new insights towards understanding the anisotropic nanoscale phase separation in strained metal oxide films.

cond-mat.mtrl-sci↗
arXiv2021

Prevalence of Extra Power-Law Spectral Components in Short Gamma-Ray Bursts

Prompt extra power-law (PL) spectral component is discovered in some bright gamma-ray bursts (GRBs), which usually dominates the spectral energy distribution below tens of keV or above about 10 MeV. However, its origin is still unclear. In this paper, we present a systematic analysis 13 Fermi short GRBs as of August 2020, with the contemporaneous keV-MeV and GeV detections at the prompt emission phase. We find that the extra PL component is a ubiquitous spectral feature for short GRBs, showing up in all 13 analyzed GRBs. The PL indices are mostly harder than -2.0, which may be well reproduced by considering the electromagnetic cascade induced by ultra-relativistic protons or electrons accelerated in the prompt emission phase. The average flux of these extra PL components positively correlates with that of the main spectral components, which implies they may share the same physical origin.

astro-ph.HE↗
arXiv2021

Edge-featured Graph Neural Architecture Search

Graph neural networks (GNNs) have been successfully applied to learning representation on graphs in many relational tasks. Recently, researchers study neural architecture search (NAS) to reduce the dependence of human expertise and explore better GNN architectures, but they over-emphasize entity features and ignore latent relation information concealed in the edges. To solve this problem, we incorporate edge features into graph search space and propose Edge-featured Graph Neural Architecture Search to find the optimal GNN architecture. Specifically, we design rich entity and edge updating operations to learn high-order representations, which convey more generic message passing mechanisms. Moreover, the architecture topology in our search space allows to explore complex feature dependence of both entities and edges, which can be efficiently optimized by differentiable search strategy. Experiments at three graph tasks on six datasets show EGNAS can search better GNNs with higher performance than current state-of-the-art human-designed and searched-based GNNs.

cs.LG↗
arXiv2021

Multi-Modulation Network for Audio-Visual Event Localization

We study the problem of localizing audio-visual events that are both audible and visible in a video. Existing works focus on encoding and aligning audio and visual features at the segment level while neglecting informative correlation between segments of the two modalities and between multi-scale event proposals. We propose a novel MultiModulation Network (M2N) to learn the above correlation and leverage it as semantic guidance to modulate the related auditory, visual, and fused features. In particular, during feature encoding, we propose cross-modal normalization and intra-modal normalization. The former modulates the features of two modalities by establishing and exploiting the cross-modal relationship. The latter modulates the features of a single modality with the event-relevant semantic guidance of the same modality. In the fusion stage,we propose a multi-scale proposal modulating module and a multi-alignment segment modulating module to introduce multi-scale event proposals and enable dense matching between cross-modal segments. With the auditory, visual, and fused features modulated by the correlation information regarding audio-visual events, M2N performs accurate event localization. Extensive experiments conducted on the AVE dataset demonstrate that our proposed method outperforms the state of the art in both supervised event localization and cross-modality localization.

cs.CV↗
arXiv2021

Fast Batch Nuclear-norm Maximization and Minimization for Robust Domain Adaptation

Due to the domain discrepancy in visual domain adaptation, the performance of source model degrades when bumping into the high data density near decision boundary in target domain. A common solution is to minimize the Shannon Entropy to push the decision boundary away from the high density area. However, entropy minimization also leads to severe reduction of prediction diversity, and unfortunately brings harm to the domain adaptation. In this paper, we investigate the prediction discriminability and diversity by studying the structure of the classification output matrix of a randomly selected data batch. We find by theoretical analysis that the prediction discriminability and diversity could be separately measured by the Frobenius-norm and rank of the batch output matrix. The nuclear-norm is an upperbound of the former, and a convex approximation of the latter. Accordingly, we propose Batch Nuclear-norm Maximization and Minimization, which performs nuclear-norm maximization on the target output matrix to enhance the target prediction ability, and nuclear-norm minimization on the source batch output matrix to increase applicability of the source domain knowledge. We further approximate the nuclear-norm by L_{1,2}-norm, and design multi-batch optimization for stable solution on large number of categories. The fast approximation method achieves O(n^2) computational complexity and better convergence property. Experiments show that our method could boost the adaptation accuracy and robustness under three typical domain adaptation scenarios. The code is available at https://github.com/cuishuhao/BNM.

cs.CV↗
arXiv2021

Magnetic-field-induced robust zero Hall plateau state in MnBi$_2$Te$_4$ Chern insulator

The intrinsic antiferromagnetic topological insulator MnBi2Te4 provides an ideal platform for exploring exotic topological quantum phenomena. Recently, the Chern insulator and axion insulator phases have been realized in few-layer MnBi2Te4 devices at low magnetic field regime. However, the fate of MnBi2Te4 in high magnetic field has never been explored in experiment. In this work, we report transport studies of exfoliated MnBi2Te4 flakes in pulsed magnetic fields up to 61.5 T. In the high-field limit, the Chern insulator phase with Chern number C = -1 evolves into a robust zero Hall resistance plateau state. Nonlocal transport measurements and theoretical calculations demonstrate that the charge transport in the zero Hall plateau state is conducted by two counter-propagating edge states that arise from the combined effects of Landau levels and large Zeeman effect in strong magnetic fields. Our result demonstrates the intricate interplay among intrinsic magnetic order, external magnetic field, and nontrivial band topology in MnBi2Te4.

cond-mat.mes-hall↗
arXiv2021

TreeBERT: A Tree-Based Pre-Trained Model for Programming Language

Source code can be parsed into the abstract syntax tree (AST) based on defined syntax rules. However, in pre-training, little work has considered the incorporation of tree structure into the learning process. In this paper, we present TreeBERT, a tree-based pre-trained model for improving programming language-oriented generation tasks. To utilize tree structure, TreeBERT represents the AST corresponding to the code as a set of composition paths and introduces node position embedding. The model is trained by tree masked language modeling (TMLM) and node order prediction (NOP) with a hybrid objective. TMLM uses a novel masking strategy designed according to the tree's characteristics to help the model understand the AST and infer the missing semantics of the AST. With NOP, TreeBERT extracts the syntactical structure by learning the order constraints of nodes in AST. We pre-trained TreeBERT on datasets covering multiple programming languages. On code summarization and code documentation tasks, TreeBERT outperforms other pre-trained models and state-of-the-art models designed for these tasks. Furthermore, TreeBERT performs well when transferred to the pre-trained unseen programming language.

cs.LG↗
arXiv2021

Energy distribution of solutions to defocusing semi-linear wave equation in higher dimensional space

The topic of this paper is a semi-linear, defocusing wave equation $u_{t t}-Δu=-|u|^{p-1} u$ in sub-conformal case in the higher dimensional space whose initial data are radical and come with a finite energy. We prove some decay estimates of the the solutions if initial data decay at a certain rate as the spatial variable tends to infinity. A combination of this property with a method of characteristic lines give a scattering result if the initial data satisfy $$E_κ\left(u_{0}, u_{1}\right)=\int_{\mathbb{R}^{d}}\left(|x|^κ+1\right)\left(\frac{1}{2}\left|\nabla u_{0}(x)\right|^{2}+\frac{1}{2}\left|u_{1}(x)\right|^{2}+\frac{1}{p+1}\left|u_{0}(x)\right|^{p+1}\right) d x<+\infty.$$ Here $κ=\frac{(2-d)p+(d+2)}{p+1}$.

math.AP↗
arXiv2021

Rethinking Graph Neural Architecture Search from Message-passing

Graph neural networks (GNNs) emerged recently as a standard toolkit for learning from data on graphs. Current GNN designing works depend on immense human expertise to explore different message-passing mechanisms, and require manual enumeration to determine the proper message-passing depth. Inspired by the strong searching capability of neural architecture search (NAS) in CNN, this paper proposes Graph Neural Architecture Search (GNAS) with novel-designed search space. The GNAS can automatically learn better architecture with the optimal depth of message passing on the graph. Specifically, we design Graph Neural Architecture Paradigm (GAP) with tree-topology computation procedure and two types of fine-grained atomic operations (feature filtering and neighbor aggregation) from message-passing mechanism to construct powerful graph network search space. Feature filtering performs adaptive feature selection, and neighbor aggregation captures structural information and calculates neighbors' statistics. Experiments show that our GNAS can search for better GNNs with multiple message-passing mechanisms and optimal message-passing depth. The searched network achieves remarkable improvement over state-of-the-art manual designed and search-based GNNs on five large-scale datasets at three classical graph tasks. Codes can be found at https://github.com/phython96/GNAS-MP.

cs.LG↗
arXiv2021

Highly Efficient Ultrathin Light Emitting Diodes based on Perovskite Nanocrystals

Light-emitting diodes based on perovskite nanocrystals (PNCs-LEDs) have gained great interest for next-generation display and lighting technologies prized for their color purity, high brightness and luminous efficiency approaching the intrinsic limit imposed by extraction of electroluminescence from the device structure. Although the time is ripe for the development of effective light outcoupling strategies to further boost the device performance, this technologically relevant aspect of PNC-LEDs is still without a definitive solution. Here, following theoretical guidelines and without the integration of complex photonic structures, we realize stable PNC-LEDs with EQE as high as 29.2% (average EQE=24.7%), which substantially break the outcoupling limit of common PNC-LEDs and systematically surpass any previous perovskite-based device. Key to such unprecedented performance is channeling the recombination zone in PNC emissive layers as thin as 10 nm, which we achieve by finely balancing the electron and hole transport using CsPbBr3 PNCs resurfaced with a nickel oxide layer. The ultra-thin approach general and, in principle, applicable to other perovskite nanostructures for fabricating highly efficient, color tunable transparent LEDs ideal for unobtrusive screens and displays and is compatible with the integration of photonic components for further enhanced performance.

physics.optics↗
arXiv2021

Artificial Lateral Line Based Relative State Estimation for Two Adjacent Robotic Fish

The lateral line enables fish to efficiently sense the surrounding environment, thus assisting flow-related fish behaviours. Inspired by this phenomenon, varieties of artificial lateral line systems (ALLSs) have been developed and applied to underwater robots. This article focuses on using the pressure sensor arrays based on ALLS-measured hydrodynamic pressure variations (HPVs) for estimating the relative state between two adjacent robotic fish with leader-follower formation. The relative states include the relative oscillating frequency, amplitude, and offset of the upstream robotic fish to the downstream robotic fish, the relative vertical distance, the relative yaw angle, the relative pitch angle, and the relative roll angle between the two adjacent robotic fish. Regression model between the ALLS-measured and the mentioned relative states is investigated, and regression model-based relative state estimation is conducted. Specifically, two criteria are proposed firstly to investigate not only the sensitivity of each pressure sensor to the variations of relative state but also the insufficiency and redundancy of the pressure sensors. And thus the pressure sensors used for regression analysis are determined. Then four typical regression methods, including random forest algorithm, support vector regression, back propagation neural network, and multiple linear regression method are used for establishing regression models between the ALLS-measured HPVs and the relative states. Then regression effects of the four methods are compared and discussed. Finally, random forest-based method, which has the best regression effect, is used to estimate relative yaw angle and oscillating amplitude using the ALLS-measured HPVs and exhibits excellent estimation performance. This work contributes to local relative estimation for a group of underwater robots, which has always been a challenge.

eess.SP↗