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Yang Xue

Publications and source records attributed to Yang Xue.

35 records · Page 2Linked to original sources
arXiv2023

GridFormer: Towards Accurate Table Structure Recognition via Grid Prediction

All tables can be represented as grids. Based on this observation, we propose GridFormer, a novel approach for interpreting unconstrained table structures by predicting the vertex and edge of a grid. First, we propose a flexible table representation in the form of an MXN grid. In this representation, the vertexes and edges of the grid store the localization and adjacency information of the table. Then, we introduce a DETR-style table structure recognizer to efficiently predict this multi-objective information of the grid in a single shot. Specifically, given a set of learned row and column queries, the recognizer directly outputs the vertexes and edges information of the corresponding rows and columns. Extensive experiments on five challenging benchmarks which include wired, wireless, multi-merge-cell, oriented, and distorted tables demonstrate the competitive performance of our model over other methods.

cs.CV↗
arXiv2022

Two-dimensional half Chern-Weyl semimetal with multiple screw axes

Half topological states of matter and two-dimensional (2D) magnetism have gained much attention recently. In this paper, we propose a special topological semimetal phase called a 2D half Chern-Weyl semimetal (HCWS), which is a 2D magnetic Weyl semimetal bound to the half Chern insulator phase by symmetry, and the two phases can be converted to each other by manipulating the magnetization direction. We provide the symmetry conditions to realize this state in 2D systems with multiple screw axes. Tight-binding models with multiple basis and a predicted 2D material, monolayer TiTe, are shown as the concrete examples for HCWSs. The TiTe monolayer was shown to have a high ferremagnetic Curie temperature (~966 K) as well as a Coulomb correlation-enhanced spin-orbit coupling (SOC), and further demonstrates the effect of correlation-enhanced SOC on magnetocrystalline anisotropy energy and energy gap opening. Our work reveals a state with switchable and spin-resolved half body charge currents as well as half boundary charge currents, and will provide a platform for novel and high-performance topological spintronics devices.

cond-mat.mes-hall↗
arXiv2022

HelixFold: An Efficient Implementation of AlphaFold2 using PaddlePaddle

Accurate protein structure prediction can significantly accelerate the development of life science. The accuracy of AlphaFold2, a frontier end-to-end structure prediction system, is already close to that of the experimental determination techniques. Due to the complex model architecture and large memory consumption, it requires lots of computational resources and time to implement the training and inference of AlphaFold2 from scratch. The cost of running the original AlphaFold2 is expensive for most individuals and institutions. Therefore, reducing this cost could accelerate the development of life science. We implement AlphaFold2 using PaddlePaddle, namely HelixFold, to improve training and inference speed and reduce memory consumption. The performance is improved by operator fusion, tensor fusion, and hybrid parallelism computation, while the memory is optimized through Recompute, BFloat16, and memory read/write in-place. Compared with the original AlphaFold2 (implemented with Jax) and OpenFold (implemented with PyTorch), HelixFold needs only 7.5 days to complete the full end-to-end training and only 5.3 days when using hybrid parallelism, while both AlphaFold2 and OpenFold take about 11 days. HelixFold saves 1x training time. We verified that HelixFold's accuracy could be on par with AlphaFold2 on the CASP14 and CAMEO datasets. HelixFold's code is available on GitHub for free download: https://github.com/PaddlePaddle/PaddleHelix/tree/dev/apps/protein_folding/helixfold, and we also provide stable web services on https://paddlehelix.baidu.com/app/drug/protein/forecast.

cs.DC↗
arXiv2022

Retardation effect and dark state in a waveguide QED setup with rectangle cross section

In this paper, we investigate the dynamics of a two-atom system which couples to a quasi-one dimensional waveguide with rectangle cross section. The waveguide supports different TM and TE modes and the former ones play as environment by on-demand choosing the dipole moment of the atoms. Such environment induces the interaction and collective dissipation of the atoms. When both of the two atoms are located in the middle of the waveguide, we observe a retardation effect, which is broken by moving one of the atom to be off-centered. To preserve the complete dissipation of the system via dark state mechanism, we propose a scheme where the connection of the atoms are perpendicular to the axis of the waveguide. We hope our study will be useful in quantum information processing based on state-to-art waveguide structure.

quant-ph↗
arXiv2022

GIRAFFE HD: A High-Resolution 3D-aware Generative Model

3D-aware generative models have shown that the introduction of 3D information can lead to more controllable image generation. In particular, the current state-of-the-art model GIRAFFE can control each object's rotation, translation, scale, and scene camera pose without corresponding supervision. However, GIRAFFE only operates well when the image resolution is low. We propose GIRAFFE HD, a high-resolution 3D-aware generative model that inherits all of GIRAFFE's controllable features while generating high-quality, high-resolution images ($512^2$ resolution and above). The key idea is to leverage a style-based neural renderer, and to independently generate the foreground and background to force their disentanglement while imposing consistency constraints to stitch them together to composite a coherent final image. We demonstrate state-of-the-art 3D controllable high-resolution image generation on multiple natural image datasets.

cs.CV↗
arXiv2021

Multimodal Pre-Training Model for Sequence-based Prediction of Protein-Protein Interaction

Protein-protein interactions (PPIs) are essentials for many biological processes where two or more proteins physically bind together to achieve their functions. Modeling PPIs is useful for many biomedical applications, such as vaccine design, antibody therapeutics, and peptide drug discovery. Pre-training a protein model to learn effective representation is critical for PPIs. Most pre-training models for PPIs are sequence-based, which naively adopt the language models used in natural language processing to amino acid sequences. More advanced works utilize the structure-aware pre-training technique, taking advantage of the contact maps of known protein structures. However, neither sequences nor contact maps can fully characterize structures and functions of the proteins, which are closely related to the PPI problem. Inspired by this insight, we propose a multimodal protein pre-training model with three modalities: sequence, structure, and function (S2F). Notably, instead of using contact maps to learn the amino acid-level rigid structures, we encode the structure feature with the topology complex of point clouds of heavy atoms. It allows our model to learn structural information about not only the backbones but also the side chains. Moreover, our model incorporates the knowledge from the functional description of proteins extracted from literature or manual annotations. Our experiments show that the S2F learns protein embeddings that achieve good performances on a variety of PPIs tasks, including cross-species PPI, antibody-antigen affinity prediction, antibody neutralization prediction for SARS-CoV-2, and mutation-driven binding affinity change prediction.

q-bio.BM↗
arXiv2021

Airborne Quantum Key Distribution with Boundary Layer Effects

Airborne quantum key distribution (QKD) is now becoming a flexible bond between terrestrial fiber and satellite, which is an efficient solution to establish a mobile, on-demand, and real-time coverage quantum network. Furthermore, When the aircraft is flying at a high speed, usually larger than 0.3 Ma, the produced boundary layer will impair the performance of aircraft-based QKD. The boundary layer would introduce random wavefront aberration, jitter and extra intensity attenuation to the transmitted photons. However, previous airborne QKD implementations only considered the influences from atmospheric turbulence and molecular scattering, but ignored the boundary layer effects. In this article, we propose a detailed performance evaluation scheme of airborne QKD with boundary layer effects and estimate the overall photon transmission efficiency, quantum bit error rate and final secure key rate. Through simulations and modeling, in our proposed airborne QKD scenario, the boundary layer would introduce 3.5dB loss to the transmitted photons and decrease 70.7% of the secure key rate, which shows that the aero-optical effects caused by the boundary layer can not be ignored. With tolerated quantum bit error rate set to 10%, the suggested quantum communication azimuth angle between the aircraft and the ground station is within 60 degrees. Moreover, the optimal beacon laser module and adaptive optics module are suggested to be employed to improve the performance of airborne QKD system. Our detailed airborne QKD evaluation study can be performed to the future airborne quantum communication designs.

quant-ph↗
arXiv2021

Proactive Interaction Framework for Intelligent Social Receptionist Robots

Proactive human-robot interaction (HRI) allows the receptionist robots to actively greet people and offer services based on vision, which has been found to improve acceptability and customer satisfaction. Existing approaches are either based on multi-stage decision processes or based on end-to-end decision models. However, the rule-based approaches require sedulous expert efforts and only handle minimal pre-defined scenarios. On the other hand, existing works with end-to-end models are limited to very general greetings or few behavior patterns (typically less than 10). To address those challenges, we propose a new end-to-end framework, the TransFormer with Visual Tokens for Human-Robot Interaction (TFVT-HRI). The proposed framework extracts visual tokens of relative objects from an RGB camera first. To ensure the correct interpretation of the scenario, a transformer decision model is then employed to process the visual tokens, which is augmented with the temporal and spatial information. It predicts the appropriate action to take in each scenario and identifies the right target. Our data is collected from an in-service receptionist robot in an office building, which is then annotated by experts for appropriate proactive behavior. The action set includes 1000+ diverse patterns by combining language, emoji expression, and body motions. We compare our model with other SOTA end-to-end models on both offline test sets and online user experiments in realistic office building environments to validate this framework. It is demonstrated that the decision model achieves SOTA performance in action triggering and selection, resulting in more humanness and intelligence when compared with the previous reactive reception policies.

cs.RO↗
arXiv2019

Air-Writing Translater: A Novel Unsupervised Domain Adaptation Method for Inertia-Trajectory Translation of In-air Handwriting

As a new way of human-computer interaction, inertial sensor based in-air handwriting can provide a natural and unconstrained interaction to express more complex and richer information in 3D space. However, most of the existing in-air handwriting work is mainly focused on handwritten character recognition, which makes these work suffer from poor readability of inertial signal and lack of labeled samples. To address these two problems, we use unsupervised domain adaptation method to reconstruct the trajectory of inertial signal and generate inertial samples using online handwritten trajectories. In this paper, we propose an AirWriting Translater model to learn the bi-directional translation between trajectory domain and inertial domain in the absence of paired inertial and trajectory samples. Through semantic-level adversarial training and latent classification loss, the proposed model learns to extract domain-invariant content between inertial signal and trajectory, while preserving semantic consistency during the translation across the two domains. We carefully design the architecture, so that the proposed framework can accept inputs of arbitrary length and translate between different sampling rates. We also conduct experiments on two public datasets: 6DMG (in-air handwriting dataset) and CT (handwritten trajectory dataset), the results on the two datasets demonstrate that the proposed network successes in both Inertia-to Trajectory and Trajectory-to-Inertia translation tasks.

cs.CV↗
arXiv2019

Novel Chern insulators with half-metallic edge states

The central target of spintronics research is to achieve flexible control of highly efficient and spin-polarized electronic currents. Based on first-principles calculations and k-p models, we demonstrate that Cu2S/MnSe heterostructures are a novel type of Chern insulators with half-metallic chiral edge states and a very high Fermi velocity (0.87 * 10^6 m/s). The full spin-polarization of the edge states is found to be robust against the tuning of the chemical potential. Unlike the mechanisms reported previously, this heterostructure has quadratic bands with a normal band order, that is, the p/d-like band is below the s-like band. Charge transfer between the Cu2S moiety and the substrate results in variation in the occupied bands, which together with spin-orbit coupling, triggers the appearance of the topological state in the system. These results imply that numerous ordinary semiconductors with normal band order may convert into Chern insulators with half-metallic chiral edge states through this mechanism, providing a strategy to find a rich variety of materials for dissipationless, 100% spin-polarized and high-speed spintronic devices.

cond-mat.mtrl-sci↗
arXiv2018

Origin of topologically trivial states and topological phase transitions in low-buckled plumbene

Combining tight-binding (TB) models with first-principles calculations, we investigate electronic and topological properties of plumbene. Different from the other two-dimensional (2D) topologically nontrivial insulators in group IVA (from graphene to stanene), low-buckled plumbene is a topologically trivial insulator. The plumbene without spin-orbit coupling exhibits simultaneously two kinds of degeneracies, i.e., quadratic non-Dirac and linear Dirac band dispersions around the Γand K/K' points, respectively. Our TB model calculations show that it is the coupling between the two topological states around the Γand K/K' points that triggers the global topologically trivial property of plumbene. Quantum anomalous Hall effects with Chern numbers of 2 or -2 can be, however, achieved after an exchange field is introduced. When the plumbene is functionalized with ethynyl (PbC2H), quantum spin Hall effects appear due to the breaking of the coupling effect of the local topological states.

cond-mat.mtrl-sci↗
arXiv2018

Coupling effect of topological states and Chern insulators in two-dimensional triangular lattices

We investigate topological states of two-dimensional (2D) triangular lattices with multi-orbitals. Tight-binding model calculations of a 2D triangular lattice based on $\emph{p}_{x}$ and \emph{p}_{y} orbitals exhibit very interesting doubly degenerate energy points at different positions ($Γ$ and K/K$^{\prime}$) in momentum space, with quadratic non-Dirac and linear Dirac band dispersions, respectively. Counterintuitively, the system shows a global topologically trivial rather than nontrivial state with consideration of spin-orbit coupling due to the "destructive interference effect" between the topological states at the $Γ$ and K/K$^{\prime}$ points. The topologically nontrivial state can emerge by introducing another set of triangular lattices to the system (bitriangular lattices) due to the breakdown of the interference effect. With first-principles calculations, we predict an intrinsic Chern insulating behavior (quantum anomalous Hall effect) in a family of 2D triangular lattice metal-organic framework of Co(C$_{21}$N$_{3}$H$_{15}$) (TPyB-Co) from this scheme. Our results provide a different path and theoretical guidance for the search for and design of new 2D topological quantum materials.

cond-mat.mes-hall↗
arXiv2017

Strong magnetization and Chern insulators in compressed graphene/CrI$_{3}$ van der Waals heterostructures

Graphene-based heterostructures are a promising material system for designing the topologically nontrivial Chern insulating devices. Recently, a two-dimensional (2D) monolayer ferromagnetic insulator CrI$_{3}$ was successfully synthesized in experiments [Huang et al., Nature 546, 270 (2017)]. Here, these two interesting materials are proposed to build a heterostructure (Gr/CrI$_{3}$). Our first-principles calculations show that the system forms a van der Waals (vdW) heterostructure, relatively facilely fabricated in experiments. A Chern insulating state is acquired in the Gr/CrI$_{3}$ heterostructure if the vdW gap is compressed to certain extents by applying an external pressure. Amazingly, very strong magnetization (about 150 meV) is found in graphene, induced by the substrate CrI$_{3}$, despite the vdW interactions between them. A low-energy effective model is employed to understand the mechanism. The work functions, contact types, and band alignments of the Gr/CrI$_{3}$ heterostructure system are also studied. Our work demonstrates that the Gr/CrI$_{3}$ heterostructure is a promising system to observe the quantum anomalous Hall effect at high temperatures (up to 45 K) in experiments.

cond-mat.mtrl-sci↗
arXiv2017

Chern insulators without band inversion in MoS2 monolayers with 3d adatoms

Electronic and topological properties of MoS2 monolayers endowed with 3d transition metal (TM) adatoms (V-Fe) are explored by using ab initio methods and k.p models. Without the consideration of the Hubbard U interaction, the V, Cr, and Fe adatoms tend to locate on the top of the Mo atoms, while the most stable site for the Mn atom is at the hollow position of the Mo-S hexagon. After the Hubbard U is applied, the most stable sites of all the systems become the top of the Mo atoms. Chern insulators without band inversion are achieved in these systems. The V and Fe adsorption systems are the best candidates to produce the topological states. The k.p model calculations indicate that these topological states are determined by the TM magnetism, the C3v crystal field from the MoS2 substrate, and the TM atomic spin-orbit coupling (SOC). The special two-meron pseudospin texture is found to contribute to the topology. The apparent difference between the Berry curvatures for the V and Fe adsorption systems are also explored. Our results widen the understanding to the Chern insulators and are helpful for the applications of the MoS2 monolayers in the future electronics and spintronics.

cond-mat.mtrl-sci↗
arXiv2016

Quantum Spin-quantum Anomalous Hall Effect with Tunable Edge States in Sb Monolayer-based Heterostructures

A novel topological insulator with tunable edge states, called quantum spin-quantum anomalous Hall (QSQAH) insulator, is predicted in a heterostructure of a hydrogenated Sb (SbH) monolayer on a LaFeO3 substrate by using ab initio methods. The substrate induces a drastic staggered exchange field in the SbH film, which plays an important role to generate the QSQAH effect. A topologically nontrivial band gap (up to 35 meV) is opened by Rashba spin-orbit coupling, which can be enlarged by strain and electric field. To understand the underlying physical mechanism of the QSQAH effect, a tight-binding model based on px and py orbitals is constructed. With the model, the exotic behaviors of the edge states in the heterostructure are investigated. Dissipationless chiral charge edge states related to one valley are found to emerge along the both sides of the sample, while low-dissipation spin edge states related to the other valley flow only along one side of the sample. These edge states can be tuned flexibly by polarization-sensitive photoluminescence controls and/or chemical edge modifications. Such flexible manipulations of the charge, spin, and valley degrees of freedom provide a promising route towards applications in electronics, spintronics, and valleytronics.

cond-mat.mtrl-sci↗
arXiv2015

Recent Development in Analog Computation - A Brief Overview

The recent development in analog computation is reviewed in this paper. Analog computation was used in many applications where power and energy efficiency is of paramount importance. It is shown that by using innovative architecture and circuit design, analog computation systems can achieve much higher energy efficiency than their digital counterparts, as they are able to exploit the computational power inherent to the devices and physics. However, these systems do suffer from some disadvantages, such as lower accuracy and speed, and designers have come up with novel approaches to overcome them. The paper provides an overview of analog computation systems, from basic components such as memory and arithmetic elements, to architecture and system design.

cs.ET↗