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Ying Liu

Publications and source records attributed to Ying Liu.

At least 199 records · Page 11Linked to original sources
arXiv2022

Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks

Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets. Recent financial studies show that the momentum spillover effect plays a significant role in stock fluctuation. However, previous studies typically only learn the simple connection information among related companies, which inevitably fail to model complex relations of listed companies in the real financial market. To address this issue, we first construct a more comprehensive Market Knowledge Graph (MKG) which contains bi-typed entities including listed companies and their associated executives, and hybrid-relations including the explicit relations and implicit relations. Afterward, we propose DanSmp, a novel Dual Attention Networks to learn the momentum spillover signals based upon the constructed MKG for stock prediction. The empirical experiments on our constructed datasets against nine SOTA baselines demonstrate that the proposed DanSmp is capable of improving stock prediction with the constructed MKG.

q-fin.ST↗
arXiv2022

Exploring Native Atomic Defects in NiTe2

Nickel ditelluride (NiTe2), a new discovered type-II Dirac semimetal whose Dirac node lies close to its Fermi level, is expected to exhibit exotic phenomena including Lifshitz transition and superconductivity. As we know, defects are inevitable for transition metal dichalcogenides and have significant impacts on the optical and electronic properties. However, the systematic study of defects in NiTe2 is still lack. Here, by using high-resolution scanning tunneling microscopy combined with first-principles calculations, the point defects including the vacancy, intercalation and antisite defects in NiTe2 are systematically investigated. We identified five main types native defects and revealed that the growth condition could affect the type of native defects. By controlling the ratio of ingredient during synthesis, the types of point defects are expected to be manipulated, especially antisite defects. Additionally, we find native defects could slightly dope the topological surface states. Our results provide a facile way to manipulate defects for future optimizing the electronic properties of NiTe2 and other related materials.

cond-mat.mtrl-sci↗
arXiv2021

Topological quantum catalyst: the case of two-dimensional traversing nodal line states associated with high catalytic performance for hydrogen evolution reaction

Topological quantum catalysts (TQCs), where metallic surface states from nontrivial band topology serve as the mechanism to favor heterogeneous catalysis processes, have been well demonstrated in three dimensional (3D) examples but have been rarely discussed in 2D scale. Here, we develop a design scheme to realize 2D TQCs with showing traversing nodal line at the Brillouin zone boundary, large Fermi arc on the edge, and nearly zero Gibbs free energy (ΔGH*) for hydrogen evolution reaction (HER). We demonstrate the 2D Cu2C2N4 sheet is a such example. The material manifests an open nodal line traversing the whole k-path S-Y. It shows a long Fermi arc that spans the entire edge boundary, which is robust against spin-orbit coupling and the H adsorption. As the result, the edge of Cu2C2N4 sheet is relatively active for HER catalysis with possessing a ΔGH* as low as 0.10 eV, which is comparable with that of Pt and superior to other traditional catalysts and 3D TQCs as well. Our work offers an effective route to develop high performance HER catalysis without containing noble metals by utilizing 2D TQCs with traversing nodal line.

cond-mat.mtrl-sci↗
arXiv2021

Theoretical realization of hybrid Weyl state and associated high catalytic performance for hydrogen evolution in NiSi

For electrochemical hydrogen evolution reaction (HER), developing high-performance catalysts without containing precious metals has been a major research focus in the current. Herein, we show the feasibility of HER catalytic enhancement in Ni-based materials based on topological engineering from hybrid Weyl states. Via a high-throughput computational screening from 140 000 materials, we identify a chiral compound NiSi is a hybrid Weyl semimetal (WSM) with showing bulk type-I and type-II Weyl nodes and long surface Fermi arcs near the Fermi level. Sufficient evidences verify that topological charge carriers participate in the HER process, and make the certain surface of NiSi highly active with the Gibbs free energy nearly zero (0.07 eV), which is even lower than Pt and locates on the top of the volcano plots. This work opens up a new routine to develop no-precious-metal-containing HER catalysts via topological engineering, rather than traditional defect engineering, doping engineering, or strain engineering.

cond-mat.mtrl-sci↗
arXiv2021

CSSR: A Context-Aware Sequential Software Service Recommendation Model

We propose a novel software service recommendation model to help users find their suitable repositories in GitHub. Our model first designs a novel context-induced repository graph embedding method to leverage rich contextual information of repositories to alleviate the difficulties caused by the data sparsity issue. It then leverages sequence information of user-repository interactions for the first time in the software service recommendation field. Specifically, a deep-learning based sequential recommendation technique is adopted to capture the dynamics of user preferences. Comprehensive experiments have been conducted on a large dataset collected from GitHub against a list of existing methods. The results illustrate the superiority of our method in various aspects.

cs.IR↗
arXiv2021

Phase-dependent epitaxy for antimonene growth on silver substrate

Antimonene is a novel two-dimensional topological semiconductor material with strain driven tunable electronic structure for future electronic and spintronic devices, but the growth of clean antimonene is not fully understood. In this work, the growth process of antimonene on silver substrate has been studied in detail by using density functional theory and particle swarm optimization algorithms. The results show that, in addition to the experimental reported flat honeycomb and $β$-phase antimonene, $α$-phase antimonene was observed to be able to grow on thus substrates, and the phases of antimonene were deeply dependent on the reconstructed supercells and surface alloys. It has been demonstrated that the surface alloys on substrate play an active role in the growth of antimonene.

cond-mat.mtrl-sci↗
arXiv2021

Automated assessment of disease severity of COVID-19 using artificial intelligence with synthetic chest CT

Background: Triage of patients is important to control the pandemic of coronavirus disease 2019 (COVID-19), especially during the peak of the pandemic when clinical resources become extremely limited. Purpose: To develop a method that automatically segments and quantifies lung and pneumonia lesions with synthetic chest CT and assess disease severity in COVID-19 patients. Materials and Methods: In this study, we incorporated data augmentation to generate synthetic chest CT images using public available datasets (285 datasets from "Lung Nodule Analysis 2016"). The synthetic images and masks were used to train a 2D U-net neural network and tested on 203 COVID-19 datasets to generate lung and lesion segmentations. Disease severity scores (DL: damage load; DS: damage score) were calculated based on the segmentations. Correlations between DL/DS and clinical lab tests were evaluated using Pearson's method. A p-value < 0.05 was considered as statistical significant. Results: Automatic lung and lesion segmentations were compared with manual annotations. For lung segmentation, the median values of dice similarity coefficient, Jaccard index and average surface distance, were 98.56%, 97.15% and 0.49 mm, respectively. The same metrics for lesion segmentation were 76.95%, 62.54% and 2.36 mm, respectively. Significant (p << 0.05) correlations were found between DL/DS and percentage lymphocytes tests, with r-values of -0.561 and -0.501, respectively. Conclusion: An AI system that based on thoracic radiographic and data augmentation was proposed to segment lung and lesions in COVID-19 patients. Correlations between imaging findings and clinical lab tests suggested the value of this system as a potential tool to assess disease severity of COVID-19.

eess.IV↗
arXiv2021

Monolayer RhB4: half-auxeticity and almost ideal spin-orbit Dirac point semimetal

Structural-property relationship, the connection between materials' structures and their properties, is central to the materials research. Especially at reduced dimensions, novel structural motifs often generate unique physical properties.Motivated by a recent work reporting a novel half auxetic effect in monolayer PdB4 with a hypercoordinated structure, here, we extensively explore similar 2D transition metal boride structures MB4 with M covering 3d and 4d elements.Our investigation screens out one stable candidate, the monolayer RhB4. We find that monolayer RhB4 also shows half auxeticity, i.e., the material always expands in a lateral in-plane direction in response to an applied strain in the other direction, regardless of whether the strain is positive or negative.We show that this special mechanical character is intimately tied to the hypercoordinated structure with the M\c{opyright}B8 structural motif. Furthermore, regarding electronic properties, monolayer RhB4 is found to be the first example of an almost ideal 2D spin-orbit Dirac point semimetal.The low-energy band structure is clean, with a pair of fourfold degenerate Dirac points robust under spin-orbit coupling located close to the Fermi level. These Dirac points are enforced by the nonsymmorphic space group symmetry which is also determined by the lattice structure. Our work deepens the fundamental understanding of structural-property relationship in reduced dimensions. The half auxeticity and the spin-orbit Dirac points will make monolayer RhB4 a promising platform for nanomechanics and nanoelectronics applications.

cond-mat.mtrl-sci↗
arXiv2021

Visual-Semantic Transformer for Scene Text Recognition

Modeling semantic information is helpful for scene text recognition. In this work, we propose to model semantic and visual information jointly with a Visual-Semantic Transformer (VST). The VST first explicitly extracts primary semantic information from visual feature maps with a transformer module and a primary visual-semantic alignment module. The semantic information is then joined with the visual feature maps (viewed as a sequence) to form a pseudo multi-domain sequence combining visual and semantic information, which is subsequently fed into an transformer-based interaction module to enable learning of interactions between visual and semantic features. In this way, the visual features can be enhanced by the semantic information and vice versus. The enhanced version of visual features are further decoded by a secondary visual-semantic alignment module which shares weights with the primary one. Finally, the decoded visual features and the enhanced semantic features are jointly processed by the third transformer module obtaining the final text prediction. Experiments on seven public benchmarks including regular/ irregular text recognition datasets verifies the effectiveness our proposed model, reaching state of the art on four of the seven benchmarks.

cs.CV↗
arXiv2021

Unsupervised COVID-19 Lesion Segmentation in CT Using Cycle Consistent Generative Adversarial Network

COVID-19 has become a global pandemic and is still posing a severe health risk to the public. Accurate and efficient segmentation of pneumonia lesions in CT scans is vital for treatment decision-making. We proposed a novel unsupervised approach using cycle consistent generative adversarial network (cycle-GAN) which automates and accelerates the process of lesion delineation. The workflow includes lung volume segmentation, "synthetic" healthy lung generation, infected and healthy image subtraction, and binary lesion mask creation. The lung volume volume was firstly delineated using a pre-trained U-net and worked as the input for the later network. The cycle-GAN was developed to generate synthetic "healthy" lung CT images from infected lung images. After that, the pneumonia lesions are extracted by subtracting the synthetic "healthy" lung CT images from the "infected" lung CT images. A median filter and K-means clustering were then applied to contour the lesions. The auto segmentation approach was validated on two public datasets (Coronacases and Radiopedia). The Dice coefficients reached 0.748 and 0.730, respectively, for the Coronacases and Radiopedia datasets. Meanwhile, the precision and sensitivity for lesion segmentationdetection are 0.813 and 0.735 for the Coronacases dataset, and 0.773 and 0.726 for the Radiopedia dataset. The performance is comparable to existing supervised segmentation networks and outperforms previous unsupervised ones. The proposed unsupervised segmentation method achieved high accuracy and efficiency in automatic COVID-19 lesion delineation. The segmentation result can serve as a baseline for further manual modification and a quality assurance tool for lesion diagnosis. Furthermore, due to its unsupervised nature, the result is not influenced by physicians' experience which otherwise is crucial for supervised methods.

eess.IV↗
arXiv2021

K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce

Existing pre-trained language models (PLMs) have demonstrated the effectiveness of self-supervised learning for a broad range of natural language processing (NLP) tasks. However, most of them are not explicitly aware of domain-specific knowledge, which is essential for downstream tasks in many domains, such as tasks in e-commerce scenarios. In this paper, we propose K-PLUG, a knowledge-injected pre-trained language model based on the encoder-decoder transformer that can be transferred to both natural language understanding and generation tasks. We verify our method in a diverse range of e-commerce scenarios that require domain-specific knowledge. Specifically, we propose five knowledge-aware self-supervised pre-training objectives to formulate the learning of domain-specific knowledge, including e-commerce domain-specific knowledge-bases, aspects of product entities, categories of product entities, and unique selling propositions of product entities. K-PLUG achieves new state-of-the-art results on a suite of domain-specific NLP tasks, including product knowledge base completion, abstractive product summarization, and multi-turn dialogue, significantly outperforms baselines across the board, which demonstrates that the proposed method effectively learns a diverse set of domain-specific knowledge for both language understanding and generation tasks.

cs.CL↗
arXiv2021

Identifying super-spreaders in information-epidemic coevolving dynamics on multiplex networks

Identifying super-spreaders in epidemics is important to suppress the spreading of disease especially when the medical resource is limited.In the modern society, the information on epidemics transmits swiftly through various communication channels which contributes much to the suppression of epidemics. Here we study on the identification of super-spreaders in the information-disease coupled spreading dynamics. Firstly, we find that the centralities in physical contact layer are no longer effective to identify super-spreaders in epidemics, which is due to the suppression effects from the information spreading. Then by considering the structural and dynamical couplings between the communication layer and physical contact layer, we propose a centrality measure called coupling-sensitive centrality to identify super-spreaders in disease spreading. Simulation results on synthesized and real-world multiplex networks show that the proposed measure is not only much more accurate than centralities on the single network, but also outperforms two typical multilayer centralities in identifying super-spreaders. These findings imply that considering the structural and dynamical couplings between layers is very necessary in identifying the key roles in the coupled multilayer systems.

physics.soc-ph↗
arXiv2021

Multiple-Fold Fermions and Topological Fermi Arcs Induced Catalytic Enhancement in Nanoporous Electride C12A7

Topological materials are recently regarded as the idea catalysts due to the protected surface metallic states and high carrier mobility, however the fundamental mechanism and the underlying relationship between the catalytic performance and topological states are in debate. Here, by means of symmetry analysis and first-principles calculations, we discover that the electride material of C12A7 hosts the multiple-fold fermions due to the interstitial-electrons, with the sixfold- and fourfold- degenerate points locating at high symmetric points near the Fermi energy, which are identified as the underlying reason of the enhanced catalytic ability in C12A7-based catalysts. The multiple-fold fermions exhibit much longer Fermi arcs on the (001) surface than traditional Weyl/Dirac fermions, the surface is thus highly chemical active and possesses a low Gibbs free energy for the hydrogen evolution reaction. The underlying relationship between catalytic performance and the topological surface state is explicitly verified by artificially hole doping, external strain and similar electride without the Fermi arcs, where the Gibbs free energies are significantly increased when the Fermi arcs is shifted to higher energy level. This work offers a guiding principle for understanding catalytic nature of electrides and the topological quantum catalysts.

cond-mat.mtrl-sci↗
arXiv2021

A rigorous and efficient approach to finding and quantifying symmetries in complex networks

Symmetries are fundamental to dynamical processes in complex networks such as cluster synchronization, which have attracted a great deal of current research. Finding symmetric nodes in large complex networks, however, has relied on automorphism groups in algebraic group theory, which are solvable in quasipolynomial time. We articulate a conceptually appealing and computationally extremely efficient approach to finding and characterizing all symmetric nodes by introducing a structural position vector (SPV) for each and every node in the network. We prove mathematically that nodes with the identical SPV are symmetrical to each other. Utilizing six representative complex networks from the real world, we demonstrate that all symmetric nodes can be found in linear time, and the SPVs can not only characterize the similarity of nodes but also quantify the nodal influences in spreading dynamics on the network. Our SPV-based framework, in additional to being rigorously justified, provides a physically intuitive way to uncover, understand and exploit symmetric structures in complex networks.

physics.soc-ph↗
arXiv2021

Low-Dose CT Denoising Using a Structure-Preserving Kernel Prediction Network

Low-dose CT has been a key diagnostic imaging modality to reduce the potential risk of radiation overdose to patient health. Despite recent advances, CNN-based approaches typically apply filters in a spatially invariant way and adopt similar pixel-level losses, which treat all regions of the CT image equally and can be inefficient when fine-grained structures coexist with non-uniformly distributed noises. To address this issue, we propose a Structure-preserving Kernel Prediction Network (StructKPN) that combines the kernel prediction network with a structure-aware loss function that utilizes the pixel gradient statistics and guides the model towards spatially-variant filters that enhance noise removal, prevent over-smoothing and preserve detailed structures for different regions in CT imaging. Extensive experiments demonstrated that our approach achieved superior performance on both synthetic and non-synthetic datasets, and better preserves structures that are highly desired in clinical screening and low-dose protocol optimization.

eess.IV↗
arXiv2021

Anisotropic Berry phase in the Dirac nodal-line semimetal ZrSiS: The effect of spin-orbit coupling

The topological nodal-line semimetals (NLSMs) possess a loop of Dirac nodes in the k space with linear dispersion, different from the point nodes in Dirac/Weyl semimetals. While the quantum transport associated with the topologically nontrivial Dirac fermions has been investigated extensively, features uniquely associated with the extended nodal lines remain to be demonstrated. Here, we investigate the quantum oscillations (QOs) in the nodal-line semimetal ZrSiS, with the electron transport along the c axis, and magnetic field rotating in the ab plane. The extremal orbits identified through the field orientation dependence of the QOs interlock with the nodal line, leading to a nonzero Berry phase. Most importantly, the Berry phase shows a significant dependence on the magnetic field orientation, which we argue to be due to the finite spin-orbit coupling gap. Our results demonstrate the importance of the spin-orbit coupling and the nodal-line dispersion in understanding the quantum transport of NLSMs.

cond-mat.mtrl-sci↗
arXiv2021

The relative importance of structure and dynamics on node influence in reversible spreading processes

The reversible spreading processes with repeated infection widely exist in nature and human society, such as gonorrhea propagation and meme spreading. Identifying influential spreaders is an important issue in the reversible spreading dynamics on complex networks, which has been given much attention. Except for structural centrality, the nodes' dynamical states play a significant role in their spreading influence in the reversible spreading processes. By integrating the number of outgoing edges and infection risks of node's neighbors into structural centrality, a new measure for identifying influential spreaders is articulated which considers the relative importance of structure and dynamics on node influence. The number of outgoing edges and infection risks of neighbors represent the positive effect of the local structural characteristic and the negative effect of the dynamical states of nodes in identifying influential spreaders, respectively. We find that an appropriate combination of these two characteristics can greatly improve the accuracy of the proposed measure in identifying the most influential spreaders. Notably, compared with the positive effect of the local structural characteristic, slightly weakening the negative effect of dynamical states of nodes can make the proposed measure play the best performance. Quantitatively understanding the relative importance of structure and dynamics on node influence provides a significant insight into identifying influential nodes in the reversible spreading processes.

physics.soc-ph↗