arXiv subjects

Ying Ding

Publications and source records attributed to Ying Ding.

At least 127 records · Page 7Linked to original sources

arXiv2020

A Simultaneous Inference Procedure to Identify Subgroups from RCTs with Survival Outcomes: Application to Analysis of AMD Progression Studies

With the uptake of targeted therapies, instead of the "one-fits-all" approach, modern randomized clinical trials (RCTs) often aim to develop treatments that target a subgroup of patients. Motivated by analyzing the Age-Related Eye Disease Study (AREDS) data, a large RCT to study the efficacy of nutritional supplements in delaying the progression of an eye disease, age-related macular degeneration (AMD), we develop a simultaneous inference procedure to identify and infer subgroups with differential treatment efficacy in RCTs with survival outcome. Specifically, we formulate the multiple testing problem through contrasts and construct their simultaneous confidence intervals, which control both within- and across- marker multiplicity appropriately. Realistic simulations are conducted using real genotype data to evaluate the method performance under various scenarios. The method is then applied to AREDS to assess the efficacy of antioxidants and zinc combination in delaying AMD progression. Multiple gene regions including ESRRB-VASH1 on chromosome 14 have been identified with subgroups showing differential efficacy. We further validate our findings in an independent subsequent RCT, AREDS2, by discovering consistent differential treatment responses in the targeted and non-targeted subgroups been identified from AREDS. This simultaneous inference approach provides a step forward to confidently identify and infer subgroups in modern drug development.

stat.AP↗

arXiv2019

Co-contributorship Network and Division of Labor in Individual Scientific Collaborations

Collaborations are pervasive in current science. Collaborations have been studied and encouraged in many disciplines. However, little is known how a team really functions from the detailed division of labor within. In this research, we investigate the patterns of scientific collaboration and division of labor within individual scholarly articles by analyzing their co-contributorship networks. Co-contributorship networks are constructed by performing the one-mode projection of the author-task bipartite networks obtained from 138,787 papers published in PLoS journals. Given a paper, we define three types of contributors: Specialists, Team-players, and Versatiles. Specialists are those who contribute to all their tasks alone; team-players are those who contribute to every task with other collaborators; and versatiles are those who do both. We find that team-players are the majority and they tend to contribute to the five most common tasks as expected, such as "data analysis" and "performing experiments". The specialists and versatiles are more prevalent than expected by a random-graph null model. Versatiles tend to be senior authors associated with funding and supervisions. Specialists are associated with two contrasting roles: the supervising role as team leaders or marginal and specialized contributions.

cs.SI↗

arXiv2019

Copula-based Semiparametric Regression Method for Bivariate Data under General Interval Censoring

This research is motivated by discovering and underpinning genetic causes for the progression of a bilateral eye disease, Age-related Macular Degeneration (AMD), of which the primary outcomes, progression times to late-AMD, are bivariate and interval-censored due to intermittent assessment times. We propose a novel class of copula-based semiparametric transformation models for bivariate data under general interval censoring, which includes the case 1 interval censoring (current status data) and case 2 interval censoring. Specifically, the joint likelihood is modeled through a two-parameter Archimedean copula, which can flexibly characterize the dependence between the two margins in both tails. The marginal distributions are modeled through semiparametric transformation models using sieves, with the proportional hazards or odds model being a special case. We develop a computationally efficient sieve maximum likelihood estimation procedure for the unknown parameters, together with a generalized score test for the regression parameter(s). For the proposed sieve estimators of finite-dimensional parameters, we establish their asymptotic normality and efficiency. Extensive simulations are conducted to evaluate the performance of the proposed method in finite samples. Finally, we apply our method to a genome-wide analysis of AMD progression using the Age-Related Eye Disease Study (AREDS) data, to successfully identify novel risk variants associated with the disease progression. We also produce predicted joint and conditional progression-free probabilities, for patients with different genetic characteristics.

stat.ME↗

arXiv2019

Analyzing Linguistic Complexity and Scientific Impact

The number of publications and the number of citations received have become the most common indicators of scholarly success. In this context, scientific writing increasingly plays an important role in scholars' scientific careers. To understand the relationship between scientific writing and scientific impact, this paper selected 12 variables of linguistic complexity as a proxy for depicting scientific writing. We then analyzed these features from 36,400 full-text Biology articles and 1,797 full-text Psychology articles. These features were compared to the scientific impact of articles, grouped into high, medium, and low categories. The results suggested no practical significant relationship between linguistic complexity and citation strata in either discipline. This suggests that textual complexity plays little role in scientific impact in our data sets.

cs.CL↗

arXiv2019

Visualizing the charge order and topological defects in an overdoped (Bi,Pb)$_2$Sr$_2$CuO$_{6+x}$ superconductor

Electronic charge order is a symmetry breaking state in high-$T_\mathrm{c}$ cuprate superconductors. In scanning tunneling microscopy, the detected charge-order-induced modulation is an electronic response of the charge order. For an overdoped (Bi,Pb)$_2$Sr$_2$CuO$_{6+x}$ sample, we apply scanning tunneling microscopy to explore local properties of the charge order. The ordering wavevector is non-dispersive with energy, which can be confirmed and determined. By extracting its order-parameter field, we identify dislocations in the stripe structure of the electronic modulation, which correspond to topological defects with an integer winding number of $\pm 1$. Through differential conductance maps over a series of reduced energies, the development of different response of the charge order is observed and a spatial evolution of topological defects is detected. The intensity of charge-order-induced modulation increases with energy and reaches its maximum when approaching the pseudogap energy. In this evolution, the topological defects decrease in density and migrate in space. Furthermore, we observe appearance and disappearance of closely spaced pairs of defects as energy changes. Our experimental results could inspire further studies of the charge order in both high-$T_\mathrm{c}$ cuprate superconductors and other charge density wave materials.

cond-mat.supr-con↗

arXiv2019

Evolution of Incommensurate Superstructure and Electronic Structure with Pb Substitution in (Bi$_{2-x}$Pb$_{x}$)Sr$_2$CaCu$_2$O$_{8+δ}$ Superconductors

High-quality Bi$_{2-x}$Pb$_{x}$Sr$_2$CaCu$_2$O$_{8+δ}$ (Bi2212) single crystals have been successfully grown by the traveling solvent floating zone technique with a wide range of Pb substitution ($x=0-0.8$). The samples are characterized by transmission electron microscope (TEM) and measured by high resolution laser-based angle-resolved photoemission spectroscopy (ARPES) with different photon energies. A systematic evolution of the electronic structure and superstructure with Pb substitution has been revealed for the first time. The superstructure shows a significant change with Pb substitution and the incommensurate modulation vector ($\textbf{Q}$) decreases with increasing Pb substitution. In the meantime, the superstructure intensity from ARPES measurements also decreases dramatically with increasing Pb concentration. The superstructure in Bi2212 can be effectively suppressed by Pb substitution and it nearly disappears with a Pb substitution of $x=0.8$. We also find that the superstructure bands in ARPES measurements depend sensitively on the photon energy of lasers used; they can become even stronger than the main band when using a laser photon energy of 10.897 eV. These results provide important information on the origin of the incommensurate superstructure and its control and suppression in bismuth-based high temperature superconductors.

cond-mat.supr-con↗

arXiv2019

edge2vec: Representation learning using edge semantics for biomedical knowledge discovery

Representation learning provides new and powerful graph analytical approaches and tools for the highly valued data science challenge of mining knowledge graphs. Since previous graph analytical methods have mostly focused on homogeneous graphs, an important current challenge is extending this methodology for richly heterogeneous graphs and knowledge domains. The biomedical sciences are such a domain, reflecting the complexity of biology, with entities such as genes, proteins, drugs, diseases, and phenotypes, and relationships such as gene co-expression, biochemical regulation, and biomolecular inhibition or activation. Therefore, the semantics of edges and nodes are critical for representation learning and knowledge discovery in real world biomedical problems. In this paper, we propose the edge2vec model, which represents graphs considering edge semantics. An edge-type transition matrix is trained by an Expectation-Maximization approach, and a stochastic gradient descent model is employed to learn node embedding on a heterogeneous graph via the trained transition matrix. edge2vec is validated on three biomedical domain tasks: biomedical entity classification, compound-gene bioactivity prediction, and biomedical information retrieval. Results show that by considering edge-types into node embedding learning in heterogeneous graphs, \textbf{edge2vec}\ significantly outperforms state-of-the-art models on all three tasks. We propose this method for its added value relative to existing graph analytical methodology, and in the real world context of biomedical knowledge discovery applicability.

cs.IR↗

arXiv2019

Quasiparticle interference and charge order in a heavily overdoped non-superconducting cuprate

One of the key issues in unraveling the mystery of high Tc superconductivity in the cuprates is to understand the normal state outside the superconducting dome. Here we perform scanning tunneling microscopy and spectroscopy measurements on a heavily overdoped, non-superconducting (Bi,Pb)2Sr2CuO6+x cuprate. Spectroscopic imaging reveals dispersive quasiparticle interferences and the Fourier transforms uncover the evolution of momentum space topology. More interestingly, we observe nanoscale patches of static charge order with sqrt(2)*sqrt(2) periodicity. Both the dispersive quasiparticle interference and static charge order can be qualitatively explained by theoretical calculations, which reveal the unique electronic structure of strongly overdoped cuprate.

cond-mat.supr-con↗

arXiv2019

Gene-based Association Analysis for Bivariate Time-to-event Data through Functional Regression with Copula Models

Several gene-based association tests for time-to-event traits have been proposed recently, to detect whether a gene region (containing multiple variants), as a set, is associated with the survival outcome. However, for bivariate survival outcomes, to the best of our knowledge, there is no statistical method that can be directly applied for gene-based association analysis. Motivated by a genetic study to discover gene regions associated with the progression of a bilateral eye disease, Age-related Macular Degeneration (AMD), we implement a novel functional regression method under the copula framework. Specifically, the effects of variants within a gene region are modeled through a functional linear model, which then contributes to the marginal survival functions within the copula. Generalized score test and likelihood ratio test statistics are derived to test for the association between bivariate survival traits and the genetic region. Extensive simulation studies are conducted to evaluate the type-I error control and power performance of the proposed approach, with comparisons to several existing methods for a single survival trait, as well as the marginal Cox functional regression model using the robust sandwich estimator for bivariate survival traits. Finally, we apply our method to a large AMD study, the Age-related Eye Disease Study (AREDS), to identify gene regions that are associated with AMD progression.

stat.AP↗

arXiv2019

Emergence of Superconductivity from Fully Incoherent Normal State in an Iron-Based Superconductor (Ba$_{0.6}$K$_{0.4}$)Fe$_2$As$_2$

In unconventional superconductors, it is generally believed that understanding the physical properties of the normal state is a pre-requisite for understanding the superconductivity mechanism. In conventional superconductors like niobium or lead, the normal state is a Fermi liquid with a well-defined Fermi surface and well-defined quasipartcles along the Fermi surface. Superconductivity is realized in this case by the Fermi surface instability in the superconducting state and the formation and condensation of the electron pairs (Cooper pairing). The high temperature cuprate superconductors, on the other hand, represent another extreme case that superconductivity can be realized in the underdoped region where there is neither well-defined Fermi surface due to the pseudogap formation nor quasiparticles near the antinodal regions in the normal state. Here we report a novel scenario that superconductivity is realized in a system with well-defined Fermi surface but without quasiparticles along the Fermi surface in the normal state. High resolution laser-based angle-resolved photoemission measurements have been performed on an optimally-doped iron-based superconductor (Ba$_{0.6}$K$_{0.4}$)Fe$_2$As$_2$. We find that, while sharp superconducting coherence peaks emerge in the superconducting state on the hole-like Fermi surface sheets, no quasiparticle peak is present in the normal state. Its electronic behaviours deviate strongly from a Fermi liquid system. The superconducting gap of such a system exhibits an unusual temperature dependence that it is nearly a constant in the superconducting state and abruptly closes at T$_c$. These observations have provided a new platform to study unconventional superconductivity in a non-Fermi liquid system.

cond-mat.supr-con↗

arXiv2019

Disappearance of Superconductivity and a Concomitant Lifshitz Transition in Heavily-Overdoped Bi2Sr2CuO6 Superconductor Revealed by Angle-Resolved Photoemission Spectroscopy

By partially doping Pb to effectively suppress the superstructure in single-layered cuprate Bi2Sr2CuO6+δ(Pb-Bi2201) and annealing them in vacuum or in high pressure oxygen atmosphere, a series of high quality Pb-Bi2201 single crystals are obtained with Tc covering from 17 K to non-supercondcuting in the overdoped region. High resolution angle resolved photoemission spectroscopy (ARPES) measurements are carried out on these samples to investigate the evolution of the Fermi surface topology with doping in the normal state. Clear and complete Fermi surface are observed and quantitatively analyzed in all these overdoped Pb-Bi2201 samples. A Lifshitz transition from hole-like Fermi surface to electron like Fermi surface with increasing doping is observed at a doping level of ~0.35. This transition coincides with the change that the sample undergoes from superconducting to non-superconducting states. Our results reveal the emergence of an electron-like Fermi surface and the existence of a Lifshitz transition in heavily overdoped Bi2201 samples. They provide important information in understanding the connection between the disappearance of superconductivity and the Lifshitz transition in the overdoped region.

cond-mat.supr-con↗

arXiv2018

Examining Scientific Writing Styles from the Perspective of Linguistic Complexity

Publishing articles in high-impact English journals is difficult for scholars around the world, especially for non-native English-speaking scholars (NNESs), most of whom struggle with proficiency in English. In order to uncover the differences in English scientific writing between native English-speaking scholars (NESs) and NNESs, we collected a large-scale data set containing more than 150,000 full-text articles published in PLoS between 2006 and 2015. We divided these articles into three groups according to the ethnic backgrounds of the first and corresponding authors, obtained by Ethnea, and examined the scientific writing styles in English from a two-fold perspective of linguistic complexity: (1) syntactic complexity, including measurements of sentence length and sentence complexity; and (2) lexical complexity, including measurements of lexical diversity, lexical density, and lexical sophistication. The observations suggest marginal differences between groups in syntactical and lexical complexity.

cs.CL↗

arXiv2018

Evidence for Multiple Underlying Fermi Surface and Isotropic Energy Gap in the Cuprate Parent Compound Ca$_2$CuO$_2$Cl$_2$

The parent compounds of the high-temperature cuprate superconductors are Mott insulators. It has been generally agreed that understanding the physics of the doped Mott insulators is essential to understanding the mechanism of high temperature superconductivity. A natural starting point is to elucidate the basic electronic structure of the parent compound. Here we report comprehensive high resolution angle-resolved photoemission measurements on Ca$_2$CuO$_2$Cl$_2$, a Mott insulator and a prototypical parent compound of the cuprates. Multiple underlying Fermi surface sheets are revealed for the first time. The high energy waterfall-like band dispersions exhibit different behavior near the nodal and antinodal regions. Two distinct energy scales are identified: a d-wave-like low energy peak dispersion and a nearly isotropic lower Hubbard band gap. These observations provide new information on the electronic structure of the cuprate parent compound, which is important for understanding the anomalous physical properties and superconductivity mechanism of the high temperature cuprate superconductors.

cond-mat.supr-con↗

arXiv2018

Electronic effect of doped oxygen atoms in Bi2201 superconductors determined by scanning tunneling microscopy

The oxygen dopants are essential in tuning electronic properties of Bi$_2$Sr$_2$Ca$_{n-1}$Cu$_n$O$_{2n+4+δ}$ superconductors. Here we apply the technique of scanning tunneling microscopy and spectroscopy to study the influence of oxygen dopants in an optimally doped Bi$_2$Sr$_{2-x}$La$_x$CuO$_{6+δ}$ and an overdoped Bi$_{2-y}$Pb$_y$Sr$_2$CuO$_{6+δ}$. In both samples, we find that interstitial oxygen atoms on the SrO layers dominate over the other two forms of oxygen dopants, oxygen vacancies on the SrO layers and interstitial oxygen atoms on the BiO layers. The hole doping is estimated from the oxygen concentration, as compared to the result extracted from the measured Fermi surface. The precise spatial location is employed to obtain a negative correlation between the oxygen dopants and the inhomogeneous pseudogap.

cond-mat.supr-con↗

arXiv2018

The study of electronic nematicity in an overdoped (Bi, Pb)$_2$Sr$_2$CuO$_{6+δ}$ superconductor using scanning tunneling spectroscopy

The pseudogap (PG) state and its related intra-unit-cell symmetry breaking remain the focus in the research of cuprate superconductors. Although the nematicity has been studied in Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$, especially underdoped samples, its behavior in other cuprates and different doping regions is still unclear. Here we apply a scanning tunneling microscope to explore an overdoped (Bi, Pb)$_2$Sr$_2$CuO$_{6+δ}$ with a large Fermi surface (FS). The establishment of a nematic order and its real-space distribution is visualized as the energy scale approaches the PG.

cond-mat.supr-con↗

arXiv2018

Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews

Although latent factor models (e.g., matrix factorization) achieve good accuracy in rating prediction, they suffer from several problems including cold-start, non-transparency, and suboptimal recommendation for local users or items. In this paper, we employ textual review information with ratings to tackle these limitations. Firstly, we apply a proposed aspect-aware topic model (ATM) on the review text to model user preferences and item features from different aspects, and estimate the aspect importance of a user towards an item. The aspect importance is then integrated into a novel aspect-aware latent factor model (ALFM), which learns user's and item's latent factors based on ratings. In particular, ALFM introduces a weighted matrix to associate those latent factors with the same set of aspects discovered by ATM, such that the latent factors could be used to estimate aspect ratings. Finally, the overall rating is computed via a linear combination of the aspect ratings, which are weighted by the corresponding aspect importance. To this end, our model could alleviate the data sparsity problem and gain good interpretability for recommendation. Besides, an aspect rating is weighted by an aspect importance, which is dependent on the targeted user's preferences and targeted item's features. Therefore, it is expected that the proposed method can model a user's preferences on an item more accurately for each user-item pair locally. Comprehensive experimental studies have been conducted on 19 datasets from Amazon and Yelp 2017 Challenge dataset. Results show that our method achieves significant improvement compared with strong baseline methods, especially for users with only few ratings. Moreover, our model could interpret the recommendation results in depth.

cs.IR↗

arXiv2017

Evidence of Electron-Hole Imbalance in WTe2 from High-Resolution Angle-Resolved Photoemission Spectroscopy

WTe2 has attracted a great deal of attention because it exhibits extremely large and nonsaturating magnetoresistance. The underlying origin of such a giant magnetoresistance is still under debate. Utilizing laser-based angle-resolved photoemission spectroscopy with high energy and momentum resolutions, we reveal the complete electronic structure of WTe2. This makes it possible to determine accurately the electron and hole concentrations and their temperature dependence. We find that, with increasing the temperature, the overall electron concentration increases while the total hole concentration decreases. It indicates that the electron-hole compensation, if it exists, can only occur in a narrow temperature range, and in most of the temperature range there is an electron-hole imbalance. Our results are not consistent with the perfect electron-hole compensation picture that is commonly considered to be the cause of the unusual magnetoresistance in WTe2. We identified a flat band near the Brillouin zone center that is close to the Fermi level and exhibits a pronounced temperature dependence. Such a flat band can play an important role in dictating the transport properties of WTe2. Our results provide new insight on understanding the origin of the unusual magnetoresistance in WTe2.

cond-mat.mtrl-sci↗

arXiv2017

Electronic structure of heavy fermion system CePt2In7 from angle-resolved photoemission spectroscopy

We have carried out high-resolution angle-resolved photoemission measurements on the Cebased heavy fermion compound CePt2In7 that exhibits stronger two-dimensional character than the prototypical heavy fermion system CeCoIn5. Multiple Fermi surface sheets and a complex band structure are clearly resolved. We have also performed detailed band structure calculations on CePt2In7. The good agreement found between our measurements and the calculations suggests that the band renormalization effect is rather weak in CePt2In7. A comparison of the common features of the electronic structure of CePt2In7 and CeCoIn5 indicates that CeCoIn5 shows a much stronger band renormalization effect than CePt2In7. These results provide new information for understanding the heavy fermion behaviors and unconventional superconductivity in Ce-based heavy fermion systems.

cond-mat.str-el↗