arXiv subjects

Guohua Wang

Publications and source records attributed to Guohua Wang.

22 records · Page 2Linked to original sources

arXiv2019

NbSeTe -A New Layered Transition Metal Dichalcogenide Superconductor

Transition metal dichalcogenides (TMDCs) usually exhibit layered polytypic structures due to the weak interlayer coupling. 2H-NbSe2 is one of the most widely studied in the pristine TMDC family due to its high superconducting transition temperature (Tc = 7.3K) and the occurrence of a charge-density wave (CDW) order below 33 K. The coexistence of CDW with superconductivity poses an intriguing open question about the relationship between Fermi surface nesting and Cooper pairing. Past studies of this issue have mostly been focused on doping 2H-NbSe2 by 3d transition metals without significantly changing its crystal structure. Here we replaced the Se by Te in 2H-NbSe2 in order to design a new 1T polytype layered TMDC NbSeTe, which adopts a trigonal structure with space group P-3m1. We successfully grew large size and high-quality single crystals of 1T-NbSeTe via the vapor transport method using I2 as the transport agent. Temperature-dependent resistivity and specific heat data revealed a bulk Tc at 1.3 K, which is the first observation of superconductivity in pure 1T-NbSeTe phase. This compound enlarged the family of superconducting TMDCs and provides an opportunity to study the interplay between CDW and superconductivity in the trigonal structure.

cond-mat.supr-con↗

arXiv2019

The Unusual Suppression of Superconducting Transition Temperature in Double-Doping 2H-NbSe$_2$

2H-NbSe2 is one of the most widely researched transition metal dichalcogenide (TMD) superconductors, which undergoes charge-density wave (CDW) transition at TCDW about 33 K and superconducting transition at Tc of 7.3 K. To explore the relation between its superconductivity and Fermi surface nesting, we combined S substitution with Cu intercalation in 2H-NbSe2 to make CuxNbSe2-ySy. Upon systematic substitution of S and intercalation of Cu ions into 2H-NbSe2, we found that when the Cu and S contents increases, the Tc decreases in CuxNbSe2-ySy. While at higher x and y values, Tc keeps a constant value near 2 K, which is not commonly observed for a layered TMD. For comparison, we found the simultaneous substitution of Nb by Cu and Se by S in CuxNb1-xSe2-ySy lowered the Tc substantially faster. We construct a superconducting phase diagrams for our double-doping compounds in contrast with the related single-ions doping systems.

cond-mat.supr-con↗

arXiv2019

A Dynamic Evolutionary Framework for Timeline Generation based on Distributed Representations

Given the collection of timestamped web documents related to the evolving topic, timeline summarization (TS) highlights its most important events in the form of relevant summaries to represent the development of a topic over time. Most of the previous work focuses on fully-observable ranking models and depends on hand-designed features or complex mechanisms that may not generalize well. We present a novel dynamic framework for evolutionary timeline generation leveraging distributed representations, which dynamically finds the most likely sequence of evolutionary summaries in the timeline, called the Viterbi timeline, and reduces the impact of events that irrelevant or repeated to the topic. The assumptions of the coherence and the global view run through our model. We explore adjacent relevance to constrain timeline coherence and make sure the events evolve on the same topic with a global view. Experimental results demonstrate that our framework is feasible to extract summaries for timeline generation, outperforms various competitive baselines, and achieves the state-of-the-art performance as an unsupervised approach.

cs.CL↗

arXiv2015

A Robust Approach for Identifying Gene-Environment Interactions for Prognosis

For many complex diseases, prognosis is of essential importance. It has been shown that, beyond the main effects of genetic (G) and environmental (E) risk factors, the gene-environment (G$\times$E) interactions also play a critical role. In practice, the prognosis outcome data can be contaminated, and most of the existing methods are not robust to data contamination. In the literature, it has been shown that even a single contaminated observation can lead to severely biased model estimation. In this study, we describe prognosis using an accelerated failure time (AFT) model. An exponential squared loss is proposed to accommodate possible data contamination. A penalization approach is adopted for regularized estimation and marker selection. The proposed method is realized using an effective coordinate descent (CD) and minorization maximization (MM) algorithm. Simulation shows that without contamination, the proposed method has performance comparable to or better than the unrobust alternative. With contamination, it outperforms the unrobust alternative and, under certain scenarios, can be superior to the robust method based on quantile regression. The proposed method is applied to the analysis of TCGA (The Cancer Genome Atlas) lung cancer data. It identifies interactions different from those using the alternatives. The identified marker have important implications and satisfactory stability.

stat.AP↗