arXiv subjects

Danqing Wang

Publications and source records attributed to Danqing Wang.

47 records · Page 3Linked to original sources

arXiv2020

Imaging moiré flat bands in 3D reconstructed WSe2/WS2 superlattices

Moiré superlattices in transition metal dichalcogenide (TMD) heterostructures can host novel correlated quantum phenomena due to the interplay of narrow moiré flat bands and strong, long-range Coulomb interactions1-5. However, microscopic knowledge of the atomically-reconstructed moiré superlattice and resulting flat bands is still lacking, which is critical for fundamental understanding and control of the correlated moiré phenomena. Here we quantitatively study the moiré flat bands in three-dimensional (3D) reconstructed WSe2/WS2 moiré superlattices by comparing scanning tunneling spectroscopy (STS) of high quality exfoliated TMD heterostructure devices with ab initio simulations of TMD moiré superlattices. A strong 3D buckling reconstruction accompanied by large in-plane strain redistribution is identified in our WSe2/WS2 moiré heterostructures. STS imaging demonstrates that this results in a remarkably narrow and highly localized K-point moiré flat band at the valence band edge of the heterostructure. A series of moiré flat bands are observed at different energies that exhibit varying degrees of localization. Our observations contradict previous simplified theoretical models but agree quantitatively with ab initio simulations that fully capture the 3D structural reconstruction. Here the strain redistribution and 3D buckling dominate the effective moiré potential and result in moiré flat bands at the Brillouin zone K points.

cond-mat.mes-hall↗

arXiv2020

Heterogeneous Graph Neural Networks for Extractive Document Summarization

As a crucial step in extractive document summarization, learning cross-sentence relations has been explored by a plethora of approaches. An intuitive way is to put them in the graph-based neural network, which has a more complex structure for capturing inter-sentence relationships. In this paper, we present a heterogeneous graph-based neural network for extractive summarization (HeterSumGraph), which contains semantic nodes of different granularity levels apart from sentences. These additional nodes act as the intermediary between sentences and enrich the cross-sentence relations. Besides, our graph structure is flexible in natural extension from a single-document setting to multi-document via introducing document nodes. To our knowledge, we are the first one to introduce different types of nodes into graph-based neural networks for extractive document summarization and perform a comprehensive qualitative analysis to investigate their benefits. The code will be released on Github

cs.CL↗

arXiv2020

Extractive Summarization as Text Matching

This paper creates a paradigm shift with regard to the way we build neural extractive summarization systems. Instead of following the commonly used framework of extracting sentences individually and modeling the relationship between sentences, we formulate the extractive summarization task as a semantic text matching problem, in which a source document and candidate summaries will be (extracted from the original text) matched in a semantic space. Notably, this paradigm shift to semantic matching framework is well-grounded in our comprehensive analysis of the inherent gap between sentence-level and summary-level extractors based on the property of the dataset. Besides, even instantiating the framework with a simple form of a matching model, we have driven the state-of-the-art extractive result on CNN/DailyMail to a new level (44.41 in ROUGE-1). Experiments on the other five datasets also show the effectiveness of the matching framework. We believe the power of this matching-based summarization framework has not been fully exploited. To encourage more instantiations in the future, we have released our codes, processed dataset, as well as generated summaries in https://github.com/maszhongming/MatchSum.

cs.CL↗

arXiv2019

Moiré lattice-induced formation and tuning of hybrid dipolar excitons in twisted WS$_2$/MoSe$_2$ heterobilayers

Moiré superlattices formed in van der Waals bilayers have enabled the creation and manipulation of new quantum states, as is exemplified by the discovery of superconducting and correlated insulating states in twisted bilayer graphene near the magic angle. Twisted bilayer semiconductors may lead to tunable exciton lattices and topological states, yet signatures of moiré excitons have been reported only in closely angularly-aligned bilayers. Here we report tuning of moiré lattice in WS$_{2}$ /MoSe$_{2}$ bilayers over a wide range of twist angles, leading to the continuous tuning of moiré lattice induced interlayer excitons and their hybridization with optically bright intralayer excitons. A pronounced revival of the hybrid excitons takes place near commensurate twist angles, 21.8°and 38.2°, due to interlayer tunneling between states connected by a moiré reciprocal lattice vector. From the angle dependence, we obtain the effective mass of the interlayer excitons and the electron inter-layer tunneling strength. These findings pave the way for understanding and engineering rich moiré-lattice induced phenomena in angle-twisted semiconductor van dar Waals heterostructures.

cond-mat.mes-hall↗

arXiv2019

Optical detection of Mott and generalized Wigner crystal states in WSe2/WS2 moiré superlattices

Moiré superlattices are emerging as a new route for engineering strongly correlated electronic states in two-dimensional van der Waals heterostructures, as recently demonstrated in the correlated insulating and superconducting states in magic-angle twisted bilayer graphene and ABC trilayer graphene/boron nitride moiré superlattices. Transition metal dichalcogenide (TMDC) moiré heterostructures provide another exciting model system to explore correlated quantum phenomena, with the addition of strong light-matter interactions and large spin-orbital coupling. Here we report the optical detection of strongly correlated phases in semiconducting WSe2/WS2 moiré superlattices. Our sensitive optical detection technique reveals a Mott insulator state at one hole per superlattice site (ν = 1), and surprising insulating phases at fractional filling factors ν = 1/3 and 2/3, which we assign to generalized Wigner crystallization on an underlying lattice. Furthermore, the unique spin-valley optical selection rules of TMDC heterostructures allow us to optically create and investigate low-energy spin excited states in the Mott insulator. We reveal an especially slow spin relaxation lifetime of many microseconds in the Mott insulating state, orders-of-magnitude longer than that of charge excitations. Our studies highlight novel correlated physics that can emerge in moiré superlattices beyond graphene.

cond-mat.mes-hall↗

arXiv2019

A Closer Look at Data Bias in Neural Extractive Summarization Models

In this paper, we take stock of the current state of summarization datasets and explore how different factors of datasets influence the generalization behaviour of neural extractive summarization models. Specifically, we first propose several properties of datasets, which matter for the generalization of summarization models. Then we build the connection between priors residing in datasets and model designs, analyzing how different properties of datasets influence the choices of model structure design and training methods. Finally, by taking a typical dataset as an example, we rethink the process of the model design based on the experience of the above analysis. We demonstrate that when we have a deep understanding of the characteristics of datasets, a simple approach can bring significant improvements to the existing state-of-the-art model.A

cs.CL↗

arXiv2019

Exploring Domain Shift in Extractive Text Summarization

Although domain shift has been well explored in many NLP applications, it still has received little attention in the domain of extractive text summarization. As a result, the model is under-utilizing the nature of the training data due to ignoring the difference in the distribution of training sets and shows poor generalization on the unseen domain. With the above limitation in mind, in this paper, we first extend the conventional definition of the domain from categories into data sources for the text summarization task. Then we re-purpose a multi-domain summarization dataset and verify how the gap between different domains influences the performance of neural summarization models. Furthermore, we investigate four learning strategies and examine their abilities to deal with the domain shift problem. Experimental results on three different settings show their different characteristics in our new testbed. Our source code including \textit{BERT-based}, \textit{meta-learning} methods for multi-domain summarization learning and the re-purposed dataset \textsc{Multi-SUM} will be available on our project: \url{http://pfliu.com/TransferSum/}.

cs.CL↗

arXiv2019

Searching for Effective Neural Extractive Summarization: What Works and What's Next

The recent years have seen remarkable success in the use of deep neural networks on text summarization. However, there is no clear understanding of \textit{why} they perform so well, or \textit{how} they might be improved. In this paper, we seek to better understand how neural extractive summarization systems could benefit from different types of model architectures, transferable knowledge and learning schemas. Additionally, we find an effective way to improve current frameworks and achieve the state-of-the-art result on CNN/DailyMail by a large margin based on our observations and analyses. Hopefully, our work could provide more clues for future research on extractive summarization.

cs.CL↗

arXiv2019

Resolving spin, valley, and moiré quasi-angular momentum of interlayer excitons in WSe2/WS2 heterostructures

Moiré superlattices provide a powerful way to engineer properties of electrons and excitons in two-dimensional van der Waals heterostructures. The moiré effect can be especially strong for interlayer excitons, where electrons and holes reside in different layers and can be addressed separately. In particular, it was recently proposed that the moiré superlattice potential not only localizes interlayer exciton states at different superlattice positions, but also hosts an emerging moiré quasi-angular momentum (QAM) that periodically switches the optical selection rules for interlayer excitons at different moiré sites. Here we report the observation of multiple interlayer exciton states coexisting in a WSe2/WS2 moiré superlattice and unambiguously determine their spin, valley, and moiré QAM through novel resonant optical pump-probe spectroscopy and photoluminescence excitation spectroscopy. We demonstrate that interlayer excitons localized at different moiré sites can exhibit opposite optical selection rules due to the spatially-varying moiré QAM. Our observation reveals new opportunities to engineer interlayer exciton states and valley physics with moiré superlattices for optoelectronic and valleytronic applications.

cond-mat.mes-hall↗

arXiv2018

Observation of Moiré Excitons in WSe2/WS2 Heterostructure Superlattices

Moiré superlattices provide a powerful tool to engineer novel quantum phenomena in two-dimensional (2D) heterostructures, where the interactions between the atomically thin layers qualitatively change the electronic band structure of the superlattice. For example, mini-Dirac points, tunable Mott insulator states, and the Hofstadter butterfly can emerge in different types of graphene/boron nitride moiré superlattices, while correlated insulating states and superconductivity have been reported in twisted bilayer graphene moiré superlattices. In addition to their dramatic effects on the single particle states, moiré superlattices were recently predicted to host novel excited states, such as moiré exciton bands. Here we report the first observation of moiré superlattice exciton states in nearly aligned WSe2/WS2 heterostructures. These moiré exciton states manifest as multiple emergent peaks around the original WSe2 A exciton resonance in the absorption spectra, and they exhibit gate dependences that are distinctly different from that of the A exciton in WSe2 monolayers and in large-twist-angle WSe2/WS2 heterostructures. The observed phenomena can be described by a theoretical model where the periodic moiré potential is much stronger than the exciton kinetic energy and creates multiple flat exciton minibands. The moiré exciton bands provide an attractive platform to explore and control novel excited state of matter, such as topological excitons and a correlated exciton Hubbard model, in transition metal dichalcogenides.

cond-mat.mes-hall↗

arXiv2014

Distinctive Signature of Indium Gallium Nitride Quantum Dot Lasing in Microdisks Cavities

Low threshold lasers realized within compact, high quality optical cavities enable a variety of nanophotonics applications. Gallium nitride (GaN) materials containing indium gallium nitride (InGaN) quantum dots and quantum wells offer an outstanding platform to study light matter interactions and realize practical devices such as efficient light emitting diodes and nanolasers. Despite progress in the growth and characterization of InGaN quantum dots, their advantages as the gain medium in low threshold lasers have not been clearly demonstrated. This work seeks to better understand the reasons for these limitations by focusing on the simpler, limited-mode microdisk cavities, and by carrying out comparisons of lasing dynamics in those cavities using varying gain media including InGaN quantum wells, fragmented quantum wells, and a combination of fragmented quantum wells with quantum dots. For each gain medium, we utilize the distinctive, high quality (Q~5500) modes of the cavities, and the change in the highest intensity mode as a function of pump power to better understand the dominant radiative processes. The variations of threshold power and lasing wavelength as a function of gain medium help us identify the possible limitations to lower-threshold lasing with quantum dot active medium. In addition, we have identified a distinctive lasing signature for quantum dot materials, which consistently lase at wavelengths shorter than the peak of the room temperature gain emission. These findings not only provide better understanding of lasing in nitride-based quantum dot cavity systems, but also shed insight into the more fundamental issues of light matter coupling in such systems.

physics.optics↗