A5013831155- 2D Materials and Applications
- Topological Materials and Phenomena
- Topic Modeling
- Advanced Condensed Matter Physics
- Natural Language Processing Techniques
- Graphene research and applications
- Inorganic Chemistry and Materials
- Advanced Image Processing Techniques
- Phase-change materials and chalcogenides
- Multimodal Machine Learning Applications
- Enhanced Oil Recovery Techniques
- Seismic Imaging and Inversion Techniques
- Advanced Optical Sensing Technologies
- Algebraic structures and combinatorial models
- Rare-earth and actinide compounds
- Advanced Topology and Set Theory
- Geomagnetism and Paleomagnetism Studies
- Advanced Banach Space Theory
- Advanced Thermoelectric Materials and Devices
- CO2 Sequestration and Geologic Interactions
- Physics of Superconductivity and Magnetism
- Advanced Topics in Algebra
- Mathematical and Theoretical Analysis
- Machine Learning in Materials Science
- Chalcogenide Semiconductor Thin Films
SLAC National Accelerator Laboratory
2017-2023
Stanford University
2017-2023
Meta (Israel)
2018-2019
Chinese Academy of Sciences
2012
Tianjin University
2003
The excitonic insulator (EI) is a Bose-Einstein condensation (BEC) of excitons bound by electron-hole interaction in solid, which could support high-temperature BEC transition. material realization EI has been challenged the difficulty distinguishing it from conventional charge density wave (CDW) state. In limit, preformed exciton gas phase hallmark to distinguish CDW, yet direct experimental evidence lacking. Here we report distinct correlated beyond 2×2 CDW ground state emerging monolayer...
In the past decade, topological materials have been continuously attracting interest of condensed-matter physics community because their unique band structures and transport properties. Recently, ZrTe${}_{5}$ is becoming a promising platform to study phase transitions, as it could possibly be 3D Dirac semimetal, weak insulator (TI), or strong TI, which are distinguished by whether there finite gap surface state (TSS). This paper performs systematic high-momentum-resolution photoemission on...
We study the microscopic origins of photocurrent generation in topological insulator Bi$_2$Se$_3$ via time- and angle-resolved photoemission spectroscopy. image unoccupied band structure as it evolves following a circularly polarized optical excitation observe an asymmetric electron population momentum space, which is spectroscopic signature photocurrent. By analyzing rise times we identify occupied electronic states are coupled by excitation. conclude that photocurrents can only be excited...
Deep convolutional neural networks (DCNNs) have recently demonstrated high-quality results in single-image super-resolution (SR). DCNNs often suffer from over-parametrization and large amounts of redundancy, which inefficient inference high memory usage, preventing massive applications on mobile devices. As a way to significantly reduce model size computation time, binarized network has only been shown excel semantic-level tasks such as image classification recognition. However, little...
Relational database management systems (RDBMSs) are powerful because they able to optimize and answer queries against any relational database. A natural language interface (NLI) for a database, on the other hand, is tailored support that specific In this work, we introduce general purpose transfer-learnable NLI with goal of learning one model can be used as We adopt data principle separating its schema, but additional idiosyncrasy complexity languages. Specifically, an automatic annotation...
A natural language interface (NLI) to structured query is intriguing due its wide industrial applications and high economical values. In this work, we tackle the problem of domain adaptation for NLI with limited data on target domain. Two important approaches are considered: (a) effective general-knowledge-learning source semantic parsing, (b) augmentation We present a Structured Query Inference Network (SQIN) enhance learning adaptation, by separating schema information from NL decoding SQL...
Deep convolutional neural networks (DCNNs) have recently demonstrated high-quality results in single-image super-resolution (SR). DCNNs often suffer from over-parametrization and large amounts of redundancy, which inefficient inference high memory usage, preventing massive applications on mobile devices. As a way to significantly reduce model size computation time, binarized network has only been shown excel semantic-level tasks such as image classification recognition. However, little...
A natural language interface (NLI) to structured query is intriguing due its wide industrial applications and high economical values. In this work, we tackle the problem of domain adaptation for NLI with limited data on target domain. Two important approaches are considered: (a) effective general-knowledge-learning source semantic parsing, (b) augmentation We present a Structured Query Inference Network (SQIN) enhance learning adaptation, by separating schema information from NL decoding SQL...