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36 results on '"Shen, Jialiang"'

2. Exciton Assisted Deeply Subwavelength Nano-Photonics

Author

Ling, Haonan, Manna, Arnab, Shen, Jialiang, Tung, Ho-Ting, Sharp, David, Fröch, Johannes, Dai, Siyuan, Majumdar, Arka, and Davoyan, Artur R.

Subjects
Physics - Optics, Physics - Applied Physics
Abstract

The wave nature of light sets a fundamental diffraction limit that challenges confinement and control of light in nanoscale structures with dimensions significantly smaller than the wavelength. Here, we demonstrate van der Waals MoS_2 nano-photonic devices with dimensions as small as ~ \lambda/16 (~60 nm at 1000 nm excitation wavelength). This deep subwavelength light confinement is achieved by exploiting the coupling between MoS_2 excitons and photons. We validate deep subwavelength light control via far- and near-field measurements. Our near-field measurements reveal detailed imaging of excitation, evolution, and guidance of fields in MoS_2 nanodevices, whereas our far-field study examines highly confined integrated photonics. Exciton-driven nano-photonics at a fraction of a wavelength demonstrated here could dramatically reduce the size of integrated photonic devices and opto-electronic circuits with potential applications in optical information science and engineering., Comment: 39 pages, 32 figures

Published
2023
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3. A Machine Learning Approach for Predicting Human Preference for Graph Layouts

Author

Cai, Shijun, Hong, Seok-Hee, Shen, Jialiang, and Liu, Tongliang

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract

Understanding what graph layout human prefer and why they prefer is significant and challenging due to the highly complex visual perception and cognition system in human brain. In this paper, we present the first machine learning approach for predicting human preference for graph layouts. In general, the data sets with human preference labels are limited and insufficient for training deep networks. To address this, we train our deep learning model by employing the transfer learning method, e.g., exploiting the quality metrics, such as shape-based metrics, edge crossing and stress, which are shown to be correlated to human preference on graph layouts. Experimental results using the ground truth human preference data sets show that our model can successfully predict human preference for graph layouts. To our best knowledge, this is the first approach for predicting qualitative evaluation of graph layouts using human preference experiment data., Comment: 9 pages, 9 figures, PacificVis_Notes

Published
2021

4. ASDN: A Deep Convolutional Network for Arbitrary Scale Image Super-Resolution

Author

Shen, Jialiang, Wang, Yucheng, and Zhang, Jian

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Deep convolutional neural networks have significantly improved the peak signal-to-noise ratio of SuperResolution (SR). However, image viewer applications commonly allow users to zoom the images to arbitrary magnification scales, thus far imposing a large number of required training scales at a tremendous computational cost. To obtain a more computationally efficient model for arbitrary scale SR, this paper employs a Laplacian pyramid method to reconstruct any-scale high-resolution (HR) images using the high-frequency image details in a Laplacian Frequency Representation. For SR of small-scales (between 1 and 2), images are constructed by interpolation from a sparse set of precalculated Laplacian pyramid levels. SR of larger scales is computed by recursion from small scales, which significantly reduces the computational cost. For a full comparison, fixed- and any-scale experiments are conducted using various benchmarks. At fixed scales, ASDN outperforms predefined upsampling methods (e.g., SRCNN, VDSR, DRRN) by about 1 dB in PSNR. At any-scale, ASDN generally exceeds Meta-SR on many scales.

Published
2020

5. Deep Bi-Dense Networks for Image Super-Resolution

Author

Wang, Yucheng, Shen, Jialiang, and Zhang, Jian

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper proposes Deep Bi-Dense Networks (DBDN) for single image super-resolution. Our approach extends previous intra-block dense connection approaches by including novel inter-block dense connections. In this way, feature information propagates from a single dense block to all subsequent blocks, instead of to a single successor. To build a DBDN, we firstly construct intra-dense blocks, which extract and compress abundant local features via densely connected convolutional layers and compression layers for further feature learning. Then, we use an inter-block dense net to connect intra-dense blocks, which allow each intra-dense block propagates its own local features to all successors. Additionally, our bi-dense construction connects each block to the output, alleviating the vanishing gradient problems in training. The evaluation of our proposed method on five benchmark datasets shows that our DBDN outperforms the state of the art in SISR with a moderate number of network parameters., Comment: DICTA 2018

Published
2018

6. Phonon Polaritons in Monolayers of Hexagonal Boron Nitride

Author

Dai, Siyuan, Fang, Wenjing, Rivera, Nicholas, Stehle, Yijing, Jiang, Bor‐Yuan, Shen, Jialiang, Tay, Roland Yingjie, Ciccarino, Christopher J, Ma, Qiong, Rodan‐Legrain, Daniel, Jarillo‐Herrero, Pablo, Teo, Edwin Hang Tong, Fogler, Michael M, Narang, Prineha, Kong, Jing, and Basov, Dimitri N

Subjects
Quantum Physics, Physical Sciences, Condensed Matter Physics, monolayers, phonon polaritons, van der Waals materials, Chemical Sciences, Engineering, Nanoscience & Nanotechnology, Chemical sciences, Physical sciences
Abstract

Phonon polaritons in van der Waals materials reveal significant confinement accompanied with long propagation length: important virtues for tasks pertaining to the control of light and energy flow at the nanoscale. While previous studies of phonon polaritons have relied on relatively thick samples, here reported is the first observation of surface phonon polaritons in single atomic layers and bilayers of hexagonal boron nitride (hBN). Using antenna-based near-field microscopy, propagating surface phonon polaritons in mono- and bilayer hBN microcrystals are imaged. Phonon polaritons in monolayer hBN are confined in a volume about one million times smaller than the free-space photons. Both the polariton dispersion and their wavelength-thickness scaling law are altered compared to those of hBN bulk counterparts. These changes are attributed to phonon hardening in monolayer-thick crystals. The data reported here have bearing on applications of polaritons in metasurfaces and ultrathin optical elements.

Published
2019

10. Discovering Graph Patterns for Fact Checking in Knowledge Graphs

Author

Lin, Peng, Song, Qi, Shen, Jialiang, Wu, Yinghui, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Pei, Jian, editor, Manolopoulos, Yannis, editor, Sadiq, Shazia, editor, and Li, Jianxin, editor

Published
2018
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12. Thickness-scaling phonon resonance: A systematic study of hexagonal boron nitride from monolayers to bulk crystals.

Author

Jiang, Xiaojie, Chen, Mingyuan, Li, Jiahan, Fathi-Hafshejani, Parvin, Shen, Jialiang, Jin, Yiming, Cai, Wei, Mahjouri-Samani, Masoud, Edgar, James H., and Dai, Siyuan

Subjects
BORON nitride, PHONONS, RESONANCE, CRYSTALS, OPTICAL materials, MONOMOLECULAR films
Abstract

Phonons are important lattice vibrations that affect the thermal, electronic, and optical properties of materials. In this work, we studied infrared phonon resonance in a prototype van der Waals (vdW) material—hexagonal boron nitride (hBN)—with the thickness ranging from monolayers to bulk, especially on ultra-thin crystals with atomic layers smaller than 20. Our combined experimental and modeling results show a systematic increase in the intensity of in-plane phonon resonance at the increasing number of layers in hBN, with a sensitivity down to one atomic layer. While the thickness-dependence of the phonon resonance reveals the antenna nature of our nanoscope, the linear thickness-scaling of the phonon polariton wavelength indicates the preservation of electromagnetic hyperbolicity in ultra-thin hBN layers. Our conclusions should be generic for fundamental resonances in vdW materials and heterostructures where the number of constituent layers can be conveniently controlled. The thickness-dependent phonon resonance and phonon polaritons revealed in our work also suggest vdW engineering opportunities for desired thermal and nanophotonic functionalities. [ABSTRACT FROM AUTHOR]

Published
2022
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22. Nanoplasmonic Sandwich Immunoassay for Tumor-Derived Exosome Detection and Exosomal PD-L1 Profiling

Author

Wang, Chuanyu, primary, Huang, Chung-Hui, additional, Gao, Zhuangqiang, additional, Shen, Jialiang, additional, He, Jiacheng, additional, MacLachlan, Alana, additional, Ma, Chao, additional, Chang, Ya, additional, Yang, Wen, additional, Cai, Yuxin, additional, Lou, Yang, additional, Dai, Siyuan, additional, Chen, Weiqiang, additional, Li, Feng, additional, and Chen, Pengyu, additional

Published
2021
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23. Fine Grain Image Classification with Label Noise

Author

Shen, Jialiang

Abstract

Deep models training from clean data have achieved tremendous success in fine-grained image classification. Yet, they prone to generate wrong sample label because of faint difference in visual representation between each category, resulting in significant performance degradation when encountering noisy labels. Existing approaches to handle label noise, though proved effective for generic object recognition, usually fail on fine-grained data due to insufficient data and severely overfitting on noisy samples according to ConvNets memorization effects. In this paper, we propose a novel sample selection and relabel combined approach that enhance robust learning of ConvNets on noisy data for fine-grained visual categorization. Our method employs an adaptive label correction strategy to make full use of data which acted as a semi-supervised manner, ensuring effective learning on limited data. Moreover, our approach considers the criteria of exploring the effectiveness on both global class-prototype and part class-prototype similarity in correcting labels of samples. We evaluate our method on three standard benchmarks of fine-grained recognition. Experimental results show that our method outperforms the existing label noisy methods by a large margin. Further, our approach is non-sensitive to hyper-parameters setting, and the class-prototype similarity based label correction method can be added as a component in general fine-grained visual categorization algorithms to against label noise.

Published
2021

25. Hyperbolic phonon polaritons with positive and negative phase velocities in suspended α-MoO3.

Author

Shen, Jialiang, Zheng, Zhiren, Dinh, Thao, Wang, Chuanyu, Chen, Mingyuan, Chen, Pengyu, Ma, Qiong, Jarillo-Herrero, Pablo, Kang, Lixing, and Dai, Siyuan

Subjects
*PHONONS, *PHASE velocity, *ELECTROMAGNETIC theory, *BORON nitride, *POLARITONS, *OPTICAL properties
Abstract

Sample suspension is a valuable method to improve the mechanical, thermal, electronic, and optical properties of low-dimensional materials. In terms of confined light-matter waves—the polaritons, sample suspension can elongate the wavelength of polaritons with a positive phase velocity. Previous work demonstrates a wavelength elongation of ∼10% for hyperbolic phonon polaritons (HPPs) in uniaxial crystals of hexagonal boron nitride (hBN). In this work, we report the alteration of HPPs in biaxial α-phase molybdenum trioxide (α-MoO3) by sample suspension. Our combined infrared nano-imaging experiments and electromagnetic theory reveal a wavelength elongation > 60% and a propagation length increase > 140%, due to the simultaneous wavelength elongation and dissipation elimination in the suspended specimen. We have also examined HPPs in α-MoO3 with a negative phase velocity. The sample suspension shortens the HPP wavelength and simultaneously reduces the dissipation due to the unique permittivity tensor. The HPPs with improved figures of merits in the suspended specimen may be developed for nano-polaritonic circuits, biochemical sensing, emission engineering, and energy transfer. [ABSTRACT FROM AUTHOR]

Published
2022
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26. Hyperbolic phonon polaritons with positive and negative phase velocities in suspended α-MoO3.

Author

Shen, Jialiang, Zheng, Zhiren, Dinh, Thao, Wang, Chuanyu, Chen, Mingyuan, Chen, Pengyu, Ma, Qiong, Jarillo-Herrero, Pablo, Kang, Lixing, and Dai, Siyuan

Subjects
PHONONS, PHASE velocity, ELECTROMAGNETIC theory, BORON nitride, POLARITONS, OPTICAL properties
Abstract

Sample suspension is a valuable method to improve the mechanical, thermal, electronic, and optical properties of low-dimensional materials. In terms of confined light-matter waves—the polaritons, sample suspension can elongate the wavelength of polaritons with a positive phase velocity. Previous work demonstrates a wavelength elongation of ∼10% for hyperbolic phonon polaritons (HPPs) in uniaxial crystals of hexagonal boron nitride (hBN). In this work, we report the alteration of HPPs in biaxial α-phase molybdenum trioxide (α-MoO3) by sample suspension. Our combined infrared nano-imaging experiments and electromagnetic theory reveal a wavelength elongation > 60% and a propagation length increase > 140%, due to the simultaneous wavelength elongation and dissipation elimination in the suspended specimen. We have also examined HPPs in α-MoO3 with a negative phase velocity. The sample suspension shortens the HPP wavelength and simultaneously reduces the dissipation due to the unique permittivity tensor. The HPPs with improved figures of merits in the suspended specimen may be developed for nano-polaritonic circuits, biochemical sensing, emission engineering, and energy transfer. [ABSTRACT FROM AUTHOR]

Published
2022
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27. Single Image Super-Resolution via Deep Dense Network

Author

Shen, Jialiang

Abstract

University of Technology Sydney. Faculty of Engineering and Information Technology. Image Super-Resolution (SR) is a research field of computer vision, which enhances the resolution of an imaging system. The need for high resolution is common in computer vision applications for better performance in pattern recognition and analysis of images. However, recovering of the HR image from LR image is a highly ill-posed problem. In this thesis, the image SR problem is solved from three aspects with deep dense network models, including improving reconstruction accuracy, optimizing model training-time memory consumption, and extending effective SR scale ranges. Chapter 1 introduces the importance of image SR reconstruction and summarizes the challenges of image SR problem. Chapter 2 reviews the existing image SR methods, analyses their limitations and explains some related fundamental theories. Chapter 3 proposes a bi-dense model to improve image SR performance based on the dense connections for feature reuse. The bi-dense network does not only reuse local feature layers in the dense block, but also reuses the block information in the network to archive excellent performance with a moderate number of parameters. Chapter 4 evaluates the memory consumption of the vanilla dense model for image SR. For solving this problem, we introduce shared memory strategy into image SR by proposing a memory-optimized deep dense network. Chapter 5 discovers most of the deep SR methods are inefficient or impractical for generating SR of any scale factor, and proposes a novel Any-Scale Deep Network (ASDN), which requires few training scales to achieve one unified network for any-scale SR. In order to design such a powerful network architecture, we propose Laplacian Frequency Representation to predict SR results of the small ratio range and Recursive Deployment for SR of any larger scale. In this way, the required training data and update periods are substantially decreased to optimize the any-scale SR network. All these algorithms are aimed to solve the single image SR problem. These algorithms are tested on many public datasets and the results on those datasets demonstrate superior performance of our approach over the state-of-the-art methods and validate the effectiveness and correctness of our methods.

Published
2019

33. Phonon Polaritons and Hyperbolic Response in van der Waals Materials.

Author

Hu, Guangwei, Shen, Jialiang, Qiu, Cheng‐Wei, Alù, Andrea, and Dai, Siyuan

Subjects
*PHONONS, *QUANTUM optics, *HIGH resolution imaging, *OPTICAL elements, *CIRCUIT elements, *POLARITONS, *METAMATERIALS
Abstract

Phonon polaritons provide useful opportunities, complementary to those provided by plasmon polaritons, in the study of the interaction of light with matter at small scales. The focus of this review is on phonon polaritons in low‐dimensional van der Waals (vdW) materials and heterostructures. Phonon polaritons confined in vdW materials exhibit large electromagnetic localization and are easy to hybridize with other collective modes. The extreme optical anisotropy in vdW systems produces the natural hyperbolic dispersion, enabling the access to deep subdiffractional optics and often yielding improved figures of merit over hyperbolic metamaterials. These virtues hold promises for practical nanophotonic applications, including optical sensing, super‐resolution imaging, energy and emission engineering, quantum optics, and a next generation of optical circuit elements. [ABSTRACT FROM AUTHOR]

Published
2020
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36. Fabrication and Characterization of Blends of P(VDF-CTFE) 91/9 and PMMA

Author

Shen, Jialiang

Subjects
Materials Engineering
Abstract

Transparent and flexible polymer blends with a uniform thickness of films about 8 microns were successfully and simply fabricated by a spin-coating method. PMMA polymer and P(VDF-CTFE) 91/9 mol. % (VC91) copolymer were mixed and dissolved in DMF solvent, with different volume fraction of PMMA from 0 vol. % to 100 vol. %. Dielectric properties, polarization-electric field (P-E) hysteresis loops and differential scanning calorimetry (DSC) were measured in this study. At room temperature, dielectric constant decreased, while dielectric loss decreased at low frequency and increased at high frequency, with increasing volume fraction of PMMA. Temperature dependence of dielectric properties were investigated and explained by several relaxation processes. With increasing PMMA content, breakdown fields increased, and maximal polarization and remnant polarization decreased. Energy-storage performances were calculated from P-E loops. Maximum charged and discharged energy density were about 26.4 J/cm3 and 10.4 J/cm3, obtaining in PMMA-VC91 blends with 10 vol. % and 30 vol. % of PMMA, respectively. Also, efficiency increased as the addition of PMMA. The phase transitions, such as glass transition, fusion, crystallization, were investigated by DSC, assisting to understand the dielectric behaviors of PMMA-VC91 blends.

Published
2019

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