arXiv subjects

Zihao Wang

Publications and source records attributed to Zihao Wang.

350 records · Page 20Linked to original sources

arXiv2018

Computational multifocal microscopy

Despite recent advances, high performance single-shot 3D microscopy remains an elusive task. By introducing designed diffractive optical elements (DOEs), one is capable of converting a microscope into a 3D "kaleidoscope", in which case the snapshot image consists of an array of tiles and each tile focuses on different depths. However, the acquired multifocal microscopic (MFM) image suffers from multiple sources of degradation, which prevents MFM from further applications. We propose a unifying computational framework which simplifies the imaging system and achieves 3D reconstruction via computation. Our optical configuration omits chromatic correction grating and redesigns the multifocal grating to enlarge the tracking area. Our proposed setup features only one single grating in addition to a regular microscope. The aberration correction, along with Poisson and background denoising, are incorporated in our deconvolution-based fully-automated algorithm, which requires no empirical parameter-tuning. In experiments, we achieve the spatial resolutions of $0.35$um (lateral) and $0.5$um (axial), which are comparable to the resolution that can be achieved with confocal deconvolution microscopy. We demonstrate a 3D video of moving bacteria recorded at $25$ frames per second using our proposed computational multifocal microscopy technique.

physics.optics↗

arXiv2018

Magnon-assisted tunnelling in van der Waals heterostructures based on CrBr3

The growing family of two-dimensional (2D) materials that are now available can be used to assemble van der Waals heterostructures with a wide range of properties. Of particular interest are tunnelling heterostructures, which have been used to study the electronic states both in the tunnelling barrier and in the emitter and collector contacts. Recently, 2D ferromagnets have been studied theoretically and experimentally. Here we investigate electron tunnelling through a thin (2-6 layers) ferromagnetic CrBr3 barrier. For devices with non-magnetic barriers, conservation of momentum can be relaxed by phonon-assisted tunnelling or by tunnelling through localised states. In the case of our ferromagnetic barrier the dominant tunnelling mechanisms are the emission of magnons at low temperatures or scattering of electrons on localised magnetic excitations above the Curie temperature. Tunnelling with magnon emission offers the possibility of injecting spin into the collector electrode.

cond-mat.mes-hall↗

arXiv2017

Deep Recurrent Generative Decoder for Abstractive Text Summarization

We propose a new framework for abstractive text summarization based on a sequence-to-sequence oriented encoder-decoder model equipped with a deep recurrent generative decoder (DRGN). Latent structure information implied in the target summaries is learned based on a recurrent latent random model for improving the summarization quality. Neural variational inference is employed to address the intractable posterior inference for the recurrent latent variables. Abstractive summaries are generated based on both the generative latent variables and the discriminative deterministic states. Extensive experiments on some benchmark datasets in different languages show that DRGN achieves improvements over the state-of-the-art methods.

cs.CL↗

arXiv2017

Neural Rating Regression with Abstractive Tips Generation for Recommendation

Recently, some E-commerce sites launch a new interaction box called Tips on their mobile apps. Users can express their experience and feelings or provide suggestions using short texts typically several words or one sentence. In essence, writing some tips and giving a numerical rating are two facets of a user's product assessment action, expressing the user experience and feelings. Jointly modeling these two facets is helpful for designing a better recommendation system. While some existing models integrate text information such as item specifications or user reviews into user and item latent factors for improving the rating prediction, no existing works consider tips for improving recommendation quality. We propose a deep learning based framework named NRT which can simultaneously predict precise ratings and generate abstractive tips with good linguistic quality simulating user experience and feelings. For abstractive tips generation, gated recurrent neural networks are employed to "translate" user and item latent representations into a concise sentence. Extensive experiments on benchmark datasets from different domains show that NRT achieves significant improvements over the state-of-the-art methods. Moreover, the generated tips can vividly predict the user experience and feelings.

cs.CL↗

arXiv2017

Competitive and Penalized Clustering Auto-encoder

The paper has been withdrawn since more effective experiments should be completed. Auto-encoders (AE) has been widely applied in different fields of machine learning. However, as a deep model, there are a large amount of learnable parameters in the AE, which would cause over-fitting and slow learning speed in practice. Many researchers have been study the intrinsic structure of AE and showed different useful methods to regularize those parameters. In this paper, we present a novel regularization method based on a clustering algorithm which is able to classify the parameters into different groups. With this regularization, parameters in a given group have approximate equivalent values and over-fitting problem could be alleviated. Moreover, due to the competitive behavior of clustering algorithm, this model also overcomes some intrinsic problems of clustering algorithms like the determination of number of clusters. Experiments on handwritten digits recognition verify the effectiveness of our novel model.

cs.LG↗

arXiv2016

Subsampled Phase Retrieval for On-chip Lensless Holographic Video

On-chip holographic video is a convenient way to monitor biological samples simultaneously at high spatial resolution and over a wide field-of-view. However, due to the limited readout rate of digital detector arrays, one often faces a tradeoff between the per-frame pixel count and frame rate of the captured video. In this report, we propose a subsampled phase retrieval (SPR) algorithm to overcome the spatial-temporal trade-off in holographic video. Compared to traditional phase retrieval approaches, our SPR algorithm uses over an order of magnitude less pixel measurements while maintaining suitable reconstruction quality. We use an on-chip holographic video setup with pixel sub-sampling to experimentally demonstrate a factor of 5.5 increase in sensor frame rate while monitoring the in vivo movement of Peranema microorganisms.

physics.optics↗

arXiv2016

Compressive Holographic Video

Compressed sensing has been discussed separately in spatial and temporal domains. Compressive holography has been introduced as a method that allows 3D tomographic reconstruction at different depths from a single 2D image. Coded exposure is a temporal compressed sensing method for high speed video acquisition. In this work, we combine compressive holography and coded exposure techniques and extend the discussion to 4D reconstruction in space and time from one coded captured image. In our prototype, digital in-line holography was used for imaging macroscopic, fast moving objects. The pixel-wise temporal modulation was implemented by a digital micromirror device. In this paper we demonstrate $10\times$ temporal super resolution with multiple depths recovery from a single image. Two examples are presented for the purpose of recording subtle vibrations and tracking small particles within 5 ms.

cs.CV↗

arXiv2015

On-Chip Quantum Interference from a Single Silicon Ring Resonator Source

Here we demonstrate quantum interference of photons on a Silicon chip produced from a single ring resonator photon source. The source is seamlessly integrated with a Mach-Zehnder interferometer, which path entangles degenerate bi-photons produced via spontaneous four wave mixing in the Silicon ring resonator. The resulting bi-photon N00N state is controlled by varying the relative phase of the integrated Mach-Zehnder interferometer, resulting in high two-photon interference visibilities of V~96%. Furthermore, we show that the interference can be produced using pump wavelengths tuned to all of the ring resonances accessible with our tunable lasers (C+L band). This work is a key demonstration towards the simplified integration of multiple photon sources and quantum circuits together on a monolithic chip, in turn, enabling quantum information chips with much greater complexity and functionality.

quant-ph↗