arXiv subjects

Tao Xu

Publications and source records attributed to Tao Xu.

At least 145 records · Page 8Linked to original sources

arXiv2018

Defect Engineering for Modulating the Trap States in Two-dimensional Photoconductor

Defect induced trap states are essential in determining the performance of semiconductor photodetectors. The de-trap time of carriers from a deep trap could be prolonged by several orders of magnitude as compared to shallow trap, resulting in additional decay/response time of the device. Here, we demonstrate that the trap states in two-dimensional ReS2 could be efficiently modulated by defect engineering through molecule decoration. The deep traps that greatly prolong the response time could be mostly filled by Protoporphyrin (H2PP) molecules. At the same time, carrier recombination and shallow traps would in-turn play dominant roles in determining the decay time of the device, which can be several orders of magnitude faster than the as-prepared device. Moreover, the specific detectivity of the device is enhanced (as high as ~1.89 x 10^13 Jones) due to the significant reduction of dark current through charge transfer between ReS2 and molecules. Defect engineering of trap states therefore provides a solution to achieve photodetectors with both high responsivity and fast response.

cond-mat.mtrl-sci↗

arXiv2018

Low-Temperature Eutectic Synthesis of PtTe2 with Weak Antilocalization and Controlled Layer Thinning

Metallic transition metal dichalcogenides (TMDs) have exhibited various exotic physical properties and hold the promise of novel optoelectronic and topological devices applications. However, the synthesis of metallic TMDs is based on gas-phase methods and requires high temperature condition. As an alternative to the gas-phase synthetic approach, lower temperature eutectic liquid-phase synthesis presents a very promising approach with the potential for larger-scale and controllable growth of high-quality thin metallic TMDs single crystals. Herein, we report the first realization of low-temperature eutectic liquid-phase synthesis of type-II Dirac semimetal PtTe2 single crystals with thickness ranging from 2 to 200 nm. The electrical measurement of synthesized PtTe2 reveals a record-high conductivity of as high as 3.3*106 S/m at room temperature. Besides, we experimentally identify the weak antilocalization behavior in the type-II Dirac semimetal PtTe2 for the first time. Furthermore, we develop a simple and general strategy to obtain atomically-thin PtTe2 crystal by thinning as-synthesized bulk samples, which can still retain highly crystalline and exhibits excellent electric conductivity. Our results of controllable and scalable low-temperature eutectic liquid-phase synthesis and layer-by-layer thinning of high-quality thin PtTe2 single crystals offer a simple and general approach for obtaining different thickness metallic TMDs with high-melting point transition metal.

cond-mat.mtrl-sci↗

arXiv2018

Segmentation of Drosophila Heart in Optical Coherence Microscopy Images Using Convolutional Neural Networks

Convolutional neural networks are powerful tools for image segmentation and classification. Here, we use this method to identify and mark the heart region of Drosophila at different developmental stages in the cross-sectional images acquired by a custom optical coherence microscopy (OCM) system. With our well-trained convolutional neural network model, the heart regions through multiple heartbeat cycles can be marked with an intersection over union (IOU) of ~86%. Various morphological and dynamical cardiac parameters can be quantified accurately with automatically segmented heart regions. This study demonstrates an efficient heart segmentation method to analyze OCM images of the beating heart in Drosophila.

cs.CV↗

arXiv2018

StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks

Although Generative Adversarial Networks (GANs) have shown remarkable success in various tasks, they still face challenges in generating high quality images. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aiming at generating high-resolution photo-realistic images. First, we propose a two-stage generative adversarial network architecture, StackGAN-v1, for text-to-image synthesis. The Stage-I GAN sketches the primitive shape and colors of the object based on given text description, yielding low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details. Second, an advanced multi-stage generative adversarial network architecture, StackGAN-v2, is proposed for both conditional and unconditional generative tasks. Our StackGAN-v2 consists of multiple generators and discriminators in a tree-like structure; images at multiple scales corresponding to the same scene are generated from different branches of the tree. StackGAN-v2 shows more stable training behavior than StackGAN-v1 by jointly approximating multiple distributions. Extensive experiments demonstrate that the proposed stacked generative adversarial networks significantly outperform other state-of-the-art methods in generating photo-realistic images.

cs.CV↗

arXiv2018

A Synergized Pulsing-Imaging Network (SPIN)

Currently, the deep neural network is the mainstream for machine learning, and being actively developed for biomedical imaging applications with an increasing emphasis on tomographic reconstruction for MRI, CT, and other imaging modalities. Multiple deep-learning-based approaches were applied to MRI image reconstruction from k-space samples to final images. Each of these studies assumes a given pulse sequence that produces incomplete and/or inconsistent data in the Fourier space, and targets a trained neural network that recovers an underlying image as close as possible to the ground truth. For the first time, in this paper we view data acquisition and the image reconstruction as the two key parts of an integrated MRI process, and optimize both the pulse sequence and the reconstruction scheme seamlessly in the machine learning framework. Our pilot simulation results show an exemplary embodiment of our new MRI strategy. Clearly, this work can be extended to other imaging modalities and their combinations as well, such as ultrasound imaging, and also potentially simultaneous emission-transmission tomography aided by polarized radiotracers.

physics.med-ph↗

arXiv2018

GPU Accelerated Cascade Hashing Image Matching for Large Scale 3D Reconstruction

Image feature point matching is a key step in Structure from Motion(SFM). However, it is becoming more and more time consuming because the number of images is getting larger and larger. In this paper, we proposed a GPU accelerated image matching method with improved Cascade Hashing. Firstly, we propose a Disk-Memory-GPU data exchange strategy and optimize the load order of data, so that the proposed method can deal with big data. Next, we parallelize the Cascade Hashing method on GPU. An improved parallel reduction and an improved parallel hashing ranking are proposed to fulfill this task. Finally, extensive experiments show that our image matching is about 20 times faster than SiftGPU on the same graphics card, nearly 100 times faster than the CPU CasHash method and hundreds of times faster than the CPU Kd-Tree based matching method. Further more, we introduce the epipolar constraint to the proposed method, and use the epipolar geometry to guide the feature matching procedure, which further reduces the matching cost.

cs.CV↗

arXiv2018

Probing anomalous $WWγ$ Triple Gauge Bosons Coupling at LHeC

The precision measurement of the $WWγ$ vertex at the future Large Hadron electron Collider (LHeC) at CERN is discussed in this paper. We propose to measure this vertex in the $e^{-} p\to e^{-}W^{\pm}j$ channel as a complement to the conventional charged current $ν_{e}γj$ channel. In addition to the cross section measurement, $χ^{2}$ method studies of angular variables provide powerful tools to probe the anomalous structure of triple gauge boson couplings. We study the distribution of the well-known azimuthal angle between the final state forward electron and jet in this vector-boson fusion (VBF) process. On the other hand, full reconstruction of leptonic $W$ decay opens a new opportunity to measure $W$ polarization that is also sensitive to the anomalous triple gauge boson couplings. Taking into consideration the superior determination of parton distribution functions~(PDFs) based on future LHeC data, the constraints of $λ_γ$ and $Δκ_γ$ might reach up to $\mathcal{O}(10^{-3})$ level in the most ideal case with the 2--3~ab$^{-1}$ data set, which shows a potential advantage compared to those from LHC and LEP data.

hep-ph↗

arXiv2018

New Methods of Studying Valley Fitness Landscapes

The word "valley" is a popular term used in intuitively describing fitness landscapes. What is a valley on a fitness landscape? How to identify the direction and location of a valley if it exists? However, such questions are seldom rigorously studied in evolutionary optimization especially when the search space is a high dimensional continuous space. This paper presents two methods of studying valleys on a fitness landscape. The first method is based on the topological homeomorphism. It establishes a rigorous definition of a valley. A valley is regarded as a one-dimensional manifold. The second method takes a different viewpoint from statistics. It provides an algorithm of identifying the valley direction and location using principle component analysis.

cs.NE↗

arXiv2018

Realizing spin-dependent gauge field with biaxial metamaterials

Artificial magnetic field in electromagnetism is becoming an emerging way as a robust control of light based on its geometric and topological nature. Other than demonstrating topological photonics properties in the diffractive regime using photonic crystals or arrays of waveguides, it will be of great interest if similar manipulations can be done simply in the long wavelength limit, in which only a few optical parameters can be used to describe the system, making the future optical component design much easier. Here, by designing and fabricating a metamaterial with split dispersion surface, we provide a straight-forward experimental realization of spin-dependent gauge field in the real space using a biaxial material. A "magnetic force bending" for light of desired pseudospins is visualized experimentally by such a gauge field as a manifestation of optical spin Hall effect. Such a demonstration is potentially useful to develop pseudospin optics, topological components and spin-enabled transformation optical devices.

physics.optics↗

arXiv2018

On the Discrimination-Generalization Tradeoff in GANs

Generative adversarial training can be generally understood as minimizing certain moment matching loss defined by a set of discriminator functions, typically neural networks. The discriminator set should be large enough to be able to uniquely identify the true distribution (discriminative), and also be small enough to go beyond memorizing samples (generalizable). In this paper, we show that a discriminator set is guaranteed to be discriminative whenever its linear span is dense in the set of bounded continuous functions. This is a very mild condition satisfied even by neural networks with a single neuron. Further, we develop generalization bounds between the learned distribution and true distribution under different evaluation metrics. When evaluated with neural distance, our bounds show that generalization is guaranteed as long as the discriminator set is small enough, regardless of the size of the generator or hypothesis set. When evaluated with KL divergence, our bound provides an explanation on the counter-intuitive behaviors of testing likelihood in GAN training. Our analysis sheds lights on understanding the practical performance of GANs.

cs.LG↗

arXiv2018

Optimize transfer learning for lung diseases in bronchoscopy using a new concept: sequential fine-tuning

Bronchoscopy inspection as a follow-up procedure from the radiological imaging plays a key role in lung disease diagnosis and determining treatment plans for the patients. Doctors needs to make a decision whether to biopsy the patients timely when performing bronchoscopy. However, the doctors also needs to be very selective with biopsies as biopsies may cause uncontrollable bleeding of the lung tissue which is life-threaten. To help doctors to be more selective on biopsies and provide a second opinion on diagnosis, in this work, we propose a computer-aided diagnosis (CAD) system for lung diseases including cancers and tuberculosis (TB). The system is developed based on transfer learning. We propose a novel transfer learning method: sentential fine-tuning . Compared to traditional fine-tuning methods, our methods achieves the best performance. We obtained a overall accuracy of 77.0% a dataset of 81 normal cases, 76 tuberculosis cases and 277 lung cancer cases while the other traditional transfer learning methods achieve an accuracy of 73% and 68%. . The detection accuracy of our method for cancers, TB and normal cases are 87%, 54% and 91% respectively. This indicates that the CAD system has potential to improve lung disease diagnosis accuracy in bronchoscopy and it also might be used to be more selective with biopsies.

cs.CV↗

arXiv2017

AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks

In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation. With a novel attentional generative network, the AttnGAN can synthesize fine-grained details at different subregions of the image by paying attentions to the relevant words in the natural language description. In addition, a deep attentional multimodal similarity model is proposed to compute a fine-grained image-text matching loss for training the generator. The proposed AttnGAN significantly outperforms the previous state of the art, boosting the best reported inception score by 14.14% on the CUB dataset and 170.25% on the more challenging COCO dataset. A detailed analysis is also performed by visualizing the attention layers of the AttnGAN. It for the first time shows that the layered attentional GAN is able to automatically select the condition at the word level for generating different parts of the image.

cs.CV↗

arXiv2017

Mixed interactions of localized waves in the three-component coupled derivative nonlinear Schrödinger equations

The Darboux transformation of the three-component coupled derivative nonlinear Schrödinger equations is constructed, based on the special vector solution elaborately generated from the corresponding Lax pair, various interactions of localized waves are derived. Here, we focus on the higher-order interactional solutions among higher-order rogue waves (RWs), multi-soliton and multi-breather. Instead of considering various arrangements among the three components $q_1$, $q_2$ and $q_3$, we define the same combination as the same type solution. Based on our method, these interactional solutions are completely classified into six types among these three components $q_1$, $q_2$ and $q_3$. In these six types interactional solutions, there are four mixed interactions of localized waves in three different components. In particular, the free parameters $α$ and $β$ paly an important role in dynamics structures of the interactional solutions, for example, different nonlinear localized waves merge with each other by increasing the absolute values of $α$ and $β$.

nlin.SI↗

arXiv2017

StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks

Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications. Samples generated by existing text-to-image approaches can roughly reflect the meaning of the given descriptions, but they fail to contain necessary details and vivid object parts. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) to generate 256x256 photo-realistic images conditioned on text descriptions. We decompose the hard problem into more manageable sub-problems through a sketch-refinement process. The Stage-I GAN sketches the primitive shape and colors of the object based on the given text description, yielding Stage-I low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details. It is able to rectify defects in Stage-I results and add compelling details with the refinement process. To improve the diversity of the synthesized images and stabilize the training of the conditional-GAN, we introduce a novel Conditioning Augmentation technique that encourages smoothness in the latent conditioning manifold. Extensive experiments and comparisons with state-of-the-arts on benchmark datasets demonstrate that the proposed method achieves significant improvements on generating photo-realistic images conditioned on text descriptions.

cs.CV↗

arXiv2017

SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image Segmentation

Inspired by classic generative adversarial networks (GAN), we propose a novel end-to-end adversarial neural network, called SegAN, for the task of medical image segmentation. Since image segmentation requires dense, pixel-level labeling, the single scalar real/fake output of a classic GAN's discriminator may be ineffective in producing stable and sufficient gradient feedback to the networks. Instead, we use a fully convolutional neural network as the segmentor to generate segmentation label maps, and propose a novel adversarial critic network with a multi-scale $L_1$ loss function to force the critic and segmentor to learn both global and local features that capture long- and short-range spatial relationships between pixels. In our SegAN framework, the segmentor and critic networks are trained in an alternating fashion in a min-max game: The critic takes as input a pair of images, (original_image $*$ predicted_label_map, original_image $*$ ground_truth_label_map), and then is trained by maximizing a multi-scale loss function; The segmentor is trained with only gradients passed along by the critic, with the aim to minimize the multi-scale loss function. We show that such a SegAN framework is more effective and stable for the segmentation task, and it leads to better performance than the state-of-the-art U-net segmentation method. We tested our SegAN method using datasets from the MICCAI BRATS brain tumor segmentation challenge. Extensive experimental results demonstrate the effectiveness of the proposed SegAN with multi-scale loss: on BRATS 2013 SegAN gives performance comparable to the state-of-the-art for whole tumor and tumor core segmentation while achieves better precision and sensitivity for Gd-enhance tumor core segmentation; on BRATS 2015 SegAN achieves better performance than the state-of-the-art in both dice score and precision.

cs.CV↗

arXiv2017

Localized nonlinear waves of the three-component coupled Hirota equation by the generalized Darboux transformation

In this paper, We extend the two-component coupled Hirota equation to the three-component one, and reconstruct the Lax pair with $4\times4$ matrixes of this three-component coupled system including higher-order effects such as third-order dispersion, self-steepening and delayed nonlinear response. Combining the generalized Darboux transformation and a specific vector solution of this $4\times4$ matrix spectral problem, we study higher-order localized nonlinear waves in this three-component coupled system. Then, the semi-rational and multi-parametric solutions of this system are derived in our paper. Owing to these more free parameters in the interactional solutions than those in single- and two-component Hirota equation, this three-component coupled system has more abundant and fascinating localized nonlinear wave solutions structures. Besides, in the first- and second-order localized waves, we get a variety of new and appealing combinations among these three components $q_1, q_2$ and $q_3$. Instead of considering various arrangements of the three potential functions, we consider the same combination as the same type solution. Moreover, the phenomenon that these nonlinear localized waves merge with each other observably, may appears by increasing the absolute values of two free parameters $α, β$. These results further uncover some striking dynamic structures in multi-component coupled system.

nlin.SI↗

arXiv2017

Darboux transformation of nonisospectral coupled Gross-Pitaevskii equation and its multi-component generalization

We extend one component Gross-Pitaevskii equation to two component coupled case with the damping term, linear and parabolic density profiles, then give the Lax pair and infinitely-many conservations laws of this coupled system. The system is nonautonomous, that is, it admits a nonisospectral linear eigenvalue problem. In fact, the Darboux transformation for this kind of inhomogeneous system which is essentially different from the isospectral case, we reconstruct the Darboux transformation for this coupled Gross-Pitaevskii equation. Multi nonautonomous solitons, one breather and the first-order rogue wave are also obtained by the Darboux transformation. When $β>0$, the amplitudes and velocities of solitons decay exponentially as $t$ increases, otherwise, they increase exponentially as $t$ increases. Meanwhile, the real part $Re(ξ_j)$'s~$(j=1,2,3,\dots)$ of new spectral parameters determine the direction of solitions' propagation and $α$ affects the localization of solitons. Choosing $Re(ξ_1)=Re(ξ_2)$, the two-soliton bound state is obtained. From nonzero background seed solutions, we construct one nonautonomous breather on curved background and find that this breather has some deformations along the direction of $t$ due to the exponential decaying term. Besides, $β$ determines the degree of this curved background, if we set $β>0$, the amplitude of the breather becomes small till being zero as $t$ increases. Through taking appropriate limit about the breather, the first-order rogue wave can be acquired. Finally, we give multi-component generalization of Gross-Pitaevskii equation and its Lax pair with nonisospectral parameter, meanwhile, Darboux transformation about this multi-component generalization is also constructed.

nlin.SI↗

arXiv2017

Collider Phenomenology of $e^{-}e^{-}\to W^{-}W^{-}$

Whether in nature exists Majorana neutrino is one of the most fundamental questions in physics. It is directly related to the violation of accidental $U(1)$-lepton number symmetry(LNV). Enormous efforts had been put into such test and among them, one conventional experiment is the neutrinoless double-beta decay ($0νββ$). On the other hand, there have been proposals of future electron-positron colliders such as International Linear Collider (ILC) for precision measurements of Higgs boson or new discovered beyond SM particles and it can potentially be converted into an electron-electron collider. Such collider enables a new probe of Majorana neutrino via the inverse $0νββ$ process($e^{-}e^{-}\to W^- W^-$) as an alternative and complementary test to the conventional neutrinoless double-beta decay experiments. In this paper, we illustrate the searching strategies of $e^{-}e^{-}\to W^- W^-$ at future electron colliders, and find that the channels of pure hadronic, semi-leptonic with muon and pure leptonic with muons have the most discovery potential.

hep-ph↗