arXiv subjects

Yi Zhang

Publications and source records attributed to Yi Zhang.

At least 1,135 records · Page 63Linked to original sources

arXiv2018

Semileptonic $B$ and $B_s$ decays involving scalar and axial-vector mesons

We report our theoretical calculations on the branching fractions for the semileptonic $B$ and $B_s$ decays, i.e., $B (B_s) \to (P,\, V,\, S,\,A) \ell ν_\ell$, where $P$ and $V$ denote the pseudoscalar and vector mesons, respectively, while $S$ denotes the scalar meson with mass above 1 GeV and $A$ the axial-vector meson. The branching fractions for the semileptonic $B\to P$ and $V$ modes have been measured very well in experiment and our theoretical values are in good agreement with them. The ones for $B\to S$ and $A$ modes are our theoretical predictions. There is little experimental information on the semileptonic $B_s$ decays although much theoretical effort has been done. In addition, we predict the branching fractions of $B\to D^*_0(2400) \ell \barν_\ell$ and $B_s\to D^{*-}_{s0}(2317) \ell \barν_\ell$ as $(2.31\pm 0.25)\times 10^{-3}$ and $(3.07\pm0.34)\times 10^{-3}$, in order, assuming them as the conventional mesons with quark-antiquark configuration. The high luminosity $e^+e^-$ collider SuperKEKB/Belle-II is running, with the data sample enhanced by a factor of 40 compared to Belle, which will provide huge opportunity for the test of the theoretical predictions and further help understand the inner structure of these scalar and axial vector mesons, e.g., the glueball content of $f_0(1710)$ and the mixing angles for the axial-vector mesons. These decay channels can also be accessed by the LHCb experiment.

hep-ph↗

arXiv2018

Weighted infinitesimal unitary bialgebras on matrix algebras and weighted associative Yang-Baxter equations

We equip a matrix algebra with a weighted infinitesimal unitary bialgebraic structure, via a construction of a suitable coproduct. Furthermore, an infinitesimal unitary Hopf algebra, under the view of Aguiar, is constructed on a matrix algebra. By exploring the relationship between weighted infinitesimal bialgebras and pre-Lie algebras, we construct a pre-Lie algebraic structure and then a new Lie algebraic structure on a matrix algebra. We also introduce the weighted associative Yang-Baxter equations (AYBEs) and obtain the relationship between solutions of weighted AYBEs and weighted infinitesimal unitary bialgebras. We give a bijection between the solutions of the associative Yang-Baxter equation of weight $λ$ and Rota-Baxter operators of weight $-λ$ on matrix algebras. As a consequence, weighted quasitriangular infinitesimal unitary bialgebras are constructed, which generalize the results studied by Aguiar. Finally, We show that any weighted quasitriangular infinitesimal unitary bialgebra can be made into a dendriform algebra.

math.RA↗

arXiv2018

Sparse-View CT Reconstruction via Convolutional Sparse Coding

Traditional dictionary learning based CT reconstruction methods are patch-based and the features learned with these methods often contain shifted versions of the same features. To deal with these problems, the convolutional sparse coding (CSC) has been proposed and introduced into various applications. In this paper, inspired by the successful applications of CSC in the field of signal processing, we propose a novel sparse-view CT reconstruction method based on CSC with gradient regularization on feature maps. By directly working on whole image, which need not to divide the image into overlapped patches like dictionary learning based methods, the proposed method can maintain more details and avoid the artifacts caused by patch aggregation. Experimental results demonstrate that the proposed method has better performance than several existing algorithms in both qualitative and quantitative aspects.

physics.med-ph↗

arXiv2018

Systematic Quantum Cluster Typical Medium Method For the Study of Localization in Strongly Disordered Electronic Systems

Great progress has been made in the last several years towards understanding the properties of disordered electronic systems. In part, this is made possible by recent advances in quantum effective medium methods which enable the study of disorder and electron-electronic interactions on equal footing. They include dynamical mean field theory and the coherent potential approximation, and their cluster extension, the dynamical cluster approximation. Despite their successes, these methods do not enable the first-principles study of the strongly disordered regime, including the effects of electronic localization. The main focus of this review is the recently developed typical medium dynamical cluster approximation for disordered electronic systems. This method has been constructed to capture disorder-induced localization, and is based on a mapping of a lattice onto a quantum cluster embedded in an effective typical medium, which is determined self-consistently. Here we provide an overview of various recent applications of the typical medium dynamical cluster approximation to a variety of models and systems, including single and multi-band Anderson model, and models with local and off-diagonal disorder. We then present the application of the method to realistic systems in the framework of the density functional theory.

cond-mat.dis-nn↗

arXiv2018

A Deep Reinforced Sequence-to-Set Model for Multi-Label Text Classification

Multi-label text classification (MLTC) aims to assign multiple labels to each sample in the dataset. The labels usually have internal correlations. However, traditional methods tend to ignore the correlations between labels. In order to capture the correlations between labels, the sequence-to-sequence (Seq2Seq) model views the MLTC task as a sequence generation problem, which achieves excellent performance on this task. However, the Seq2Seq model is not suitable for the MLTC task in essence. The reason is that it requires humans to predefine the order of the output labels, while some of the output labels in the MLTC task are essentially an unordered set rather than an ordered sequence. This conflicts with the strict requirement of the Seq2Seq model for the label order. In this paper, we propose a novel sequence-to-set framework utilizing deep reinforcement learning, which not only captures the correlations between labels, but also reduces the dependence on the label order. Extensive experimental results show that our proposed method outperforms the competitive baselines by a large margin.

cs.CL↗

arXiv2018

UnrealStereo: Controlling Hazardous Factors to Analyze Stereo Vision

A reliable stereo algorithm is critical for many robotics applications. But textureless and specular regions can easily cause failure by making feature matching difficult. Understanding whether an algorithm is robust to these hazardous regions is important. Although many stereo benchmarks have been developed to evaluate performance, it is hard to quantify the effect of hazardous regions in real images because the location and severity of these regions are unknown. In this paper, we develop a synthetic image generation tool enabling to control hazardous factors, such as making objects more specular or transparent, to produce hazardous regions at different degrees. The densely controlled sampling strategy in virtual worlds enables to effectively stress test stereo algorithms by varying the types and degrees of the hazard. We generate a large synthetic image dataset with automatically computed hazardous regions and analyze algorithms on these regions. The observations from synthetic images are further validated by annotating hazardous regions in real-world datasets Middlebury and KITTI (which gives a sparse sampling of the hazards). Our synthetic image generation tool is based on a game engine Unreal Engine 4 and will be open-source along with the virtual scenes in our experiments. Many publicly available realistic game contents can be used by our tool to provide an enormous resource for development and evaluation of algorithms.

cs.CV↗

arXiv2018

CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)

Computed tomography (CT) is widely used in screening, diagnosis, and image-guided therapy for both clinical and research purposes. Since CT involves ionizing radiation, an overarching thrust of related technical research is development of novel methods enabling ultrahigh quality imaging with fine structural details while reducing the X-ray radiation. In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs. We also include the joint constraints in the loss function to facilitate structural preservation. In this deep imaging process, we incorporate deep convolutional neural network (CNN), residual learning, and network in network techniques for feature extraction and restoration. In contrast to the current trend of increasing network depth and complexity to boost the CT imaging performance, which limit its real-world applications by imposing considerable computational and memory overheads, we apply a parallel $1\times1$ CNN to compress the output of the hidden layer and optimize the number of layers and the number of filters for each convolutional layer. Quantitative and qualitative evaluations demonstrate that our proposed model is accurate, efficient and robust for super-resolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three large-scale CT datasets, and obtain promising results as compared to the other state-of-the-art methods.

eess.IV↗

arXiv2018

Controlled Layer-by-Layer Oxidation of MoTe2 via O3 Exposure

Growing uniform oxides with various thickness on TMDs is one of the biggest challenges to integrate TMDs into complementary metal oxide semiconductor (CMOS) logic circuits. Here, we report a layer-by-layer oxidation of atomically thin MoTe2 flakes via ozone (O3) exposure. The thickness of MoOx oxide film could be tuning with atomic-level accuracy simply by varying O3 exposure time. Additionally, MoOx-covered MoTe2 shows a hole-dominated transport behavior. Our findings point to a simple and effective strategy for growing homogenous surface oxide film on MoTe2, which is promising for several purposes in metal-oxide-semiconductor transistor, ranging from surface passivation to dielectric layers.

cond-mat.mtrl-sci↗

arXiv2018

A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation

Narrative story generation is a challenging problem because it demands the generated sentences with tight semantic connections, which has not been well studied by most existing generative models. To address this problem, we propose a skeleton-based model to promote the coherence of generated stories. Different from traditional models that generate a complete sentence at a stroke, the proposed model first generates the most critical phrases, called skeleton, and then expands the skeleton to a complete and fluent sentence. The skeleton is not manually defined, but learned by a reinforcement learning method. Compared to the state-of-the-art models, our skeleton-based model can generate significantly more coherent text according to human evaluation and automatic evaluation. The G-score is improved by 20.1% in the human evaluation. The code is available at https://github.com/lancopku/Skeleton-Based-Generation-Model

cs.CL↗

arXiv2018

Learning Sentiment Memories for Sentiment Modification without Parallel Data

The task of sentiment modification requires reversing the sentiment of the input and preserving the sentiment-independent content. However, aligned sentences with the same content but different sentiments are usually unavailable. Due to the lack of such parallel data, it is hard to extract sentiment independent content and reverse the sentiment in an unsupervised way. Previous work usually can not reconcile sentiment transformation and content preservation. In this paper, motivated by the fact the non-emotional context (e.g., "staff") provides strong cues for the occurrence of emotional words (e.g., "friendly"), we propose a novel method that automatically extracts appropriate sentiment information from learned sentiment memories according to specific context. Experiments show that our method substantially improves the content preservation degree and achieves the state-of-the-art performance.

cs.CL↗

arXiv2018

A fast spatial resolution optimizing method for track-ion using a GEM detector based on the time information

In this work, a fast method of vertex reconstructing for incident ions in GEM detectors was proposed. As inspired by the Time Projection Chamber (TPC), the time information of the consecutive signal samples from Front End Electronics (FEE) was employed. To demonstrate the method, an experiment involving a 2D-readout THGEM detector with an APV-25 FEE was taken. With one experiment dependent parameter in the analysis, the proposed method led to a spatial resolution of 0.45 mm, compared with the same number of 9.10 mm from the traditional center of gravity method.

physics.ins-det↗

arXiv2018

Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising

Computed tomography (CT) is a popular medical imaging modality in clinical applications. At the same time, the x-ray radiation dose associated with CT scans raises public concerns due to its potential risks to the patients. Over the past years, major efforts have been dedicated to the development of Low-Dose CT (LDCT) methods. However, the radiation dose reduction compromises the signal-to-noise ratio (SNR), leading to strong noise and artifacts that down-grade CT image quality. In this paper, we propose a novel 3D noise reduction method, called Structure-sensitive Multi-scale Generative Adversarial Net (SMGAN), to improve the LDCT image quality. Specifically, we incorporate three-dimensional (3D) volumetric information to improve the image quality. Also, different loss functions for training denoising models are investigated. Experiments show that the proposed method can effectively preserve structural and texture information from normal-dose CT (NDCT) images, and significantly suppress noise and artifacts. Qualitative visual assessments by three experienced radiologists demonstrate that the proposed method retrieves more detailed information, and outperforms competing methods.

cs.CV↗

arXiv2018

SampleAhead: Online Classifier-Sampler Communication for Learning from Synthesized Data

State-of-the-art techniques of artificial intelligence, in particular deep learning, are mostly data-driven. However, collecting and manually labeling a large scale dataset is both difficult and expensive. A promising alternative is to introduce synthesized training data, so that the dataset size can be significantly enlarged with little human labor. But, this raises an important problem in active vision: given an {\bf infinite} data space, how to effectively sample a {\bf finite} subset to train a visual classifier? This paper presents an approach for learning from synthesized data effectively. The motivation is straightforward -- increasing the probability of seeing difficult training data. We introduce a module named {\bf SampleAhead} to formulate the learning process into an online communication between a {\em classifier} and a {\em sampler}, and update them iteratively. In each round, we adjust the sampling distribution according to the classification results, and train the classifier using the data sampled from the updated distribution. Experiments are performed by introducing synthesized images rendered from ShapeNet models to assist PASCAL3D+ classification. Our approach enjoys higher classification accuracy, especially in the scenario of a limited number of training samples. This demonstrates its efficiency in exploring the infinite data space.

cs.CV↗

arXiv2018

Cosmological Model Independent Time Delay Method

We propose a Cosmological Model Independent Time Delay (CMITD) method where the Lorentz invariance violation (LIV) variable $K(z)$ is constructed by observational data instead of cosmological model. The simulated time delay data show the CMITD method could present the validity of LIV test. And, the errors in the propagating process is critical for the existence and magnitude of LIV.

astro-ph.CO↗

arXiv2018

On the Nature of Localization in Ti doped Si

Intermediate band semiconductors hold the promise to significantly improve the efficiency of solar cells, but only if the intermediate impurity band is metallic. We apply a recently developed first principles method to investigate the origin of electron localization in Ti doped Si, a promising candidate for intermediate band solar cells. Although Anderson localization is often overlooked in the context of intermediate band solar cells, our results show that in Ti doped Si it plays a more important role in the metal insulator transition than Mott localization. Implications for the theory of intermediate band solar cells are discussed.

cond-mat.dis-nn↗

arXiv2018

The Ratio $\mathcal{R}(D)$ and the $D$-meson Distribution Amplitude

In this paper, we calculate the $B\to D$ transition form factors (TFFs) within the light-cone sum rules (LCSR) and predict the ratio $\mathcal{R}(D)$. More accurate $D$-meson distribution amplitudes (DAs) are essential to get a more accurate theoretical prediction. We construct a new model for the twist-3 DAs $ϕ^p_{3;D}$ and $ϕ^σ_{3;D}$ based on the QCD sum rules under the background field theory for their moments as we have done for constructing the leading-twist DA $ϕ_{2;D}$. As an application, we observe that the twist-3 contributions are sizable in whole $q^2$-region. Taking the twist-2 and twist-3 DAs into consideration, we obtain $f^{B\to D}_{+,0}(0) = 0.659^{+0.029}_{-0.032}$. As a combination of the Lattice QCD and the QCD LCSR predictions on the TFFs $f^{B\to D}_{+,0}(q^2)$, we predict $\mathcal{R}(D) = 0.320^{+0.018}_{-0.021}$, which improves is about $1.5σ$ deviation from the HFAG average of the Belle and BABAR data. At present the data are still of large errors, and we need further accurate measurements of the experiment to confirm whether there is signal of new physics from the ratio $\mathcal{R}(D)$.

hep-ph↗

arXiv2018

Conditional Tail-Related Risk Estimation Using Composite Asymmetric Least Squares and Empirical Likelihood

In this article, by using composite asymmetric least squares (CALS) and empirical likelihood, we propose a two-step procedure to estimate the conditional value at risk (VaR) and conditional expected shortfall (ES) for the GARCH series. First, we perform asymmetric least square regressions at several significance levels to model the volatility structure and separate it from the innovation process in the GARCH model. Note that expectile can serve as a bond to make up the gap from VaR estimation to ES estimation because there exists a bijective mapping from expectiles to specific quantile, and ES can be induced by expectile through a simple formula. Then, we introduce the empirical likelihood method to determine the relation above; this method is data-driven and distribution-free. Theoretical studies guarantee the asymptotic properties, such as consistency and the asymptotic normal distribution of the estimator obtained by our proposed method. A Monte Carlo experiment and an empirical application are conducted to evaluate the performance of the proposed method. The results indicate that our proposed estimation method is competitive with some alternative existing tail-related risk estimation methods.

math.ST↗

arXiv2018

Does Higher Order LSTM Have Better Accuracy for Segmenting and Labeling Sequence Data?

Existing neural models usually predict the tag of the current token independent of the neighboring tags. The popular LSTM-CRF model considers the tag dependencies between every two consecutive tags. However, it is hard for existing neural models to take longer distance dependencies of tags into consideration. The scalability is mainly limited by the complex model structures and the cost of dynamic programming during training. In our work, we first design a new model called "high order LSTM" to predict multiple tags for the current token which contains not only the current tag but also the previous several tags. We call the number of tags in one prediction as "order". Then we propose a new method called Multi-Order BiLSTM (MO-BiLSTM) which combines low order and high order LSTMs together. MO-BiLSTM keeps the scalability to high order models with a pruning technique. We evaluate MO-BiLSTM on all-phrase chunking and NER datasets. Experiment results show that MO-BiLSTM achieves the state-of-the-art result in chunking and highly competitive results in two NER datasets.

cs.CL↗