arXiv subjects

Yi Zhang

Publications and source records attributed to Yi Zhang.

At least 1,153 records · Page 64Linked to original sources

arXiv2018

Quantum limit transport and destruction of the Weyl nodes in TaAs

Weyl fermions are a new ingredient for correlated states of electronic matter. A key difficulty has been that real materials also contain non-Weyl quasiparticles, and disentangling the experimental signatures has proven challenging. We use magnetic fields up to 95 tesla to drive the Weyl semimetal TaAs far into its quantum limit (QL), where only the purely chiral 0th Landau levels (LLs) of the Weyl fermions are occupied. We find the electrical resistivity to be nearly independent of magnetic field up to 50 tesla: unusual for conventional metals but consistent with the chiral anomaly for Weyl fermions. Above 50 tesla we observe a two-order-of-magnitude increase in resistivity, indicating that a gap opens in the chiral LLs. Above 80 tesla we observe strong ultrasonic attenuation below 2 kelvin, suggesting a mesoscopically-textured state of matter. These results point the way to inducing new correlated states of matter in the QL of Weyl semimetals.

cond-mat.str-el↗

arXiv2018

Influence of Earth-Directed Coronal Mass Ejections on the Sun's Shadow Observed by the Tibet-III Air Shower Array

We examine the possible influence of Earth-directed coronal mass ejections (ECMEs) on the Sun's shadow in the 3~TeV cosmic-ray intensity observed by the Tibet-III air shower (AS) array. We confirm a clear solar-cycle variation of the intensity deficit in the Sun's shadow during ten years between 2000 and 2009. This solar-cycle variation is overall reproduced by our Monte Carlo (MC) simulations of the Sun's shadow based on the potential field model of the solar magnetic field averaged over each solar rotation period. We find, however, that the magnitude of the observed intensity deficit in the Sun's shadow is significantly less than that predicted by MC simulations, particularly during the period around solar maximum when a significant number of ECMEs is recorded. The $χ^2$ tests of the agreement between the observations and the MC simulations show that the difference is larger during the periods when the ECMEs occur, and the difference is reduced if the periods of ECMEs are excluded from the analysis. This suggests the first experimental evidence of the ECMEs affecting the Sun's shadow observed in the 3~TeV cosmic-ray intensity.

astro-ph.HE↗

arXiv2018

Conversational Recommender System

A personalized conversational sales agent could have much commercial potential. E-commerce companies such as Amazon, eBay, JD, Alibaba etc. are piloting such kind of agents with their users. However, the research on this topic is very limited and existing solutions are either based on single round adhoc search engine or traditional multi round dialog system. They usually only utilize user inputs in the current session, ignoring users' long term preferences. On the other hand, it is well known that sales conversion rate can be greatly improved based on recommender systems, which learn user preferences based on past purchasing behavior and optimize business oriented metrics such as conversion rate or expected revenue. In this work, we propose to integrate research in dialog systems and recommender systems into a novel and unified deep reinforcement learning framework to build a personalized conversational recommendation agent that optimizes a per session based utility function.

cs.IR↗

arXiv2018

Multifractal Study of Quasiparticle Localization in Disordered Superconductors

The thermal metal to thermal insulator transition due to random disorder is studied in the context of the symmetries of the Bogoliubov de Gennes Hamiltonian. We focus on a three dimensional system with gapless s-wave pairing that possesses time reversal and spin rotational symmetry. The quasiparticle excitations (bogolons) undergo a metal insulator transition as the disorder increases. We determine the critical disorder strength and correlation exponent first by the transfer matrix method (TMM). We then apply a multifractal finite sized scaling (MFSS) of the bogolon wavefunction obtained from large scale diagonalization of the Hamiltonian and obtain the critical disorder strength and exponent, in agreement with those found by TMM.

cond-mat.dis-nn↗

arXiv2018

On (shape-)Wilf-equivalence for words

Stankova and West showed that for any non-negative integer $s$ and any permutation $γ$ of $\{4,5,\dots,s+3\}$ there are as many permutations that avoid $231γ$ as there are that avoid $312γ$. We extend this result to the setting of words.

math.CO↗

arXiv2018

Design of a Non-vacuum-cooling Compact Scientific CCD Camera

CCD was born in Bell Laboratories in 1969 and has been widely used in various fields. Its ultra-low noise and high quantum efficiency make it work well in particle physics, high energy physics, nuclear physics and astrophysics. Nowadays, more and more CCD cameras have been developed for medical diagnosis, scientific experiments, aerospace, military exploration and other fields. For the wide range of CCD cameras, a Non-vacuum-cooling compact (NVCC) scientific CCD camera has been developed, including FPGA-based low noise clock and bias driver circuit, data acquisition circuit, STM32-based temperature control design. At the same time, the readout noise of the imaging system is studied emphatically. The scheme to generate the CCD clock and the bias driving circuit through ultralow noise LDOs is proposed. The camera was tested in a variety of environments, and the test results show that the system can run at a maximum rate of 5M pixels/s and readout noise is as low as 9.29e^- when the CCD readout speed is 500K pixels/s. Finally, a series of stability tests were carried out on the camera system.

physics.ins-det↗

arXiv2018

STS Classification with Dual-stream CNN

The structured time series (STS) classification problem requires the modeling of interweaved spatiotemporal dependency. most previous STS classification methods model the spatial and temporal dependencies independently. Due to the complexity of the STS data, we argue that a desirable STS classification method should be a holistic framework that can be made as adaptive and flexible as possible. This motivates us to design a deep neural network with such merits. Inspired by the dual-stream hypothesis in neural science, we propose a novel dual-stream framework for modeling the interweaved spatiotemporal dependency, and develop a convolutional neural network within this framework that aims to achieve high adaptability and flexibility in STS configurations from various diagonals, i.e., sequential order, dependency range and features. The proposed architecture is highly modularized and scalable, making it easy to be adapted to specific tasks. The effectiveness of our model is demonstrated through experiments on synthetic data as well as benchmark datasets for skeleton based activity recognition.

cs.CV↗

arXiv2018

The First Release of the AST3-1 Point Source Catalogue from Dome A, Antarctica

The three Antarctic Survey Telescopes (AST3) aim to carry out time domain imaging survey at Dome A, Antarctica. The first of the three telescopes (AST3-1) was successfully deployed on January 2012. AST3-1 is a 500\,mm aperture modified Schmidt telescope with a 680\,mm diameter primary mirror. AST3-1 is equipped with a SDSS $i$ filter and a 10k $\times$ 10k frame transfer CCD camera, reduced to 5k $\times$ 10k by electronic shuttering, resulting in a 4.3 deg$^2$ field-of-view. To verify the capability of AST3-1 for a variety of science goals, extensive commissioning was carried out between March and May 2012. The commissioning included a survey covering 2000 deg$^2$ as well as the entire Large and Small Magellanic Clouds. Frequent repeated images were made of the center of the Large Magellanic Cloud, a selected exoplanet transit field, and fields including some Wolf-Rayet stars. Here we present the data reduction and photometric measurements of the point sources observed by AST3-1. We have achieved a survey depth of 19.3\,mag in 60 s exposures with 5\,mmag precision in the light curves of bright stars. The facility achieves sub-mmag photometric precision under stable survey conditions, approaching its photon noise limit. These results demonstrate that AST3-1 at Dome A is extraordinarily competitive in time-domain astronomy, including both quick searches for faint transients and the detection of tiny transit signals.

astro-ph.IM↗

arXiv2018

Regularizing Output Distribution of Abstractive Chinese Social Media Text Summarization for Improved Semantic Consistency

Abstractive text summarization is a highly difficult problem, and the sequence-to-sequence model has shown success in improving the performance on the task. However, the generated summaries are often inconsistent with the source content in semantics. In such cases, when generating summaries, the model selects semantically unrelated words with respect to the source content as the most probable output. The problem can be attributed to heuristically constructed training data, where summaries can be unrelated to the source content, thus containing semantically unrelated words and spurious word correspondence. In this paper, we propose a regularization approach for the sequence-to-sequence model and make use of what the model has learned to regularize the learning objective to alleviate the effect of the problem. In addition, we propose a practical human evaluation method to address the problem that the existing automatic evaluation method does not evaluate the semantic consistency with the source content properly. Experimental results demonstrate the effectiveness of the proposed approach, which outperforms almost all the existing models. Especially, the proposed approach improves the semantic consistency by 4\% in terms of human evaluation.

cs.CL↗

arXiv2018

N2RPP: An Adversarial Network to Rebuild Plantar Pressure for ACLD Patients

Foot is a vital part of human, and lots of valuable information is embedded. Plantar pressure is one of which contains this information and it describes human walking features. It is proved that once one has trouble with lower limb, the distribution of plantar pressure will change to some degree. Plantar pressure can be converted into images according to some simple standards. In this paper, we take full advantage of these plantar pressure images for medical usage. We present N2RPP, a generative adversarial network (GAN) based method to rebuild plantar pressure images of anterior cruciate ligament deficiency (ACLD) patients from low dimension features, which are extracted from an autoencoder. Through the result of experiments, the extracted features are a useful representation to describe and rebuild plantar pressure images. According to N2RPP's results, we find out that there are several noteworthy differences between normal people and patients. This can provide doctors a rough direction of adjusting plantar pressure to a better distribution to reduce patients' sore and pain during the rehabilitation treatment for ACLD.

cs.CV↗

arXiv2018

Algebraic dual polynomials for the equivalence of curl-curl problems

In this paper we will consider two curl-curl equation in two dimensions. One curl-curl problem for a scalar quantity $F$ and one problem for a vector field $\bf{E}$. For Dirichlet boundary conditions $\bf{n} \times \bf{E} =$ $ \hat{E}_{\dashv}$ on $\bf{E}$ and Neumann boundary conditions $\bf{n} \times \mbox{curl}$ $F=\hat{E}_{\dashv}$, we expect the solutions to satisfy $\bf{E}=\mbox{curl}$ $F$. When we use algebraic dual polynomial representations, these identities continue to hold at the discrete level. Equivalence will be proved and illustrated with a computational example.

math.NA↗

arXiv2018

3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network

Low-dose computed tomography (CT) has attracted a major attention in the medical imaging field, since CT-associated x-ray radiation carries health risks for patients. The reduction of CT radiation dose, however, compromises the signal-to-noise ratio, and may compromise the image quality and the diagnostic performance. Recently, deep-learning-based algorithms have achieved promising results in low-dose CT denoising, especially convolutional neural network (CNN) and generative adversarial network (GAN). This article introduces a Contracting Path-based Convolutional Encoder-decoder (CPCE) network in 2D and 3D configurations within the GAN framework for low-dose CT denoising. A novel feature of our approach is that an initial 3D CPCE denoising model can be directly obtained by extending a trained 2D CNN and then fine-tuned to incorporate 3D spatial information from adjacent slices. Based on the transfer learning from 2D to 3D, the 3D network converges faster and achieves a better denoising performance than that trained from scratch. By comparing the CPCE with recently published methods based on the simulated Mayo dataset and the real MGH dataset, we demonstrate that the 3D CPCE denoising model has a better performance, suppressing image noise and preserving subtle structures.

cs.CV↗

arXiv2018

Rational Solutions of High-Order Algebraic Ordinary Differential Equations

We consider algebraic ordinary differential equations (AODEs) and study their polynomial and rational solutions. A sufficient condition for an AODE to have a degree bound for its polynomial solutions is presented. An AODE satisfying this condition is called \emph{noncritical}. We prove that usual low order classes of AODEs are noncritical. For rational solutions, we determine a class of AODEs, which are called \emph{maximally comparable}, such that the poles of their rational solutions are recognizable from their coefficients. This generalizes a fact from linear AODEs, that the poles of their rational solutions are the zeros of the corresponding highest coefficient. An algorithm for determining all rational solutions, if there is any, of certain maximally comparable AODEs, which covers $78.54\%$ AODEs from a standard differential equations collection by Kamke, is presented.

cs.SC↗

arXiv2018

A Grouping Based Cooperative Driving Strategy for CAVs Merging Problems

In general, there are two kinds of cooperative driving strategies, planning based strategy and ad hoc negotiation based strategy, for connected and automated vehicles (CAVs) merging problems. The planning based strategy aims to find the global optimal passing order, but it is time-consuming when the number of considered vehicles is large. In contrast, the ad hoc negotiation based strategy runs fast, but it always finds a local optimal solution. In this paper, we propose a grouping based cooperative driving strategy to make a good tradeoff between time consumption and coordination performance. The key idea is to fix the passing orders for some vehicles whose inter-vehicle headways are small enough (e.g., smaller than the pre-selected grouping threshold). From the viewpoint of optimization, this method reduces the size of the solution space. A brief analysis shows that the sub-optimal passing order found by the grouping based strategy has a high probability to be close to the global optimal passing order, if the grouping threshold is appropriately chosen. A series of simulation experiments are carried out to validate that the proposed strategy can yield a satisfied coordination performance with less time consumption and is promising to be used in practice.

eess.SY↗

arXiv2018

Impulsive Control for G-AIMD Dynamics with Relaxed and Hard Constraints

Motivated by various applications from Internet congestion control to power control in smart grids and electric vehicle charging, we study Generalized Additive Increase Multiplicative Decrease (G-AIMD) dynamics under impulsive control in continuous time with the time average alpha-fairness criterion. We first show that the control under relaxed constraints can be described by a threshold. Then, we propose a Whittle-type index heuristic for the hard constraint problem. We prove that in the homogeneous case the index policy is asymptotically optimal when the number of users is large.

math.OC↗

arXiv2018

The Strong Decays of $P-$wave Mixing Heavy-Light $1^+$ States

Many $P-$wave mixing heavy-light $1^+$ states have not been discovered by experiment, some of them have been discovered but without the information of width, or with large uncertainty widths. In this paper, we study the strong decays of $P-$wave mixing heavy-light $1^+$ states $D^0$, $D^\pm$, $D_s^\pm$, $B^0$, $B^\pm$ and $B_s$ by the improved Bethe-Salpeter(B-S) method in two conditions of mixing angle $θ$: one is $θ=35.3^\circ$; another is considering the correction to mixing angle $θ=35.3^\circ+θ_1$. And we get some valuable predictions of the strong decay widths: $Γ(D_1^{\prime0})=232$ MeV, $Γ(D_1^0)=21.5$ MeV, $Γ(D_1^{\prime\pm})=232$ MeV, $Γ(D_1^\pm)=21.5$ MeV, $Γ(D_{s1}^{\prime\pm})=0.0101$ MeV, $Γ(D_{s1}^{\pm})=0.950$ MeV, $Γ(B_1^{\prime\pm})=263$ MeV, $Γ(B_1^{\pm})=16.8$ MeV, $Γ(B_{s1}^{\prime})=0.01987$ MeV and $Γ(B_{s1})=0.412$ MeV. We find that the decay widths of $D_{s1}^{\pm}$ and $B_{s1}$ are very sensitive to the mixing angle. And our results will provide the theoretical assistance by the future experiments.

hep-ph↗

arXiv2018

Few-View CT Reconstruction with Group-Sparsity Regularization

Classical total variation (TV) based iterative reconstruction algorithms assume that the signal is piecewise smooth, which causes reconstruction results to suffer from the over-smoothing effect. To address this problem, this work presents a novel computed tomography (CT) reconstruction method for the few-view problem called the group-sparsity regularization-based simultaneous algebraic reconstruction technique (GSR-SART). Group-based sparse representation, which utilizes the concept of a group as the basic unit of sparse representation instead of a patch, is introduced as the image domain prior regularization term to eliminate the over-smoothing effect. By grouping the nonlocal patches into different clusters with similarity measured by Euclidean distance, the sparsity and nonlocal similarity in a single image are simultaneously explored. The split Bregman iteration algorithm is applied to obtain the numerical scheme. Experimental results demonstrate that our method both qualitatively and quantitatively outperforms several existing reconstruction methods, including filtered back projection, expectation maximization, SART, and TV-based projections onto convex sets.

physics.med-ph↗

arXiv2018

Not-So-Random Features

We propose a principled method for kernel learning, which relies on a Fourier-analytic characterization of translation-invariant or rotation-invariant kernels. Our method produces a sequence of feature maps, iteratively refining the SVM margin. We provide rigorous guarantees for optimality and generalization, interpreting our algorithm as online equilibrium-finding dynamics in a certain two-player min-max game. Evaluations on synthetic and real-world datasets demonstrate scalability and consistent improvements over related random features-based methods.

cs.LG↗