arXiv subjects

Yu Liu

Publications and source records attributed to Yu Liu.

At least 1,081 records · Page 60Linked to original sources

arXiv2017

On a small-scale EUV wave: the driving mechanism and the associated oscillating filament

We present observations of a small-scale Extreme-ultraviolet (EUV) wave that was associated with a mini-filament eruption and a GOES B1.9 micro-fare in the quiet Sun region. The initiation of the event was due to the photospheric magnetic emergence and cancellation in the eruption source region, which first caused the ejection of a small plasma ejecta, then the ejecta impacted on a nearby mini-filament and thereby led to the filament's eruption and the associated fare. During the filament eruption, an EUV wave at a speed of 182 { 317 km/s was formed ahead of an expanding coronal loop, which propagated faster than the expanding loop and showed obvious deceleration and refection during the propagation. In addition, the EUV wave further resulted in the transverse oscillation of a remote filament whose period and damping time are 15 and 60 minutes, respectively. Based on the observational results, we propose that the small-scale EUV wave should be a fast-mode magnetosonic wave that was driven by the the expanding coronal loop. Moreover, with the application of filament seismology, it is estimated that the radial magnetic field strength is about 7 Gauss. The observations also suggest that small-scale EUV waves associated with miniature solar eruptions share similar driving mechanism and observational characteristics with their large-scale counterparts.

astro-ph.SR↗

arXiv2017

Effect of Massive Neutrinos on the Position of Cold Dark Matter Halo: Revealed via Delaunay Triangulation Void

Using cosmological $N$-body simulation which coevolves cold dark matter (CDM) and neutrino particles, we discover the local effect of massive neutrinos on the spatial distribution of CDM halos, reflected on properties of the Delaunay Triangulation (DT) voids. Smaller voids are generally in regions with higher neutrino abundance and so their surrounding halos are impacted by a stronger neutrino free streaming. This makes the voids larger (surrounding halos being washed outward the void center). On the contrary, larger voids are generally in regions with lower neutrino abundance and so their surrounding halos are less impacted by neutrino free streaming, making the voids smaller (surrounding halos being squeezed toward the void center). This characteristic change of the spatial distribution of the halos suppresses the 2-point correlation function of halos on scales $\sim$ 1 Mpc$/h$ and significantly skews the number function of the DT voids, which serve as measurable neutrino effects in current or future galaxy surveys.

astro-ph.CO↗

arXiv2017

Rethinking Feature Discrimination and Polymerization for Large-scale Recognition

Feature matters. How to train a deep network to acquire discriminative features across categories and polymerized features within classes has always been at the core of many computer vision tasks, specially for large-scale recognition systems where test identities are unseen during training and the number of classes could be at million scale. In this paper, we address this problem based on the simple intuition that the cosine distance of features in high-dimensional space should be close enough within one class and far away across categories. To this end, we proposed the congenerous cosine (COCO) algorithm to simultaneously optimize the cosine similarity among data. It inherits the softmax property to make inter-class features discriminative as well as shares the idea of class centroid in metric learning. Unlike previous work where the center is a temporal, statistical variable within one mini-batch during training, the formulated centroid is responsible for clustering inner-class features to enforce them polymerized around the network truncus. COCO is bundled with discriminative training and learned end-to-end with stable convergence. Experiments on five benchmarks have been extensively conducted to verify the effectiveness of our approach on both small-scale classification task and large-scale human recognition problem.

cs.CV↗

arXiv2017

MLBench: How Good Are Machine Learning Clouds for Binary Classification Tasks on Structured Data?

We conduct an empirical study of machine learning functionalities provided by major cloud service providers, which we call machine learning clouds. Machine learning clouds hold the promise of hiding all the sophistication of running large-scale machine learning: Instead of specifying how to run a machine learning task, users only specify what machine learning task to run and the cloud figures out the rest. Raising the level of abstraction, however, rarely comes free - a performance penalty is possible. How good, then, are current machine learning clouds on real-world machine learning workloads? We study this question with a focus on binary classication problems. We present mlbench, a novel benchmark constructed by harvesting datasets from Kaggle competitions. We then compare the performance of the top winning code available from Kaggle with that of running machine learning clouds from both Azure and Amazon on mlbench. Our comparative study reveals the strength and weakness of existing machine learning clouds and points out potential future directions for improvement.

cs.DC↗

arXiv2017

Tuning of few-electron states and optical absorption anisotropy in GaAs quantum rings

The electronic and optical properties of a GaAs quantum ring (QR) with few electrons in the presence of the Rashba spin-orbit interaction (RSOI) and the Dresselhaus spin-orbit interaction (DSOI) have been investigated theoretically. Configuration interaction (CI) method is employed to calculate the eigenvalues and eigenstates of the multiple-electron QR accurately. Our numerical results demonstrate that the symmetry breaking induced by the RSOI and DSOI leads to an anisotropic distribution of multi-electron states. The Coulomb interaction offers additional modulation of the electron distribution and thus the optical absorption indices in the quantum rings. By tuning the magnetic/electric fields and/or electron numbers in a quantum ring, one can change its optical property significantly. Our theory provides a new way to control multi-electron states and optical properties of QR by hybrid modulations or by electrical means only.

cond-mat.mes-hall↗

arXiv2017

Towards diluted magnetism in TaAs

Magnetism in Weyl semimetals is desired to investigate the interaction between the magnetic moments and Weyl fermions, e.g. to explore anomalous quantum Hall phenomena. Here we demonstrate that proton irradiation is an effective tool to induce ferromagnetism in the Weyl semimetal TaAs. The intrinsic magnetism is observed with a transition temperature above room temperature. The magnetic moments from d states are found to be localized around Ta atoms. Further, the first-principles calculations indicate that the d states localized on the nearest-neighbor Ta atoms of As vacancy sites are responsible for the observed magnetic moments and the long-ranged magnetic order. The results show the feasibility of inducing ferromagnetism in Weyl semimetals so that they may facilitate the applications of this material in spintronics.

cond-mat.mtrl-sci↗

arXiv2017

Numerical Approximation of Random Periodic Solutions of Stochastic Differential Equations

In this paper, we discuss the numerical approximation of random periodic solutions (r.p.s.) of stochastic differential equations (SDEs) with multiplicative noise. We prove the existence of the random periodic solution as the limit of the pull-back flow when the starting time tends to $-\infty$ along the multiple integrals of the period. As the random periodic solution is not explicitly constructible, it is useful to study the numerical approximation. We discretise the SDE using the Euler-Maruyama scheme and moldiflied Milstein scheme. Subsequently we obtain the existence of the random periodic solution as the limit of the pull-back of the discretised SDE. We prove that the latter is an approximated random periodic solution with an error to the exact one at the rate of $\sqrt {Δt}$ in the mean-square sense in Euler-Maruyama method and $Δt$ in the Milstein method. We also obtain the weak convergence result for the approximation of the periodic measure.

math.NA↗

arXiv2017

Real-time detection and resolution of atom bumping in crystallographic models

A basic principle in crystal structure determination is that there should be proper distances between adjacent atoms. Therefore, detection of atom bumping is of fundamental significance in structure determination, especially in the direct space method where crystallographic models are just randomly generated. Presented in this article is an algorithm that detects atom bonding in a unit cell based on the sweep and prune algorithm of axis-aligned bounding boxes (AABBs) and running in $O(n \log n)$ time bound, where $n$ is the total number of atoms in the unit cell. This algorithm only needs the positions of individual atoms in the unit cell and does not require any prior knowledge of existing bonds, and is thus suitable for modelling of inorganic crystals where the bonding relations are often unknown a priori. With this algorithm, computation routines requiring bonding information, eg. anti-bumping and computation of coordination numbers and valences, can be performed efficiently. As an example application, an evaluation function for atom bumping is proposed, which can be used for real-time elimination of crystallographic models with unreasonably short bonds during the procedure of global optimisation in the direct space method.

cond-mat.mtrl-sci↗

arXiv2017

The radiation mechanism of fast radio bursts

Fast radio bursts are radio transients observed mainly around 1.5 GHz. Their peak frequency decreases at a rate of 100 ~ 500 MHz/s and some of them have a broader pulse with an exponentially decaying tail. Common assumptions for fast radio bursts include a dispersion effect resulting in the peak frequency drifting and a scattering effect resulting in pulse broadening. These assumptions attribute the abnormally large dispersion measure and scattering measure to the environmental medium of the host galaxy. Here we show that the radiation of fast radio bursts can be explained as an undulator radiation and the large dispersion measure can be due to a motion effect mainly from the rotation of the source which is probably variable stars. In our scenario, the pulse broadening is near-field effects and the pulse itself represents a Fresnel diffraction pattern sweeping the observer. Our work is the first analysis of properties of fast radio bursts in the context of a special mechanism of the radiation instead of a special propagation environment of the radiation.

astro-ph.HE↗

arXiv2017

Large magnetoresistance in type-II Weyl semimetal WP$_2$

We report magnetotransport study on type-II Weyl semimetal WP$_2$ single crystals. Magnetoresistance (MR) exhibits a nonsaturating $H^{n}$ field dependence (14,300\% at 2 K and 9 T) whereas systematic violation of Kohler's rule was observed. Quantum oscillations reveal a complex multiband electronic structure. The cyclotron effective mass close to the mass of free electron m$_e$ was observed in quantum oscillations along $b$-axis, while reduced effective mass of about 0.5$m_e$ was observed in $a$-axis quantum oscillations, suggesting Fermi surface anisotropy. Temperature dependence of the resistivity shows a large upturn that cannot be explained by the multi-band magnetoresistance of conventional metals. Even though crystal structure of WP$_{2}$ is not layered as in transition metal dichalcogenides, quantum oscillations suggest partial two-dimensional character.

cond-mat.mtrl-sci↗

arXiv2017

Successive Two-sided loop Jets Caused by Magnetic Reconnection between Two adjacent Filamentary Threads

We present observational analysis of two successive two-sided loop jets observed by the ground-based New Vacuum Solar Telescope (NVST) and the space-borne Solar Dynamics Observatory ( SDO). The two successive two-sided loop jets manifested similar evolution process and both were associated with the interaction of two small-scale adjacent filamentary threads, magnetic emerging and cancellation processes at the jet's source region. High temporal and high spatial resolution observations reveal that the two adjacent ends of the two filamentary threads are rooted in opposite magnetic polarities within the source region. The two threads approached to each other, and then an obvious brightening patch is observed at the interaction position. Subsequently, a pair of hot plasma ejections are observed heading to opposite directions along the paths of the two filamentary threads, and with a typical speed of two-sided loop jets of the order 150 km/s. Close to the end of the second jet, we report the formation of a bright hot loop structure at the source region, which suggests the formation of new loops during the interaction. Based on the observational results, we propose that the observed two-sided loop jets are caused by the magnetic reconnection between the two adjacent filamentary threads, largely different from the previous scenario that a two-sided loop jet is generated by magnetic reconnection between an emerging bipole and the overlying horizontal magnetic fields.

astro-ph.SR↗

arXiv2017

Topological phase in $1D$ topological Kondo insulator: $Z_{2}$ topological insulator, Haldane-like phase and Kondo breakdown

We have simulated a half-filled $1D$ $p$-wave periodic Anderson model with numerically exact projector quantum Monte Carlo technique, and the system is indeed located in the Haldane-like state as detected in previous works on the $p$-wave Kondo lattice model, though the soluble non-interacting limit corresponds to the conventional $Z_{2}$ topological insulator. The site-resolved magnetization in an open boundary system and strange correlator for the periodic boundary have been used to identify the mentioned topological states. Interestingly, the edge magnetization in the Haldane-like state is not saturated to unit magnetic moment due to the intrinsic charge fluctuation in our periodic Anderson-like model, which is beyond the description of the Kondo lattice-like model in existing literature. The finding here underlies the correlation driven topological state in this prototypical interacting topological state of matter and naive use of non-interacting picture should be taken care. Moreover, no trace of the surface Kondo breakdown at zero temperature is observed and it is suspected that frustration-like interaction may be crucial in inducing such radical destruction of Kondo screening. The findings here may be relevant to our understanding of interacting topological materials like topological Kondo insulator candidate SmB$_{6}$.

cond-mat.str-el↗

arXiv2017

Scale-Aware Face Detection

Convolutional neural network (CNN) based face detectors are inefficient in handling faces of diverse scales. They rely on either fitting a large single model to faces across a large scale range or multi-scale testing. Both are computationally expensive. We propose Scale-aware Face Detector (SAFD) to handle scale explicitly using CNN, and achieve better performance with less computation cost. Prior to detection, an efficient CNN predicts the scale distribution histogram of the faces. Then the scale histogram guides the zoom-in and zoom-out of the image. Since the faces will be approximately in uniform scale after zoom, they can be detected accurately even with much smaller CNN. Actually, more than 99% of the faces in AFW can be covered with less than two zooms per image. Extensive experiments on FDDB, MALF and AFW show advantages of SAFD.

cs.CV↗

arXiv2017

Magnetotransport properties of MoP$_2$

We report magnetotransport and de Haas-van Alphen (dHvA) effect studies on MoP$_2$ single crystals, predicted to be type-2 Weyl semimetal with four pairs of robust Weyl points located below the Fermi level and long Fermi arcs. The temperature dependence of resistivity shows a peak before saturation, which does not move with magnetic field. Large nonsaturating magnetoresistance (MR) was observed, and the field dependence of MR exhibits a crossover from semicalssical weak-field $B^2$ dependence to the high-field linear-field dependence, indicating the presence of Dirac linear energy dispersion. In addition, systematic violation of Kohler's rule was observed, consistent with multiband electronic transport. Strong spin-orbit coupling (SOC) splitting has an effect on dHvA measurements whereas the angular-dependent dHvA orbit frequencies agree well with the calculated Fermi surface. The cyclotron effective mass $\sim$ 1.6$m_e$ indicates the bands might be trivial, possibly since the Weyl points are located below the Fermi level. Interestingly, quasi-two dimensional(2D) band structure is observed even though the crystal structure of MoP$_2$ is not layered.

cond-mat.mtrl-sci↗

arXiv2017

Critical behavior of quasi-two-dimensional semiconducting ferromagnet CrGeTe$_3$

The critical properties of the single-crystalline semiconducting ferromagnet CrGeTe$_3$ were investigated by bulk dc magnetization around the paramagnetic to ferromagnetic phase transition. Critical exponents $β= 0.200\pm0.003$ with critical temperature $T_c = 62.65\pm0.07$ K and $γ= 1.28\pm0.03$ with $T_c = 62.75\pm0.06$ K are obtained by the Kouvel-Fisher method whereas $δ= 7.96\pm0.01$ is obtained by the critical isotherm analysis at $T_c = 62.7$ K. These critical exponents obey the Widom scaling relation $δ= 1+γ/β$, indicating self-consistency of the obtained values. With these critical exponents the isotherm $M(H)$ curves below and above the critical temperatures collapse into two independent universal branches, obeying the single scaling equation $m = f_\pm(h)$, where $m$ and $h$ are renormalized magnetization and field, respectively. The determined exponents match well with those calculated from the results of renormalization group approach for a two-dimensional Ising system coupled with long-range interaction between spins decaying as $J(r)\approx r^{-(d+σ)}$ with $σ=1.52$.

cond-mat.mtrl-sci↗

arXiv2017

Unsupervised Sequence Classification using Sequential Output Statistics

We consider learning a sequence classifier without labeled data by using sequential output statistics. The problem is highly valuable since obtaining labels in training data is often costly, while the sequential output statistics (e.g., language models) could be obtained independently of input data and thus with low or no cost. To address the problem, we propose an unsupervised learning cost function and study its properties. We show that, compared to earlier works, it is less inclined to be stuck in trivial solutions and avoids the need for a strong generative model. Although it is harder to optimize in its functional form, a stochastic primal-dual gradient method is developed to effectively solve the problem. Experiment results on real-world datasets demonstrate that the new unsupervised learning method gives drastically lower errors than other baseline methods. Specifically, it reaches test errors about twice of those obtained by fully supervised learning.

cs.LG↗

arXiv2017

Interaction between magnetic moments and itinerant carriers in d0 ferromagnetic SiC

Elucidating the interaction between magnetic moments and itinerant carriers is an important step to spintronic applications. Here, we investigate magnetic and transport properties in d0 ferromagnetic SiC single crystals prepared by postimplantation pulsed laser annealing. Magnetic moments are contributed by the p states of carbon atoms, but their magnetic circular dichroism is different from that in semi-insulating SiC samples. The anomalous Hall effect and negative magnetoresistance indicate the influence of d0 spin order on free carriers. The ferromagnetism is relatively weak in N-implanted SiC compared with that in Al-implanted SiC after annealing. The results suggest that d0 magnetic moments and itinerant carriers can interact with each other, which will facilitate the development of SiC spintronic devices with d0 ferromagnetism.

cond-mat.mtrl-sci↗

arXiv2017

Quality Aware Network for Set to Set Recognition

This paper targets on the problem of set to set recognition, which learns the metric between two image sets. Images in each set belong to the same identity. Since images in a set can be complementary, they hopefully lead to higher accuracy in practical applications. However, the quality of each sample cannot be guaranteed, and samples with poor quality will hurt the metric. In this paper, the quality aware network (QAN) is proposed to confront this problem, where the quality of each sample can be automatically learned although such information is not explicitly provided in the training stage. The network has two branches, where the first branch extracts appearance feature embedding for each sample and the other branch predicts quality score for each sample. Features and quality scores of all samples in a set are then aggregated to generate the final feature embedding. We show that the two branches can be trained in an end-to-end manner given only the set-level identity annotation. Analysis on gradient spread of this mechanism indicates that the quality learned by the network is beneficial to set-to-set recognition and simplifies the distribution that the network needs to fit. Experiments on both face verification and person re-identification show advantages of the proposed QAN. The source code and network structure can be downloaded at https://github.com/sciencefans/Quality-Aware-Network.

cs.CV↗