arXiv subjects

Chang Liu

Publications and source records attributed to Chang Liu.

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

arXiv2017

How Much Data is Enough? A Statistical Approach with Case Study on Longitudinal Driving Behavior

Big data has shown its uniquely powerful ability to reveal, model, and understand driver behaviors. The amount of data affects the experiment cost and conclusions in the analysis. Insufficient data may lead to inaccurate models while excessive data waste resources. For projects that cost millions of dollars, it is critical to determine the right amount of data needed. However, how to decide the appropriate amount has not been fully studied in the realm of driver behaviors. This paper systematically investigates this issue to estimate how much naturalistic driving data (NDD) is needed for understanding driver behaviors from a statistical point of view. A general assessment method is proposed using a Gaussian kernel density estimation to catch the underlying characteristics of driver behaviors. We then apply the Kullback-Liebler divergence method to measure the similarity between density functions with differing amounts of NDD. A max-minimum approach is used to compute the appropriate amount of NDD. To validate our proposed method, we investigated the car-following case using NDD collected from the University of Michigan Safety Pilot Model Deployment (SPMD) program. We demonstrate that from a statistical perspective, the proposed approach can provide an appropriate amount of NDD capable of capturing most features of the normal car-following behavior, which is consistent with the experiment settings in many literatures.

cs.LG↗

arXiv2017

Large-area epitaxial growth of MoSe2 via an incandescent molybdenum source

We have developed an incandescent Mo source to fabricate large-area single-crystalline MoSe2 thin films. The as-grown MoSe2 thin films were characterized using transmission electron microscopy, energy dispersive X-ray spectroscopy, atomic force microscopy, Raman spectroscopy, photoluminescence, reflection high energy electron diffraction (RHEED) and angular resolved photoemission spectroscopy (ARPES). A new Raman characteristic peak at 1591 cm-1 was identified. Results from Raman spectroscopy, photoluminescence, RHEED and ARPES studies consistently reveal that large-area single crystalline mono-layer of MoSe2 could be achieved by this technique. This technique enjoys several advantages over conventional approaches and could be extended to the growth of other two-dimensional layered materials containing a low-vapor-pressure element.

cond-mat.mtrl-sci↗

arXiv2017

Predicting Solar Flares Using SDO/HMI Vector Magnetic Data Product and Random Forest Algorithm

Adverse space weather effects can often be traced to solar flares, prediction of which has drawn significant research interests. The Helioseismic and Magnetic Imager (HMI) produces full-disk vector magnetograms with continuous high cadence, while flare prediction efforts utilizing this unprecedented data source are still limited. Here we report results of flare prediction using physical parameters provided by the Space-weather HMI Active Region Patches (SHARP) and related data products. We survey X-ray flares occurred from 2010 May to 2016 December, and categorize their source regions into four classes (B, C, M, and X) according to the maximum GOES magnitude of flares they generated. We then retrieve SHARP related parameters for each selected region at the beginning of its flare date to build a database. Finally, we train a machine-learning algorithm, called random forest (RF), to predict the occurrence of a certain class of flares in a given active region within 24 hours, evaluate the classifier performance using the 10-fold cross validation scheme, and characterize the results using standard performance metrics. Compared to previous works, our experiments indicate that using the HMI parameters and RF is a valid method for flare forecasting with fairly reasonable prediction performance. To our knowledge, this is the first time that RF is used to make multi-class predictions of solar flares. We also find that the total unsigned quantities of vertical current, current helicity, and flux near polarity inversion line are among the most important parameters for classifying flaring regions into different classes.

astro-ph.SR↗

arXiv2017

Witnessing a Large-scale Slipping Magnetic Reconnection along a Dimming Channel during a Solar Flare

We report the intriguing large-scale dynamic phenomena associated with the M6.5 flare~(SOL2015-06-22T18:23) in NOAA active region 12371, observed by RHESSI, Fermi, and the Atmospheric Image Assembly (AIA) and Magnetic Imager (HMI) on the Solar Dynamic Observatory (SDO). The most interesting feature of this event is a third ribbon (R3) arising in the decay phase, propagating along a dimming channel (seen in EUV passbands) towards a neighboring sunspot. The propagation of R3 occurs in the presence of hard X-ray footpoint emission, and is broadly visible at temperatures from 0.6~MK to over 10~MK through the Differential Emission Measure (DEM) analysis. The coronal loops then undergo an apparent slipping motion following the same path of R3, after a $\sim$ 80~min delay. To understand the underlying physics, we investigate the magnetic configuration and the thermal structure of the flaring region. Our results are in favor of a slipping-type reconnection followed by the thermodynamic evolution of coronal loops. In comparison with those previously reported slipping reconnection events, this one proceeds across a particularly long distance ($\sim$60 Mm) over a long period of time ($\sim$50 min), and shows two clearly distinguished phases: the propagation of the footpoint brightening driven by nonthermal particle injection and the apparent slippage of loops governed by plasma heating and subsequent cooling.

astro-ph.SR↗

arXiv2017

Regularizing Face Verification Nets For Pain Intensity Regression

Limited labeled data are available for the research of estimating facial expression intensities. For instance, the ability to train deep networks for automated pain assessment is limited by small datasets with labels of patient-reported pain intensities. Fortunately, fine-tuning from a data-extensive pre-trained domain, such as face verification, can alleviate this problem. In this paper, we propose a network that fine-tunes a state-of-the-art face verification network using a regularized regression loss and additional data with expression labels. In this way, the expression intensity regression task can benefit from the rich feature representations trained on a huge amount of data for face verification. The proposed regularized deep regressor is applied to estimate the pain expression intensity and verified on the widely-used UNBC-McMaster Shoulder-Pain dataset, achieving the state-of-the-art performance. A weighted evaluation metric is also proposed to address the imbalance issue of different pain intensities.

cs.CV↗

arXiv2017

Flare differentially rotates sunspot on Sun's surface

Sunspots are concentrations of magnetic field visible on the solar surface (photosphere). It was considered implausible that solar flares, as resulted from magnetic reconnection in the tenuous corona, would cause a direct perturbation of the dense photosphere involving bulk motion. Here we report the sudden flare-induced rotation of a sunspot using the unprecedented spatiotemporal resolution of the 1.6 m New Solar Telescope, supplemented by magnetic data from the Solar Dynamics Observatory. It is clearly observed that the rotation is non-uniform over the sunspot: as the flare ribbon sweeps across, its different portions accelerate (up to 50 deg per hr) at different times corresponding to peaks of flare hard X-ray emission. The rotation may be driven by the surface Lorentz-force change due to the back reaction of coronal magnetic restructuring and is accompanied by a downward Poynting flux. These results have direct consequences for our understanding of energy and momentum transportation in the flare-related phenomena.

astro-ph.SR↗

arXiv2017

Self-supporting structure design in additive manufacturing through explicit topology optimization

One of the challenging issues in additive manufacturing (AM) oriented topology optimization is how to design structures that are self-supportive in a manufacture process without introducing additional supporting materials. In the present contribution, it is intended to resolve this problem under an explicit topology optimization framework where optimal structural topology can be found by optimizing a set of explicit geometry parameters. Two solution approaches established based on the Moving Morphable Components (MMC) and Moving Morphable Voids (MMV) frameworks, respectively, are proposed and some theoretical issues associated with AM oriented topology optimization are also analyzed. Numerical examples provided demonstrate the effectiveness of the proposed methods.

physics.app-ph↗

arXiv2017

Multi-photon boson-sampling machines beating early classical computers

Boson sampling is considered as a strong candidate to demonstrate the quantum computational supremacy over classical computers. However, previous proof-of-principle experiments suffered from small photon number and low sampling rates owing to the inefficiencies of the single-photon sources and multi-port optical interferometers. Here, we develop two central components for high-performance boson sampling: robust multi-photon interferometers with 0.99 transmission rate, and actively demultiplexed single-photon sources from a quantum-dot-micropillar with simultaneously high efficiency, purity and indistinguishability. We implement and validate 3-, 4-, and 5-photon boson sampling, and achieve sampling rates of 4.96 kHz, 151 Hz, and 4 Hz, respectively, which are over 24,000 times faster than the previous experiments, and over 220 times faster than obtaining one sample through calculating the matrices permanent using the first electronic computer (ENIAC) and transistorized computer (TRADIC) in the human history. Our architecture is feasible to be scaled up to larger number of photons and with higher rate to race against classical computers, and might provide experimental evidence against the Extended Church-Turing Thesis.

quant-ph↗

arXiv2017

On the apparent permeability of porous media in rarefied gas flows

The apparent gas permeability of the porous medium is an important parameter in the prediction of unconventional gas production, which was first investigated systematically by Klinkenberg in 1941 and found to increase with the reciprocal mean gas pressure (or equivalently, the Knudsen number). Although the underlying rarefaction effects are well-known, the reason that the correction factor in Klinkenberg's famous equation decreases when the Knudsen number increases has not been fully understood. Most of the studies idealize the porous medium as a bundle of straight cylindrical tubes, however, according to the gas kinetic theory, this only results in an increase of the correction factor with the Knudsen number, which clearly contradicts Klinkenberg's experimental observations. Here, by solving the Bhatnagar-Gross-Krook equation in simplified (but not simple) porous media, we identify, for the first time, two key factors that can explain Klinkenberg's experimental results: the tortuous flow path and the non-unitary tangential momentum accommodation coefficient for the gas-surface interaction. Moreover, we find that Klinkenberg's results can only be observed when the ratio between the apparent and intrinsic permeabilities is $\lesssim30$; at large ratios (or Knudsen numbers) the correction factor increases with the Knudsen number. Our numerical results could also serve as benchmarking cases to assess the accuracy of macroscopic models and/or numerical schemes for the modeling/simulation of rarefied gas flows in complex geometries over a wide range of gas rarefaction.

physics.flu-dyn↗

arXiv2017

Experimental quantum channel simulation

Quantum simulation is of great importance in quantum information science. Here, we report an experimental quantum channel simulator imbued with an algorithm for imitating the behavior of a general class of quantum systems. The reported quantum channel simulator consists of four single-qubit gates and one controlled-NOT gate. All types of quantum channels can be decomposed by the algorithm and implemented on this device. We deploy our system to simulate various quantum channels, such as quantum-noise channels and weak quantum measurement. Our results advance experimental quantum channel simulation, which is integral to the goal of quantum information processing.

quant-ph↗

arXiv2017

High-resolution observations of flare precursors in the low solar atmosphere

Solar flares are generally believed to be powered by free magnetic energy stored in the corona, but the build up of coronal energy alone may be insufficient for the imminent flare occurrence. The flare onset mechanism is a critical but less understood problem, insights into which could be gained from small-scale energy releases known as precursors, which are observed as small pre-flare brightenings in various wavelengths, and also from certain small-scale magnetic configurations such as the opposite polarity fluxes, where magnetic orientation of small bipoles is opposite to that of the ambient main polarities. However, high-resolution observations of flare precursors together with the associated photospheric magnetic field dynamics are lacking. Here we study precursors of a flare using unprecedented spatiotemporal resolution of the 1.6 m New Solar Telescope, complemented by novel microwave data. Two episodes of precursor brightenings are initiated at a small-scale magnetic channel (a form of opposite polarity fluxes) with multiple polarity inversions and enhanced magnetic fluxes and currents, lying near the footpoints of sheared magnetic loops. The low-atmospheric origin of these precursor emissions is corroborated by microwave spectra. We propose that the emerging magnetic channel field interacts with the sheared arcades to cause precursor brightenings at the main flare core region. These high-resolution results provide evidence of low-atmospheric small-scale energy release and possible relationship to the onset of the main flare.

astro-ph.SR↗

arXiv2017

Feature Analysis and Selection for Training an End-to-End Autonomous Vehicle Controller Using the Deep Learning Approach

Deep learning-based approaches have been widely used for training controllers for autonomous vehicles due to their powerful ability to approximate nonlinear functions or policies. However, the training process usually requires large labeled data sets and takes a lot of time. In this paper, we analyze the influences of features on the performance of controllers trained using the convolutional neural networks (CNNs), which gives a guideline of feature selection to reduce computation cost. We collect a large set of data using The Open Racing Car Simulator (TORCS) and classify the image features into three categories (sky-related, roadside-related, and road-related features).We then design two experimental frameworks to investigate the importance of each single feature for training a CNN controller.The first framework uses the training data with all three features included to train a controller, which is then tested with data that has one feature removed to evaluate the feature's effects. The second framework is trained with the data that has one feature excluded, while all three features are included in the test data. Different driving scenarios are selected to test and analyze the trained controllers using the two experimental frameworks. The experiment results show that (1) the road-related features are indispensable for training the controller, (2) the roadside-related features are useful to improve the generalizability of the controller to scenarios with complicated roadside information, and (3) the sky-related features have limited contribution to train an end-to-end autonomous vehicle controller.

cs.CV↗

arXiv2017

A novel artificial condensed matter lattice and a new platform for one-dimensional topological phases

Engineered lattices in condensed matter physics, such as cold atom optical lattices or photonic crystals, can have fundamentally different properties from naturally-occurring electronic crystals. Here, we report a novel type of artificial quantum matter lattice. Our lattice is a multilayer heterostructure built from alternating thin films of topological and trivial insulators. Each interface within the heterostructure hosts a set of topologically-protected interface states, and by making the layers sufficiently thin, we demonstrate for the first time a hybridization of interface states across layers. In this way, our heterostructure forms an emergent atomic chain, where the interfaces act as lattice sites and the interface states act as atomic orbitals, as seen from our measurements by angle-resolved photoemission spectroscopy (ARPES). By changing the composition of the heterostructure, we can directly control hopping between lattice sites. We realize a topological and a trivial phase in our superlattice band structure. We argue that the superlattice may be characterized in a significant way by a one-dimensional topological invariant, closely related to the invariant of the Su-Schrieffer-Heeger model. Our topological insulator heterostructure demonstrates a novel experimental platform where we can engineer band structures by directly controlling how electrons hop between lattice sites.

cond-mat.mtrl-sci↗

arXiv2017

A Unified Gas Kinetic Scheme for Multi-scale and Multi-component Plasma Transport

A unified gas kinetic scheme (UGKS) for multi-scale and multi-component plasma transport is constructed. The current scheme is a direct modeling method, where the time evolution solutions from the Vlasov-BGK equations of electron and ion and the Maxwell equations are used to construct a scale-dependent plasma simulation model. As a result, with the changing of modeling scales of mesh size and time step and with a variation of Knudsen number and Larmor radius, the discretized governing equations for a wide range of plasma evolution regimes can be obtained. The physics recovered in UGKS ranges from the Vlasov equation in the kinetic scale to different-type magneto-hydrodynamic (MHD) equations in the hydrodynamic scale. The key dynamics in UGKS is the un-splitting treatment of particle collision, acceleration, and transport in the construction of numerical flux across a cell interface. At the same time, the plasma evolution is coupled with the Maxwell equations in an implicit way, which automatically provides a smooth transition between the Ampere's law and the Ohm's law for the calculation of electric field. The time step of UGKS is not limited by the relaxation time, the cyclotron period, and the speed of light in the MHD regime. The UGKS is validated by numerical test cases, such as the Landau damping and two stream instability in the kinetic regime, and the Brio-Wu shock tube problem and the Orszag- Tang MHD turbulence problem in the hydrodynamic regime. The scheme is also used to study the geospace environment modeling (GEM), such as the challenging magnetic reconnection problem in the transition regime. Overall, the UGKS is a physically reliable multi-scale plasma simulation method. It provides a powerful and unified approach for the study of plasma physics.

physics.comp-ph↗

arXiv2017

A comment on `An improved macroscale model for gas slip flow in porous media'

In a recent paper by Lasseux, Valdés-Parada and Porter (J.~Fluid~Mech. \textbf{805} (2016) 118-146), it is found that the apparent gas permeability of the porous media is a nonlinear function of the Knudsen number. However, this result is highly questionable, because the adopted Navier-Stokes equations and the first-order velocity-slip boundary condition are first-order (in terms of the Knudsen number) approximations of the Boltzmann equation and the kinetic boundary condition for rarefied gas flows. Our numerical simulations based on the Bhatnagar-Gross-Krook kinetic equation and regularized 20-moment equations prove that the Navier-Stokes equations with the first-order velocity-slip boundary condition are only accurate at a very small Knudsen number limit, where the apparent gas permeability is a linear function of the Knudsen number.

physics.flu-dyn↗

arXiv2017

Delving into Transferable Adversarial Examples and Black-box Attacks

An intriguing property of deep neural networks is the existence of adversarial examples, which can transfer among different architectures. These transferable adversarial examples may severely hinder deep neural network-based applications. Previous works mostly study the transferability using small scale datasets. In this work, we are the first to conduct an extensive study of the transferability over large models and a large scale dataset, and we are also the first to study the transferability of targeted adversarial examples with their target labels. We study both non-targeted and targeted adversarial examples, and show that while transferable non-targeted adversarial examples are easy to find, targeted adversarial examples generated using existing approaches almost never transfer with their target labels. Therefore, we propose novel ensemble-based approaches to generating transferable adversarial examples. Using such approaches, we observe a large proportion of targeted adversarial examples that are able to transfer with their target labels for the first time. We also present some geometric studies to help understanding the transferable adversarial examples. Finally, we show that the adversarial examples generated using ensemble-based approaches can successfully attack Clarifai.com, which is a black-box image classification system.

cs.LG↗

arXiv2017

Sonication-assisted synthesis of semiconducting atomic crystals of germanium monoselenide: An isostructural and isoelectronic analog of black phosphorous

Monochalcogenides of germanium (or tin) are considered as stable isoelectronic and isostructural analogue of black phosphorous. Their two-dimensional (2D) forms have been just predicted to shown strong thickness-dependent physical properties, and even indirect to direct band gap crossover at the monolayer limit. Here, we demonstrate the synthesis of atomically thin GeSe by direct sonication-assisted exfoliation of bulk microcrystalline powders in solvents. The exfoliated few-layer GeSe sheets characterize high crystallinity with lateral sizes over 100 nm and, importantly, strong resistance against oxidation and degradation in ambient conditions. Density functional theory calculation combined with optical characterizations confirm the layer-number dependent optical bandgap, which, for few-layer sheets, is optimal for solar light harvesting, and promising for relevant applications, such as optoelectronics and photonics.

cond-mat.mtrl-sci↗

arXiv2017

Two-Scale Oscillons

Oscillons are spatially stationary, quasi-periodic solutions of nonlinear field theories seen in settings ranging from granular systems, low temperature condensates and early universe cosmology. We describe a new class of oscillon in which the spatial envelope can have "off centre" maxima and pulsate on timescales much longer than the fundamental frequency. These are exact solutions of the 1-D sine-Gordon equation and we demonstrate numerically that similar solutions exist in up to three dimensions for a range of potentials. The dynamics of these solutions match key properties of oscillons that may form after cosmological inflation in string-motivated monodromy scenarios.

hep-th↗