arXiv subjects

Chao Liu

Publications and source records attributed to Chao Liu.

At least 379 records · Page 21Linked to original sources

arXiv2018

Cosmological Newtonian limits on large spacetime scales

We establish the existence of $1$-parameter families of $ε$-dependent solutions to the Einstein-Euler equations with a positive cosmological constant $Λ>0$ and a linear equation of state $p=ε^2 K ρ$, $0<K\leq 1/3$, for the parameter values $0<ε< ε_0$. These solutions exist globally on the manifold $M=(0,1]\times \mathbb{R}^3$, are future complete, and converge as $ε\searrow 0$ to solutions of the cosmological Poisson-Euler equations. They represent inhomogeneous, nonlinear perturbations of a FLRW fluid solution where the inhomogeneities are driven by localized matter fluctuations that evolve to good approximation according to Newtonian gravity.

gr-qc↗

arXiv2018

Implementing SCRUM to develop a connected robot

Agile methods are receiving a growing interest from industry and these approaches are nowadays well accepted and deployed in software engineering. However, some issues remain to introduce agility in systems engineering. The objective of this paper is to show an agile management implementation in an educational project consisting in developing a connected mobile robot, and to evaluate the issues and benefits of adopting an agile approach. Among the most famous agile management methods, SCRUM has been chosen to lead this experiment. This paper first presents the project and how students traditionally manage it, then it describes how Scrum could be used instead. It evaluates the difficulties and interests to introduce agility in this project, and concludes on the ability of Scrum to design, test and progressively integrate the system, thus providing an operational prototype more quickly.

cs.SE↗

arXiv2018

Progress in the Construction and Testing of the Tianlai Radio Interferometers

The Tianlai Pathfinder is designed to demonstrate the feasibility of using a wide field of view radio interferometers to map the density of neutral hydrogen in the Universe after the Epoch of Reionizaton. This approach, called 21~cm intensity-mapping, promises an inexpensive means for surveying the large-scale structure of the cosmos. The Tianlai Pathfinder presently consists of an array of three, 15~m $\times$ 40~m cylinder telescopes and an array of sixteen, 6~m diameter dish antennas located in a radio-quiet part of western China. The two types of arrays were chosen to determine the advantages and disadvantages of each approach. The primary goal of the Pathfinder is to make 3D maps by surveying neutral hydrogen over large areas of the sky %$20,000 {\rm deg}^2$ in two different redshift ranges: first at $1.03 > z > 0.78$ ($700 - 800$~MHz) and later at $0.21 > z > 0.12$ ($1170 - 1270$~MHz). The most significant challenge to $21$~cm intensity-mapping is the removal of strong foreground radiation that dwarfs the cosmological signal. It requires exquisite knowledge of the instrumental response, i.e. calibration. In this paper, we provide an overview of the status of the Pathfinder and discuss the details of some of the analysis that we have carried out to measure the beam function of both arrays. We compare electromagnetic simulations of the arrays to measurements, discuss measurements of the gain and phase stability of the instrument, and provide a brief overview of the data processing pipeline.

astro-ph.IM↗

arXiv2018

Behavior-based evaluation of session satisfaction

Nowadays, web search becomes more and more popular all over the world. Many researchers and developers have done lots of studies on behaviors of search users. In practice, the full understanding of these behaviors can not only help to evaluate the usefulness of newly-developed ranking algorithms and other changes of search engine, but also to guide the growth direction of search engine. As far as we know, most of past work are mainly focused on single search evaluation, which do promote the rapid development of search engine in early stage. However,these page-level behaviors are so limited that can no longer give explicit feedbacks on minor changes of the search engine. We think that it will be more accurate and sensitive when more information on search session are provided. In this paper, a session level evaluation method is proposed. The session-level features are retrieved and carefully analyzed. Some linear and non-linear features which can reflect the final degree of satisfaction are chosen and adopted in evaluation models. A two-layer hybrid evaluation model with different granularity, which can achieve good precision and recall, is designed and trained. Lots of real experiments are evaluated by the model, the result shows it achieved a higher accuracy performance than traditional page-level evaluation metrics. Furthermore, for practical application, it is important to interpret the reason of each session's satisfaction judgement. In all, a session-level evaluation model with improved performance and well capability on interpretation is proposed and applied in real practice in search engine companies.

cs.IR↗

arXiv2018

Newtonian Limits of Isolated Cosmological Systems on Long Time Scales

We establish the existence of $1$-parameter families of $ε$-dependent solutions to the Einstein-Euler equations with a positive cosmological constant $Λ>0$ and a linear equation of state $p=ε^2 K ρ$, $0<K\leq 1/3$, for the parameter values $0<ε< ε_0$. These solutions exist globally to the future, converge as $ε\searrow 0$ to solutions of the cosmological Poison-Euler equations of Newtonian gravity, and are inhomogeneous non-linear perturbations of FLRW fluid solutions.

gr-qc↗

arXiv2018

Data-driven root-cause analysis for distributed system anomalies

Modern distributed cyber-physical systems encounter a large variety of anomalies and in many cases, they are vulnerable to catastrophic fault propagation scenarios due to strong connectivity among the sub-systems. In this regard, root-cause analysis becomes highly intractable due to complex fault propagation mechanisms in combination with diverse operating modes. This paper presents a new data-driven framework for root-cause analysis for addressing such issues. The framework is based on a spatiotemporal feature extraction scheme for distributed cyber-physical systems built on the concept of symbolic dynamics for discovering and representing causal interactions among subsystems of a complex system. We present two approaches for root-cause analysis, namely the sequential state switching ($S^3$, based on free energy concept of a Restricted Boltzmann Machine, RBM) and artificial anomaly association ($A^3$, a multi-class classification framework using deep neural networks, DNN). Synthetic data from cases with failed pattern(s) and anomalous node are simulated to validate the proposed approaches, then compared with the performance of vector autoregressive (VAR) model-based root-cause analysis. Real dataset based on Tennessee Eastman process (TEP) is also used for validation. The results show that: (1) $S^3$ and $A^3$ approaches can obtain high accuracy in root-cause analysis and successfully handle multiple nominal operation modes, and (2) the proposed tool-chain is shown to be scalable while maintaining high accuracy.

cs.LG↗

arXiv2018

Root-cause Analysis for Time-series Anomalies via Spatiotemporal Graphical Modeling in Distributed Complex Systems

Performance monitoring, anomaly detection, and root-cause analysis in complex cyber-physical systems (CPSs) are often highly intractable due to widely diverse operational modes, disparate data types, and complex fault propagation mechanisms. This paper presents a new data-driven framework for root-cause analysis, based on a spatiotemporal graphical modeling approach built on the concept of symbolic dynamics for discovering and representing causal interactions among sub-systems of complex CPSs. We formulate the root-cause analysis problem as a minimization problem via the proposed inference based metric and present two approximate approaches for root-cause analysis, namely the sequential state switching ($S^3$, based on free energy concept of a restricted Boltzmann machine, RBM) and artificial anomaly association ($A^3$, a classification framework using deep neural networks, DNN). Synthetic data from cases with failed pattern(s) and anomalous node(s) are simulated to validate the proposed approaches. Real dataset based on Tennessee Eastman process (TEP) is also used for comparison with other approaches. The results show that: (1) $S^3$ and $A^3$ approaches can obtain high accuracy in root-cause analysis under both pattern-based and node-based fault scenarios, in addition to successfully handling multiple nominal operating modes, (2) the proposed tool-chain is shown to be scalable while maintaining high accuracy, and (3) the proposed framework is robust and adaptive in different fault conditions and performs better in comparison with the state-of-the-art methods.

stat.ML↗

arXiv2018

Predicting County Level Corn Yields Using Deep Long Short Term Memory Models

Corn yield prediction is beneficial as it provides valuable information about production and prices prior the harvest. Publicly available high-quality corn yield prediction can help address emergent information asymmetry problems and in doing so improve price efficiency in futures markets. This paper is the first to employ Long Short-Term Memory (LSTM), a special form of Recurrent Neural Network (RNN) method to predict corn yields. A cross sectional time series of county-level corn yield and hourly weather data made the sample space large enough to use deep learning technics. LSTM is efficient in time series prediction with complex inner relations, which makes it suitable for this task. The empirical results from county level data in Iowa show promising predictive power relative to existing survey based methods.

stat.ML↗

arXiv2018

The R-Process Alliance: 2MASS J09544277+5246414, the Most Actinide-Enhanced R-II Star Known

We report the discovery of a new actinide-boost star, 2MASS J09544277+5246414, originally identified as a very bright (V = 10.1), extremely metal-poor ([Fe/H] = -2.99) K giant in the LAMOST survey, and found to be highly r-process-enhanced (r-II; [Eu/Fe]= +1.28]), during the snapshot phase of the R-Process Alliance (RPA). Based on a high S/N, high-resolution spectrum obtained with the Harlan J. Smith 2.7-m telescope, this star is the first confirmed actinide-boost star found by RPA efforts. With an enhancement of [Th/Eu] = +0.37, 2MASS J09544277+5246414 is also the most actinide-enhanced r-II star yet discovered, and only the sixth metal-poor star with a measured uranium abundance ([U/Fe] = +1.40). Using the Th/U chronometer, we estimate an age of 13.0+/-4.7 Gyr for this star. The unambiguous actinide-boost signature of this extremely metal-poor star, combined with additional r-process-enhanced and actinide-boost stars identified by the RPA, will provide strong constraints on the nature and origin of the r-process at early times.

astro-ph.SR↗

arXiv2018

Mapping the Milky Way with LAMOST III: Complicated spatial structure in the outer disc

We present {complexity} of the Galactic outer disc {by fitting the stellar volume densities of the red giant branch stars with a two-disc component model}. {The discs are confirmed to} extend to $R\sim19$\,kpc. The radial density profile of the discs shows {two breaks at $R\sim11$ and $\sim14$\,kpc, respectively, which} separate the radial profile into three segments with different scale lengths of $2.12\pm0.26$, $1.18\pm0.08$, and $2.72$\,kpc at $R<11$, $11\leq R\leq14$, and $R>14$\,kpc, respectively. The first {break} is likely due to the sudden drop in the radial profile of the thin disc, which may be an evidence of the radial migration. {Beyond $14$\,kpc, the thick disc becomes prominent and the transition from thin to thick disc leads to the second break.} This implies that the geometrically defined thick disc is more {radially} extended than the thin disc. This is also supported by the larger scale length of the thick disc than that of the thin disc. Meanwhile, {the scale height of the thicker component increases from $0.637_{-0.036}^{+0.056}$ at $R=8$ to $1.284_{-0.079}^{+0.086}$\,kpc at $R=19$\,kpc, showing an intensive flared disc}. Moreover, rich substructures are displayed in the residuals of the stellar density. Among them, the substructures $D14+2.0$ and $O14-1.5$ show a north-south asymmetry, which can be essentially explained by southward shifting of the thick disc. However, no significant overdensity is found for the Monoceros ring. Finally, the thick disc shows a ripple-like feature with unclear origin at $9<R<10.5$\,kpc.

astro-ph.GA↗

arXiv2018

Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application

In recent years, an increasing popularity of deep learning model for intelligent condition monitoring and diagnosis as well as prognostics used for mechanical systems and structures has been observed. In the previous studies, however, a major assumption accepted by default, is that the training and testing data are taking from same feature distribution. Unfortunately, this assumption is mostly invalid in real application, resulting in a certain lack of applicability for the traditional diagnosis approaches. Inspired by the idea of transfer learning that leverages the knowledge learnt from rich labeled data in source domain to facilitate diagnosing a new but similar target task, a new intelligent fault diagnosis framework, i.e., deep transfer network (DTN), which generalizes deep learning model to domain adaptation scenario, is proposed in this paper. By extending the marginal distribution adaptation (MDA) to joint distribution adaptation (JDA), the proposed framework can exploit the discrimination structures associated with the labeled data in source domain to adapt the conditional distribution of unlabeled target data, and thus guarantee a more accurate distribution matching. Extensive empirical evaluations on three fault datasets validate the applicability and practicability of DTN, while achieving many state-of-the-art transfer results in terms of diverse operating conditions, fault severities and fault types.

cs.LG↗

arXiv2018

Probing the collective dynamics of nuclear spin bath in a rare-earth ion doped crystal

Probing collective spin dynamics is a current challenge in the field of magnetic resonance spectroscopy and has important applications in material analysis and quantum information protocols. Recently, the rare-earth ion doped crystals are an attractive candidate for making long-lived quantum memory. Further enhancement of its performance would benefit from the direct knowledge on the dynamics of nuclear-spin bath in the crystal. Here we detect the collective dynamics of nuclear-spin bath located around the rare-earth ions in a crystal using dynamical decoupling spectroscopy method. From the measured spectrum, we analyze the configuration of the spin bath and characterize the flip-flop time between two correlated nuclear spins in a long time scale ($\sim $1s). Furthermore, we experimentally demonstrate that the rare-earth ions can serve as a magnetic quantum sensor for external magnetic field. These results suggest that the rare-earth ion is a useful probe for complex spin dynamics in solids and enable quantum sensing in the low-frequency regime, revealing promising possibilities for applications in diverse fields.

quant-ph↗

arXiv2018

Misclassified B stars in the Kepler field

Stellar fundamental parameters are important in the asteroseismic study of Kepler light curves. However, the most used estimates in the Kepler Input Catalog (KIC) are not accurate enough for hot stars. Using a sample of B stars from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) spectral survey, we confirmed the systematic underestimation in KIC effective temperature and overestimation in KIC surface gravity. The good agreement between LAMOST and other follow-up observations proved the accuracy of effective temperature and surface gravity of B stars derived from LAMOST low-resolution spectra. By searching through LAMOST data, we found four misclassified main-sequence B stars in the Kepler field, which had been previously classified as A-type variables. We present spectroscopic and detailed frequency analysis of these four stars based on LAMOST spectra and Kepler photometry.

astro-ph.SR↗

arXiv2018

Carbon stars identified from LAMOST DR4 using Machine Learning

In this work, we present a catalog of 2651 carbon stars from the fourth Data Release (DR4) of the Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST). Using an efficient machine-learning algorithm, we find out these stars from more than seven million spectra. As a by-product, 17 carbon-enhanced metal-poor (CEMP) turnoff star candidates are also reported in this paper, and they are preliminarily identified by their atmospheric parameters. Except for 176 stars that could not be given spectral types, we classify the other 2475 carbon stars into five subtypes including 864 C-H, 226 C-R, 400 C-J, 266 C-N, and 719 barium stars based on a series of spectral features. Furthermore, we divide the C-J stars into three subtypes of CJ( H), C-J(R), C-J(N), and about 90% of them are cool N-type stars as expected from previous literature. Beside spectroscopic classification, we also match these carbon stars to multiple broadband photometries. Using ultraviolet photometry data, we find that 25 carbon stars have FUV detections and they are likely to be in binary systems with compact white dwarf companions.

astro-ph.SR↗

arXiv2018

A Raman-Heterodyne Study of the Hyperfine Interaction of the Optically-Excited State $^5$D$_0$ of Eu$^{3+}$:Y$_2$SiO$_5$

The spin coherence time of $^{151}$Eu$^{3+}$ which substitutes the yttrium at site 1 in Y$_2$SiO$_5$ crystal has been extended to 6 hours in a recent work [\textit{Nature} \textbf{517}, 177 (2015)]. To make this long-lived spin coherence useful for optical quantum memory applications, we experimentally characterize the hyperfine interaction of the optically-excited state $^5$D$_0$ using Raman-heterodyne-detected nuclear magnetic resonance. The effective spin Hamiltonians for excited and ground state are fitted based on the experimental spectra obtained in 200 magnetic fields with various orientations. To show the correctness of the fitted parameters and potential application in quantum memory protocols, we also characterize the ground-state hyperfine interaction and predict the critical magnetic field which produces the 6-hour-long coherence time. The complete energy level structure for both the $^7$F$_0$ ground state and $^5$D$_0$ excited state at the critical magnetic field are obtained. These results enable the design of quantum memory protocols and the optimization of optical pumping strategy for realization of photonic quantum memory with hour-long lifetime.

quant-ph↗

arXiv2018

Discovery and Characterization of 3000+ Main-Sequence Binaries from APOGEE Spectra

We develop a data-driven spectral model for identifying and characterizing spatially unresolved multiple-star systems and apply it to APOGEE DR13 spectra of main-sequence stars. Binaries and triples are identified as targets whose spectra can be significantly better fit by a superposition of two or three model spectra, drawn from the same isochrone, than any single-star model. From an initial sample of $\sim$20,000 main-sequence targets, we identify $\sim$2,500 binaries in which both the primary and secondary star contribute detectably to the spectrum, simultaneously fitting for the velocities and stellar parameters of both components. We additionally identify and fit $\sim$200 triple systems, as well as $\sim$700 velocity-variable systems in which the secondary does not contribute detectably to the spectrum. Our model simplifies the process of simultaneously fitting single- or multi-epoch spectra with composite models and does not depend on a velocity offset between the two components of a binary, making it sensitive to traditionally undetectable systems with periods of hundreds or thousands of years. In agreement with conventional expectations, almost all the spectrally-identified binaries with measured parallaxes fall above the main sequence in the color-magnitude diagram. We find excellent agreement between spectrally and dynamically inferred mass ratios for the $\sim$600 binaries in which a dynamical mass ratio can be measured from multi-epoch radial velocities. We obtain full orbital solutions for 64 systems, including 14 close binaries within hierarchical triples. We make available catalogs of stellar parameters, abundances, mass ratios, and orbital parameters.

astro-ph.SR↗

arXiv2017

Rediscovering the Galactic outer disk with LAMOST data

From the derived stellar density profile using LAMOST giant stars, we find that the Galactic disk does not show truncation or break, but smoothly transit to the halo from 19 kpc. The scale length of the outer disk is only $1.6\pm0.1$\,kpc, substantially smaller than previous results. This implies that the shapes of the inner and outer disk are different. Meanwhile, the disk flaring is not only found in older populations, but also in younger population. Moreover, the vertical oscillations of the disk are identified in a wide range or $R$ from 8 to 14 kpc. We also find that the velocity dispersion profile as a function of the Galactocentric radius is flat with scale length of $26.3\pm3.2$\,kpc. We confirm that the radial velocity profile in outer disk is significantly affected by asymmetric motion. The bar with either a slower or a faster pattern speed can induce the similar radial asymmetric motion.

astro-ph.GA↗

arXiv2017

The age-velocity dispersion relation of the Galactic discs from LAMOST-Gaia data

We present the age-velocity dispersion relation (AVR) in three dimensions in the solar neighbourhood using 3,564 commonly observed sub-giant/red-giant branch stars selected from LAMOST, which gives the age and radial velocity, and \emph{Gaia}, which measures the distance and proper motion. The stars are separated into metal-poor (${\rm [Fe/H]<-0.2}$\,dex and metal-rich (${\rm [Fe/H]>-0.2}$\,dex) groups, so that the metal-rich stars are mostly $α$-poor, while the metal-poor group are mostly contributed by $α$-enhanced stars. Thus, the old and metal-poor stars likely belong to the chemically defined thick disc population, while the metal-rich sample is dominated by the thin disc. The AVR for the metal-poor sample shows an abrupt increase at $\gtrsim7$\,Gyr, which is contributed by the thick disc component. On the other hand, most of the thin disc stars with ${\rm [Fe/H]>-0.2}$\,dex display a power-law like AVR with indices of about 0.3--0.4 and 0.5 for the in-plane and vertical dispersions, respectively. This is consistent with the scenario that the disc is gradually heated by the spiral arms and/or the giant molecular clouds. Moreover, the older thin disc stars ($>7$\,Gyr) have a rounder velocity ellipsoid, i.e. $σ_ϕ/σ_{\rm z}$ is close to 1.0, probably due to the more efficient heating in vertical direction. Particularly for the old metal-poor sample located with $|z|>270$\,pc, the vertical dispersion is even larger than its azimuthal counterpart. Finally, the vertex deviations and the tilt angles are plausibly around zero with large uncertainties.

astro-ph.GA↗