arXiv subjects

Lu Li

Publications and source records attributed to Lu Li.

At least 163 records · Page 9Linked to original sources

arXiv2020

Modelling unresolved binaries of open clusters in color-magnitude diagram. I. method and application of NGC3532

The binary properties of open clusters place crucial constraints on star formation theory and clusters' dynamical evolution. We develop a comprehensive approach that models the color-magnitude diagram (CMD) of the cluster members as the mixture of single stars and photometric unresolved binaries. This method enables us to infer the binary properties, including the binary fraction $f_\mathrm{b}$ and binary mass-ratio distribution index $γ_q$ when a power-law is assumed, with high accuracy and precision, which were unfeasible in conventional methods. We employ a modified Gaussian process to determine the main sequence ridge line and its scatter from the observed CMD as model input. As a first example, we apply the method to the open cluster NGC3532 with the Gaia DR2 photometry. For the cluster members within a magnitude range corresponding to FGK dwarfs, we obtain $f_\mathrm{b} = 0.267\pm0.019$ and $γ_q = - 0.10\pm0.22$ for binaries with mass ratio $q > 0.2$. The $f_\mathrm{b}$ value is consistent with the previous work on NGC3532 and smaller than that of field stars. The close to zero $γ_q$ indicates that the mass ratios of binaries follow a nearly uniform distribution. For the first time, we unveil that the stars with smaller mass or in the inner region tend to have lower $f_\mathrm{b}$ and more positive value of $γ_q$ due to the lack of low mass-ratio binaries. The clear dependences of binary properties on mass and radius are most likely caused by the internal dynamics.

astro-ph.GA↗

arXiv2020

Unveiling the Hierarchical Structure of Open Star Clusters: the Perseus Double Cluster

We introduce a new kinematic method to investigate the structure of open star clusters. We adopt a hierarchical clustering algorithm that uses the celestial coordinates and the proper motions of the stars in the field of view of the cluster to estimate a proxy of the pairwise binding energy of the stars and arrange them in a binary tree. The cluster substructures and their members are identified by trimming the tree at two thresholds, according to the $σ$-plateau method. Testing the algorithm on 100 mock catalogs shows that, on average, the membership of the identified clusters is $(91.5\pm 3.5)$\% complete and the fraction of unrelated stars is $(10.4\pm 2.0)$\%. We apply the algorithm to the stars in the field of view of the Perseus double cluster from the Data Release 2 of Gaia. This approach identifies a single structure, Sub1, that separates into two substructures, Sub1-1 and Sub1-2. These substructures coincide with $h$ Per and $χ$ Per: the distributions of the proper motions and the color-magnitude diagrams of the members of Sub1-1 and Sub1-2 are fully consistent with those of $h$ Per and $χ$ Per reported in the literature. These results suggest that our hierarchical clustering algorithm can be a powerful tool to unveil the complex kinematic information of star clusters.

astro-ph.GA↗

arXiv2020

Linearly implicit local and global energy-preserving methods for PDEs with a cubic Hamiltonian

We present linearly implicit methods that preserve discrete approximations to local and global energy conservation laws for multi-symplectic PDEs with cubic invariants. The methods are tested on the one-dimensional Korteweg-de Vries equation and the two-dimensional Zakharov-Kuznetsov equation; the numerical simulations confirm the conservative properties of the methods, and demonstrate their good stability properties and superior running speed when compared to fully implicit schemes.

math.NA↗

arXiv2020

Exploring open cluster properties with Gaia and LAMOST

In Gaia DR2, the unprecedented high-precision level reached in sub-mas for astrometry and mmag for photometry. Using cluster members identified with these astrometry and photometry in Gaia DR2, we can obtain a reliable determination of cluster properties. However, because of the shortcoming of Gaia spectroscopic observation in dealing with densely crowded cluster region, the number of radial velocity and metallicity for cluster member stars from Gaia DR2 is still lacking. In this study, we aim to improve the cluster properties by combining the LAMOST spectra. In particular, we provide the list of cluster members with spectroscopic parameters as an add-value catalog in LAMOST DR5, which can be used to perform detailed study for a better understanding on the stellar properties, by using their spectra and fundamental properties from the host cluster. We cross-matched the spectroscopic catalog in LAMOST DR5 with the identified cluster members in Cantat-Gaudin et al.2018 and then used members with spectroscopic parameters to derive statistical properties of open clusters. We obtained a list of 8811 members with spectroscopic parameters and a catalog of 295 cluster properties. In addition, we study the radial and vertical metallicity gradient and age-metallicity relation with the compiled open clusters as tracers, finding slopes of -0.053$\pm$0.004 dex kpc$^{-1}$, -0.252$\pm$0.039 dex kpc$^{-1}$ and 0.022$\pm$0.008 dex Gyr$^{-1}$, respectively. Both slopes of metallicity distribution relation for young clusters (0.1 Gyr < Age < 2 Gyr) and the age-metallicity relation for clusters within 6 Gyr are consistent with literature results. In order to fully study the chemical evolution history in the disk, more spectroscopic observations for old and distant open clusters are needed for further investigation.

astro-ph.GA↗

arXiv2020

Organ size increases with obesity and correlates with cancer risk

Obesity increases significantly cancer risk in various organs. Although this has been recognized for decades, the mechanism through which this happens has never been explained. Here, we show that the volumes of kidneys, pancreas, and liver are strongly correlated (median correlation = 0.625; P-value<10-47) with the body mass index (BMI) of an individual. We also find a significant relationship between the increase in organ volume and the increase in cancer risk (P-value<10-12). These results provide a mechanism explaining why obese individuals have higher cancer risk in several organs: the larger the organ volume the more cells at risk of becoming cancerous. These findings are important for a better understanding of the effects obesity has on cancer risk and, more generally, for the development of better preventive strategies to limit the mortality caused by obesity.

q-bio.TO↗

arXiv2020

Linearly implicit structure-preserving schemes for Hamiltonian systems

Kahan's method and a two-step generalization of the discrete gradient method are both linearly implicit methods that can preserve a modified energy for Hamiltonian systems with a cubic Hamiltonian. These methods are here investigated and compared. The schemes are applied to the Korteweg-de Vries equation and the Camassa-Holm equation, and the numerical results are presented and analysed.

math.NA↗

arXiv2020

Learning Spatiotemporal Features of Ride-sourcing Services with Fusion Convolutional Network

To collectively forecast the demand for ride-sourcing services in all regions of a city, the deep learning approaches have been applied with commendable results. However, the local statistical differences throughout the geographical layout of the city make the spatial stationarity assumption of the convolution invalid, which limits the performance of CNNs on the demand forecasting task. In this paper, we propose a novel deep learning framework called LC-ST-FCN (locally connected spatiotemporal fully-convolutional neural network) to address the unique challenges of the region-level demand forecasting problem within one end-to-end architecture (E2E). We first employ the 3D convolutional layers to fuse the spatial and temporal information existed in the input and then feed the spatiotemporal features extracted by the 3D convolutional layers to the subsequent 2D convolutional layers. Afterward, the prediction value of each region is obtained by the locally connected convolutional layers which relax the parameter sharing scheme. We evaluate the proposed model on a real dataset from a ride-sourcing service platform (DiDiChuxing) and observe significant improvements compared with a bunch of baseline models. Besides, we also illustrate the effectiveness of our proposed model by visualizing how different types of convolutional layers transform their input and capture useful features. The visualization results show that fully convolutional architecture enables the model to better localize the related regions. And the locally connected layers play an important role in dealing with the local statistical differences and activating useful regions.

cs.LG↗

arXiv2020

CLUENER2020: Fine-grained Named Entity Recognition Dataset and Benchmark for Chinese

In this paper, we introduce the NER dataset from CLUE organization (CLUENER2020), a well-defined fine-grained dataset for named entity recognition in Chinese. CLUENER2020 contains 10 categories. Apart from common labels like person, organization, and location, it contains more diverse categories. It is more challenging than current other Chinese NER datasets and could better reflect real-world applications. For comparison, we implement several state-of-the-art baselines as sequence labeling tasks and report human performance, as well as its analysis. To facilitate future work on fine-grained NER for Chinese, we release our dataset, baselines, and leader-board.

cs.CL↗

arXiv2019

How to Evaluate the Next System: Automatic Dialogue Evaluation from the Perspective of Continual Learning

Automatic dialogue evaluation plays a crucial role in open-domain dialogue research. Previous works train neural networks with limited annotation for conducting automatic dialogue evaluation, which would naturally affect the evaluation fairness as dialogue systems close to the scope of training corpus would have more preference than the other ones. In this paper, we study alleviating this problem from the perspective of continual learning: given an existing neural dialogue evaluator and the next system to be evaluated, we fine-tune the learned neural evaluator by selectively forgetting/updating its parameters, to jointly fit dialogue systems have been and will be evaluated. Our motivation is to seek for a lifelong and low-cost automatic evaluation for dialogue systems, rather than to reconstruct the evaluator over and over again. Experimental results show that our continual evaluator achieves comparable performance with reconstructing new evaluators, while requires significantly lower resources.

cs.CL↗

arXiv2019

Quantum spin liquids by geometric lattice design

On a lattice composed of triangular plaquettes where antiferromagnetic exchange interactions between localized spins cannot be simultaneously satisfied, the system becomes geometrically frustrated with magnetically disordered phases remarkably different from a simple paramagnet. Spin liquid belongs to one of these exotic states, in which a macroscopic degeneracy of the ground state gives rise to the rich spectrum of collective phenomena. Here, we report on the discovery of a new magnetic state in the heterostructures derived from a single unit cell (111)-oriented spinel CoCr2O4 sandwiched between nonmagnetic Al2O3 spacers. The artificial quasi-two-dimensional material composed of three triangle and one kagome atomic planes shows a degree of magnetic frustration which is almost two orders of magnitude enlarged compared to the bulk crystals. Combined resonant X-ray absorption and torque magnetometry measurements confirm that the designer system exhibits no sign of spin ordering down to 30 mK, implying a possible realization of a quantum spin liquid state in the two dimensional limit.

cond-mat.str-el↗

arXiv2019

Leveraging access mode declarations in a model for memory consistency in heterogeneous systems

On a system that exposes disjoint memory spaces to the software, a program has to address memory consistency issues and perform data transfers so that it always accesses valid data. Several approaches exist to ensure the consistency of the memory accessed. Here we are interested in the verification of a declarative approach where each component of a computation is annotated with an access mode declaring which part of the memory is read or written by the component. The programming framework uses the component annotations to guarantee the validity of the memory accesses. This is the mechanism used in VectorPU, a C++ library for programming CPU-GPU heterogeneous systems. This article proves the correctness of the software cache-coherence mechanism used in VectorPU. Beyond the scope of VectorPU, this article provides a simple and effective formalisation of memory consistency mechanisms based on the explicit declaration of the effect of each component on each memory space. The formalism we propose also takes into account arrays for which a single validity status is stored for the whole array; additional mechanisms for dealing with overlapping arrays are also studied.

cs.DC↗

arXiv2019

Dynamic Partial Sufficient Dimension Reduction

Sufficient dimension reduction aims for reduction of dimensionality of a regression without loss of information by replacing the original predictor with its lower-dimensional subspace. Partial (sufficient) dimension reduction arises when the predictors naturally fall into two sets, X and W, and we seek dimension reduction on X alone while considering all predictors in the regression analysis. Though partial dimension reduction is a very general problem, only very few research results are available when W is continuous. To the best of our knowledge, these methods generally perform poorly when X and W are related, furthermore, none can deal with the situation where the reduced lower-dimensional subspace of X varies dynamically with W. In this paper, We develop a novel dynamic partial dimension reduction method, which could handle the dynamic dimension reduction issue and also allows the dependency of X on W. The asymptotic consistency of our method is investigated. Extensive numerical studies and real data analysis show that our {\it Dynamic Partial Dimension Reduction} method has superior performance comparing to the existing methods.

stat.ME↗

arXiv2019

Gaia Parallax of Milky Way Globular Clusters -- A Solution of Mixture Model

We have established a mixture model approach to derive the parallax of the Milky Way globular clusters. It avoids the problem of cluster membership determination and provides a completely independent astrometrical solution by purely using the parallax data. This method is validated with simulated clusters of \cite{2017MNRAS.467..412P}. We have resolved 120 real globular clusters by the mixture model using parallaxes of the second data release of \gaia. They construct the largest direct parallax sample up to now. In comparing with other direct parallax results based on cluster members, including 75 clusters of \cite{2018A&A...616A..12G}, our method presents its accuracy, especially for some particular clusters. A systematic offset of $-27.6\pm1.7$ $μ$as, together with a scatter of $22.8\pm1.3$ $μ$as is found in comparing with other indirect parallax measurements. They are consistent with the global value and the variation of the zero-point of current \gaia parallaxes. Distances of several specific nearby globular clusters are discussed while the closest ones can reach high precisions, even taking the systematic error into account.

astro-ph.GA↗

arXiv2019

Observation data pre-processing and scientific data products generation of POLAR

POLAR is a compact space-borne detector initially designed to measure the polarization of hard X-rays emitted from Gamma-Ray Bursts in the energy range 50-500keV. This instrument was launched successfully onboard the Chinese space laboratory Tiangong-2 (TG-2) on 2016 September 15. After being switched on a few days later, tens of gigabytes of raw detection data were produced in-orbit by POLAR and transferred to the ground every day. Before the launch date, a full pipeline and related software were designed and developed for the purpose of quickly pre-processing all the raw data from POLAR, which include both science data and engineering data, then to generate the high level scientific data products that are suitable for later science analysis. This pipeline has been successfully applied for use by the POLAR Science Data Center in the Institute of High Energy Physics (IHEP) after POLAR was launched and switched on. A detailed introduction to the pipeline and some of the core relevant algorithms are presented in this paper.

astro-ph.IM↗

arXiv2019

Revealing the Complicated Story of the Cetus Stream with StarGO

We use a novel cluster identification tool StarGO to explore the metal poor ([Fe/H] $<$ -1.5) outer stellar halo (d $>$ 15 kpc) of the Milky Way using data from Gaia, LAMOST and SDSS. Our method is built using an unsupervised learning algorithm, a self-organizing map, which trains a 2-D neural network to learn the topological structures of a data set from an n-D input space. Using a 4-D space of angular momentum and orbital energy, we identify three distinct groups corresponding to the Sagittarius, Orphan, and Cetus Streams. For the first time we are able to discover a northern counterpart to the Cetus stream. We test the robustness of this new detection using mock data and find that the significance is more than 5-sigma. We also find that the existing southern counterpart bifurcates into two clumps with different radial velocities. By exploiting the visualization power of StarGO, we attach MW globular clusters to the same trained neural network. The Sagittarius stream is found to have five related clusters, confirming recent literature studies, and the Cetus stream has one associated cluster, NGC 5824. This latter association has previously been postulated, but can only now be truly confirmed thanks to the high-precision Gaia proper motions and large numbers of stellar spectra from LAMOST. The large metallicity dispersion of the stream indicates that the progenitor cannot be a globular cluster. Given the mean metallicity of the stream, we propose that the stream is the result of a merger of a low-mass dwarf galaxy that hosted a large nuclear star cluster (NGC 5824).

astro-ph.GA↗

arXiv2019

Unconventional thermal metallic state of charge-neutral fermions in an insulator

Quantum oscillations (QOs) in transport and thermodynamic parameters at high magnetic fields are an unambiguous signature of the Fermi surface, the defining characteristic of a metal. Therefore, recent observations of QOs in insulating SmB$_6$ and YbB$_{12}$, in particular the QOs of the resistivity $ρ_{xx}$ in YbB$_{12}$, have been a big surprise, pointing to the formation of a novel state of quantum matter. Despite the large charge gap inferred from the insulating behaviour of $ρ_{xx}$, these compounds seemingly host a Fermi surface at high magnetic fields. However, the nature of the ground state in zero field has been little explored. Here we report the use of low-temperature heat-transport measurements to discover gapless, itinerant, charge-neutral excitations in the ground state of YbB$_{12}$. At zero field, despite $ρ_{xx}$ being far larger than that of conventional metals, a sizable linear temperature dependent term in the thermal conductivity is clearly resolved in the zero-temperature limit ($κ_{xx}/T(T\rightarrow0)=κ_{xx}^0/T\neq0$). Such a residual $κ_{xx}^0/T$ term at zero field, which is absent in SmB$_6$, leads to a spectacular violation of the Wiedemann-Franz law: the Lorenz ratio $L=κ_{xx}ρ_{xx}/T$ is $10^{4}$-$10^{5}$ times larger than that expected in conventional metals. These data indicate that YbB$_{12}$ is a charge insulator but a thermal metal, suggesting the presence of itinerant neutral fermions. Remarkably, more insulating crystals with larger activation energies exhibit a larger amplitude of the resistive QOs as well as a larger $κ_{xx}^0/T$, in stark contrast to conventional metals. Moreover, we find that these fermions couple to magnetic field, despite their charge neutrality. Our findings expose novel gapless and highly itinerant, charge-neutral quasiparticles in this unconventional quantum state.

cond-mat.str-el↗

arXiv2019

Quantum Oscillations of Electrical Resistivity in an Insulator

In metals, orbital motions of conduction electrons on the Fermi surface are quantized in magnetic fields, which is manifested by quantum oscillations in electrical resistivity. This Landau quantization is generally absent in insulators. Here we report a notable exception in an insulator, ytterbium dodecaboride (YbB12). Despite much larger than that of metals, the resistivity of YbB12 exhibits profound quantum oscillations. This unconventional oscillation is shown to arise from the insulating bulk, yet the temperature dependence of their amplitude follows the conventional Fermi liquid theory of metals. The large effective masses indicate the presence of Fermi surface consisting of strongly correlated electrons. Our result reveals a mysterious bipartite ground state of YbB12: it is both a charge insulator and a strongly correlated metal.

cond-mat.str-el↗

arXiv2019

Quantifying the resource content of quantum channels: An operational approach

We propose a general method to operationally quantify the resourcefulness of quantum channels via channel discrimination, an important information processing task. A main result is that the maximum success probability of distinguishing a given channel from the set of free channels by free probe states is exactly characterized by the resource generating power, i.e. the maximum amount of resource produced by the action of the channel, given by the trace distance to the set of free states. We apply this framework to the resource theory of quantum coherence, as an informative example. The general results can also be easily applied to other resource theories such as entanglement, magic states, and asymmetry.

quant-ph↗