arXiv subjects

Xu Chen

Publications and source records attributed to Xu Chen.

At least 505 records · Page 28Linked to original sources

arXiv2019

Efficient Contour Computation of Group-based Skyline

Skyline, aiming at finding a Pareto optimal subset of points in a multi-dimensional dataset, has gained great interest due to its extensive use for multi-criteria analysis and decision making. The skyline consists of all points that are not dominated by any other points. It is a candidate set of the optimal solution, which depends on a specific evaluation criterion for optimum. However, conventional skyline queries, which return individual points, are inadequate in group querying case since optimal combinations are required. To address this gap, we study the skyline computation in the group level and propose efficient methods to find the Group-based skyline (G-skyline). For computing the front $l$ skyline layers, we lay out an efficient approach that does the search concurrently on each dimension and investigates each point in the subspace. After that, we present a novel structure to construct the G-skyline with a queue of combinations of the first-layer points. We further demonstrate that the G-skyline is a complete candidate set of top-$l$ solutions, which is the main superiority over previous group-based skyline definitions. However, as G-skyline is complete, it contains a large number of groups which can make it impractical. To represent the "contour" of the G-skyline, we define the Representative G-skyline (RG-skyline). Then, we propose a Group-based clustering (G-clustering) algorithm to find out RG-skyline groups. Experimental results show that our algorithms are several orders of magnitude faster than the previous work.

cs.DB↗

arXiv2019

Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition

Action recognition with skeleton data has recently attracted much attention in computer vision. Previous studies are mostly based on fixed skeleton graphs, only capturing local physical dependencies among joints, which may miss implicit joint correlations. To capture richer dependencies, we introduce an encoder-decoder structure, called A-link inference module, to capture action-specific latent dependencies, i.e. actional links, directly from actions. We also extend the existing skeleton graphs to represent higher-order dependencies, i.e. structural links. Combing the two types of links into a generalized skeleton graph, we further propose the actional-structural graph convolution network (AS-GCN), which stacks actional-structural graph convolution and temporal convolution as a basic building block, to learn both spatial and temporal features for action recognition. A future pose prediction head is added in parallel to the recognition head to help capture more detailed action patterns through self-supervision. We validate AS-GCN in action recognition using two skeleton data sets, NTU-RGB+D and Kinetics. The proposed AS-GCN achieves consistently large improvement compared to the state-of-the-art methods. As a side product, AS-GCN also shows promising results for future pose prediction.

cs.CV↗

arXiv2019

Contact binaries at the short period cut-off I. statistics and the first photometric investigations of ten totally eclipsing systems

The period distribution of contact binaries exhibits a very sharp short period cut-off at 0.22 days. In order to provide valuable information on this short period limit, we observed ten totally eclipsing contact binaries with orbital periods near this cut-off. By detailed analysis using W-D code, we determined that two of these systems are A-subtype contact binaries while the others are W-subtype contact binaries and all the targets show shallow contact configurations. Half of the targets exhibit stellar spot activity and four of them have third light. A statistical work on well studied USPCBs was carried out, and physical parameters of fifty-five USPCBs were obtained. Some common properties for these systems were derived. Due to the study of the period-color diagram of USPCBs, we found that the period-color relation of USPCBs is different from other W UMa type contact binaries and USPCBs are metal poor old stars. In addition, the evolutionary states of these systems were discussed by constructing the color-density diagram. We derived that the evolutionary states of the two components of USPCBs show identical characteristics of other contact binaries despite slower evolutionary status caused by smaller mass. We suggested that both the fully convective limit claimed by Rucinski (1992) and the AML theory recommended by Stepien (2006) can produce the short period cut-off and a tertiary companion is very important during the formation of the short period contact binaries by removing angular momentum from the host eclipsing pairs under certain circumstances.

astro-ph.SR↗

arXiv2019

Locations of optical and $γ$-ray emitting regions in the jet of PMN J2345-1555

We collect long term $γ$-ray, optical and radio $15$ GHz light curves of quasar object PMN J2345-1555. The correlation analyses between them are performed via the local cross-correlation function (LCCF). We found that all the optical $V$, $R$ band and the infrared $J$ band are correlated with the radio 15 GHz at beyond $3σ$ significance level, and the lag times are $-221.81^{+6.26}_{-6.72}$, $-201.38^{+6.42}_{-6.02}$ and $-192.27^{+8.26}_{-7.37}$ days, respectively. The $γ$-ray is strongly correlated with optical, but weakly correlated with the radio. We present that time lags between different frequencies can be used as an alternative parameter to derive the core-shift measurement. For this target, the magnetic field and particle density at 1 parsec in jet are derived to be $0.61$ Gauss and $1533/γ_{\rm min}$ cm$^{-3}$, respectively. The black hole mass and the 15 GHz core position in jet are estimated to be $10^{8.44} {\rm M}_{\odot}$ and $30$ parsec, respectively. The lag times enable us to derive that the optical and the $γ$-ray emitting regions coincide, which are located at $4.26^{+0.83}_{-0.79}$ pc away from 15 GHz core position in jet and beyond the broad line region (BLR). We found that a $3σ$ correlation between the color index and the radio light curve, which indicates that opacity may play an important role in the variation. The $δV-δR$ behaviors are complex, while the $R-J$ shows a bluer when brighter trend. As hinted from radio images, we proposed a positional dependent spectral index model to explain the color index behaviors, which is complementary for the shock in jet model. The curvature effects and contribution from accretion disk may also affect variables of blazars in many aspects.

astro-ph.HE↗

arXiv2019

Characterizing optical variability of OJ 287 in 2016 - 2017

We report on a recent multi-band optical photometric and polarimetric observational campaign of the blazar OJ 287 which was carried out during September 2016 -- December 2017. We employed nine telescopes in Bulgaria, China, Georgia, Japan, Serbia, Spain and the United States. We collected over 1800 photometric image frames in BVRI bands and over 100 polarimetric measurements over ~175 nights. In 11 nights with many quasi-simultaneous multi-band (V, R, I) observations, we did not detect any genuine intraday variability in flux or color. On longer timescales, multiple flaring events were seen. Large changes in color with respect to time and in a color--magnitude diagram were seen, and while only a weak systematic variability trend was noticed in color with respect to time, the color--magnitude diagram shows a bluer-when-brighter trend. Large changes in the degree of polarization, and substantial swings in the polarization angle were detected. The fractional Stokes parameters of the polarization showed a systematic trend with time in the beginning of these observations, followed by chaotic changes and then an apparently systematic variation at the end. These polarization changes coincide with the detection and duration of the source at very high energies as seen by VERITAS. The spectral index shows a systematic variation with time and V-band magnitude. We briefly discuss possible physical mechanisms that could explain the observed flux, color, polarization, and spectral variability.

astro-ph.HE↗

arXiv2018

Edge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergy

As the backbone technology of machine learning, deep neural networks (DNNs) have have quickly ascended to the spotlight. Running DNNs on resource-constrained mobile devices is, however, by no means trivial, since it incurs high performance and energy overhead. While offloading DNNs to the cloud for execution suffers unpredictable performance, due to the uncontrolled long wide-area network latency. To address these challenges, in this paper, we propose Edgent, a collaborative and on-demand DNN co-inference framework with device-edge synergy. Edgent pursues two design knobs: (1) DNN partitioning that adaptively partitions DNN computation between device and edge, in order to leverage hybrid computation resources in proximity for real-time DNN inference. (2) DNN right-sizing that accelerates DNN inference through early-exit at a proper intermediate DNN layer to further reduce the computation latency. The prototype implementation and extensive evaluations based on Raspberry Pi demonstrate Edgent's effectiveness in enabling on-demand low-latency edge intelligence.

cs.DC↗

arXiv2018

Ramp-based Twin Support Vector Clustering

Traditional plane-based clustering methods measure the cost of within-cluster and between-cluster by quadratic, linear or some other unbounded functions, which may amplify the impact of cost. This letter introduces a ramp cost function into the plane-based clustering to propose a new clustering method, called ramp-based twin support vector clustering (RampTWSVC). RampTWSVC is more robust because of its boundness, and thus it is more easier to find the intrinsic clusters than other plane-based clustering methods. The non-convex programming problem in RampTWSVC is solved efficiently through an alternating iteration algorithm, and its local solution can be obtained in a finite number of iterations theoretically. In addition, the nonlinear manifold-based formation of RampTWSVC is also proposed by kernel trick. Experimental results on several benchmark datasets show the better performance of our RampTWSVC compared with other plane-based clustering methods.

cs.LG↗

arXiv2018

Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision

Joint representation learning of words and entities benefits many NLP tasks, but has not been well explored in cross-lingual settings. In this paper, we propose a novel method for joint representation learning of cross-lingual words and entities. It captures mutually complementary knowledge, and enables cross-lingual inferences among knowledge bases and texts. Our method does not require parallel corpora, and automatically generates comparable data via distant supervision using multi-lingual knowledge bases. We utilize two types of regularizers to align cross-lingual words and entities, and design knowledge attention and cross-lingual attention to further reduce noises. We conducted a series of experiments on three tasks: word translation, entity relatedness, and cross-lingual entity linking. The results, both qualitatively and quantitatively, demonstrate the significance of our method.

cs.CL↗

arXiv2018

Robust Active Learning for Electrocardiographic Signal Classification

The classification of electrocardiographic (ECG) signals is a challenging problem for healthcare industry. Traditional supervised learning methods require a large number of labeled data which is usually expensive and difficult to obtain for ECG signals. Active learning is well-suited for ECG signal classification as it aims at selecting the best set of labeled data in order to maximize the classification performance. Motivated by the fact that ECG data are usually heavily unbalanced among different classes and the class labels are noisy as they are manually labeled, this paper proposes a novel solution based on robust active learning for addressing these challenges. The key idea is to first apply the clustering of the data in a low dimensional embedded space and then select the most information instances within local clusters. By selecting the most informative instances relying on local average minimal distances, the algorithm tends to select the data for labelling in a more diversified way. Finally, the robustness of the model is further enhanced by adding a novel noisy label reduction scheme after the selection of the labeled data. Experiments on the ECG signal classification from the MIT-BIH arrhythmia database demonstrate the effectiveness of the proposed algorithm.

eess.SP↗

arXiv2018

Porosity-mediated High-performance Thermoelectric Materials

Whether porosity can effectively improve thermoelectric performance is still an open question. Herein we report that thermoelectric performance can be significantly enhanced by creating porosity in n-type Mg3.225Mn0.025Sb1.5Bi0.49Te0.01, with a ZT of ~0.9 at 323 K and ~1.6 at 723 K, making the average ZT much higher for better performance. The large improvement at room temperature is significant considering that such a ZT value is comparable to the best ZT at this temperature in n-type Bi2Te3. The enhancement was mainly from the improved electrical mobility and multi-scale phonon scattering, particularly from the well-dispersed bismuth nano-precipitates in the porous structure. We further extend this approach to other thermoelectric materials such as half-Heuslers Nb0.56V0.24Ti0.2FeSb and Hf0.25Zr0.75NiSn0.99Sb0.01 and Bi0.5Sb1.5Te3 showing similar improvements, further advancing thermoelectric materials for applications.

cond-mat.mtrl-sci↗

arXiv2018

Variational Collaborative Learning for User Probabilistic Representation

Collaborative filtering (CF) has been successfully employed by many modern recommender systems. Conventional CF-based methods use the user-item interaction data as the sole information source to recommend items to users. However, CF-based methods are known for suffering from cold start problems and data sparsity problems. Hybrid models that utilize auxiliary information on top of interaction data have increasingly gained attention. A few "collaborative learning"-based models, which tightly bridges two heterogeneous learners through mutual regularization, are recently proposed for the hybrid recommendation. However, the "collaboration" in the existing methods are actually asynchronous due to the alternative optimization of the two learners. Leveraging the recent advances in variational autoencoder~(VAE), we here propose a model consisting of two streams of mutual linked VAEs, named variational collaborative model (VCM). Unlike the mutual regularization used in previous works where two learners are optimized asynchronously, VCM enables a synchronous collaborative learning mechanism. Besides, the two stream VAEs setup allows VCM to fully leverages the Bayesian probabilistic representations in collaborative learning. Extensive experiments on three real-life datasets have shown that VCM outperforms several state-of-art methods.

cs.LG↗

arXiv2018

Aesthetic-based Clothing Recommendation

Recently, product images have gained increasing attention in clothing recommendation since the visual appearance of clothing products has a significant impact on consumers' decision. Most existing methods rely on conventional features to represent an image, such as the visual features extracted by convolutional neural networks (CNN features) and the scale-invariant feature transform algorithm (SIFT features), color histograms, and so on. Nevertheless, one important type of features, the \emph{aesthetic features}, is seldom considered. It plays a vital role in clothing recommendation since a users' decision depends largely on whether the clothing is in line with her aesthetics, however the conventional image features cannot portray this directly. To bridge this gap, we propose to introduce the aesthetic information, which is highly relevant with user preference, into clothing recommender systems. To achieve this, we first present the aesthetic features extracted by a pre-trained neural network, which is a brain-inspired deep structure trained for the aesthetic assessment task. Considering that the aesthetic preference varies significantly from user to user and by time, we then propose a new tensor factorization model to incorporate the aesthetic features in a personalized manner. We conduct extensive experiments on real-world datasets, which demonstrate that our approach can capture the aesthetic preference of users and significantly outperform several state-of-the-art recommendation methods.

cs.IR↗

arXiv2018

Follow Me at the Edge: Mobility-Aware Dynamic Service Placement for Mobile Edge Computing

Mobile edge computing is a new computing paradigm, which pushes cloud computing capabilities away from the centralized cloud to the network edge. However, with the sinking of computing capabilities, the new challenge incurred by user mobility arises: since end-users typically move erratically, the services should be dynamically migrated among multiple edges to maintain the service performance, i.e., user-perceived latency. Tackling this problem is non-trivial since frequent service migration would greatly increase the operational cost. To address this challenge in terms of the performance-cost trade-off, in this paper we study the mobile edge service performance optimization problem under long-term cost budget constraint. To address user mobility which is typically unpredictable, we apply Lyapunov optimization to decompose the long-term optimization problem into a series of real-time optimization problems which do not require a priori knowledge such as user mobility. As the decomposed problem is NP-hard, we first design an approximation algorithm based on Markov approximation to seek a near-optimal solution. To make our solution scalable and amenable to future 5G application scenario with large-scale user devices, we further propose a distributed approximation scheme with greatly reduced time complexity, based on the technique of best response update. Rigorous theoretical analysis and extensive evaluations demonstrate the efficacy of the proposed centralized and distributed schemes.

cs.NI↗

arXiv2018

First Investigation on the Radiation Field of the Gas-Filled Three-Axis Cylindrical Hohlraum

A novel ignition hohlraum named three-axis cylindrical hohlraum (TACH) is designed for indirect-drive inertial confinement fusion. TACH is a kind of 6 laser entrance holes (LEHs) hohlraum, which is orthogonally jointed of three cylindrical hohlraums. The first experiment on the radiation field of TACH was performed on Shenguang III laser facility. 24 laser beams were elected and injected into 6 LEHs quasi-symmetrically. Total laser energy was about 59 kJ, and the peak radiation temperature reached about 192 eV. Radiation temperature and pinhole images in gas-filled hohlraum are largely identical but with minor differences with those in vacuum hohlraum. All laser energy can be totally delivered into hohlraum in 3 ns duration even without filled gas in the hohlraum of 1.4 mm diameter. Plasma filling cannot be obviously suppressed even with 0.5 atm pressure gas in the small hohlraum. Backscattering fractions of vacuum hohlraum and gas-filled hohlraum are both lower than 2%. Experimental study of this new kind of hohlraum can provide guidance for future target design and implosion experiment.

physics.plasm-ph↗

arXiv2018

First Integrated Implosion Experiment of Three-Axis Cylindrical Hohlraum at the SGIII Laser Facility

The first integrated implosion experiment of three-axis cylindrical hohlraum (TACH) was accomplished at the SGIII laser facility. 24 laser beams of the SGIII laser facility were carefully chosen and quasi-symmetrically injected into the TACH, in which a highly symmetric radiation filed was generated with a peak radiation temperature of ~190eV. Driven by the radiation field, the neutron yield of a deuterium gas filled capsule reached ~1e9, and the corresponding yield over clean (YOC) was ~40% for a convergence ratio (Cr) of ~17. The X-ray self-emission image of imploded capsule cores was nearly round, and the backscatter fraction of laser beams was less than 1.25%. This experiment preliminarily demonstrated the major performance of TACH, such as the robustness of symmetry, and a laser plasma instability (LPI) behavior similar to that of the outer ring of traditional cylindrical hohlraum.

physics.plasm-ph↗

arXiv2018

Fictitious GAN: Training GANs with Historical Models

Generative adversarial networks (GANs) are powerful tools for learning generative models. In practice, the training may suffer from lack of convergence. GANs are commonly viewed as a two-player zero-sum game between two neural networks. Here, we leverage this game theoretic view to study the convergence behavior of the training process. Inspired by the fictitious play learning process, a novel training method, referred to as Fictitious GAN, is introduced. Fictitious GAN trains the deep neural networks using a mixture of historical models. Specifically, the discriminator (resp. generator) is updated according to the best-response to the mixture outputs from a sequence of previously trained generators (resp. discriminators). It is shown that Fictitious GAN can effectively resolve some convergence issues that cannot be resolved by the standard training approach. It is proved that asymptotically the average of the generator outputs has the same distribution as the data samples.

cs.LG↗

arXiv2018

$I=0$ $ππ$ $s$-wave scattering length from lattice QCD

We deliver lattice results for the $I=0$ $ππ$ elastic $s$-wave scattering length calculated with the MILC $N_f=3$ flavors of the Asqtad-improved staggered fermions. The scattering phase shifts are determined by Lüscher's formula from the energy-eigenvalues of $ππ$ systems at one center of mass frame and four moving frames using the moving wall source technique. Our measurements are good enough to resolve the scattering length $a$ and effective range $r$, moreover, it allows us to roughly estimate the shape parameter $P$. Using our lattice results, the scattering length $a$ and effective range $r$ at the physical point are extrapolated by chiral perturbation theory. Our results are reasonably consistent with the Roy equation determinations and the newer experimental data. Numerical computations are carried out with two MILC fine ($a\approx0.09$~fm, $L^3 \times T = 40^3\times 96$) and one MILC superfine ($a\approx0.06$~fm, $L^3 \times T = 48^3\times 144$) lattice ensembles at three pion masses of $m_π\sim247~{\rm MeV}$, $249~{\rm MeV}$, and $314~{\rm MeV}$, respectively.

hep-lat↗

arXiv2018

The ever-surprising blazar OJ 287: multi-wavelength study and appearance of a new component in X-rays

We present a multi-wavelength spectral and temporal investigation of OJ 287 emission during its strong optical-to-X-ray activity between July 2016 - July 2017. The daily $γ$-ray fluxes from \emph{Fermi}-LAT are consistent with no variability. The strong optical-to-X-ray variability is accompanied by a change in power-law spectral index of the X-ray spectrum from $< 2$ to $>2$, with variations often associated with changes in optical polarization properties. Cross-correlations between optical-to-X-ray emission during four continuous segments show simultaneous optical-ultraviolet (UV) variations while the X-ray and UV/optical are simultaneous only during the middle two segments. In the first segment, the results suggest X-rays lag the optical/UV, while in the last segment X-rays lead by $\sim$ 5-6 days. The last segment also shows a systematic trend with variations appearing first at higher energies followed by lower energy ones. The LAT spectrum before the VHE activity is similar to preceding quiescent state spectrum while it hardens during VHE activity period and is consistent with the extrapolated VHE spectrum during the latter. Overall, the broadband spectral energy distributions (SEDs) during high activity periods are a combination of a typical OJ 287 SED and an HBL SED, and can be explained in a two-zone leptonic model, with the second zone located at parsec scales, beyond the broad line region, being responsible for the HBL-like spectrum. The change of polarization properties from systematic to chaotic and back to systematic, before, during and after the VHE activity, suggest dynamic roles for magnetic fields and turbulence.

astro-ph.HE↗