arXiv subjects

Xi Liu

Publications and source records attributed to Xi Liu.

At least 109 records · Page 6Linked to original sources

arXiv2019

ICDAR 2019 Robust Reading Challenge on Reading Chinese Text on Signboard

Chinese scene text reading is one of the most challenging problems in computer vision and has attracted great interest. Different from English text, Chinese has more than 6000 commonly used characters and Chinesecharacters can be arranged in various layouts with numerous fonts. The Chinese signboards in street view are a good choice for Chinese scene text images since they have different backgrounds, fonts and layouts. We organized a competition called ICDAR2019-ReCTS, which mainly focuses on reading Chinese text on signboard. This report presents the final results of the competition. A large-scale dataset of 25,000 annotated signboard images, in which all the text lines and characters are annotated with locations and transcriptions, were released. Four tasks, namely character recognition, text line recognition, text line detection and end-to-end recognition were set up. Besides, considering the Chinese text ambiguity issue, we proposed a multi ground truth (multi-GT) evaluation method to make evaluation fairer. The competition started on March 1, 2019 and ended on April 30, 2019. 262 submissions from 46 teams are received. Most of the participants come from universities, research institutes, and tech companies in China. There are also some participants from the United States, Australia, Singapore, and Korea. 21 teams submit results for Task 1, 23 teams submit results for Task 2, 24 teams submit results for Task 3, and 13 teams submit results for Task 4. The official website for the competition is http://rrc.cvc.uab.es/?ch=12.

cs.CV↗

arXiv2019

Direct visualization of the nematic superconductivity in Cu$_x$Bi$_2$Se$_3$

Cu$_x$Bi$_2$Se$_3$ hosts both topological surface states and bulk superconductivity. It has been identified recently as a topological superconductor (TSC) with an extraordinary nematic, i. e. C2-symmetric, superconducting state and odd-parity pairing. Here, using scanning tunneling microscopy (STM), we directly examine the response of the superconductivity of CuxBi2Se3 to magnetic field. Under out-of-plane fields, we discover elongated magnetic vortices hosting zero-bias conductance peaks consistent with the Majorana bound states expected in a TSC. Under in-plane fields, the average superconducting gap exhibits two-fold symmetry with field orientation; the long C2 symmetry axes are pinned to the dihedral mirror planes under B//=0.5 T but rotate slightly under B//=1.0 T. Moreover, a nodeless Δ4x gap structure is semi-quantitatively determined for the first time. Our data paint a microscopic picture of the nematic superconductivity in CuxBi2Se3 and pose strong constraints on theory.

cond-mat.supr-con↗

arXiv2019

Piecewise Stationary Modeling of Random Processes Over Graphs With an Application to Traffic Prediction

Stationarity is a key assumption in many statistical models for random processes. With recent developments in the field of graph signal processing, the conventional notion of wide-sense stationarity has been extended to random processes defined on the vertices of graphs. It has been shown that well-known spectral graph kernel methods assume that the underlying random process over a graph is stationary. While many approaches have been proposed, both in machine learning and signal processing literature, to model stationary random processes over graphs, they are too restrictive to characterize real-world datasets as most of them are non-stationary processes. In this paper, to well-characterize a non-stationary process over graph, we propose a novel model and a computationally efficient algorithm that partitions a large graph into disjoint clusters such that the process is stationary on each of the clusters but independent across clusters. We evaluate our model for traffic prediction on a large-scale dataset of fine-grained highway travel times in the Dallas--Fort Worth area. The accuracy of our method is very close to the state-of-the-art graph based deep learning methods while the computational complexity of our model is substantially smaller.

eess.SP↗

arXiv2019

Stay With Me: Lifetime Maximization Through Heteroscedastic Linear Bandits With Reneging

Sequential decision making for lifetime maximization is a critical problem in many real-world applications, such as medical treatment and portfolio selection. In these applications, a "reneging" phenomenon, where participants may disengage from future interactions after observing an unsatisfiable outcome, is rather prevalent. To address the above issue, this paper proposes a model of heteroscedastic linear bandits with reneging, which allows each participant to have a distinct "satisfaction level," with any interaction outcome falling short of that level resulting in that participant reneging. Moreover, it allows the variance of the outcome to be context-dependent. Based on this model, we develop a UCB-type policy, namely HR-UCB, and prove that it achieves $\mathcal{O}\big(\sqrt{{T}(\log({T}))^{3}}\big)$ regret. Finally, we validate the performance of HR-UCB via simulations.

cs.LG↗

arXiv2019

Micro- and Macro-Level Churn Analysis of Large-Scale Mobile Games

As mobile devices become more and more popular, mobile gaming has emerged as a promising market with billion-dollar revenues. A variety of mobile game platforms and services have been developed around the world. A critical challenge for these platforms and services is to understand the churn behavior in mobile games, which usually involves churn at micro level (between an app and a specific user) and macro level (between an app and all its users). Accurate micro-level churn prediction and macro-level churn ranking will benefit many stakeholders such as game developers, advertisers, and platform operators. In this paper, we present the first large-scale churn analysis for mobile games that supports both micro-level churn prediction and macro-level churn ranking. For micro-level churn prediction, in view of the common limitations of the state-of-the-art methods built upon traditional machine learning models, we devise a novel semi-supervised and inductive embedding model that jointly learns the prediction function and the embedding function for user-app relationships. We model these two functions by deep neural networks with a unique edge embedding technique that is able to capture both contextual information and relationship dynamics. We also design a novel attributed random walk technique that takes into consideration both topological adjacency and attribute similarities. To address macro-level churn ranking, we propose to construct a relationship graph with estimated micro-level churn probabilities as edge weights and adapt link analysis algorithms on the graph. We devise a simple algorithm SimSum and adapt two more advanced algorithms PageRank and HITS. The performance of our solutions for the two-level churn analysis problems is evaluated on real-world data collected from the Samsung Game Launcher platform.

cs.LG↗

arXiv2018

Evidence of nematic order and nodal superconducting gap along [110] direction in RbFe2As2

Unconventional superconductivity often intertwines with various forms of order, such as the "nematic" order which breaks the rotational symmetry of the lattice. Investigation of these ordered phases sheds crucial light on the superconductivity itself. Here we report a low-temperature scanning tunneling microscopy (STM) study on RbFe2As2, a heavily hole-doped Fe-based superconductor (FeSC). We observe significant symmetry breaking in its electronic structure and magnetic vortex which differentiates the (π, π) and (π, -π) directions of the unfolded Brillouin zone (BZ). It is thus a novel nematic state, distinct from the nematicity of undoped/lightly-doped FeSCs which breaks the (π, 0) / (0, π) equivalence. Moreover, we observe a clear "V"-shaped superconducting gap which can be well fitted with a nodal gap function. The gap is found to be suppressed on surface Rb vacancies and at step edges, and the suppression is particularly strong at the [110]-oriented edges. This is possibly due to a dx2-y2 like pairing component with nodes along the [110] directions. We further demonstrated that such (π, π) nematic state can be suppressed via surface electron doping to RbFe2As2, and the superconductivity is subsequently enhanced. Our results thus highlight the intimate connection between nematicity and superconducting pairing in iron-based superconductors.

cond-mat.supr-con↗

arXiv2018

Streaming Network Embedding through Local Actions

Recently, considerable research attention has been paid to network embedding, a popular approach to construct feature vectors of vertices. Due to the curse of dimensionality and sparsity in graphical datasets, this approach has become indispensable for machine learning tasks over large networks. The majority of existing literature has considered this technique under the assumption that the network is static. However, networks in many applications, nodes and edges accrue to a growing network as a streaming. A small number of very recent results have addressed the problem of embedding for dynamic networks. However, they either rely on knowledge of vertex attributes, suffer high-time complexity or need to be re-trained without closed-form expression. Thus the approach of adapting the existing methods to the streaming environment faces non-trivial technical challenges. These challenges motivate developing new approaches to the problems of streaming network embedding. In this paper, We propose a new framework that is able to generate latent features for new vertices with high efficiency and low complexity under specified iteration rounds. We formulate a constrained optimization problem for the modification of the representation resulting from a stream arrival. We show this problem has no closed-form solution and instead develop an online approximation solution. Our solution follows three steps: (1) identify vertices affected by new vertices, (2) generate latent features for new vertices, and (3) update the latent features of the most affected vertices. The generated representations are provably feasible and not far from the optimal ones in terms of expectation. Multi-class classification and clustering on five real-world networks demonstrate that our model can efficiently update vertex representations and simultaneously achieve comparable or even better performance.

cs.LG↗

arXiv2018

Safe Intersection Management for Mixed Transportation Systems with Human-Driven and Autonomous Vehicles

Most recent studies on establishing intersection safety focus on the situation where all vehicles are fully autonomous. However, currently most vehicles are human-driven and so we will need to transition through regimes featuring a varying proportion of human-driven vehicles ranging from 100\% to 0\% before realizing such a fully autonomous future -- if ever. We will, therefore, need to address the safety of hybrid systems featuring an arbitrary mixture of human-driven and autonomous vehicles. In fact, recent incidents involving autonomous vehicles have already highlighted the need to study the safety of autonomous vehicles co-existing with human-driven vehicles. Motivated by this we address the design of provably safe intersection management for mixed traffic consisting of a mix of human-driven vehicles (HVs) as well as autonomous vehicles (AVs). To analyze such mixed traffic, we model HVs as nearsighted and with relatively loose constraints, permitting worst-case behavior while AVs are considered as capable of following much tighter constraints. HVs are allowed freedom to change their speed at any time while AVs are only allowed to change their speed beginning of a time slot through a Model Predictive Controller (MPC). AVs are assumed to possess a shorter response time and stronger braking capability than HVs in collision avoidance. Moreover, AVs obtain the permissions of passing through the intersection through vehicle-to-infrastructure (V2I) communication, while HVs achieve the same objective by following traffic lights. Taking the above differences into consideration, we propose a provably safe intersection management for mixed traffic comprised of an MPC-based protocol for AVs along with a coordination protocol for traffic lights...

eess.SY↗

arXiv2018

Towards Provably Safe Mixed Transportation Systems with Human-driven and Automated Vehicles

Currently, we are in an environment where the fraction of automated vehicles is negligibly small. We anticipate that this fraction will increase in coming decades before if ever, we have a fully automated transportation system. Motivated by this we address the problem of provable safety of mixed traffic consisting of both intelligent vehicles (IVs) as well as human-driven vehicles (HVs). An important issue that arises is that such mixed systems may well have lesser throughput than all human traffic systems if the automated vehicles are expected to remain provably safe with respect to human traffic. This necessitates the consideration of strategies such as platooning of automated vehicles in order to increase the throughput. In this paper, we address the design of provably safe systems consisting of a mix of automated and human-driven vehicles including the use of platooning by automated vehicles. We design motion planing policies and coordination rules for participants in this novel mixed system. HVs are considered as nearsighted and modeled with relatively loose constraints, while IVs are considered as capable of following much tighter constraints. HVs are expected to follow reasonable and simple rules. IVs are designed to move under a model predictive control (MPC) based motion plans and coordination protocols. Our contribution of this paper is in showing how to integrate these two types of models safely into a mixed system. System safety is proved in single lane scenarios, as well as in multi-lane situations allowing lane changes.

eess.SY↗

arXiv2018

A Semi-Supervised and Inductive Embedding Model for Churn Prediction of Large-Scale Mobile Games

Mobile gaming has emerged as a promising market with billion-dollar revenues. A variety of mobile game platforms and services have been developed around the world. One critical challenge for these platforms and services is to understand user churn behavior in mobile games. Accurate churn prediction will benefit many stakeholders such as game developers, advertisers, and platform operators. In this paper, we present the first large-scale churn prediction solution for mobile games. In view of the common limitations of the state-of-the-art methods built upon traditional machine learning models, we devise a novel semi-supervised and inductive embedding model that jointly learns the prediction function and the embedding function for user-app relationships. We model these two functions by deep neural networks with a unique edge embedding technique that is able to capture both contextual information and relationship dynamics. We also design a novel attributed random walk technique that takes into consideration both topological adjacency and attribute similarities. To evaluate the performance of our solution, we collect real-world data from the Samsung Game Launcher platform that includes tens of thousands of games and hundreds of millions of user-app interactions. The experimental results with this data demonstrate the superiority of our proposed model against existing state-of-the-art methods.

cs.LG↗

arXiv2017

High-order harmonic generation of doped semiconductor

We investigate the high-order harmonic generation (HHG) in doped semiconductors. The HHG is simulated with the single-electron time-dependent Schrödinger equation (TDSE). The results show that the high-order harmonics in the second plateau generated from the doped semiconductors is about 1 to 3 orders of magnitude higher than those from the undoped semiconductor. The results are explained based on the analysis of the energy band structure and the time-dependent population imaging. Our work indicates that doping can effectively control the HHG in semiconductor.

physics.optics↗

arXiv2017

Time-dependent population imaging for solid high harmonic generation

We propose an intuitive method, called time-dependent population imaging (TDPI), to map the dynamical processes of high harmonic generation (HHG) in solids by solving the time-dependent Schrödinger equation (TDSE). It is shown that the real-time dynamical characteristics of HHG in solids, such as the instantaneous photon energies of emitted harmonics, can be read directly from the energy-resolved population oscillations of electrons in the TDPIs. Meanwhile, the short and long trajectories of solid HHG are illustrated clearly from TDPI. By using the TDPI, we also investigate the effects of carrier-envelope phase (CEP) in few-cycle pulses and intuitively demonstrate the HHG dynamics driven by two-color fields. Our results show that the TDPI provides a powerful tool to study the ultrafast dynamics in strong fields for various laser-solid configurations and gain an insight into HHG processes in solids.

physics.atom-ph↗

arXiv2017

Adaptive Neighboring Selection Algorithm Based on Curvature Prediction in Manifold Learning

Recently manifold learning algorithm for dimensionality reduction attracts more and more interests, and various linear and nonlinear, global and local algorithms are proposed. The key step of manifold learning algorithm is the neighboring region selection. However, so far for the references we know, few of which propose a generally accepted algorithm to well select the neighboring region. So in this paper, we propose an adaptive neighboring selection algorithm, which successfully applies the LLE and ISOMAP algorithms in the test. It is an algorithm that can find the optimal K nearest neighbors of the data points on the manifold. And the theoretical basis of the algorithm is the approximated curvature of the data point on the manifold. Based on Riemann Geometry, Jacob matrix is a proper mathematical concept to predict the approximated curvature. By verifying the proposed algorithm on embedding Swiss roll from R3 to R2 based on LLE and ISOMAP algorithm, the simulation results show that the proposed adaptive neighboring selection algorithm is feasible and able to find the optimal value of K, making the residual variance relatively small and better visualization of the results. By quantitative analysis, the embedding quality measured by residual variance is increased 45.45% after using the proposed algorithm in LLE.

stat.ME↗

arXiv2017

Throughput-Optimal Scheduling for Multi-Hop Networked Transportation Systems With Switch-Over Delay

The emerging connected-vehicle technology provides a new dimension in developing more intelligent traffic control algorithms for signalized intersections in networked transportation systems. An important challenge for the scheduling problem in networked transportation systems is the switch-over delay caused by the guard time before any traffic signal change. The switch-over delay can result in significant loss of system capacity and hence needs to be accommodated in the scheduling design. To tackle this challenge, we propose a distributed online scheduling policy that extends the well-known Max-Pressure policy to address switch-over delay by introducing a bias factor toward the current schedule. We prove that the proposed policy is throughput-optimal with switch-over delay. Furthermore, the proposed policy remains optimal when there are both connected signalized intersections and conventional fixed-time ones in the system. With connected-vehicle technology, the proposed policy can be easily incorporated into the current transportation systems without additional infrastructure. Through extensive simulation in VISSIM, we show that our policy indeed outperforms the existing popular policies.

cs.NI↗

arXiv2017

Full-reference image quality assessment-based B-mode ultrasound image similarity measure

During the last decades, the number of new full-reference image quality assessment algorithms has been increasing drastically. Yet, despite of the remarkable progress that has been made, the medical ultrasound image similarity measurement remains largely unsolved due to a high level of speckle noise contamination. Potential applications of the ultrasound image similarity measurement seem evident in several aspects. To name a few, ultrasound imaging quality assessment, abnormal function region detection, etc. In this paper, a comparative study was made on full-reference image quality assessment methods for ultrasound image visual structural similarity measure. Moreover, based on the image similarity index, a generic ultrasound motion tracking re-initialization framework is given in this work. The experiments are conducted on synthetic data and real-ultrasound liver data and the results demonstrate that, with proposed similarity-based tracking re-initialization, the mean error of landmarks tracking can be decreased from 2 mm to about 1.5 mm in the ultrasound liver sequence.

cs.CV↗

arXiv2017

Delay-Optimal Scheduling for Queueing Systems with Switching Overhead

We study the scheduling polices for asymptotically optimal delay in queueing systems with switching overhead. Such systems consist of a single server that serves multiple queues, and some capacity is lost whenever the server switches to serve a different set of queues. The capacity loss due to this switching overhead can be significant in many emerging applications, and needs to be explicitly addressed in the design of scheduling policies. For example, in 60GHz wireless networks with directional antennas, base stations need to train and reconfigure their beam patterns whenever they switch from one client to another. Considerable switching overhead can also be observed in many other queueing systems such as transportation networks and manufacturing systems. While the celebrated Max-Weight policy achieves asymptotically optimal average delay for systems without switching overhead, it fails to preserve throughput-optimality, let alone delay-optimality, when switching overhead is taken into account. We propose a class of Biased Max-Weight scheduling policies that explicitly takes switching overhead into account. The Biased Max-Weight policy can use either queue length or head-of-line waiting time as an indicator of the system status. We prove that our policies not only are throughput-optimal, but also can be made arbitrarily close to the asymptotic lower bound on average delay. To validate the performance of the proposed policies, we provide extensive simulation with various system topologies and different traffic patterns. We show that the proposed policies indeed achieve much better delay performance than that of the state-of-the-art policy.

cs.PF↗

arXiv2016

Multi-resource Fair Allocation with Bounded Number of Tasks in Cloud Computing Systems

Dominant resource fairness (DRF) is a popular mechanism for multi-resource allocation in cloud computing systems. In this paper, we consider a problem of multi-resource fair allocation with bounded number of tasks. Firstly, we propose the lexicographically max-min normalized share (LMMNS) fair allocation mechanism, which is a natural generalization of DRF, and design a non-trivial optimal algorithm to find a LMMNS fair allocation, whose running time is linear in the number of users. Secondly, we prove that LMMNS satisfies envy-freeness (EF) and group strategy-proofness (GSP), and analysis the approximation ratios of LMMNS, by exploiting the properties of the optimal solution. Thirdly, we propose a modified version of LMMNS, which is the second mechanism satisfying sharing incentive, EF, and GSP. Finally, we have implemented LMMNS, and show that it has a good average-case performance, especially when the number of resources is 2.

cs.GT↗

arXiv2016

Helicity reversion in high harmonic generation driven by bichromatic counterrotating circularly polarized laser fields

We investigate the polarization properties of high harmonics generated with the bichromatic counterrotating circularly polarized (BCCP) laser fields by numerically solving time-dependent Schrödinger equation (TDSE). It is found that, the helicity of the elliptically polarized harmonic emission is reversed at particular harmonic orders. Based on the time-frequency analysis and the classical three-step model, the correspondence between the positions of helicity reversions and the classical trajectories of continuum electrons is established. It is shown that, the electrons ionized at one lobe of laser field can be divided into different groups based on the different lobes they recombine at, and the harmonics generated by adjacent groups have opposite helicities. Our study performs a detailed analysis of high harmonics in terms of electron trajectories and depicts a clear and intuitive physical picture of the HHG process in BCCP laser field.

physics.atom-ph↗