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Xu Chen

Publications and source records attributed to Xu Chen.

At least 523 records · Page 29Linked to original sources
arXiv2018

Efficient Resource Allocation for On-Demand Mobile-Edge Cloud Computing

Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. Aiming at provisioning flexible on-demand mobile-edge cloud service, in this paper we propose a comprehensive framework consisting of a resource-efficient computation offloading mechanism for users and a joint communication and computation (JCC) resource allocation mechanism for network operator. Specifically, we first study the resource-efficient computation offloading problem for a user, in order to reduce user's resource occupation by determining its optimal communication and computation resource profile with minimum resource occupation and meanwhile satisfying the QoS constraint. We then tackle the critical problem of user admission control for JCC resource allocation, in order to properly select the set of users for resource demand satisfaction. We show the admission control problem is NP-hard, and hence develop an efficient approximation solution of a low complexity by carefully designing the user ranking criteria and rigourously derive its performance guarantee. To prevent the manipulation that some users may untruthfully report their valuations in acquiring mobile-edge cloud service, we further resort to the powerful tool of critical value approach to design truthful pricing scheme for JCC resource allocation. Extensive performance evaluation demonstrates that the proposed schemes can achieve superior performance for on-demand mobile-edge cloud computing.

cs.DC↗
arXiv2018

Topology Control for Energy-Efficient Localization in Mobile Underwater Sensor Networks using Stackelberg Game

The characteristics of mobile Underwater Sensor Networks (UWSNs), such as low communication bandwidth, large propagation delay, and sparse deployment, pose challenging issues for successful localization of sensor nodes. In addition, sensor nodes in UWSNs are usually powered by batteries whose replacements introduce high cost and complexity. Thus, the critical problem in UWSNs is to enable each sensor node to find enough anchor nodes in order to localize itself, with minimum energy costs. In this paper, an Energy-Efficient Localization Algorithm (EELA) is proposed to analyze the decentralized interactions among sensor nodes and anchor nodes. A Single-Leader-Multi-Follower Stackelberg game is utilized to formulate the topology control problem of sensor nodes and anchor nodes by exploiting their available communication opportunities. In this game, the sensor node acts as a leader taking into account factors such as 'two-hop' anchor nodes and energy consumption, while anchor nodes act as multiple followers, considering their ability to localize sensor nodes and their energy consumption. We prove that both players select best responses and reach a socially optimal Stackelberg Nash Equilibrium. Simulation results demonstrate that the proposed EELA improves the perfor- mance of localization in UWSNs significantly, and in particular the energy cost of sensor nodes. Compared to the baseline schemes, the energy consumption per node is about 48% lower in EELA, while providing a desirable localization coverage, under reasonable error and delay.

cs.NI↗
arXiv2018

Learning over Knowledge-Base Embeddings for Recommendation

State-of-the-art recommendation algorithms -- especially the collaborative filtering (CF) based approaches with shallow or deep models -- usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or explicit feedbacks. Though structured knowledge bases were considered in content-based approaches, they have been largely neglected recently due to the availability of vast amount of data, and the learning power of many complex models. However, structured knowledge bases exhibit unique advantages in personalized recommendation systems. When the explicit knowledge about users and items is considered for recommendation, the system could provide highly customized recommendations based on users' historical behaviors. A great challenge for using knowledge bases for recommendation is how to integrated large-scale structured and unstructured data, while taking advantage of collaborative filtering for highly accurate performance. Recent achievements on knowledge base embedding sheds light on this problem, which makes it possible to learn user and item representations while preserving the structure of their relationship with external knowledge. In this work, we propose to reason over knowledge base embeddings for personalized recommendation. Specifically, we propose a knowledge base representation learning approach to embed heterogeneous entities for recommendation. Experimental results on real-world dataset verified the superior performance of our approach compared with state-of-the-art baselines.

cs.IR↗
arXiv2018

Benefits from Superposed Hawkes Processes

The superposition of temporal point processes has been studied for many years, although the usefulness of such models for practical applications has not be fully developed. We investigate superposed Hawkes process as an important class of such models, with properties studied in the framework of least squares estimation. The superposition of Hawkes processes is demonstrated to be beneficial for tightening the upper bound of excess risk under certain conditions, and we show the feasibility of the benefit in typical situations. The usefulness of superposed Hawkes processes is verified on synthetic data, and its potential to solve the cold-start problem of recommendation systems is demonstrated on real-world data.

stat.ML↗
arXiv2018

Superposition-Assisted Stochastic Optimization for Hawkes Processes

We consider the learning of multi-agent Hawkes processes, a model containing multiple Hawkes processes with shared endogenous impact functions and different exogenous intensities. In the framework of stochastic maximum likelihood estimation, we explore the associated risk bound. Further, we consider the superposition of Hawkes processes within the model, and demonstrate that under certain conditions such an operation is beneficial for tightening the risk bound. Accordingly, we propose a stochastic optimization algorithm assisted with a diversity-driven superposition strategy, achieving better learning results with improved convergence properties. The effectiveness of the proposed method is verified on synthetic data, and its potential to solve the cold-start problem of sequential recommendation systems is demonstrated on real-world data.

stat.ML↗
arXiv2018

Training Generative Adversarial Networks via Primal-Dual Subgradient Methods: A Lagrangian Perspective on GAN

We relate the minimax game of generative adversarial networks (GANs) to finding the saddle points of the Lagrangian function for a convex optimization problem, where the discriminator outputs and the distribution of generator outputs play the roles of primal variables and dual variables, respectively. This formulation shows the connection between the standard GAN training process and the primal-dual subgradient methods for convex optimization. The inherent connection does not only provide a theoretical convergence proof for training GANs in the function space, but also inspires a novel objective function for training. The modified objective function forces the distribution of generator outputs to be updated along the direction according to the primal-dual subgradient methods. A toy example shows that the proposed method is able to resolve mode collapse, which in this case cannot be avoided by the standard GAN or Wasserstein GAN. Experiments on both Gaussian mixture synthetic data and real-world image datasets demonstrate the performance of the proposed method on generating diverse samples.

cs.LG↗
arXiv2018

Visually Explainable Recommendation

Images account for a significant part of user decisions in many application scenarios, such as product images in e-commerce, or user image posts in social networks. It is intuitive that user preferences on the visual patterns of image (e.g., hue, texture, color, etc) can be highly personalized, and this provides us with highly discriminative features to make personalized recommendations. Previous work that takes advantage of images for recommendation usually transforms the images into latent representation vectors, which are adopted by a recommendation component to assist personalized user/item profiling and recommendation. However, such vectors are hardly useful in terms of providing visual explanations to users about why a particular item is recommended, and thus weakens the explainability of recommendation systems. As a step towards explainable recommendation models, we propose visually explainable recommendation based on attentive neural networks to model the user attention on images, under the supervision of both implicit feedback and textual reviews. By this, we can not only provide recommendation results to the users, but also tell the users why an item is recommended by providing intuitive visual highlights in a personalized manner. Experimental results show that our models are not only able to improve the recommendation performance, but also can provide persuasive visual explanations for the users to take the recommendations.

cs.IR↗
arXiv2017

Texture Object Segmentation Based on Affine Invariant Texture Detection

To solve the issue of segmenting rich texture images, a novel detection methods based on the affine invariable principle is proposed. Considering the similarity between the texture areas, we first take the affine transform to get numerous shapes, and utilize the KLT algorithm to verify the similarity. The transforms include rotation, proportional transformation and perspective deformation to cope with a variety of situations. Then we propose an improved LBP method combining canny edge detection to handle the boundary in the segmentation process. Moreover, human-computer interaction of this method which helps splitting the matched texture area from the original images is user-friendly.

eess.IV↗
arXiv2017

Can Early Joining Participants Contribute More? - Timeliness Sensitive Incentivization for Crowdsensing

This paper investigates the incentive mechanism design from a novel and practically important perspective in which mobile users as contributors do not join simultaneously and a requester desires large efforts from early contributors. A two-stage Tullock contest framework is constructed:at the second stage the potential contributors compete for splittable reward by exerting efforts, and at the first stage the requester can orchestrate the incentive mechanism to maximize his crowdsensing efficiency given the rewarding budget. A general reward discrimination mechanism is developed for timeliness sensitive crowdsensing where an earlier contributor usually has a larger maximum achievable reward and thus allocates more efforts. Owning to the lack of joining time information, two practical implementations, namely earliest-n and termination time, are announced to the contributors. For each of them, we formulate a Stackelberg Bayesian game in which the joining time of a contributor is his type and not available to his opponents. The uniqueness of Bayesian Nash equilibrium (BNE) is proved in each strategy. To maximize the requester's efficiency, we compute the optimal number of rewarded contributors in the earliest-n scheme and the optimal deadline in the termination time scheme. Our contest framework is applicable not only to the closed crowdsensing with fixed number of contributors, but also to the open crowdsensing that the arrival of contributors is governed by a stochastic process. Extensive simulations manifest that with appropriate reward discriminations, the requester is able to achieve a much higher efficiency with the optimal selection of the number of rewarded contributiors and the termination time.

cs.GT↗
arXiv2017

A Remark on the Localization formulas about two Killing vector fields

In this article, we will discuss a localization formulas of equivariant cohomology about two Killing vector fields on the set of zero points ${\rm{Zero}}(X_{M}-\sqrt{-1}Y_{M})=\{x\in M \mid |Y_{M}(x)|=|X_{M}(x)|=0 \}.$ As application, we use it to get formulas about characteristic numbers and to get a Duistermaat-Heckman type formula on symplectic manifold.

math.DG↗
arXiv2017

Localization Formulas About Two Killing Vector Fields

In this article, we will discuss the smooth $(X_{M}+\sqrt{-1}Y_{M})$-invariant forms on $M$ and to establish a localization formulas. As an application, we get a localization formulas for characteristic numbers.

math.DG↗
arXiv2017

Exploiting Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing

In this article we propose a novel Device-to-Device (D2D) Crowd framework for 5G mobile edge computing, where a massive crowd of devices at the network edge leverage the network-assisted D2D collaboration for computation and communication resource sharing among each other. A key objective of this framework is to achieve energy-efficient collaborative task executions at network-edge for mobile users. Specifically, we first introduce the D2D Crowd system model in details, and then formulate the energy-efficient D2D Crowd task assignment problem by taking into account the necessary constraints. We next propose a graph matching based optimal task assignment policy, and further evaluate its performance through extensive numerical study, which shows a superior performance of more than 50% energy consumption reduction over the case of local task executions. Finally, we also discuss the directions of extending the D2D Crowd framework by taking into variety of application factors.

cs.NI↗
arXiv2017

To Bond or not to Bond: An Optimal Channel Allocation Algorithm For Flexible Dynamic Channel Bonding in WLANs

IEEE 802.11 has evolved from 802.11a/b/g/n to 802.11ac to meet rapidly increasing data rate requirements in WLANs. One important technique adopted in 802.11ac is the channel bonding (CB) scheme that combines multiple 20MHz channels for a single transmission in 5GHz band. In order to effectively access channel after a series of contention operations, 802.11ac specifies two different CB operations: Dynamic Channel Bonding (DCB) and Static Channel Bonding (SCB). This paper proposes an optimal channel allocation algorithm to achieve maximal throughputs in DCB WLANs. Specifically, we first adopt a continuous-time Markov Chain (CTMC) model to analyze the equilibrium throughputs. Based on the throughput analysis, we then construct an integer nonlinear programming (INLP) model with the target of maximizing system throughputs. By solving the INLP model, we then propose an optimal channel allocation algorithm based on the Branch-and-Bound Method (BBM). It turns out that the maximal throughput performance can be achieved under the channel allocation scheme with the least overlapped channels among WLANs. Simulations show that the proposed channel allocation algorithm can achieve the maximal system throughput under various network settings. Our analysis on the optimal channel allocation schemes brings new insights into the design and optimization of future WLANs, especially for those adopting channel bonding technique.

cs.NI↗
arXiv2017

Capacity of Gaussian Many-Access Channels

Classical multiuser information theory studies the fundamental limits of models with a fixed (often small) number of users as the coding blocklength goes to infinity. This work proposes a new paradigm, referred to as {\em many-user information theory}, where the number of users is allowed to grow with the blocklength. This paradigm is motivated by emerging systems with a massive number of users in an area, such as machine-to-machine communication systems and sensor networks. The focus of the current paper is the {\em many-access} channel model, which consists of a single receiver and many transmitters, whose number increases unboundedly with the blocklength. Moreover, an unknown subset of transmitters may transmit in a given block and need to be identified. A new notion of capacity is introduced and characterized for the Gaussian many-access channel with random user activities. The capacity can be achieved by first detecting the set of active users and then decoding their messages.

cs.IT↗
arXiv2016

Paired-move multiple-try stochastic search for Bayesian variable selection

Variable selection is a key issue when analyzing high-dimensional data. The explosion of data with large sample sizes and dimensionality brings new challenges to this problem in both inference accuracy and computational complexity. To alleviate these problems, we propose a new scalable Markov chain Monte Carlo (MCMC) sampling algorithm for "large $p$ small $n$" scenarios by generalizing multiple-try Metropolis to discrete model spaces and further incorporating neighborhood-based stochastic search. The proof of reversibility of the proposed MCMC algorithm is provided. Extensive simulation studies are performed to examine the efficiency of the new algorithm compared with existing methods. A real data example is provided to illustrate the prediction performances of the new algorithm.

stat.ME↗
arXiv2016

Content-Centric and Software-Defined Networking with Big Data

Many communities have researched the application of novel network architectures such as Content-Centric Networking (CCN) and Software-Defined Networking (SDN) to build the future Internet. Another emerging technology which is big data analysis has also won lots of attentions from academia to industry. Many splendid researches have been done on CCN, SDN, and big data, which all have addressed separately in the traditional literature. In this paper, we propose a novel network paradigm to jointly consider CCN, SDN, and big data, and provide the architecture internal data flow, big data processing and use cases which indicate the benefits and applicability. Simulation results are exhibited to show the potential benefits relating to the proposed network paradigm. We refer to this novel paradigm as Data-Driven Networking (DDN).

cs.NI↗
arXiv2016

Sparse Channel Estimation for Massive MIMO with 1-bit Feedback per Dimension

In massive multiple-input multiple-output (MIMO) systems, acquisition of the channel state information at the transmitter side (CSIT) is crucial. In this paper, a practical CSIT estimation scheme is proposed for frequency division duplexing (FDD) massive MIMO systems. Specifically, each received pilot symbol is first quantized to one bit per dimension at the receiver side and then the quantized bits are fed back to the transmitter. A joint one-bit compressed sensing algorithm is implemented at the transmitter to recover the channel matrices. The algorithm leverages the hidden joint sparsity structure in the user channel matrices to minimize the training and feedback overhead, which is considered to be a major challenge for FDD systems. Moreover, the one-bit compressed sensing algorithm accurately recovers the channel directions for beamforming. The one-bit feedback mechanism can be implemented in practical systems using the uplink control channel. Simulation results show that the proposed scheme nearly achieves the maximum output signal-to-noise-ratio for beamforming based on the estimated CSIT.

cs.NI↗