A Listing of Current Books

Abstract—We investigate cooperative strategies for relay-aided multi-source multi-destination wireless networks with backhaul support. Each source multicasts information to all destinations using a shared relay. We study cooperative strategies based on different network coding (NC) schemes, namely, finite field NC (FNC), linear NC (LNC), and lattice coding. To further exploit the backhaul connection, we also propose NC-based beam-forming (NBF). We measure the performance in term of achievable rates over Gaussian channels and observe significant gains over a benchmark scheme. The benefit of using backhaul is also clearly demonstrated in most of scenarios. I

Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification

Multiple kernel learning (MKL) method is generally believed to perform better than single kernel method. However, some empirical studies show that this is not always true: the combination of multiple kernels may even yield an even worse performance than using a single kernel. There are two possible reasons for the failure: (i) most existing MKL methods assume that the optimal kernel is a linear combination of base kernels, which may not hold true; and (ii) some kernel weights are inappropriately assigned due to noises and carelessly designed algorithms. In this paper, we propose a novel MKL framework by following two intuitive assumptions: (i) each kernel is a perturbation of the consensus kernel; and (ii) the kernel that is close to the consensus kernel should be assigned a large weight. Impressively, the proposed method can automatically assign an appropriate weight to each kernel without introducing additional parameters, as existing methods do. The proposed framework is integrated into a unified framework for graph-based clustering and semi-supervised classification. We have conducted experiments on multiple benchmark datasets and our empirical results verify the superiority of the proposed framework.Comment: Accepted by IJCAI 2018, Code is availabl

Federal Estate Tax: Joint Wills and the Marital Deduction

The framework of network equivalence theory developed by Koetter et al. introduces a notion of channel emulation to construct noiseless networks as upper/lower　bounding models for the original noisy network. This paper presents scalable upper bounding models for wireless networks, by firstly extending the ``one-shot'' bounding models developed by Calmon et al. and then integrating them with network equivalence tools. A channel decoupling method is proposed to decompose wireless networks into decoupled multiple-access channels (MACs) and broadcast channels (BCs). The main advantages of the proposed method is its simplicity and the fact that it can be extended easily to large networks with a complexity that grows linearly with the number of nodes. It is demonstrated that the resulting upper bounds can approach the capacity in some setups.QC 20140619VR International Postdo

An Economic User-Centric WiFi Offloading Algorithm for Heterogeneous Network

An economic user-centric WiFi offloading algorithm is proposed to satisfy the major concerns of wireless users, who wish to have better network performance with even less network expense. Thus in this paper both system throughput and network expense are considered, and the goal of the proposed offloading algorithm is to obtain an optimal offloading ratio, which can both maximize the system throughput and minimize the network expense. Firstly, a practical system model is set up on the basis of a typical scenario of heterogeneous network. In this model, the average throughput of both cellular network and WiFi network is analyzed carefully. Then an economic user-centric WiFi offloading algorithm is proposed with an evaluation function to evaluate the system, and the optimal offloading ratio can be obtained by minimizing the evaluation function. At last, numerical results represent a direct calculating process of the optimal offloading ratio. These results in return validate the efficiency of the proposed offloading algorithm as well

DWCox: A density-weighted Cox model for outlier-robust prediction of prostate cancer survival

Reliable predictions on the risk and survival time of prostate cancer patients based on their clinical records can help guide their treatment and provide hints about the disease mechanism. The Cox regression is currently a commonly accepted approach for such tasks in clinical applications. More complex methods, like ensemble approaches, have the potential of reaching better prediction accuracy at the cost of increased training difficulty and worse result interpretability. Better performance on a specific data set may also be obtained by extensive manual exploration in the data space, but such developed models are subject to overfitting and usually not directly applicable to a different data set. We propose \model, a density-weighted Cox model that has improved robustness against outliers and thus can provide more accurate predictions of prostate cancer survival. \Model~assigns weights to the training data according to their local kernel density in the feature space, and incorporates those weights into the partial likelihood function. A linear regression is then used to predict the actual survival times from the predicted risks. In \challengefull, \model~obtained the best average ranking in prediction accuracy on the risk and survival time. The success of \model~is remarkable given that it is one of the smallest and most interpretable models submitted to the challenge. In simulations, \model~performed consistently better than a standard Cox model when the training data contained many sparsely distributed outliers. Although developed for prostate cancer patients, \model~can be easily re-trained and applied to other survival analysis problems. \Model~is implemented in R and can be downloaded from https://github.com/JinfengXiao/DWCox

Vorticity and Spin Polarization in Heavy Ion Collisions: Transport Models

Heavy ion collisions generate strong fluid vorticty in the produced hot quark-gluon matter which could in turn induce measurable spin polarization of hadrons. We review recent progress on the vorticity formation and spin polarization in heavy ion collisions with transport models. We present an introduction to the fluid vorticity in non-relativistic and relativistic hydrodynamics and address various properties of the vorticity formed in heavy ion collisions. We discuss the spin polarization in a vortical fluid using the Wigner function formalism in which we derive the freeze-out formula for the spin polarization. Finally we give a brief overview of recent theoretical results for both the global and local spin polarization of $\Lambda$ and $\bar\Lambda$ hyperons.Comment: V2: 29 pages, 15 figures, published in Lecture Notes in Physics vol.987, "Strongly Interacting Matter under Rotation", page 281-30

Solving for Dispersivity in Field Dispersion Test of Unsteady Flow in Mixing Flow Field: Mass Transport Modeling

AbstractA combined groundwater flow and mass transport model was constructed to simulate the migration of contaminants and to obtain dispersion parameters from a field dispersion test in unsteady flow in mixing flow field in groundwater. Aquifer parameters were obtained by a pumping test. Tracer tests were carried out in order to characterize the characteristics of groundwater flow and to determine the velocity of the pollutant diffusion process from the source to the pumping well. Groundwater head and velocity were analyzed in the groundwater flow model and the total dissolved solids (TDS) concentration was computed in the mass transport model. The observed drawdown and the observed TDS concentration were found to respectively match closely with the computed drawdown and TDS concentration