arXiv subjects

Ning Zhang

Publications and source records attributed to Ning Zhang.

At least 361 records · Page 20Linked to original sources

arXiv2016

Catalyzing Cloud-Fog Interoperation in 5G Wireless Networks: An SDN Approach

The piling up storage and compute stacks in cloud data center are expected to accommodate the majority of internet traffic in the future. However, as the number of mobile devices significantly increases, getting massive data into and out of the cloud wirelessly inflicts high pressure on the bandwidth, and meanwhile induces unpredictable latency. Fog computing, which advocates extending clouds to network edge, guarantees low latency and location-aware service provisioning. In this article, we consider fog computing as an ideal complement rather than a substitute of cloud computing, and we propose a software defined networking (SDN) enabled framework for cloud-fog interoperation, aiming at improving quality of experience and optimizing network resource usage. Two case studies are provided to illuminate the feasibility and advantage of the proposed framework. At last, potential research issues are presented for further investigation.

cs.NI↗

arXiv2016

AutoScaler: Scale-Attention Networks for Visual Correspondence

Finding visual correspondence between local features is key to many computer vision problems. While defining features with larger contextual scales usually implies greater discriminativeness, it could also lead to less spatial accuracy of the features. We propose AutoScaler, a scale-attention network to explicitly optimize this trade-off in visual correspondence tasks. Our network consists of a weight-sharing feature network to compute multi-scale feature maps and an attention network to combine them optimally in the scale space. This allows our network to have adaptive receptive field sizes over different scales of the input. The entire network is trained end-to-end in a siamese framework for visual correspondence tasks. Our method achieves favorable results compared to state-of-the-art methods on challenging optical flow and semantic matching benchmarks, including Sintel, KITTI and CUB-2011. We also show that our method can generalize to improve hand-crafted descriptors (e.g Daisy) on general visual correspondence tasks. Finally, our attention network can generate visually interpretable scale attention maps.

cs.CV↗

arXiv2016

Competing Mechanisms between Dislocation and Phase Transformation in Plastic Deformation of Single Crystalline Yttria-Stabilized Tetragonal Zirconia Nanopillars

Molecular dynamics (MD) is employed to investigate the plastic deformation mechanisms of single crystalline yttria-stabilized tetragonal zirconia (YSTZ) nanopillars under uniaxial compression. Simulation results show that the nanoscale plastic deformation of YSTZ is strongly dependent on the crystallographic orientation of zirconia nanopillars. For the first time, the experimental explored tetragonal to monoclinic phase transformation is reproduced by MD simulations in some particular loading directions. Three distinct mechanisms of dislocation, phase transformation, and a combination of dislocation and phase transformation are identified when applying compressive loading along different directions. The strength of zirconia nanopillars exhibits a sensitive behavior depending on the failure mechanisms, such that the dislocation-mediated deformation leads to the lowest strength, while the phase transformation-dominated deformation results in the highest strength.

cond-mat.mtrl-sci↗

arXiv2016

A Hierarchical Distributed Processing Framework for Big Image Data

This paper introduces an effective processing framework nominated ICP (Image Cloud Processing) to powerfully cope with the data explosion in image processing field. While most previous researches focus on optimizing the image processing algorithms to gain higher efficiency, our work dedicates to providing a general framework for those image processing algorithms, which can be implemented in parallel so as to achieve a boost in time efficiency without compromising the results performance along with the increasing image scale. The proposed ICP framework consists of two mechanisms, i.e. SICP (Static ICP) and DICP (Dynamic ICP). Specifically, SICP is aimed at processing the big image data pre-stored in the distributed system, while DICP is proposed for dynamic input. To accomplish SICP, two novel data representations named P-Image and Big-Image are designed to cooperate with MapReduce to achieve more optimized configuration and higher efficiency. DICP is implemented through a parallel processing procedure working with the traditional processing mechanism of the distributed system. Representative results of comprehensive experiments on the challenging ImageNet dataset are selected to validate the capacity of our proposed ICP framework over the traditional state-of-the-art methods, both in time efficiency and quality of results.

cs.CV↗

arXiv2016

Phylogenomic Analyses of Large-scale Nuclear Genes Provide New Insights into the Evolutionary Relationships within the Rosids

The Rosids is one of the largest groups of flowering plants, with 140 families and ~70,000 species. Previous phylogenetic studies of the rosids have primarily utilized organelle genes that likely differ in evolutionary histories from nuclear genes. To better understand the evolutionary history of rosids, it is necessary to investigate their phylogenetic relationships using nuclear genes. Here, we employed large-scale phylogenomic datasets composed of nuclear genes, including 891 clusters of putative orthologous genes. Combined with comprehensive taxon sampling covering 63 species representing 14 out of the 17 orders, we reconstructed the rosids phylogeny with coalescence and concatenation methods, yielding similar tree topologies from all datasets. However, these topologies did not agree on the placement of Zygophyllales. Through comprehensive analyses, we found that missing data and gene tree heterogeneity were potential factors that may mislead concatenation methods, in particular, large amounts of missing data under high gene tree heterogeneity. Our results provided new insights into the deep phylogenetic relationships of the rosids, and demonstrated that coalescence methods may effectively resolve the phylogenetic relationships of the rosids with missing data under high gene tree heterogeneity.

q-bio.PE↗

arXiv2016

Fully Computable Error Bounds for Eigenvalue Problem

This paper is concerned with the computable error estimates for the eigenvalue problem which is solved by the general conforming finite element methods on the general meshes. Based on the computable error estimate, we can give an asymptotically lower bound of the general eigenvalues. Furthermore, we also give a guaranteed upper bound of the error estimates for the first eigenfunction approximation and a guaranteed lower bound of the first eigenvalue based on computable error estimator. Some numerical examples are presented to validate the theoretical results deduced in this paper.

math.NA↗

arXiv2016

Compact Bilinear Pooling

Bilinear models has been shown to achieve impressive performance on a wide range of visual tasks, such as semantic segmentation, fine grained recognition and face recognition. However, bilinear features are high dimensional, typically on the order of hundreds of thousands to a few million, which makes them impractical for subsequent analysis. We propose two compact bilinear representations with the same discriminative power as the full bilinear representation but with only a few thousand dimensions. Our compact representations allow back-propagation of classification errors enabling an end-to-end optimization of the visual recognition system. The compact bilinear representations are derived through a novel kernelized analysis of bilinear pooling which provide insights into the discriminative power of bilinear pooling, and a platform for further research in compact pooling methods. Experimentation illustrate the utility of the proposed representations for image classification and few-shot learning across several datasets.

cs.CV↗

arXiv2016

Energy Harvesting-Aided Spectrum Sensing and Data Transmission in Heterogeneous Cognitive Radio Sensor Network

The incorporation of Cognitive Radio (CR) and Energy Harvesting (EH) capabilities in wireless sensor networks enables spectrum and energy efficient heterogeneous cognitive radio sensor networks (HCRSNs). The new networking paradigm of HCRSNs consists of EH-enabled spectrum sensors and battery powered data sensors. Spectrum sensors can cooperatively scan the licensed spectrum for available channels, while data sensors monitor an area of interest and transmit sensed data to the sink over those channels. In this work, we propose a resource allocation solution for the HCRSN to achieve the sustainability of spectrum sensors and conserve energy of data sensors. The proposed solution is achieved by two algorithms that operate in tandem, a spectrum sensor scheduling algorithm and a data sensor resource allocation algorithm. The spectrum sensor scheduling algorithm allocates channels to spectrum sensors such that the average detected available time for the channels is maximized, while the EH dynamics are considered and PU transmissions are protected. The data sensor resource allocation algorithm allocates the transmission time, power and channels such that the energy consumption of the data sensors is minimized. Extensive simulation results demonstrate that the energy consumption of the data sensors can be significantly reduced while maintaining the sustainability of the spectrum sensors.

cs.NI↗

arXiv2016

Aleksandrov projection problem for convex lattice sets

Let $K$ and $L$ be origin-symmetric convex integer polytopes in $\mathbb{R}^n$. We study a discrete analogue of the Aleksandrov projection problem. If for every $u\in \mathbb{Z}^n$, the sets $(K\cap \mathbb{Z}^n)|u^\perp$ and $(L\cap \mathbb{Z}^n)|u^\perp$ have the same number of points, is then $K=L$? We give a positive answer to this problem in $\mathbb{Z}^2$ under an additional hypothesis that $(2K\cap \mathbb{Z}^2)|u^\perp$ and $(2L\cap \mathbb{Z}^2)|u^\perp$ have the same number of points.

math.MG↗

arXiv2016

Energy-Aware Traffic Offloading for Green Heterogeneous Networks

With small cell base stations (SBSs) densely deployed in addition to conventional macro base stations (MBSs), the heterogeneous cellular network (HCN) architecture can effectively boost network capacity. To support the huge power demand of HCNs, renewable energy harvesting technologies can be leveraged. In this paper, we aim to make efficient use of the harvested energy for on-grid power saving while satisfying the quality of service (QoS) requirement. To this end, energy-aware traffic offloading schemes are proposed, whereby user associations, ON-OFF states of SBSs, and power control are jointly optimized according to the statistical information of energy arrival and traffic load. Specifically, for the single SBS case, the power saving gain achieved by activating the SBS is derived in closed form, based on which the SBS activation condition and optimal traffic offloading amount are obtained. Furthermore, a two-stage energy-aware traffic offloading (TEATO) scheme is proposed for the multiple-SBS case, considering various operating characteristics of SBSs with different power sources. Simulation results demonstrate that the proposed scheme can achieve more than 50% power saving gain for typical daily traffic and solar energy profiles, compared with the conventional traffic offloading schemes.

cs.NI↗

arXiv2016

Ultrafast transient dynamics in composite multiferroics

We investigate theoretically the dynamic multiferroic response of coupled ferroelectric/ferromagnetic composites upon excitation by a photo-induced acoustic strain pulse. Two magnetoelectric mechanisms are considered: interface strain- and charge-mediated magnetoelectric couplings. The former results in demagnetization, depolarization and repolarization within tens of picoseconds via respectively magnetostriction and piezoelectricity. Charge magnetoelectric interaction affects the ferroelectric/ferromagnetic feedback response leading to magnetization recovery. Experimental realization based on time-resolved x-ray diffraction is suggested. The findings indicate the potential of composite multiferroics for photo-steered, high-speed, multi-state electronic devices.

cond-mat.mes-hall↗

arXiv2015

Fine-grained pose prediction, normalization, and recognition

Pose variation and subtle differences in appearance are key challenges to fine-grained classification. While deep networks have markedly improved general recognition, many approaches to fine-grained recognition rely on anchoring networks to parts for better accuracy. Identifying parts to find correspondence discounts pose variation so that features can be tuned to appearance. To this end previous methods have examined how to find parts and extract pose-normalized features. These methods have generally separated fine-grained recognition into stages which first localize parts using hand-engineered and coarsely-localized proposal features, and then separately learn deep descriptors centered on inferred part positions. We unify these steps in an end-to-end trainable network supervised by keypoint locations and class labels that localizes parts by a fully convolutional network to focus the learning of feature representations for the fine-grained classification task. Experiments on the popular CUB200 dataset show that our method is state-of-the-art and suggest a continuing role for strong supervision.

cs.CV↗

arXiv2015

Sparse learning of maximum likelihood model for optimization of complex loss function

Traditional machine learning methods usually minimize a simple loss function to learn a predictive model, and then use a complex performance measure to measure the prediction performance. However, minimizing a simple loss function cannot guarantee that an optimal performance. In this paper, we study the problem of optimizing the complex performance measure directly to obtain a predictive model. We proposed to construct a maximum likelihood model for this problem, and to learn the model parameter, we minimize a com- plex loss function corresponding to the desired complex performance measure. To optimize the loss function, we approximate the upper bound of the complex loss. We also propose impose the sparsity to the model parameter to obtain a sparse model. An objective is constructed by combining the upper bound of the loss function and the sparsity of the model parameter, and we develop an iterative algorithm to minimize it by using the fast iterative shrinkage- thresholding algorithm framework. The experiments on optimization on three different complex performance measures, including F-score, receiver operating characteristic curve, and recall precision curve break even point, over three real-world applications, aircraft event recognition of civil aviation safety, in- trusion detection in wireless mesh networks, and image classification, show the advantages of the proposed method over state-of-the-art methods.

cs.LG↗

arXiv2015

Non-central sections of convex bodies

We study the following open problem, suggested by Barker and Larman. Let $K$ and $L$ be convex bodies in $\mathbb R^n$ ($n\ge 2$) that contain a Euclidean ball $B$ in their interiors. If $\mathrm{vol}_{n-1}(K\cap H) = \mathrm{vol}_{n-1}(L\cap H)$ for every hyperplane $H$ that supports $B$, does it follow that $K=L$? We discuss various modifications of this problem. In particular, we show that in $\mathbb R^2$ the answer is positive if the above condition is true for two disks, none of which is contained in the other. We also study some higher dimensional analogues.

math.MG↗

arXiv2015

Spatial Traffic Shaping in Heterogeneous Cellular Networks with Energy Harvesting

Energy harvesting (EH), which explores renewable energy as a supplementary power source, is a promising 5G technology to support the huge energy demand of heterogeneous cellular networks (HCN). However, the random arrival of renewable energy brings great challenges to network management. By adjusting the distribution of traffic load in spatial domain, traffic shaping helps to balance the cell-level power demand and supply, and thus improves the utilization of renewable energy. In this paper, we investigate the power saving performance of traffic shaping in an analytical way, based on the statistic information of energy arrival and traffic load. Specifically, an energy-optimal traffic shaping scheme (EOTS) is devised for HCNs with EH, whereby the on-off state of the off-grid small cell and the amount of offloading traffic are adjusted dynamically with the energy variation, to minimize the on-grid power consumption. Numerical results are given to demonstrate that for the daily traffic and solar energy profiles, EOTS scheme can significantly reduce the energy consumption, compared with the greedy method where users are always offloaded to the off-grid small cell with priority.

cs.NI↗

arXiv2015

Dynamic Channel Access for Energy Efficient Data Gathering in Cognitive Radio Sensor Networks

Wireless sensor networks (WSNs) operating in the license-free spectrum suffer from uncontrolled interference as those spectrum bands become increasingly crowded. The emerging cognitive radio sensor networks (CRSNs) provide a promising solution to address this challenge by enabling sensor nodes to opportunistically access licensed channels. However, since sensor nodes have to consume considerable energy to support CR functionalities, such as channel sensing and switching, the opportunistic channel accessing should be carefully devised for improving the energy efficiency in CRSN. To this end, we investigate the dynamic channel accessing problem to improve the energy efficiency for a clustered CRSN. Under the primary users' protection requirement, we study the resource allocation issues to maximize the energy efficiency of utilizing a licensed channel for intra-cluster and inter-cluster data transmission, respectively. With the consideration of the energy consumption in channel sensing and switching, we further determine the condition when sensor nodes should sense and switch to a licensed channel for improving the energy efficiency, according to the packet loss rate of the license-free channel. In addition, two dynamic channel accessing schemes are proposed to identify the channel sensing and switching sequences for intra-cluster and inter-cluster data transmission, respectively. Extensive simulation results demonstrate that the proposed channel accessing schemes can significantly reduce the energy consumption in CRSNs.

cs.NI↗

arXiv2015

Existence of solutions for a higher order Kirchhoff type problem with exponential critical growth

The higher order Kirchhoff type equation $$\int_{\mathbb{R}^{2m}}(|\nabla^m u|^2 +\sum_{γ=0}^{m-1}a_γ(x)|\nabla^γu|^2)dx \left((-Δ)^m u+\sum_{γ=0}^{m-1}(-1)^γ\nabla^γ\cdot(a_γ(x)\nabla^γu)\right) =\frac{f(x,u)}{|x|^β}+εh(x)\ \ \text{in}\ \ \mathbb{R}^{2m}$$ is considered in this paper. We assume that the nonlinearity of the equation has exponential critical growth and prove that, for a positive $ε$ which is small enough, there are two distinct nontrivial solutions to the equation. When $ε=0$, we also prove that the equation has a nontrivial mountain-pass type solution.

math.AP↗

arXiv2015

Time-Critical Influence Maximization in Social Networks with Time-Delayed Diffusion Process

Influence maximization is a problem of finding a small set of highly influential users, also known as seeds, in a social network such that the spread of influence under certain propagation models is maximized. In this paper, we consider time-critical influence maximization, in which one wants to maximize influence spread within a given deadline. Since timing is considered in the optimization, we also extend the Independent Cascade (IC) model and the Linear Threshold (LT) model to incorporate the time delay aspect of influence diffusion among individuals in social networks. We show that time-critical influence maximization under the time-delayed IC and LT models maintains desired properties such as submodularity, which allows a greedy approximation algorithm to achieve an approximation ratio of $1-1/e$. To overcome the inefficiency of the greedy algorithm, we design two heuristic algorithms: the first one is based on a dynamic programming procedure that computes exact influence in tree structures and directed acyclic subgraphs, while the second one converts the problem to one in the original models and then applies existing fast heuristic algorithms to it. Our simulation results demonstrate that our algorithms achieve the same level of influence spread as the greedy algorithm while running a few orders of magnitude faster, and they also outperform existing fast heuristics that disregard the deadline constraint and delays in diffusion.

cs.SI↗