arXiv subjects

Nan Wang

Publications and source records attributed to Nan Wang.

At least 199 records · Page 11Linked to original sources

arXiv2018

Impact parameter smearing effects on isospin sensitive observables in heavy ion collisions

The validity of impact parameter estimation from the multiplicity of charged particles at low-intermediate energies is checked within the framework of ImQMD model. The simulations show that the multiplicity of charged particles cannot estimate the impact parameter of heavy ion collisions very well, especially for central collisions at the beam energies lower than $\sim$70 MeV/u due to the large fluctuations of the multiplicity of charged particles. The simulation results for the central collisions defined by the charged particle multiplicity are compared to those by using impact parameter b=2 fm and it shows that the charge distribution for $^{112}$Sn +$^{112}$Sn at 50 MeV/u is different evidently for two cases; and the chosen isospin sensitive observable, the coalescence invariant single neutron to proton yield ratio, reduces less than 15\% for neutron-rich systems $^{124,132}$Sn +$^{124}$Sn at $E_{beam}$=50 MeV/u, while the coalescence invariant double neutron to proton yield ratio does not have obvious difference. The sensitivity of the chosen isospin sensitive observables to effective mass splitting is studied for central collisions defined by the multiplicity of charged particles. Our results show that the sensitivity is enhanced for $^{132}$Sn+$^{124}$Sn relative to that for $^{124}$Sn+$^{124}$Sn, and this reaction system should be measured in future experiments to study the effective mass splitting by heavy ion collisions.

nucl-th↗

arXiv2018

"Exclusive-OR" operation with Fano resonant MEMS Metamaterial

In recent years, a range of reconfigurable metamaterials controlled with thermal, electric, magnetic and optical signals have been developed for dynamically manipulating the intensity, phase and wave-front of electromagnetic radiation across the broad electromagnetic spectrum ranging from microwave to optics. Here for the first time, we demonstrate a reconfigurable metasurface performing exclusive OR (XOR) logical operation using two independent control electrical inputs and an optical read-out in the form of far-field Fano intensity states at terahertz wavelengths. At the same time, the near field resonant confinement enables a universal NAND logical operation. Further, by using a single electrical input control and an optical readout, a logical NOT operation is achieved at the far-field intensity states of the Fano resonance. The proposed reconfigurable microcantilever based Fano design that exhibits multiple logical operations can create a versatile platform for realization of programmable and randomly accessible metamaterials with enhanced electro-optical performance, multichannel data processing and encryption techniques for high speed wireless networks which are now being pushed towards terahertz wavelengths.

physics.app-ph↗

arXiv2018

Structural and Chemical Orders in Ni64.5Zr35.5 Metallic Glass by Molecular Dynamics Simulation

The atomic structure of Ni64.5Zr35.5 metallic glass has been investigated by molecular dynamics (MD) simulations. The calculated structure factors from the MD glassy sample at room temperature agree well with the X-ray diffraction (XRD) and neutron diffraction (ND) experimental data. Using the pairwise cluster alignment and clique analysis methods, we show that there are three types dominant short-range order (SRO) motifs around Ni atoms in the glass sample of Ni64.5Zr35.5, i.e., Mixed-Icosahedron(ICO)-Cube, Twined-Cube and icosahedron-like clusters. Furthermore, chemical order and medium-range order (MRO) analysis show that the Mixed-ICO-Cube and Twined-Cube clusters exhibit the characteristics of the crystalline B2 phase. Our simulation results suggest that the weak glass-forming ability (GFA) of Ni64.5Zr35.5 can be attributed to the competition between the glass forming ICO SRO and the crystalline Mixed-ICO-Cube and Twined-Cube motifs.

cond-mat.mes-hall↗

arXiv2017

Pipeline Generative Adversarial Networks for Facial Images Generation with Multiple Attributes

Generative Adversarial Networks are proved to be efficient on various kinds of image generation tasks. However, it is still a challenge if we want to generate images precisely. Many researchers focus on how to generate images with one attribute. But image generation under multiple attributes is still a tough work. In this paper, we try to generate a variety of face images under multiple constraints using a pipeline process. The Pip-GAN (Pipeline Generative Adversarial Network) we present employs a pipeline network structure which can generate a complex facial image step by step using a neutral face image. We applied our method on two face image databases and demonstrate its ability to generate convincing novel images of unseen identities under multiple conditions previously.

cs.CV↗

arXiv2017

Edge-as-a-Service: Towards Distributed Cloud Architectures

We present an Edge-as-a-Service (EaaS) platform for realising distributed cloud architectures and integrating the edge of the network in the computing ecosystem. The EaaS platform is underpinned by (i) a lightweight discovery protocol that identifies edge nodes and make them publicly accessible in a computing environment, and (ii) a scalable resource provisioning mechanism for offloading workloads from the cloud on to the edge for servicing multiple user requests. We validate the feasibility of EaaS on an online game use-case to highlight the improvement in the QoS of the application hosted on our cloud-edge platform. On this platform we demonstrate (i) low overheads of less than 6%, (ii) reduced data traffic to the cloud by up to 95% and (iii) minimised application latency between 40%-60%.

cs.DC↗

arXiv2017

Phase-field model of vapor-liquid-solid nanowire growth

We present a multi-phase-field model to describe quantitatively nanowire growth by the vapor-liquid-solid (VLS) process. The free-energy functional of this model depends on three non-conserved order parameters that distinguish the vapor, liquid, and solid phases. The evolution equations for those order parameters describe basic kinetic processes including the rapid (quasi-instantaneous) equilibration of the liquid catalyst to a droplet shape with constant mean curvature, the slow incorporation of growth atoms at the droplet surface, and crystallization within the droplet. The standard constraint that the sum of the phase fields equals unity and the conservation of the number of catalyst atoms, which relates the catalyst volume to the concentration of growth atoms inside the droplet, are handled via separate Lagrange multipliers. An analysis of the model is presented that rigorously maps the phase-field equations to a desired set of sharp-interface equations for the evolution of the phase boundaries under the constraint of force-balance at three-phase junctions given by the Young-Herring relation that includes torque term related to the anisotropy of the solid-liquid and solid-vapor interface excess free energies. Numerical examples of growth in two dimensions are presented. The simulations reproduce many of the salient features of nanowire growth observed experimentally, including growth normal to the substrate with tapering of the side walls, transitions between different growth orientations, and crawling growth along the substrate.

cond-mat.mtrl-sci↗

arXiv2017

ENORM: A Framework For Edge NOde Resource Management

Current computing techniques using the cloud as a centralised server will become untenable as billions of devices get connected to the Internet. This raises the need for fog computing, which leverages computing at the edge of the network on nodes, such as routers, base stations and switches, along with the cloud. However, to realise fog computing the challenge of managing edge nodes will need to be addressed. This paper is motivated to address the resource management challenge. We develop the first framework to manage edge nodes, namely the Edge NOde Resource Management (ENORM) framework. Mechanisms for provisioning and auto-scaling edge node resources are proposed. The feasibility of the framework is demonstrated on a PokeMon Go-like online game use-case. The benefits of using ENORM are observed by reduced application latency between 20% - 80% and reduced data transfer and communication frequency between the edge node and the cloud by up to 95\%. These results highlight the potential of fog computing for improving the quality of service and experience.

cs.DC↗

arXiv2017

Three-dimensional vortex structures and dynamics in hexagonal manganites

Hexagonal manganites REMnO3 (RE, rare earths) have attracted significant attention due to their potential applications as multiferroic materials and the intriguing physics associated with the topological defects. The two-dimensional (2D) and 3D domain and vortex structure evolution of REMnO3 is predicted using the phase-field method based on a thermodynamic potential constructed from first-principles calculations. In 3D spaces, vortex lines show three types of topological changes, i.e. shrinking, coalescence, and splitting, with the latter two caused by the interaction and exchange of vortex loops. Compared to the coarsening rate of the isotropic XY model, the six-fold degeneracy gives rise to negligible differences with the vortex-antivortex annihilation controlling the scaling dynamics, whereas the anisotropy of interfacial energy results in a deviation. The temporal evolution of domain and vortex structures serves as a platform to fully explore the mesoscale mechanisms for the 0-D and 1-D topological defects.

cond-mat.mtrl-sci↗

arXiv2017

Magnetic Flux Tailoring through Lenz Lenses in Toroidal Diamond Indenter Cells: A New Pathway to High Pressure Nuclear Magnetic Resonance

A new pathway to nuclear magnetic resonance spectroscopy in high pressure diamond anvil cells is introduced, using inductively coupled broadband passive electro-magnetic lenses to locally amplify the magnetic flux at the isolated sample, leading to an increase in sensitivity. The lenses are adopted for the geometrical restrictions imposed by a toroidal diamond indenter cell, and yield high signal-to-noise ratios at pressures as high as 72 GPa, at initial sample volumes of only 230 pl. The corresponding levels of detection, LODt, are found to be up to four orders of magnitude lower compared to formerly used solenoidal micro-coils in diamond anvil cells, as shown by Proton-NMR measurements on paraffin oil. This approach opens up the field of ultra-high pressure sciences for one of the most versatile spectroscopic methods available in a pressure range unprecedended up to now.

physics.ins-det↗

arXiv2017

Feasibility of Fog Computing

As billions of devices get connected to the Internet, it will not be sustainable to use the cloud as a centralised server. The way forward is to decentralise computations away from the cloud towards the edge of the network closer to the user. This reduces the latency of communication between a user device and the cloud, and is the premise of 'fog computing' defined in this paper. The aim of this paper is to highlight the feasibility and the benefits in improving the Quality-of-Service and Experience by using fog computing. For an online game use-case, we found that the average response time for a user is improved by 20% when using the edge of the network in comparison to using a cloud-only model. It was also observed that the volume of traffic between the edge and the cloud server is reduced by over 90% for the use-case. The preliminary results highlight the potential of fog computing in achieving a sustainable computing model and highlights the benefits of integrating the edge of the network into the computing ecosystem.

cs.DC↗

arXiv2016

Challenges and Opportunities in Edge Computing

Many cloud-based applications employ a data centre as a central server to process data that is generated by edge devices, such as smartphones, tablets and wearables. This model places ever increasing demands on communication and computational infrastructure with inevitable adverse effect on Quality-of-Service and Experience. The concept of Edge Computing is predicated on moving some of this computational load towards the edge of the network to harness computational capabilities that are currently untapped in edge nodes, such as base stations, routers and switches. This position paper considers the challenges and opportunities that arise out of this new direction in the computing landscape.

cs.DC↗

arXiv2016

A Machine Learning Analysis of Twitter Sentiment to the Sandy Hook Shootings

Gun related violence is a complex issue and accounts for a large proportion of violent incidents. In the research reported in this paper, we set out to investigate the pro-gun and anti-gun sentiments expressed on a social media platform, namely Twitter, in response to the 2012 Sandy Hook Elementary School shooting in Connecticut, USA. Machine learning techniques are applied to classify a data corpus of over 700,000 tweets. The sentiments are captured using a public sentiment score that considers the volume of tweets as well as population. A web-based interactive tool is developed to visualise the sentiments and is available at http://www.gunsontwitter.com. The key findings from this research are: (i) There are elevated rates of both pro-gun and anti-gun sentiments on the day of the shooting. Surprisingly, the pro-gun sentiment remains high for a number of days following the event but the anti-gun sentiment quickly falls to pre-event levels. (ii) There is a different public response from each state, with the highest pro-gun sentiment not coming from those with highest gun ownership levels but rather from California, Texas and New York.

cs.SI↗

arXiv2016

Analytical formulation of lunar cratering asymmetries

We formulate the lunar cratering distribution and verify the cratering asymmetries generated by the main-belt asteroids (MBAs) as well as the near-Earth objects (NEOs). Based on a planar model that excludes the terrestrial and lunar gravitations on the impactors and assuming the impactor encounter speed with Earth $v_{\rm{enc}}$ is higher than the lunar orbital speed $v_{\rm{M}}$, we rigorously integrated the lunar cratering distribution, and derived its approximation to the first order of $v_{\rm{M}}/v_{\rm{enc}}$. Numerical simulations of lunar bombardment by the MBAs during the late heavy bombardment were performed with an Earth-Moon distance $a_{\rm{M}}$ = 20--60 Earth radii in five cases. The analytical model directly proves the existence of a leading/trailing asymmetry and the absence of near/far asymmetry. The approximate form of the leading/trailing asymmetry is $(1 + A_1 \cosβ)$, which decreases as the apex distance $β$ increases. The numerical simulations show evidence of a pole/equator asymmetry as well as the leading/trailing asymmetry, and the former is empirically described as $(1 + A_2 \cos2φ)$, which decreases as the latitude modulus $|φ|$ increases. The amplitudes $A_{1,2}$ are reliable measurements of asymmetries. Our analysis explicitly indicates the quantitative relations between cratering distribution and bombardment conditions (impactor properties and the lunar orbital status) like $A_1 \propto v_{\rm{M}}/v_{\rm{enc}}$, resulting in a method for reproducing the bombardment conditions through measuring the asymmetry. Mutual confirmation between analytical model and numerical simulations is found in terms of the cratering distribution and its variation with $a_{\rm{M}}$. Estimates of $A_1$ for crater density distributions generated by the MBAs and the NEOs are 0.101--0.159 and 0.117, respectively.

astro-ph.EP↗

arXiv2016

Determining proportions of lunar crater populations by fitting crater size distribution

We determine the proportions of two mixed crater populations distinguishable by size distributions on the Moon. A "multiple power-law" model is built to formulate crater size distribution $N(D) \propto D^{-α}$ whose slope $α$ varies with crater diameter $D$. Fitted size distribution of lunar highland craters characterized by $α= 1.17 \pm 0.04$, $1.88 \pm 0.07$, $3.17 \pm 0.10$ and $1.40 \pm 0.15$ for consecutive $D$ intervals divided by 49, 120 and 251 km and that of lunar Class 1 craters with a single slope $α= 1.96 \pm 0.14$, are taken as Population 1 and 2 crater size distribution respectively, whose sum is then fitted to the size distribution of global lunar craters with $D$ between 10 and 100 km. Estimated crater densities of Population 1 and 2 are $44 \times 10^{-5}$ and $5 \times 10^{-5}$ km$^{-2}$ respectively, leading to the proportion of the latter $10 \%$. The results underlines the need for considering the Population 1 craters and the relevant impactors, the primordial main-belt asteroids which dominated the late heavy bombardment.

astro-ph.EP↗

arXiv2015

A new boosting algorithm based on dual averaging scheme

The fields of machine learning and mathematical optimization increasingly intertwined. The special topic on supervised learning and convex optimization examines this interplay. The training part of most supervised learning algorithms can usually be reduced to an optimization problem that minimizes a loss between model predictions and training data. While most optimization techniques focus on accuracy and speed of convergence, the qualities of good optimization algorithm from the machine learning perspective can be quite different since machine learning is more than fitting the data. Better optimization algorithms that minimize the training loss can possibly give very poor generalization performance. In this paper, we examine a particular kind of machine learning algorithm, boosting, whose training process can be viewed as functional coordinate descent on the exponential loss. We study the relation between optimization techniques and machine learning by implementing a new boosting algorithm. DABoost, based on dual-averaging scheme and study its generalization performance. We show that DABoost, although slower in reducing the training error, in general enjoys a better generalization error than AdaBoost.

cs.LG↗

arXiv2014

Macroscopic models of collective motion with repulsion

We study a system of self-propelled particles which interact with their neighbors via alignment and repulsion. The particle velocities result from self-propulsion and repulsion by close neighbors. The direction of self-propulsion is continuously aligned to that of the neighbors, up to some noise. A continuum model is derived starting from a mean-field kinetic description of the particle system. It leads to a set of non conservative hydrodynamic equations. We provide a numerical validation of the continuum model by comparison with the particle model. We also provide comparisons with other self-propelled particle models with alignment and repulsion.

math-ph↗

arXiv2014

Modeling correlations in spontaneous activity of visual cortex with centered Gaussian-binary deep Boltzmann machines

Spontaneous cortical activity -- the ongoing cortical activities in absence of intentional sensory input -- is considered to play a vital role in many aspects of both normal brain functions and mental dysfunctions. We present a centered Gaussian-binary Deep Boltzmann Machine (GDBM) for modeling the activity in early cortical visual areas and relate the random sampling in GDBMs to the spontaneous cortical activity. After training the proposed model on natural image patches, we show that the samples collected from the model's probability distribution encompass similar activity patterns as found in the spontaneous activity. Specifically, filters having the same orientation preference tend to be active together during random sampling. Our work demonstrates the centered GDBM is a meaningful model approach for basic receptive field properties and the emergence of spontaneous activity patterns in early cortical visual areas. Besides, we show empirically that centered GDBMs do not suffer from the difficulties during training as GDBMs do and can be properly trained without the layer-wise pretraining.

cs.NE↗

arXiv2014

Gaussian-binary Restricted Boltzmann Machines on Modeling Natural Image Statistics

We present a theoretical analysis of Gaussian-binary restricted Boltzmann machines (GRBMs) from the perspective of density models. The key aspect of this analysis is to show that GRBMs can be formulated as a constrained mixture of Gaussians, which gives a much better insight into the model's capabilities and limitations. We show that GRBMs are capable of learning meaningful features both in a two-dimensional blind source separation task and in modeling natural images. Further, we show that reported difficulties in training GRBMs are due to the failure of the training algorithm rather than the model itself. Based on our analysis we are able to propose several training recipes, which allowed successful and fast training in our experiments. Finally, we discuss the relationship of GRBMs to several modifications that have been proposed to improve the model.

cs.NE↗