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D. Kim

Publications and source records attributed to D. Kim.

At least 109 records · Page 6Linked to original sources
arXiv2009

Disassortativity of random critical branching trees

Random critical branching trees (CBTs) are generated by the multiplicative branching process, where the branching number is determined stochastically, independent of the degree of their ancestor. Here we show analytically that despite this stochastic independence, there exists the degree-degree correlation (DDC) in the CBT and it is disassortative. Moreover, the skeletons of fractal networks, the maximum spanning trees formed by the edge betweenness centrality, behave similarly to the CBT in the DDC. This analytic solution and observation support the argument that the fractal scaling in complex networks originates from the disassortativity in the DDC.

cond-mat.stat-mech↗
arXiv2008

Innovative in silico approaches to address avian flu using grid technology

The recent years have seen the emergence of diseases which have spread very quickly all around the world either through human travels like SARS or animal migration like avian flu. Among the biggest challenges raised by infectious emerging diseases, one is related to the constant mutation of the viruses which turns them into continuously moving targets for drug and vaccine discovery. Another challenge is related to the early detection and surveillance of the diseases as new cases can appear just anywhere due to the globalization of exchanges and the circulation of people and animals around the earth, as recently demonstrated by the avian flu epidemics. For 3 years now, a collaboration of teams in Europe and Asia has been exploring some innovative in silico approaches to better tackle avian flu taking advantage of the very large computing resources available on international grid infrastructures. Grids were used to study the impact of mutations on the effectiveness of existing drugs against H5N1 and to find potentially new leads active on mutated strains. Grids allow also the integration of distributed data in a completely secured way. The paper presents how we are currently exploring how to integrate the existing data sources towards a global surveillance network for molecular epidemiology.

q-bio.PE↗
arXiv2008

Tuning the exciton g-factor in single InAs/InP quantum dots

Photoluminescence data from single, self-assembled InAs/InP quantum dots in magnetic fields up to 7 T are presented. Exciton g-factors are obtained for dots of varying height, corresponding to ground state emission energies ranging from 780 meV to 1100 meV. A monotonic increase of the g-factor from -2 to +1.2 is observed as the dot height decreases. The trend is well reproduced by sp3 tight binding calculations, which show that the hole g-factor is sensitive to confinement effects through orbital angular momentum mixing between the light-hole and heavy-hole valence bands. We demonstrate tunability of the exciton g-factor by manipulating the quantum dot dimensions using pyramidal InP nanotemplates.

cond-mat.mes-hall↗
arXiv2008

Segregation in the annihilation of two-species reaction-diffusion processes on fractal scale-free networks

In the reaction-diffusion process $A+B \to \varnothing$ on random scale-free (SF) networks with the degree exponent $γ$, the particle density decays with time in a power law with an exponent $α$ when initial densities of each species are the same. The exponent $α$ is $α> 1$ for $2 < γ< 3$ and $α=1$ for $γ\ge 3$. Here, we examine the reaction process on fractal SF networks, finding that $α< 1$ even for $2 < γ< 3$. This slowly decaying behavior originates from the segregation effect: Fractal SF networks contain local hubs, which are repulsive to each other. Those hubs attract particles and accelerate the reaction, and then create domains containing the same species of particles. It follows that the reaction takes place at the non-hub boundaries between those domains and thus the particle density decays slowly. Since many real SF networks are fractal, the segregation effect has to be taken into account in the reaction kinetics among heterogeneous particles.

cond-mat.dis-nn↗
arXiv2008

Jamming transition in traffic flow under the priority queuing protocol

Packet traffic in complex networks undergoes the jamming transition from free-flow to congested state as the number of packets in the system increases. Here we study such jamming transition when queues are operated by the priority queuing protocol and packets are guided by the dynamic routing protocol. We introduce a minimal model in which there are two types of packets distinguished by whether priority is assigned. Based on numerical simulations, we show that traffic is improved in the congested region under the priority queuing protocol, and it is worsened in the free-flow region. Also, we find that at the transition point, the waiting-time distribution follows a power law, and the power spectrum of traffic exhibits a crossover between two 1/f^a behaviors with exponent a ~ 1 and 1 < a < 2 in low and high frequency regime, respectively. This crossover is originated from a characteristic waiting time of packets in the queue.

cond-mat.other↗
arXiv2008

Modular synchronization in complex networks with a gauge Kuramoto model

We modify the Kuramoto model for synchronization on complex networks by introducing a gauge term that depends on the edge betweenness centrality (BC). The gauge term introduces additional phase difference between two vertices from 0 to $π$ as the BC on the edge between them increases from the minimum to the maximum in the network. When the network has a modular structure, the model generates the phase synchronization within each module, however, not over the entire system. Based on this feature, we can distinguish modules in complex networks, with relatively little computational time of $\mathcal{O}(NL)$, where $N$ and $L$ are the number of vertices and edges in the system, respectively. We also examine the synchronization of the modified Kuramoto model and compare it with that of the original Kuramoto model in several complex networks.

cond-mat.dis-nn↗
arXiv2008

Self-similarity in Fractal and Non-fractal Networks

We study the origin of scale invariance (SI) of the degree distribution in scale-free (SF) networks with a degree exponent $γ$ under coarse graining. A varying number of vertices belonging to a community or a box in a fractal analysis is grouped into a supernode, where the box mass $M$ follows a power-law distribution, $P_m(M)\sim M^{-η}$. The renormalized degree $k^{\prime}$ of a supernode scales with its box mass $M$ as $k^{\prime} \sim M^θ$. The two exponents $η$ and $θ$ can be nontrivial as $η\ne γ$ and $θ<1$. They act as relevant parameters in determining the self-similarity, i.e., the SI of the degree distribution, as follows: The self-similarity appears either when $γ\le η$ or under the condition $θ=(η-1)/(γ-1)$ when $γ> η$, irrespective of whether the original SF network is fractal or non-fractal. Thus, fractality and self-similarity are disparate notions in SF networks.

cond-mat.stat-mech↗
arXiv2007

Production and application of metal-based nanoparticles

A number of metal-based nanopowders such kinds as Fe, Co, Fe/Co alloy, Fe/C, Fe/organic shell were successfully produced by aerosol synthesis method. The mechanism of nanoparticles formation and the influence of experimental parameters on shape, size distribution, structure, chemical and phase composition of oxide-, carbon-, or organic- coated nanoparticles were evaluated. The sizes of particles can be varied from 6-100 nm with narrow size distribution. The several application fields of synthesized nanoparticles have been studied.

cond-mat.mtrl-sci↗
arXiv2007

Spectral densities of scale-free networks

The spectral densities of the weighted Laplacian, random walk and weighted adjacency matrices associated with a random complex network are studied using the replica method. The link weights are parametrized by a weight exponent $β$. Explicit results are obtained for scale-free networks in the limit of large mean degree after the thermodynamic limit, for arbitrary degree exponent and $β$.

cond-mat.stat-mech↗
arXiv2007

Design and performance of the silicon sensors for the CMS barrel pixel detector

The CMS experiment at the LHC includes a hybrid silicon pixel detector for the reconstruction of charged tracks and of the interaction vertices. The barrel region consists of n-in-n sensors with 100X150 um^2 cell size processed on diffusion oxygenated float zone silicon. A biasing grid is implemented and pixel isolation is achieved with the moderated p-spray technique. An extensive test program was carried out on the H2 beam line of the CERN SPS. In this paper we describe the sensor layout, the beam test setup and the results obtained with both irradiated and non-irradiated prototype devices. Measurements of charge collection, hit detection efficiency, Lorentz angle and spatial resolution are presented.

physics.ins-det↗
arXiv2007

Scale-free random branching tree in supercritical phase

We study the size and the lifetime distributions of scale-free random branching tree in which $k$ branches are generated from a node at each time step with probability $q_k\sim k^{-γ}$. In particular, we focus on finite-size trees in a supercritical phase, where the mean branching number $C=\sum_k k q_k$ is larger than 1. The tree-size distribution $p(s)$ exhibits a crossover behavior when $2 < γ< 3$; A characteristic tree size $s_c$ exists such that for $s \ll s_c$, $p(s)\sim s^{-γ/(γ-1)}$ and for $s \gg s_c$, $p(s)\sim s^{-3/2}\exp(-s/s_c)$, where $s_c$ scales as $\sim (C-1)^{-(γ-1)/(γ-2)}$. For $γ> 3$, it follows the conventional mean-field solution, $p(s)\sim s^{-3/2}\exp(-s/s_c)$ with $s_c\sim (C-1)^{-2}$. The lifetime distribution is also derived. It behaves as $\ell(t)\sim t^{-(γ-1)/(γ-2)}$ for $2 < γ< 3$, and $\sim t^{-2}$ for $γ> 3$ when branching step $t \ll t_c \sim (C-1)^{-1}$, and $\ell(t)\sim \exp(-t/t_c)$ for all $γ> 2$ when $t \gg t_c$. The analytic solutions are corroborated by numerical results.

cond-mat.stat-mech↗
arXiv2007

A box-covering algorithm for fractal scaling in scale-free networks

A random sequential box-covering algorithm recently introduced to measure the fractal dimension in scale-free networks is investigated. The algorithm contains Monte Carlo sequential steps of choosing the position of the center of each box, and thereby, vertices in preassigned boxes can divide subsequent boxes into more than one pieces, but divided boxes are counted once. We find that such box-split allowance in the algorithm is a crucial ingredient necessary to obtain the fractal scaling for fractal networks; however, it is inessential for regular lattice and conventional fractal objects embedded in the Euclidean space. Next the algorithm is viewed from the cluster-growing perspective that boxes are allowed to overlap and thereby, vertices can belong to more than one box. Then, the number of distinct boxes a vertex belongs to is distributed in a heterogeneous manner for SF fractal networks, while it is of Poisson-type for the conventional fractal objects.

cond-mat.stat-mech↗
arXiv2007

Detection of M31 Binaries via High-Cadence Pixel-Lensing Surveys

The Angstrom Project is using a distributed network of two-meter class telescopes to conduct a high cadence pixel-lensing survey of the bulge of the Andromeda Galaxy (M31). With the expansion of global telescope network, the detection efficiency of pixel-lensing surveys is rapidly improving. In this paper, we estimate the detection rate of binary lens events expected from high-cadence pixel-lensing surveys toward M31 such as the Angstrom Project based on detailed simulation of events and application of realistic observational conditions. Under the conservative detection criteria that only high signal-to-noise caustic-crossing events with long enough durations between caustic crossings can be firmly identified as binary lens events, we estimate that the rate would be $Γ_{\rm b}\sim (7-15)f_{\rm b}(N/50)$ per season, where $f_{\rm b}$ is the fraction of binaries with projected separations of $10^{-3} {\rm AU}<\tilde{d}<10^3 {\rm AU}$ out of all lenses and $N$ is the rate of stellar pixel-lensing events. We find that detected binaries would have mass ratios distributed over a wide range of $q\gtrsim 0.1$ but with separations populated within a narrow range of $1 {\rm AU}\lesssim \tilde{d}\lesssim 5 {\rm AU}$. Implementation of an alert system and subsequent follow-up observations would be important not only for the increase of the binary lens event rate but also for the characterization of lens matter.

astro-ph↗
arXiv2006

Internet data packet transport: from global topology to local queueing dynamics

We study structural feature and evolution of the Internet at the autonomous systems level. Extracting relevant parameters for the growth dynamics of the Internet topology, we construct a toy model for the Internet evolution, which includes the ingredients of multiplicative stochastic evolution of nodes and edges and adaptive rewiring of edges. The model reproduces successfully structural features of the Internet at a fundamental level. We also introduce a quantity called the load as the capacity of node needed for handling the communication traffic and study its time-dependent behavior at the hubs across years. The load at hub increases with network size $N$ as $\sim N^{1.8}$. Finally, we study data packet traffic in the microscopic scale. The average delay time of data packets in a queueing system is calculated, in particular, when the number of arrival channels is scale-free. We show that when the number of arriving data packets follows a power law distribution, $\sim n^{-λ}$, the queue length distribution decays as $n^{1-λ}$ and the average delay time at the hub diverges as $\sim N^{(3-λ)/(γ-1)}$ in the $N \to \infty$ limit when $2 < λ< 3$, $γ$ being the network degree exponent.

physics.soc-ph↗
arXiv2006

Synchronization transition of heterogeneously coupled oscillators on scale-free networks

We investigate the synchronization transition of the modified Kuramoto model where the oscillators form a scale-free network with degree exponent $λ$. An oscillator of degree $k_i$ is coupled to its neighboring oscillators with asymmetric and degree-dependent coupling in the form of $\couplingcoeff k_i^{η-1}$. By invoking the mean-field approach, we determine the synchronization transition point $J_c$, which is zero (finite) when $η> λ-2$ ($η< λ-2$). We find eight different synchronization transition behaviors depending on the values of $η$ and $λ$, and derive the critical exponents associated with the order parameter and the finite-size scaling in each case. The synchronization transition is also studied from the perspective of cluster formation of synchronized vertices. The cluster-size distribution and the largest cluster size as a function of the system size are derived for each case using the generating function technique. Our analytic results are confirmed by numerical simulations.

cond-mat.stat-mech↗
arXiv2006

Simulation of Heavily Irradiated Silicon Pixel Detectors

We show that doubly peaked electric fields are necessary to describe grazing-angle charge collection measurements of irradiated silicon pixel sensors. A model of irradiated silicon based upon two defect levels with opposite charge states and the trapping of charge carriers can be tuned to produce a good description of the measured charge collection profiles in the fluence range from 0.5x10^{14} Neq/cm^2 to 5.9x10^{14} Neq/cm^2. The model correctly predicts the variation in the profiles as the temperature is changed from -10C to -25C. The measured charge collection profiles are inconsistent with the linearly-varying electric fields predicted by the usual description based upon a uniform effective doping density. This observation calls into question the practice of using effective doping densities to characterize irradiated silicon. The model is now being used to calibrate pixel hit reconstruction algorithms for CMS.

physics.ins-det↗
arXiv2006

Fractality in complex networks: critical and supercritical skeletons

Fractal scaling--a power-law behavior of the number of boxes needed to tile a given network with respect to the lateral size of the box--is studied. We introduce a new box-covering algorithm that is a modified version of the original algorithm introduced by Song et al. [Nature (London) 433, 392 (2005)]; this algorithm enables effective computation and easy implementation. Fractal networks are viewed as comprising a skeleton and shortcuts. The skeleton, embedded underneath the original network, is a special type of spanning tree based on the edge betweenness centrality; it provides a scaffold for the fractality of the network. When the skeleton is regarded as a branching tree, it exhibits a plateau in the mean branching number as a function of the distance from a root. Based on these observations, we construct a fractal network model by combining a random branching tree and local shortcuts. The scaffold branching tree can be either critical or supercritical, depending on the small-worldness of a given network. For the network constructed from the critical (supercritical) branching tree, the average number of vertices within a given box grows with the lateral size of the box according to a power-law (an exponential) form in the cluster-growing method. The distribution of box masses, i.e., the number of vertices within each box, follows a power law P_m(M) sim M^{-eta}. The exponent eta depends on the box lateral size ell_B. For small values of ell_B, eta is equal to the degree exponent gamma of a given scale-free network, whereas eta approaches the exponent tau=gamma/(gamma-1) as ell_B increases, which is the exponent of the cluster-size distribution of the random branching tree. We also study the perimeter of a given box as a function of the box mass.

cond-mat.stat-mech↗