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during the last two decades , several important advances have been made toward the analysis , modeling , and control of networked spreading processes .

important advances in the analysis and containment of spreading processes over static networks have been made during the last decade .

using the null geodesic method and hamilton-jacobi method , much fruit has been achieved .

using the null geodesic method and the hamilton-jacobi method , much fruit has been achieved .

the exchange-correlation potential was calculated using the generalized gradient approximation as proposed by pedrew , burke , and ernzerhof .

the exchange correlation approximation is treated with the generalized gradient approximation of perdew , burke , and ernzerhof .

both methods provide similar estimates for the galactic component of the total brightness temperature towards the north polar cap .

both methods yield similar estimates for the total galactic temperature towards each reference location .

graphene is a two-dimensional material with the unique .

graphene is a hexagonal lattice built out of two inter-penetrating triangular sub-lattices a and b .

in addition to wysteria several other mpc dsls have been proposed in the literature .

in addition to wysteria , several other mpc dsls have been proposed in the literature .

first , these tools often require pro ling runs on the gpu to determine the best performing resource speci cations .

first , these tools often require profiling runs on the gpu to determine the best performing resource specifications .

a clique is a group of friends where everybody knows everybody else .

a clique is a fully connected subset of vertices , and thus an independent set in the complementary graph g where vertices i and j are connected whenever e and vice versa .

originally introduced by elfes et al , grid maps are well-suited for sensor fusion and enable the use of efficient convolutional operations due to their dense grid structure .

first introduced in , grid maps are well-suited for sensor fusion and enable the use of efficient convolutional operations due to their dense grid structure .

generic hyperbolicity in the logistic family .

non-uniform hyperbolicity and universal bounds for s-unimodal maps .

the graviton background should interact with the cosmic microwave one in this model .

the redshift is caused by forehead collisions with gravitons in this model .

recent success in computer vision and image retrieval are closely related to convolutional neural networks .

data-driven approaches in robotics have gained popularity based on the success of deep neural network methods in other domains including computer vision .

additional standards are required within the user layer to enable user authentication to proprietary datasets and storage elements as well as interoperability amongst vo applications .

hence some standards are required within the user layer to enable user authentication to non-public datasets and storage elements and to enable interoperability amongst vo applications .

the symbol p denotes a fixed finite prime of q and denotes the real prime .

the symbol p denotes the cauchy principal value .

in the course of a proof of index theorems in noncommutative geometry , connes and moscovici defined a noncommutative hopf algebra .

the connes-moscovici hopf algebra h cm was introduced in in the context of noncommutative geometry .

generative adversarial networks are a family of generative models that implicitly estimate the data distribution in an unsupervised manner .

generative adversarial networks are a class of algorithms for modeling a probability distribution given a set of samples from the data probability distribution ρ data .

deep learning methods have been widely used in recent years with its high successes especially in image classification .

deep learning has recently been applied very successfully in areas such as image recognition .

another improved measure for the contrastto-noise-ratio called generalized-cnr was recently proposed .

recently , an improved measure for the contrast-to-noiseratio called generalized-cnr is proposed .

chen et al proposed a dcnn-based system for semantic segmentation and used a crf for postprocessing .

shakeri et al adapted the work of chen et al for semantic segmentation of natural images using fcnn .

narasimhan and agarwal develop a structural svm based method which directly optimizes the pauc score .

narasimhan and agarwal develop structural svm based methods which directly optimize the pauc score .

deep reinforcement learning has demonstrated success in policy search for tasks in domains like game playing and robotic control .

deep reinforcement learning has mastered human-level control policies in a wide variety of tasks .

in recent years , convolutional neural networks have achieved outstanding performance in a variety of machine learning tasks , especially in computer vision , such as image classification .

deep neural networks have demonstrated their success in many machine learning and computer vision applications , including image classification .

deep learning has succeeded in making hierarchical neural networks perform excellently in various practical applications .

deep learning has been used successfully in many applications , and is considered to be one of the most cuttingedge artificial intelligence techniques .

ito et al adopting an intrusive approach , placed six marker points on driver body to predict some typical driving operations .

ito et al , adopting an intrusive approach , placed six marker points on the driver body to predict some typical driving operations .

from these preparations , we can calculate the β-function from our simulation data .

in order to estimate σ , we utilized the β-function , which is readily accessible from our simulation data .

in this paper , we employ the susceptible-infected-recovered model to simulate the spreading process on networks .

in this paper , we make use of the well-known susceptible-infected-remove model to simulate the spreading process on networks .

esposito , new invariants in the one-loop divergences on man ifolds with boundary , class .

esposito , gauge-averaging functionals for euclidean maxwell theory in the presence of boundaries , class .

the part which describes the strong nuclear force is called quantum chromodynamics .

quantum chromodynamics is the matrix model of greatest physical interest .

the imaginary contributions to the on-shell self-energy are determined , in effect , by the unitarity condition .

so both the renormalized mass and the imaginary part of the on-shell self-energy become gauge-independent .

although string theory is a proposed fundamental theory of quantum gravity , geometries with closed timelike curves have resurfaced as solutions to its low energy equations of motion .

string theory is the leading candidate for a theory of quantum gravity .

this small chunk forms the majority of requests that come from different users at different times , which is referred to as asynchronous content reuse .

this small portion forms the majority of requests that come from different users at different times , which is referred to as asynchronous content reuse .

a more general approach is given by bounded matrix factorization .

most traditional cf methods are based on matrix factorization .

this theory contains all fields expected from the study of d1-d5-p string world-sheet amplitudes .

this theory contains all the fields expected from d1-d5-p string world-sheet calculations .

an orbifold is a generalisation of the orbit space of a smooth effective finite group action on a manifold6 .

the new space obtained in this way is called an orbifold .

most microorganisms can be preserved for several years in cryogenic environments without losing vitality .

most microorganisms can be preserved for many years in cryogenic environments .

among these methods , chou et al propose a multi-scale linear regressor which only applies to affine deformations and lowrank approximations of non-linear deformations .

chou et al propose a multi-scale linear regressor , which is restricted to the prediction of affine transformations and low-rank approximations of nonrigid deformations .

deep convolutional neural networks have emerged as highly effective models for these large-scale visual recognition tasks .

recently , deep neural networks have gained the attention of numerous researchers outperforming state-of-the-art approaches on various computer vision tasks .

one of the most widely applied regularisation techniques currently used for deep networks is dropout .

the most successful noise-based regularizer for neural networks is dropout .

the modification is a chern-simons term that does not exist in even dimensions 1 as due to the coupling of the 4-d bulk theory to a defect 3-d cft .

the other modification is a straightforward high-energy correction of the background quantities h and ρ via the modified friedmann equations .

ek dra also is a long period binary star where the secondary is much fainter than the primary .

ek dra is a young object and it is likely that its age lies within 50 to 125 myrs depending on the criteria one applies , eg if one only regards the activity .

therefore one can use the all electron density to evaluate the lattice constant if the soft correction is not added .

furthermore , we test if the lattice constant can be evaluated using only the valence electron density .

it has been shown that the effective capacity of neural networks is sufficient for memorizing the entire training dataset .

it is well known that deep networks can perfectly fit the training set on randomly labelled data , while necessarily achieving chance level performance on the test set .

deep convolutional neural networks have made significant breakthroughs in many visual understanding tasks including image classification .

convolutional neural networks have achieved notable successes in a variety of visual recognition tasks , such as image classification .

we use the ms coco dataset , containing 113k images for training and 5k each for validation and test , with 5 captions for each image .

here , we train on the ms coco dataset , which contains approximately 120,000 training images with instance segmentation masks for each object in 80 categories .

the recent advances in object detection have been driven mainly by the development of deep neural networks .

object detection has achieved significant progress in recent years using deep neural networks .

the coefficient d is the decay rate for the predator y , and f , the efficiency of its predation .

the first non-vanishing coefficient an is called the leading coefficient .

finally on the theoretical side the is a prerequisite for studying d0 lifetime ratios for the different charm hadrons provide the best , since most inclusive observables to probe hadrodynamics at the charm scale .

on the theoretical side , there is the challenge of dealing with the consequences to hopping transport due to multioccupation of electronic states , and the connection between the single-particle dos and conductivity in nonequilibrium situations .

during training , batch normalization is inserted immediately after every linear layer .

for efficient training , the batch normalization technique is applied at each layer .

the construction of the moonshine module vertex operator algebra by frenkel , lepowsky and meurman can in fact be viewed as the answer to an important special case of this problem .

the construction of the moonshine module vertex operator algebra by frenkel , lepowsky and meurman can in fact be viewed in retrospect as the answer to an important special case of this problem .

region-based convolutional neural networks have been recognized as one of the most effective tools for object detection .

convolutional neural networks have proven to be effective models for tackling a variety of visual tasks .

in this paper , we employ the cdf-based scheduling policy for further analysis .

in this paper , we consider a system that employs the cumulative distribution function based scheduling policy .

although other work has been done on the subject , no work done to date has succeeded in giving any sort of genuinely deep understanding of discrete torsion .

although other work has been done on the subject , until recently no work has succeeded in giving any sort of genuinely deep understanding of discrete torsion .

note that the giveon-kutasov duality it is interesting to consider generic real mass parameters for our theories .

one can also see that this has to be the case from the giveon-kutasov duality in these theories .

we now give an alternative condition for localisability in terms of fourier transforms .

with an additional constraint we can get strong localisability .

if an orbifold m is a manifold , then the above cq coincides with the group of usual q-dimensional singular chains of m .

an orbifold is a topological space modeled on quotient spaces of a finite group actions .

here , we note that the large mass enhancement occurs in the middle of the intermediate-valent regime .

then , we have found that , in the intermediate-valent regime , the effective mass is enhanced substantially .

such data combinations of pseudo-detectors is called time-delay interferometry .

this post-processing data technique is known as time-delay interferometry .

the category of crossed modules of r-algebroids is equivalent to the category of double r-algebroids with thin structure .

therefore , an object in the category of algebroids yields also an associated crossed module .

this notion and its asymptotic version has been studied in recent years as a geometric invariant , see for example .

many results have appeared recently associated to this dimension , see , for example .

finally , we also studied the effects of couplings fluctuations and imperfect initialization .

we also have studied the effects of imperfections caused by couplings fluctuations and imperfect initialization .

the resurgence of convolutional neural networks has led to a wave of unprecedented advances for image classification using end-to-end hierarchical feature learning architectures .

more recently , convolutional neural networks have achieved unprecedented performance in a wide range of image classification problems .

generative adversarial networks are models capable of mapping noise vectors into realistic samples from a data distribution .

generative adversarial networks use large , unrestricted neural networks to transform samples from a fixed base distribution .

to analyze the spectra we used the standard spectral fitting software xspec .

we reduced these spectra using the eso-midas software .

in section 4 , we consider its su affine analogue and also give the explicit derivation of the moduli dependent metrics .

in section 3 we perform the calculation of the wilson loop by doing an analytic continuation of previous results in euclidean ads space .

such photons arising predominantly from the hard process are called prompt photons .

indeed , for such photons there is a possibility to use mirrors to reflect the photons .

the extended yale face database b contains 16,128 images of 28 human subjects under 9 poses and 64 illumination conditions .

the extended yaleb database contains 2432 front face images of 38 individuals and each subject having around 64 near frontal images under different illuminations .

moreover , the non-linear bias in the weakly non-linear regime only affects the monopole order of these statistics .

the above theory can be used to construct estimators which constrain the bias in the weakly non-linear regime .

rickayzen , in superconductivity , edited by r .

maple in advances in superconductivity , edited by b .

standard deviations for the uncertainties are shown in brackets after the main part of the number and are applicable to the last significant digits .

statistical uncertainties are shown in brackets after the respective values in units of the last significant digits .

the detections of gravitational waves in the last years by the ligo and virgo interferometric observatories , consistent with the merger of binary black hole systems , has opened a new era of gw astronomy leading to unprecedented discoveries .

the gravitational-wave events from binary black holes and binary neutron stars have been detected by the ligo-virgo collaboration , marking the dawn of the gw astronomy .

the least-square fits were performed using the mpfit algorithm under the idl 23 environment .

the crossmatches were performed with in a postgresqla database using the q3c indexing scheme .

in recent years , methods using convolution neural network have been successful in the classification of image recognition .

recently , deep convolution neural networks have been applied to solve the stereo matching problem .

it has been predicted theoretically that free-standing biaxially strained srtio 3 under electrical short-circuit boundary conditions can develop an electric polarization .

free-standing srtio 3 at zero temperature has been predicted to develop an in-plane polarization in the direction under biaxial tensile strain .

rastogi and nath proposed an algorithm which perturbs the discrete fourier transform of the entire time series and reconstructs a released version from the inverse dft .

rastogi and nath proposed the fourier perturbation algorithm f p a k that transforms the raw series into frequency domain , perturbs the first k coefficients , and then reconstructs the series with the perturbed k coefficients .

reinforcement learning is the problem of learning from interaction with the environment to achieve a goal .

reinforcement learning involves an agent interacting with an environment in order to achieve an explicit goal or goals .

in the following we will give an example of a simple quantum code which is also known as repetition code .

one of the simplest classical errorcorrecting codes is called repetition code 0 is encoded as 000 and 1 as 111 .

in recent years , convolutional neural networks methods have demonstrated highly accurate and reliable performance across a variety of computer-vision related tasks , including image classification .

deep convolutional neural networks have achieved remarkable accuracy for tasks in a wide range of application domains including image processing , machine translation , and speech recognition .

the energy dependence of leakage is in both options rather weak .

the energy dependence is in all cases rather similar .

in recent years , convolutional neural networks have achieved outstanding performance in a variety of machine learning tasks , especially in computer vision , such as image classification .

convolutional neural networks have significantly boosted the performance of a variety of visual analysis tasks , such as image classification in recent years due to its high capacity in learning discriminative features .

the two-point amplitude vanishes by the same arguments as at four loops , since there are at least seven insertions of momenta in the amplitude .

the one-point amplitude vanishes because of momentum conservation and the fact that there are insertions of momenta .

their geometry is the subject of the present article .

this geometry is the one for which the permeation mode is expected .

the other ingredient is the brane action , which is added to s5 .

another ingredient is the partial elimination ideal theory .

baryons consist of three quarks , while mesons are made of a quark and an antiquark .

the latter two are called the hybrid baryons .

this is equivalently recognized as stochastic block models .

one of the best known such models is the stochastic block model .

in recent years , convolutional neural networks have achieved outstanding performance in a variety of machine learning tasks , especially in computer vision , such as image classification .

deep convolutional neural networks have revolutionized computer vision , achieving unprecedented performance in high-level vision tasks such as classification .

in this work , we adopt a version of the model introduced by karloff , suri , and vassilvitskii .

in this paper we will work with the computational model introduced by karloff , suri , and vassilvitskii .

in particular , the minimum of a submodular function can be found in strongly polynomial time .

as with the special case of min-cut , the general problem of submodular minimization is solvable in polynomial time .

the droplet consists of free fermions on the topology of a sphere .

the droplet is a diamondlike shaped curve with four cusps .

the quantum lobachevsky spaces play the role of homogeneous spaces with respect to the twisted quantum lorentz groups .

for three members of quantum lobachevsky spaces the casimir operators give rise to the two-body relativistic open toda lattice hamiltonians .

cross- situational learning of object-word mapping using neural modeling fields .

rapid word learning under uncertainty via cross-situational statistics .

pathak et al developed a context encoder in an unsupervised learning algorithm for image inpainting .

pathak et al proposed a network to generate contents of an arbitrary image region conditioned on its surroundings .

one of them is the cusp-core problem , indicated by the discrepancy between increasing dark matter halo profile towards the centre of the galaxy from cdm n-body simulations .

one of them is the cusp-core problem , indicated by the discrepancy between increasing dark matter halo profile towards the center of galaxy from n-body simulation .

ngc 4100 this galaxy is a member of the ursa major cluster .

both ngc 4733 this galaxy is a low luminosity virgo elliptical .

we will see that the amplitudes need to be generalised to be consistent with brst invariance .

as we will see , these properties of the supergravity construction are essential for the brst consistency of the amplitude .

allouche and shallit have generalised the notion of automatic sequences to a wider class of regular sequences and demonstrated its ubiquity and links with multiple branches of mathematics and computer science .

allouche and shallit have generalized the notion of automatic sequences to a wider class of regular sequences and demonstrated their ubiquity and links with multiple branches of mathematics and computer science .

as discussed in , in this case the mechanism which tells us that exactly three spatial dimensions become macroscopic does not work .

in this case the mechanism which tells us that exactly three spatial dimensions become macroscopic does not work .

we follow notation and a basic definition of stability conditions on a triangulated category d from the original article due to bridgeland .

we first recall the definition of stability conditions on a triangulated category c introduced by bridgeland .

moreover , bekenstein has introduced an elegant picture for the quantization of the area of the event horizon , being defined in terms of planck areas .

moreover , bekenstein has conjointly introduced an elegant picture for the quantization of the area of the event horizon , being defined as an integral multiple planck area .

denote the set of isomorphism classes of finite-dimensional λ-modules by .

denote by mod-λ the category of finite-dimensional λ-modules .

deep learning has been very successful in a broad range of data analysis and learning problems .

deep reinforcement learning has mastered human-level control policies in a wide variety of tasks .