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this is the only vacuum in the regular region of these coordinates , because the metric is regular there .
this is the unique vacuum in the finite region of these coordinates where the metric is regular .
recent studies on generative adversarial networks have achieved impressive success in generating images .
first proposed by in 2014 , generative adversarial networks have been quite successful at generating realistic images .
multi-task learning has proven effective in many computer vision problems .
multi-task learning has recently been employed in image classification .
the iib matrix model is a convincing candidate of non-perturbative type iib superstring theory .
a large n reduced model has been proposed as a nonperturbative formulation of type iib superstring theory .
cross approximation is a basic reduction technique for the approximation of large low-rank matrices .
cross approximation is an iterative matrix approximation technique .
weitsman , the cohomology rings of symplectic quotients .
kogan , localization theorems by symplectic cuts .
the classical limit for quantum mechanical correlation functions .
the classical field limit of nonrelativistic bosons .
the theory of coherent sheaves over weighted projective lines is due to geigle-lenzing .
indeed , the theory of coherent sheaves on the weighted projective lines of tubular type was constructed by geigle and lenzing in .
generalized spatial dirichlet process models .
variational inference for dirichlet process mixtures .
secondly , the critical eigenvalue of the state matrix can be used as a measure of proximity to instability as discussed in extensive literature .
for instance , the critical eigenvalue of the state matrix can be used as a measure of proximity to instability as discussed in extensive literature .
this fact is known in field theory on curved spaces as an instance of conformal triviality .
this anomaly is relevant for the applications of quantum field theory on curved spacetime .
the scale rotation is modeled as a linear torsional spring which rotates about a fixed point .
accordingly , the scales rotation is modeled as a linear torsional spring .
in effect , academic recruitment is often described as an informal process , in which a few powerful professors promote or select new professors through mechanisms of cooptation .
in effect , academic recruitment is often reported as an informal process in which a few powerful professors select new ones through cooptation mechanisms .
these points are in the first brillouin zone shown in the inset .
these points are in the first brilp louin zone shown on the right of the figure .
in particular , the random code capacity region is not necessarily achievable using deterministic codes .
in particular , the divided-randomness capacity region is not necessarily achievable using deterministic codes .
powerful deep neural networks have been created and investigated for high-level computer vision tasks such as image classification .
deep neural networks have achieved significant improvements in many computer vision tasks .
the physical basis of this assumption is a spatial finiteness of the many-nucleon system .
the physical basis for this assumption is the general uniformity of h ii region abundances in dwarf galaxies .
we use a generative advasarial network to model the stochastic distribution .
to build such a generator we use a generative adversarial network structure .
the deep learning models are trained by using the adam optimizer .
the network is trained using the adam optimizer over the ccc metric .
for example , donahue et al propose long-term recurrent convolution network for activity recognition and video captioning .
donahue et al combine convolutional layers and long-range temporal recursion to propose long-term recurrent convolutional networks for visual recognition and description .
the multi-column deep neural network was the first cnn used for hccr .
the multi-column deep neural network , composed of several cnns , was the first cnn used for hccr .
the cms particle-flow event algorithm reconstructs and identifies individual particles with an optimized combination of information from the various elements of the cms detector .
events are processed using the particle-flow algorithm , which is designed to reconstruct and identify all particles using the optimal combination of information from the elements of the cms detector .
hd 168076 hd 168076 is a member of the young open cluster ngc 6611 which we use in this analysis .
hd 161796 hd 161796 is a high galactic latitude f3 ib supergiant .
in , goodfellow et al proposed the fast gradient sign method for generating adversarial samples .
goodfellow et al introduced the fast gradient sign method to find adversarial perturbations .
our aim in the remainder of this subsection is to clarify when these frobenius algebras are canonical .
the aim of the next two subsections will be to show how frobenius algebras arise in category theory and subfactor theory .
the ssb of laser beams coupled into an effective transverse dwp created in the self-focusing photorefractive medium was demonstrated in ref .
on the other hand , the ssb of laser beams coupled into an effective transverse dwp created in a self-focusing photorefractive medium has been demonstrated in ref .
all the convolutional layers , apart from the last one , are followed by batch normalization units .
every convolutional layers are followed by a batch normalization layer and a relu .
li et al proved that for a multicast session , symbol-wise linear algebraic coding over a finite field is always sufficient .
li et al further showed that linear network coding with finite alphabet size is sufficient for this multicast .
now we can define substructures in interval polynomial semirings .
we can define substructures and special elements in group interval semirings .
it is an experimental fact that the bosons do not show generational behavior .
this is because the bosons do not show generational behavior .
the physics objects are jets , clustered using the jet finding algorithm with the tracks assigned to the vertex as inputs , and the associated missing transverse momentum , taken as the negative vector sum of the p t of those jets .
the physics objects are the objects reconstructed by a jet finding algorithm applied to all charged particle tracks associated with the vertex and also the corresponding missing transverse momentum .
welded knotted objects first appeared in a work of fenn-rimanyi-rourke in the more algebraic context of braids .
these welded knotted objects first appeared in a work of fenn-rimanyi-rourke in the more algebraic context of braids .
compact examples of manifolds with holonomy g 2 were obtained first by joyce .
complete examples of riemannian 7-manifolds with holonomy g 2 were constructed by bryant and salamon .
deep neural networks have been widely applied and achieved state-of-art performance on a variety of tasks including image recognition .
recently , deep neural networks have substantially improved the state-of-the-art performances of various challenging classification tasks , including image based object recognition .
liu , electromagnetic fields in ferrofluids , phys .
liu , shear excited sound in magnetic fluid , phys .
in recent years , deep convolutional neural networks have been shown to be exceptionally effective for image classification .
specifically , convolutional neural networks have shown their powerful abilities on image representation .
the ergodic hypothesis implies the difference between two subactions is always constant in the set π 1 , .
the ergodic hypothesis implies the difference between two subactions is always constant on the set π 1 , .
without loss of generalization , we compare our proposed algorithm with two main-stream baselines trained using neural networks , u-net .
we compare performance of the proposed architecture for the per-pixel classification task at hand with two well-adopted cnns for segmentation , fcn-8 .
deep neural networks have been significantly successful in many artificial intelligence tasks such as im- age classification .
convolutional neural networks have achieved significant progress in computer vision tasks such as image classification .
these modulated standing virtual waves form the virtual replica systems .
the interference of bivacuum reference waves with the object waves forms the primary virtual replica .
string theory is a central candidate , with the feature that the assumptions underlying the cpt theorem are invalid for strings , which are extended objects .
however , string theory is a theory which is still under construction .
the rp sterile neutrino parameters required to produce this signal have been recently calculated .
the sterile neutrino parameters required to produce this signal have been recently calculated .
recent work has shown the benefits of rdma for specific relational operators and key-value stores .
recent research has shown the benefits of rdma for specific operators and key-value stores .
since f -gravity is a gauge theory , like general relativity , it is crucial the choice of suitable coordinates in order to correctly formulate the problem .
since gravity is the universal force which couples to all energy sources , the decay of the inflaton is necessarily 3 accompanied by non-thermal emission of kk gravitons with masses comparable to mφ .
moreover , because the jced factor graph is loopy , even non-approximate spa is not guaranteed to yield the correct output distributions , because exact inference is np hard .
due to the cycles within the factor graph , exact inference is np-hard , and so we settle for approximate mmse inference .
for cub dataset , we use cnn-rnn textual features as class attributes , similar to the approaches mentioned in table 5 and 2 .
for textual stream , we apply cnn-rnn as the text encoder to learn a correspondence function with images .
generative adversarial networkis a generative model composed of two neural networks which are trained in opposite directions .
a generative adversarial network consists of two neural networks trained in opposition to each other .
deep convolutional neural networks have recently shown immense success for various image recognition tasks , such as object recognition .
deep neural networks have achieved impressive accuracy in many application domains such as image classification and localization , object detection , speech recognition and video classification .
very close analogues of x i in h n are used in where they represent the murphy operators .
the element p m is used in for describing the mth power sum of the murphy operators in h n , independently of n .
the segmentation part of our model is a modified version of the u-net architecture .
the first part of our network loosely follows the idea of an autoencoder with the u-net architecture .
the monoid m is a group if and only if each mapping in m is a bijection .
we say that a monoid m is a gaussian monoid if it is atomic , gcd definition 2 point 3 .
in 1994 peter shor developed a polynomial time quantum algorithm for factoring integers and solving discrete logarithm problems .
in 1994 , peter shor described an efficient , polynomial time , quantum algorithm for the discrete logarithm problem .
recently , deep convolutional neural networks has been successfully applied for tracking .
representative methods include convolutional neural networks based trackers .
we use the following five real dtns data-sets gathered by over almost two years , referred to as kaist , ncsu , new york city , orlando and north carolina state fair .
we use the following three real data-sets gathered by , referred to as north carolina state fair , ncsu , and kaist .
while the axion is a priori a goldstone boson of the spontaneously broken peccei-quinn symmetry u p q , qcd instanton effects induce an axion potential with residual zn symmetry .
axion is a possible candidate of light particle and might show the long-distance interference phenomenon .
the face from each frame of videos was automatically detected using viola and jones face detection algorithm .
the face from each frame of videos was automatically detected using the viola and jones face detection algorithm .
batch normalization and leaky relu activations are applied after every convolution .
batch normalization is applied right after each convolution and before the activation following .
normalized cut is a common objective in the context of spectral clustering .
spectral clustering is a standard application of spectral embedding .
the em algorithm is the canonical algorithm to use in the case of unobserved variables .
in this case we can use the hard em algorithm for parameter estimation with latent variables .
for comparison , we obtained null distributions from 100 random networks with equivalent degree and strength distributions 29 .
for comparison , we constructed benchmark distributions from 100 random networks with equivalent size , degree distribution , and strength distribution 19 .
since f is non-convex and only lipschitz differentiable away from zero , convergence analysis of admm in this case is beyond the current theory .
since f is non-convex and only lipschitz differentiable away from zero , convergence analysis of admm is beyond the current theory .
optical quantum computation using clus ter states .
ensemble quantum computing by nmr spectroscopy .
to train a thinner and deeper student network using kd , romero et al propose hint training that trains a hidden layer with a convolutional regressor .
in romero et al , the approach uses knowledge distillation with an intermediate hint layer to train a thinner but deeper student network containing fewer parameters to outperform the teacher network .
to mitigate this effect , charged candidates associated with vertices other than the primary one are discarded from clustering , and an offset correction is applied to the p t of the jet to subtract the remaining contributions .
to mitigate this effect , tracks identified to be originating from pileup vertices are discarded prior to the clustering , and an offset correction is applied to correct for remaining contributions .
according to the brown-henneaux analysis , for generic ads 3 gravity theories the asymptotic symmetry group consists of two copies of the virasoro algebra corresponding to the two-dimensional conformal symmetry .
in the three dimensional asymptotic ads 3 cases , this symmetry algebra consists of two copies of virasoro algebra at the brown-henneaux central charge .
convolutional neural networks have achieved state-of-the-art results on several computer vision tasks .
deep convolutional neural networks have been prevailed in various computer vision tasks , such as objection classification .
a hopf algebra h in z2-mod is called a super hopf algebra .
a hopf algebra is a bialgebra h with an antipode s .
in the present paper we give some basic theory of these maps .
here we give some basic theory for these maps .
the residual learning framework fixes this problem and achieves precise classification .
the residual network model is selected for learning robust features and increasing its efficiency .
recent progress in string theory has stimulated interest in solitons in noncommutative field theories .
there has recently been much interest , particularly from string theorists , in noncommutative geometry .
the chirality is a variable whose absolute value corresponds to a volume of a parallelepiped spanned by three spins , and whose sign represents the handedness of the spin structure .
the chirality is a discrete vortices , which can have an arbitrary integer winding number .
let us first study the hypersurface case more closely .
let us treat the first case exemplarily with more details .
herwigver this variable must to be set equal to the name of the object file corresponding to the version of herwig linked to the package .
the former variable must be set equal to the object file name of the version of herwig currently adopted .
the performance of visual scene recognition tasks has been significantly boosted by recent advances of deep learning algorithms .
visual recognition has made a significant progress due to the widespread use of deep learning architectures .
convolutional neural networks have become the dominant approach for many computer vision tasks .
deep neural networks have shown remarkable success in many computer vision tasks such as image classification .
following , we apply bn on the output of each convolutional layer before applying relu non-linearity .
we use batch normalization between each convolutional layer and which is followed by relu non-linearity .
we adopt the 56-layer resnet as the feature extractor network where the dimension is 64 .
for a resnet architecture , we use the final pooling layer .
examples of such kbs are freebase , wikipedia , google knowledge graph and yago .
examples of such kbs are freebase , yago and wikidata .
these architectures learn hierarchical layers of representations to perform pattern recognition and have demonstrated impressive results on many pattern recognition tasks .
in recent years , the deep learning models like the cnns have been shown to achieve superior performance in numerous visual perception tasks .
any other orbit consists of precisely one cofinality class .
this orbit is the innermost boundary of circular orbits for all particles .
this higgs field is the remnant of the a0 field in the four-dimensional theory .
the higgs field is the a0 field of the 4d-theory , and therefore the number of degrees of freedom is conserved in this process .
since nobody knows for sure what strategy is the best one , different approaches should be investigated .
since nobody knows for sure which strategy is the best one , different approaches should be investigated .
more recently , convolutional neural networks have achieved unprecedented performance in a wide range of image classification problems .
convolutional neural networks have become a highly active area of research due to strong results in areas such as image classification , among others .
the red , green and blue points show these different analyses using the true , elliptical and circular beams , respectively .
the dark and light blue points are from the analysis of the first data set , with the circular and elliptical beams respectively .
qi extended some nice properties of matrices to higher order tensors .
qi extended some nice properties of symmetric matrices to higher order symmetric tensors .
in the case thatt is approximately low-rank , one can instead compute only the top r singular values and singular vectors either using deterministic methods , such as lanczos bidiagonalization , at a reduced cost of oflops .
in the case that t is approximately low-rank , one can instead compute only the top r singular values and singular vectors either using deterministic methods , such as lanczos bidiagonalization , at a reduced cost of oflops .
the free energy on t 4 is a function of the ratio r of the noncommutativity and r of the matrix size .
furthermore , the free energy is a concave function of the distance between the defects .
deep convolutional neural networks are very successful at performing visual tasks such as image classification .
in recent years , deep convolutional neural networks have been shown to be exceptionally effective for image classification .
cosmological inflation manifests itself as the most appealing scenario for solving the problems of the big bang initial conditions , namely , the flatness and horizon problems .
cosmic inflation is a plausible solution for the problems of the standard big bang scenario , such as the flatness and the horizon problems .
for the office-31 and visda dataset , we utilize the resnet-50 with an embedding layer to represent the generator g .
for the rgb branch we use the convolutional layers of a pre-trained resnet-18 network .
this suggests that controllability can be cast as a structural property of the graph defined by a and b , as captured in the graph-theoretic concept of structural controllability described by lin in .
the controllability property is thus at its core a structural property of the graph defined by a and b , as captured in the graph-theoretic concept of structural controllability described by lin in .
finally , we show experimental results that confirm the theoretical predictions .
these theoretical predictions are confirmed by experimental results .
solar , atmospheric , reactor and accelerator neutrino experiments have confirmed the existence of flavour oscillations of active neutrinos , implying that neutrinos have non-zero mass .
in the last decades , neutrino oscillation experiments have provided conclusive evidence that neutrinos have non-zero masses .
jade alglave , daniel kroening , john lugton , vincent nimal , and michael tautschnig .
susmit sarkar , kayvan memarian , scott owens , mark batty , peter sewell , luc maranget , jade alglave , and derek williams .
it is shown that all possible transitions can be described in terms of one universal invariant function whose explicit expression is lorenz structure dependent .
next , we show that all possible transitions can be described in terms of only one universal invariant function .
szegedy et al showed that training on a combination of discovered adversarial examples and clean examples somewhat regularizes the deep neural network .
szegedy et al found that deep neural networks learn input-output mappings that are fairly discontinuous .
yang et al represented person-specific and age-specific factors independently using sparse representation hidden factor analysis .
represented person-specific and age-specific factors independently using sparse representation hidden factor analysis .
a distinctive example of this type of particles is the axion , which arises when the strong cp problem is solved through the pecceiquinn mechanism .
the prime example of this is the axion , a scalar particle originally proposed to explain the strong constraints on the existence of charge-parity violating terms in the strong nuclear force sector .
guruswami and vardy later proved in that this problem applied to the family of reed-solomon codes is also np-hard .
when the error distance increases , guruswami and vardy showed that that maximum-likelihood decoding is np-hard for the family of reed-solomon codes .
deep convolutional neural networks have shown tremendous success in a variety of computer vision tasks , such as image classification .
deep convolutional neural networks have been successful in many computer vision tasks including image classification .