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recently , deep learning based algorithms have shown great success in object detection and classification tasks .

recently , deep convolutional neural networks show promising performances in various computer vision tasks such as object classification , localization .

the explanation that the o viii emission arises in an accretion disc seems unlikely since the optical emission .

it is , therefore , worth investigating if the emission in o viii might be originating in a ring of narrow radial width in the disc .

the proposed technique works with all types of queries including range queries and fuzzy match queries .

the proposed algorithm works well in the case of range and fuzzy match queries .

the cyg x-1 is the permanent source of x-ray radiation and one of brightest sources of soft gamma-radiation possessing high variability .

cyg x-1 is a high-mass x-ray binary system that accretes from the stellar wind of the young , massive companion .

confinement is a key emergent phenomenon in qcd .

confinement is a long distance effect , and a particular way to study such effects is to formulate the theory in a finite volume .

for coherent states these tomograms are the multivariable gaussian distribution functions .

for the fock states the tomograms are expressed in terms of multivariable hermite polynomials .

one particular example of high order methods is the discontinuous galerkin method introduced by reed and hill .

the discontinuous galerkin method , as a class of finite element methods , has been very popular in the cfd community .

one of such observables is the spatial variance σ2 x by measuring samples of x on a grid of points .

one of the simplest observables is a partial cross section , ie , the differential cross section integrated over all azimuthal angles , but over a limited range in θ .

solid and long-dashed curves are for neutron star and dotted curves are drawn for accretion around black hole .

solid curves are for neutron star and dotted one is drawn for accretion around black hole .

kipf and welling further propose a simplified graph convolutional network based on the first-order approximation of spectral filters .

specifically , kipf and welling proposed graph-convolutional networks for semi-supervised graph classification .

most text categorization problems are linearly separable and random forests are not optimal for very high dimensional sparse feature vectors .

most text classification problems are linearly separable , and more complex kernel functions can be slower and often do not increase accuracy .

it may be verified that this polyhedron is a regular octahedron .

arbitrarily close to this polyhedron is a smooth surface with a region of gaussian curvature more than q close to each vertex of p .

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 .

recent image analysis approaches have shown the strengths of convolutional neural networks which surpass state-of-the-art methods in virtually all fields , such as object classification , and many other tasks .

convolutional neural networks have achieved impressive state-of-the-art results on image classification .

convolutional neural networks have achieved great success on visual recognition tasks .

in recent years , deep convolutional neural networks have proven to be highly effective general models for a multitude of computer vision problems .

in recent years , neural networks have been effectively applied in various problems such as voice recognition .

for the particular model discussed above , the spinon fermi surface is perfectly nested .

in particular , it is interesting to consider an sdw instability of the spinon fermi surface .

several recent modifications have been proposed to improve the stability of gamp , including damping .

several recent modifications have been proposed to improve the stability of amp , including damping .

in 2d rational cfts , it was found of the primary operator , which is a kind of topological quantity .

in 2d rational cfts , it was found of the primary operator associated .

next , we provide an outer bound for the capacity region of the degraded gaussian mimo multi-receiver wiretap channel .

next , we consider the outer bound we proposed for the degraded discrete memoryless channel .

in general , such solutions do not allow time travel .

thus , in general , these wormhole solutions do not allow time travel .

we say that a couple , which is the trivial center .

then the couple is a cosymplectic structure on k and rk is the reeb vector of k .

superluminal signaling for space-time structure .

space-time transformation for superluminal signaling .

hutzler , the physics of foams , oxford university press , oxford .

larson , the structure and rheology of complex fluids , oxford university press , new york .

recently , a simple method , called the extended sampling method , was proposed using the the far field data due to one incident wave .

recently , a new method , called the extended sampling method , was developed to obtain the size and shape of the target using the scattering data of one incident wave .

this result motivated the work by wyner who introduced the notion of wiretap channel .

the classical degraded form of this channel was introduced by wyner , who determined the secrecy capacity of this channel .

the crab pulsar is the quintessential astrophysical object for which the unconfined split-monopole field geometry remains a key model element .

the crab pulsar is the only other pulsar for which optical polarization measurements are available .

describes the power dissipated by the system when this power is provided by the thermal bath .

describes the power which the system returns back to the thermal bath .

the grayscale is the midplane density of the model .

the grayscale is a liner stretch from 0 counts .

the errorbars in angle are the errors on the mean of the inclination angles within that bin .

the errorbars in azimuthal angles are the standard errors on the mean of azimuthal angles within that bin .

mccuaig found a polynomial-time algorithm to determine whether a bipartite graph has a pfaffian orientation .

mccuaig found a polynomial-time algorithm to show whether a bipartite graph has a pfaffian orientation .

this machine consists of 64 300 mhz mips 10000 processors with an aggregate memory of 16 gbytes .

this machine consists of 128 nodes , each with dual intel 800mhz itanium processors , and a myrinet network interconnect .

these algebras are not commutative , but they are right-commutative .

this algebra is commutative and satisfies identity .

therefore , in two dimensions and for strong disorder strength the charge conjugation symmetry is not restored at high temperature .

however , the behavior of the charge conjugation symmetry is the same as that at zero temperature .

in recent years , neural network approaches have significantly advanced the state of the art in computer vision tasks such as classification .

deep learning , especially convolutional neural networks , has revolutionized various machine learning tasks with grid-like input data , such as image classification .

throughput optimal control of cooperative relay networks .

energy optimal control for time varying wireless networks .

closer to our work , the authors of let the curing rates be proportional to the degree of each node -independent of the current state of the network , which means that curing resources may be wasted on healthy nodes .

closer to our work , the authors of let the curing rates be proportional to the degree of each node -but again independent of the current state of the network , which means that curing resources may be wasted on healthy nodes .

deep convolutional neural networks have achieved state-of-the-art performance on several image processing and computer vision tasks like image classification , object detection , and segmentation .

in particular , convolutional neural network architectures have enabled superior performance over alternative approaches in classification and pattern recognition problems in computer vision .

convolutional neural networks have witnessed great improvement on a series of vision tasks such as object classification .

deep neural networks have been significantly successful in many artificial intelligence tasks such as im- age classification .

cluster algebras were introduced by fomin and zelevinsky in in order to model the properties of the dual canonical basis of a quantized enveloping algebra and to study total positivity .

cluster algebras were introduced by fomin-zelevinsky in a series of four articles to give an algebraic framework for the study of total positivity and dual canonical bases in lie theory .

the peaks at even harmonics are suppressed due to the symmetry of the modulation .

harmonics at even multiples are suppressed because of the symmetry of the modulation .

birch , ac , braun , dc , hanasoge , sm , cameron , r .

scharmer , gb , gudiksen , bv , kiselman , d .

the hypercube is a popular interconnection network because of its structural properties .

in the hypercube there is a range of failure probabilities in which short paths exist with high probability , yet finding them must involve querying essentially the entire network .

this lagrangian is the one describing the electroweak symmetry breaking sector at scales well below the higgs mass and it has been thoroughly investigated using unitarization techniques in the past .

the lagrangian is a function of the field and it is this integral which we are concerned with .

in the last few years , convolutional neural networks have demonstrated outstanding performances in various applications including image recognition , object detection , and recently speech acoustic modeling .

in recent years , convolutional neural networks have been employed successfully for numerous applications in computer vision and robotics such as object detection and many others , often outperforming the conventional feature-based methods .

the study of truncated toeplitz operators has been largely motivated by a seminal paper of sarason .

truncated toeplitz operators were introduced by sarason , and have received much attention since then .

the three prominent resonance mass regions observed in the inclusive cross section are indicated by arrows , and labeled in the top plots .

the locations of the three prominent resonances observed in the unseparated cross section measurements are labeled at the top .

to mitigate overfitting , we apply both the dropout method and l2 weight regularization .

to mitigate overfitting , we apply the dropout method to regularize our model .

for this reason swas called the entropy exchange of the operation by shumacher .

for this reason swas called the entropy exchange of the operation φ by shumacher .

deep neural networks have powered many research areas from computer vision , natural language processing to biology and e-commerce .

deep neural networks have achieved outstanding performances on many computer vision tasks .

dwork et al consider a related kemeny optimization problem , where the goal is to determine the total ordering that minimizes the sum of the distances to different permutations .

dwork et al considered a related kemeny optimization problem , where the goal is to determine the total ordering that minimizes the sum of the distances to different permutations .

deep learning using convolutional neural networks has achieved excellent performance for a wide range of tasks , such as image recognition .

convolutional neural networks have seen tremendous success across different problems including image classification .

the most popular generative model approaches are generative adversarial networks , variational autoencoders .

currently , generative adversarial networks are among the most successful approaches in building such models .

zeitouni , random polynomials having few or no real zeros , e-print archive , math .

zelditch , random polynomials with prescribed newton polytope , i , e-print archive , math .

the upper left panel shows the column density radial profile of the halo .

the lower left panel shows the luminosity radial profile .

d eep learning approaches have achieved tremendous success in classification problems , etc .

recently , deep learning approaches have achieved tremendous success in classification problems , etc .

however , it is well-known that dnns are vulnerable to adversarial perturbations .

recent work , however , has demonstrated that dnns are vulnerable to adversarial perturbations .

the generalizedgradient approximation of perdew-burke-ernzerhof is employed as the exchange-correlation functional .

we use the generalized gradient approximation with the exchange-correlation functional of perdew , burke and ernzerhof .

we regularize each model by applying dropout to the output layer and penalizing the l2 norm of the parameters .

to make our baseline competitive , we apply several regularization techniques such as dropout in the output layer and within the lstm .

thus we have almost the theory described by eq .

this gives us precisely the matter content of the theory in eq .

in particular , similarly to the d-wave case , the crossover in the s-channel takes place only if the coupling is larger of some minimal value , otherwise the cooper pairing scenario takes place at any small carrier density .

in particular , the two-particle bound states in the s-channel exist only if the coupling constant is larger of the threshold value , similar to the d-wave pairing case .

deep convolutional neural networks have achieved significant success in a wide range of studies .

deep neural networks have exhibited great performance in computer vision tasks in recent years .

a relaxation that works particularly well for ldpc codes is given by the following approach .

a relaxation which works particularly well for ldpc codes is given by the following approach .

convolutional neural networks have achieved state-of-the-art results on several computer vision tasks .

convolutional neural networks have achieved state-of-the-art accuracy in computer vision tasks such as image recognition .

the centerpiece is a computer program called emili .

the centerpiece is the study of the singular values of partial fourier transforms .

recently , deep neural networks has achieved great success on computer vision .

recently deep neural networks have attained impressive performance in many fields such as image classification .

for posterior inference , we develop a variational expectation maximization algorithm to find a maximum a posteriori estimate of model parameters .

we use expectation-maximization to find the maximum a posteriori estimates of the unknown parameters of the model .

we present experimental results on the bounding box detection track of the challenging ms coco benchmark .

we explore the visual actions that are present in the recently collected ms coco image dataset .

the major motivation to investigate the backreaction on the geometry is that it is necessary for calculating the entanglement entropy using the ryu-takayanagi proposal .

the application of the ryu-takayanagi approach for the holographic entanglement entropy requires to include the effect of the backreaction of the ads 2 hypersurface on the ads 3 geometry .

previous works showed that certain network applications , like load-balancers , can work around eventual consistency and still deliver acceptable performance .

levin et al show in that distributed network functions such as load-balancers can work around eventual consistency and still deliver performance sufficient for production deployments .

both fits are equally good , illustrating that noise in the data is not affecting our inferred teff and log g .

both fits are equally good , showing that noise in the data is not affecting our derived parameters .

deep neural networks have significantly advanced the state-of-the-art performance for various machine learning problems .

deep convolutional neural networks have achieved significant successes in numerous computer vision tasks such as image classification .

kim et al tackled overfitting by using a deeply-recursive convolutional network .

kim et al propose a deep recursive convolutional neural network for image super-resolution .

the generalized gradient approximation of perdew , burke and ernzerhof was used to describe the exchangecorrelation potential .

generalized gradient approximation was used for the exchangecorrelation energy , within the perdew-burke-ernzerhof functional form .

by generalizing the classical atiyah-bott localization to the virtual localization , one can compute the equivariant gromov-witten invariants of an algebraic gkm manifold in terms of summing over gkm graphs .

if x admits a torus action and the fixed point set is compact , we can define torus-equivariant orbifold gromovwitten invariants using the atiyah-bott style localization on the moduli space of stable maps .

it was first shown in , that this semi-distance vanishes identically for the l 2 right-invariant metric on the diffeomorphisms group of any compact manifold .

it was shown in that the geodesic distance on the group diff c vanishes for the l 2 -metric and is positive for the h 1 -metric .

the matched filtering and oversampling procedure is similar to that in and described below .

the oversampling procedure is similar to that in and described below .

any other orbit consists of precisely one cofinality class .

consequently the orbit is a conic section .

a hilbert space is a hilbert module over c .

then the hilbert space is a product of n spaces c2 .

snow , on the ampleness of homogeneous vector bundles , trans .

snow , the nef value and defect of homogeneous line bundles , trans .

below this point the transition is of first order .

below these points the transitions are of first order .

in attentionbased encoder-decoder , the decoder part can be initialized by a lm trained from text data , which helps the model convergence and domain adaptation .

the decoder part of s2s can be initialized by a lm trained from text data , which helps the model convergence and domain adaptation .

when the system is spatially homogeneous , this can give rise to so-called generalized gibbs ensembles .

however , the nonthermal steady states of integrable systems can be described by a generalized gibbs ensemble .

tilted-optical-pulse front is fulfilled to obtain optimal thz beam characteristics and .

coincides with the tilted-optical-pulse front is fulfilled to obtain optimal thz beam .

here , dust extinction is a critical issue in estimating the cosmic sfr from the uv-luminosity density .

dust extinction as we commented above there is a strong bias in dust extinction estimates toward higher extinction values when nir data are used .

when the symmetry algebra is the standard , globally well-defined bms algebra , we show that the extension vanishes .

we assume that the symmetry algebra is the finite field analog of the de sitter algebra so and consider spinless irreducible representations it is shown that the finite field analog of complex of this algebra .

it is useful to compare the photon number and stress tensor approaches at this point .

this calculation shows the agreement between the photon number and the stress tensor approaches .

type ii string models with possible vanishing cosmological constant in perturbation theory were studied in refs .

type ii string models with vanishing perturbative contributions to the cosmological constant were studied in refs .

chronic kidney disease is a world-wide public health problem , which was responsible for approximately 956,200 deaths globally in 2013 .

chronic kidney disease is a widely common clinical condition which is responsible for approximately 956,200 deaths globally in 2013 .

yang et al identifies the scalability problem of cqa networks that we study here-namely , the volume of question content eventually subsumes the capacity of the answerers within the community .

notably , yang et al noted the scalability problem of cqa-namely , the volume of questions eventually subsumes the capacity of the answerers within the community .

instead , we adopt the framework of generative adversarial networks .

we train the network using the original generative adversarial network .

on sequential monte carlo sampling methods for bayesian filtering .

on particle methods for parameter estimation in state-space models .

recent advances in machine learning and data analytics have yielded transformative results across diverse scientific disciplines , including image recognition .

in recent years , deep neural networks have revolutionized machine-learning tasks such as image classification , speech recognition , and language translation .

for many of the issues pertaining to the phenomenology of these models , it will be beneficial to abandon the fermionic realization and to resort to the bosonic , or geometric description .

for many issues pertaining to the phenomenology of the relevant string vacua , it will prove beneficial to abandon the free fermionic realization and to resort to the bosonic , or geometrical description .

within a decade , performances on classical vision problems such as image classification have been largely improved by the use of deep learning frameworks .

over the last few years , deep learning has achieved impressive results on various visual understanding tasks , such as image classification .

deep neural networks have shown remarkable success in many computer vision tasks such as image classification .

convolutional neural networks , as one of the widely used deep learning methods , have been proven to be very successful for object recognition in images .

to accelerate convergence , we add batch normalization after each convolution and learn the scale and shift parameters during training .

to make the training stable , we also add batchnormalization layers before the relu and before the exponentiation .

by using of these bases , the quantum displacements can be realized .

by these bases , we can realize the quantum displacements , and discuss their possible forms .

kiros et al adopted an encoder-decoder framework coupled with a contrastive loss to train a joint visual-semantic embedding .

an encoder-decoder framework is presented by kiros et al utilizing a joint multimodal space in which the lstm is a big success .

such stochastic and irregular topology forms a so-called poissonvoronoi tessellation .

such stochastic and irregular topology forms a so-called poisson-voronoi tessellation .

chevalley , invariants of finite groups generated by reflections .

lusztig , irreducible representations of finite classical groups .