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mimo systems with only one on-beam are discussed in , where the performance analysis is derived by geometric arguments in the g n,1 .
mimo systems with only one on-beam are discussed in , where the beamforming codebook design criterion and performance analysis are derived by geometric arguments in the grassmann manifold g n,1 .
convolutional neural networks have recently achieved great success on various visual recognition tasks .
convolutional neural networks have achieved great success in many fields , such as object classification , face recognition .
succinct representation of balanced parentheses and static trees .
succinct representation of balanced parentheses , static trees and planar graphs .
deep neural networks have achieved significant success in a wide variety of challenging applications from object and face detection to speech processing , and from autonomous cars to medical image analysis .
deep convolutional neural networks have been successfully applied to a wide range of computer vision tasks , such as image classification , due to their powerful end-to-end learnable representations .
since the matrix v is symmetric and positive definite , we can employ a conjugate gradient method to solve the linear system .
in case the matrix a in is symmetric and positive definite , the preconditioned conjugate gradient method can be applied .
a nilmanifold is a homogeneous space 2 point 3 .
a compact quotient of a nilpotent lie group n by a discrete subgroup is called a nilmanifold .
for a comprehensive discussion of all these notions and more we refer to the monographs .
we refer to for more details and references regarding all these notions .
recently convolutional neural networks have performed very well on image classification tasks and are pervasive in machine learning and computer vision .
convolutional neural networks have recently been very successful on a variety of recognition and classification tasks .
the thereby formed three-flavor quark matter , composed of a mix of u , d and s quarks , is known as strange quark matter .
quark matter is a fermi gas of 3a quarks which , together , constitute a single color-singlet baryon with baryon number a .
the electron exchange correlation potential is treated with the generalized gradient of the perdew-bruke-ernzerhof functional .
the exchange-correlation functional was treated with the pbe parametrization of the generalized gradient approximation .
the greyscale is the hst f569w filter image , the red contours correspond to the diffuse x-ray component .
the greyscale is the 850 µm intensity , and is displayed to show the location of the filament .
it is mistaken to label cp2-avoidance solutions as being unsuitable for peer-to-peer co-editing .
therefore , it is incorrect to label cp2-avoidance solutions as being unsuitable for deployment in peer-2-peer co-editing environments .
it was shown in that this is an equality for sufficiently large t when the associated graded ring is cohen-macaulay .
brodmann eisenbud and huneke showed that equality holds , if the associated graded ring gr i is cohen-macaulay .
the exact values of the complexity are presently known only for a finite number of tabulated manifolds .
the exact values of the complexity are presently known only for certain infinite series of irreducible boundary irreducible 3-manifolds .
dmt is widely accepted as a useful performance analysis tool in cooperative systems .
dmt is accepted as a useful performance analysis tool in cooperative systems .
convolutional neural networks have recently advanced general object detection substantially .
deep learning and especially convolutional neural networks have revolutionized image-based tasks , eg , image classification .
this phase has also been reproduced numerically using lattice monte carlo simulations by dotera and gemma .
this phase has also been reproduced numerically using lattice monte carlo simulations .
dispersion of recovered properties of artificial clusters , assuming availability of ubih magnitudes and varying observational errors , as indicated in the legend .
dispersion of recovered properties of artificial clusters , assuming availability of ubih and various input metallicities , as indicated in the legend .
the horizontal bars show the statistical uncertainty and the full vertical lines show the total uncertainty on each point .
in each plot , the horizontal bars show the statistical uncertainty and the full vertical lines show the total uncertainty on each point .
this cohomology class is the first k-invariant of x 8 mihai d .
an equivalence class is called a cohomology class .
then , using a map between yang-mills multiplet and dilaton weyl multiplet , we obtain an off-shell riemann tensor squared invariant .
using a map between the yang-mills multiplet and the dilaton weyl multiplet , we reconstruct the off-shell supersymmetric riemann squared action .
all first-principles calculations were carried out using the vienna ab initio simulation package based on dft .
main dft calculations were performed using the projector augmented-wave method .
first of all , we describe the cornerstone notion of involutive division which , together with a monomial ordering , determines properties of an involutive basis .
for this purpose , we describe first the notion of an involutive division that , together with a monomial ordering , determines properties of an involutive basis .
in the next section , we discuss frw spaces and their application to cosmology in more detail .
in the next section , we discuss current observational constraints on the form of the inflationary potential .
these models can be trained on large corpora of text to predict a word from its context or vice versa .
such representations are trained by exploiting co-occurrences in words in large text corpuses .
some works fit a body surface model to images using substantial manual interaction typically for the task of image manipulation .
some works fit a body model to images using manual intervention with the goal of image manipulation .
deep neural networks have achieved recordbreaking accuracy in many image classification tasks .
convolutional neural networks have achieved impressive state-of-the-art results on image classification .
sandia is a multiprogram laboratory operated by sandia corp .
sandia is a multiprogram laboratory operated by sandia corporation , a lockheed martin company , for the us dept .
convolutional neural networks have achieved significant progress in computer vision tasks such as image classification .
convolutional neural networks have achieved superior performance in many visual tasks , such as object detection and segmentation .
van tuyl , splittable ideals and the resolutions of monomial ideals .
van tuyl , monomial ideals , edge ideals of hypergraphs , and their graded betti numbers .
what is highly nontrivial is the fact that this homeomorphism must be david .
this is highly non-trivial , since it is a measure of integration in an infinite-dimensional space .
in particular , the work focuses on universal , fixed-rate , lossless compression of individual sequences using finite-state encoders and decoders , which was then further developed to the well-known lempel-ziv algorithm .
in particular , the work focuses on universal , fixed-rate , lossless compression of individual sequences using finite-state encoders and decoders , which was then further developed to the famous lempel-ziv algorithm .
three pairs of helmholtz coils are used to compensate for the residual magnetic field .
the residual magnetic field is compensated by three pairs of helmholtz coils .
we give here a brief introduction in the notation of caccioppoli sets and functions of bounded variations , but for a detailed introduction we refer to .
we refer to the book for the definitions and the main properties of bv and sbv functions , sets of finite perimeter , and caccioppoli partitions .
recently , neural networks have achieved very impressive success on a wide range of fields like computer vision .
recently , deep neural networks have achieved impressive results for many image classification tasks .
polar codes proposed by arikan , are the first explicit construction of a family of codes that provably achieve the channel capacity for any binary-input , symmetric , memoryless channel .
polar codes , proposed by arikan , achieve the symmetric capacity of binary-input discrete memoryless channels under a low-complexity successive cancellation decoder .
selectivity of channels and proteins allows ions to carry specific signals .
selectivity of channels allows ions to provide energy for biological function .
in recent years , deep neural networks have achieved great success in a variety of machine learning tasks .
nowadays , deep neural networks , especially deep cnns , have shown explosive successes in both high-level vision problems .
we use the resnet101 implementation provided by the pytorch repository .
we implemented the proposed approach using the pytorch framework .
a famous example is the formation of topological defects , described by the kibble-zurek mechanism , when a system is driven across a continuous phase transition .
a prototypical example is the kz mechanism for the formation of topological defects when the temperature is slowly changed across a continuous transition , from the disordered to the ordered phase .
chen et al proposed a trainable nonlinear reaction diffusion model which learns a modified fields of experts image prior by unfolding a fixed number of gradient descent inference steps .
similar to csf , chen et al extended foe priors and proposed a trainable nonlinear reaction diffusion model for both image denoising and super-resolution .
l can be thought of as the multilayer , multi-output unit analogue of the loss function optimized by the single neuron model , where it stems directly from the particular chosen form of the learning rule .
l can be thought of as the multi-area , multioutput unit analogue of the loss function optimized by the single neuron model , where it stems directly from the particular chosen form of the learning rule .
deep learning or deep neural networks have achieved extraordinary performance in many application domains such as image classification .
since 2012 , neural networks and deep architectures have proven very effective in application areas such as computer vision .
in contrast to , we use the ilsvrc 2012 dataset which consists of 1000 object classes .
we evaluate our method on the imagenet 2012 classification dataset which has 1000 object classes .
we also demonstrate how these methods might be useful for other x-ray instruments .
the methods presented here could be generalized to other x-ray instruments as well .
these particles are expected to interact with the lxe via the axio-electric effect with coupling constant g ae , leading to electronic recoil signatures .
these are expected to interact with the lxe target via the axio-electric effect , leading to observable electronic recoil signatures .
yang et al presented a stacked attention network that refined the joint features by recursively attending question-related image regions , which leads to better qa accuracy .
for the same task , yang et al employed multiple stacked spatial attention models , in which the spatial attention map is successively refined .
deep neural networks have been widely used in many artificial intelligence applications including computer vision .
deep neural networks have transformed the machine learning field and are now widely used in many applications .
the asymmetry in the traffic in the two directions can lead to a significant degradation in the performance of xor in twr .
the asymmetry in the traffic in the two directions can lead to significant degradation in the performance of xor in twr .
deep convolutional neural networks have made significant breakthroughs in many visual understanding tasks including image classification .
deep learning based models have emerged as an extremely powerful framework to deal with different kinds of vision problems including image classification .
in each semester seminar are organized to check the performance of students .
like seminar , in each semester general proficiency tests are organized .
the torsion-free connection consistent with this metric is given by .
this form defines bi-invariant distance consistent with the killing metric .
deep neural networks are extremely important in various applications including computer vision , speech recognition , and natural language processing .
over the past few years , neural networks has been widely used in some domains , such as large vocabulary continuous speech recognition .
all other parameters are the same as those in case b .
the mc in this case is identical with that in case a .
this is why com has been developed as an extension of a novel approach to programming , called concept-oriented programming , savinov , 2005b , savinov , 2008a , savinov , 2009b .
it is also tightly integrated with a novel approach to programming , called concept-oriented programming , savinov , 2005c , savinov , 2008 , savinov , 2009b , savinov , 2012a .
atzei et al analyzed possible attacks on ethereum smart contracts with emphasis on the dao attacks .
in a similar context , atzei et al also explored various attacks limited to ethereum smart contracts .
the tt samples are normalized to the next-to-next-to-leading-order cross section calculation .
the cross sections used for normalization are computed at nlo or next-to-nlo .
the fidelity is then a measure of how well the selected state matches the original signal state selected by alice .
this fidelity is a direct consequence of the failure probability of the post-processing procedure .
conductance is a quantity defined in electrical networks as the inverse of resistance .
the conductance is the product of the soliton density by their mobility .
deep feedforward neural networks , with multiple hidden layers , have achieved remarkable performance across many domains .
convolutional neural networks have achieved great success in classification and detection on the imagenet .
in this situation , x is called a compactification of x over y .
this compactification is called warped compactification .
in this section , we give a brief review of stability conditions on derived category of coherent sheaves on a k3 surface , see for details .
in this section we recall from the basic theory of bridgeland stability conditions in the special case of a k3 surface .
deep convolutional neural networks have achieved significant success in a wide range of studies .
in recent years , deep convolutional neural networks have set the state-of-the-art on a broad range of computer vision tasks .
the invariant mass is calculated for an event by combining the four-momenta of the three jets .
the invariant mass is calculated for an event by combining the fourmomenta of the photon and the jet .
deep neural networks , have also been successfully applied in other fields , such as speech recognition .
deep neural networks are used in many recent applications such as image recognition .
the corresponding variety vp is called a wonderful compactification of g .
this approach is known as dynamical compactification .
thus we conclude that the observed quark masses seem to be consistent with the simple scaling laws .
we show that the u-type quark masses obey the scaling law very well .
the recent spectre attack has shown that this behavior can be exploited to expose information that is otherwise inaccessible .
the recent meltdown and spectre attacks have shown that this behavior can be exploited to expose information that is otherwise inaccessible .
similar network structures have been successfully applied to semantic segmentation tasks in medical imaging .
more precisely , cnns have excelled at semantic segmentation tasks in medical imaging , such as the em isbi 2012 dataset .
the supersymmetric solutions of this theory have been completely classified by solving killing spinor equations .
all supersymmetric solutions to this theory have been classified according to whether there exists a timelike or null killing vector .
the dashed line gives again the distribution corresponding to our open-diagrams mechanism .
the dashed line corresponds to the open-diagrams contribution .
the discrepancy is a consequence of the changes made to the collision step , as the exchange of momentum between the mpcd particles and the colloid , via vp , changes the effective number of local brownian collisions .
the discrepancy d is a special case of our definition , by letting p be an equally weighted measure on a sequence .
the first-principles density functional theory calculations in this work were carried out using the vienna ab-initio simulation package .
the atomistic first principles calculations were performed within the density functional theory framework as implemented in the vienna ab initio simulation package .
in the tables θi denotes a sum over the relevant size cycles containing i , extending our previous notation .
in the tables , si denotes sedimentation-type increment .
the 168er nucleus is a good candidate to test current fusion models description of deformation since it has a large quadrupole deformation with an insignificant hexadecapole deformation .
the nucleus is a complex entity , built of nucleons held together by the strong force .
the largest category of proteins represented in the hc class are those associated with various diseases .
the largest percentage of proteins in the hc class is from viral nucleocapsid protein families .
we compare the proposed method with 10 recent state-ofthe-art methods on aforementioned datasets , which include gmr .
we quantitatively and qualitatively compare the proposed approach with several comparison methods including gbvs .
as for 3d segmentation , huang et al propose 3d-fcnn which predict coarse voxel-level semantic label .
huang et al present a 3d-fcnn for 3d semantic segmentation which produces coarse voxellevel segmentation .
locally , the equilibrium is a sink , ie all eigenvalues of the limiting linear system are real and negative .
this equilibrium is a specific nash equilibrium of the strategic-form game .
while convolutional neural networks have achieved unprecedented performance when learning on structured data , their application to unstructured data such as point clouds is still fairly new .
in recent years , convolutional neural networks have shown excellent performance on classification problems when large-scale labeled datasets are available .
saturation is a natural mechanism to restore unitarity .
this saturation is the result of quanvalue is tum interference .
the weights are initialized using the standard truncated normal distribution , and a first order gradient-based technique is used for optimization .
the model parameters are optimized with a gradient-based stochastic adam optimizer by minimizing the loss function .
deep neural networks have been widely applied to various applications and achieved great successes .
deep neural networks have achieved great success in various tasks , including but not limited to image classification .
in particular , we focus on the convergence in the presence of inter-processor communication delays , which has been identi ed as an important problem in .
in particular , we focus on the convergence in the presence of inter-processor communication delays , which has been identified as a significant open problem in .
the profiles consist of a double exponential .
the profiles consist of a broad emission , a narrow emission , and a shortward absorption trough .
we extract features using a pre-trained residual network trained on this classification task .
we implement a naive baseline using a pre-trained resnet-101 network .
the atom is coupled , besides the cavity mode , to the photon field in the lateral direction .
the right mirror of the cavity is weakly transmissive , through which the cavity mode is coupled to the external photon field .
the dotted lines represent the initial temperature-density relation adopted in the models .
the initial igm temperature-density relations are represented by the dotted lines in all panels .
the architecture uses vgg-16 as the base network , removing the last fully connected layers , so that the network consists of convolutional , relu , and max-pooling layers .
the encoder part of the network is identical in architecture to vgg-16 , omitting the final pooling and fully connected layers .
problems of the same complexity as graph isomorphism are called isomorphism complete .
a coherent sheaf f is called reflexive if θ is an isomorphism .
the top 2 best accuracy was obtained by the methods of kwapisz et al , which are statistically equivalents .
the top 2 best accuracy was obtained by the methods of kwapisz et al , which are statistically equivalent .
in recent days , deep learning has been remarkably advanced and successfully applied in numerous fields .
in the past decade , the broad applications of deep learning techniques are the most inspiring advancements of machine learning .
however , high resolution follow-up observations are needed to investigate this .
these scenarios can be further tested with high-resolution observations .
deep convolutional neural networks have significantly improved the performance of computer vision systems .
convolutional neural networks have recently been applied to various computer vision tasks such as image classification .
a subset of machine learning known as deep learning has evolved as one of the most compelling and cutting-edge topics of research and has demonstrated tremendous increase in accuracy in the areas of image and speech recognition .
a branch of machine learning , called deep learning , has attracted worldwide interest in recent years due to its excellent performance in multiple areas including speech recognition , image classification and natural language processing .
the exchange correlation term was described using the gga functional proposed by perdew , burke , and ernzerhof .
the electron exchangecorrelation functional was treated using the generalized gradient approximation in the form proposed by perdew , burke , and ernzerhof .
the stable marriage problem is a well-known matching problem introduced by gale and shapley .
the stable marriage problem is a well-known problem that has been defined by gale and shapley in 1962 .
laser 2 is the reference laser on the d2 line .
this laser is a semiconductor diode device , operated with an extended cavity by use of a littrow-mounted diffraction grating .
deep neural networks have recently become a standard architecture due to their significant performance improvement over the traditional machine learning models in a number of fields , such as image recognition .
deep neural networks have become an extremely popular learning technique , with significant deployment in a wide variety of practical domains such as image classification .
recent generative models such as generative adversarial networks are capable of generating more realistic images .
more recent approaches use the generative adversarial network to align and transfer images from different domains .