sentence1
stringlengths
16
446
sentence2
stringlengths
14
436
we choose the standard residual network with 34 layers as our feature extractor φusing the last pooling layer as the face representation .
we use the 18-layer deep residual network as the encoder of deeplle after removing the first mean pooling layer , the global average pooling layer and the softmax layer .
the most promising results so far are based on conditional generative adversarial networks .
recent progress in generative adversarial networks shows promising results for learning data distributions .
its completion is the an tions on m with values in everywhere .
this completion is the goal of the lines below .
multiple-input multiple-output systems with largescale transmit antenna arrays , often called massive mimo , have been of great interest in recent years because of their potential to dramatically improve spectral efficiency of future wireless systems .
wireless base stations with large numbers of transmit antennas , which are also known as massive mimo systems , have attracted considerable research attention recently .
arnold , an interior penalty finite element method with discontinuous elements , siam j .
wheeler , an elliptic collocation-finite element method with interior penalties , siam j .
this qec method can be adopted to produce the resource state for measurement-based quantum computation .
the family of vbqc protocols are conveniently presented in the measurement-based quantum computation model .
we use expectation-maximization to find the maximum a posteriori estimates of the unknown parameters of the model .
for the parameter estimation of each gaussian component of each gmm , we use expectation maximization .
the dominant feature is a remarkable , sharp van hove singularity 65 mev below ef , which was traced to an extremely flat band around the m point of the brillouin zone .
one dominant feature is a maximum at about 1 7 mev , a second occurs at about 7 mev .
each network was trained with adam for 100 epochs , optimizing the mean squared error .
the network was trained to minimize the categorical cross-entropy using adam was used to prevent overfitting .
among them , the alexnet is the simplest , with 5 convolutional layer and 3 fully-connected layers .
most of the architectures follow the alexnet , which is an 8 layers network with 5 convolutional layers and 3 full-connected layers .
the random forest was built using the python scikit-learn package .
the data were downloaded using the scikit-learn python package .
an offset correction is applied to remove the additional energy included in the jets that come from pileup .
a jet area method is used to correct for the remaining pile-up contributions .
exchange-correlation energies are taken into account by the generalized gradient approximation using perdew-burke-ernzerhof functional .
to account for the exchange-correlation potential we have used the generalized gradient approximation as formulated by perdew , burke and ernzerhof .
neural networks have proved their efficiency in solving classification problems in areas such as computer vision .
deep convolutional neural networks have improved performance of many tasks in computer vision , such as image recognition .
it suffices to show that y is a connected graph .
now it suffices to show that if s is a direct factor of soc would be arbitrarily large , as required .
changes introduced to the radial emission pattern of the frequency-filtered maximum pulse amplitude for emission from the shower maximum by the automatic bin inactivation algorithm .
changes introduced by the smart sampling algorithm in the radial emission pattern of the rectanglefiltered maximum pulse amplitude for emission from the shower maximum .
on congruences and continued fractions for some classical combinatorial quantities .
on the representation of certain asymptotic series as convergent continued fractions .
batch normalization is used in each hidden layer in p and q .
a batch normalization layer is added after each convolution layer .
the generalized gradient approximation of perdew , burke and ernzerhof was employed for the exchange-correlation potential .
the perdew , burke and ernzerhof parametrisation of the generalised gradient approximation was employed to describe the exchange correlation function .
the quark phase consists of the three quark flavors u , d , s and electrons in equilibrium with respect to weak interactions .
we consider the quark phase consists of u , d , s quarks and electron , and the hadron phase proton , neutron and electron .
in view of this , we refer to the numbers fk , n as the k-generalized fibonacci numbers .
interestingly , many of these enumerations can be given in terms of k-generalized fibonacci numbers .
the belle detector is a large-solid-angle general purpose spectrometer that consists of a silicon vertex detector crystals located inside a superconducting solenoid coil that provides a 1 5 t magnetic field .
the belle detector is a large-solid-angle magnetic spectrometer that consists of a three layer silicon vertex detector , a barrel-like array of time-of-flight scintillation counters located inside a superconducting solenoidal coil that provides a 1 5 t magnetic field .
in addition , the instantaneous values and distribution information of secondary-secondary channel power gains is assumed to be available at the ctx .
in addition , the estimated instantaneous values and distribution information of the secondary-secondary channel power gains is assumed to be available at the ctx .
a family of methods restrict the output to a single object 3d model .
a first class of methods relies on a large set of reference 3d shapes for training .
perdew-burke-ernzerhof functional was used to treat the electronic exchange correlation .
the generalized gradient approximation was used to account for the exchange and correlations .
this construction is similar to that of , between noncommutative field theories and string field theories .
this propagator can be thought of as arising from loop corrections to a noncommutative scalar field theory in euclidean spacetime .
there are many interference methods to covert the oam modes into identifiable intensity patterns , such as holographic detection with plasmonic photodiodes 10 and diffraction patterns of various apertures .
various interference methods have been developed to convert oam modes into identifiable intensity patterns , such as holographic detection with plasmonic photodiodes 10 and the generation of diffraction patterns using various apertures .
due to the large bandwidth available , millimeter wave communication over the spectrum above 28ghz has emerged as a key enabling technology for the fifth-generation wireless systems .
millimeter wave communication is a promising technology for addressing the high throughput requirement for the fifth generation mobile networks .
we assume the reader is generally familiar with quantum computation , .
we assume the reader is familiar with the basics of quantum computation .
the solid line represents the dependence obtained by fitting according to eq .
the solid lines represent the dependencies obtained by fitting according to eq .
recent results have shown that generating these adversarial examples are inexpensive .
previous works have proved generating this kind of adversarial examples can be very cheap and effective .
this discrepancy is a significant fraction of the total range of ionization-energy dependent velocities seen in the orion nebula outflow .
this discrepancy is a result of the absence of nearby rich clusters in the south .
the gravitino is the lsp , all cascades will ultimately end in a gravitino .
the gravitino is the lightest supersymmetric particle whose mass ranges from the ev to the gev .
generically , yamabe metrics are unique in their conformal class .
thus , in general , uniqueness of yamabe metrics fails for positive conformal classes .
we use the coco dataset with 82,783 training and 40,504 validation images .
we use the mscoco 2014 dataset for training and present results on the coco validation set .
examples of problems for which convex relaxation are known include binary classification .
examples of problems for which tight convex relaxations are known include binary classification , sparse and low-rank approximation .
various systems can be represented using graph theory , for instance , internet networks , social networks , electrical circuits , and biochemical pathways .
complex networks appear in different categories such as social networks , citation networks , collaboration networks , and communication networks .
the proof proceeds by induction over the number of symbols in s .
the proof will be by induction over the number of symbols in s .
epresentation learning is to make the classifiers easier perform the given task by learning useful information and extracting essential features from the data .
r epresentation learning is to learn transformations of the data that makes it easier to extract useful information when building classifiers or other predictors .
we adapt nonconforming tensor product meshes using the p4est library .
we adapt nonconforming tensor product meshes using the third party p4est library .
more recently , convolutional neural networks have achieved unprecedented performance in a wide range of image classification problems .
deep learning methods have achieved impressive performance in object recognition and classification by using large networks trained with millions of data examples .
the exchange-correlation potential was calculated using the generalized gradient approximation as proposed by pedrew , burke , and ernzerhof .
the exchange-correlation functional was treated using the generalized gradient approximation parametrized by perdew , burke , and ernzerhof .
the coordinate ξ of the half disk is called the local coordinate .
the 0th coordinate is the time coordinate , which is related to the measured time but may be not identical to it .
therefore the power spectrum is the superposition of lorenzians weighted by the same gaussian function introduced to describe the polidispersity in the correlation analysis .
the power spectrum is the most important statistic that can be measured from large scale structure .
in the first group , generative adversarial networks provides an appropriate solution to model the data generation .
thus , we propose a denoising method by utilizing generative adversarial networks .
in recent years , deep learning techniques have achieved profound breakthroughs in many computer vision applications , including the classification of natural and medical images .
designing deeper and wider convolutional neural networks has led to significant breakthroughs in many machine learning tasks , such as image classification .
these numbers are generated by the so-called e str -polynomials and , as it was shown in , they are the right quantities to establish several mirror-symmetry identities for calabi-yau varieties .
these numbers are generated by the so-called e strpolynomials and , as it was shown in , they are the right quantities to establish several mirror-symmetry identities for calabi-yau varieties .
in , kontsevich proves the existence of a formal deformation quantization on any poisson manifold .
in 1997 , kontsevich proved the existence of deformation quantization of regular poisson manifolds .
convolutional neural networks have become the dominant approach for many computer vision tasks .
in recent years , convolutional neural networks has achieved remarkable results in a wide range of computer vision applications .
it is interesting to investigate the variations of the overall maximum amplification vs frequency for different values of damping .
the analysis of overall maximum amplification values is necessary to investigate the whole site effects versus frequency .
a pair of male and female wolves reproduce an offspring when they satisfy the rp term .
a pair of male and female sheep reproduce an offspring when they satisfy the rp term .
this sequence is called the orbit of z under h .
as the orbit of y is connected then so is the closure of such orbit which , by minimality , is all of y .
for tracking problems like optical flow a time-dependent velocity may be necessary .
for tracking problems like optical flow a non-stationary velocity may be necessary .
for implementations of these learners , we used the open source tool scikit-learn .
here , we make use of an extensive open source machine learning library in python called scikit-learn .
in a diversity analysis for cooperative fd noma systems was provided to prove that the use of the direct link overcomes the lack of diversity for the far user which otherwise serves as a limitation of fd relaying .
the authors in provided a diversity analysis for cooperative full-duplex noma systems and proved that the use of the direct link overcomes the lack of diversity for the noma-weak user which is otherwise inherent to the full-duplex relaying .
convolutional neural networks have recently achieved the state-of-the-art performance in many image analysis tasks .
deep convolutional neural networks have achieved huge success in solving problems related to computer vision , such as image classification .
secondly , it would be interesting to generalize the present work to the case of non-abelian groups and to develop a discrete version of non-abelian routh reduction .
secondly , it would be interesting to generalize the present work to the case of nonabelian groups and to develop a discrete version of nonabelian routh reduction , .
convolutional neural networks have achieved tremendous progress on many pattern recognition tasks , especially large-scale images recognition problems .
deep learning has brought significant breakthroughs in many computer vision tasks , including object detection .
from the numerics in , we expect that the value of the logarithmic negativity in this regime will again be constant in time , and lower than the value in regime ii .
from the numerical results in we expect the logarithmic negativity to saturate to a constant value in this limit .
a finite spin-foam-based theory of three and four dimensional quantum gravity .
anomaly-free formulation of non perturbative , lorentzian quantum gravity .
in this section , we briefly outline a link between variational autoencoders and information dropout .
in this section , we outline the connection between variational autoencoders and information dropout .
a detailed treatment of the approach to gr , adopted here can be found in the excellent review bymaluf .
a complete account of the various approaches to this subject can be found in refsand the references cited therein .
the other ingredient is a finite set of bs factors such that the sequences generated from them by the mapping cover all bs factors in a given fixed point .
another ingredient is the partial elimination ideal theory .
the higgs is the only particle in the standard model which has not yet been observed experimentally .
the endomorphism φ is called a higgs field .
a hilbert space is a hilbert module over c .
the projective hilbert space is is called the projective hilbert space .
the power spectrum is the sum of the squares of the sine and cosine fourier transforms , plotted in log-log as a function of the wavenumber , k .
the power spectrum is a two point statistic , which depends on the square of the bias .
this also identifies x with the focal surface of a congruence of lines s of bidegree .
let us see how it arises as a focal surface of a congruence of bidegree and hence it is isomorphic to a surface x h .
this is significantly smaller than the uncertainties associated with the current seismic measurements .
however , this increase remains significantly smaller than the uncertainties associated with current seismic measurements .
a heegaard splitting is called a heegaard diagram of m .
a heegaard diagram is a triple consisting of , and the points in β are points in σ which flow into the index two critical points .
this approach has since been broadly applied to study the conductance properties of molecules in stm .
this approach has been used to study various transport phenomena in nanostructures .
each convolutional layer is followed by batch norm , before using the leaky-relu activation function .
each convolutional layer is followed by batch normalization and relu .
recently , deep convolutional neural networks have led to substantial improvements for numerous computer vision tasks like object detection , often achieving human-level performance .
convolutional neural networks have achieved tremendous progress on many pattern recognition tasks , especially large-scale images recognition problems .
we implement the hcn model based on pytorch architecture as 2d cnn for image feature learning .
we choose faster r-cnn architecture as our two-stage object detector that provides accurate bounding boxes .
lattice qcd is the only fully non-perturbative framework at the present time to calculate the low energy hadronic observables from qcd .
lattice qcd is a powerful method to study the non-perturbative nature of qcd .
we employed the perdew-burke-ernzerhof exchange correlation functional in the generalized gradient approximation .
the exchange and correlation effects were taken into account within the generalized gradient approximation .
for any connected set of zeros , we can define the index .
we can define an index for every isolated set of zeros .
the generalized gradient approximation was used in conjunction with the perdew , burke , and ernzerhof density functional .
the exchange-correlation energy was described by the revised perdew-burke-ernzerhof exchange functional .
the ordinate is the integrated time during which the detector exchange threshold has been lower than ht .
the ordinate is the number of the clusters .
recently , generative adversarial networks have been introduced to overcome precisely this limitation .
optimizing this directly is hard , but generative adversarial networks in theory should also optimize this objective .
deep learning algorithms have recently been applied to many tasks in computer vision and achieved state-of-the-art results , for instance , in object recognition .
recently , deep convolutional neural networks have taken the computer vision field by storm , significantly improving the state-of-the-art performances in many visual tasks , such as face recognition .
to reduce the high cost of memory and computation during search , tree-based acceleration structures of memory are proposed .
to reduce the runtime and improve memory efficiency , tree-structure based search are proposed .
among them , bert , achieved new state-of-the-art results on various nlp tasks including question answering .
in particular , bert achieved stateof-the-art results when performing various tasks including the single-turn machine comprehension dataset squad .
deep convolutional neural networks achieve impressive performance on many computer vision tasks , including image classification .
since 2012 , neural networks and deep architectures have proven very effective in application areas such as computer vision .
we previously applied gated cnn architectures for voice conversion , and their effectiveness has already been confirmed .
we previously employed gated cnn architectures for voice conversion , and their effectiveness has already been confirmed .
the photon selected is the most energetic one in the events .
the photon is the state orthogonal to the neutral electroweak boson zµ .
cosmic strings are linear topological defects generated in symmetry breaking phase transitions in the early universe due to the kibble mechanism .
cosmic strings are line-like topological defects which m a y form during phase transitions in the early universe .
these approaches generally rely on the use of generative adversarial networks .
for that purpose , we utilize the idea of generative adversarial networks .
since the torsion is a left module map , it follows that the torsion vanishes entirely .
for any g-structure , that part of the torsion that is left unchanged under all possible changes of pseudoconnection is known as the intrinsic torsion .
the recent interest in these theories is motivated by works that establish a connection between noncommutative geometry and string theory .
recently , there has been much interest in noncommutative field theory motivated by the string theory .
the relationship between egocentric and top-view videos has been explored in tasks such as human identification .
the relationship between egocentric and top-view information has been explored in tasks such as human identification .
the first known example is the 16 dimensional tori constructed by milnor in 1964 14 .
the first known example is the 16 dimensional tori constructed by milnor in 1964 13 .
the quartic couplings involving two charged and two neutral higgs fields are given by eq .
the quadrilinear couplings among four neutral higgs fields are given by eq .
moreover , it was both noted in that when supp ν is a uniqueness set for all the entire functions on c d then k is universal .
from this observation , it was immediately concluded in that if supp ν is a uniqueness set for all the entire functions on c d then k is universal .
we evaluate our proposed approach on the benchmark datasets of sentiment and emotion analysis , namely cmu multi-modal opinion sentiment and emotion intensity dataset .
we train our model on cmu multimodal opinion sentiment and emotion intensity dataset which is available on cmu multimodal data sdk .
the vacuum is a solution of the field equations .
a vector with such a property is called a vacuum .
models based on convolutional neural networks and recurrent neural networks have achieved remarkable performance in many tasks , such as image classification .
supervised training of deep convolution networks is clearly highly effective for image classification , as shown by results on imagenet .
electronic structure calculations were performed in vasp , with projector augmented-wave pseudopotentials .
the calculations of atomic geometry and band structure were performed based on the dft approach , as implemented in the vasp code .
and 7to results for different collision systems at sps energies .
and 7to results for different collision systems at rhic 5 energies .
large-scale deep convolutional neural networks have been successfully applied to a wide variety of applications such as image classification .
convolution-based deep neural networks have performed exceedingly well on 2d representation learning tasks .
some supersymmetric solutions of the six-dimensional yang-mills theory on calabi-yau threefold were obtained in .
some supersymmetric solutions of six-dimensional yang-mills theory on calabi-yau threefold were obtained in .