HHousen/ParaSCI · Datasets at Hugging Face

sentence1 stringlengths 16 446	sentence2 stringlengths 14 436
convolutional neural networks have surpassed many traditional machine learning approaches in solving several computer vision tasks such as classification and others .	recently , supervised deep learning approaches such as large-scale deep convolutional neural networks have been immensely successful in many high-level computer vision tasks such as image recognition .
power , temperature and em emissions from a computer are an important set of physical side channels , and have been used by many attackers to identify sensitive data .	power , temperature , and em emissions from a computer are a set of physical side channels that have been used by many attackers to compromise security .
the thin horizontal line is the area of the minimum number of connected pixels needed to detect an object .	the thin horizontal line is the high temperature limit .
we confirm that this is the case based on the hβ emission line image .	we can confirm that this is the case by checking boundary conditions .
neural word embeddings , as popularized by the word2vec algorithm , are a way to represent words in a low-dimensional space without requiring outside supervision .	word embeddings are an increasingly popular and important technique that turns words into high dimensional vectors .
in the standard model , electroweak symmetry breaking is achieved by introducing a complex scalar doublet , leading to the prediction of the higgs boson .	in the standard model , the masses of fermions are generated by their yukawa coupling to the higgs field , whose existence was confirmed by the higgs boson discovery .
we used the medical subfigure classification dataset from the image conference and labs of the evaluation forum 2016 competition .	we used the medical subfigure classification dataset used in the imageclef 2016 competition .
the top row contains the simulations without the effects of cosmic variance .	the second row contains simulations with all of the attenuation factors , including cosmic variance .
all the network weights are initialized using glorot initialization .	the colorization network is initialized with xavier initialization .
batch normalization is added after each convolution layer to accelerate convergence .	filters , relus , batch normalization and dropout are used at each hidden layer .
in this paper , we focus on a variant of the rcp dynamical system which was introduced in .	in this paper , we focus on the proportionally fair variant of rcp which was introduced in .
all calculations were performed within density functional theory as implemented in the vienna ab initio simulation package .	all the calculations were performed using vienna ab initio simulation package , which is based on the density functional theory .
more recently , convolutional network architectures have evolved to classify much more complex images .	recently , convolutional neural networks have been effectively applied to image classification .
since string theory is a proposed fundamental theory of quantum gravity , one may expect that stringy corrections to the low energy effective theory resolve causality violations .	string theory is a rather radical generalization of quantum field theory whereby the fundamental objects are extended , one-dimensional lines or loops in nature , if we are very far away from it we will be able to see its point-like oscillations , and hence measure the elementary particles that it produces .
in , sahai and waters introduced attribute-based encryption to achieve fine-grained encrypted access control .	in , sahai and waters proposed the attribute-based encryption to realize access control on encrypted data .
in section 3 , for the most general class of ads 5 solutions of there is a consistent kaluza-klein truncation to minimal gauged supergravity in five dimensions .	in this section we show that for the most general class of supersymmetric ads 5 solutions of there is a consistent truncation to minimal five-dimensional gauged supergravity .
convolutional neural networks have demonstrated their promising performance on various real-world applications .	recently , neural networks have achieved very impressive success on a wide range of fields like computer vision .
in addition , the weak additivity conjecture was confirmed for generalized dephasing channels .	in addition , the weak additivity conjecture was confirmed for degradable channels .
however , tanay and griffin demonstrated image classes that do not suffer from adversarial examples for linear classifier , which is not in-line with the linearity hypothesis .	however , tanay and griffin later constructed the image classes that do not suffer from the adversarial examples for the linear classifiers .
karpathy et al suggest cnns for large scale video classification using a dataset of 1 million youtube videos , spanning 487 classes .	karpathy et al provided an extensive empirical evaluation of cnns on large-scale video classification .
local modulations of chiral symmetry transformations a .	non-local modulations of symmetry transformations a .
focusing on the distribution relation of the eisenstein-kronecker series , we then prove the p-adic analogues of the kronecker limit formulas .	we then prove p-adic analogues of the first and second kronecker limit formulas by using the distribution relation of the kronecker theta function .
an intriguing issue is the possibility to gain an insight into the existence of a finite t0 by means of measurements of the violation of the fluctuation-dissipation relation in off 11 equilibrium experiments .	an intriguing issue is the presence of helium in our atmosphere and in particular its isotope3he .
convolutional neural networks have delivered state of the art performance on highly challenging tasks such as speech and image recognition .	convolutional neural networks have successfully tackled classic computer vision problems such as image classification , where the input image has a grid-like structure .
using l1-norm regularization has the additional benefit of keeping the solution sparse .	as reported in , the sparsity induced by l 1 -norm optimization can help to deal with the noise in the data to some extent .
the twist technique is applied to two quarks running from the source to the current and from the current to the sink .	the twist technique is applied to the quark running from the source to the current .
in recent years , neural networks have been effectively applied in various problems such as voice recognition .	deep neural networks have shown tremendous success in several computer vision tasks in recent years .
for this purpose , we use celeba dataset which includes 40 binary attributes of the human face .	we apply celeba dataset that has 202,599 face images with 40 binary attributes .
the hierarchical bayesian optimization algorithm evolves a population of candidate solutions to the given problem .	the hierarchical bayesian optimization algorithm evolves a population of candidate solutions .
we can adress this because the partition function of the theory on s 3 can be exactly computed by the localization technique .	here z 3d is the supersymmetric partition function of the dimensionally reduced theory on s 3 , which in favorable cases can be computed by localization .
deep convolutional neural networks have been used to achieve state-of-the-art performances in many supervised computer vision tasks such as image classification .	deep learning has been used as a dramatically powerful tool in computer vision tasks such as image recognition .
we now illustrate this by the following example .	this would be made explicit by the following example .
in another direction , it was showed by binev , dahmen and devore for the first time that afem for poisson equation in the plane has optimal computational complexity by using a critical coarsening step .	in another direction , it was shown by binev , dahmen and devore for the first time that afem for poisson equation in the plane has optimal computational complexity by using a special coarsening step .
generalized gradient approximation with the perdew , burke and ernzerhof exchange correlation functional is used for estimating the exchange correlation energy .	the perdew-burke-ernzerhof generalized gradient approximation was used as the exchange-correlation functional .
at present , deep neural networks have been applied to detect salient objects .	recently , deep neural networks has been adopted to saliency detection .
after the pivotal work of krizhevsky et al , deep convolutional neural networks quickly became the dominant tool in computer vision , establishing new state-ofthe-art results for a large variety of tasks , such as human pose estimation .	ever since the alexnet by krizhevsky et al succeeded in the 2012 imagenet competition , convolutional neural networks have become a popular tool for computer vision tasks , as they perform exceedingly well in a variety of settings .
we also used the extended stochastic gradient descent adam algorithm to optimize the loss function .	we used stochastic gradient descent with adam updates for optimization .
deep neural networks perform impressively well in classic machine learning areas such as image classification , segmentation , speech recognition and language translation .	deep neural networks have demonstrated success in many machine learning tasks , including image recognition , speech recognition , and even modelling mathematical learning , among many other domains .
the bosonic sector of this theory with a simple vector field is called emad gravity .	gravity is the only physics in our simulation , meaning we can in principle interpret the results at different mass scales by a corresponding scaling of time and length .
deep neural networks have exhibited great performance in computer vision tasks in recent years .	deep convolutional neural networks have made great progress in visual recognition challenges such as object classification .
we use a neural encoder-decoder architecture with a hard attention mechanism .	we use a neural sequence-to-sequence architecture with a hard attention mechanism .
a linear time algorithm for embedding graphs in an arbitrary surface .	a fixed-parameter algorithm for the directed feedback vertex set problem .
one of the quasiparticles in qcd is the constituent quark .	the quasi-particles consist of states belonging to the γ and the m pocket .
for the backbone network , we use resnet50 as the building foundation for feature map extraction .	for imagenet , we use a resnet-50 architecture using the code from the tensorpack repository .
due to this reason , there is an interest to exploit the underutilized millimeter wave frequencies for cellular applications .	due to this fact , there is an interest to exploit the underutilized millimeter wave frequencies for cellular applications .
most recently , the field of deep learning or deep neural networks has advanced rapidly leading to breakthroughs in both high-level and low-level vision problems .	recent development of deep convolutional neural networks has led to great success in a variety of tasks including image classfication and others .
after this brief survey of some specific examples , let us make some general statements .	finally , let us make some comments on the coherent states themselves .
deep convolutional neural networks have achieved dramatic accuracy improvements in many areas of computer vision .	convolutional neural networks have achieved significant progress in computer vision tasks such as image classification .
in the last section we have defined the concept of bisets .	we in this chapter introduce the concept of binear-rings and smarandache binear-rings .
that is , the moving platform can admit several positions and orientations in the workspace for one given set of input joint values .	that is , the mobile platform can admit several positions and orientations in the workspace for one given set of input joint values .
this asymmetry is a pure direct-cp-violating observable .	perhaps the cause for this asymmetry is the fact that time evolution is often irreversible .
a maximal collection of pairwise disjoint curves in sm is called a pants decomposition .	a pants decomposition of s is a maximal collection of distinct isotopy classes of pairwise disjoint essential simple closed curves on s .
deep convolutional neural networks have recently shown immense success for various image recognition tasks , such as object recognition .	deep neural networks have achieved recordbreaking accuracy in many image classification tasks .
the single particle level diagrams are computed within the woods-saxon superasymmetric two-center shell model .	the single particle level schemes are obtained within the two center woods-saxon shell model .
then we apply the multidimensional edgeworth expansion method and investigate weakly nonlinear evolution of the genus statistics and the area statistics .	then we discuss weakly nonlinear effects of the genus and the area statistics using the multidimensional edgeworth expansion method explored by matsubara .
for this reason , we only consider this model with the effect of relativistic blurring .	we considered this model with and without incorporating the effects of relativistic blurring on the overall spectrum .
we evaluate our approach on the flickr30k entities dataset which contains 31 , 783 images , each annotated with five sentences .	we use the standard flickr30k entities dataset for evaluating our proposed approach .
at the end of 2015 , they proposed a deep architecture of 152 layers , called residual network which was built on top their previous findings and achieved state of the art on imagenet previously held by themselves .	at the end of 2015 , they proposed a deep architecture of 152 layers , called residual network which was built on top their previous findings and achieved state of the art on imagenet previously held by themselves .
again s duality is a symmetry of iib theory with symmetry group sl .	the duality is a tool that untangles the mutual connection , the mutual changeability and the transitions between different objects .
duality of this kind is known as 3d mirror symmetry .	the pairs of manifolds satisfying this symmetry are known as mirror pairs , and this duality is also called mirror symmetry .
deep learning techniques have shown promising performance in many applications such as object detection , natural language process , and synthetic images generation .	deep neural networks have proved astoundingly effective at a wide range of empirical tasks , from image classification to playing go , and even modeling human learning .
deep neural networks have achieved a great success on many tasks such as image classification when a large set of labeled examples are available .	large-scale deep convolutional neural networks have been successfully applied to a wide variety of applications such as image classification .
to enhance the convergence of the derived power series , references suggest the use of pade approximants or continued fractions , which , however , are not optimized .	to enhance the convergence of the derived power series , references suggest the use of pade approximants or continued fractions , whose orders are not optimized yet .
before , evaporation is the dominant process due to the initial collapse and heating of the diffuse gas .	if evaporation is the dominant process and the rate is larger than the bondi accretion rate in the galactic center , the disk will be depleted within a certain time and no persistent disk will exist .
to repeat our experiments , we have made all source codes and data sets publicly available .	to repeat our experiments , we have made all source codes and data sets publicly available .
we use an implementation of faster r-cnn with a vgg-16 as its backbone .	we instead use a network based on vgg-16 network as base architecture .
recent development of deep neural networks has driven the remarkable improvements in semantic segmentation .	recently , tremendous advances in semantic segmentation have been made relying on deep convolutional neural networks .
the energy spectrum is a smooth function of k and one level at the band edge coupling .	namely , the energy spectrum consists of 6m bands in this case .
machine learning models based on deep neural networks have achieved unprecedented performance across a wide range of tasks .	deep neural networks have recently achieved great success in computer vision , speech recognition , and natural language processing .
liu pointed out that the commonly used conflict coefficient in evidence theory is not reasonable to represent the conflict degree between two pieces of evidence .	in , liu noted that the classical conflict coefficient k can not effectively measure the degree of conflict between two bodies of evidence .
topology , lattices , and logic programming .	topology and the semantics of logic programs .
neural networks have made remarkable progress in achieving encouraging results in digital image processing .	convolutional neural networks have led to leap-forward in a large number of computer vision applications .
the exchange-correlation potential was calculated using the generalized gradient approximation as proposed by pedrew , burke , and ernzerhof .	the projector-augmented wave method was employed in this study , and for the exchange-correlation potential , the generalized gradient approximation with the perdew , burke , and ernzerhof functional was used .
we minimize the cross-entropy loss using adam optimizer with a fixed learning rate of 1e-5 .	we use the adam gradient optimization method with the categorical cross-entropy loss function .
calculation of the influence functional we may now calculate the influence functional with the semiclassical approximation for system variables inserted .	the semiclassical approximation to the decoherence functional and probabilities we may now compute the decoherence functional .
our acoustic model is a convolutional neural network , with gated linear units .	we employ a continuous dynamical system that resembles a convolution neural network when discretized .
for that we denote by finrings the category of finite commutative rings .	we denote by abfin the category of finite abelian groups .
a phrase-based alignment model for natural language inference .	a machine-learning approach to textual entailment recognition .
deep convolutional neural networks have enabled unparalleled breakthroughs in a variety of visual tasks , such as image classification .	convolutional neural networks have achieved impressive state-of-the-art results on image classification .
generation and control of greenberger-horne-zeilinger entanglement in superconducting circuits .	theory of the bloch-wave oscillations in small josephson junctions .
the last column shows the fraction of free planets around the mbh .	the third column shows the rate of hvp production .
the radius of gyration is a purely geometric quantity .	radius of gyration is a parameter frequently used to measure the global compactness of a conformation .
the best upper bound on the chromatic number of k t -minor-free graphs is oindependently due to kostochka .	in general , kostochka independently proved that every k t -minor-free graph is o-degnerate .
cognitive radio is a promising technology offering enhanced spectrum efficiency via dynamic spectrum access .	cognitive radio presents a promising approach to implement intelligent wireless communications systems .
an isomorphism is a bijective homomorphism .	a g-action on x is called smart if 0 is an isomorphism .
gu and je shapiro , kernels of hankel operators and hyponormality of toeplitz operators , math .	hwang and wy lee , hyponormality of toeplitz operators with rational symbols , math .
we evaluate the renormalization of these couplings at one loop using lattice perturbation theory .	we proceed to calculate these divergences at one loop using lattice perturbation theory .
the exchangecorrelation energy functional is described with the spin-polarized generalized gradient approximation as parameterized by perdew-burke-ernzerhof .	the exchange-correlation interactions are treated by the generalized gradient approximation formulated by perdew , burke , and ernzerhof .
in particular , we use the k-means algorithm and interpret the resulting centers of clusters as the representative values .	we cluster the cns using the k-mean clustering algorithm , which needs an input parameter to indicate the number of expected clusters .
ss wishes to acknowledge financial support from the project influs .	sc wishes to acknowledge financial support from the erg eu grant and cometa .
up to the jacobian of the relevant coordinate transformation , that mode agrees with the tachyon of .	in fact , up to the jacobian of the relevant transformation , that mode agrees with the result of .
newman , robustness of community structure in networks , phys .	newman , community structure in directed networks , phys .
the number of available vms at the cloud server may change over time as some of them might become unavailable due to failure or malicious attacks .	the availability of remaining vms at the cloud server may change over time as some of them might become unavailable due to failure or malicious attacks .
kontsevich gave an explicit formula for the deformation quantization of a poisson structure on r n in terms of a formal power series and established the global existence on arbitrary poisson manifolds using formal geometry .	kontsevich gave the proof of existence and classification of star products on an arbitrary poisson manifolds as a consequence of his formality theorem .
we will also show that the vortex solitons with small vortex components are not only linearly but also nonlinearly stable .	we also prove that both vortex and dipole vector solitons are linearly stable in the neighborhood of the bifurcation point .
it is important to note that none of the cnn-based methods is able to match the precision of active search , especially when computing the orientation of the camera .	it is important to note that we can still not match the precision of state-ofthe-art sift-based methods , especially when computing the orientation of the camera .
this can be a problem , since in the internet the ip address usually identifies the sender of a message .	this is a problem , since in the internet the network address usually identifies the sender of a message .
finally , has theoretically shown the optimality of frequency flat precoding by proving that dominant subspaces of the frequency domain channel matrices of different subcarriers are equivalent .	besides , by proving the dominant subspaces of frequency domain channel matrices at different subcarriers are equivalent , has theoretically revealed the optimality of the frequency-flat precoding .
algorithms for encoding needed states in one to four blocks of c e require at most c 1 n 2 gates for some constant c 1 .	algorithms for encoding states in one or two blocks of c e require at most c 1 n 2 gates for some constant c 1 .
however , it is important to stress that is the only chaotic jerk system containing nonlinearity depending only on x .	it is important to stress that is the only chaotic jerk system containing nonlinearity depending only on x .

convolutional neural networks have surpassed many traditional machine learning approaches in solving several computer vision tasks such as classification and others .

recently , supervised deep learning approaches such as large-scale deep convolutional neural networks have been immensely successful in many high-level computer vision tasks such as image recognition .

power , temperature and em emissions from a computer are an important set of physical side channels , and have been used by many attackers to identify sensitive data .

power , temperature , and em emissions from a computer are a set of physical side channels that have been used by many attackers to compromise security .

the thin horizontal line is the area of the minimum number of connected pixels needed to detect an object .

the thin horizontal line is the high temperature limit .

we confirm that this is the case based on the hβ emission line image .

we can confirm that this is the case by checking boundary conditions .

neural word embeddings , as popularized by the word2vec algorithm , are a way to represent words in a low-dimensional space without requiring outside supervision .

word embeddings are an increasingly popular and important technique that turns words into high dimensional vectors .

in the standard model , electroweak symmetry breaking is achieved by introducing a complex scalar doublet , leading to the prediction of the higgs boson .

in the standard model , the masses of fermions are generated by their yukawa coupling to the higgs field , whose existence was confirmed by the higgs boson discovery .

we used the medical subfigure classification dataset from the image conference and labs of the evaluation forum 2016 competition .

we used the medical subfigure classification dataset used in the imageclef 2016 competition .

the top row contains the simulations without the effects of cosmic variance .

the second row contains simulations with all of the attenuation factors , including cosmic variance .

all the network weights are initialized using glorot initialization .

the colorization network is initialized with xavier initialization .

batch normalization is added after each convolution layer to accelerate convergence .

filters , relus , batch normalization and dropout are used at each hidden layer .

in this paper , we focus on a variant of the rcp dynamical system which was introduced in .

in this paper , we focus on the proportionally fair variant of rcp which was introduced in .

all calculations were performed within density functional theory as implemented in the vienna ab initio simulation package .

all the calculations were performed using vienna ab initio simulation package , which is based on the density functional theory .

more recently , convolutional network architectures have evolved to classify much more complex images .

recently , convolutional neural networks have been effectively applied to image classification .

since string theory is a proposed fundamental theory of quantum gravity , one may expect that stringy corrections to the low energy effective theory resolve causality violations .

string theory is a rather radical generalization of quantum field theory whereby the fundamental objects are extended , one-dimensional lines or loops in nature , if we are very far away from it we will be able to see its point-like oscillations , and hence measure the elementary particles that it produces .

in , sahai and waters introduced attribute-based encryption to achieve fine-grained encrypted access control .

in , sahai and waters proposed the attribute-based encryption to realize access control on encrypted data .

in section 3 , for the most general class of ads 5 solutions of there is a consistent kaluza-klein truncation to minimal gauged supergravity in five dimensions .

in this section we show that for the most general class of supersymmetric ads 5 solutions of there is a consistent truncation to minimal five-dimensional gauged supergravity .

convolutional neural networks have demonstrated their promising performance on various real-world applications .

recently , neural networks have achieved very impressive success on a wide range of fields like computer vision .

in addition , the weak additivity conjecture was confirmed for generalized dephasing channels .

in addition , the weak additivity conjecture was confirmed for degradable channels .

however , tanay and griffin demonstrated image classes that do not suffer from adversarial examples for linear classifier , which is not in-line with the linearity hypothesis .

however , tanay and griffin later constructed the image classes that do not suffer from the adversarial examples for the linear classifiers .

karpathy et al suggest cnns for large scale video classification using a dataset of 1 million youtube videos , spanning 487 classes .

karpathy et al provided an extensive empirical evaluation of cnns on large-scale video classification .

local modulations of chiral symmetry transformations a .

non-local modulations of symmetry transformations a .

focusing on the distribution relation of the eisenstein-kronecker series , we then prove the p-adic analogues of the kronecker limit formulas .

we then prove p-adic analogues of the first and second kronecker limit formulas by using the distribution relation of the kronecker theta function .

an intriguing issue is the possibility to gain an insight into the existence of a finite t0 by means of measurements of the violation of the fluctuation-dissipation relation in off 11 equilibrium experiments .

an intriguing issue is the presence of helium in our atmosphere and in particular its isotope3he .

convolutional neural networks have delivered state of the art performance on highly challenging tasks such as speech and image recognition .

convolutional neural networks have successfully tackled classic computer vision problems such as image classification , where the input image has a grid-like structure .

using l1-norm regularization has the additional benefit of keeping the solution sparse .

as reported in , the sparsity induced by l 1 -norm optimization can help to deal with the noise in the data to some extent .

the twist technique is applied to two quarks running from the source to the current and from the current to the sink .

the twist technique is applied to the quark running from the source to the current .

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

deep neural networks have shown tremendous success in several computer vision tasks in recent years .

for this purpose , we use celeba dataset which includes 40 binary attributes of the human face .

we apply celeba dataset that has 202,599 face images with 40 binary attributes .

the hierarchical bayesian optimization algorithm evolves a population of candidate solutions to the given problem .

the hierarchical bayesian optimization algorithm evolves a population of candidate solutions .

we can adress this because the partition function of the theory on s 3 can be exactly computed by the localization technique .

here z 3d is the supersymmetric partition function of the dimensionally reduced theory on s 3 , which in favorable cases can be computed by localization .

deep convolutional neural networks have been used to achieve state-of-the-art performances in many supervised computer vision tasks such as image classification .

deep learning has been used as a dramatically powerful tool in computer vision tasks such as image recognition .

we now illustrate this by the following example .

this would be made explicit by the following example .

in another direction , it was showed by binev , dahmen and devore for the first time that afem for poisson equation in the plane has optimal computational complexity by using a critical coarsening step .

in another direction , it was shown by binev , dahmen and devore for the first time that afem for poisson equation in the plane has optimal computational complexity by using a special coarsening step .

generalized gradient approximation with the perdew , burke and ernzerhof exchange correlation functional is used for estimating the exchange correlation energy .

the perdew-burke-ernzerhof generalized gradient approximation was used as the exchange-correlation functional .

at present , deep neural networks have been applied to detect salient objects .

recently , deep neural networks has been adopted to saliency detection .

after the pivotal work of krizhevsky et al , deep convolutional neural networks quickly became the dominant tool in computer vision , establishing new state-ofthe-art results for a large variety of tasks , such as human pose estimation .

ever since the alexnet by krizhevsky et al succeeded in the 2012 imagenet competition , convolutional neural networks have become a popular tool for computer vision tasks , as they perform exceedingly well in a variety of settings .

we also used the extended stochastic gradient descent adam algorithm to optimize the loss function .

we used stochastic gradient descent with adam updates for optimization .

deep neural networks perform impressively well in classic machine learning areas such as image classification , segmentation , speech recognition and language translation .

deep neural networks have demonstrated success in many machine learning tasks , including image recognition , speech recognition , and even modelling mathematical learning , among many other domains .

the bosonic sector of this theory with a simple vector field is called emad gravity .

gravity is the only physics in our simulation , meaning we can in principle interpret the results at different mass scales by a corresponding scaling of time and length .

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

deep convolutional neural networks have made great progress in visual recognition challenges such as object classification .

we use a neural encoder-decoder architecture with a hard attention mechanism .

we use a neural sequence-to-sequence architecture with a hard attention mechanism .

a linear time algorithm for embedding graphs in an arbitrary surface .

a fixed-parameter algorithm for the directed feedback vertex set problem .

one of the quasiparticles in qcd is the constituent quark .

the quasi-particles consist of states belonging to the γ and the m pocket .

for the backbone network , we use resnet50 as the building foundation for feature map extraction .

for imagenet , we use a resnet-50 architecture using the code from the tensorpack repository .

due to this reason , there is an interest to exploit the underutilized millimeter wave frequencies for cellular applications .

due to this fact , there is an interest to exploit the underutilized millimeter wave frequencies for cellular applications .

most recently , the field of deep learning or deep neural networks has advanced rapidly leading to breakthroughs in both high-level and low-level vision problems .

recent development of deep convolutional neural networks has led to great success in a variety of tasks including image classfication and others .

after this brief survey of some specific examples , let us make some general statements .

finally , let us make some comments on the coherent states themselves .

deep convolutional neural networks have achieved dramatic accuracy improvements in many areas of computer vision .

convolutional neural networks have achieved significant progress in computer vision tasks such as image classification .

in the last section we have defined the concept of bisets .

we in this chapter introduce the concept of binear-rings and smarandache binear-rings .

that is , the moving platform can admit several positions and orientations in the workspace for one given set of input joint values .

that is , the mobile platform can admit several positions and orientations in the workspace for one given set of input joint values .

this asymmetry is a pure direct-cp-violating observable .

perhaps the cause for this asymmetry is the fact that time evolution is often irreversible .

a maximal collection of pairwise disjoint curves in sm is called a pants decomposition .

a pants decomposition of s is a maximal collection of distinct isotopy classes of pairwise disjoint essential simple closed curves on s .

deep convolutional neural networks have recently shown immense success for various image recognition tasks , such as object recognition .

deep neural networks have achieved recordbreaking accuracy in many image classification tasks .

the single particle level diagrams are computed within the woods-saxon superasymmetric two-center shell model .

the single particle level schemes are obtained within the two center woods-saxon shell model .

then we apply the multidimensional edgeworth expansion method and investigate weakly nonlinear evolution of the genus statistics and the area statistics .

then we discuss weakly nonlinear effects of the genus and the area statistics using the multidimensional edgeworth expansion method explored by matsubara .

for this reason , we only consider this model with the effect of relativistic blurring .

we considered this model with and without incorporating the effects of relativistic blurring on the overall spectrum .

we evaluate our approach on the flickr30k entities dataset which contains 31 , 783 images , each annotated with five sentences .

we use the standard flickr30k entities dataset for evaluating our proposed approach .

at the end of 2015 , they proposed a deep architecture of 152 layers , called residual network which was built on top their previous findings and achieved state of the art on imagenet previously held by themselves .

again s duality is a symmetry of iib theory with symmetry group sl .

the duality is a tool that untangles the mutual connection , the mutual changeability and the transitions between different objects .

duality of this kind is known as 3d mirror symmetry .

the pairs of manifolds satisfying this symmetry are known as mirror pairs , and this duality is also called mirror symmetry .

deep learning techniques have shown promising performance in many applications such as object detection , natural language process , and synthetic images generation .

deep neural networks have proved astoundingly effective at a wide range of empirical tasks , from image classification to playing go , and even modeling human learning .

deep neural networks have achieved a great success on many tasks such as image classification when a large set of labeled examples are available .

large-scale deep convolutional neural networks have been successfully applied to a wide variety of applications such as image classification .

to enhance the convergence of the derived power series , references suggest the use of pade approximants or continued fractions , which , however , are not optimized .

to enhance the convergence of the derived power series , references suggest the use of pade approximants or continued fractions , whose orders are not optimized yet .

before , evaporation is the dominant process due to the initial collapse and heating of the diffuse gas .

if evaporation is the dominant process and the rate is larger than the bondi accretion rate in the galactic center , the disk will be depleted within a certain time and no persistent disk will exist .

to repeat our experiments , we have made all source codes and data sets publicly available .

we use an implementation of faster r-cnn with a vgg-16 as its backbone .

we instead use a network based on vgg-16 network as base architecture .

recent development of deep neural networks has driven the remarkable improvements in semantic segmentation .

recently , tremendous advances in semantic segmentation have been made relying on deep convolutional neural networks .

the energy spectrum is a smooth function of k and one level at the band edge coupling .

namely , the energy spectrum consists of 6m bands in this case .

machine learning models based on deep neural networks have achieved unprecedented performance across a wide range of tasks .

deep neural networks have recently achieved great success in computer vision , speech recognition , and natural language processing .

liu pointed out that the commonly used conflict coefficient in evidence theory is not reasonable to represent the conflict degree between two pieces of evidence .

in , liu noted that the classical conflict coefficient k can not effectively measure the degree of conflict between two bodies of evidence .

topology , lattices , and logic programming .

topology and the semantics of logic programs .

neural networks have made remarkable progress in achieving encouraging results in digital image processing .

convolutional neural networks have led to leap-forward in a large number of computer vision applications .

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

the projector-augmented wave method was employed in this study , and for the exchange-correlation potential , the generalized gradient approximation with the perdew , burke , and ernzerhof functional was used .

we minimize the cross-entropy loss using adam optimizer with a fixed learning rate of 1e-5 .

we use the adam gradient optimization method with the categorical cross-entropy loss function .

calculation of the influence functional we may now calculate the influence functional with the semiclassical approximation for system variables inserted .

the semiclassical approximation to the decoherence functional and probabilities we may now compute the decoherence functional .

our acoustic model is a convolutional neural network , with gated linear units .

we employ a continuous dynamical system that resembles a convolution neural network when discretized .

for that we denote by finrings the category of finite commutative rings .

we denote by abfin the category of finite abelian groups .

a phrase-based alignment model for natural language inference .

a machine-learning approach to textual entailment recognition .

deep convolutional neural networks have enabled unparalleled breakthroughs in a variety of visual tasks , such as image classification .

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

generation and control of greenberger-horne-zeilinger entanglement in superconducting circuits .

theory of the bloch-wave oscillations in small josephson junctions .

the last column shows the fraction of free planets around the mbh .

the third column shows the rate of hvp production .

the radius of gyration is a purely geometric quantity .

radius of gyration is a parameter frequently used to measure the global compactness of a conformation .

the best upper bound on the chromatic number of k t -minor-free graphs is oindependently due to kostochka .

in general , kostochka independently proved that every k t -minor-free graph is o-degnerate .

cognitive radio is a promising technology offering enhanced spectrum efficiency via dynamic spectrum access .

cognitive radio presents a promising approach to implement intelligent wireless communications systems .

an isomorphism is a bijective homomorphism .

a g-action on x is called smart if 0 is an isomorphism .

gu and je shapiro , kernels of hankel operators and hyponormality of toeplitz operators , math .

hwang and wy lee , hyponormality of toeplitz operators with rational symbols , math .

we evaluate the renormalization of these couplings at one loop using lattice perturbation theory .

we proceed to calculate these divergences at one loop using lattice perturbation theory .

the exchangecorrelation energy functional is described with the spin-polarized generalized gradient approximation as parameterized by perdew-burke-ernzerhof .

the exchange-correlation interactions are treated by the generalized gradient approximation formulated by perdew , burke , and ernzerhof .

in particular , we use the k-means algorithm and interpret the resulting centers of clusters as the representative values .

we cluster the cns using the k-mean clustering algorithm , which needs an input parameter to indicate the number of expected clusters .

ss wishes to acknowledge financial support from the project influs .

sc wishes to acknowledge financial support from the erg eu grant and cometa .

up to the jacobian of the relevant coordinate transformation , that mode agrees with the tachyon of .

in fact , up to the jacobian of the relevant transformation , that mode agrees with the result of .

newman , robustness of community structure in networks , phys .

newman , community structure in directed networks , phys .

the number of available vms at the cloud server may change over time as some of them might become unavailable due to failure or malicious attacks .

the availability of remaining vms at the cloud server may change over time as some of them might become unavailable due to failure or malicious attacks .

kontsevich gave an explicit formula for the deformation quantization of a poisson structure on r n in terms of a formal power series and established the global existence on arbitrary poisson manifolds using formal geometry .

kontsevich gave the proof of existence and classification of star products on an arbitrary poisson manifolds as a consequence of his formality theorem .

we will also show that the vortex solitons with small vortex components are not only linearly but also nonlinearly stable .

we also prove that both vortex and dipole vector solitons are linearly stable in the neighborhood of the bifurcation point .

it is important to note that none of the cnn-based methods is able to match the precision of active search , especially when computing the orientation of the camera .

it is important to note that we can still not match the precision of state-ofthe-art sift-based methods , especially when computing the orientation of the camera .

this can be a problem , since in the internet the ip address usually identifies the sender of a message .

this is a problem , since in the internet the network address usually identifies the sender of a message .

finally , has theoretically shown the optimality of frequency flat precoding by proving that dominant subspaces of the frequency domain channel matrices of different subcarriers are equivalent .

besides , by proving the dominant subspaces of frequency domain channel matrices at different subcarriers are equivalent , has theoretically revealed the optimality of the frequency-flat precoding .

algorithms for encoding needed states in one to four blocks of c e require at most c 1 n 2 gates for some constant c 1 .

algorithms for encoding states in one or two blocks of c e require at most c 1 n 2 gates for some constant c 1 .

however , it is important to stress that is the only chaotic jerk system containing nonlinearity depending only on x .

it is important to stress that is the only chaotic jerk system containing nonlinearity depending only on x .