HHousen/ParaSCI · Datasets at Hugging Face

sentence1 stringlengths 16 446	sentence2 stringlengths 14 436
ellipses denote the trivial uniform background that clearly does not scatter .	for ellipses it is the pa of the dust ellipse major axis .
the obstruction is the equivalence class of the projective module , modulo stably free modules .	the first obstruction h 9 is called the primary obstruction .
the lower panels show the case all the distortion effects are incorporated .	the lower panels show the case all the effects are incorporated .
generative adversarial networks are a powerful framework to learn generative models of natural images .	generative adversarial networks are one of the main approaches to learning such models in a fully unsupervised fashion .
in recent years , deep convolution neural networks have achieved promising performance on many artificial intelligence tasks , including image recognition .	convolutional neural networks have achieved superior performance in many visual tasks , such as object detection and segmentation .
the exchange correlation energy was described by the generalized gradient approximation using the pbe functional .	the exchange-correlation energy of electrons is treated within a generalized gradient approximation with the functional parameterized by perdew , burke and ernzerhof .
convolutional neural networks have achieved significant progress in computer vision tasks such as image classification .	deep neural networks have shown improvement in state-of-the-art in different tasks , such as image classification .
more recently , a neural network language model was proposed in bengio et al where word vector representations are simultaneously learned along with a statistical language model .	bengio et al first proposed the concept of a neural language model , and this concept has been explored mainly with recurrent neural networks .
in the cognitive sciences , the famous perception experiments of michotte et al led to a long line of research exploring the cognitive biases that humans possess when attempting to link cause and effect .	the famous perception experiments of michotte et al have led to a long thread of experiments exploring how and why cognitive biases affect causal attribution .
hong and luo provided a proof on the convergence of the directly extended q-block admm under some restrictive assumptions including a global error bound condition with a sufficiently small step-length τ .	hong and luo present a convergence proof for the n block admm when the functions are convex , but under many assumptions that are difficult to verify in practice .
the wire-tap channel model was first introduced by wyner in his seminal paper .	the wiretap channel was first introduced in the seminal paper by wyner .
in particular , the deep convolutional neural network has dominated complex visual perception tasks such as object recognition .	deep neural networks have shown remarkable success in many computer vision tasks such as image classification .
over the past few years , convolutional neural networks have become the leading approach in computer vision .	deep neural networks have recently achieved performance breakthroughs in many of computer vision tasks .
tolar , quantum kinematics on smooth manifolds .	tolar , on global properties of quantum systems .
let us obtain further constraints on other bare parameters of the hls through the wilsonian matching for the currents correlators .	from the above assumptions we can determine the nf dependences of other three bare parameters through the wilsonian matching .
in fact , the general upper bound in is a single-shot upper bound on the secret key length for a multiparty secret key agreement problem with side information at the eavesdropper .	in fact , the general upper bound in is a single-shot upper bound on the secret key length for a multiparty secret key agreement problem .
deep learning systems have surpassed other algorithms and even humans at several tasks .	deep neural networks have recently achieved huge success in various machine learning tasks .
recently , deep convolutional neural networks have achieved great successes in computer vision topics such as image classification .	deep neural networks have been adopted to improve the performance of state-of-the-arts on a wide variety of tasks in computer vision , including image classification .
the perdew-burkeernzerhof generalized-gradient approximation is employed to describe the exchange and correlation functional .	we adopt the generalized gradient approximation of perdew , burke , and ernzerhof exchange-correlation functional .
convolutional neural networks have shown extraordinary success in a large variety of computer vision tasks , such as image recognition .	convolutional neural networks have shown excellent performance in various visual recognition problems such as image classification .
fontaine-laffaille weights of the hilbert modular varieties .	hodge-tate weights of the hilbert modular varieties .
if most of the polarization stays in the 13c , it is the small portion transfered to the proton system the one described by the theory above .	the polarization is the sum with the height of the dot .
notice that the zf solution used in and most early works use a conventional approach based on the singular value decomposition of the rsi channel .	notice that the zf solution used in and most early works uses a conventional approach based on the singular value decomposition of the loopback self-interference channel .
in our work , we utilize resnet-50 as the backbone network for its ease-of-use .	we adopt resnet101 as our network architecture due to its good trade-off between accuracy and efficiency .
convolutional neural networks algorithms have been achieving outstanding classification capabilities in several complex tasks such as image recognition .	c onvolutional neural networks have achieved state-of-the-art performance on various visual recognition tasks such as image classification .
deep learning has led to significant improvements in many computer vision tasks such as image classification .	deep neural networks have shown tremendous success in image recognition tasks , even surpassing human capability .
deep learning or representation learning has received increasing interest in recent years owing to their success in several applications .	in recent years , deep neural networks have achieved great success in a variety of machine learning tasks .
powerful deep neural networks have been created and investigated for high-level computer vision tasks such as image classification .	convolutional neural networks have recently been applied to various computer vision tasks such as image classification .
in this subsection , we briefly present the decentralized coded caching scheme in .	in this paper , we mainly focus on the decentralized coded caching scheme in .
the higgs particle is a useful mathematical device but we lack a more basic reason for its presence .	here the higgs particle is a pseudo-goldstone boson of a global symmetry g which is spontaneously broken at a scale f to a subgroup h .
the dynamics of single straight cracks are well described by linear elastic fracture mechanics .	the accepted approach to describing crack dynamics is linear elastic fracture mechanics .
the pseudoscalar meson-exchange interaction produces spin-spin and tensor forces .	the scalar meson-exchange interaction produces only central and spin-orbit forces .
however , as electromagnetism is a long range interaction , one might expect large qed finite volume effects on a periodic lattice .	since electromagnetism is a non-metric aspect of the universal connection , given a stellar object , such as magnetar , with an immense magnetic field that is comparable in curvature to its gravitational field , bending of light should be significantly different than that predicted by general relativity .
string theory appears to contain an enormous landscape of metastable vacua , with a corresponding diversity of low-energy physics .	string theory suggests the existence of a multitude of vacua characterized by different values of the low-energy constants of nature .
first principles calculations were carried out with vasp code , which gives solutions to density functional theory using pseudopotentials or the projector-augmented wave method .	the atomistic first principles calculations were performed within the density functional theory framework as implemented in the vienna ab initio simulation package .
the neutralino , if it is the lsp , is a natural candidate for dark matter .	the neutralino is a majorana particle and therefore its own antiparticle .
deep convolutional neural networks have made significant progress in classification problems , which have shown to generate good results when provided sufficient data .	deep neural networks have shown tremendous success in image recognition tasks , even surpassing human capability .
secondly , the most crucial part of the ionization history of the universe is related to the large-scale structure and galaxy formation and is called late reionization .	most importantly , reionization is a highly nonlinear process , and constructing an accurate model for the sizes and spatial distribution of hii regions is challenging .
this higgs field is the remnant of the a0 field in the four-dimensional theory .	the endomorphism φ is called a higgs field .
choy et al propose a unified framework for single and multi-view reconstruction by using a 3d recurrent neural network based on long-short-term memory .	choy et al propose a unified framework for single and multi-view object generation reading images sequentially .
convolutional neural networks have gained remarkable success on a variety of visual recognition tasks .	deep neural networks have seen great success in many cognitive applications such as image classification .
the performance of a wiretap channel was first introduced by wyner in which a source is trying to communicate with the destination in the presence of an external eavesdropper .	the notion of secrecy capacity was introduced by wyner in the context of the wire-tap channel , in which a legitimate transmitter intends to send a confidential message to a legitimate receiver by hiding it from a degraded eavesdropper .
deep learning has obviously improved the performance of many computer vision tasks such as classification .	in recent years , deep learning methods have shown remarkable performance for a variety of computer vision tasks such as real-time object detection .
two representative deep models are convolutional neural networks and recurrent neural networks .	moreover , these models are frequently based on recurrent neural networks only .
the knowledge of the position of this singularity allows us to use a powerful resummation method known as conformal mapping , that will be reviewed in the next section .	with the knowledge of the large order behaviour , a more efficient method could be developed , combining a borel transformation and a conformal mapping , which we briefly present in next section .
this is obtained by introducing a monolithic velocity vector field valid everywhere , found by applying the volume averaging procedure .	this is achieved by introducing a monolithic velocity vector field u i , defined everywhere and found by applying the volume averaging procedure .
nonrenormalizability is a family property of all sisters .	its non-renormalizability is a consequence of exactly this problem .
an arbitrary abelian periodic group variety is definable .	the set of all strongly permutative semigroup varieties is definable .
recent success in computer vision and image retrieval are closely related to convolutional neural networks .	in recent years the use of deep convolutional neural networks has significantly improved the detection of objects .
in an absorptive medium , this is determined by the behaviour of the exponential damping factor entering the spectrum and the formation time of radiation .	for an absorptive medium , this is driven by the behaviour of an exponential damping factor in the spectrum , which is related to the formation time of radiation in the matter .
total cross section for cc and nc coherent single pion antineutrino and neutrino production .	total cross section of coherent single pion neutrino and antineutrino production .
if we assume that gravity is the primary force determining the large-scale distribution of matter and that galaxies trace mass at least on large scales , then we can directly compare our best fit λ cdm model .	gravity , which is the simplest model in higher-curvature theories of gravity .
tudose , revival structure of stark wave packets , phys .	tudose , revivals of rydberg wave packets , phys .
in 1888 pincherle gave the integrals involving product of gamma functions along vertical lines .	in 1888 pincherle studied the integrals involving product of gamma functions along vertical lines .
this perturbation is a strong error in the output y that can be attributed to an unexpected fault in a measurement device or even human errors and does not belong to the natural evolution of the tumour .	the perturbation is normalised so that it is a fifth of the equilibrium density profile .
generative adversarial networks approach the training of deep generative models from a game theory perspective using a minimax game .	generative adversarial networks are an innovative technique for training generative models to produce realistic examples from a data distribution .
with this understanding we need not modify our dirac bracket .	the addition of the conditions above requires us to modify the dirac bracket slightly .
recently , deep neural network based methods have lead to breakthroughs in several vision tasks , such as classification .	in recent years , convolutional neural networks s have emerged as the most powerful technique for image classification .
poggi , rt de souza , mb tsang , and hm xu , de fields , k .	zhu , wg lynch , dr bowman , rt de souza , ck gelbke , yd kim , l .
as in , for ease of presentation , we assume that the number of channels is equal to the number of sessions .	as in , for ease of presentation , we assume that the number of users is equal to the number of channels .
infogan disentangles hidden dimensions on unlabeled data by mutual information regularization .	infogan learns interpretable representations by introducing latent codes .
deep convolutional neural networks have exhibited significant effectiveness in tackling and modeling image data .	recently , convolutional neural networks have achieved remarkable success on many vision tasks such as object recognition .
by a result of hartshorne , connectedness in codimension 1 is a necessary condition for cohenmacaulayness .	a result of hartshorne , asserts that a cohen-macaulay ring is connected in codimension 1 .
close to the phase transition and close to the critical point large fluctuations are predicted .	close to the critical point an increase of fluctuations is predicted .
interacting quantum many-particle systems in the presence of disorder are of great interest not only in condensed matter physics .	interacting quantum many-particle systems with disorder pose fundamental challenges for theory and experiment not only in condensed matter physics .
deep convolutional neural networks have demonstrated superior performance in various computer vision tasks .	convolutional neural networks have achieved superior performance in many visual tasks , such as object detection and segmentation .
we notice that the intermittence slopes φq increase with the increasing orders q .	we notice that all values increase with increasing the orders q .
low-density parity-check codes have found widespread acceptance in different areas due to their superior performance and low complexity decoding .	low-density parity-check codes have been widely applied to communication and data storage systems due to their capacity approaching performance .
the gan approach is a framework for training generative models , which we briefly explain in the context of image data .	gans are a framework for training generative deep models via an adversarial process .
special interest has been paid to the enhancement of spin-orbit coupling in graphene since a topological state , a quantum spin hall phase , was theoretically shown to emerge .	additionally , the observation of substantial spin-orbit coupling in novel 2d dirac materials could give rise to quantum spin hall effect .
because the computation of the fom is time consuming , in this section , we construct a small dimensional rom by utilizing the pod method .	because the computation of the fom is time consuming , in this section , we construct a small dimensional reduced order model by utilizing the proper orthogonal decomposition method .
deep convolutional neural networks have made significant breakthroughs in many visual understanding tasks including image classification .	large-scale deep convolutional neural networks have been successfully applied to a wide variety of applications such as image classification .
it was suggested originally by hufnagel 1 in 1966 and given its name by fried 2 in 1978 .	this method was originally suggested by hufnagel 1 in 1966 and given its name by fried 2 in 1978 .
the exchange and correlation functional employed was the generalized gradient approximation in the parametrization due to perdew-burke-ernzerhof .	the perdew-burke-erzerhof and mbjlda parameterizations of the exchange-correlation energy were employed .
when the genus is 0 or 1 , there are complete classification results for holomorphic vector bundles on σ g , due to grothendieck for the case of the riemann sphere , and to atiyah for the case of elliptic curves .	there are complete classification results for holomorphic vector bundles for rational and elliptic curves , due to grothendieck for the case of the riemann sphere .
table 6 compares the hwgq-net with the recent xnor-net , on the imagenet classification task .	table 2 compares the accuracy of lcnn , xnor-net , and the original model .
these deformations will consist of finitely many steps .	the associated quantum groups are called standard deformations of gl .
we use the 50-layer residual network as the feature extractor f θ , which outputs a 256-dimensional feature vector .	in our proposed lfsr , we use a sr residual network as the generator g , which includes 16 residual blocks , and following sub-pixel convolution layers .
since the matter is induced from the extra dimension , this theory is also called the induced matter theory .	this theory is called the induced matter theory .
perel , in optical orientation , edited by f .	titkov , in optical orientation , edited by f .
there is no ordering ambiguity in this approach .	this is the well known ordering ambiguity .
quantum chromodynamics calculations on the lattice predict , under these conditions , the formation of a quark-gluon plasma , where colour confinement vanishes and chiral symmetry is partially restored .	under these conditions , quantum chromodynamics calculations on the lattice predict a transition to a quark-gluon plasma in which colour confinement vanishes and chiral symmetry is partially restored .
deep neural networks have demonstrated impressive performance in many fields of research with applications ranging from image classification , just to name a few .	deep neural networks have achieved impressive performance on tasks across a variety of domains , including vision .
dropout is put vertically between lstm layers to improve regularization .	drop out layers were added between lstm layers as a method to curb overfitting .
we remark that the computation of the spectral and nuclear norm of tensors is np-hard .	as shown in , it is np-hard to compute extreme eigenvalues of tensors .
as the orbit of y is connected then so is the closure of such orbit which , by minimality , is all of y .	moreover , since an orbit is a point , the differential character is just a smooth character of t .
supersymmetric field theories in non-commutative superspace have recently attracted considerable interest in view of the fact that they describe some superstring effective actions in the background of space-time supergravity fields .	noncommutative field theories have received much attention recently , as they were found to arise in string theory in the presence of d-branes and a constant ns b-field background .
worst-case robust distributed ed with demand side management was proposed for grid-connected microgrids .	worstcase robust distributed ed is proposed for grid-connected microgrids with ders in .
deep neural networks have shown remarkable success in many domains , such as computer vision .	deep neural networks , have also been successfully applied in other fields , such as speech recognition .
wu and negi in defined the effective capacity as the maximum constant arrival rate that can be supported by a given channel service process while also satisfying a statistical qos requirement specified by the qos exponent θ .	inspired by the theory of effective bandwidth , wu and negi introduced effective capacity in , which is defined as the maximum constant arrival rate that a given service process can support in order to guarantee a statistical qos requirement specified by θ .
the gravitino is a stable superweakly massive particle from its observed planckian spectrum .	if the gravitino is the lsp all mssm particles will eventually decay into it , and since it couples gravitationally one would naively expect those decays to be extremely slow .
performing dmap computation with a compactly supported kernel reveals these two disjoint sets .	performing dmap computations with a compactly supported kernel reveals these two disjoint sets since the markov chain is reducible .
first , we will assume that every orbit in m is a real hypersurface .	when the orbit , otherwise it is a repetition .
this interval is the range over which the sn data are even minimally consistent with the empirical models of sne ia .	the interval a m is the mass and a the spin parameter of the black hole .
variety reasoning for multiset constraint propagation .	representations in constraint programming .
deep neural networks have produced state-ofthe-art results in applications such as computer vision .	deep neural networks have been evolved to powerful predictive models with remarkable performance on computer vision tasks .
then we analyze the situation of high losses in the channel .	first we analyze the case of low losses in the channel .
the monoid so obtained is called the plactic monoid .	this monoid is the free monoid over the alphabet a .
the eckart-young theorem states that a solution to this problem is given by truncating the singular value decomposition of m to the top k singular values .	the eckart-young theorem establishes that the solution l τ of is easily obtained via the singular value decomposition .
we compare it with the basic per-flow polling method proposed in .	we compare it with the basic perflow querying method proposed in .

ellipses denote the trivial uniform background that clearly does not scatter .

for ellipses it is the pa of the dust ellipse major axis .

the obstruction is the equivalence class of the projective module , modulo stably free modules .

the first obstruction h 9 is called the primary obstruction .

the lower panels show the case all the distortion effects are incorporated .

the lower panels show the case all the effects are incorporated .

generative adversarial networks are a powerful framework to learn generative models of natural images .

generative adversarial networks are one of the main approaches to learning such models in a fully unsupervised fashion .

in recent years , deep convolution neural networks have achieved promising performance on many artificial intelligence tasks , including image recognition .

convolutional neural networks have achieved superior performance in many visual tasks , such as object detection and segmentation .

the exchange correlation energy was described by the generalized gradient approximation using the pbe functional .

the exchange-correlation energy of electrons is treated within a generalized gradient approximation with the functional parameterized by perdew , burke and ernzerhof .

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

deep neural networks have shown improvement in state-of-the-art in different tasks , such as image classification .

more recently , a neural network language model was proposed in bengio et al where word vector representations are simultaneously learned along with a statistical language model .

bengio et al first proposed the concept of a neural language model , and this concept has been explored mainly with recurrent neural networks .

in the cognitive sciences , the famous perception experiments of michotte et al led to a long line of research exploring the cognitive biases that humans possess when attempting to link cause and effect .

the famous perception experiments of michotte et al have led to a long thread of experiments exploring how and why cognitive biases affect causal attribution .

hong and luo provided a proof on the convergence of the directly extended q-block admm under some restrictive assumptions including a global error bound condition with a sufficiently small step-length τ .

hong and luo present a convergence proof for the n block admm when the functions are convex , but under many assumptions that are difficult to verify in practice .

the wire-tap channel model was first introduced by wyner in his seminal paper .

the wiretap channel was first introduced in the seminal paper by wyner .

in particular , the deep convolutional neural network has dominated complex visual perception tasks such as object recognition .

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

over the past few years , convolutional neural networks have become the leading approach in computer vision .

deep neural networks have recently achieved performance breakthroughs in many of computer vision tasks .

tolar , quantum kinematics on smooth manifolds .

tolar , on global properties of quantum systems .

let us obtain further constraints on other bare parameters of the hls through the wilsonian matching for the currents correlators .

from the above assumptions we can determine the nf dependences of other three bare parameters through the wilsonian matching .

in fact , the general upper bound in is a single-shot upper bound on the secret key length for a multiparty secret key agreement problem with side information at the eavesdropper .

in fact , the general upper bound in is a single-shot upper bound on the secret key length for a multiparty secret key agreement problem .

deep learning systems have surpassed other algorithms and even humans at several tasks .

deep neural networks have recently achieved huge success in various machine learning tasks .

recently , deep convolutional neural networks have achieved great successes in computer vision topics such as image classification .

deep neural networks have been adopted to improve the performance of state-of-the-arts on a wide variety of tasks in computer vision , including image classification .

the perdew-burkeernzerhof generalized-gradient approximation is employed to describe the exchange and correlation functional .

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

convolutional neural networks have shown extraordinary success in a large variety of computer vision tasks , such as image recognition .

convolutional neural networks have shown excellent performance in various visual recognition problems such as image classification .

fontaine-laffaille weights of the hilbert modular varieties .

hodge-tate weights of the hilbert modular varieties .

if most of the polarization stays in the 13c , it is the small portion transfered to the proton system the one described by the theory above .

the polarization is the sum with the height of the dot .

notice that the zf solution used in and most early works use a conventional approach based on the singular value decomposition of the rsi channel .

notice that the zf solution used in and most early works uses a conventional approach based on the singular value decomposition of the loopback self-interference channel .

in our work , we utilize resnet-50 as the backbone network for its ease-of-use .

we adopt resnet101 as our network architecture due to its good trade-off between accuracy and efficiency .

convolutional neural networks algorithms have been achieving outstanding classification capabilities in several complex tasks such as image recognition .

c onvolutional neural networks have achieved state-of-the-art performance on various visual recognition tasks such as image classification .

deep learning has led to significant improvements in many computer vision tasks such as image classification .

deep neural networks have shown tremendous success in image recognition tasks , even surpassing human capability .

deep learning or representation learning has received increasing interest in recent years owing to their success in several applications .

in recent years , deep neural networks have achieved great success in a variety of machine learning tasks .

powerful deep neural networks have been created and investigated for high-level computer vision tasks such as image classification .

convolutional neural networks have recently been applied to various computer vision tasks such as image classification .

in this subsection , we briefly present the decentralized coded caching scheme in .

in this paper , we mainly focus on the decentralized coded caching scheme in .

the higgs particle is a useful mathematical device but we lack a more basic reason for its presence .

here the higgs particle is a pseudo-goldstone boson of a global symmetry g which is spontaneously broken at a scale f to a subgroup h .

the dynamics of single straight cracks are well described by linear elastic fracture mechanics .

the accepted approach to describing crack dynamics is linear elastic fracture mechanics .

the pseudoscalar meson-exchange interaction produces spin-spin and tensor forces .

the scalar meson-exchange interaction produces only central and spin-orbit forces .

however , as electromagnetism is a long range interaction , one might expect large qed finite volume effects on a periodic lattice .

since electromagnetism is a non-metric aspect of the universal connection , given a stellar object , such as magnetar , with an immense magnetic field that is comparable in curvature to its gravitational field , bending of light should be significantly different than that predicted by general relativity .

string theory appears to contain an enormous landscape of metastable vacua , with a corresponding diversity of low-energy physics .

string theory suggests the existence of a multitude of vacua characterized by different values of the low-energy constants of nature .

first principles calculations were carried out with vasp code , which gives solutions to density functional theory using pseudopotentials or the projector-augmented wave method .

the atomistic first principles calculations were performed within the density functional theory framework as implemented in the vienna ab initio simulation package .

the neutralino , if it is the lsp , is a natural candidate for dark matter .

the neutralino is a majorana particle and therefore its own antiparticle .

deep convolutional neural networks have made significant progress in classification problems , which have shown to generate good results when provided sufficient data .

deep neural networks have shown tremendous success in image recognition tasks , even surpassing human capability .

secondly , the most crucial part of the ionization history of the universe is related to the large-scale structure and galaxy formation and is called late reionization .

most importantly , reionization is a highly nonlinear process , and constructing an accurate model for the sizes and spatial distribution of hii regions is challenging .

this higgs field is the remnant of the a0 field in the four-dimensional theory .

the endomorphism φ is called a higgs field .

choy et al propose a unified framework for single and multi-view reconstruction by using a 3d recurrent neural network based on long-short-term memory .

choy et al propose a unified framework for single and multi-view object generation reading images sequentially .

convolutional neural networks have gained remarkable success on a variety of visual recognition tasks .

deep neural networks have seen great success in many cognitive applications such as image classification .

the performance of a wiretap channel was first introduced by wyner in which a source is trying to communicate with the destination in the presence of an external eavesdropper .

the notion of secrecy capacity was introduced by wyner in the context of the wire-tap channel , in which a legitimate transmitter intends to send a confidential message to a legitimate receiver by hiding it from a degraded eavesdropper .

deep learning has obviously improved the performance of many computer vision tasks such as classification .

in recent years , deep learning methods have shown remarkable performance for a variety of computer vision tasks such as real-time object detection .

two representative deep models are convolutional neural networks and recurrent neural networks .

moreover , these models are frequently based on recurrent neural networks only .

the knowledge of the position of this singularity allows us to use a powerful resummation method known as conformal mapping , that will be reviewed in the next section .

with the knowledge of the large order behaviour , a more efficient method could be developed , combining a borel transformation and a conformal mapping , which we briefly present in next section .

this is obtained by introducing a monolithic velocity vector field valid everywhere , found by applying the volume averaging procedure .

this is achieved by introducing a monolithic velocity vector field u i , defined everywhere and found by applying the volume averaging procedure .

nonrenormalizability is a family property of all sisters .

its non-renormalizability is a consequence of exactly this problem .

an arbitrary abelian periodic group variety is definable .

the set of all strongly permutative semigroup varieties is definable .

recent success in computer vision and image retrieval are closely related to convolutional neural networks .

in recent years the use of deep convolutional neural networks has significantly improved the detection of objects .

in an absorptive medium , this is determined by the behaviour of the exponential damping factor entering the spectrum and the formation time of radiation .

for an absorptive medium , this is driven by the behaviour of an exponential damping factor in the spectrum , which is related to the formation time of radiation in the matter .

total cross section for cc and nc coherent single pion antineutrino and neutrino production .

total cross section of coherent single pion neutrino and antineutrino production .

if we assume that gravity is the primary force determining the large-scale distribution of matter and that galaxies trace mass at least on large scales , then we can directly compare our best fit λ cdm model .

gravity , which is the simplest model in higher-curvature theories of gravity .

tudose , revival structure of stark wave packets , phys .

tudose , revivals of rydberg wave packets , phys .

in 1888 pincherle gave the integrals involving product of gamma functions along vertical lines .

in 1888 pincherle studied the integrals involving product of gamma functions along vertical lines .

this perturbation is a strong error in the output y that can be attributed to an unexpected fault in a measurement device or even human errors and does not belong to the natural evolution of the tumour .

the perturbation is normalised so that it is a fifth of the equilibrium density profile .

generative adversarial networks approach the training of deep generative models from a game theory perspective using a minimax game .

generative adversarial networks are an innovative technique for training generative models to produce realistic examples from a data distribution .

with this understanding we need not modify our dirac bracket .

the addition of the conditions above requires us to modify the dirac bracket slightly .

recently , deep neural network based methods have lead to breakthroughs in several vision tasks , such as classification .

in recent years , convolutional neural networks s have emerged as the most powerful technique for image classification .

poggi , rt de souza , mb tsang , and hm xu , de fields , k .

zhu , wg lynch , dr bowman , rt de souza , ck gelbke , yd kim , l .

as in , for ease of presentation , we assume that the number of channels is equal to the number of sessions .

as in , for ease of presentation , we assume that the number of users is equal to the number of channels .

infogan disentangles hidden dimensions on unlabeled data by mutual information regularization .

infogan learns interpretable representations by introducing latent codes .

deep convolutional neural networks have exhibited significant effectiveness in tackling and modeling image data .

recently , convolutional neural networks have achieved remarkable success on many vision tasks such as object recognition .

by a result of hartshorne , connectedness in codimension 1 is a necessary condition for cohenmacaulayness .

a result of hartshorne , asserts that a cohen-macaulay ring is connected in codimension 1 .

close to the phase transition and close to the critical point large fluctuations are predicted .

close to the critical point an increase of fluctuations is predicted .

interacting quantum many-particle systems in the presence of disorder are of great interest not only in condensed matter physics .

interacting quantum many-particle systems with disorder pose fundamental challenges for theory and experiment not only in condensed matter physics .

deep convolutional neural networks have demonstrated superior performance in various computer vision tasks .

convolutional neural networks have achieved superior performance in many visual tasks , such as object detection and segmentation .

we notice that the intermittence slopes φq increase with the increasing orders q .

we notice that all values increase with increasing the orders q .

low-density parity-check codes have found widespread acceptance in different areas due to their superior performance and low complexity decoding .

low-density parity-check codes have been widely applied to communication and data storage systems due to their capacity approaching performance .

the gan approach is a framework for training generative models , which we briefly explain in the context of image data .

gans are a framework for training generative deep models via an adversarial process .

special interest has been paid to the enhancement of spin-orbit coupling in graphene since a topological state , a quantum spin hall phase , was theoretically shown to emerge .

additionally , the observation of substantial spin-orbit coupling in novel 2d dirac materials could give rise to quantum spin hall effect .

because the computation of the fom is time consuming , in this section , we construct a small dimensional rom by utilizing the pod method .

because the computation of the fom is time consuming , in this section , we construct a small dimensional reduced order model by utilizing the proper orthogonal decomposition method .

deep convolutional neural networks have made significant breakthroughs in many visual understanding tasks including image classification .

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

it was suggested originally by hufnagel 1 in 1966 and given its name by fried 2 in 1978 .

this method was originally suggested by hufnagel 1 in 1966 and given its name by fried 2 in 1978 .

the exchange and correlation functional employed was the generalized gradient approximation in the parametrization due to perdew-burke-ernzerhof .

the perdew-burke-erzerhof and mbjlda parameterizations of the exchange-correlation energy were employed .

when the genus is 0 or 1 , there are complete classification results for holomorphic vector bundles on σ g , due to grothendieck for the case of the riemann sphere , and to atiyah for the case of elliptic curves .

there are complete classification results for holomorphic vector bundles for rational and elliptic curves , due to grothendieck for the case of the riemann sphere .

table 6 compares the hwgq-net with the recent xnor-net , on the imagenet classification task .

table 2 compares the accuracy of lcnn , xnor-net , and the original model .

these deformations will consist of finitely many steps .

the associated quantum groups are called standard deformations of gl .

we use the 50-layer residual network as the feature extractor f θ , which outputs a 256-dimensional feature vector .

in our proposed lfsr , we use a sr residual network as the generator g , which includes 16 residual blocks , and following sub-pixel convolution layers .

since the matter is induced from the extra dimension , this theory is also called the induced matter theory .

this theory is called the induced matter theory .

perel , in optical orientation , edited by f .

titkov , in optical orientation , edited by f .

there is no ordering ambiguity in this approach .

this is the well known ordering ambiguity .

quantum chromodynamics calculations on the lattice predict , under these conditions , the formation of a quark-gluon plasma , where colour confinement vanishes and chiral symmetry is partially restored .

under these conditions , quantum chromodynamics calculations on the lattice predict a transition to a quark-gluon plasma in which colour confinement vanishes and chiral symmetry is partially restored .

deep neural networks have demonstrated impressive performance in many fields of research with applications ranging from image classification , just to name a few .

deep neural networks have achieved impressive performance on tasks across a variety of domains , including vision .

dropout is put vertically between lstm layers to improve regularization .

drop out layers were added between lstm layers as a method to curb overfitting .

we remark that the computation of the spectral and nuclear norm of tensors is np-hard .

as shown in , it is np-hard to compute extreme eigenvalues of tensors .

as the orbit of y is connected then so is the closure of such orbit which , by minimality , is all of y .

moreover , since an orbit is a point , the differential character is just a smooth character of t .

supersymmetric field theories in non-commutative superspace have recently attracted considerable interest in view of the fact that they describe some superstring effective actions in the background of space-time supergravity fields .

noncommutative field theories have received much attention recently , as they were found to arise in string theory in the presence of d-branes and a constant ns b-field background .

worst-case robust distributed ed with demand side management was proposed for grid-connected microgrids .

worstcase robust distributed ed is proposed for grid-connected microgrids with ders in .

deep neural networks have shown remarkable success in many domains , such as computer vision .

deep neural networks , have also been successfully applied in other fields , such as speech recognition .

wu and negi in defined the effective capacity as the maximum constant arrival rate that can be supported by a given channel service process while also satisfying a statistical qos requirement specified by the qos exponent θ .

inspired by the theory of effective bandwidth , wu and negi introduced effective capacity in , which is defined as the maximum constant arrival rate that a given service process can support in order to guarantee a statistical qos requirement specified by θ .

the gravitino is a stable superweakly massive particle from its observed planckian spectrum .

if the gravitino is the lsp all mssm particles will eventually decay into it , and since it couples gravitationally one would naively expect those decays to be extremely slow .

performing dmap computation with a compactly supported kernel reveals these two disjoint sets .

performing dmap computations with a compactly supported kernel reveals these two disjoint sets since the markov chain is reducible .

first , we will assume that every orbit in m is a real hypersurface .

when the orbit , otherwise it is a repetition .

this interval is the range over which the sn data are even minimally consistent with the empirical models of sne ia .

the interval a m is the mass and a the spin parameter of the black hole .

variety reasoning for multiset constraint propagation .

representations in constraint programming .

deep neural networks have produced state-ofthe-art results in applications such as computer vision .

deep neural networks have been evolved to powerful predictive models with remarkable performance on computer vision tasks .

then we analyze the situation of high losses in the channel .

first we analyze the case of low losses in the channel .

the monoid so obtained is called the plactic monoid .

this monoid is the free monoid over the alphabet a .

the eckart-young theorem states that a solution to this problem is given by truncating the singular value decomposition of m to the top k singular values .

the eckart-young theorem establishes that the solution l τ of is easily obtained via the singular value decomposition .

we compare it with the basic per-flow polling method proposed in .

we compare it with the basic perflow querying method proposed in .