HHousen/ParaSCI · Datasets at Hugging Face

sentence1 stringlengths 16 446	sentence2 stringlengths 14 436
to deal with such environment , hausknecht and stone introduced the deep recurrent q-networks .	hausknecht and stone proposed deep recurrent q-network based on a long short term memory network .
the candidate from particle physics responsible for driving inflation is a scalar field , which is called the inflaton field , φ .	the inflaton is a natural candidate for a ngb as we have discussed , because of the necessary flatness of the potential .
recent years have yielded rapid advances in the field of deep learning , largely due to the unparalleled effectiveness of convolutional neural networks on a variety of difficult problems .	recent successes in machine learning have shown that difficult perceptual tasks can be tackled efficiently using deep neural networks .
even though fj is not the first operational model of nominally-typed oop , yet fj is the most widely known operational model of a nominally-typed mainstream oo language , namely java .	even though fj is not the first operational model of nominally-typed oop , fj is the most widely known operational model of a nominally-typed mainstream oo language , namely java .
eamformers are used to steer the beam of a phased array antenna by controlling the phase added to the signal at each antenna element .	obfns are used in wireless communication systems to steer phased array antennas in the desired direction by making use of positive interference of synchronized signals .
on explicit bounds for the spectral gap on compact manifolds .	on the spectral gap for compact manifolds .
willis , amenable and weakly amenable banach algebras with compact multiplication .	dales , amenability and weak amenability for beurling and lipschitz algebras .
now we proceed on to define the notion of s-definite special ideal related with the ring contained in s-definite special field .	we now proceed on to define notions analogues to galois group of automorphisms for s-definite special field .
omniglot is a dataset of over 1623 characters from 50 different alphabets .	omniglot is a set of images of 1623 handwritten characters from 50 alphabets with 20 examples for each class .
the derived functors of rational k-theory and cyclic homology .	the derived functor of negative cyclic homology .
carlevaris-bianco and eustice introduce the generic linear constraint factors and the nonlinear graph sparsification method , respectively .	carlevaris-bianco and eustice introduce the generic linear constraint factors and the nonlinear graph sparsification , respectively .
dbscan is a density-based data clustering algorithm because it finds a number of clusters starting from the estimated density distribution of corresponding nodes .	dbscan is a density-based algorithm which discovers clusters with arbitrary shape using minimal number of input parameters .
it follows that the probability that the first set of arcs forms .	it follows that the probability that the first set of arcs forms an .
hashednets is a recent technique to reduce model sizes by using a hash function to randomly group connection weights , so that all connections within the same hash bucket share a single parameter value .	hashednets is a recent technique to reduce model size by using a hash function to randomly group connection weights , so that all connections within the same hash bucket share a single parameter value .
the space spanned by ideal boson-fermion states that are selected with the aid of the above particle number considerations is called the ideal subspace .	let s be the subspace of t which is the union of all arcs joining postcritical points .
such models have been applied to machine translation , image caption generation , or dialogue systems .	examples include machine translation , image captioning and dialogue systems .
the classical morrey spaces l p , λ were introduced by morrey in 1938 to study the local behavior of solutions of second order elliptic partial differential equations .	the classical morrey space l p , λ was introduced by morrey in connection with elliptic partial differential equations .
deep neural networks are powerful representation learning models which achieve near-human performance in image recognition tasks .	deep neural networks are widely used for many applications including object recognition .
hester and hirschberg have provided an extensive survey on average case analysis of list accessing algorithms .	hester and hirschberg have provided an extensive survey of average case analysis of list update algorithms .
the overall error performance is dominated by the minimum singular value λns .	further optimization of angles are based upon minimizing the average error performance .
among these algorithms , we are specially interested in sabmp as it is capable of mmse estimation even if the distribution of the unknown vector is not available .	among such sparse recovery methods , we are interested specifically in sabmp due to its nature of fulfilling our requirements .
deep convolutional neural networks have demonstrated great success in various machine intelligence areas and enabled new advancement for applications such as video content understanding .	in recent years , deep learning methods , and in particular convolutional neural networks , have achieved considerable success in a range of computer vision applications including object recognition .
there has been much previous work studying a 1 and related sets by exploiting this fourier structure .	there has been much previous work studying a and related sets by exploiting this fourier structure .
we use the svm classifiers from scikit-learn with an rbf kernel and a scaled gamma .	as classifier we use a traditional model , a support vector machine with linear kernel implemented in scikit-learn .
large-scale multiple-input multiple-output systems , also known as massive mimo systems , are considered as a promising technique for next generations of wireless communication networks .	cloud radio access network and massive multiple-input multiple-output are regarded as two key technologies for future wireless systems .
deep neural networks have been widely applied and achieved state-of-art performance on a variety of tasks including image recognition .	deep neural networks have achieved great success across a broad range of domains , such as computer vision , speech processing and natural language processing .
recently , deterministic deep neural networks have demonstrated state-of-the-art performance on many supervised tasks , eg , speech recognition .	in the last decade , convolutional neural networks have shown state of the art accuracy on a variety of visual recognition tasks such as image classification .
the main drawback of the results in is that if a controller fails to exist for the symbolic model , nothing can be concluded regarding the existence of a controller for the original control system .	however , analogously to the results in the main drawback of these approaches is that if a controller fails to exist for the proposed symbolic models , nothing can be concluded regarding the existence of a controller for the original control system .
it follows from theorem 3 6 that c is a subset of d .	so we see that theorem c is a direct consequence of theorem b .
the embedding subnetwork is comprised of a sequence of 4 residual blocks , each comprised of two 2d-convolutional layers .	the content encoder is made of several 2d convolutional layers followed by several residual blocks .
the pileup contribution to the jet energy is estimated on an event-by-event basis using the jet area method described in and is subtracted from the overall jet p t .	the contribution to the jet energy from pile-up is estimated on an event-by-event basis using the jet area method described in ref , and is subtracted from the overall jet p t .
the lagrangian consists of three pieces that have to be investigated separately .	for the lagrangian in is the hamiltonian .
this property appeared recently in the paper in connection with property ptq for discrete quantum groups , where g with representations of bounded degree was termed low .	the latter class has recently appeared in the literature in connection with property for discrete quantum groups .
the squid is a uniquely superconducting electronic device discovered in the mid-1960s that is most sensitive detector of magnetic fields .	the squid comprises two identical josephson junctions with critical current ic and capacitance cj .
in recent years , convolutional neural networks have become the dominant approach for various tasks including classification .	along this direction , convolutional neural networks have been very successful in various computer vision and natural language processing tasks in recent years .
several other works with focus on classifying users based on their mobility periodicity , or a combination of mobility statistics .	several other works with focus on classifying users based on their mobility periodicity .
the origin of this violation is a highly intermittent dynamics characterized by large fluctuations and strongly non-gaussian statistics .	the origin of the violation is the difference between the force acting on the tagged particle , f1 , and the effective force , feff 1 .
our general conclusion regarding gradient descent methods is that in such cases the faster gradient descent methods offer substantial advantages .	our general conclusion is that in situations where many steepest descent steps are required , thus building slowness into the solution procedure , the faster gradient descent method offers substantial advantages .
data reduction was performed with the miriad software package .	data reduction was carried out with the miriad software package using standard procedures .
for a faster processing time , we consider a rather shallow pretrained cnn architecture , namely vgg-f .	in order to obtain automatic features , we experiment with multiple cnn architectures , such as vgg-face .
several researchers have observed that the main bottleneck in mapreduce is the network .	however , it is consistently reported that the network is typically the bottleneck for large-scale mapreduce tasks .
firstly , it has been proved in that any completely monotonic function can be approximated as a sum of exponential functions .	firstly , it has been shown in that any completely monotonic function can be approximated as a sum of exponential functions .
agosta et al approached the security and privacy problems involved in the key derivation algorithm adopted by the widespread z-wave home automation protocol .	agosta et al approached the security and privacy problems involved the key derivation algorithm adopted by the widespread z-wave home automation protocol .
particularly , generative adversarial networks and variational auto-encoders have shown significant promise in this direction .	two notable approaches in this area are variational auto-encoders as well as generative adversarial networks .
zwicklbauer et al introduce a method to annotate table headers by mining column content .	by mining column content , zwicklbauer et al propose a method to annotate table headers .
recently , deep neural networks has achieved great success on computer vision .	recently , deep learning methods have made remarkable progress in computer vision and machine learning .
we now proceed as in that carries a natural structure of ν-twisted module for the vertex operator algebra s .	we proceed as in that carries a natural structure of ν-twisted module for s .
this feature can be realized by pre-cancellation of multiuser interference using the optimal dirty paper coding .	it is well known that the capacity of the vector gbc can be achieved by nonlinear dirty paper coding .
the p-wave pairing is dominated by the vertex correction .	the vertex correction suppresses the p-wave pairing .
deep neural networks have been continuously achieving breakthroughs in many challenging ai domains , such as image recognition .	deep convolutional neural networks have made significant breakthroughs in many visual understanding tasks including image classification .
this frame structure requires accurate synchronization among sus , achievable using techniques developed in .	this frame structure requires accurate synchronization among sus , which can be achieved using techniques developed in .
cosmic strings are linear topological defects that can be produced in the early universe via phase transitions .	cosmic strings are line-like topological defects which m a y form during phase transitions in the early universe .
in this section we shall introduce the reader to the new resummation technique for the multipolar waveform introduced in ref .	here , we shall briefly explain the new , parameter-free resummation technique for the multipolar waveform introduced in ref .
as figure 1 shows , the feature learning method is an upgraded version of the skip-gram architecture , which was originally developed for natural language processing and word embedding .	the feature learning methods are based on the skip-gram architecture , which is originally developed for natural language processing and word embedding .
as stated in , the hard parameter sharing paradigm is still pervasive for neural-network based mtl .	current mtl deep model research is dominated by the direct parameters sharing approach .
we prove the following result theorem a .	the precise result we prove is the following theorem c .
it may be shown that for suitable choices of ψ the wavelet transform is invertible , we will not use that property here .	it may be shown that , for suitable choices of ψ , the wavelet transform is invertible , but we will not use that property here .
deep neural networks have been significantly successful in many artificial intelligence tasks such as im- age classification .	in recent years , convolutional neural networks have achieved significant success in many computer vision tasks , including the super-resolution problem .
bridgeland introduced the notion of a stability condition on a triangulated category in .	the notion of stability conditions on a c-linear triangulated category was introduced by bridgeland in .
fundamentally , 2d gabor filter refers a linear filter whose impulse response function defines as the multiplication of harmonic function and gaussian function .	two-dimensional gabor filter refers a linear filter whose impulse response function is defined as the multiplication of harmonic function and gaussian function .
we argue that the definition of the correlation function should be consistent with this equivalence structure .	we require that indistinguishable observables must lead to the same correlation function .
as graphene is a zero band gap material , one of the routes to make use of graphene in electronics industry is to create a small band gap by means of functionalization with external chemical agents .	nevertheless , graphene is a semimetal , ie a zero bandgap material with negligible density of states at the fermi energy , which limits its suitability for electronic or optoelectronic devices .
the molecular atomization energies in these databases were computed using the hybrid density functional pbe0 .	molecular atomization energies from these databases were computed using the pbe0 hybrid density functional .
when the polytope is a 4-dimensional simplex , the concept of reflexive simplex .	a polytope is the convex hull of a point set , and conv denotes the convex hull of a .
fortunately , a special class of nonlinear theory of massive gravity has been successfully found by de rham , gabadadze , and tolley , dubbed as drgt massive gravity theory .	however , more recently a ghost-free nonlinear theory of a massive spin-2 field -de rham-gabadadze-tolley massive gravity -was proposed .
by analyzing the formation of the black ring horizon in this system , we are able to understand the feature of the black ring formation in n-particle systems in higher-dimensions .	using this method , we are able to calculate the initial data of moving particles and understand the effect of motion on the ring horizon formation .
we consider the fifth order ssp ts methods m2 , m3 , and m3 , and compare their performance to the ssp mdrkmethod in , and the non-ssp dormand prince method .	we also consider the sixth order ssp ts methods m2 , m3 , and m3 , as well as the mdrkfrom and the non-ssprkmethod .
some recent studies have shown that deep learning algorithms are successfully used for medical imaging applications .	machine learning algorithms have been successfully applied to diagnostic and prognostic endeavors in pathology .
latent dirichlet allocation is one of the best-known topic models .	latent dirichlet allocation -shown in figure 9a -is one of the most popular topic models .
string theory is a perturbative approach depending on the choice of the background metric , while loop quantum gravity is a canonical approach that does not treat time on an equal footing with space .	within string theory this is the case for the dilaton field whose expectation value is related to the gauge coupling constant at the unification scale .
the data are compared with an nlo qcd calculation based on collinear factorisation and dglap evolution .	the data are compared with two qcd calculations , one based on collinear factorisation in nlo .
the dashed vertical line represents the significance level of the tests .	the dashed vertical line represents the significance level of the tests each species .
as we will show , there is general agreement between model and observation for various properties of the galaxy population .	as we can see , the reasonably good agreement between model prediction and observation also extends to the k-band , in which galaxy luminosity traces the stellar mass .
deep generative networks including variational auto-encoders and generative adversarial nets have enabled effective modeling of high-dimensional data with a low-dimensional latent space .	deep generative models such as variational autoencoders and generative adversarial networks have played a prominent role in the advancement of unsupervised learning .
in this paper we adopt resnet owing to its good performance and simplicity .	in this study we use u-resnet , a hybrid of the unet design pattern .
however we should note that this generating function does not include the winding mode of the theory .	note that the nontrivial reflection amplitude does not exist in this case .
the methods presented in is based on solving the integral equation then computing the function h by to obtain the boundary values of the mapping function .	the method described in requires solving a boundary integral equation with the generalized neumann kernel .
in the case where the cyclic prefix is equal to or longer than the channel length , the hybrid ls-lmmse algorithm will apply directly the lmmse channel estimation technique .	it is clear that in this case where the channel length exceeds the cyclic prefix length , the hybrid ls-lmmse channel estimator shows its true efficiency .
it is anticipated that either of these schemes , and potentially any scheme , may come with an associated gain compression , as in refwhere harmonic injection was used to set the fundamental output phase at a cost of reducing output power .	it is anticipated that either of these schemes , and potentially any scheme , may come with an associated gain compression , as in where harmonic injection was used to set the fundamental output phase at a cost of reducing output power .
furthermore , while diffusion is the mechanism whereby interfaces move in the ising model with ncop , as stated by is obeyed , showing a non brownian character .	while diffusion is the primary mechanism for dampening fluctuations at high density , viscosity becomes more important at small net baryon density .
our choice of the positive-frequency wightman function is motivated by the fact that this function will also determine the response of a unruh-de witt detector .	the correlation function can be obtained from the complete set of solutions and hence the response of an unruh-dewitt detector .
for the svm and other experiments , we used the implementation from scikit-learn .	we used the scikit-learn implementation with lof , osvm , if , knn , svm , rf and nn .
moreover , many recent works showed that neural networks can be successfully used in a number of tasks in natural language processing , such as machine translation .	recurrent neural networks , as a class of deep convolutional networks , have recently shown great promise in tackling many sequence modelling tasks in machine learning , such as automatic speech recognition .
a manifold for which the riemannian measure is the only 2 microlocal lift is called quantum uniquely ergodic .	if the sample has the shape of a grain , it is called a quantum dot .
recently , generative adversarial networks has shown outstanding performance in conditional transfer of pixel-level knowledge .	in particular , generative adversarial network variants have achieved state-of-the-art results for image-to-image translation task .
single photons are very robust carriers of quantum information that can be used for quantum simulations as well as other applications in quantum technology .	single photon emitters are a fundamental element for many quantum information processing and communication technologies .
each node also periodically checks it backpointer and inter-level constraints .	each node periodically checks to see if its backpointer or inter-level constraints have been violated .
more recently , many end-to-end cnn models have also been proposed for image deblurring .	more recently , many end-to-end cnn models for image deblurring have also been proposed .
in related work , address the question of how many original packets are revealed before the whole block is decoded in a fountain code setting .	in related work , address the question of how many original packets are revealed before the whole block is decoded .
for a topological space y denote by sh the abelian category of sheaves of complex vector spaces on y .	if x is any topological space , let 2x denote the set of all closed subsets of x .
the data were reduced using standard procedures in miriad .	the data were reduced using the miriad package .
it remains to ensure gauge invariance , which is the most crucial ingredient .	gauge invariance is the main issue we addressed .
consider a luttinger liquid in one dimension with forward scattering short range mutual interactions in the presence of a scalar potential that is localized near an origin .	consider a luttinger liquid in one dimension with forward scattering short range mutual interactions in the presence of a heavy particle moving with speed small compared with the fermi velocity v f .
the dft calculations are done within the generalized gradient approximation and the perdewburke-ernzerhof exchange correlation function .	the exchange and correlation effects were treated within the generalized gradient approximation .
convolutional neural networks have achieved remarkable success in many computer vision domains such as classification .	for image segmentation , fully convolutional neural networks have set the benchmark performance in medical imaging .
it is not easy to explain the origin of this effect within our fully relativistic model .	it is not easy to understand why the obtained results are so different from well-established non-relativistic predictions .
generative adversarial networks provide an important approach for learning a generative model which generates samples from the real-world data distribution .	generative adversarial networks were first introduced in 2014 as a generative model that attempts to capture the underlying distribution of complex real world data sets .
the generalized gradient approximation parameterized by perdew-burkeernzerhof for the exchange-correlation functional was used .	the exchange and correlation functional employed was the generalized gradient approximation in the parametrization due to perdew-burke-ernzerhof .
a hilbert space is a hilbert module over c .	to begin with , we recall that two different orthonormal bases a and are called b of a d-dimensional hilbert space h b .
with minor modifications , the analysis and calculations in refs can be employed to show how the model works , which are not redescribed here .	with some modifications , the analysis and calculations in references can be employed to show how the model works , which are described in appendix .

to deal with such environment , hausknecht and stone introduced the deep recurrent q-networks .

hausknecht and stone proposed deep recurrent q-network based on a long short term memory network .

the candidate from particle physics responsible for driving inflation is a scalar field , which is called the inflaton field , φ .

the inflaton is a natural candidate for a ngb as we have discussed , because of the necessary flatness of the potential .

recent years have yielded rapid advances in the field of deep learning , largely due to the unparalleled effectiveness of convolutional neural networks on a variety of difficult problems .

recent successes in machine learning have shown that difficult perceptual tasks can be tackled efficiently using deep neural networks .

even though fj is not the first operational model of nominally-typed oop , yet fj is the most widely known operational model of a nominally-typed mainstream oo language , namely java .

even though fj is not the first operational model of nominally-typed oop , fj is the most widely known operational model of a nominally-typed mainstream oo language , namely java .

eamformers are used to steer the beam of a phased array antenna by controlling the phase added to the signal at each antenna element .

obfns are used in wireless communication systems to steer phased array antennas in the desired direction by making use of positive interference of synchronized signals .

on explicit bounds for the spectral gap on compact manifolds .

on the spectral gap for compact manifolds .

willis , amenable and weakly amenable banach algebras with compact multiplication .

dales , amenability and weak amenability for beurling and lipschitz algebras .

now we proceed on to define the notion of s-definite special ideal related with the ring contained in s-definite special field .

we now proceed on to define notions analogues to galois group of automorphisms for s-definite special field .

omniglot is a dataset of over 1623 characters from 50 different alphabets .

omniglot is a set of images of 1623 handwritten characters from 50 alphabets with 20 examples for each class .

the derived functors of rational k-theory and cyclic homology .

the derived functor of negative cyclic homology .

carlevaris-bianco and eustice introduce the generic linear constraint factors and the nonlinear graph sparsification method , respectively .

carlevaris-bianco and eustice introduce the generic linear constraint factors and the nonlinear graph sparsification , respectively .

dbscan is a density-based data clustering algorithm because it finds a number of clusters starting from the estimated density distribution of corresponding nodes .

dbscan is a density-based algorithm which discovers clusters with arbitrary shape using minimal number of input parameters .

it follows that the probability that the first set of arcs forms .

it follows that the probability that the first set of arcs forms an .

hashednets is a recent technique to reduce model sizes by using a hash function to randomly group connection weights , so that all connections within the same hash bucket share a single parameter value .

hashednets is a recent technique to reduce model size by using a hash function to randomly group connection weights , so that all connections within the same hash bucket share a single parameter value .

the space spanned by ideal boson-fermion states that are selected with the aid of the above particle number considerations is called the ideal subspace .

let s be the subspace of t which is the union of all arcs joining postcritical points .

such models have been applied to machine translation , image caption generation , or dialogue systems .

examples include machine translation , image captioning and dialogue systems .

the classical morrey spaces l p , λ were introduced by morrey in 1938 to study the local behavior of solutions of second order elliptic partial differential equations .

the classical morrey space l p , λ was introduced by morrey in connection with elliptic partial differential equations .

deep neural networks are powerful representation learning models which achieve near-human performance in image recognition tasks .

deep neural networks are widely used for many applications including object recognition .

hester and hirschberg have provided an extensive survey on average case analysis of list accessing algorithms .

hester and hirschberg have provided an extensive survey of average case analysis of list update algorithms .

the overall error performance is dominated by the minimum singular value λns .

further optimization of angles are based upon minimizing the average error performance .

among these algorithms , we are specially interested in sabmp as it is capable of mmse estimation even if the distribution of the unknown vector is not available .

among such sparse recovery methods , we are interested specifically in sabmp due to its nature of fulfilling our requirements .

deep convolutional neural networks have demonstrated great success in various machine intelligence areas and enabled new advancement for applications such as video content understanding .

in recent years , deep learning methods , and in particular convolutional neural networks , have achieved considerable success in a range of computer vision applications including object recognition .

there has been much previous work studying a 1 and related sets by exploiting this fourier structure .

there has been much previous work studying a and related sets by exploiting this fourier structure .

we use the svm classifiers from scikit-learn with an rbf kernel and a scaled gamma .

as classifier we use a traditional model , a support vector machine with linear kernel implemented in scikit-learn .

large-scale multiple-input multiple-output systems , also known as massive mimo systems , are considered as a promising technique for next generations of wireless communication networks .

cloud radio access network and massive multiple-input multiple-output are regarded as two key technologies for future wireless systems .

deep neural networks have been widely applied and achieved state-of-art performance on a variety of tasks including image recognition .

deep neural networks have achieved great success across a broad range of domains , such as computer vision , speech processing and natural language processing .

recently , deterministic deep neural networks have demonstrated state-of-the-art performance on many supervised tasks , eg , speech recognition .

in the last decade , convolutional neural networks have shown state of the art accuracy on a variety of visual recognition tasks such as image classification .

the main drawback of the results in is that if a controller fails to exist for the symbolic model , nothing can be concluded regarding the existence of a controller for the original control system .

however , analogously to the results in the main drawback of these approaches is that if a controller fails to exist for the proposed symbolic models , nothing can be concluded regarding the existence of a controller for the original control system .

it follows from theorem 3 6 that c is a subset of d .

so we see that theorem c is a direct consequence of theorem b .

the embedding subnetwork is comprised of a sequence of 4 residual blocks , each comprised of two 2d-convolutional layers .

the content encoder is made of several 2d convolutional layers followed by several residual blocks .

the pileup contribution to the jet energy is estimated on an event-by-event basis using the jet area method described in and is subtracted from the overall jet p t .

the contribution to the jet energy from pile-up is estimated on an event-by-event basis using the jet area method described in ref , and is subtracted from the overall jet p t .

the lagrangian consists of three pieces that have to be investigated separately .

for the lagrangian in is the hamiltonian .

this property appeared recently in the paper in connection with property ptq for discrete quantum groups , where g with representations of bounded degree was termed low .

the latter class has recently appeared in the literature in connection with property for discrete quantum groups .

the squid is a uniquely superconducting electronic device discovered in the mid-1960s that is most sensitive detector of magnetic fields .

the squid comprises two identical josephson junctions with critical current ic and capacitance cj .

in recent years , convolutional neural networks have become the dominant approach for various tasks including classification .

along this direction , convolutional neural networks have been very successful in various computer vision and natural language processing tasks in recent years .

several other works with focus on classifying users based on their mobility periodicity , or a combination of mobility statistics .

several other works with focus on classifying users based on their mobility periodicity .

the origin of this violation is a highly intermittent dynamics characterized by large fluctuations and strongly non-gaussian statistics .

the origin of the violation is the difference between the force acting on the tagged particle , f1 , and the effective force , feff 1 .

our general conclusion regarding gradient descent methods is that in such cases the faster gradient descent methods offer substantial advantages .

our general conclusion is that in situations where many steepest descent steps are required , thus building slowness into the solution procedure , the faster gradient descent method offers substantial advantages .

data reduction was performed with the miriad software package .

data reduction was carried out with the miriad software package using standard procedures .

for a faster processing time , we consider a rather shallow pretrained cnn architecture , namely vgg-f .

in order to obtain automatic features , we experiment with multiple cnn architectures , such as vgg-face .

several researchers have observed that the main bottleneck in mapreduce is the network .

however , it is consistently reported that the network is typically the bottleneck for large-scale mapreduce tasks .

firstly , it has been proved in that any completely monotonic function can be approximated as a sum of exponential functions .

firstly , it has been shown in that any completely monotonic function can be approximated as a sum of exponential functions .

agosta et al approached the security and privacy problems involved in the key derivation algorithm adopted by the widespread z-wave home automation protocol .

agosta et al approached the security and privacy problems involved the key derivation algorithm adopted by the widespread z-wave home automation protocol .

particularly , generative adversarial networks and variational auto-encoders have shown significant promise in this direction .

two notable approaches in this area are variational auto-encoders as well as generative adversarial networks .

zwicklbauer et al introduce a method to annotate table headers by mining column content .

by mining column content , zwicklbauer et al propose a method to annotate table headers .

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

recently , deep learning methods have made remarkable progress in computer vision and machine learning .

we now proceed as in that carries a natural structure of ν-twisted module for the vertex operator algebra s .

we proceed as in that carries a natural structure of ν-twisted module for s .

this feature can be realized by pre-cancellation of multiuser interference using the optimal dirty paper coding .

it is well known that the capacity of the vector gbc can be achieved by nonlinear dirty paper coding .

the p-wave pairing is dominated by the vertex correction .

the vertex correction suppresses the p-wave pairing .

deep neural networks have been continuously achieving breakthroughs in many challenging ai domains , such as image recognition .

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

this frame structure requires accurate synchronization among sus , achievable using techniques developed in .

this frame structure requires accurate synchronization among sus , which can be achieved using techniques developed in .

cosmic strings are linear topological defects that can be produced in the early universe via phase transitions .

cosmic strings are line-like topological defects which m a y form during phase transitions in the early universe .

in this section we shall introduce the reader to the new resummation technique for the multipolar waveform introduced in ref .

here , we shall briefly explain the new , parameter-free resummation technique for the multipolar waveform introduced in ref .

as figure 1 shows , the feature learning method is an upgraded version of the skip-gram architecture , which was originally developed for natural language processing and word embedding .

the feature learning methods are based on the skip-gram architecture , which is originally developed for natural language processing and word embedding .

as stated in , the hard parameter sharing paradigm is still pervasive for neural-network based mtl .

current mtl deep model research is dominated by the direct parameters sharing approach .

we prove the following result theorem a .

the precise result we prove is the following theorem c .

it may be shown that for suitable choices of ψ the wavelet transform is invertible , we will not use that property here .

it may be shown that , for suitable choices of ψ , the wavelet transform is invertible , but we will not use that property here .

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

in recent years , convolutional neural networks have achieved significant success in many computer vision tasks , including the super-resolution problem .

bridgeland introduced the notion of a stability condition on a triangulated category in .

the notion of stability conditions on a c-linear triangulated category was introduced by bridgeland in .

fundamentally , 2d gabor filter refers a linear filter whose impulse response function defines as the multiplication of harmonic function and gaussian function .

two-dimensional gabor filter refers a linear filter whose impulse response function is defined as the multiplication of harmonic function and gaussian function .

we argue that the definition of the correlation function should be consistent with this equivalence structure .

we require that indistinguishable observables must lead to the same correlation function .

as graphene is a zero band gap material , one of the routes to make use of graphene in electronics industry is to create a small band gap by means of functionalization with external chemical agents .

nevertheless , graphene is a semimetal , ie a zero bandgap material with negligible density of states at the fermi energy , which limits its suitability for electronic or optoelectronic devices .

the molecular atomization energies in these databases were computed using the hybrid density functional pbe0 .

molecular atomization energies from these databases were computed using the pbe0 hybrid density functional .

when the polytope is a 4-dimensional simplex , the concept of reflexive simplex .

a polytope is the convex hull of a point set , and conv denotes the convex hull of a .

fortunately , a special class of nonlinear theory of massive gravity has been successfully found by de rham , gabadadze , and tolley , dubbed as drgt massive gravity theory .

however , more recently a ghost-free nonlinear theory of a massive spin-2 field -de rham-gabadadze-tolley massive gravity -was proposed .

by analyzing the formation of the black ring horizon in this system , we are able to understand the feature of the black ring formation in n-particle systems in higher-dimensions .

using this method , we are able to calculate the initial data of moving particles and understand the effect of motion on the ring horizon formation .

we consider the fifth order ssp ts methods m2 , m3 , and m3 , and compare their performance to the ssp mdrkmethod in , and the non-ssp dormand prince method .

we also consider the sixth order ssp ts methods m2 , m3 , and m3 , as well as the mdrkfrom and the non-ssprkmethod .

some recent studies have shown that deep learning algorithms are successfully used for medical imaging applications .

machine learning algorithms have been successfully applied to diagnostic and prognostic endeavors in pathology .

latent dirichlet allocation is one of the best-known topic models .

latent dirichlet allocation -shown in figure 9a -is one of the most popular topic models .

string theory is a perturbative approach depending on the choice of the background metric , while loop quantum gravity is a canonical approach that does not treat time on an equal footing with space .

within string theory this is the case for the dilaton field whose expectation value is related to the gauge coupling constant at the unification scale .

the data are compared with an nlo qcd calculation based on collinear factorisation and dglap evolution .

the data are compared with two qcd calculations , one based on collinear factorisation in nlo .

the dashed vertical line represents the significance level of the tests .

the dashed vertical line represents the significance level of the tests each species .

as we will show , there is general agreement between model and observation for various properties of the galaxy population .

as we can see , the reasonably good agreement between model prediction and observation also extends to the k-band , in which galaxy luminosity traces the stellar mass .

deep generative networks including variational auto-encoders and generative adversarial nets have enabled effective modeling of high-dimensional data with a low-dimensional latent space .

deep generative models such as variational autoencoders and generative adversarial networks have played a prominent role in the advancement of unsupervised learning .

in this paper we adopt resnet owing to its good performance and simplicity .

in this study we use u-resnet , a hybrid of the unet design pattern .

however we should note that this generating function does not include the winding mode of the theory .

note that the nontrivial reflection amplitude does not exist in this case .

the methods presented in is based on solving the integral equation then computing the function h by to obtain the boundary values of the mapping function .

the method described in requires solving a boundary integral equation with the generalized neumann kernel .

in the case where the cyclic prefix is equal to or longer than the channel length , the hybrid ls-lmmse algorithm will apply directly the lmmse channel estimation technique .

it is clear that in this case where the channel length exceeds the cyclic prefix length , the hybrid ls-lmmse channel estimator shows its true efficiency .

it is anticipated that either of these schemes , and potentially any scheme , may come with an associated gain compression , as in refwhere harmonic injection was used to set the fundamental output phase at a cost of reducing output power .

it is anticipated that either of these schemes , and potentially any scheme , may come with an associated gain compression , as in where harmonic injection was used to set the fundamental output phase at a cost of reducing output power .

furthermore , while diffusion is the mechanism whereby interfaces move in the ising model with ncop , as stated by is obeyed , showing a non brownian character .

while diffusion is the primary mechanism for dampening fluctuations at high density , viscosity becomes more important at small net baryon density .

our choice of the positive-frequency wightman function is motivated by the fact that this function will also determine the response of a unruh-de witt detector .

the correlation function can be obtained from the complete set of solutions and hence the response of an unruh-dewitt detector .

for the svm and other experiments , we used the implementation from scikit-learn .

we used the scikit-learn implementation with lof , osvm , if , knn , svm , rf and nn .

moreover , many recent works showed that neural networks can be successfully used in a number of tasks in natural language processing , such as machine translation .

recurrent neural networks , as a class of deep convolutional networks , have recently shown great promise in tackling many sequence modelling tasks in machine learning , such as automatic speech recognition .

a manifold for which the riemannian measure is the only 2 microlocal lift is called quantum uniquely ergodic .

if the sample has the shape of a grain , it is called a quantum dot .

recently , generative adversarial networks has shown outstanding performance in conditional transfer of pixel-level knowledge .

in particular , generative adversarial network variants have achieved state-of-the-art results for image-to-image translation task .

single photons are very robust carriers of quantum information that can be used for quantum simulations as well as other applications in quantum technology .

single photon emitters are a fundamental element for many quantum information processing and communication technologies .

each node also periodically checks it backpointer and inter-level constraints .

each node periodically checks to see if its backpointer or inter-level constraints have been violated .

more recently , many end-to-end cnn models have also been proposed for image deblurring .

more recently , many end-to-end cnn models for image deblurring have also been proposed .

in related work , address the question of how many original packets are revealed before the whole block is decoded in a fountain code setting .

in related work , address the question of how many original packets are revealed before the whole block is decoded .

for a topological space y denote by sh the abelian category of sheaves of complex vector spaces on y .

if x is any topological space , let 2x denote the set of all closed subsets of x .

the data were reduced using standard procedures in miriad .

the data were reduced using the miriad package .

it remains to ensure gauge invariance , which is the most crucial ingredient .

gauge invariance is the main issue we addressed .

consider a luttinger liquid in one dimension with forward scattering short range mutual interactions in the presence of a scalar potential that is localized near an origin .

consider a luttinger liquid in one dimension with forward scattering short range mutual interactions in the presence of a heavy particle moving with speed small compared with the fermi velocity v f .

the dft calculations are done within the generalized gradient approximation and the perdewburke-ernzerhof exchange correlation function .

the exchange and correlation effects were treated within the generalized gradient approximation .

convolutional neural networks have achieved remarkable success in many computer vision domains such as classification .

for image segmentation , fully convolutional neural networks have set the benchmark performance in medical imaging .

it is not easy to explain the origin of this effect within our fully relativistic model .

it is not easy to understand why the obtained results are so different from well-established non-relativistic predictions .

generative adversarial networks provide an important approach for learning a generative model which generates samples from the real-world data distribution .

generative adversarial networks were first introduced in 2014 as a generative model that attempts to capture the underlying distribution of complex real world data sets .

the generalized gradient approximation parameterized by perdew-burkeernzerhof for the exchange-correlation functional was used .

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

a hilbert space is a hilbert module over c .

to begin with , we recall that two different orthonormal bases a and are called b of a d-dimensional hilbert space h b .

with minor modifications , the analysis and calculations in refs can be employed to show how the model works , which are not redescribed here .

with some modifications , the analysis and calculations in references can be employed to show how the model works , which are described in appendix .