HHousen/ParaSCI · Datasets at Hugging Face

sentence1 stringlengths 16 446	sentence2 stringlengths 14 436
now we proceed on to define smarandache homomorphism .	now we proceed on to define the concept of bisemigroup birings .
this gives naturally an xx coupling which is proportional to the tunneling amplitude and , thus , it is strong and less experimentally demanding .	this gives naturally an xx coupling which is proportional to the tunnelling amplitude and , thus , it is strong and less experimentally demanding .
the data were reduced using the herschel interactive processing environment .	the spectral analysis was performed using the xspec package .
in some applications , deep neural networks have been shown to outperform conventional machine learning methods and even human experts .	deep neural networks have shown remarkable success in many domains , such as computer vision .
guiding waves in such waveguides is an intrinsic property of pbg crystals .	the possibility to guide electromagnetic modes in such waveguides is intrinsic property of pbg crystals .
niizeki , monte carlo simulation of the ising model on the penrose lattice , j .	dotera , high temperature expansion for the ising model on the penrose lattice , j .
in an intriguing confluence of computational geometry and networking , geometric routing has shown how simple geometric rules can replace cumbersome routing tables to facilitate effective message passing in a network .	in an intriguing confluence of computational geometry , graph drawing , and networking , geometric routing has shown how simple geometric rules can replace cumbersome routing tables to facilitate effective message passing in a network .
rendall , on the einstein-vlasov system with hyperbolic symmetry .	rendall , theorems on existence and global dynamics for the einstein equations .
all wave packets have initially vanishing angular momentum and vanishing average radial momentum .	however , in our quantum mechanical wave packet the average radial momentum vanishes .
wengler , for the lep collaborations , these proceedings .	roth , for the lep-ew working group , these proceedings .
recently , convolutional neural networks have been deployed successfully in a variety of applications , including imagenet classication .	convolutional neural networks has shown phenomenal results for many computer vision applications .
deep learning has shown promising results in machine vision , object recognition , speech recognition , and language modeling .	deep convolutional neural networks have achieved significant successes in numerous computer vision tasks such as image classification .
carbon is the fourth most abundant element and has a lower ionization potential .	carbon is the largest contributor to the uncertainty in the calculation of the p-p , 13n , and 15o neutrino fluxes .
new data structures for orthogonal range queries .	improved algorithms for the range next value problem and applications .
we adopted the projector augmented wave potentials and the generalized gradient approximation within the perdew-burke-ernzerhof exchange- correlation function .	for exchange and correlation we applied the gradient corrected approach using the generalized gradient approximation functional following the approach suggested by perdew-burke-ernzerhof .
the following figures , which appear in both , show two such surfaces .	the following figures , which appear in both , shows two such surfaces .
powerful deep neural networks have been created and investigated for high-level computer vision tasks such as image classification .	machine learning models , especially deep neural networks , have been deployed prominently in many real-world applications , such as image classification .
batch normalization is used in new added layers to accelerate training process .	batch normalization activation function are also used after each convolutional layer to improve the training process .
deep neural networks have demonstrated extraordinary success in a variety of fields such as computer vision .	deep neural networks have made great strides in many computer vision tasks such as image classification .
deep neural networks perform impressively well in classic machine learning areas such as image classification , segmentation , speech recognition and language translation .	deep convolutional neural networks have achieved great success in various computer vision tasks such as image classification .
statins are drugs that lower cholesterol and help to slow down the furring up of arteries with fats .	beta blockers are drugs that reduce the work of the heart thereby reducing the risk of angina .
since 2012 , neural networks and deep architectures have proven very effective in application areas such as computer vision .	deep convolutional neural networks have already achieved tremendous success on a variety of computer vision tasks such as image classification among many others .
this decrease is a selection effect due to the magnitude detection limits of the stripe 82 area , which will limit the number of galaxies found at higher redshifts .	this decrease is the consequence of the decay of the bipolar outflow phenomenum during the protostellar evolution .
the lagrangian is the constant brane tension integrated over the hyperarea of the brane .	lagrangian is the simplest system with second time derivatives .
the proof is by induction on the rank of mm .	the proof is by induction on the rank of g .
it was pointed out in refthat the non-gaussianity f n l is proportional to γ 2 .	it was pointed out in ref that the non-gaussianity f n l is proportional to γ 2 .
in the last few years , convolutional neural networks have demonstrated outstanding performances in various applications including image recognition , object detection , and recently speech acoustic modeling .	in the last several years , convolutional neural networks have made great achievement in kinds of computer vision tasks , such as classification .
sgr b2 is a giant molecular cloud complex , located close to the galactic centre .	sgr b2 comprises at least four components .
weak continuous measurements of multiqubits systems .	quantum many-body phenomena in coupled cavity arrays .
the subject of this article is a normal form algorithm for the brieskorn lattice replacing this sequence of full divisions by a sequence of partial divisions .	the subject of this article is the kac equation without cutoff .
but , if the neutrino is a dirac particle , all fermions appear in doublets of dirac particles .	neutrino is a massless fermion in the standard model .
convolutional neural networks have exhibited superior performance in various visual tasks , for example , object classification and detection .	recently , convolutional neural networks have achieved remarkable success on many vision tasks such as object recognition .
deep convolutional neural networks have been successful in many computer vision tasks including image classification .	deep learning models have accomplished superior performance in several machine learning problems , which use either visual or audio sources .
we evaluate our method on the pascal voc 2012 image segmentation benchmark , which has 21 semantic classes , including background .	following previous work , we evaluate our instance segmentation performance on the pas-cal voc 2012 validation set which comprises of 1449 images with high-quality annotations .
these objects include the jets , clustered using the jet finding algorithm , with the tracks assigned to the vertex as inputs , and the associated missing transverse momentum , taken as the negative vector sum of the p t of those jets .	here the physics objects are the jets , clustered using the jet finding algorithm with the tracks assigned to the vertex as inputs , and the associated missing transverse momentum , taken as the negative vector p t sum of those jets .
ridge regression and empirical risk minimization .	ordinary least squares and empirical risk minimization .
as a result of non-convexity and high-dimensionality , it was shown that training a general neural network model is np-hard .	unfortunately , it was shown in 1992 that training a very simple neural network is indeed np-hard .
using a bayesian setting in , the authors addressed an algorithm that constructs bayesian uncertainty sets in a safe manner .	in , the authors designed a robustification procedure that builds safe uncertainty sets upon optimal value functions .
deep convolutional neural networks have shown tremendous success in a variety of computer vision tasks , such as image classification .	convolutional neural networks have achieved state-of-the-art performance on the object detection task .
deep neural networks , particularly deep convolutional neural networks , have provided significant improvement in visual tasks such as face recognition , attribute prediction and image classification .	convolutional neural networks have achieved state-of-the-art performance on visual tasks such as image and video recognition in the last few years .
in particular , cosmic strings are one-dimensional topological defects which lead to a scale-invariant spectrum of cosmological perturbations .	cosmic strings are one-dimensional topological defects , which arise naturally in field theories .
we prove the required by induction on the length of s .	we prove the lemma by induction on the length of τ .
doersch et al train the network by predicting the relative positions between sampled patches from an image as self-supervised information .	doersch et al train a model that predicts the spatial relation between image patches of the image .
in reinforcement learning , the goal of an agent is to learn a policy that maximizes long-term returns by sequentially interacting with an unknown environment .	in reinforcement learning , a behaving agent interacts with its environment and tries to maximize the expected future reward it receives from the environment as a consequence of this interaction .
since , deep neural networks have become ubiquitous in most computer vision applications .	deep convolutional neural networks have led to major breakthroughs in many computer vision tasks .
some studies have treated this as a discriminative multiinstance learning problem .	some methods formulate this problem as multiple instance learning .
the class age has the ramsey property and consists of rigid elements .	the class age has the ramsey property as well as the ordering property relative to age .
liu et al proposed to combine conditional random field and the deep neural network for depth estimation .	liu et al presented a deep convolutional neural field model for depth estimation .
collective cognition and decision-making in human and animal groups have received significant attention in a broad scientific community .	collective decision-making in animal groups has received significant attention in a broad scientific community .
manifolds with ricci curvature bounded below .	manifolds with non-negative ricci curvature .
we will survey the theory of non-commutative boundaries for unital operator algebras , and we refer the reader to for a more in-depth treatment of the theory .	since we use extensively the basics of operator space theory , we refer the reader to the monographs for further details .
this feature gave rise to the first proposal of trapped ion based qubit operations by cirac and zoller .	this idea was outlined in the original proposal for quantum information processing using trapped ions .
this soliton is the solution of the field equation , and there are infinite solutions .	the soliton is the unique lowest mass solution for all spacetimes in its class .
in order to exploit the temporal context of the input video , we integrate 3d convolutions into mask r-cnn on top of a resnet-101 backbone .	for each input image , we use the resnet-34 as the backbone structure for basic feature extraction .
generative adversarial networks are one of the dominant approaches for learning generative models in contemporary machine learning research , which provide a flexible algorithm for learning in latent variable models .	generative adversarial networks , introduced by goodfellow et al , have become a widely popular framework for generative modeling using deep neural networks .
unlike smooth models that involve simple components , the new data reviewed here clearly exhibit many irregular structures , such as the sagittarius dwarf tidal stream and the virgo and pisces overdensities in the halo , and the monoceros stream closer to the galactic plane .	unlike the smooth models with simple components that have been used on local scales , new data on larger scales indicate the presence of much more irregular structures , such as the sgr dwarf tidal stream and the virgo and pisces overden sities in the halo , and the monoceros stream closer to the galactic plane .
gauge theory there is a first order phase change , so e is discontinuous while p is continuous .	higher gauge theory is a generalization which describes how strings and membranes change as they move through spacetime .
every perfectly regular countably compact space has countable tightness .	in particular , every countable space has countable tightness .
we adopt the region proposal network from faster rcnn for the object localization .	we use the faster r-cnn from for object detection and recognition .
convolutional neural networks have achieved great success on visual recognition tasks .	deep convolutional networks made great progress in recent years in the field of computer vision .
yao et al propose to use a 3-d convolutional neural networks for modeling video clip dynamic temporal structure and an attention mechanism to select the most relevant temporal clips .	yao et al propose a temporal attention mechanism to automatically select the most relevant temporal segments in the task of video description .
the generalization to several order parameters is also considered .	it can be easily generalized to the case several order parameters .
this is a184271 in the on-line encyclopedia of integer sequences .	this is sequence a000111 in the online encyclopedia of integer sequences .
further measurements and more extensive theoretical and numerical analysis are needed for fully understanding this behavior .	further experimental measurements with high-quality samples are desired to clarify this issue .
therefore , it suffices to show that θ is a morphism of coalgebras .	it suffices to show that β is a monomorphism .
for the gmu component , we use batch normalization applied to each modality matrix .	for regularization , we use batch normalization for all mlps .
a plane wave basis-set with a high cutoff energy of 900 ev was used to expand the wave-function together with the projector augmented wave method as implemented in the vienna ab initio simulation package vasp .	the vienna ab initio simulation package was employed to calculate bulk band structure with the generalized gradient approximation density functional and the projector augmented wave method .
a rigid 3d pendulum is a rigid body supported by a fixed , frictionless pivot , acted on by uniform gravitational force .	a 3d pendulum is a rigid body supported by a fixed frictionless pivot acting under the influence of a uniform gravitational field .
graph signal processing generalizes the classical signal processing for analyzing the structured data on noneuclidean spaces .	graph signal processing extends tools from classical signal processing to deal with data defined on networks and other irregular domains .
to round the fractional solution , we now use randomized swap rounding over the matroid m τ .	after obtaining y , we use swap rounding to round the fractional solution to integral solutions .
deep convolutional neural networks have made significant progress in classification problems , which have shown to generate good results when provided sufficient data .	artificial neural networks , especially with deep network structures , have achieved great success on machine learning applications such as image classification .
object recognition is one of the most challenging problems in computer vision , and is catalysed by the swift development of deep learning in recent years .	visual recognition has made a significant progress due to the widespread use of deep learning architectures .
we can only expect qualitative descriptions and rules for most phenomena of consciousness .	we can only expect general relationships between brain activity and phenomena of consciousness .
our choice is motivated by the strong approximation capabilities of such functions , discussed in , as well as the efficient computation of constraints in the lp problems of the previous section .	our choice is motivated by the strong approximation capabilities of such functions , discussed in , as well as the fact that for certain types of sets one can compute integrals over grbfs analytically .
deep neural networks are now the driving technology for numerous application domains , such as computer vision .	deep neural networks have achieved state-of-the-art performance in many application areas , such as computer vision and natural language processing .
we also adopt the expectation over transformation framework and model the uncertainties of viewing parameters within the optimization procedure .	to make our adversarial examples robust under most physical conditions , we adopt expectation over transformationframework proposed by athalye et al and model such uncertainties within our optimization process .
intuitively , the response time is the minimum delay between a discrete action and a discrete action of the controller .	since the response time is a critical issue for the acceptance of an ir system by its users , the use of time-consuming algorithms to evaluate term-positional information at query time is generally inappropriate .
we initialize the network parameters with the method of he et al .	we initialize all convolutional filters using the method of he et al .
staudacher , monte carlo approach to m-theory , phys .	wheater , rotational symmetry breaking in multi-matrix models , phys .
this remarkable relation is called the rank-size duality .	the duality is a trivial geometrical reflection .
this kind of module has been employed successfully in state-of-the-art deeplabv3 for semantic segmentation .	this kind of module has been employed successfully in the state-of-the- art deeplabv3 for semantic segmentation .
theoretically , the time complexity of step 1 is ot rank , where t rank is the time complexity of the rank operation on strings over the alphabet σ .	theoretically , the time complexity of step 1 is ot rank , where t rank is the time complexity of the rank operation on bwt b .
in this work , we have strived to make the simplest assumptions consistent with our present body of knowledge .	our philosophy in this work is to explore the implications of the simplest assumptions which appear consistent with the existing evidence .
since the hamiltonian constraint is a constant of motion , there is no need to constrain the path-integral representing the evolution operator .	since the hamiltonian constraint is a constant of motion , there is no need to constrain the path integral representing the evolution operator .
in this section , we introduce the threats to validity , following the structure suggested by yin , reporting construct validity , internal validity , external validity , and reliability .	in this section , we will introduce the threats to validity following the structure suggested by yin , reporting construct validity , internal validity , external validity , and reliability .
cognitive radio is a promising paradigm to increase the spectrum usage efficiency and alleviate spectrum scarcity problems in wireless networks .	cognitive radio has been recognized as a promising solution to spectrum scarcity and spectrum under-utilization by allowing the secondary users to access the spectrum of the primary user when the latter is idle .
priezzhev , critical exponents for boundary avalanches in two-dimensional abelian sandpile , j .	priezzhev , the upper critical dimension of the abelian sandpile model , j .
the coulomb interaction consists of a product of the charge density .	coulomb interaction is the nonlinearity essential for entanglement generation , and , in its absence , the singlet state vanishes .
these results support that the large negative mr arises from the ferromagnetic correlation in conducting layers .	nonetheless , the ferrimagnetism and the large negative mr in the crystals suggest that there also exists ferromagnetic correlation .
in the past few years , deep convolutional neural networks have shown promising results on object detection .	boosted by the development of deep convolutional neural network , the accuracy of object detection has been improved greatly .
for example , refused measurement-based quantum computing to construct a multiprover interactive proof system for bqp with a classical verifier .	for example , refused measurement-based quantum computing to show that the verifier needs only single-qubit measurements in qma and qam .
for instance , korepin has introduced in the six-vertex model with domain-wall boundary conditions as a building block of correlation functions of the xxz spin-chain .	in the course of studying scalar products of bethe vectors , korepin has also introduced the so called six-vertex model with domain-wall boundary conditions .
glauber , high energy physics and nuclear structure , edited by s .	yennie , hadronic interactions of electrons and photons , edited by j .
so in this step , we use the gensim implementation of word2vec to build a word2vec model based on the collection of tweets .	we therefore train a 500-dimensional word2vec model on english tags of 30 million flickr images , using the skip-gram algorithm .
huang et al propose a new model which increases the global context-aware to enrich the semantic information of words .	huang et al propose a neural model to utilize the global context in addition to the local information .
we will give experimental evidence to show that the k-flats algorithm is improved by such initialization .	in particular , we give experimental evidence to show that the k-flats algorithm is improved by initialization that is based on the local best-fit heuristic .
over the past few years , neural networks has been widely used in some domains , such as large vocabulary continuous speech recognition .	deep neural networks have achieved state-of-the-art performance on tasks such as image recognition in the last few years .
deep neural networks are at the core of state-of-the-art models for supervised tasks like image recognition and speech recognition .	deep convolutional neural networks have been the state-of-the-art methods for tackling vision tasks .
below the critical temperature , the decrease of the emissivity causes overall suppression of the cooling rate , in particular at lower temperature .	in the superconducting phase , the emissivity decreases and causes suppression of the cooling rate .
the propagator s is the summed propagator including all radiative corrections .	the propagator of this field is the difference of two-point function and the permuted one .

now we proceed on to define smarandache homomorphism .

now we proceed on to define the concept of bisemigroup birings .

this gives naturally an xx coupling which is proportional to the tunneling amplitude and , thus , it is strong and less experimentally demanding .

this gives naturally an xx coupling which is proportional to the tunnelling amplitude and , thus , it is strong and less experimentally demanding .

the data were reduced using the herschel interactive processing environment .

the spectral analysis was performed using the xspec package .

in some applications , deep neural networks have been shown to outperform conventional machine learning methods and even human experts .

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

guiding waves in such waveguides is an intrinsic property of pbg crystals .

the possibility to guide electromagnetic modes in such waveguides is intrinsic property of pbg crystals .

niizeki , monte carlo simulation of the ising model on the penrose lattice , j .

dotera , high temperature expansion for the ising model on the penrose lattice , j .

in an intriguing confluence of computational geometry and networking , geometric routing has shown how simple geometric rules can replace cumbersome routing tables to facilitate effective message passing in a network .

in an intriguing confluence of computational geometry , graph drawing , and networking , geometric routing has shown how simple geometric rules can replace cumbersome routing tables to facilitate effective message passing in a network .

rendall , on the einstein-vlasov system with hyperbolic symmetry .

rendall , theorems on existence and global dynamics for the einstein equations .

all wave packets have initially vanishing angular momentum and vanishing average radial momentum .

however , in our quantum mechanical wave packet the average radial momentum vanishes .

wengler , for the lep collaborations , these proceedings .

roth , for the lep-ew working group , these proceedings .

recently , convolutional neural networks have been deployed successfully in a variety of applications , including imagenet classication .

convolutional neural networks has shown phenomenal results for many computer vision applications .

deep learning has shown promising results in machine vision , object recognition , speech recognition , and language modeling .

deep convolutional neural networks have achieved significant successes in numerous computer vision tasks such as image classification .

carbon is the fourth most abundant element and has a lower ionization potential .

carbon is the largest contributor to the uncertainty in the calculation of the p-p , 13n , and 15o neutrino fluxes .

new data structures for orthogonal range queries .

improved algorithms for the range next value problem and applications .

we adopted the projector augmented wave potentials and the generalized gradient approximation within the perdew-burke-ernzerhof exchange- correlation function .

for exchange and correlation we applied the gradient corrected approach using the generalized gradient approximation functional following the approach suggested by perdew-burke-ernzerhof .

the following figures , which appear in both , show two such surfaces .

the following figures , which appear in both , shows two such surfaces .

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

machine learning models , especially deep neural networks , have been deployed prominently in many real-world applications , such as image classification .

batch normalization is used in new added layers to accelerate training process .

batch normalization activation function are also used after each convolutional layer to improve the training process .

deep neural networks have demonstrated extraordinary success in a variety of fields such as computer vision .

deep neural networks have made great strides in many computer vision tasks such as image classification .

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

deep convolutional neural networks have achieved great success in various computer vision tasks such as image classification .

statins are drugs that lower cholesterol and help to slow down the furring up of arteries with fats .

beta blockers are drugs that reduce the work of the heart thereby reducing the risk of angina .

since 2012 , neural networks and deep architectures have proven very effective in application areas such as computer vision .

deep convolutional neural networks have already achieved tremendous success on a variety of computer vision tasks such as image classification among many others .

this decrease is a selection effect due to the magnitude detection limits of the stripe 82 area , which will limit the number of galaxies found at higher redshifts .

this decrease is the consequence of the decay of the bipolar outflow phenomenum during the protostellar evolution .

the lagrangian is the constant brane tension integrated over the hyperarea of the brane .

lagrangian is the simplest system with second time derivatives .

the proof is by induction on the rank of mm .

the proof is by induction on the rank of g .

it was pointed out in refthat the non-gaussianity f n l is proportional to γ 2 .

it was pointed out in ref that the non-gaussianity f n l is proportional to γ 2 .

in the last few years , convolutional neural networks have demonstrated outstanding performances in various applications including image recognition , object detection , and recently speech acoustic modeling .

in the last several years , convolutional neural networks have made great achievement in kinds of computer vision tasks , such as classification .

sgr b2 is a giant molecular cloud complex , located close to the galactic centre .

sgr b2 comprises at least four components .

weak continuous measurements of multiqubits systems .

quantum many-body phenomena in coupled cavity arrays .

the subject of this article is a normal form algorithm for the brieskorn lattice replacing this sequence of full divisions by a sequence of partial divisions .

the subject of this article is the kac equation without cutoff .

but , if the neutrino is a dirac particle , all fermions appear in doublets of dirac particles .

neutrino is a massless fermion in the standard model .

convolutional neural networks have exhibited superior performance in various visual tasks , for example , object classification and detection .

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

deep convolutional neural networks have been successful in many computer vision tasks including image classification .

deep learning models have accomplished superior performance in several machine learning problems , which use either visual or audio sources .

we evaluate our method on the pascal voc 2012 image segmentation benchmark , which has 21 semantic classes , including background .

following previous work , we evaluate our instance segmentation performance on the pas-cal voc 2012 validation set which comprises of 1449 images with high-quality annotations .

these objects include the jets , clustered using the jet finding algorithm , with the tracks assigned to the vertex as inputs , and the associated missing transverse momentum , taken as the negative vector sum of the p t of those jets .

here the physics objects are the jets , clustered using the jet finding algorithm with the tracks assigned to the vertex as inputs , and the associated missing transverse momentum , taken as the negative vector p t sum of those jets .

ridge regression and empirical risk minimization .

ordinary least squares and empirical risk minimization .

as a result of non-convexity and high-dimensionality , it was shown that training a general neural network model is np-hard .

unfortunately , it was shown in 1992 that training a very simple neural network is indeed np-hard .

using a bayesian setting in , the authors addressed an algorithm that constructs bayesian uncertainty sets in a safe manner .

in , the authors designed a robustification procedure that builds safe uncertainty sets upon optimal value functions .

deep convolutional neural networks have shown tremendous success in a variety of computer vision tasks , such as image classification .

convolutional neural networks have achieved state-of-the-art performance on the object detection task .

deep neural networks , particularly deep convolutional neural networks , have provided significant improvement in visual tasks such as face recognition , attribute prediction and image classification .

convolutional neural networks have achieved state-of-the-art performance on visual tasks such as image and video recognition in the last few years .

in particular , cosmic strings are one-dimensional topological defects which lead to a scale-invariant spectrum of cosmological perturbations .

cosmic strings are one-dimensional topological defects , which arise naturally in field theories .

we prove the required by induction on the length of s .

we prove the lemma by induction on the length of τ .

doersch et al train the network by predicting the relative positions between sampled patches from an image as self-supervised information .

doersch et al train a model that predicts the spatial relation between image patches of the image .

in reinforcement learning , the goal of an agent is to learn a policy that maximizes long-term returns by sequentially interacting with an unknown environment .

in reinforcement learning , a behaving agent interacts with its environment and tries to maximize the expected future reward it receives from the environment as a consequence of this interaction .

since , deep neural networks have become ubiquitous in most computer vision applications .

deep convolutional neural networks have led to major breakthroughs in many computer vision tasks .

some studies have treated this as a discriminative multiinstance learning problem .

some methods formulate this problem as multiple instance learning .

the class age has the ramsey property and consists of rigid elements .

the class age has the ramsey property as well as the ordering property relative to age .

liu et al proposed to combine conditional random field and the deep neural network for depth estimation .

liu et al presented a deep convolutional neural field model for depth estimation .

collective cognition and decision-making in human and animal groups have received significant attention in a broad scientific community .

collective decision-making in animal groups has received significant attention in a broad scientific community .

manifolds with ricci curvature bounded below .

manifolds with non-negative ricci curvature .

we will survey the theory of non-commutative boundaries for unital operator algebras , and we refer the reader to for a more in-depth treatment of the theory .

since we use extensively the basics of operator space theory , we refer the reader to the monographs for further details .

this feature gave rise to the first proposal of trapped ion based qubit operations by cirac and zoller .

this idea was outlined in the original proposal for quantum information processing using trapped ions .

this soliton is the solution of the field equation , and there are infinite solutions .

the soliton is the unique lowest mass solution for all spacetimes in its class .

in order to exploit the temporal context of the input video , we integrate 3d convolutions into mask r-cnn on top of a resnet-101 backbone .

for each input image , we use the resnet-34 as the backbone structure for basic feature extraction .

generative adversarial networks are one of the dominant approaches for learning generative models in contemporary machine learning research , which provide a flexible algorithm for learning in latent variable models .

generative adversarial networks , introduced by goodfellow et al , have become a widely popular framework for generative modeling using deep neural networks .

unlike smooth models that involve simple components , the new data reviewed here clearly exhibit many irregular structures , such as the sagittarius dwarf tidal stream and the virgo and pisces overdensities in the halo , and the monoceros stream closer to the galactic plane .

unlike the smooth models with simple components that have been used on local scales , new data on larger scales indicate the presence of much more irregular structures , such as the sgr dwarf tidal stream and the virgo and pisces overden sities in the halo , and the monoceros stream closer to the galactic plane .

gauge theory there is a first order phase change , so e is discontinuous while p is continuous .

higher gauge theory is a generalization which describes how strings and membranes change as they move through spacetime .

every perfectly regular countably compact space has countable tightness .

in particular , every countable space has countable tightness .

we adopt the region proposal network from faster rcnn for the object localization .

we use the faster r-cnn from for object detection and recognition .

convolutional neural networks have achieved great success on visual recognition tasks .

deep convolutional networks made great progress in recent years in the field of computer vision .

yao et al propose to use a 3-d convolutional neural networks for modeling video clip dynamic temporal structure and an attention mechanism to select the most relevant temporal clips .

yao et al propose a temporal attention mechanism to automatically select the most relevant temporal segments in the task of video description .

the generalization to several order parameters is also considered .

it can be easily generalized to the case several order parameters .

this is a184271 in the on-line encyclopedia of integer sequences .

this is sequence a000111 in the online encyclopedia of integer sequences .

further measurements and more extensive theoretical and numerical analysis are needed for fully understanding this behavior .

further experimental measurements with high-quality samples are desired to clarify this issue .

therefore , it suffices to show that θ is a morphism of coalgebras .

it suffices to show that β is a monomorphism .

for the gmu component , we use batch normalization applied to each modality matrix .

for regularization , we use batch normalization for all mlps .

a plane wave basis-set with a high cutoff energy of 900 ev was used to expand the wave-function together with the projector augmented wave method as implemented in the vienna ab initio simulation package vasp .

the vienna ab initio simulation package was employed to calculate bulk band structure with the generalized gradient approximation density functional and the projector augmented wave method .

a rigid 3d pendulum is a rigid body supported by a fixed , frictionless pivot , acted on by uniform gravitational force .

a 3d pendulum is a rigid body supported by a fixed frictionless pivot acting under the influence of a uniform gravitational field .

graph signal processing generalizes the classical signal processing for analyzing the structured data on noneuclidean spaces .

graph signal processing extends tools from classical signal processing to deal with data defined on networks and other irregular domains .

to round the fractional solution , we now use randomized swap rounding over the matroid m τ .

after obtaining y , we use swap rounding to round the fractional solution to integral solutions .

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

artificial neural networks , especially with deep network structures , have achieved great success on machine learning applications such as image classification .

object recognition is one of the most challenging problems in computer vision , and is catalysed by the swift development of deep learning in recent years .

visual recognition has made a significant progress due to the widespread use of deep learning architectures .

we can only expect qualitative descriptions and rules for most phenomena of consciousness .

we can only expect general relationships between brain activity and phenomena of consciousness .

our choice is motivated by the strong approximation capabilities of such functions , discussed in , as well as the efficient computation of constraints in the lp problems of the previous section .

our choice is motivated by the strong approximation capabilities of such functions , discussed in , as well as the fact that for certain types of sets one can compute integrals over grbfs analytically .

deep neural networks are now the driving technology for numerous application domains , such as computer vision .

deep neural networks have achieved state-of-the-art performance in many application areas , such as computer vision and natural language processing .

we also adopt the expectation over transformation framework and model the uncertainties of viewing parameters within the optimization procedure .

to make our adversarial examples robust under most physical conditions , we adopt expectation over transformationframework proposed by athalye et al and model such uncertainties within our optimization process .

intuitively , the response time is the minimum delay between a discrete action and a discrete action of the controller .

since the response time is a critical issue for the acceptance of an ir system by its users , the use of time-consuming algorithms to evaluate term-positional information at query time is generally inappropriate .

we initialize the network parameters with the method of he et al .

we initialize all convolutional filters using the method of he et al .

staudacher , monte carlo approach to m-theory , phys .

wheater , rotational symmetry breaking in multi-matrix models , phys .

this remarkable relation is called the rank-size duality .

the duality is a trivial geometrical reflection .

this kind of module has been employed successfully in state-of-the-art deeplabv3 for semantic segmentation .

this kind of module has been employed successfully in the state-of-the- art deeplabv3 for semantic segmentation .

theoretically , the time complexity of step 1 is ot rank , where t rank is the time complexity of the rank operation on strings over the alphabet σ .

theoretically , the time complexity of step 1 is ot rank , where t rank is the time complexity of the rank operation on bwt b .

in this work , we have strived to make the simplest assumptions consistent with our present body of knowledge .

our philosophy in this work is to explore the implications of the simplest assumptions which appear consistent with the existing evidence .

since the hamiltonian constraint is a constant of motion , there is no need to constrain the path-integral representing the evolution operator .

since the hamiltonian constraint is a constant of motion , there is no need to constrain the path integral representing the evolution operator .

in this section , we introduce the threats to validity , following the structure suggested by yin , reporting construct validity , internal validity , external validity , and reliability .

in this section , we will introduce the threats to validity following the structure suggested by yin , reporting construct validity , internal validity , external validity , and reliability .

cognitive radio is a promising paradigm to increase the spectrum usage efficiency and alleviate spectrum scarcity problems in wireless networks .

cognitive radio has been recognized as a promising solution to spectrum scarcity and spectrum under-utilization by allowing the secondary users to access the spectrum of the primary user when the latter is idle .

priezzhev , critical exponents for boundary avalanches in two-dimensional abelian sandpile , j .

priezzhev , the upper critical dimension of the abelian sandpile model , j .

the coulomb interaction consists of a product of the charge density .

coulomb interaction is the nonlinearity essential for entanglement generation , and , in its absence , the singlet state vanishes .

these results support that the large negative mr arises from the ferromagnetic correlation in conducting layers .

nonetheless , the ferrimagnetism and the large negative mr in the crystals suggest that there also exists ferromagnetic correlation .

in the past few years , deep convolutional neural networks have shown promising results on object detection .

boosted by the development of deep convolutional neural network , the accuracy of object detection has been improved greatly .

for example , refused measurement-based quantum computing to construct a multiprover interactive proof system for bqp with a classical verifier .

for example , refused measurement-based quantum computing to show that the verifier needs only single-qubit measurements in qma and qam .

for instance , korepin has introduced in the six-vertex model with domain-wall boundary conditions as a building block of correlation functions of the xxz spin-chain .

in the course of studying scalar products of bethe vectors , korepin has also introduced the so called six-vertex model with domain-wall boundary conditions .

glauber , high energy physics and nuclear structure , edited by s .

yennie , hadronic interactions of electrons and photons , edited by j .

so in this step , we use the gensim implementation of word2vec to build a word2vec model based on the collection of tweets .

we therefore train a 500-dimensional word2vec model on english tags of 30 million flickr images , using the skip-gram algorithm .

huang et al propose a new model which increases the global context-aware to enrich the semantic information of words .

huang et al propose a neural model to utilize the global context in addition to the local information .

we will give experimental evidence to show that the k-flats algorithm is improved by such initialization .

in particular , we give experimental evidence to show that the k-flats algorithm is improved by initialization that is based on the local best-fit heuristic .

over the past few years , neural networks has been widely used in some domains , such as large vocabulary continuous speech recognition .

deep neural networks have achieved state-of-the-art performance on tasks such as image recognition in the last few years .

deep neural networks are at the core of state-of-the-art models for supervised tasks like image recognition and speech recognition .

deep convolutional neural networks have been the state-of-the-art methods for tackling vision tasks .

below the critical temperature , the decrease of the emissivity causes overall suppression of the cooling rate , in particular at lower temperature .

in the superconducting phase , the emissivity decreases and causes suppression of the cooling rate .

the propagator s is the summed propagator including all radiative corrections .

the propagator of this field is the difference of two-point function and the permuted one .