HHousen/ParaSCI · Datasets at Hugging Face

sentence1 stringlengths 16 446	sentence2 stringlengths 14 436
section two introduces smarandache lie algebras and basic properties of lie algebras .	chapter four is devoted to the introduction of several new concepts in lie algebras and smarandache lie algebras .
we may again need to take account of the landau levels .	here we should comment about the anti-particle contribution .
in the present case , however , the entropy release may be smeared out to some extent by the enlargement of the embedding space dimensions .	naively , one would expect that the latent-heat release is smeared out by the enlargement of the embedding spatial dimensions .
one can consider orbifolds and discrete quotients of minkowski spacetime acting non-trivially on time .	as an example one can consider orbifold quotients of minkowski space-time which non-trivially act on both space and time .
recently deep neural networks have attained impressive performance in many fields such as image classification .	deep neural networks have made great strides in many computer vision tasks such as image classification .
the quasi-optical bridge transmits the microwaves from the source to the probe head .	the wire-grid above the probe head separates the resonance signal from the reflected wave .
the first general proof of the positive energy theorem was done by schoen and yau .	the positive mass theorem first proved by schoen and yau in 1981 using spinors .
however nucleation is a stochastic phenomenon and therefore dimer formation may also occur on vicinal terraces every now and then , while on bottom terraces it is an exceedingly rare event .	since nucleation is a rare event , the population of clusters is very low , and the interaction between clusters is negligible .
first-principles calculations were performed within the framework of density functional theory using the projector-augmented wave method .	electronic localized function calculations on the basis of density function theory was performed using the vienna ab initio simulation package .
convolutional neural networks have witnessed great improvement on a series of vision tasks such as object classification .	deep convolutional neural networks have made significant progress in classification problems , which have shown to generate good results when provided sufficient data .
for instance the max-min hill climbing algorithm firstly builds the skeleton of a bayesian network using conditional independence tests and then performs a bayesian-scoring greedy hill-climbing search to orient the edges .	for example , the max-min hill climbing algorithm estimates the skeleton using a constraint-based method , and then orients the edges by using a greedy search algorithm .
we evaluate our method using the kitti eigen split , which has 697 test images from 29 scenes .	we use the 22600 training images from 28 scenes and 697 test images from another 29 scenes based on eigen split .
in order to overcome this problem , he et al proposed the deep residual learning framework to learn the residual of the identity map .	to overcome these problems , he et al proposed residual learning technique to ease the training of networks and enables them to be substantially deeper .
according to the inflationary paradigm the universe undergoes inflation when dominated by the potential density of a scalar field , which is called the inflaton field .	the inflaton field is a light field , it is frozen on large scales , and it is important to use the full equation of motion equation including metric terms for its fluctuations qk .
similarly , zhou et al show that this approach also works for images distorted with noise .	similarly , zhou et al show the effectiveness of fine-tuning for both noisy and blurry images .
other nonlinear dimensionality reduction methods include manifold learning methods such as isomap , locally linear embedding , among others .	typical manifold learning methods include the locally linear embedding , the local tangent space alignment method , the laplacian eigenmap .
one-hop opportunistic network coding scheme is considered for video streaming over wireless networks in .	one-hop opportunistic nc scheme is improved for video streaming over wireless networks in .
we have incorporated the tcaf model in xspec as a local model to achieve this spectral fitting .	we modelled the spectra using the projct model implemented in the xspec spectral fitting package .
in this section , we evaluate our approach on coco dataset , which has 80 object categories .	we evaluate the proposed san on ms coco dataset that has 80 object categories .
the phase space is the moduli space of meromorphic functions of certain class on a fixed riemann surface σ , which is either a sphere , a cylinder or a torus .	the phase space is a four-dimensional euclidean space spanned by the canonical coordinates p ir2 .
we show that relativistic effects on the spectral function of the density fluctuation appears only in the width of sound modes , irrespective of the choice of the frame .	we have shown that the relativistic effect on the spectral function of the density fluctuation appears only in the width of the lorentzian function .
convolutional neural networks have shown significant success in challenging tasks in image classification and recognition .	c onvolutional neural networks have achieved state-of-the-art performance on various visual recognition tasks such as image classification .
the physical interpretation is the following .	the physical interpretation is that there is a flux attached to each particle .
the base system utilizes a faster r-cnn head as the object detection module .	the object detection module structure is identical to that of the faster-rcnn .
this tower is a fibrant replacement for bu .	consider a tower denote a cell datum for hn .
on the other hand , cluster algebras are known to have quantum analogues , the so-called quantum cluster algebras .	cluster algebras have the quantum counterparts , called quantum cluster algebras .
deep learning has led to series of breakthroughs in many fields of applied machine learning , especially in image classification or natural language processing .	convolutional neural networks have shown outstanding performance in many fundamental areas in computer vision , enabled by the availability of large-scale annotated datasets .
the filled symbols denote sources in the robust sample .	the filled symbols denote cluster core galaxies and the open symbols galaxies from the cluster periphery .
we minimize the cross entropy loss using gradient-based optimization and the adam update rule .	in the proposed network we employ the categorical cross-entropy loss function , which is minimized using the adam optimization method .
the electron-exchange correlation energy is described by using the functional of perdew , burke , and ernzerhof based within the generalized gradient approximation .	the exchange-correlation energy of electrons is treated within a generalized gradient approximation with the functional parameterized by perdew , burke and ernzerhof .
linsker showed that despite spontaneous neural activity in layer a being uncorrelated , the gaussian connectivity distributions introduce spatial correlations in the inputs to layer b neurons .	linsker showed that , under gaussian synaptic connectivity distributions , correlation between two neurons in the second layer was a gaussian function of their radial separation distance in the laminar .
millimeter wave communication is a promising technique for meeting the ever-increasing mobile traffic demand in next generation wireless communication systems due to vast swaths of available spectrum .	massive multiple input multiple output technology is one of the promising means for achieving very high energy and spectrum efficiency requirements of the future 5g networks .
for both variants , we use a resnet-50 until the final convolutional layer of the 4-th stage .	for the office-31 and visda dataset , we utilize the resnet-50 with an embedding layer to represent the generator g .
visual recognition from images has witnessed tremendous success in recent years with the advent of deep convolutional neural networks .	the performance of object detectors has been dramatically improved thanks to the advance of deep convolutional neural networks .
the ordinate is the flux normalized to the maximum .	the ordinate is the type assigned from the total flux .
an isomorphism is a morphism which is bijective on both edges and vertices .	this isomorphism is a projectivization of a natural isomorphism between a space dual to a factor-space and a subspace of a dual space dual to the kernel of the factorization .
recent developments in learning image feature representations for object and place recognition have made image retrieval a viable method for localization .	in recent years , methods using convolution neural network have been successful in the classification of image recognition .
our algorithm is evaluated on the dataset provided by the miccai 2015 gland segmentation challenge contest and achieves state-of-the-art performance among all participants and other popular methods of instance segmentation .	our algorithm is evaluated on the dataset provided by miccai 2015 gland segmentation challenge contest and achieves state-of-the-art performance among all participants and other popular methods of instance segmentation .
word embeddings learned using neural methods have been shown to be tremendously effective on several nlp tasks .	non-linear interactions within a local context have been shown to improve empirical performance in various tasks .
deshpande , the eic project , these proceedings .	stasto , qcd at the lhec , these proceedings .
convolutional neural networks have been demonstrated to have state-of-the-art performances in many computer vision tasks such as image classification .	deep learning has been used as a dramatically powerful tool in computer vision tasks such as image recognition .
let us now prove the divisibility on the left .	let us now give another description of the elementary y .
for the feature extractor , we use the resnet-101 pre-trained on imagenet with the parameters fixed and truncated at the conv4 23 layer .	for the image-sentence matching model , we use resnet-101 pre-trained on imagenet as the cnn image encoder and one-layer bi-directional gru with 512 hidden units as the sentence encoder .
these too have the same forms as those given in the definitions of each order gauge invariant variable for the perturbations of an arbitrary field .	these indeed do have the same forms as the definitions for each order gauge invariant variable for an arbitrary field .
finally , to accelerate deep network training , we use batch normalization before the activation function in each graph convolutional layer .	to accelerate the training of the siamese network , we add a batch normalization layer after each convolutional layer .
now we will define level four interval polynomial groupoids .	now we proceed onto define matrix groupoids of type ii .
a multi-source multi-hop setting in broadcast wireless networks was investigated in and a fundamental lower bound on the average aoi was derived .	a multiple-source multihop setting in broadcast wireless networks was investigated in and a fundamental lower bound on the average aoi was derived .
the electrons of this band may lower their coulomb energy by occupying an atomiclike state represented by these wannier functions .	the electrons may lower their coulomb energy by occupying an atomiclike state reprein sented by these spin-dependent wannier functions .
li et al propose new objective functions based on maximum mutual information for neural conversation models to generate an informative and relevant response to a given message .	li et al use maximum mutual information as the objective function , as a way of encouraging informative agent responses .
the other variant of uses quasi-cyclic low density parity-check codes .	the other one tries to combine these two positive aspects by requiring quasi-cyclic ldpc codes .
c onvolutional neural networks are attracting interest in the fields of image recognition .	convolutional neural networks have recently exhibited great performance in various fields such as computer vision .
the paradigm shift from heuristic reasoning to machine learning has transformed computer vision and natural language processing over the last few years and is starting to impact more traditional fields of science .	in recent years , advances in deep learning and reinforcement learning have led to tremendous progress across many areas of natural language processing and gameplay .
in recent years , convolutional neural networks has achieved remarkable results in a wide range of computer vision applications .	convolutional neural networks have shown its great effectiveness in computer vision tasks .
in recent years , deep convolutional neural networks represented by alexnet have shown strong ability on various kinds of large-scale computer vision tasks , including object detection , localization and classification .	recently , deep convolutional neural networks show promising performances in various computer vision tasks such as object classification , localization .
from the work of juschenko and monod , it is known that the topological full group of a z cantor minimal system is a countable amenable group .	juschenko and monod have shown that topological full groups of minimal z-actions on the cantor space are amenable .
recent breakthroughs in object detection are driven by supervised approaches with deep convolutional neural networks .	region-based approaches with convolutional neural networks have achieved great success in object detection .
bierstone and pd milman , relations among analytic functions i , ann .	bierstone and pd milman , composite differentiable functions , ann .
for visual stream , we apply the widely-used model of 19-layers vggnet with batch normalization .	for the convolutional layers , we use the vgg16 model pre-trained on imagenet data .
in recent years , convolutional neural networks methods have demonstrated highly accurate and reliable performance across a variety of computer-vision related tasks , including image classification .	over the past few years deep convolutional neural networks have emerged as the method of choice for the majority of computer vision tasks that require learning from data .
next , we deal with the linear susceptibility for the bond-orientational order parameters .	now , we turn to the linear susceptibility for the global bond-orientational order parameters near the melting transition .
deep neural networks have been extensively applied in many fields , such as image recognition .	in recent years , deep neural networks have gained enormous popularity for a wide spectrum of applications , ranging from image recognition .
the lowest order deviation from the mb distribution is characterized by the flatness of the distribution , which is called the fourth cumulant or the kurtosis .	however , the kurtosis , which is a measure of volatility surprises , is in fact minimum at the open of the market , when the volatility is at its peak .
gyrokinetic simulation is an important tool for investigating properties of the low frequency turbulence in magnetized plasmas .	gyrokinetics is one of the major frameworks used in theoretical and numerical studies of low-frequency turbulence in magnetized fusion plasmas .
since first coined by in 1982 , noninterference property has become the main criterion for software security .	noninterference is commonly known as the baseline property of information flow security .
these deep learning based approaches have been applied to high-level vision challenges such as image recognition and object detection .	deep learning based models have emerged as an extremely powerful framework to deal with different kinds of vision problems including image classification .
to further characterize the transport in the nanowire devices , effective mobility was extracted using the y-function method , which agrees reasonably well with the split-cv method and allows for the suppression of the series resistance effect .	the low field mobility is extracted using the y-function method to further elucidate the transport performance of the nanowire devices , confirming the enhanced mobility for smaller nanowires .
motivated by the generic failure of these local models , we propose an original model for turbulence which incorporates global properties of the flow .	motivated by this failure , we propose an original turbulence model which is derived using global considerations .
recent studies have revealed that structured or group sparsity can offer powerful reconstruction performance for image denoising .	recent studies have shown that structured or group sparsity can offer more promising performance for image restoration tasks .
the ellipses denote terms whi h are rst order in 4 spa e-time derivatives .	ellipses denote uncertainties on structural parameters and correspond to 1σ confidence contours .
their magnitude gives a measure of the inaccuracy of the separable ansatz .	its magnitude is a decreasing function of l , since as the levels are further from ef their contribution to the pairing fluctuations is smaller .
we use resnet as our backbone network , owing to its consistently good performance on many vision tasks .	we adopt resnet101 as our network architecture due to its good trade-off between accuracy and efficiency .
the rotation curves presented here are not corrected for the effects of beam smearing .	these rotation curves are not corrected for the effects of beam smearing .
convolutional neural networks have recently been very successful on a variety of recognition and classification tasks .	deep convolutional neural networks have demonstrated superior performance in various computer vision tasks .
quantum entanglement is a quite amazing physical phenomenon , and has attracted intensive interest in recent years due to its possible applications in quantum computation , teleportation and cryptography , as well as to its connections to quantum chaos .	quantum entanglement is a mathematically well-defined concept and quite universal in the sense that entanglement in a composite system means its possession of quantum-mechanical correlation , regardless of its usefulness for a particular task .
any such subspace is called a cartan subspace of p .	a non-zero summand vλ in this decomposition is called the weight subspace for the weight λ .
deep neural networks are extremely important in various applications including computer vision , speech recognition , and natural language processing .	deep neural networks have recently led to significant improvements in many fields , such as image classification .
recently , deep convolution neural networks have been applied to solve the stereo matching problem .	the use of deep convolutional networks has recently advanced the accuracy of stereo matching algorithms considerably .
note however that the second order approximation with respect to the transfer hamiltonian that we keep here implies that coherences between many-body states with different particle numbers are excluded .	it is known that the form of the gme that we use here , in the lowest coupling order , does not guarantee the positivity of the diagonal elements of ρ , ie the probabilities of many-body states .
for slc loops , tiwari proved that the problem is decidable when the update is linear and the variables range over r .	tiwari showed that termination of single-path linear loops is decidable over r d .
note that there is not any form of centralized index , nor any flooding of queries , nor any form of partitioned global index .	note that in ddd there is not any form of centralized index , nor any flooding of queries , nor any form of partitioned global index .
we used perdew-burke-ernzerhof pseudopotentials from the standard solid state pseudopotentials efficiency database .	we use the generalised gradient approximation to density functional theory proposed by perdew , burke , and ernzerhof .
deep convolutional neural networks have made significant breakthroughs in many visual understanding tasks including image classification .	neural networks have achieved state-of-the-art results in a wide variety of supervised learning tasks , such as image recognition .
echoing the arguments of claussen and osborne , science education in new zealand , and internationally , needs to highlight the utility value of physics in culture , scientific literacy , and employment .	echoing the arguments of claussen and osborne , science education in new zealand , and internationally , needs to highlight the utility value of physics in culture , in boosting scientific literacy , and employment .
mann et al and wood et al found strong evidence that the accretion of moderately volatile elements into earth occurred mostly during the late stages of accretion , but before core formation was complete .	mann et al and wood et al found evidence that the accretion of moderately volatile elements to the earth occurred mostly before core formation was complete .
a general framework for automatic termination analysis of logic programs .	a framework for analyzing the termination of definite logic programs with respect to call patterns .
we use the adaptive moment algorithm for training the model .	we use adam optimizer in the stochastic gradient descent setting to train all models .
recent developments showed that neural networks can be applied successfully in many technical applications like computer vision .	in recent years , machine learning with deep neural networks has surged into popularity in many application areas .
the number in the lower right corner of each panel indicates the total number of standard-star observations represented .	the number in the bottom right corner of each panel indicates the total number of stellar observations that went into these averages .
we discuss how the classification of markov equations for different geometries into families relates the rg flows of the corresponding gauge theories .	we will discuss how the connection between markov equations for different singularities can be exploited to relate rg cascades for these geometries .
deep learning is attracting much attention in the fields of visual object recognition , speech recognition , object detection , among many others .	deep learning has recently seen enormous success in challenging problems such as object recognition in natural images , automatic speech recognition and machine translation .
our proposed method is implemented based on the deep learning library pytorch .	we implement our system with the open-source deep learning framework pytorch .
quantum computers can solve certain problems , such as simulation of quantum-mechanical systems , exponentially faster than the best classical algorithms known .	quantum algorithms can solve certain computational problems much faster than classical computers , and most likely will be of great impact once quantum computers are available .
chun , some fourth-order iterative methods for solving nonlinear equa tions , appl .	wang , fourth-order iterative methods free from second derivative , appl .
the study of complex networks has received an enormous amount of attention from the scientific community in recent years .	in the last twenty years very significant advances in the understanding of complex systems have been obtained using network theory .
vaswani et al proposed an attentional model for machine translation eschewing recurrent architectures .	vaswani et al demonstrated that machine translation models could achieve state-of-the-art results by solely using a self-attention model .
convolutional neural networks have recently been very successful on a variety of recognition and classification tasks .	recently , deep neural networks have enabled breakthroughs on computer vision and natural language processing tasks .
they proposed to use generative adversarial networks to avoid the blurring artifacts .	this motivates the recent studies to explore generative adversarial nets .
radio emission is a sure sign that relativistic electrons are present and flux levels , sharp radio edges , etc .	thus the radio emission is a key to interpret the physics of cosmic rays , and conversely , any attempt to understand cosmic rays should also try to understand the properties of the radio emission .
nanda et al propose an approach to select regression tests in systems that contain frequent changes in non-code artifacts .	nanda et al introduce a regression test selection technique to selects a subset of existing test cases .
we employed the resnet34 architecture for learning the feature embedding .	for this purpose , we designed a specific computational block inspired by the resnet residual blocks .

section two introduces smarandache lie algebras and basic properties of lie algebras .

chapter four is devoted to the introduction of several new concepts in lie algebras and smarandache lie algebras .

we may again need to take account of the landau levels .

here we should comment about the anti-particle contribution .

in the present case , however , the entropy release may be smeared out to some extent by the enlargement of the embedding space dimensions .

naively , one would expect that the latent-heat release is smeared out by the enlargement of the embedding spatial dimensions .

one can consider orbifolds and discrete quotients of minkowski spacetime acting non-trivially on time .

as an example one can consider orbifold quotients of minkowski space-time which non-trivially act on both space and time .

recently deep neural networks have attained impressive performance in many fields such as image classification .

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

the quasi-optical bridge transmits the microwaves from the source to the probe head .

the wire-grid above the probe head separates the resonance signal from the reflected wave .

the first general proof of the positive energy theorem was done by schoen and yau .

the positive mass theorem first proved by schoen and yau in 1981 using spinors .

however nucleation is a stochastic phenomenon and therefore dimer formation may also occur on vicinal terraces every now and then , while on bottom terraces it is an exceedingly rare event .

since nucleation is a rare event , the population of clusters is very low , and the interaction between clusters is negligible .

first-principles calculations were performed within the framework of density functional theory using the projector-augmented wave method .

electronic localized function calculations on the basis of density function theory was performed using the vienna ab initio simulation package .

convolutional neural networks have witnessed great improvement on a series of vision tasks such as object classification .

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

for instance the max-min hill climbing algorithm firstly builds the skeleton of a bayesian network using conditional independence tests and then performs a bayesian-scoring greedy hill-climbing search to orient the edges .

for example , the max-min hill climbing algorithm estimates the skeleton using a constraint-based method , and then orients the edges by using a greedy search algorithm .

we evaluate our method using the kitti eigen split , which has 697 test images from 29 scenes .

we use the 22600 training images from 28 scenes and 697 test images from another 29 scenes based on eigen split .

in order to overcome this problem , he et al proposed the deep residual learning framework to learn the residual of the identity map .

to overcome these problems , he et al proposed residual learning technique to ease the training of networks and enables them to be substantially deeper .

according to the inflationary paradigm the universe undergoes inflation when dominated by the potential density of a scalar field , which is called the inflaton field .

the inflaton field is a light field , it is frozen on large scales , and it is important to use the full equation of motion equation including metric terms for its fluctuations qk .

similarly , zhou et al show that this approach also works for images distorted with noise .

similarly , zhou et al show the effectiveness of fine-tuning for both noisy and blurry images .

other nonlinear dimensionality reduction methods include manifold learning methods such as isomap , locally linear embedding , among others .

typical manifold learning methods include the locally linear embedding , the local tangent space alignment method , the laplacian eigenmap .

one-hop opportunistic network coding scheme is considered for video streaming over wireless networks in .

one-hop opportunistic nc scheme is improved for video streaming over wireless networks in .

we have incorporated the tcaf model in xspec as a local model to achieve this spectral fitting .

we modelled the spectra using the projct model implemented in the xspec spectral fitting package .

in this section , we evaluate our approach on coco dataset , which has 80 object categories .

we evaluate the proposed san on ms coco dataset that has 80 object categories .

the phase space is the moduli space of meromorphic functions of certain class on a fixed riemann surface σ , which is either a sphere , a cylinder or a torus .

the phase space is a four-dimensional euclidean space spanned by the canonical coordinates p ir2 .

we show that relativistic effects on the spectral function of the density fluctuation appears only in the width of sound modes , irrespective of the choice of the frame .

we have shown that the relativistic effect on the spectral function of the density fluctuation appears only in the width of the lorentzian function .

convolutional neural networks have shown significant success in challenging tasks in image classification and recognition .

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

the physical interpretation is the following .

the physical interpretation is that there is a flux attached to each particle .

the base system utilizes a faster r-cnn head as the object detection module .

the object detection module structure is identical to that of the faster-rcnn .

this tower is a fibrant replacement for bu .

consider a tower denote a cell datum for hn .

on the other hand , cluster algebras are known to have quantum analogues , the so-called quantum cluster algebras .

cluster algebras have the quantum counterparts , called quantum cluster algebras .

deep learning has led to series of breakthroughs in many fields of applied machine learning , especially in image classification or natural language processing .

convolutional neural networks have shown outstanding performance in many fundamental areas in computer vision , enabled by the availability of large-scale annotated datasets .

the filled symbols denote sources in the robust sample .

the filled symbols denote cluster core galaxies and the open symbols galaxies from the cluster periphery .

we minimize the cross entropy loss using gradient-based optimization and the adam update rule .

in the proposed network we employ the categorical cross-entropy loss function , which is minimized using the adam optimization method .

the electron-exchange correlation energy is described by using the functional of perdew , burke , and ernzerhof based within the generalized gradient approximation .

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

linsker showed that despite spontaneous neural activity in layer a being uncorrelated , the gaussian connectivity distributions introduce spatial correlations in the inputs to layer b neurons .

linsker showed that , under gaussian synaptic connectivity distributions , correlation between two neurons in the second layer was a gaussian function of their radial separation distance in the laminar .

millimeter wave communication is a promising technique for meeting the ever-increasing mobile traffic demand in next generation wireless communication systems due to vast swaths of available spectrum .

massive multiple input multiple output technology is one of the promising means for achieving very high energy and spectrum efficiency requirements of the future 5g networks .

for both variants , we use a resnet-50 until the final convolutional layer of the 4-th stage .

for the office-31 and visda dataset , we utilize the resnet-50 with an embedding layer to represent the generator g .

visual recognition from images has witnessed tremendous success in recent years with the advent of deep convolutional neural networks .

the performance of object detectors has been dramatically improved thanks to the advance of deep convolutional neural networks .

the ordinate is the flux normalized to the maximum .

the ordinate is the type assigned from the total flux .

an isomorphism is a morphism which is bijective on both edges and vertices .

this isomorphism is a projectivization of a natural isomorphism between a space dual to a factor-space and a subspace of a dual space dual to the kernel of the factorization .

recent developments in learning image feature representations for object and place recognition have made image retrieval a viable method for localization .

in recent years , methods using convolution neural network have been successful in the classification of image recognition .

our algorithm is evaluated on the dataset provided by the miccai 2015 gland segmentation challenge contest and achieves state-of-the-art performance among all participants and other popular methods of instance segmentation .

our algorithm is evaluated on the dataset provided by miccai 2015 gland segmentation challenge contest and achieves state-of-the-art performance among all participants and other popular methods of instance segmentation .

word embeddings learned using neural methods have been shown to be tremendously effective on several nlp tasks .

non-linear interactions within a local context have been shown to improve empirical performance in various tasks .

deshpande , the eic project , these proceedings .

stasto , qcd at the lhec , these proceedings .

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

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

let us now prove the divisibility on the left .

let us now give another description of the elementary y .

for the feature extractor , we use the resnet-101 pre-trained on imagenet with the parameters fixed and truncated at the conv4 23 layer .

for the image-sentence matching model , we use resnet-101 pre-trained on imagenet as the cnn image encoder and one-layer bi-directional gru with 512 hidden units as the sentence encoder .

these too have the same forms as those given in the definitions of each order gauge invariant variable for the perturbations of an arbitrary field .

these indeed do have the same forms as the definitions for each order gauge invariant variable for an arbitrary field .

finally , to accelerate deep network training , we use batch normalization before the activation function in each graph convolutional layer .

to accelerate the training of the siamese network , we add a batch normalization layer after each convolutional layer .

now we will define level four interval polynomial groupoids .

now we proceed onto define matrix groupoids of type ii .

a multi-source multi-hop setting in broadcast wireless networks was investigated in and a fundamental lower bound on the average aoi was derived .

a multiple-source multihop setting in broadcast wireless networks was investigated in and a fundamental lower bound on the average aoi was derived .

the electrons of this band may lower their coulomb energy by occupying an atomiclike state represented by these wannier functions .

the electrons may lower their coulomb energy by occupying an atomiclike state reprein sented by these spin-dependent wannier functions .

li et al propose new objective functions based on maximum mutual information for neural conversation models to generate an informative and relevant response to a given message .

li et al use maximum mutual information as the objective function , as a way of encouraging informative agent responses .

the other variant of uses quasi-cyclic low density parity-check codes .

the other one tries to combine these two positive aspects by requiring quasi-cyclic ldpc codes .

c onvolutional neural networks are attracting interest in the fields of image recognition .

convolutional neural networks have recently exhibited great performance in various fields such as computer vision .

the paradigm shift from heuristic reasoning to machine learning has transformed computer vision and natural language processing over the last few years and is starting to impact more traditional fields of science .

in recent years , advances in deep learning and reinforcement learning have led to tremendous progress across many areas of natural language processing and gameplay .

in recent years , convolutional neural networks has achieved remarkable results in a wide range of computer vision applications .

convolutional neural networks have shown its great effectiveness in computer vision tasks .

in recent years , deep convolutional neural networks represented by alexnet have shown strong ability on various kinds of large-scale computer vision tasks , including object detection , localization and classification .

recently , deep convolutional neural networks show promising performances in various computer vision tasks such as object classification , localization .

from the work of juschenko and monod , it is known that the topological full group of a z cantor minimal system is a countable amenable group .

juschenko and monod have shown that topological full groups of minimal z-actions on the cantor space are amenable .

recent breakthroughs in object detection are driven by supervised approaches with deep convolutional neural networks .

region-based approaches with convolutional neural networks have achieved great success in object detection .

bierstone and pd milman , relations among analytic functions i , ann .

bierstone and pd milman , composite differentiable functions , ann .

for visual stream , we apply the widely-used model of 19-layers vggnet with batch normalization .

for the convolutional layers , we use the vgg16 model pre-trained on imagenet data .

in recent years , convolutional neural networks methods have demonstrated highly accurate and reliable performance across a variety of computer-vision related tasks , including image classification .

over the past few years deep convolutional neural networks have emerged as the method of choice for the majority of computer vision tasks that require learning from data .

next , we deal with the linear susceptibility for the bond-orientational order parameters .

now , we turn to the linear susceptibility for the global bond-orientational order parameters near the melting transition .

deep neural networks have been extensively applied in many fields , such as image recognition .

in recent years , deep neural networks have gained enormous popularity for a wide spectrum of applications , ranging from image recognition .

the lowest order deviation from the mb distribution is characterized by the flatness of the distribution , which is called the fourth cumulant or the kurtosis .

however , the kurtosis , which is a measure of volatility surprises , is in fact minimum at the open of the market , when the volatility is at its peak .

gyrokinetic simulation is an important tool for investigating properties of the low frequency turbulence in magnetized plasmas .

gyrokinetics is one of the major frameworks used in theoretical and numerical studies of low-frequency turbulence in magnetized fusion plasmas .

since first coined by in 1982 , noninterference property has become the main criterion for software security .

noninterference is commonly known as the baseline property of information flow security .

these deep learning based approaches have been applied to high-level vision challenges such as image recognition and object detection .

deep learning based models have emerged as an extremely powerful framework to deal with different kinds of vision problems including image classification .

to further characterize the transport in the nanowire devices , effective mobility was extracted using the y-function method , which agrees reasonably well with the split-cv method and allows for the suppression of the series resistance effect .

the low field mobility is extracted using the y-function method to further elucidate the transport performance of the nanowire devices , confirming the enhanced mobility for smaller nanowires .

motivated by the generic failure of these local models , we propose an original model for turbulence which incorporates global properties of the flow .

motivated by this failure , we propose an original turbulence model which is derived using global considerations .

recent studies have revealed that structured or group sparsity can offer powerful reconstruction performance for image denoising .

recent studies have shown that structured or group sparsity can offer more promising performance for image restoration tasks .

the ellipses denote terms whi h are rst order in 4 spa e-time derivatives .

ellipses denote uncertainties on structural parameters and correspond to 1σ confidence contours .

their magnitude gives a measure of the inaccuracy of the separable ansatz .

its magnitude is a decreasing function of l , since as the levels are further from ef their contribution to the pairing fluctuations is smaller .

we use resnet as our backbone network , owing to its consistently good performance on many vision tasks .

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

the rotation curves presented here are not corrected for the effects of beam smearing .

these rotation curves are not corrected for the effects of beam smearing .

convolutional neural networks have recently been very successful on a variety of recognition and classification tasks .

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

quantum entanglement is a quite amazing physical phenomenon , and has attracted intensive interest in recent years due to its possible applications in quantum computation , teleportation and cryptography , as well as to its connections to quantum chaos .

quantum entanglement is a mathematically well-defined concept and quite universal in the sense that entanglement in a composite system means its possession of quantum-mechanical correlation , regardless of its usefulness for a particular task .

any such subspace is called a cartan subspace of p .

a non-zero summand vλ in this decomposition is called the weight subspace for the weight λ .

deep neural networks are extremely important in various applications including computer vision , speech recognition , and natural language processing .

deep neural networks have recently led to significant improvements in many fields , such as image classification .

recently , deep convolution neural networks have been applied to solve the stereo matching problem .

the use of deep convolutional networks has recently advanced the accuracy of stereo matching algorithms considerably .

note however that the second order approximation with respect to the transfer hamiltonian that we keep here implies that coherences between many-body states with different particle numbers are excluded .

it is known that the form of the gme that we use here , in the lowest coupling order , does not guarantee the positivity of the diagonal elements of ρ , ie the probabilities of many-body states .

for slc loops , tiwari proved that the problem is decidable when the update is linear and the variables range over r .

tiwari showed that termination of single-path linear loops is decidable over r d .

note that there is not any form of centralized index , nor any flooding of queries , nor any form of partitioned global index .

note that in ddd there is not any form of centralized index , nor any flooding of queries , nor any form of partitioned global index .

we used perdew-burke-ernzerhof pseudopotentials from the standard solid state pseudopotentials efficiency database .

we use the generalised gradient approximation to density functional theory proposed by perdew , burke , and ernzerhof .

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

neural networks have achieved state-of-the-art results in a wide variety of supervised learning tasks , such as image recognition .

echoing the arguments of claussen and osborne , science education in new zealand , and internationally , needs to highlight the utility value of physics in culture , scientific literacy , and employment .

echoing the arguments of claussen and osborne , science education in new zealand , and internationally , needs to highlight the utility value of physics in culture , in boosting scientific literacy , and employment .

mann et al and wood et al found strong evidence that the accretion of moderately volatile elements into earth occurred mostly during the late stages of accretion , but before core formation was complete .

mann et al and wood et al found evidence that the accretion of moderately volatile elements to the earth occurred mostly before core formation was complete .

a general framework for automatic termination analysis of logic programs .

a framework for analyzing the termination of definite logic programs with respect to call patterns .

we use the adaptive moment algorithm for training the model .

we use adam optimizer in the stochastic gradient descent setting to train all models .

recent developments showed that neural networks can be applied successfully in many technical applications like computer vision .

in recent years , machine learning with deep neural networks has surged into popularity in many application areas .

the number in the lower right corner of each panel indicates the total number of standard-star observations represented .

the number in the bottom right corner of each panel indicates the total number of stellar observations that went into these averages .

we discuss how the classification of markov equations for different geometries into families relates the rg flows of the corresponding gauge theories .

we will discuss how the connection between markov equations for different singularities can be exploited to relate rg cascades for these geometries .

deep learning is attracting much attention in the fields of visual object recognition , speech recognition , object detection , among many others .

deep learning has recently seen enormous success in challenging problems such as object recognition in natural images , automatic speech recognition and machine translation .

our proposed method is implemented based on the deep learning library pytorch .

we implement our system with the open-source deep learning framework pytorch .

quantum computers can solve certain problems , such as simulation of quantum-mechanical systems , exponentially faster than the best classical algorithms known .

quantum algorithms can solve certain computational problems much faster than classical computers , and most likely will be of great impact once quantum computers are available .

chun , some fourth-order iterative methods for solving nonlinear equa tions , appl .

wang , fourth-order iterative methods free from second derivative , appl .

the study of complex networks has received an enormous amount of attention from the scientific community in recent years .

in the last twenty years very significant advances in the understanding of complex systems have been obtained using network theory .

vaswani et al proposed an attentional model for machine translation eschewing recurrent architectures .

vaswani et al demonstrated that machine translation models could achieve state-of-the-art results by solely using a self-attention model .

convolutional neural networks have recently been very successful on a variety of recognition and classification tasks .

recently , deep neural networks have enabled breakthroughs on computer vision and natural language processing tasks .

they proposed to use generative adversarial networks to avoid the blurring artifacts .

this motivates the recent studies to explore generative adversarial nets .

radio emission is a sure sign that relativistic electrons are present and flux levels , sharp radio edges , etc .

thus the radio emission is a key to interpret the physics of cosmic rays , and conversely , any attempt to understand cosmic rays should also try to understand the properties of the radio emission .

nanda et al propose an approach to select regression tests in systems that contain frequent changes in non-code artifacts .

nanda et al introduce a regression test selection technique to selects a subset of existing test cases .

we employed the resnet34 architecture for learning the feature embedding .

for this purpose , we designed a specific computational block inspired by the resnet residual blocks .