HHousen/ParaSCI · Datasets at Hugging Face

sentence1 stringlengths 16 446	sentence2 stringlengths 14 436
in , hammons et al showed that some important binary nonlinear codes can be obtained from cyclic codes over z 4 through the gray map .	in a groundbreaking paper , hammons et al showed that certain non-linear codes which have more codewords than any linear code are images of linear codes over z 4 under the non-linear gray map .
neustupa and pokorny proved certain regularity of one component imply regularity of the other components of the solutions .	pokorny proved that the regularity of one component implies regularity of the other components of the solution .
bpr is a classic approach for learning preference ranking models from implicit feedback .	bprmf is a quite strong matrix factorization ranking model for item recommendation with implicit feedback .
we will follow this approach for the general case .	we will follow closely the ideas in the aforementioned papers .
more importantly , string theory is a perfectly quantum mechanical theory which does include gravity .	if string theory is a consistent , unitary s-matrix theory , as it is believed to be , then it is reasonable 1 to expect that the cosmic singularity should be resolved within string theory , or a future development of it , in a satisfactory way .
this term can then have significant implications for the value of the gauge couplings .	this applies to the case of two compact dimensions which contribute to the radiative corrections to the gauge couplings .
only in hindsight has its success spurred mathematical analysis , which has shown that certain versions are strongly hyperbolic and thus have a well-posed cauchy problem .	only in hindsight has its success spurred mathematical analysis , which showed that certain versions were strongly hyperbolic and thus had a well-posed cauchy problem .
in the mixed and weak interference regimes , a simple messagesplitting in the hk region is to within two bits for all channel parameters .	in mixed and weak interference , a simple rate splitting in the hk region is optimal to within one bit .
convolutional neural networks have achieved notable successes in a variety of visual recognition tasks , such as image classification .	convolutional neural networks have proven to be effective models for tackling a variety of visual tasks .
for these reasons , it is likely that ride-sharing platforms will , at least initially , adopt a hybrid or mixed framework in which avs operate alongside conventional , human-driven vehicles .	therefore , it is likely that ride-sharing platforms will initially adopt a mixed framework in which avs operate alongside conventional , human-driven vehicles .
particularly , the large dnn models exceed the limited on-chip memory of mobile devices , and thus they have to be accommodated by the off-chip memory , which consumes significantly more energy .	the large dnn models exceed the limited on-chip memory of mobile devices , and thus they have to be accommodated by the off-chip memory , which unfortunately consumes significantly more energy .
many progress has been made in recent years with the rapid development of convolutional neural networks .	many deep learning structures have been proposed based on convolutional neural networks .
goodfellow et al introduced fgs to find adversarial perturbations .	goodfellow et al introduced a more efficient algorithm to form adversarial perturbations .
a series of social experiments made by him and his joint researcher suggested that all people in usa are connected through about 6 intermediate acquaintances .	a series of social experiments made by him and his joint researcher suggest that all people in usa are connected through about 6 intermediate acquaintances .
the dot-dashed curve shows the corrections from the effective range term .	the dashed curve in shows the corrections from the logarithmic term at the next order .
for exchange-correlation functional , we used generalised gradient approximations as parameterised by perdew and wang .	we used the generalized-gradient approximation to model the exchange-correlation effects .
convolutional neural networks have proven to be effective models for tackling a variety of visual tasks .	deep convolutional neural networks have achieved great success in computer vision related tasks such as image classification , and so on .
in contrast , the preferential attachment model proposed in provides a justification for the emergence of power-law degree distributions in real-world networks .	in his seminal paper , barabasi et al have also predicted that the growth and preferential attachment are jointly responsible for the emergence of the scale-free property in real networks .
current induced switching of domains in magnetic multilayer devices .	current-driven excitation of magnetic multilayers .
software libraries are commonly used nowadays to support development , providing source code reuse , improving productivity , and , consequently , decreasing costs .	modern software systems are commonly implemented with the support of frameworks , which provide feature reuse , improve productivity , and decrease costs .
more precisely , this statement is only true under some special geometric conditions , such as similar base and mobile platforms .	in fact , this statement is only true under some special geometric conditions , such as similar base and mobile platforms .
for this purpose , we can use a parameter learning approach as utilized in batch normalization .	we use a batch normalization for convergence acceleration during training .
convolutional neural networks have powerful pattern-recognition capabilities that have recently given dramatic improvements in important tasks such as image classification .	in recent years , deep convolutional neural networks have proven to be highly effective general models for a multitude of computer vision problems .
quantitative comparison on attribute generation by fid score on celeba testing set .	quantitative comparison on attribute generation by f1 score and fid score from celeba testing set .
the corresponding ground state expectation value is called the chiral condensate .	the condensate is a fine effect , and we may 8 expect that the proper setting of bcs is important for its evaluation .
in low-rank matrix recovery , the schatten 1-norm is commonly used as convex surrogate for the rank of a matrix .	in order to find a low-rank solution , the nuclear norm is widely used in matrix inverse problems .
now we proceed onto define smarandache pseudo seminear-ring .	now we proceed on to define smarandache semivector spaces .
overlaid is a band representing the variation of the result under various spin-alignment assumptions on the non-prompt and prompt components .	overlaid is the fit with an absorbed power law model described in the text .
recently , however , many works have shown linear convergence rates for douglas-rachford splitting , peaceman-rachford splitting and admm in different settings .	recently , however , many works have shown linear convergence rates for douglas-rachford splitting and its dual version , admm , see , .
our asynchronous svrg on distributed-memory architecture has faster convergence rate than asysg-con in .	our asynchronous svrg on shared-memory architecture has faster convergence rate than asysg-incon in .
neural network learning has underpinned state of the art empirical results in numerous applied machine learning tasks .	in recent years , deep neural networks have achieved significant breakthroughs in many machine learning tasks .
deep neural networks have been shown to be very efficient in image processing tasks such as content classification .	deep neural networks have been widely applied and achieved state-of-art performance on a variety of tasks including image recognition .
first-principles calculations in this work were conducted using the vienna ab initio simulation package within the perdew-burke-ernzerhof functional for the exchange correlation potential .	dft calculations were performed as implemented in the vienna ab initio simulation package 30 , 31 using the perdew-burke-ernzerhof generalized gradient approximation exchange-correlation functional .
for deep neural networks , dropout is the most popular regularization method .	dropout is a widely-used regularization technique for deep neural network .
convolutional neural networks have been largely responsible for the significant progress achieved on visual recognition tasks in recent years .	convolutional neural networks have dramatically improved in recent years , their performance now exceeding that of other visual recognition algorithms .
ozawa , concepts of conditional expectations in quantum theory , j .	ozawa , on information gain by quantum measurements of continuous observables , j .
color x 0 x 1 , x 1 x 2 , x 3 x 4 , x 4 x 0 with 3 , 2 , 1 , 4 , respectively , and color x 2 x 3 from with respect to 2 and u φ .	color x 0 x 1 , x 1 x 2 , x 3 x 4 , x 4 x 0 with 2 , 5 , 5 , 4 , respectively , and then color x 2 x 3 from with respect to 5 and u φ .
our qr algorithm does not require the unique id assumption in and is thus more robust to jamming attack .	our quasi-random algorithm is an extension of the construction in without the need of the unique id assumption .
lavi and swamy have presented a randomized mechanism that is truthful-in-expectation , and achieves o-approximation for general valuations .	a randomized o-approximate mech-anism that is truthful in expectation was given by lavi and swamy .
the beppo-sax satellite is a joint italian and dutch program .	the bepposax satellite is a joint italian and dutch programme .
but while the asymptotic dimension remains invariant under coarse equivalences , the asymptotic assouad-nagata dimension does not .	analogously the asymptotic assouad-nagata dimension is preserved under quasi-isometries .
generalized metrics and uniquely determined logic programs .	nonmonotonic reasoning , preferential models and cumulative logics .
our method also uses the visual space as the embedding space , because it is demonstrated helpful in alleviating the hubness problem .	here , we adopt the visual space as the embedding space and project the semantic space into it , which is also demonstrated to be helpful in improving the hubness problem .
finally , we stress again that any complete dm halo model comprising a subhalo population should be checked against kinematic constraints , which are more and more stringent for the milky way in the context of the gaia results .	finally , we stress again that any complete dm halo model comprising a subhalo population should be checked against kinematic constraints , which are increasingly stringent for the milky way in the context of the gaia results .
for e v , we use the 50-layer resnet to encode image to domain attribute-specific features of 2048 dimensions .	for image embeddings , we use the 2048 dimensional feature vectors extracted from top-layer pooling units of resnet-101 .
the electron-core interactions are described by the projector augmented wave method , and we use perdew-burke-ernzerhof parametrization of the generalized gradient approximation for the exchange-correlation functional .	the electron-electron interactions are evaluated from the exchange-correlation function under the generalized gradient approximation of perdew-burke-ernzerhof .
despite its simplicity , lattice protein models are still widely used in the contest of protein folding because of their versatility and the possibility to better understand many mechanisms of the protein dynamics .	despite its simplicity , lattice protein models are still widely used in the contest of protein folding because of their versatility and the possibility to develop coarse-grained theories and simulations for them .
mcmc kameleon as proposed by sejdinovic et al is an adaptive mh sampler approximating highly non-linear target densities πin a reproducing kernel hilbert space .	mcmc kameleon as proposed by sejdinovic et al is an adaptive mh sampler approximating highly non-linear target densities π .
later we will see that this asymmetry is a consequence of the finite bias across the dot .	such an asymmetry is the remnant of the partonic asymmetry .
also extracted from string theory is the fact that there is a second brane somewhere in the bulk .	however , string theory is a higher dimensional theory , and therefore , it is necessary to find a mechanism to get rid of the extra dimensions with some imprint left on the four dimensional physical world .
we will see from our model calculations how this disk shape well reproduces the observations .	as we will see , our model reproduces very convincingly this phenomenon .
dropout is originally introduced as a regularization technique to prevent overfitting .	dropout is a simple yet effective regularization to prevent dnns from overfitting .
here the superscripts on t denote the order of the coupling constant in the expansion .	the superscripts h denote transpose and hermitian operation respectively .
to report the impact of image features , we replace the bottomup attention features with imagenet pre-trained vgg-16 features .	we extract image features using the pre-trained vgg16 model on the imagenet dataset .
there are different methods to define the effectiveness of algorithms .	there are different methods to define the effectiveness of the algorithms .
to our knowledge , this is the first investigation of cascading phenomena in networks with geometric constraints .	to our knowledge , this work represents the first investigation of cascading phenomena in networks with geometric constraints .
with the help of quantum entanglement , an unknown quantum state can be transported from one place to another place , dubbed quantum teleportation .	it is possible to use shared entanglement together with classical communication to send quantum information using quantum teleportation .
the riemannian geometry is a deformed euclidean geometry .	the riemannian geometry is a kind of inhomogeneous physical geometry , and , hence , it uses the deformation principle .
for general discrete distributions , li and deshpande obtained an additive ptas if the deterministic version of the problem can be solved exactly in pseudopolynomial time .	for general discrete distributions , li and deshpande obtained an additive ptas if the deterministic version of the problem can be solve exactly in pseudopolynomial time .
deep neural networks have been widely adopted in many applications such as computer vision .	neural networks are commonly employed to address many complex tasks such as machine translation .
deep learning has recently vastly improved the performance of many related fields such as compute vision and speech recognition .	deep learning approaches , in particularly deep convolutional neural networks , have achieved tremendous successes in various visual recognition tasks .
recent results proved that it is unlikely that every fixed parameter tractable problem admits a polynomial kernel .	recently , several results gave evidence that there exist parameterized problems that do not admit polynomial kernels .
multi-task learning obtains shared feature representations or classifiers for related tasks .	multi-task learning leverages the task relatedness in the form of shared structures to jointly learn multiple tasks .
network visualization by semantic substrates .	dynamic social network analysis using latent space models .
in our fen network , we use the resnet-101 network as our backbone network .	for all our experiments here , we employ resnet-12 as the embedding network .
further , from the linear combination of y , y received over the fifth symbol , the receiver removes y to obtain a clean y .	further , from the linear combination of y and y received over the fifth channel use , the receiver removes y to obtain a clean y .
girdhar et al combine an encoder for 2d images and a decoder for 3d models to reconstruct 3d shapes from 2d input .	girdhar et al jointly train an encoder for 2d images , and a decoder for voxelized 3d shapes , allowing 3d reconstruction from a 2d image .
the xy model with dm interactions can be solved exactly using the jordan-wigner transformation .	this model can be exactly solved by means of the bethe ansatz , and possesses several symmetries .
we would like to thank the santa fe institute for its hospitality during the completion of this work .	acknowledgments also like to thank the doe and the nsf for their partial support of this work .
the dft computations were performed by using the plane-wave technique implemented in the vienna ab initio simulation package .	all calculations were carried out using the vienna ab-initio simulation package with the scan exchange-correlation functional .
deep neural networks have achieved impressive performance on tasks across a variety of domains , including vision .	deep learning has brought significant breakthroughs in many computer vision tasks , including object detection .
recently , deep neural networks have achieved impressive results for many image classification tasks .	recently , deep neural networks have achieved remarkable progress in computer vision .
the theory of inflation provides an attractive approach to understanding the cosmological initial conditions of our universe .	cosmological inflation is a remarkable idea that provides a convincing explanation for the isotropy and homogeneity of the universe .
neural networks have made remarkable progress in achieving encouraging results in digital image processing .	deep neural networks have demonstrated impressive performance on many machine-learning tasks such as image recognition .
threshold pressure of various gases on graphite compared with various experiments discussed in the text .	threshold pressure of various gases on ag compared with various experiments discussed in the text .
these were chosen because of their popularity in atmospheric fluid dynamics where interpolation between node sets is often required .	these were chosen because of their popularity in computational geosciences where interpolation between node sets is often required .
they are efficient for the sparse channels when there are very limited multipaths between transceivers .	they are efficient for sparse channels with very few multipaths between transceivers .
the past few years have witnessed the successful application of deep neural networks to automatic speech recognition tasks .	in the last five years , deep neural networks have enjoyed tremendous progress , achieving or surpassing human-level performance in many tasks such as speech recognition .
recent research has also shown that deep learning techniques can be used to learn useful representations for reinforcement learning problems .	in recent years , deep reinforcement learning has been shown to be adept at solving sequential decision processes with high-dimensional state spaces such as in the go game .
this phenomena is the brane version of the field identification fixed points .	one of such phenomena is the subject of this work .
moreover , later in ref , it was discussed that the analysis does not fully account for the effects of compactification , because the directions transverse to the mother branes had not been considered .	in ref , it was discussed that the analysis does not fully account for the effects of compactification , because the directions transverse to the mother brane had not been considered .
reconstruction-based methods seek to do channel pruning by minimizing the reconstruction error of feature maps between the pruned model and a pre-trained model .	to prune redundant channels , existing reconstruction-based methods usually minimize the reconstruction error of feature maps between the baseline model and the pruned one .
neural machine translation models have advanced the machine translation community in recent years .	neural machine translation has been rapidly developed in recent years .
deep learning has been applied to many supervised machine learning tasks and performed spectacularly well especially in the field of image classification .	in recent years , deep learning has demonstrated strong model capabilities and obtains very promising performances in many computer vision tasks .
the rates of transmission of information in optical waveguide links can be significantly enhanced by sending many pulse sequences through the same waveguide .	the rates of information transmission through broadband optical waveguide links can be significantly increased by transmitting many pulse sequences through the same waveguide .
other approaches , such as those by turpin et al , require global information in terms of a priori assignment , characteristics about the communication network size , or specifically oriented seed agents respectively .	other approaches , such as those by turpin et al , required global information in terms of a priori assignment , characteristics about the communication network size , or specifically oriented seed agents , respectively .
multi-task learning aims to boost the generalization performance by learning multiple related tasks simultaneously .	multi-task learning employs a shared representation of knowledge for learning several different instance of the same or related problems .
an asterisk denotes that the algorithm attaining the bound is memoryless scale-invariant .	an asterisk denotes a defective approximant .
there has been significant recent work to reduce self-interference and enable full-duplex operations on radios .	recently , there has been considerable research interest and promising results in mitigating this self-interference for building practical fullduplex radios .
to settle the sample scarcity problem , we perform a 5-fold cross-validation , following the standard setting in .	to more accurately verify the performance of padnet , we adopt a 5-fold cross-validation following the standard setting in .
chiral symmetry is a specific of fermions in even dimensional spaces .	chiral symmetry is the interchange of quarks .
large-scale deep neural networks or dnns have made breakthroughs in many fields , such as image recognition .	recently , convolutional neural networks are driving advances in computer vision , such as for image classification .
the generalized gradient approximation in the parametrization of perdew , burke and ernzerhof was used as approximation for the exchange and correlation functional .	for exchange-correlation functional , we used generalised gradient approximations as parameterised by perdew and wang .
deep convolutional neural networks have been successfully applied to a wide range of image classification tasks .	convolutional neural networks have shown excellent performance in various visual recognition problems such as image classification .
the only modification is the increase of the current quark mass , m0 .	another modification is the reduction of the compton cross section in the klein-nishina regime .
it has been known that noncommutative geometry arises quite naturally in string theory , noncommutative field theories arise on the worldvolume of d-branes .	recently it was found that noncommutative geometry arises naturally in string theory with a constant b field background .
the origins of pairwise comparisons date back to the thirteenth century .	the first written evidence of the use of pairwise comparisons dates back to the thirteenth century .
recent advances in the design of convolutional neural networks has led to state of the art performances in several tasks , including image classification .	recent advancements in computer vision and deep learning research have enabled enormous progress in many computer vision tasks , such as image classification .
we now propose an expectation-maximization algorithm to learn the prior parameters .	to circumvent this problem , we use the expectation maximization algorithm .
we used categorical crossentropy as learning objective , and selected the adam optimization algorithm .	we used adam as the learning algorithm and mean-squared-error as the objective to be minimized .

in , hammons et al showed that some important binary nonlinear codes can be obtained from cyclic codes over z 4 through the gray map .

in a groundbreaking paper , hammons et al showed that certain non-linear codes which have more codewords than any linear code are images of linear codes over z 4 under the non-linear gray map .

neustupa and pokorny proved certain regularity of one component imply regularity of the other components of the solutions .

pokorny proved that the regularity of one component implies regularity of the other components of the solution .

bpr is a classic approach for learning preference ranking models from implicit feedback .

bprmf is a quite strong matrix factorization ranking model for item recommendation with implicit feedback .

we will follow this approach for the general case .

we will follow closely the ideas in the aforementioned papers .

more importantly , string theory is a perfectly quantum mechanical theory which does include gravity .

if string theory is a consistent , unitary s-matrix theory , as it is believed to be , then it is reasonable 1 to expect that the cosmic singularity should be resolved within string theory , or a future development of it , in a satisfactory way .

this term can then have significant implications for the value of the gauge couplings .

this applies to the case of two compact dimensions which contribute to the radiative corrections to the gauge couplings .

only in hindsight has its success spurred mathematical analysis , which has shown that certain versions are strongly hyperbolic and thus have a well-posed cauchy problem .

only in hindsight has its success spurred mathematical analysis , which showed that certain versions were strongly hyperbolic and thus had a well-posed cauchy problem .

in the mixed and weak interference regimes , a simple messagesplitting in the hk region is to within two bits for all channel parameters .

in mixed and weak interference , a simple rate splitting in the hk region is optimal to within one bit .

convolutional neural networks have achieved notable successes in a variety of visual recognition tasks , such as image classification .

convolutional neural networks have proven to be effective models for tackling a variety of visual tasks .

for these reasons , it is likely that ride-sharing platforms will , at least initially , adopt a hybrid or mixed framework in which avs operate alongside conventional , human-driven vehicles .

therefore , it is likely that ride-sharing platforms will initially adopt a mixed framework in which avs operate alongside conventional , human-driven vehicles .

particularly , the large dnn models exceed the limited on-chip memory of mobile devices , and thus they have to be accommodated by the off-chip memory , which consumes significantly more energy .

the large dnn models exceed the limited on-chip memory of mobile devices , and thus they have to be accommodated by the off-chip memory , which unfortunately consumes significantly more energy .

many progress has been made in recent years with the rapid development of convolutional neural networks .

many deep learning structures have been proposed based on convolutional neural networks .

goodfellow et al introduced fgs to find adversarial perturbations .

goodfellow et al introduced a more efficient algorithm to form adversarial perturbations .

a series of social experiments made by him and his joint researcher suggested that all people in usa are connected through about 6 intermediate acquaintances .

a series of social experiments made by him and his joint researcher suggest that all people in usa are connected through about 6 intermediate acquaintances .

the dot-dashed curve shows the corrections from the effective range term .

the dashed curve in shows the corrections from the logarithmic term at the next order .

for exchange-correlation functional , we used generalised gradient approximations as parameterised by perdew and wang .

we used the generalized-gradient approximation to model the exchange-correlation effects .

convolutional neural networks have proven to be effective models for tackling a variety of visual tasks .

deep convolutional neural networks have achieved great success in computer vision related tasks such as image classification , and so on .

in contrast , the preferential attachment model proposed in provides a justification for the emergence of power-law degree distributions in real-world networks .

in his seminal paper , barabasi et al have also predicted that the growth and preferential attachment are jointly responsible for the emergence of the scale-free property in real networks .

current induced switching of domains in magnetic multilayer devices .

current-driven excitation of magnetic multilayers .

software libraries are commonly used nowadays to support development , providing source code reuse , improving productivity , and , consequently , decreasing costs .

modern software systems are commonly implemented with the support of frameworks , which provide feature reuse , improve productivity , and decrease costs .

more precisely , this statement is only true under some special geometric conditions , such as similar base and mobile platforms .

in fact , this statement is only true under some special geometric conditions , such as similar base and mobile platforms .

for this purpose , we can use a parameter learning approach as utilized in batch normalization .

we use a batch normalization for convergence acceleration during training .

convolutional neural networks have powerful pattern-recognition capabilities that have recently given dramatic improvements in important tasks such as image classification .

in recent years , deep convolutional neural networks have proven to be highly effective general models for a multitude of computer vision problems .

quantitative comparison on attribute generation by fid score on celeba testing set .

quantitative comparison on attribute generation by f1 score and fid score from celeba testing set .

the corresponding ground state expectation value is called the chiral condensate .

the condensate is a fine effect , and we may 8 expect that the proper setting of bcs is important for its evaluation .

in low-rank matrix recovery , the schatten 1-norm is commonly used as convex surrogate for the rank of a matrix .

in order to find a low-rank solution , the nuclear norm is widely used in matrix inverse problems .

now we proceed onto define smarandache pseudo seminear-ring .

now we proceed on to define smarandache semivector spaces .

overlaid is a band representing the variation of the result under various spin-alignment assumptions on the non-prompt and prompt components .

overlaid is the fit with an absorbed power law model described in the text .

recently , however , many works have shown linear convergence rates for douglas-rachford splitting , peaceman-rachford splitting and admm in different settings .

recently , however , many works have shown linear convergence rates for douglas-rachford splitting and its dual version , admm , see , .

our asynchronous svrg on distributed-memory architecture has faster convergence rate than asysg-con in .

our asynchronous svrg on shared-memory architecture has faster convergence rate than asysg-incon in .

neural network learning has underpinned state of the art empirical results in numerous applied machine learning tasks .

in recent years , deep neural networks have achieved significant breakthroughs in many machine learning tasks .

deep neural networks have been shown to be very efficient in image processing tasks such as content classification .

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

first-principles calculations in this work were conducted using the vienna ab initio simulation package within the perdew-burke-ernzerhof functional for the exchange correlation potential .

dft calculations were performed as implemented in the vienna ab initio simulation package 30 , 31 using the perdew-burke-ernzerhof generalized gradient approximation exchange-correlation functional .

for deep neural networks , dropout is the most popular regularization method .

dropout is a widely-used regularization technique for deep neural network .

convolutional neural networks have been largely responsible for the significant progress achieved on visual recognition tasks in recent years .

convolutional neural networks have dramatically improved in recent years , their performance now exceeding that of other visual recognition algorithms .

ozawa , concepts of conditional expectations in quantum theory , j .

ozawa , on information gain by quantum measurements of continuous observables , j .

color x 0 x 1 , x 1 x 2 , x 3 x 4 , x 4 x 0 with 3 , 2 , 1 , 4 , respectively , and color x 2 x 3 from with respect to 2 and u φ .

color x 0 x 1 , x 1 x 2 , x 3 x 4 , x 4 x 0 with 2 , 5 , 5 , 4 , respectively , and then color x 2 x 3 from with respect to 5 and u φ .

our qr algorithm does not require the unique id assumption in and is thus more robust to jamming attack .

our quasi-random algorithm is an extension of the construction in without the need of the unique id assumption .

lavi and swamy have presented a randomized mechanism that is truthful-in-expectation , and achieves o-approximation for general valuations .

a randomized o-approximate mech-anism that is truthful in expectation was given by lavi and swamy .

the beppo-sax satellite is a joint italian and dutch program .

the bepposax satellite is a joint italian and dutch programme .

but while the asymptotic dimension remains invariant under coarse equivalences , the asymptotic assouad-nagata dimension does not .

analogously the asymptotic assouad-nagata dimension is preserved under quasi-isometries .

generalized metrics and uniquely determined logic programs .

nonmonotonic reasoning , preferential models and cumulative logics .

our method also uses the visual space as the embedding space , because it is demonstrated helpful in alleviating the hubness problem .

here , we adopt the visual space as the embedding space and project the semantic space into it , which is also demonstrated to be helpful in improving the hubness problem .

finally , we stress again that any complete dm halo model comprising a subhalo population should be checked against kinematic constraints , which are more and more stringent for the milky way in the context of the gaia results .

finally , we stress again that any complete dm halo model comprising a subhalo population should be checked against kinematic constraints , which are increasingly stringent for the milky way in the context of the gaia results .

for e v , we use the 50-layer resnet to encode image to domain attribute-specific features of 2048 dimensions .

for image embeddings , we use the 2048 dimensional feature vectors extracted from top-layer pooling units of resnet-101 .

the electron-core interactions are described by the projector augmented wave method , and we use perdew-burke-ernzerhof parametrization of the generalized gradient approximation for the exchange-correlation functional .

the electron-electron interactions are evaluated from the exchange-correlation function under the generalized gradient approximation of perdew-burke-ernzerhof .

despite its simplicity , lattice protein models are still widely used in the contest of protein folding because of their versatility and the possibility to better understand many mechanisms of the protein dynamics .

despite its simplicity , lattice protein models are still widely used in the contest of protein folding because of their versatility and the possibility to develop coarse-grained theories and simulations for them .

mcmc kameleon as proposed by sejdinovic et al is an adaptive mh sampler approximating highly non-linear target densities πin a reproducing kernel hilbert space .

mcmc kameleon as proposed by sejdinovic et al is an adaptive mh sampler approximating highly non-linear target densities π .

later we will see that this asymmetry is a consequence of the finite bias across the dot .

such an asymmetry is the remnant of the partonic asymmetry .

also extracted from string theory is the fact that there is a second brane somewhere in the bulk .

however , string theory is a higher dimensional theory , and therefore , it is necessary to find a mechanism to get rid of the extra dimensions with some imprint left on the four dimensional physical world .

we will see from our model calculations how this disk shape well reproduces the observations .

as we will see , our model reproduces very convincingly this phenomenon .

dropout is originally introduced as a regularization technique to prevent overfitting .

dropout is a simple yet effective regularization to prevent dnns from overfitting .

here the superscripts on t denote the order of the coupling constant in the expansion .

the superscripts h denote transpose and hermitian operation respectively .

to report the impact of image features , we replace the bottomup attention features with imagenet pre-trained vgg-16 features .

we extract image features using the pre-trained vgg16 model on the imagenet dataset .

there are different methods to define the effectiveness of algorithms .

there are different methods to define the effectiveness of the algorithms .

to our knowledge , this is the first investigation of cascading phenomena in networks with geometric constraints .

to our knowledge , this work represents the first investigation of cascading phenomena in networks with geometric constraints .

with the help of quantum entanglement , an unknown quantum state can be transported from one place to another place , dubbed quantum teleportation .

it is possible to use shared entanglement together with classical communication to send quantum information using quantum teleportation .

the riemannian geometry is a deformed euclidean geometry .

the riemannian geometry is a kind of inhomogeneous physical geometry , and , hence , it uses the deformation principle .

for general discrete distributions , li and deshpande obtained an additive ptas if the deterministic version of the problem can be solved exactly in pseudopolynomial time .

for general discrete distributions , li and deshpande obtained an additive ptas if the deterministic version of the problem can be solve exactly in pseudopolynomial time .

deep neural networks have been widely adopted in many applications such as computer vision .

neural networks are commonly employed to address many complex tasks such as machine translation .

deep learning has recently vastly improved the performance of many related fields such as compute vision and speech recognition .

deep learning approaches , in particularly deep convolutional neural networks , have achieved tremendous successes in various visual recognition tasks .

recent results proved that it is unlikely that every fixed parameter tractable problem admits a polynomial kernel .

recently , several results gave evidence that there exist parameterized problems that do not admit polynomial kernels .

multi-task learning obtains shared feature representations or classifiers for related tasks .

multi-task learning leverages the task relatedness in the form of shared structures to jointly learn multiple tasks .

network visualization by semantic substrates .

dynamic social network analysis using latent space models .

in our fen network , we use the resnet-101 network as our backbone network .

for all our experiments here , we employ resnet-12 as the embedding network .

further , from the linear combination of y , y received over the fifth symbol , the receiver removes y to obtain a clean y .

further , from the linear combination of y and y received over the fifth channel use , the receiver removes y to obtain a clean y .

girdhar et al combine an encoder for 2d images and a decoder for 3d models to reconstruct 3d shapes from 2d input .

girdhar et al jointly train an encoder for 2d images , and a decoder for voxelized 3d shapes , allowing 3d reconstruction from a 2d image .

the xy model with dm interactions can be solved exactly using the jordan-wigner transformation .

this model can be exactly solved by means of the bethe ansatz , and possesses several symmetries .

we would like to thank the santa fe institute for its hospitality during the completion of this work .

acknowledgments also like to thank the doe and the nsf for their partial support of this work .

the dft computations were performed by using the plane-wave technique implemented in the vienna ab initio simulation package .

all calculations were carried out using the vienna ab-initio simulation package with the scan exchange-correlation functional .

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

deep learning has brought significant breakthroughs in many computer vision tasks , including object detection .

recently , deep neural networks have achieved impressive results for many image classification tasks .

recently , deep neural networks have achieved remarkable progress in computer vision .

the theory of inflation provides an attractive approach to understanding the cosmological initial conditions of our universe .

cosmological inflation is a remarkable idea that provides a convincing explanation for the isotropy and homogeneity of the universe .

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

deep neural networks have demonstrated impressive performance on many machine-learning tasks such as image recognition .

threshold pressure of various gases on graphite compared with various experiments discussed in the text .

threshold pressure of various gases on ag compared with various experiments discussed in the text .

these were chosen because of their popularity in atmospheric fluid dynamics where interpolation between node sets is often required .

these were chosen because of their popularity in computational geosciences where interpolation between node sets is often required .

they are efficient for the sparse channels when there are very limited multipaths between transceivers .

they are efficient for sparse channels with very few multipaths between transceivers .

the past few years have witnessed the successful application of deep neural networks to automatic speech recognition tasks .

in the last five years , deep neural networks have enjoyed tremendous progress , achieving or surpassing human-level performance in many tasks such as speech recognition .

recent research has also shown that deep learning techniques can be used to learn useful representations for reinforcement learning problems .

in recent years , deep reinforcement learning has been shown to be adept at solving sequential decision processes with high-dimensional state spaces such as in the go game .

this phenomena is the brane version of the field identification fixed points .

one of such phenomena is the subject of this work .

moreover , later in ref , it was discussed that the analysis does not fully account for the effects of compactification , because the directions transverse to the mother branes had not been considered .

in ref , it was discussed that the analysis does not fully account for the effects of compactification , because the directions transverse to the mother brane had not been considered .

reconstruction-based methods seek to do channel pruning by minimizing the reconstruction error of feature maps between the pruned model and a pre-trained model .

to prune redundant channels , existing reconstruction-based methods usually minimize the reconstruction error of feature maps between the baseline model and the pruned one .

neural machine translation models have advanced the machine translation community in recent years .

neural machine translation has been rapidly developed in recent years .

deep learning has been applied to many supervised machine learning tasks and performed spectacularly well especially in the field of image classification .

in recent years , deep learning has demonstrated strong model capabilities and obtains very promising performances in many computer vision tasks .

the rates of transmission of information in optical waveguide links can be significantly enhanced by sending many pulse sequences through the same waveguide .

the rates of information transmission through broadband optical waveguide links can be significantly increased by transmitting many pulse sequences through the same waveguide .

other approaches , such as those by turpin et al , require global information in terms of a priori assignment , characteristics about the communication network size , or specifically oriented seed agents respectively .

other approaches , such as those by turpin et al , required global information in terms of a priori assignment , characteristics about the communication network size , or specifically oriented seed agents , respectively .

multi-task learning aims to boost the generalization performance by learning multiple related tasks simultaneously .

multi-task learning employs a shared representation of knowledge for learning several different instance of the same or related problems .

an asterisk denotes that the algorithm attaining the bound is memoryless scale-invariant .

an asterisk denotes a defective approximant .

there has been significant recent work to reduce self-interference and enable full-duplex operations on radios .

recently , there has been considerable research interest and promising results in mitigating this self-interference for building practical fullduplex radios .

to settle the sample scarcity problem , we perform a 5-fold cross-validation , following the standard setting in .

to more accurately verify the performance of padnet , we adopt a 5-fold cross-validation following the standard setting in .

chiral symmetry is a specific of fermions in even dimensional spaces .

chiral symmetry is the interchange of quarks .

large-scale deep neural networks or dnns have made breakthroughs in many fields , such as image recognition .

recently , convolutional neural networks are driving advances in computer vision , such as for image classification .

the generalized gradient approximation in the parametrization of perdew , burke and ernzerhof was used as approximation for the exchange and correlation functional .

for exchange-correlation functional , we used generalised gradient approximations as parameterised by perdew and wang .

deep convolutional neural networks have been successfully applied to a wide range of image classification tasks .

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

the only modification is the increase of the current quark mass , m0 .

another modification is the reduction of the compton cross section in the klein-nishina regime .

it has been known that noncommutative geometry arises quite naturally in string theory , noncommutative field theories arise on the worldvolume of d-branes .

recently it was found that noncommutative geometry arises naturally in string theory with a constant b field background .

the origins of pairwise comparisons date back to the thirteenth century .

the first written evidence of the use of pairwise comparisons dates back to the thirteenth century .

recent advances in the design of convolutional neural networks has led to state of the art performances in several tasks , including image classification .

recent advancements in computer vision and deep learning research have enabled enormous progress in many computer vision tasks , such as image classification .

we now propose an expectation-maximization algorithm to learn the prior parameters .

to circumvent this problem , we use the expectation maximization algorithm .

we used categorical crossentropy as learning objective , and selected the adam optimization algorithm .

we used adam as the learning algorithm and mean-squared-error as the objective to be minimized .